Patent Application
20130024408
A computationally implemented method includes, but is not limited to presenting to a user a hypothesis identifying at least a relationship between a first event type and a second event type; receiving from the user one or more modifications to modify the hypothesis; and executing one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein-referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein-referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for presenting to a user a hypothesis identifying at least a relationship between a first event type and a second event type; means for receiving from the user one or more modifications to modify the hypothesis; and means for executing one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for presenting to a user a hypothesis identifying at least a relationship between a first event type and a second event type; circuitry for receiving from the user one or more modifications to modify the hypothesis; and circuitry for executing one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions presenting to a user a hypothesis identifying at least a relationship between a first event type and a second event type; one or more instructions for receiving from the user one or more modifications to modify the hypothesis; and one or more instructions for executing one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to: acquiring subjective user state data including at least a first subjective user state and a second subjective user state; acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user; and correlating the subjective user state data with the objective context data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring subjective user state data including at least a first subjective user state and a second subjective user state; means for acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user; and means for correlating the subjective user state data with the objective context data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring subjective user state data including at least a first subjective user state and a second subjective user state; circuitry for acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user; and circuitry for correlating the subjective user state data with the objective context data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions for acquiring subjective user state data including at least a first subjective user state and a second subjective user state; one or more instructions for acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user; and one or more instructions for correlating the subjective user state data with the objective context data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to: acquiring subjective user state data including data indicating at least one subjective user state associated with a user; acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user; correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence; and presenting one or more results of the correlating. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring subjective user state data including data indicating at least one subjective user state associated with a user; means for acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user; means for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence; and means for presenting one or more results of the correlating. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring subjective user state data including data indicating at least one subjective user state associated with a user; circuitry for acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user; circuitry for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence; and circuitry for presenting one or more results of the correlating. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions for acquiring subjective user state data including data indicating at least one subjective user state associated with a user; one or more instructions for acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user; one or more instructions for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence; and one or more instructions for presenting one or more results of the correlating. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to: acquiring subjective user state data including data indicating at least one subjective user state associated with a user; soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating occurrence of at least one objective occurrence; acquiring the objective occurrence data; and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring subjective user state data including data indicating at least one subjective user state associated with a user; means for soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating occurrence of at least one objective occurrence; means for acquiring the objective occurrence data; and means for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring subjective user state data including data indicating at least one subjective user state associated with a user; circuitry for soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating occurrence of at least one objective occurrence; circuitry for acquiring the objective occurrence data; and circuitry for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions for acquiring subjective user state data including data indicating at least one subjective user state associated with a user; one or more instructions for soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating occurrence of at least one objective occurrence; one or more instructions for acquiring the objective occurrence data; and one or more instructions for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to: acquiring objective occurrence data including data indicating occurrence of at least one objective occurrence; soliciting, in response to the acquisition of the objective occurrence data, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user; acquiring the subjective user state data; and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring objective occurrence data including data indicating occurrence of at least one objective occurrence; means for soliciting, in response to the acquisition of the objective occurrence data, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user; means for acquiring the subjective user state data; and means for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring objective occurrence data including data indicating occurrence of at least one objective occurrence; circuitry for soliciting, in response to the acquisition of the objective occurrence data, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user; circuitry for acquiring the subjective user state data; and circuitry for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions for acquiring objective occurrence data including data indicating occurrence of at least one objective occurrence; one or more instructions for soliciting, in response to the acquisition of the objective occurrence data, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user; one or more instructions for acquiring the subjective user state data; and one or more instructions for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to: acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user; acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence; and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user; means for acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence; and means for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user; circuitry for acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence; and circuitry for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions for acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user; one or more instructions for acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence; and one or more instructions for correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one objective occurrence, subjective user state data including data indicating incidence of at least one subjective user state associated with a user; and acquiring the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one objective occurrence, subjective user state data including data indicating incidence of at least one subjective user state associated with a user; and means for acquiring the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one objective occurrence, subjective user state data including data indicating incidence of at least one subjective user state associated with a user; and circuitry for acquiring the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one objective occurrence, subjective user state data including data indicating incidence of at least one subjective user state associated with a user; and one or more instructions for acquiring the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence; and acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence; and means for acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence; and circuitry for acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence; and one or more instructions for acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to acquiring events data including data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events, at least one of the first one or more reported events and the second one or more reported events being associated with a user; determining an events pattern based selectively on the incidences of the first one or more reported events and the second one or more reported events; and developing a hypothesis associated with the user based, at least in part, on the determined events pattern. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring events data including data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events, at least one of the first one or more reported events and the second one or more reported events being associated with a user; means for determining an events pattern based selectively on the incidences of the first one or more reported events and the second one or more reported events; and means for developing a hypothesis associated with the user based, at least in part, on the determined events pattern. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring events data including data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events, at least one of the first one or more reported events and the second one or more reported events being associated with a user; circuitry for determining an events pattern based selectively on the incidences of the first one or more reported events and the second one or more reported events; and circuitry for developing a hypothesis associated with the user based, at least in part, on the determined events pattern. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions acquiring events data including data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events, at least one of the first one or more reported events and the second one or more reported events being associated with a user; one or more instructions for determining an events pattern based selectively on the incidences of the first one or more reported events and the second one or more reported events; and one or more instructions for developing a hypothesis associated with the user based, at least in part, on the determined events pattern. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to selecting at least one hypothesis from a plurality of hypotheses relevant to a user, the selection of the at least one hypothesis being based, at least in part, on at least one reported event associated with the user; and presenting one or more advisories related to the hypothesis. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for selecting at least one hypothesis from a plurality of hypotheses relevant to a user, the selection of the at least one hypothesis being based, at least in part, on at least one reported event associated with the user; and means for presenting one or more advisories related to the hypothesis. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for selecting at least one hypothesis from a plurality of hypotheses relevant to a user, the selection of the at least one hypothesis being based, at least in part, on at least one reported event associated with the user; and circuitry for presenting one or more advisories related to the hypothesis. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions selecting at least one hypothesis from a plurality of hypotheses relevant to a user, the selection of the at least one hypothesis being based, at least in part, on at least one reported event associated with the user; and one or more instructions for presenting one or more advisories related to the hypothesis. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
A computationally implemented method includes, but is not limited to acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.
In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein referenced method aspects depending upon the design choices of the system designer.
A computationally implemented system includes, but is not limited to: means for acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and means for developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computationally implemented system includes, but is not limited to: circuitry for acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and circuitry for developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.
A computer program product including a signal-bearing medium bearing one or more instructions acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and one or more instructions for developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
a and 1b show a high-level block diagram a computing device 10 and a mobile device 30 operating in a network environment.
a shows another perspective of the hypothesis presentation module 102 of the computing device 10 of
b shows another perspective of the modification reception module 104 of the computing device 10 of
c shows another perspective of the action execution module 108 of the computing device 10 of
d shows another perspective of the mobile device 30 of
e shows another perspective of the hypothesis presentation module 102′ of the mobile device 30 of
f shows another perspective of the modification reception module 104′ of the mobile device 30 of
g shows another perspective of the action execution module 108′ of the mobile device 30 of
h shows an exemplarily user interface display displaying a visual version of a hypothesis.
i shows another exemplarily user interface display displaying another visual version of the hypothesis.
j shows another exemplarily user interface display displaying still another visual version of the hypothesis.
k shows another exemplarily user interface display displaying a visual version of another hypothesis.
a is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis presentation operation 302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis presentation operation 302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis presentation operation 302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis presentation operation 302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis presentation operation 302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis presentation operation 302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the modification reception operation 304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the modification reception operation 304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 306 of
c is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 306 of
d is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 306 of
e is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 306 of
a and 1-1b show a high-level block diagram of a network device operating in a network environment.
a shows another perspective of the subjective user state data acquisition module 1-102 of the computing device 1-10 of
b shows another perspective of the objective context data acquisition module 1-104 of the computing device 1-10 of
c shows another perspective of the correlation module 1-106 of the computing device 1-10 of
d shows another perspective of the presentation module 1-108 of the computing device 1-10 of
e shows another perspective of the one or more applications 1-126 of the computing device 1-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 1-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 1-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 1-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 1-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 1-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective context data acquisition operation 1-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective context data acquisition operation 1-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective context data acquisition operation 1-304 of
d is a high-level logic flowchart of a process depicting alternate implementations of the objective context data acquisition operation 1-304 of
e is a high-level logic flowchart of a process depicting alternate implementations of the objective context data acquisition operation 1-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 1-306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 1-306 of
a is a high-level logic flowchart of a process depicting alternate implementations of the presentation operation 1-708 of
b is a high-level logic flowchart of a process depicting alternate implementations of the presentation operation 1-708 of
a and 2-1b show a high-level block diagram of a network device operating in a network environment.
a shows another perspective of the subjective user state data acquisition module 2-102 of the computing device 2-10 of
b shows another perspective of the objective occurrence data acquisition module 2-104 of the computing device 2-10 of
c shows another perspective of the correlation module 2-106 of the computing device 2-10 of
d shows another perspective of the presentation module 2-108 of the computing device 2-10 of
e shows another perspective of the one or more applications 2-126 of the computing device 2-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 2-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 2-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 2-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 2-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 2-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
d is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
e is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
f is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
g is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
h is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
i is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
j is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
k is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 2-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 2-306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 2-306 of
c is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 2-306 of
d is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 2-306 of
a is a high-level logic flowchart of a process depicting alternate implementations of the presentation operation 2-308 of
b is a high-level logic flowchart of a process depicting alternate implementations of the presentation operation 2-308 of
a and 3-1b show a high-level block diagram of a computing device 3-10 operating in a network environment.
a shows another perspective of the subjective user state data acquisition module 3-102 of the computing device 3-10 of
b shows another perspective of the objective occurrence data solicitation module 3-103 of the computing device 3-10 of
c shows another perspective of the objective occurrence data acquisition module 3-104 of the computing device 3-10 of
d shows another perspective of the correlation module 3-106 of the computing device 3-10 of
e shows another perspective of the presentation module 3-108 of the computing device 3-10 of
f shows another perspective of the one or more applications 3-126 of the computing device 3-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 3-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 3-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 3-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 3-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 3-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 3-304 of
d is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 3-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 3-306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 3-306 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 3-306 of
a is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 3-308 of
b is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 3-308 of
a and 4-1b show a high-level block diagram of a computing device 4-10 operating in a network environment.
a shows another perspective of the objective occurrence data acquisition module 4-102 of the computing device 4-10 of
b shows another perspective of the subjective user state data solicitation module 4-103 of the computing device 4-10 of
c shows another perspective of the subjective user state data acquisition module 4-104 of the computing device 4-10 of
d shows another perspective of the correlation module 4-106 of the computing device 4-10 of
e shows another perspective of the presentation module 4-108 of the computing device 4-10 of
f shows another perspective of the one or more applications 4-126 of the computing device 4-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 4-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 4-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 4-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 4-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 4-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 4-304 of
d is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 4-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 4-306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 4-306 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 4-306 of
a is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 4-308 of
b is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 4-308 of
a and 5-1b show a high-level block diagram of a network device operating in a network environment.
a shows another perspective of the subjective user state data acquisition module 5-102 of the computing device 5-10 of
b shows another perspective of the objective occurrence data acquisition module 5-104 of the computing device 5-10 of
c shows another perspective of the correlation module 5-106 of the computing device 5-10 of
d shows another perspective of the presentation module 5-108 of the computing device 5-10 of
e shows another perspective of the one or more applications 5-126 of the computing device 5-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 5-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 5-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 5-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 5-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 5-302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 5-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
d is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
e is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
f is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
g is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 5-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 5-306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 5-306 of
c is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 5-306 of
d is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 5-306 of
e is a high-level logic flowchart of a process depicting alternate implementations of the correlation operation 5-306 of
a and 6-1b show a high-level block diagram of a mobile device 6-30 and a computing device 6-10 operating in a network environment.
a shows another perspective of the subjective user state data solicitation module 6-101 of the computing device 6-10 of
b shows another perspective of the subjective user state data acquisition module 6-102 of the computing device 6-10 of
c shows another perspective of the objective occurrence data acquisition module 6-104 of the computing device 6-10 of
d shows another perspective of the correlation module 6-106 of the computing device 6-10 of
e shows another perspective of the presentation module 6-108 of the computing device 6-10 of
f shows another perspective of the one or more applications 6-126 of the computing device 6-10 of
g shows another perspective of the mobile device 6-30 of
h shows another perspective of the subjective user state data solicitation module 6-101′ of the mobile device 6-30 of
i shows another perspective of the subjective user state data acquisition module 6-102′ of the mobile device 6-30 of
j shows another perspective of the objective occurrence data acquisition module 6-104′ of the mobile device 6-30 of
k shows another perspective of the presentation module 6-108′ of the mobile device 6-30 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
g is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data solicitation operation 6-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 6-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 6-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 6-606 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 6-606 of
a and 7-1b show a high-level block diagram of a mobile device 7-30 and a computing device 7-10 operating in a network environment.
a shows another perspective of the objective occurrence data solicitation module 7-101 of the computing device 7-10 of
b shows another perspective of the subjective user state data acquisition module 7-102 of the computing device 7-10 of
c shows another perspective of the objective occurrence data acquisition module 7-104 of the computing device 7-10 of
d shows another perspective of the correlation module 7-106 of the computing device 7-10 of
e shows another perspective of the presentation module 7-108 of the computing device 7-10 of
f shows another perspective of the one or more applications 7-126 of the computing device 7-10 of
g shows another perspective of the mobile device 7-30 of
h shows another perspective of the objective occurrence data solicitation module 7-101′ of the mobile device 7-30 of
i shows another perspective of the subjective user state data acquisition module 7-102′ of the mobile device 7-30 of
j shows another perspective of the objective occurrence data acquisition module 7-104′ of the mobile device 7-30 of
k shows another perspective of the presentation module 7-108′ of the mobile device 7-30 of
l shows another perspective of the one or more applications 7-126′ of the mobile device 7-30 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
g is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
h is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
i is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
j is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data solicitation operation 7-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 7-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 7-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 7-304 of
d is a high-level logic flowchart of a process depicting alternate implementations of the objective occurrence data acquisition operation 7-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 7-606 of
b is a high-level logic flowchart of a process depicting alternate implementations of the subjective user state data acquisition operation 7-606 of
a and 8-1b show a high-level block diagram of a mobile device 8-30 and a computing device 8-10 operating in a network environment.
a shows another perspective of the events data acquisition module 8-102 of the computing device 8-10 of
b shows another perspective of the events pattern determination module 8-104 of the computing device 8-10 of
c shows another perspective of the hypothesis development module 8-106 of the computing device 8-10 of
d shows another perspective of the action execution module 8-108 of the computing device 8-10 of
e shows another perspective of the one or more applications 8-126 of the computing device 8-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
g is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
h is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
i is a high-level logic flowchart of a process depicting alternate implementations of the events data acquisition operation 8-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation 8-306 of
b is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation 8-306 of
a is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 8-708 of
b is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 8-708 of
a and 9-1b show a high-level block diagram a computing device 9-10 operating in a network environment.
a shows another perspective of the events data acquisition module 9-102 of the computing device 9-10 of
b shows another perspective of the hypothesis selection module 9-104 of the computing device 9-10 of
c shows another perspective of the presentation module 9-106 of the computing device 9-10 of
a is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
g is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
h is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
i is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis selection operation 9-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the advisory presentation operation 9-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the advisory presentation operation 9-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the advisory presentation operation 9-304 of
a and 10-1b show a high-level block diagram of a computing device 10-10 operating in a network environment.
a shows another perspective of the events data acquisition module 10-102 of the computing device 10-10 of
b shows another perspective of the hypothesis development module 10-104 of the computing device 10-10 of
c shows another perspective of the action execution module 10-106 of the computing device 10-10 of
d shows another perspective of the one or more sensing devices 10-35a and/or 10-35b of
a is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
b is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
c is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
d is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
e is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
f is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
g is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
h is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
i is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
j is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
k is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
l is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
m is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation 10-302 of
a is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation 10-304 of
b is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation 10-304 of
c is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation 10-304 of
a is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 10-606 of
b is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 10-606 of
c is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 10-606 of
d is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation 10-606 of
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where users may report or post their latest status, personal activities, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social networking status reports in which a user may report or post for others to view their current status, activities, and/or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life. Typically, such microblog entries will describe the various “events” associated with or are of interest to the microblogger that occurs during a course of a typical day. The microblog entries are often continuously posted during the course of a typical day, and thus, by the end of a normal day, a substantial number of events may have been reported and posted.
Each of the reported events that may be posted through microblog entries may be categorized into one of at least three possible categories. The first category of events that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, incident, happening, or any other event that occurs with respect to the microblogger or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. Such events would include, for example, intake of food, medicine, or nutraceutical, certain physical characteristics of the microblogger such as blood sugar level or blood pressure, activities of the microblogger, external events such as performance of the stock market (which the microblogger may have an interest in), performance of a favorite sports team, and so forth.
Other examples of objective occurrences include, for example, external events such as the local weather, activities of others (e.g., spouse or boss), the behavior or activities of a pet or livestock, the characteristics or performances of mechanical or electronic devices such as automobiles, appliances, and computing devices, and other events that may directly or indirectly affect the microblogger.
A second category of events that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger may include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be directly reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., happiness, sadness, anger, tension, state of alertness, state of mental fatigue, jealousy, envy, and so forth), the subjective physical state of the microblogger (e.g., upset stomach, state of vision, state of hearing, pain, and so forth), and the subjective overall state of the microblogger (e.g., “good,” “bad,” state of overall wellness, overall fatigue, and so forth). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states).
A third category of events that may be reported or posted through microblog entries include “subjective observations” made by the microblogger. A subjective observation is similar to subjective user states and may be any subjective opinion, thought, or evaluation relating to any external incidence (e.g., outward looking instead of inward looking as in the case of subjective user states). Thus, the difference between subjective user states and subjective observations is that subjective user states relates to self-described subjective descriptions of the user states of one's self while subjective observations relates to subjective descriptions or opinions regarding external events. Examples of subjective observations include, for example, a microblogger's perception about the subjective user state of another person (e.g., “he seems tired”), a microblogger's perception about another person's activities (e.g., “he drank too much yesterday”), a microblogger's perception about an external event (e.g., “it was a nice day today”), and so forth. Although microblogs are being used to provide a wealth of personal information, thus far they have been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
Another potential source for valuable but yet to be fully exploited is data that may be provided by sensing devices that are used to sense and/or monitor various aspects of everyday life. Currently there are a number of sensing devices that can detect and/or monitor various user-related and nonuser-related events. For example, there are presently a number of sensing devices that can sense various physical or physiological characteristics of a person or an animal (e.g., a pet or a livestock). Examples of such devices include commonly known and used monitoring devices such as blood pressure devices, heart rate monitors, blood glucose sensors (e.g., glucometers), respiration sensor devices, temperature sensors, and so forth. Other examples of devices that can monitor physical or physiological characteristics include more exotic and sophisticated devices such as functional magnetic resonance imaging (fMRI) device, functional Near Infrared (fNIR) devices, blood cell-sorting sensing device, and so forth. Many of these devices are becoming more compact and less expensive such that they are becoming increasingly accessible for purchase and/or self-use by the general public.
Other sensing devices may be used in order to sense and/or monitor activities of a person or an animal. These would include, for example, global positioning systems (GPS), pedometers, accelerometers, and so forth. Such devices are compact and can even be incorporated into, for example, a mobile communication device such a cellular telephone or on the collar of a pet. Other sensing devices for monitoring activities of individuals (e.g., users) may be incorporated into larger machines and may be used in order to monitor the usage of the machines by the individuals. These would include, for example, sensors that are incorporated into exercise machines, automobiles, bicycles, and so forth. Today there are even toilet monitoring devices that are available to monitor the toilet usage of individuals.
Other sensing devices are also available that can monitor general environmental conditions such as environmental temperature sensor devices, humidity sensor devices, barometers, wind speed monitors, water monitoring sensors, air pollution sensor devices (e.g., devices that can measure the amount of particulates in the air such as pollen, those that measure CO2 levels, those that measure ozone levels, and so forth). Other sensing devices may be employed in order to monitor the performance or characteristics of mechanical and/or electronic devices. All the above described sensing devices may provide useful data that may indicate objectively observable events (e.g., objective occurrences).
In accordance with various embodiments, the data provided through social networking sites (e.g., via microblog entries, status entries, diary entries, and so forth) as well as, in some cases, those from sensing devices may be processed in order to develop a hypotheses that identifies the relationship between multiple event types (e.g., types of events). For example, based on past events reported by a person (e.g., a microblogger) and/or reported by sensing devices, a hypothesis such as a hypothesis may be developed relating to the person, a third party, a device, external activities, environmental conditions, or anything else that may be of interest to the person. One way to develop or create such a hypothesis is by identifying a pattern of events that repeatedly reoccurs.
Once such a hypothesis is developed, one or more actions may be executed based on the hypothesis and in response to, for example, occurrence of one or more reported events that may match or substantially match one or more of the event types identified in the hypothesis. Examples of actions that could be executed include, for example, the presentation of advisories or the prompting of one or more devices (e.g., sensing devices or home appliances) to execute one or more operations. However, the development of a hypothesis based on identifying repeatedly reoccurring patterns of events may lead to the development of a faulty or incorrect hypothesis.
As an illustration, suppose a hypothesis is developed by identifying a repetitively reoccurring pattern of events that indicate, for example, that whenever the person wakes-up late, eats ice cream, and drinks coffee, a stomach ache follows. However, merely looking at repetitively reoccurring patterns of events may result in a hypothesis that includes types of events that may not be relevant to the hypothesis or may not accurately reflect the types of events that should be included in the hypothesis. For example, in the above example, waking-up late may not be relevant to having a stomach ache. That is, the hypothesis may have been based on data that indicated that prior to past occurrences of stomachaches, the subject (e.g., user) had reported waking-up late, eating ice cream, and drinking coffee. However, the reports of waking-up late occurring prior to previous reports of stomachaches may merely have been a coincidence. As can be seen, using the technique determining repeatedly reoccurring patterns of events may result in the development of inaccurate or even false hypothesis.
Accordingly, robust methods, systems, and computer program products are provided to, among other things, present to a user a hypothesis identifying at least a relationship between a first event type and a second event type and receive from the user one or more modifications to modify the hypothesis. The methods, systems, and computer program products may then facilitate in the execution of one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. Examples of the types of actions that may be executed include, for example, the presentation of the modified hypothesis or advisories relating to the modified hypothesis. Other actions that may be executed include the prompting of mechanical and/or electronic devices to execute one or more operations based, at least in part, on the modified hypothesis. In some cases, the execution of the one or more actions, in addition to being based on the modified hypothesis, may be in response to a reported event.
The robust methods, systems, and computer program products may be employed in a variety of environments including, for example, social networking environments, blogging or microblogging environments, instant messaging (IM) environments, or any other type of environment that allows a user to, for example, maintain a diary. Further, the methods, systems, and computing program products in various embodiments may be implemented in a standalone computing device or implemented in a client/server environment.
In various implementations, a “hypothesis,” as referred to herein, may define one or more relationships or links between different types of events (i.e., event types) including defining a relationship between at least a first event type (e.g., a type of event such as a particular type of subjective user state including, for example, a subjective mental state such as “happy”) and a second event type (e.g., another type of event such as a particular type of objective occurrence, for example, favorite sports team winning a game). In some cases, a hypothesis may be represented by an events pattern that may indicate spatial or sequential (e.g., time/temporal) relationships between different event types (e.g., subjective user states, subjective observations, and/or objective occurrences). In some embodiments, a hypothesis may be further defined by an indication of the soundness (e.g., strength) of the hypothesis.
Note that for ease of explanation and illustration, the following description will describe a hypothesis as defining, for example, the sequential or spatial relationships between two, three, or four event types. However, those skilled in the art will recognize that such a hypothesis may also identify the relationships between five or more event types (e.g., a first event type, a second event type, a third event type, a fourth event type, a fifth event type, and so forth).
In some embodiments, a hypothesis may, at least in part, be defined or represented by an events pattern that indicates or suggests a spatial or a sequential (e.g., time/temporal) relationship between different event types. Such a hypothesis, in some cases, may also indicate the strength or weakness of the link between the different event types. That is, the strength or weakness (e.g., soundness) of the correlation between different event types may depend upon, for example, whether the events pattern repeatedly occurs and/or whether a contrasting events pattern has occurred that may contradict the hypothesis and therefore, weaken the hypothesis (e.g., an events pattern that indicates a person becoming tired after jogging for thirty minutes when a hypothesis suggests that a person will be energized after jogging for thirty minutes).
As briefly described above, a hypothesis may be represented by an events pattern that may indicate spatial or sequential (e.g., time or temporal) relationship or relationships between multiple event types. In some implementations, a hypothesis may indicate a temporal relationship or relationships between multiple event types. In alternative implementations a hypothesis may indicate a more specific time relationship or relationships between multiple event types. For example, a sequential pattern may represent the specific pattern of events that occurs along a timeline that may specify the specific amount of time, if there are any, between occurrences of the event types. In still other implementations, a hypothesis may indicate the specific spatial (e.g., geographical) relationship or relationships between multiple event types.
In various embodiments, a hypothesis may initially be provided to a user (e.g., a microblogger or a social networking user) that the hypothesis may or may not be directly associated with. That is, in some embodiments, a hypothesis may be initially provided that directly relates to a user. Such a hypothesis may relate to, for example, one or more subjective user states associated with the user, one or more activities associated with the user, or one or more characteristics associated with the user. In other embodiments, however, a hypothesis may be initially provided that may not be directly associated with a user. For example, a hypothesis may be initially provided that may be particularly associated with a third party (e.g., a spouse of the user, a friend, a pet, and so forth), while in other embodiments, a hypothesis may be initially provided that is directed to a device that may be, for example, operated or used by the user. In still other cases, a hypothesis may be provided that relates to one or more environmental characteristics or conditions.
In some embodiments, the hypothesis to be initially provided to a user may have been originally created based, for example, on reported events as reported by the user through, for example, blog entries, status reports, diary entries, and so forth. Alternatively, such a hypothesis may be supplied by a third party source such as a network service provider or a content provider.
After being presented with the hypothesis, the user may be provided with an opportunity to modify the presented hypothesis. Various types of modifications may be made by the user including, for example, revising or deleting one or more event types identified by the hypothesis, revising one or more relationships between the multiple event types identified by the hypothesis, or adding new event types to the hypothesis. Based on the modifications provided by the user, a modified hypothesis may be generated. In some embodiments, the user may be provided with the option to delete or deactivate the hypothesis or an option to select or revise the type of actions that may be executed based on the modified hypothesis.
Based, at least in part, on the modified hypothesis, one or more actions may be executed. Examples of the types of actions that may be executed include, for example, presenting to the user or a third party one or more advisories related to the modified hypothesis or prompting one or more devices to execute one or more operations based on the modified hypothesis. The one or more advisories that may be presented may include, for example, presentation of the modified hypothesis, presentation of a recommendation for a future action, presentation of a prediction of a future event, and/or presentation of a past event or events. Examples of the types of devices that may be prompted to execute one or more operations include, for example, sensing devices (e.g., sensing devices that can sense physiological or physical characteristics of the user or a third party, sensing devices that can sense the activities of the user or a third party, sensing devices to monitor environmental conditions, and so forth), household appliances, computing or communication devices, environmental devices (e.g., air conditioner, humidifier, air purifier, and so forth), and/or other types of electronic/mechanical devices. In some embodiments, the one or more actions may be in response to, in addition to being based on the modified hypothesis, a reported event.
a and 1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 100 may include at least a computing device 10 (see
Regardless of whether the computing device 10 is a network server or a standalone device, the computing device 10 may be designed to, among other things, present to a user 20* a hypothesis 60 that identifies at least a relationship between a first event type and a second event type, receive from the user 20* one or more modifications 61 to modify the hypothesis 60, and execute one or more actions based, at least in part, on a modified hypothesis 80 resulting, at least in part, from the reception of the one or more modifications 61. As will be further described herein, in embodiments where the computing device 10 is a server that communicates with a user 20a via the mobile device 30, the mobile device 30 may also be designed to perform the above-described operations. In the following, “*” indicates a wildcard. Thus, references to user 20* may indicate a user 20a or a user 20b of
As indicated earlier, in some embodiments, the computing device 10 may be a network server (or simply “server”) while in other embodiments the computing device 10 may be a standalone device. In the case where the computing device 10 is a network server, the computing device 10 may communicate indirectly with a user 20a, one or more third parties 50, and one or more sensing devices 35a via wireless and/or wired network 40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The wireless and/or wired network 40 may comprise of, for example, a local area network (LAN), a wireless local area network (WLAN), personal area network (PAN), Worldwide Interoperability for Microwave Access (WiMAX), public switched telephone network (PTSN), general packet radio service (GPRS), cellular networks, and/or other types of wireless or wired networks. In contrast, in embodiments where the computing device 10 is a standalone device, the computing device 10 may at least directly communicate with a user 20b (e.g., via a user interface 122) and one or more sensing devices 35b.
The mobile device 30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices that can communicate with the computing device 10. In some embodiments, the mobile device 30 may be a handheld device such as a cellular telephone, a smartphone, a Mobile Internet Device (MID), an Ultra Mobile Personal Computer (UMPC), a convergent device such as a personal digital assistant (PDA), and so forth.
In embodiments in which the computing device 10 is a standalone device, the computing device 10 may be any type of portable device (e.g., a handheld device) or non-portable device (e.g., desktop computer or workstation). For these embodiments, the computing device 10 may be any one of a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices. In some embodiments, in which the computing device 10 is a handheld device, the computing device 10 may be a cellular telephone, a smartphone, an MID, an UMPC, a convergent device such as a PDA, and so forth. In various embodiments, the computing device 10 may be a peer-to-peer network component device. In some embodiments, the computing device 10 and/or the mobile device 30 may operate via a Web 2.0 construct (e.g., Web 2.0 application 268).
The one or more sensing devices 35* may include one or more of a variety of different types of sensing/monitoring devices to sense various aspects (e.g., characteristics, features, or activities) associated with a user 20*, one or more third parties 50, one or more network and/or local devices 55, one or more external activities, one or more environmental characteristics, and so forth. Examples of such sensing devices 35* include, for example, those devices that can measure physical or physical characteristics of a subject (e.g., a user 20* or a third party 50) such as a heart rate sensor device, blood pressure sensor device, blood glucose sensor device, functional magnetic resonance imaging (fMRI) device, a functional near-infrared (fNIR) device, blood alcohol sensor device, temperature sensor device (e.g., thermometer), respiration sensor device, blood cell-sorting sensor device (e.g., to sort between different types of blood cells), and so forth. Another type of devices that may be included in the one or more sensing devices 35 includes, for example, those that can sense the activities of their subjects (e.g., user 20* or a third party 50). Examples of such devices include, for example, pedometers, accelerometers, an image capturing device (e.g., digital or video camera), toilet monitoring devices, exercise machine sensor devices, and so forth. Other types of sensing devices 35* include, for example, global positioning system (GPS) devices, environmental sensors such as a room thermometer, barometer, air quality sensor device, humidity sensor device, sensing devices to sense characteristics or operational performances of devices, and so forth.
The one or more third parties 50 depicted in
There are at least two ways that the computing device 10 may initially acquire a hypothesis 60. One way is to acquire the hypothesis 60 from a third party source such as a network service provider, a content provider, or an application provider. A second way is to self-develop the hypothesis 60. For example, in various implementations, and regardless of whether the computing device 10 is a standalone device or as a network server, a hypothesis 60 may be initially developed (e.g., created) by the computing device 10 based, at least in part, on events data that may be provided by one or more sources (e.g., a user 20*, one or more third parties 50, or one or more sensing devices 35*). The events data provided by the one or more sources may indicate past events as reported by the sources. In some cases, such data may be provided by the one or more sources via electronic entries such as blog entries (e.g., microblog entries), status reports, electronic messages (email, instant messages (IMs), etc.), diary entries, and so forth.
By identifying a repeatedly reoccurring pattern of reported events, for example, a hypothesis 60 may be developed by the computing device 10. The resulting hypothesis 60 may indicate a spatial or a sequential (temporal or specific time) relationship between at least a first event type (e.g., a type of subjective user state, a type of subjective observation, or a type of objective occurrence) and a second event type (e.g., a type of subjective user state, a type of subjective observation, or a type of objective occurrence).
The computing device 10 may then present (e.g., indicate via a user interface 122 or transmit via the wireless and/or wired network 40) to a user 20* the hypothesis 60. In embodiments where the computing device 10 is a server, the computing device 10 may present the hypothesis 60 to a user 20a by transmitting the hypothesis 60 to the mobile device 30 via the wireless and/or wired network 40. The mobile device 30 may then audibly and/or visually present the hypothesis 60 to the user 20a. On the other hand, in embodiments where the computing device 10 is a standalone device, the hypothesis 60 may be directly presented to a user 20b by audibly or visually indicating the hypothesis 60 to the user 20a via a user interface 122.
The hypothesis 60 may be presented to a user 20* (e.g., user 20a or user 20b) in a variety of different ways. For example, in various implementations, the hypothesis 60* may be presented in graphical form, in pictorial form, in textual form, in audio form and so forth. In some implementations, the hypothesis 60 to be presented may be modifiable such that one or more event types and/or their relationships (e.g., spatial or temporal/time relationships) with respect to each other that are identified by the hypothesis 60 may be revised or even deleted. Such modifiable hypothesis 60 may also allow a user 20* to add to the hypothesis 60 additional event types with respect to the event types already included in the hypothesis 60. In some implementations, the computing device 10 may present to the user 20* an option to delete or deactivate the hypothesis 60.
After presenting the hypothesis 60 to the user 20*, the computing device 10 may be designed to receive from the user 20* one or more modifications 61 to modify the hypothesis 60. In embodiments in which the computing device 10 is a server, the computing device 10 may receive the one or more modifications 61 from the user 20a through mobile device 30 and via the wireless and/or wired network 40. Note that for these embodiments, the mobile device 30 may directly receive the one or more modifications 61 from the user 20a and may then transmit the one or more modifications 61 to the computing device 10. In alternative embodiments in which the computing device 10 is a standalone device, the computing device 10 may receive the one or more modifications 61 directly from the user 20b via a user interface 122.
In various implementations, the one or more modifications 61 received from the user 20* may be for revising and/or deleting one or more event types and their relationships with respect to each other that are indicated by the hypothesis 60. In some cases, the one or more modifications 61 may also include modifications to add one or more event types with to respect to the event types already included in the hypothesis 60. In other words, the one or more modifications 61 to be received by the computing device 10 and/or by the mobile device 30 may include one or more modifications for adding one or more event types to the hypothesis 60 and their relationships (e.g., spatial or temporal relationships) with the event types already included in the hypothesis 60. Note that in some cases, the computing device 10 (as well as the mobile device 30) may receive from the user 20*, an indication of one or more actions to be executed based, at least in part, on the resulting modified hypothesis 80.
In any event, the computing device 10 may then generate a modified hypothesis 80 by modifying the hypothesis 60 based on the one or more modifications 61 received from the user 20* (user 20a or user 20b). In some embodiments, the modified hypothesis 80 may be stored in memory 140.
The computing device 10 (as well as the mobile device 30) may then execute one or more actions based, at least in part, on the modified hypothesis 80 resulting from the reception of the one or more modifications 61 by the computing device 10. Various types of actions may be executed by the computing device 10 and/or by the mobile device 30 in various alternative embodiments. For example, in some embodiments, the computing device 10 and/or the mobile device 30 may present one or more advisories 90 to a user 20* or to one or more third parties 50. For instance, in embodiments where the computing device 10 is a server, the computing device 10 may present the one or more advisories 90 to a user 20a by transmitting the one or more advisories 90 to the mobile device 30 (or to one or more third parties 50) via a wireless and/or wired network 40. The mobile device 30 may then present the one or more advisories 90 to a user 20a by audibly and/or visually indicating to the user 20a (e.g., via an audio and/or display system) the one or more advisories 90.
In embodiments in which the computing device 10 is a standalone device, the computing device 10 may present the one or more advisories 90 to a user 20b by audibly and/or visually indicating to the user 20b (e.g., via an audio and/or display system) the one or more advisories. For these embodiments, the computing device 10 may present the one or more advisories 90 to one or more third parties 50 by transmitting the one or more advisories 90 to the one or more third parties 50 via a wireless and/or wired network 40.
The one or more advisories 90 to be presented by the computing device 10 or by the mobile device 30 may be one or more of a variety of advisories that may be associated with the modified hypothesis 80 and that can be presented. For example, in some implementations, the one or more advisories 90 to be presented may include at least one form (e.g., an audio form, a graphical form, a pictorial form, a textual form, and so forth) of the modified hypothesis 80. In the same or different implementations, the one or more advisories 90 to be presented may include a prediction of a future event or an indication of an occurrence of a past reported event. In the same or different implementations, the one or more advisories 90 to be presented may include a recommendation for a future course of action and in some cases, justification for the recommendation.
In some embodiments, the computing device 10 and/or the mobile device 30 may execute one or more actions by prompting 91* one or more devices (e.g., one or more sensing devices 35* and/or one or more network/local devices 55) to execute one or more operations. For example, prompting 91* one or more sensing devices 35* to sense various characteristics associated with a user 20* or a third party 50, or prompting one or more household devices (which may be network and/or local devices 55) to perform one or more operations. Note that references to “prompting one or more to execute one or more devices” herein may be in reference to directing, instructing, activating, requesting, and so forth, one or more devices to execute one or more operations.
In embodiments in which the computing device 10 is a server, the computing device 10 may indirectly or directly prompt one or more devices. For example, in some embodiments, the computing device 10 may indirectly prompt one or more devices to execute one or more operations by transmitting to the mobile device 30 a request or instructions to prompt other devices to execute one or more operations. In response to the request or instructions transmitted by the computing device 10, the mobile device 30 may directly prompt 91′ one or more devices (e.g., sensing devices 35* and/or network and/or local devices 55) to execute one or more operations. In the same or different embodiments, the computing device 10 may alternatively or complimentarily directly prompt 91 the one or more devices (e.g., sensing devices 35 and/or network and/or local devices 55) to execute one or more operations. In embodiments in which the computing device 10 is a standalone device, the computing device 10 may directly (e.g., without going through mobile device 30) prompt 91 the one or more devices (e.g., sensing devices 35* and/or network and/or local devices 55) to execute the one or more operations.
In some embodiments, the one or more actions to be executed by the computing device 10 or by the mobile device 30 may be in response, at least in part, to a reported event. For instance, the one or more actions to be executed by the computing device 10 or by the mobile device 30 may be in response to a reported event 62 that at least substantially matches with at least one of the event types identified by the modified hypothesis 80. To illustrate, suppose the modified hypothesis 80 indicates that the gas tank of car belonging to a user 20* is always empty (e.g., a first event type) whenever a particular friend returns a car after borrowing it (e.g., a second event type). In response to receiving data (e.g., in the form of a blog entry or status report) that indicates that the particular friend has again borrowed and returned the user's car (e.g., reported event 62), and based at least in part on the modified hypothesis 80, the computing device 10 may execute one or more actions (e.g., transmitting one or more advisories such as a warning to fill-up the gas tank to the mobile device 30). In this example, the computing device 10 may execute the one or more actions because the reported event 62 at least substantially matches the second event type as identified by the modified hypothesis 80. Note that the reported event 62 that may initiate the one or more actions to be executed by the computing device 10 or the mobile device 30 (which in the above example, may execute one or more actions by audibly or visually indicating the one or more advisories 90) may be reported by a user 20*, one or more third parties 50, or from one or more sensing devices 35*.
Referring particularly now to the computing device 10 of
In various embodiments, the computing device 10 may include a hypothesis presentation module 102, a modification reception module 104, a hypothesis modification module 106, an action execution module 108, a reported event reception module 110, a hypothesis development module 112, a network interface 120 (e.g., network interface card or NIC), a user interface 122 (e.g., a display monitor, a touchscreen, a keypad or keyboard, a mouse, an audio system including a microphone and/or speakers, an image capturing system including digital and/or video camera, and/or other types of interface devices), a memory 140, and/or one or more applications 126. In some implementations, a copy of the hypothesis 60 and/or a copy of a modified hypothesis 80 may be stored in memory 140. The one or more applications 126 may include one or more communication applications 267 (e.g., email application, IM application, text messaging application, a voice recognition application, and so forth) and/or one or more Web 2.0 applications 268. Note that in various embodiments, a persistent copy of the one or more applications 126 may be stored in memory 140.
Turning now to
In some implementations, the hypothesis 60 to be presented may identify the relationships between the first, the second event type, a third event type, a fourth event type, and so forth. As will be further described herein, the hypothesis 60 to be presented by the hypothesis presentation module 102 may identify the relationship between a variety of different event types (e.g., identifying a relationship between a subjective user state and an objective occurrence, identifying a relationship between a first objective occurrence and a second objective occurrence, and so forth). In some implementations, the hypothesis 60 to be presented may have been previously developed based on data provided by the user 20*. In the same or different implementations, the hypothesis 60 to be presented may be related to the user 20*, to one or more third parties 50, to one or more devices, or to one or more environmental characteristics or conditions.
In order to present a hypothesis 60, the hypothesis presentation module 102 may further include one or more sub-modules. For instance, in various implementations, the hypothesis presentation module 102 may include a network transmission module 202 configured to transmit the hypothesis 60 to a user 20a via at least one of a wireless network and a wired network (e.g., wireless and/or wired network 40).
In the same or different implementations, the hypothesis presentation module 102 may include a user interface indication module 204 configured to indicate the hypothesis 60 to a user 20b via a user interface 122 (e.g., an audio system including one or more speakers and/or a display system including a display monitor or touchscreen). The user interface indication module 204 may, in turn, further include one or more additional sub-modules. For example, in some implementations, the user interface indication module 204 may include an audio indication module 206 configured to audibly indicate the hypothesis 60 to user 20b.
In the same or different implementations, the user interface indication module 204 may include a visual indication module 208 configured to visually indicate the hypothesis 60 to user 20b. Note that, and as will be further described herein, the visual indication module 208 may visually indicate the hypothesis 60 in a variety of different manners including, for example, in graphical form, in textual form, in pictorial form, and so forth. Further, in various implementations, the visual indication module 208 may represent the various event types and their relationships with respect to each other as indicated by the hypothesis 60 by symbolic representations (see, for example,
For example, the visual indication module 208 indicating visually to the user 20* symbolic representations that may represent the various event types indicated by the hypothesis 60 including, for example, a first symbolic representation representing the first event type, a second symbolic representation representing the second event type, a third symbolic representation representing a third event type, a fourth symbolic representation representing a fourth event type, and so forth. A symbolic representation may be, for example, an icon, an emoticon, a figure, text such as a word or phrase, and so forth. Similarly, the visual indication module 208 may indicate the relationships (e.g., spatial or temporal relationships) between the event types, as identified by the hypothesis 60, by visually indicating symbolic representations that represents the relationships between the event types. Such symbolic representations representing the relationships between the event types may include, for example, specific spacing or angle between the symbolic representations representing the event types (e.g., as set against a grid background), lines or arrows between the symbolic representations representing the event types, text including a word or phrase, and/or a combination thereof.
In some implementations, the visual indication module 208 may further include a visual attribute adjustment module 210 that is configured to indicate the strength of the hypothesis 60 by adjusting a visual attribute (e.g., boldness, color, background, and so forth) associated with at least one of the symbolic representations representing the event types and their relationships. In various implementations, the hypothesis presentation module 102 may include an editable hypothesis presentation module 212 configured to present an editable form of the hypothesis 60 to the user 20*. In some embodiments, the editable form of the hypothesis 60 to be presented by the editable hypothesis presentation module 212 may include symbolic representations representing the event types and their relationships with respect to each other that may be modified and/or deleted. In the same or different implementations, the editable form of the hypothesis 60 may be modified such that additional event types may be added with respect to the event types already identified by the hypothesis 60.
In some implementations, the hypothesis presentation module 102 of
Turning now to
As depicted in
Various types of modifications 61 for modifying the hypothesis 60 may be received by the modification reception module 104. For instance, in some implementations, modifications 61 for deleting one or more of the event types (e.g., the first event type, the second event type, and so forth) indicated by the hypothesis 60 may be received by the modification reception module 104. For example, the modification reception module 104 may receive one or more modifications 61 for deleting a third event type, a fourth event type, and so forth, indicated by the hypothesis 60.
In some implementations, the modification reception module 104 may be designed to receive one or more modifications 61 for adding additional event types (e.g., a third event type, a fourth event type, and so forth) to the hypothesis 60 and with respect to the at least first event type and the second event type already included in the hypothesis 60. Note that when adding a new event type to the hypothesis 60, the relationships (e.g., spatial or temporal) between the added event type (e.g., a third event type) and the first event type and the second event type may also be provided.
In some implementations, the modification reception module 104 may be designed to receive one or more modifications 61 for revising one or more of the event types (e.g., the first event type and the second event type) included in the hypothesis 60. In the same or different implementations, the modification reception module 104 may be configured to receive one or more modifications 61 for modifying (e.g., revising) the relationship or relationships (e.g., spatial, temporal, or specific time relationship) between the event types (e.g., the first event type, the second event type, and so forth) included in the hypothesis 60. The one or more modifications 61 to be received by the modification reception module 104 may be for modifying any type of event types including, for example, a subjective user state type, a subjective observation type, and/or an objective occurrence type.
In various implementations, the computing device 10 may include a hypothesis modification module 106 that is designed to modify the hypothesis 60 based, for example, on the one or more modifications 61 received by the modification reception module 104. As a result of modifying the hypothesis 60, a modified hypothesis 80 may be generated, which in some cases may be stored in memory 140.
c illustrates particular implementations of the action execution module 108 of
The advisory presentation module 232 may further include one or more sub-modules in various alternative implementations. For instance, in various implementations, the advisory presentation module 232 may include a user interface indication module 234 that is configured to indicate the at least one advisory 90 via a user interface 122. In the same or different implementations, the advisory presentation module 232 may include a network transmission module 236 configured to transmit the at least one advisory 90 via a wireless and/or wired network 40. The network transmission module 236 may transmit the at least one advisory 90 to, for example, a user 20a (e.g., via mobile device 30) and/or one or more third parties 50.
In the same or different implementations, the advisory presentation module 232 may include a modified hypothesis presentation module 238 configured to present one or more form of the modified hypothesis 80. For instance, presenting an audio form, a textual form, a pictorial form, a graphical form, and/or other forms of the modified hypothesis 80. The modified hypothesis presentation module 238 may present the at least one form of the modified hypothesis 80 by presenting an indication of a spatial, temporal, or specific time relationship between at least two event types indicated by the modified hypothesis 80. The at least one form of the modified hypothesis 80 presented by the modified hypothesis presentation module 238 may indicate the relationship between the event types indicated by the modified hypothesis 80 including any combination of subjective user state types, objective occurrence types, and/or subjective observation types (e.g., indicate a relationship between a first type of subjective user state and a second type of subjective user state, indicate a relationship between a type of subjective user state and a type of objective occurrence, indicate a relationship between a type of subjective user state and a type of subjective observation, and so forth) as indicated by the modified hypothesis 80.
The advisory presentation module 232 may further include other sub-modules in various implementations. For example, in some implementations, the advisory presentation module 232 may include a prediction presentation module 240 configured to present at least one advisory 90 relating to a predication of one or more future events based, at least in part, on the modified hypothesis 80. For example, predicting that “a personal passenger vehicle belonging to the user will breakdown sometime during the coming week.”
In various implementations, the advisory presentation module 232 may include a recommendation presentation module 242 configured to present at least one advisory 90 recommending a future course of action based, at least in part, on the modified hypothesis 80. For example, recommending that “the user take his personal passenger vehicle into the shop for repairs.” In some implementations, the recommendation presentation module 242 may include a justification presentation module 244 configured to present a justification for the recommendation presented by the recommendation presentation module 242. For example, indicating that “the user should take her personal passenger vehicle into the shop because the last time the user did not take her personal vehicle into the shop after driving it for 15 thousand miles without being serviced, the personal vehicle broke down.”
In some implementations, the advisory presentation module 232 may include a past event presentation module 246 configured to present an indication of one or more past events based, at least in part, on the modified hypothesis 80 (e.g., “the last time your husband went drinking, he overslept”).
In various implementations, the action execution module 108 may include a device prompting module 248 configured to prompt (e.g., as indicated by ref 91) at least one devices to execute at least one operation based, at least in part, on the modified hypothesis 80. The at least one device to be prompted to execute the at least one operation may include, for example, one or more sensing devices 35*, or one or more network/local devices 55. Network/local devices 55 are any device that may interface with a wireless and/or wired network 40 and/or any device that may be local with respect to, for example, the computing device 10. Examples of network/local devices 55 includes, for example, household devices such as household appliances, automobiles (or portions thereof), environmental devices such as air conditioners, humidifier, air purifiers, and so forth, electronic/communication devices (e.g., mobile device 30), and so forth.
In various alternative implementations, the device prompting module 248 may include one or more sub-modules. For example, in some implementations, the device prompting module 248 may include a device instruction module 250 configured to directly or indirectly instruct the at least one device (e.g., directly instructing a local device or indirectly instructing a network device via wireless and/or wired network 40) to execute the at least one operation. In the same or different implementations, the device prompting module 248 may include a device activation module 252 configured to directly or indirectly activate the at least one device (e.g., directly activating a local device or indirectly activating a network device via wireless and/or wired network 40) to execute the at least one operation. In the same or different implementations, the device prompting module 248 may include a device configuration module 254 designed to directly or indirectly configure the at least one device (e.g., directly configuring a local device or indirectly configuring a network device via wireless and/or wired network 40) to execute the at least one operation.
Referring back to the action execution module 108 of
In various implementations, the computing device 10 of
In various implementations, the computing device 10 may include a network interface 120, which may be a device designed to interface with a wireless and/or wired network 40. Examples of such devices include, for example, a network interface card (NIC) or other interface devices or systems for communicating through at least one of a wireless network or wired network 40. In some implementations, the computing device 10 may include a user interface 122. The user interface 122 may comprise any device that may interface with a user 20b. Examples of such devices include, for example, a keyboard, a display monitor, a touchscreen, a microphone, a speaker, an image capturing device such as a digital or video camera, a mouse, and so forth.
The computing device 10 may include a memory 140. The memory 140 may include any type of volatile and/or non-volatile devices used to store data. In various implementations, the memory 140 may comprise, for example, a mass storage device, a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read-only memory (EPROM), random access memory (RAM), a flash memory, a synchronous random access memory (SRAM), a dynamic random access memory (DRAM), and/or other memory devices. In various implementations, the memory 140 may store an existing hypotheses 80 and/or historical data (e.g., historical data including, for example, past events data or historical events patterns related to a user 20*, related to a subgroup of the general population that the user 20* belongs to, or related to the general population).
d illustrates particular implementations of the mobile device 30 of
For example, and similar to the computing device 10, the mobile device 30 may also include a hypothesis presentation module 102′, a modification reception module 104′, an action execution module 108′, a reported event reception module 110′, a network interface 120′, a user interface 122′, a memory 140′, and/or one or more applications 126′, which may include one or more communication applications 267′ and/or one or more Web 2.0 applications 268′. Note that in some implementations, memory 140′ may store a copy of the hypothesis 60 and/or the modified hypothesis 80′. These components and modules may generally perform the same or similar functions as their counterparts in the computing device 10 the computing device 10 with certain exceptions. For instance, with respect to the hypothesis presentation modules 102* of the mobile device 30 and the computing device 10, in the mobile device 30 case the hypothesis presentation module 102′ may present (e.g., audibly or visually indicate) a hypothesis 60 to a user 20a via a user interface 122′ while in the computing device 10 the hypothesis presentation module 102 may present a hypothesis 60 to a user 20a by transmitting the hypothesis 60 to the mobile device 30 via wireless and/or wired network 40 (e.g., in embodiments in which the computing device 10 is a server) or may present (e.g., audibly or visually indicate) the hypothesis 60 to a user 20b via a user interface 122 (e.g., in embodiments in which the computing device 10 is a standalone device). Note also that the unlike the computing device 10, the mobile device 30 may not include a hypothesis modification module 106 or a hypothesis development module 112 since operations performed by such modules may be performed by, for example, a server (e.g., computing device 10 in embodiments in which the computing device 10 is a server).
In addition to those components and modules described above, the mobile device 30 may include a modification transmission module 219 and an advisory reception module 235. The modification transmission module 219 may be designed to, among other things, transmit one or more modifications 61 (e.g., as provided by a user 20a through user interface 122′) to a server (e.g., computing device 10 in embodiments in which the computing device 10 is a server) via, for example, wireless and/or wired network 40. The advisory reception module 235 may be designed to receive one or more advisories 90 related to the modified hypothesis 80 from the computing device 10 via, for example, wireless and/or wired network 40, the modified hypothesis 80 being generated by the computing device 10 (e.g., in embodiments in which the computing device 10 is a server) based on the hypothesis 60 and the one or more modifications 61 received from the mobile device 30.
e illustrates particular implementations of the hypothesis presentation module 102′ of the mobile device 30 of
f illustrates particular implementations of the modification reception module 104′ of the mobile device 30 of
g illustrates particular implementations of the action execution module 108′ of the mobile device 30 of
There are many ways that a hypothesis 60 (or a modified hypothesis 80) may be visually or audibly indicated to a user 20*.
i is an exemplary pictorial version of the hypothesis 60 textually illustrated in
j is another exemplary pictorial version of the hypothesis 60 that was textually illustrated in
k illustrates a pictorial/graphical representation of a hypothesis 60 (e.g., a hypothesis 60 that links going to work, arriving at work, drinking coffee, learning boss plans to leave town, boss leaving town, and overall user state) being pictorially/graphically represented on a user interface display 290. In this example, most of the event types indicated by the hypothesis 60 are represented by blocks (e.g., symbolic representations 291a, 291b, 291c, 291d, and 291e) below a timeline. The overall user state is represented symbolically by a line to indicate the specific overall user state at any given moment in time. Note that by employing the robust systems and methods described herein, a user may be able to modify the hypothesis 60 depicted in the user interface display 290. That is, the user may choose to modify the hypothesis 60 by deleting symbolic representations 291a, 291b, and 291c (e.g., representing going to work, arriving at work, and drinking coffee) if the user feels that the events represented by the symbolic representations may not be relevant to the user having a very good overall user state.
The various features and characteristics of the components, modules, and sub-modules of the computing device 10 and mobile device 30 presented thus far will be described in greater detail with respect to the processes and operations to be described herein.
In
Further, in the following figures that depict various flow processes, various operations may be depicted in a box-within-a-box manner. Such depictions may indicate that an operation in an internal box may comprise an optional example embodiment of the operational step illustrated in one or more external boxes. However, it should be understood that internal box operations may be viewed as independent operations separate from any associated external boxes and may be performed in any sequence with respect to all other illustrated operations, or may be performed concurrently.
In any event, after a start operation, the operational flow 300 may move to a hypothesis presentation operation 302 for presenting to a user a hypothesis identifying at least a relationship between a first event type and a second event type. For instance, the hypothesis presentation module 102* of the mobile device 30 or the computing device 10 presenting (e.g., indicating via a user interface 122* or transmitting via wireless and/or wired network 40) to a user 20* a hypothesis 60 identifying at least a relationship between a first event type (e.g., a subjective user state, a subjective observation, or an objective occurrence) and a second event type (e.g., a subjective user state, a subjective observation, or an objective occurrence).
Next, operational flow 300 may include a modification reception operation 304 for receiving from the user one or more modifications to modify the hypothesis. For instance, the modification reception module 104* of the mobile device 30 or the computing device 10 receiving (e.g., receiving via a user interface 122 or via wireless and/or wired network 40) from the user 20* one or more modifications 61 to modify the hypothesis 60.
Finally, operation flow 300 may include an action execution operation 306 for executing one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. For instance, the action execution module 108* of the mobile device 30 or the computing device 10 executing one or more actions (e.g., presenting one or more advisories 90 or configuring a device to execute one or more operations) based, at least in part, on a modified hypothesis 80 resulting, at least in part, from the reception of the one or more modifications 61. In a more specific example, the action execution module 108′ of the mobile device 30 executing one or more actions (e.g., displaying the modified hypothesis 80 or prompting 91′ one or more devices such as one or more sensing devices 35* or network/local devices 55 to execute one or more operations) after receiving from the computing device 10 (e.g., when the computing device 10 is a server) a request for executing the one or more actions. In this example, the request may have been generated and transmitted by the computing device 10 based, at least in part, on the modified hypothesis 80.
Referring back to the hypothesis presentation operation 302, the hypothesis 60 presented through the hypothesis presentation operation 302 may be presented in a variety of different ways. For example, in some implementations, the hypothesis presentation operation 302 may include an operation 402 for transmitting to the user, via at least one of a wireless network and a wired network, the hypothesis as depicted in
In some alternative implementations, the hypothesis presentation operation 302 may include an operation 403 for indicating to the user, via a user interface, the hypothesis as depicted in
In some implementations, operation 403 may include an operation 404 for indicating audibly to the user the hypothesis as depicted in
In the same or different implementations, operation 403 may include an operation 405 for indicating visually to the user the hypothesis as depicted in
In some implementations, operation 405 may further include an operation 406 for indicating visually to the user the hypothesis via a display screen as depicted in
The hypothesis 60 to be visually indicated through operation 405 may be indicated in a variety of ways including, for example, in text form, in graphical form, in pictorial form, and so forth. For example, in various implementations, operation 405 may include an operation 407 for indicating visually to the user a first symbolic representation representing the first event type and a second symbolic representation representing the second event type as depicted in
In some implementations, operation 407 may further include an operation 408 for indicating visually to the user a third symbolic representation representing the relationship between the first event type and the second event type as depicted in
Operation 408 may include, in various implementations, an operation 409 for adjusting a visual attribute associated with at least one of the first symbolic representation, the second symbolic representation, and the third symbolic representation to indicate strength of the hypothesis as depicted in
In some alternative implementations, operation 408 may include an operation 410 for indicating visually to the user a fourth symbolic representation representing strength of the hypothesis as depicted in
In various implementations, operation 407 may include an operation 411 for indicating visually to the user a first icon representing the first event type and a second icon representing the second event type as depicted in
In alternative implementations, operation 407 may include an operation 412 for indicating visually to the user a first textual representation representing the first event type and a second textual representation representing the second event type as depicted in
Operation 412, in turn, may include an operation 413 for indicating visually to the user a textual passage including the first and second textual representations, the textual passage representing the relationship between the first event type and the second event type as depicted in
In various implementations, the hypothesis presentation operation 302 of
As further depicted in
Operation 415 may, in turn, comprise one or more additional operations in various alternative implementations. For example, in some implementations, operation 415 may include an operation 416 for presenting to the user an editable form of the hypothesis including at least a first deletable symbolic representation representing the first event type and a second deletable symbolic representation representing the second event type as depicted in
As a further illustration, suppose the user 20* is presented with the editable form of the hypothesis 60 that may have been previously developed based on events previously reported by the user 20* that indicates that the user 20* may get a stomach ache (e.g., a first event type) if the user 20* eats at a particular Mexican restaurant (e.g., a second event type). After being presented with the editable form of the hypothesis 60, the user 20* recognizes that the hypothesis 60 may have been based solely on the user 20* last reported visit to that particular restaurant when the user 20* got sick and now realizes that the cause of his stomach ache may not have been from the visit to that particular restaurant but rather eating a new dish containing a new ingredient he had never eaten before. Thus, the user 20* may want to modify the editable form of the hypothesis 60 to delete one of the event types identified by the hypothesis 60 (e.g., the second symbolic representation representing the second event type that indicates eating at the particular Mexican restaurant) and replacing the deleted event type (or the second symbolic representation) with a new event type (e.g., a third symbolic representation representing the consumption of the new dish containing the new ingredient).
In some implementations, operation 415 may include an operation 417 for presenting to the user an editable form of the hypothesis including at least a first modifiable symbolic representation representing the first event type and a second modifiable symbolic representation representing the second event type as depicted in
In some implementations, operation 415 may include an operation 418 for presenting to the user an editable form of the hypothesis including at least an editable symbolic representation representing the relationship between the first event type and the second event type as depicted in
For example, in some implementations, the editable form of the hypothesis 60 may be presented, for example, on a display monitor in graphical or pictorial form showing a first and a second icon representing the first event type and the second event type. The relationship (e.g., spatial or temporal/specific time relationship) between the first event type and the second event type may be represented in the graphical representation by spacing between the first and the second icon (e.g., the first and second icons being set against a grid background), a line between the first and the second icon, an arrow between the first and the second icon, and so forth, that may be editable. In this example, the symbolic representation representing the relationship between the first event type and the second event type would be the spacing between the first and the second icon, the line between the first and the second icon, the arrow between the first and the second icon, and so forth,
As further depicted in
In the same or different implementations, operation 418 may include an operation 420 for presenting to the user an editable form of the hypothesis including at least a modifiable symbolic representation representing the relationship between the first event type and the second event type as depicted in
In some implementations, operation 414 of
As further depicted in
In the same or different implementations, operation 421 may include an operation 423 for presenting to the user an editable form of the hypothesis including a modifiable symbolic representation representing the third event type as depicted in
In the same or different implementations, operation 421 may include an operation 424 for presenting to the user an editable form of the hypothesis including another editable symbolic representation representing a fourth event type as depicted in
In various implementations, operation 424 may further include an operation 425 for presenting to the user an editable form of the hypothesis including a deletable symbolic representation representing the fourth event type as depicted in
In the same or different implementations, operation 424 may include an operation 426 for presenting to the user an editable form of the hypothesis including a modifiable symbolic representation representing the fourth event type as depicted in
Referring back to the hypothesis presentation operation 302 of
In the same or different implementations, the hypothesis presentation operation 302 may include an operation 428 for presenting to the user an option to deactivate or ignore the hypothesis as depicted in
Various types of relationships between various types of events may be indicated by the hypothesis 60 presented in the hypothesis presentation operation 302 of
In some implementations, the hypothesis presentation operation 302 may include an operation 430 for presenting to the user a hypothesis identifying at least a spatial relationship between the first event type and the second event type as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 431 for presenting to the user a hypothesis identifying at least a relationship between at least a first subjective user state type and a second subjective user state type as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 432 for presenting to the user a hypothesis identifying at least a relationship between at least a subjective user state type and a subjective observation type as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 433 for presenting to the user a hypothesis identifying at least a relationship between at least a subjective user state type and an objective occurrence type as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 434 for presenting to the user a hypothesis identifying at least a relationship between at least a first subjective observation type and a second subjective observation type as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 435 for presenting to the user a hypothesis identifying at least a relationship between at least a subjective observation type and an objective occurrence type as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 436 for presenting to the user a hypothesis identifying at least a relationship between at least a first objective occurrence type and a second objective occurrence type as depicted in
In various implementations, the hypothesis to be presented through the hypothesis presentation operation 302 of
The hypothesis to be presented through the hypothesis presentation operation 302 of
In some implementations, the hypothesis presentation operation 302 may include an operation 439 for presenting to the user a hypothesis relating to a third party as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 440 for presenting to the user a hypothesis relating to a device as depicted in
In some implementations, the hypothesis presentation operation 302 may include an operation 441 for presenting to the user a hypothesis relating to one or more environmental characteristics as depicted in
In various embodiments, the hypothesis 60 to be presented through the hypothesis presentation operation 302 of
In various implementations, operation 442 may further include an operation 443 for presenting to the user a hypothesis identifying at least relationships between the first event type, the second event type, the third event type, and a fourth event type as depicted in
Referring back to the modification reception operation 304 of
In some implementations, operation 544 may further include an operation 545 for transmitting the one or more modifications to a server via at least one of a wireless network and a wired network as depicted in
In some implementations, the modification reception operation 304 may include an operation 546 for receiving the one or more modifications from at least one of a wireless network and a wired network as depicted in
The one or more modifications received through the modification reception operation 304 of
In some implementations, operation 547 may include an operation 548 for receiving the one or more modifications via one or more blog entries as provided by the user as depicted in
In some implementations, operation 547 may include an operation 549 for receiving the one or more modifications via one or more status reports as provided by the user as depicted in
In some implementations, operation 547 may include an operation 550 for receiving the one or more modifications via one or more electronic messages as provided by the user as depicted in
In some implementations, operation 547 may include an operation 551 for receiving the one or more modifications via one or more diary entries as provided by the user as depicted in
Various types of modifications may be received through the modification reception operation 304 of
In certain implementations, operation 552 may further include an operation 553 for receiving from the user a modification to delete at least a fourth event type from the hypothesis as depicted in
In various implementations, the modification reception operation 304 of
In some implementations, operation 554 may further include an operation 555 for receiving from the user a modification to add to the hypothesis at least a fourth event type with respect to the first event type and the second event type, and with respect to the third event type to be added to the hypothesis as depicted in
In various implementations, the modification reception operation 304 of
In some implementations, operation 556 may further include an operation 557 for receiving from the user a modification to revise the second event type of the hypothesis as depicted in
In some implementations, the modification reception operation 304 of
In some implementations, the modification reception operation 304 may include an operation 559 for receiving from the user a modification to modify at least one of the first event type and the second event type including at least one type of subjective user state as depicted in
In some implementations, the modification reception operation 304 may include an operation 560 for receiving from the user a modification to modify at least one of the first event type and the second event type including at least one type of subjective observation as depicted in
In some implementations, the modification reception operation 304 may include an operation 561 for receiving from the user a modification to modify at least one of the first event type and the second event type including at least one type of objective occurrence as depicted in
In some implementations, the modification reception operation 304 may include an operation 562 for modifying the hypothesis based on the one or more modifications to generate the modified hypothesis as depicted in
Referring back to the action execution operation 306 of
Various types of advisories may be presented through operation 663. For example, in some implementations, operation 663 may include an operation 664 for indicating the one or more advisories relating to the modified hypothesis via user interface as depicted in
In some selective implementations, operation 664 may include an operation 665 for receiving the one or more advisories from a server prior to said indicating as depicted in
In the same or different implementations, operation 663 may include an operation 666 for transmitting the one or more advisories related to the modified hypothesis via at least one of a wireless network and a wired network as depicted in
In various implementations, operation 666 may further include an operation 667 for transmitting the one or more advisories related to the modified hypothesis to the user as depicted in
In some implementations, operation 666 may include an operation 668 for transmitting the one or more advisories related to the modified hypothesis to one or more third parties as depicted in
In various implementations, the modified hypothesis 80 may be presented through operation 663. For example, in some implementations, operation 663 may include an operation 669 for presenting at least one form of the modified hypothesis as depicted in
Operation 669, in turn, may include an operation 670 for presenting an indication of a relationship between at least two event types as indicated by the modified hypothesis as depicted in
In some implementations, operation 670 may include an operation 671 for presenting an indication of a temporal or specific time relationship between the at least two event types as indicated by the modified hypothesis as depicted in
In the same or alternative implementations, operation 670 may include an operation 672 for presenting an indication of a spatial relationship between the at least two event types as indicated by the modified hypothesis as depicted in
In the same or different implementations, operation 670 may include an operation 673 for presenting an indication of a relationship between at least a first type of subjective user state and a second type of subjective user state as indicated by the modified hypothesis as depicted in
In the same or different implementations, operation 670 may include an operation 674 for presenting an indication of a relationship between at least a type of subjective user state and a type of objective occurrence as indicated by the modified hypothesis as depicted in
In the same or different implementations, operation 670 may include an operation 675 for presenting an indication of a relationship between at least a type of subjective user state and a type of subjective observation as indicated by the modified hypothesis as depicted in
In the same or different implementations, operation 670 may include an operation 676 for presenting an indication of a relationship between at least a first type of objective occurrence and a second type of objective occurrence as indicated by the modified hypothesis as depicted in
In the same or different implementations, operation 670 may include an operation 677 for presenting an indication of a relationship between at least a type of objective occurrence and a type of subjective observation as indicated by the modified hypothesis as depicted in
In the same or different implementations, operation 670 may include an operation 678 for presenting an indication of a relationship between at least a first type of subjective observation and a second type of subjective observation as indicated by the modified hypothesis as depicted in
In various implementations, operation 663 of
In various implementations, operation 663 may include an operation 680 for presenting a recommendation for a future course of action based, at least in part, on the modified hypothesis as depicted in
In some implementations, operation 680 may further include an operation 681 for presenting a justification for the recommendation as depicted in
In some implementations, operation 663 may include an operation 682 for presenting an indication of one or more past events based, at least in part, on the modified hypothesis as depicted in
Referring back to the action execution operation 306 of
Various techniques may be employed in order to prompt one or more devices to execute one or more operations in various alternative implementations. For example, in some implementations, operation 683 may include an operation 684 for instructing the one or more devices to execute the one or more operations as depicted in
In some implementations, operation 683 may include an operation 685 for activating the one or more devices to execute the one or more operations as depicted in
In some implementations, operation 683 may include an operation 686 for configuring the one or more devices to execute the one or more operations as depicted in
Various types of devices may be prompted through operation 683 in various alternative implementations. For example, in some implementations, operation 683 may include an operation 687 for prompting one or more environmental devices to execute the one or more operations as depicted in
In some implementations, operation 683 may include an operation 688 for prompting one or more household devices to execute the one or more operations as depicted in
In some implementations, operation 683 may include an operation 689 for prompting one or more sensing devices to execute the one or more operations as depicted in
In some implementations, operation 683 may include an operation 690 for prompting one or more network devices to execute the one or more operations as depicted in
Referring back to the action execution operation 306 of
The one or more actions to be executed in the action execution operation 306 of
In some implementations, operation 692 may further include an operation 693 for executing the one or more actions based on the modified hypothesis and in response to a reported event that at least substantially matches with one of at least two event types identified by the modified hypothesis as depicted in
Operation 693, in turn, may further include an operation 694 for executing the one or more actions based on the modified hypothesis and in response to a reported event that matches with one of the at least two event types identified by the modified hypothesis as depicted in
Those having skill in the art will recognize that the state of the art has progressed to the point where there is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software can become significant) a design choice representing cost vs. efficiency tradeoffs. Those having skill in the art will appreciate that there are various vehicles by which processes and/or systems and/or other technologies described herein can be effected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle; alternatively, if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware. Hence, there are several possible vehicles by which the processes and/or devices and/or other technologies described herein may be effected, none of which is inherently superior to the other in that any vehicle to be utilized is a choice dependent upon the context in which the vehicle will be deployed and the specific concerns (e.g., speed, flexibility, or predictability) of the implementer, any of which may vary. Those skilled in the art will recognize that optical aspects of implementations will typically employ optically-oriented hardware, software, and or firmware.
The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).
In a general sense, those skilled in the art will recognize that the various aspects described herein which can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof can be viewed as being composed of various types of “electrical circuitry.” Consequently, as used herein “electrical circuitry” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.
Those having skill in the art will recognize that it is common within the art to describe devices and/or processes in the fashion set forth herein, and thereafter use engineering practices to integrate such described devices and/or processes into data processing systems. That is, at least a portion of the devices and/or processes described herein can be integrated into a data processing system via a reasonable amount of experimentation. Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.
The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected”, or “operably coupled”, to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable”, to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.
While particular aspects of the present subject matter described herein have been shown and described, it will be apparent to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from the subject matter described herein and its broader aspects and, therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of the subject matter described herein. Furthermore, it is to be understood that the invention is defined by the appended claims.
It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.
In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).
In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”
II. Correlating Subjective User States with Objective Occurrences Associated with a User
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post the latest news, their thoughts and opinions on various topics, and various aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via social network status reports in which a user may report or post for others to view the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitter”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted though microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” associated with the microblogger. Objective occurrences associated with the microblogger may be any characteristic, event, happening, or aspect associated with or is of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, the local weather, the stock market (which the microblogger may have an interest in), activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblogging entries include “subjective states” of the microblogger. Subjective states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the mental state of the microblogger (e.g., “I am feeling happy”), particular physical states of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and overall state of the microblogger (e.g., “I'm good” or “I'm well”). Although microblogs are being used to provide a wealth of personal information, they have only been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided for correlating subjective user state data (e.g., that indicate subjective user states of a user) with objective context data (e.g., that indicate objective occurrences associated with the user). In other words, to determine a causal relationship between objective occurrences (e.g., cause) and subjective user states (e.g., result) associated with a user (e.g., a blogger or microblogger). For example, determining that whenever a user eats a banana (e.g., objective occurrence) the user feels “good” (e.g., subjective user state). Note that an objective occurrence does not need to precede a corresponding subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence).
As will be used herein a “subjective user state” is in reference to any state or status associated with a user (e.g., a blogger or microblogger) that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of the user (e.g., user is feeling sad), a subjective physical state (e.g., physical characteristic) that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), or the subjective overall state of the user (e.g., user is “good”). Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be categorized as a subjective mental state or as a subjective physical state. Examples of overall states of a user that may be subjective user states include, for example, user being good, bad, exhausted, lack of rest, user wellness, and so forth.
In contrast, “objective context data” may include data that indicate objective occurrences associated with the user. An objective occurrence may be any physical characteristic, event, happenings, or aspects associated with or is of interest to a user that can be objectively reported by at least a third party or a sensor device. Note, however, that such objective context data does not have to be actually provided by a sensor device or by a third party, but instead, may be reported by the user himself or herself (e.g., via microblog entries). Examples of objectively reported occurrences that could by indicated by the objective context data include, for example, a user's food, medicine, or nutraceutical intake, the user's location at any given point in time, the user's exercise routine, user's blood pressure, the weather at user's location, activities associated with third parties, the stock market, and so forth.
The term “correlating” as will be used herein is in reference to a determination of one or more relationships between at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that represents at least a first and a second subjective user state of a user and the second variable is objective context data that represents at least a first and a second objective occurrence associated with the user. Note that each of the at least first and second subjective user states represented by the subjective user state data may represent the same or similar type of subjective user state (e.g., user feels happy) but may be distinct subjective user states because they occurred at different points in time (e.g., user feels happy during a point in time and the user being happy again during another point in time). Similarly, each of the first and second objective occurrences represented by the objective context data may represent the same or similar type of objective occurrence (e.g., user eating a banana) but may be distinct objective occurrences because they occurred at different points in time (e.g., user ate a banana during a point in time and the user eating another banana during another point in time).
Various techniques may be employed for correlating the subjective user state data with the objective context data. For example, in some embodiments, correlating the objective context data with the subjective user state data may be accomplished by determining time sequential patterns or relationships between reported objective occurrences associated with a user and reported subjective user states of the user.
The following illustrative example is provided to describe how subjective user states and objective occurrences associated with a user may be correlated according to some embodiments. Suppose, for example, a user such as a microblogger reports that the user ate a banana on a Monday. The consumption of the banana, in this example, is a reported first objective occurrence associated with the user. The user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported first subjective user state. On Tuesday, the user reports that the user ate another banana (e.g., a second objective occurrence associated with the user). The user then reports that 15 minutes after eating the second banana, the user felt somewhat happy (e.g., a second subjective user state). For purposes of this example, the reporting of the consumption of the bananas may be in the form of objective context data and the reporting of the user feeling very or somewhat happy may be in the form of subjective user state data. The reported information may then be examined from different perspectives in order to determine whether there is a correlation (e.g., relationship) between the subjective user state data indicating the subjective user states (e.g., happiness of the user) and the objective context data indicating the objective occurrences associated with the user (e.g., eating bananas).
Several approaches may be employed in various alternative implementations in order to determine whether there is correlation (e.g., a relationship) between the subjective user state data and the objective context data. For example, a determination may be made as to whether there is co-occurrence, temporal sequencing, temporal proximity, and so forth, between the subjective user states (e.g., as provided by the subjective user state data) and the objective occurrences (e.g., as provided by the objective context data) associated with the user. One or more factors may be relevant in the determination of whether there is correlation between the subjective user state data and the objective context data.
One factor that may be examined in order to determine whether a relationship exists between the subjective user state data (e.g., happiness of the user) and the objective context data (e.g., consumption of bananas) is whether the first and second objective occurrences (e.g., consuming a banana) of the user are the same or similar (e.g., extent of similarity or difference). In this case, the first and second objective occurrences are the same. Note that consumption of the bananas could have been further defined. For example, the quantity or the type of bananas consumed could have been specified. If the quantity or the type of bananas consumed were not the same, then this could negatively impact the correlation (e.g., determination of a relationship) of the subjective user state data (e.g., happiness of the user) with the objective context data (e.g., eating bananas).
Another relevant factor that could be examined is whether the first and second subjective user states of the user are the same or similar (e.g., extent of similarity or difference). In this case, the first subjective user state (e.g., felt very happy) and second subjective user states (e.g., felt somewhat happy) are not the same but are similar. In this case, the comparison of the two subjective user states indicates that the two subjective user states, although not the same, are similar. This may result ultimately in a determination of a weaker correlation between the subjective user state data and the objective context data.
A third relevant factor that may be examined is whether the time difference between the first subjective user state and the first objective occurrence associated with the user (e.g., 15 minutes) and the time difference between the second subjective user state and the second objective occurrence associated with the user (e.g., 15 minutes) are the same or similar. In this case, the time difference between the first subjective user state and the first objective occurrence associated with the user (e.g., 15 minutes) and the time difference between the second subjective user state and the second objective occurrence associated with the user (e.g., 15 minutes) are indeed the same. As a result, this may indicate a relatively strong correlation between the subjective user state data (e.g., happiness of the user) and the objective context data (e.g., eating of bananas by the user). This operation is a relatively simple way of determining time sequential patterns. Note that if the time difference between the first subjective user state and the first objective occurrence associated with the user and the time difference between the second subjective user state and the second objective occurrence associated with the user (e.g., 15 minutes) were not the same or not similar, a weaker correlation or no correlation between the subjective user state data (e.g., happiness of the user) and the objective context data (e.g., eating of bananas by the user) may be concluded. Further, if the time differences were large (e.g., there was a four hour gap between the reporting of a consumption of a banana and the feeling of happiness), then this may indicate a weaker correlation between the subjective user state data (e.g., happiness of the user) and the objective context data (e.g., eating of bananas by the user).
The review of the subjective user state data and the objective context data from these perspectives may facilitate in determining whether there is a correlation between such data. That is, by examining such data from the various perspectives as described above, a determination may be made as to whether there is a sequential relationship between subjective user states (e.g., happiness of the user) and objective occurrences (e.g., consumption of bananas) associated with the user. Of course, those skilled in the art will recognize that the correlation of subjective user state data with objective context data may be made with greater confidence if more data points are obtained. For instance, in the above example, a stronger relationship may be determined between the subjective user state data (e.g., happiness of the user) and the objective context data (e.g., consumption of bananas) if additional data points with respect to the subjective user state data (e.g., a third subjective user state, a fourth subjective user state, and so forth) and the objective context data (e.g., a third objective occurrence, a fourth objective occurrence, and so forth) were obtained and analyzed.
In alternative embodiments, other techniques may be employed in order to correlate subjective user state data with objective context data. For example, one approach is to determine whether a subjective user state repeatedly occurs before, after, or at least partially concurrently with an objective occurrence. For instance, a determination may be made as to whether a user repeatedly has a stomach ache (e.g., subjective user state) each time after eating a banana (e.g., objective occurrence). In another example, a determination may be made as to whether a user repeatedly feels gloomy (e.g., subjective user state) before each time it begins to rain (e.g., objective occurrence). In still another example, a determination may be made as to whether a user repeatedly feels happy (e.g., subjective user state) each time his boss leaves town (e.g., objective occurrence).
a and 1-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 1-100 may include at least a computing device 1-10 (see
In some embodiments, the computing device 1-10 may be a network server in which case the computing device 1-10 may communicate with a user 1-20a via a mobile device 1-30 and through a wireless and/or wired network 1-40. Note that a network server as described herein may be in reference to a network server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 1-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, or some other type of mobile computing/communication device. In alternative embodiments, the computing device 1-10 may be a local computing device that communicates directly with a user 1-20b. For these embodiments, the computing device 1-10 may be any type of handheld device such as a cellular telephone or a PDA, or other types of computing/communication devices such as a laptop computer, a desktop computer, and so forth. In certain embodiments, the computing device 1-10 may be a peer-to-peer network component device. In some embodiments, the local device 1-30 may operate via web 2.0 construct.
In embodiments where the computing device 1-10 is a server, the computing device 1-10 may indirectly obtain the subjective user state data 1-60 from a user 1-20a via the mobile device 1-30. In alternative embodiments in which the computing device 1-10 is a local device, the subjective user state data 1-60 may be directly obtained from a user 1-20b. As will be further described, the computing device 1-10 may acquire the objective context data 1-70* from one or more different sources.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 1-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 1-10 is a local device communicating directly with a user 1-20b.
Assuming that the computing device 1-10 is a server, the computing device 1-10 may be configured to acquire subjective user state data 1-60 including at least a first subjective user state 1-60a and a second subjective user state 1-60b via the mobile device 1-30 and through wireless and/or wired networks 1-40. In some embodiments, the first subjective user state 1-60a and the second subjective user state 1-60b may be in the form of blog entries, such as microblog entries, or embodied in some other form of electronic messages. The first subjective user state 1-60a and the second subjective user state 1-60b may, in some instances, indicate the same, similar, or completely different subjective user state. Examples of subjective user states indicated by the first subjective user state 1-60a and the second subjective user state 1-60b include, for example, a mental state of the user 1-20a (e.g., user 1-20a is sad or angry), a physical state of the user 1-20a (e.g., physical or physiological characteristic of the user 1-20a such as the presence or absence of a stomach ache or headache), an overall state of the user 1-20a (e.g., user is “well”), or other subjective user states that only the user 1-20a can typically indicate.
The computing device 1-10 may be further configured to acquire objective context data 1-70* from one or more sources. For instance, objective context data 1-70a may be acquired, in some instances, from one or more third parties 1-50 (e.g., other users, a health care provider, a hospital, a place of employment, a content provider, and so forth). In some alternative situations, objective context data 1-70b may be acquired from one or more sensors 1-35 (e.g., blood pressure device or glucometer) sensing, for example, one or more physical characteristics of the user 1-20a. Note that the one or more sensors 1-35 may be other types of sensors for measuring and providing to the computing device 1-10 other subjective occurrences associated with user 1-20a. For example, in some cases, sensors 1-35 may include a global positioning system (GPS) device for determining the location of the user 1-20a or a physical activity sensor for measuring physical activities of the user 1-20a. Examples of a physical activity sensor include, for example, a pedometer for measuring physical activities of the user 1-20a. In some implementations, the one or more sensors 1-35 may include one or more physiological sensor devices for measuring physiological characteristics of the user 1-20a. Examples of physiological sensor devices include, for example, a blood pressure monitor, a heart rate monitor, a glucometer, and so forth. In some implementations, the one or more sensors 1-35 may include one or more image capturing devices such as a video or digital camera.
In still other situations, objective context data 1-70c may be acquired from the user 1-20a via the mobile device 1-30. For these situations, the objective context data 1-70c may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by the user 1-20a, certain physical characteristics (e.g., blood pressure or location) associated with the user 1-20a, or other aspects associated with the user 1-20a that the user 1-20a can report objectively. In still other alternative cases, objective context data 1-70d may be acquired from a memory 1-140.
In various embodiments, the context data 1-70* acquired by the computing device 1-10 may include at least a first context data indicative of a first objective occurrence associated with the user 1-20a and a second context data indicative of a second objective occurrence associated with the user 1-20a. In some implementations, the first and second context data may be acquired in the form of blog entries (e.g., microblog entries) or in other forms of electronic messages.
The computing device 1-10 may be further configured to correlate the acquired subjective user data 1-60 with the acquired context data 1-70*. By correlating the acquired subjective user data 1-60 with the acquired context data 1-70*, a determination may be made as to whether there is a relationship between the acquired subjective user data 1-60 with the acquired context data 1-70*. In some embodiments, and as will be further indicated in the operations and processes to be described herein, the computing device 1-10 may be further configured to present one or more the results of correlation. In various embodiments, the one or more correlation results 1-80 may be presented to the user 1-20a and/or to one or more third parties 1-50. The one or more third parties 1-50 may be other users such as other microbloggers, a health care provider, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the subjective user state data acquisition module 1-102 of the computing device 1-10 of
In some implementations, the reception module 1-202 may further include a text entry reception module 1-204 for receiving subjective user state data that was obtained based, at least in part, on a text entry provided by a user 1-20*. For example, in some implementations the text entry reception module 1-204 may be designed to receive subjective user state data 1-60 that was obtained based, at least in part, on a text entry (e.g., a text microblog entry) provided by a user 1-20a using a mobile device 1-30. In an alternative implementation or the same implementation, the reception module 1-202 may include an audio entry reception module 1-205 for receiving subjective user state data that was obtained based, at least in part, on an audio entry provided by a user 1-20*. For example, in some implementations the audio entry reception module 1-205 may be designed to receive subjective user state data 1-60 that was obtained based, at least in part, on an audio entry (e.g., an audio microblog entry) provided by a user 1-20a using a mobile device 1-30.
In some implementations, the subjective user state data acquisition module 1-102 may include a solicitation module 1-206 for soliciting from a user 1-20* a subjective user state. For example, the solicitation module 1-206, in some implementations, may be designed to solicit from a user 1-20b, via a user interface 1-122 (e.g., in the case where the computing device 1-10 is a local device), a subjective user state of the user 1-20b (e.g., whether the user 1-20b is feeling very good, good, bad, or very bad). The solicitation module 1-206 may further include a transmission module 1-207 for transmitting to a user 1-20a a request requesting a subjective user state 1-60*. For example, the transmission module 1-207 may be designed to transmit to a user 1-20a, via a network interface 1-122, a request requesting a subjective user state 1-60*. The solicitation module 1-206 may be used in some circumstances in order to prompt the user 1-20* to provide useful data. For instance, if the user 1-20* has reported a first subjective user state 1-60a following a first objective occurrence, then the solicitation module 1-206 may solicit from the user 1-20* a second subjective user state 1-60b following the happening of the second objective occurrence.
Referring now to
Turning now to
In the same or different implementations, the correlation module 1-106 may include a subjective user state and objective occurrence time difference determination module 1-214. As will be further described below, the subjective user state and objective occurrence time difference determination module 1-214 may be configured to determine at least an extent of time difference between a subjective user state associated with a user 1-20* and an objective occurrence associated with the user 1-20*. In the same or different implementations, the correlation module 1-106 may include a comparison module 1-216 for comparing an extent of time difference between a first subjective user state and a first objective occurrence associated with a user 1-20* with the extent of time difference between a second subjective user state and a second objective occurrence associated with the user 1-20*.
In the same or different implementations, the correlation module 1-106 may include a strength of correlation determination module 1-218 for determining a strength of correlation between subjective user state data and objective context data associated with a user 1-20*. In some implementations, the strength of correlation may be determined based, at least in part, on results provided by the objective occurrence difference determination module 1-210, the objective occurrence difference determination module 1-212, the subjective user state and objective occurrence time difference determination module 1-214 and/or the comparison module 1-216. In some implementations, and as will be further described herein, the correlation module 1-106 may include a determination module 1-219 for determining whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence associated with a user 1-20*.
d illustrates particular implementations of the presentation module 1-108 of the computing device 1-10 of
In various implementations, the presentation module 1-108 may include a transmission module 1-220 for transmitting one or more results of the correlation performed by the correlation module 1-106. For example, in the case where the computing device 1-10 is a server, the transmission module 1-220 may be configured to transmit to the user 1-20a or a third party 1-50 the one or more results of the correlation performed by the correlation module 1-106 via a network interface 1-120.
In some alternative implementations, the presentation module may include a display module 1-222 for displaying the one or more results of the correlation performed by the correlation module 1-106. For example, in the case where the computing device 1-10 is a local device, the display module 1-222 may be configured to display to the user 1-20b the one or more results of the correlation performed by the correlation module 1-106 via a user interface 1-122.
Referring back to
In various embodiments, the computing device 1-10 may include a network interface 1-120 that may facilitate in communicating with a user 1-20a and/or one or more third parties 1-50. For example, in embodiments whereby the computing device 1-10 is a server, the computing device 1-10 may include a network interface 1-120 that may be configured to receive from the user 1-20a subjective user state data 1-60. In some embodiments, objective context data 1-70a, 1-70b, or 1-70c may be received through the communication interface 1-120. Examples of a network interface 1-120 includes, for example, a network interface card (NIC).
In various embodiments, the computing device 1-10 may include a user interface 1-122 to communicate directly with a user 1-20b. For example, in embodiments in which the computing device 1-10 is a local device, the user interface 1-122 may be configured to directly receive from the user 1-20b subjective user state data 1-60. The user interface 1-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a mouse, an audio system, and/or other user interface devices.
e illustrates particular implementations of the one or more applications 1-126 of
In
Further, in
In any event, after a start operation, the operational flow 1-300 may move to a subjective user state data acquisition operation 1-302 for acquiring subjective user state data including at least a first subjective user state and a second subjective user state as performed by, for example, the computing device 1-10 of
Operational flow 1-300 further includes an objective context data acquisition operation 1-304 for acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user as performed by, for example, the computing device 1-10. For instance, the objective context data acquisition module 1-104 of the computing device 1-10 acquiring via a wireless and/or wired network 1-40 objective context data 1-70* (e.g., as provided by a third party source or by the user 1-20a) including at least a first context data 1-70* indicative of a first occurrence (e.g., cloudy weather) associated with a user 1-20* and a second context data 1-70* indicative of a second occurrence (e.g., cloudy weather) associated with the user 1-20*. Note that, and as those skilled in the art will recognize, the subjective user state data acquisition operation 1-302 does not have to be performed prior to the objective context data acquisition operation 1-304 and may be performed subsequent to the performance of the objective context data acquisition operation 1-304 or may be performed concurrently with the objective context data acquisition operation 1-304.
Finally, a correlation operation 1-306 for correlating the subjective user state data with the objective context data may be performed by, for example, the computing device 1-10. For instance, the correlation module 1-106 of the computing device 1-10 correlating the subjective user state data 1-60 with the objective context data 1-70* by determining a sequential time relationship between the subjective user state data 1-60 and the objective context data 1-70* (e.g., user 1-20* will be sad whenever it is cloudy).
In various implementations, the subjective user state data acquisition operation 1-302 may include one or more additional operations as illustrated in
In various alternative implementations, the reception operation 1-402 may further include one or more additional operations. For example, in some implementations, reception operation 1-402 may include an operation 1-404 for receiving a first subjective user state from at least one of a wireless network or a wired network as depicted in
In various implementations, the reception operation 1-402 may include an operation 1-406 for receiving a first subjective user state via an electronic message generated by the user as illustrated in
In some implementations, the reception operation 1-402 may include an operation 1-408 for receiving a first subjective user state via a first blog entry generated by the user as depicted in
In some implementations, the reception operation 1-402 may include an operation 1-409 for receiving a first subjective user state via a status report generated by the user as depicted in
In various implementations, the reception operation 1-402 may include an operation 1-410 for receiving a second subjective user state via an electronic message generated by the user as depicted in
In some implementations, the reception operation 1-402 may further include an operation 1-412 for receiving a second subjective user state via a second blog entry generated by the user as depicted in
In some implementations, the reception operation 1-402 may further include an operation 1-413 for receiving a second subjective user state via a status report generated by the user as depicted in
In various implementations, the reception operation 1-402 may include an operation 1-414 for receiving a first subjective user state that was obtained based, at least in part, on data provided by the user, the provided data indicating the first subjective user state associated with the user as depicted in
In some implementations, operation 1-414 may further include an operation 1-416 for receiving a first subjective user state that was obtained based, at least in part, on a text entry provided by the user as depicted in
In some implementations, operation 1-414 may further include an operation 1-418 for receiving a first subjective user state that was obtained based, at least in part, on an audio entry provided by the user as depicted in
In some implementations, operation 1-414 may further include an operation 1-419 for receiving a first subjective user state that was obtained based, at least in part, on an image entry provided by the user as depicted in
In various implementations, the reception operation 1-402 may include an operation 1-420 for receiving a first subjective user state indicating a subjective mental state of the user as depicted in
In some implementations, operation 1-420 may further include an operation 1-422 for receiving a first subjective user state indicating a level of the subjective mental state of the user as depicted in
The reception operation 1-402 in various implementations may include an operation 1-424 for receiving a first subjective user state indicating a subjective physical state of the user as depicted in
In some implementations, operation 1-424 may further include an operation 1-426 for receiving a first subjective user state indicating a level of the subjective physical state of the user as depicted in
In various implementations, the reception operation 1-402 may include an operation 1-428 for receiving a first subjective user state indicating a subjective overall state of the user as depicted in
In some implementations, operation 1-428 may further include an operation 1-430 for receiving a first subjective user state indicating a level of the subjective overall state of the user as depicted in
In certain implementations, the reception operation 1-402 may include an operation 1-432 for receiving a second subjective user state that was obtained based, at least in part, on data provided by the user, the provided data indicating the second subjective user state associated with the user as depicted in
In some implementations, operation 1-432 may further include an operation 1-434 for receiving a second subjective user state that was obtained based, at least in part, on a text entry provided by the user as depicted in
In some implementations, operation 1-432 may further include an operation 1-436 for receiving a second subjective user state that was obtained based, at least in part, on an audio entry provided by the user as depicted in
In some implementations, operation 1-432 may further include an operation 1-437 for receiving a second subjective user state that was obtained based, at least in part, on an image entry provided by the user. For instance, the reception module 1-202 of the computing device 1-10 receiving (e.g., via the network interface 1-120 or via the user interface 1-122) a second subjective user state 1-60b that was obtained based, at least in part, on an image entry (e.g., to capture a gesture such a “thumbs down” gesture or to capture a facial expression such as a smile made by the user 1-20*) provided by the user 1-20*.
In various implementations, the reception operation 1-402 may include an operation 1-438 for receiving a second subjective user state indicating a subjective mental state of the user as depicted in
In some implementations, operation 1-438 may further include an operation 1-440 for receiving a second subjective user state indicating a level of the subjective mental state of the user as depicted in
The reception operation 1-402, in various implementations, may include an operation 1-442 for receiving a second subjective user state indicating a subjective physical state of the user as depicted in
In some implementations, operation 1-442 may further include an operation 1-444 for receiving a second subjective user state indicating a level of the subjective physical state of the user as depicted in
In various implementations, the reception operation 1-402 may include an operation 1-446 for receiving a second subjective user state indicating a subjective overall state of the user as depicted in
In some implementations, operation 1-446 may further include an operation 1-448 for receiving a second subjective user state indicating a level of the subjective overall state of the user as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-450 for acquiring a first time stamp associated with the first subjective user state and a second time stamp associated with the second subjective user state as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-452 for acquiring subjective user state data including at least a first subjective user state and a second subjective user state that is equivalent to the first subjective user state as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-454 for acquiring subjective user state data including at least a first subjective user state and a second subjective user state that is proximately equivalent to the first subjective user state as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-455 for soliciting from the user at least one of the first subjective user state or the second subjective user state as depicted in
In some implementations, operation 1-455 may further include an operation 1-456 for transmitting to the user a request for a subjective user state as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-457 for acquiring at least one of the first subjective user state or the second subjective user state at a server as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-458 for acquiring at least one of the first subjective user state or the second subjective user state at a handheld device as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-460 for acquiring at least one of the first subjective user state or the second subjective user state at a peer-to-peer network component device as depicted in
In various implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-462 for acquiring at least one of the first subjective user state or the second subjective user via a Web 2.0 construct as depicted in
In some implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-464 for acquiring data that indicates a first subjective user state that occurred at least partially concurrently with an occurrence of a first objective occurrence associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-466 for acquiring data that indicates a second subjective user state that occurred at least partially concurrently with an occurrence of a second objective occurrence associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-468 for acquiring data that indicates a first subjective user state that occurred prior to an occurrence of a first objective occurrence associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-470 for acquiring data that indicates a second subjective user state that occurred prior to an occurrence of a second objective occurrence associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-472 for acquiring data that indicates a first subjective user state that occurred subsequent to an occurrence of a first objective occurrence associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 1-302 may include an operation 1-474 for acquiring data that indicates a second subjective user state that occurred subsequent to an occurrence of a second objective occurrence associated with the user as depicted in
Referring back to
In some implementations, the reception operation 1-502 may further include one or more additional operations. For example, in some implementations, the reception operation 1-502 may include an operation 1-504 for receiving the objective context data from at least one of a wireless network or wired network as depicted in
In some implementations, the reception operation 1-502 may include an operation 1-506 for receiving the objective context data via one or more blog entries as depicted in
In some implementations, the reception operation 1-502 may include an operation 1-507 for receiving the objective context data via one or more status reports as depicted in
In some implementations, the reception operation 1-502 may include an operation 1-508 for receiving the objective context data via a Web 2.0 construct as depicted in
In various implementations, the reception operation 1-502 may include an operation 1-510 for receiving the objective context data from one or more third party sources as depicted in
In some implementations, operation 1-510 may further include an operation 1-512 for receiving the objective context data from at least one of a health care professional, a pharmacy, a hospital, a health care organization, a health monitoring service, or a health care clinic as depicted in
In some implementations, operation 1-510 may further include an operation 1-514 for receiving the objective context data from a content provider as depicted in
In some implementations, operation 1-510 may further include an operation 1-516 for receiving the objective context data from at least one of a school, a place of employment, or a social group as depicted in
In various implementations, the reception operation 1-502 may include an operation 1-518 for receiving the objective context data from one or more sensors configured to sense one or more objective occurrences associated with the user as depicted in
In some implementations, operation 1-518 may further include an operation 1-520 for receiving the objective context data from a physical activity sensor device as depicted in
In some implementations, operation 1-518 may further include an operation 1-521 for receiving the objective context data from a global positioning system (GPS) device as depicted in
In some implementations, operation 1-518 may further include an operation 1-522 for receiving the objective context data from a physiological sensor device as depicted in
In some implementations, operation 1-518 may further include an operation 1-523 for receiving the objective context data from an image capturing device as depicted in
In various implementations, the reception operation 1-502 may include an operation 1-524 for receiving the objective context data from the user as depicted in
In various implementations, the objective context data acquisition operation 1-304 of
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-528 for acquiring at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user that is equivalent to the first objective occurrence as depicted in
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-530 for acquiring at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user that is proximately equivalent to the first objective occurrence as depicted in
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-532 for acquiring a first time stamp associated with the first objective occurrence and a second time stamp associated with the second objective occurrence as depicted in
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-534 for acquiring a first context data indicative of a first activity performed by the user and a second context data indicative of a second activity performed by the user as depicted in
In some implementations, operation 1-534 may also include an operation 1-536 for acquiring a first context data indicative of an ingestion by the user of a first medicine and a second context data indicative of an ingestion by the user of a second medicine as depicted in
In some implementations, operation 1-534 may also include an operation 1-538 for acquiring a first context data indicative of an ingestion by the user of a first food and a second context data indicative of an ingestion by the user of a second food as depicted in
In some implementations, operation 1-534 may also include an operation 1-540 for acquiring a first context data indicative of an ingestion by the user of a first nutraceutical and a second context data indicative of an ingestion by the user of a second nutraceutical as depicted in
In some implementations, operation 1-534 may also include an operation 1-542 for acquiring a first context data indicative of a first exercise routine executed by the user and a second context data indicative of a second exercise routine executed by the user as depicted in
In some implementations, operation 1-534 may also include an operation 1-544 for acquiring a first context data indicative of a first social activity executed by the user and a second context data indicative of a second social activity executed by the user as depicted in
In some implementations, operation 1-534 may also include an operation 1-546 for acquiring a first context data indicative of a first work activity executed by the user and a second context data indicative of a second work activity executed by the user as depicted in
In various implementations, the objective context data acquisition operation 1-304 of
In some implementations, operation 1-548 may further include an operation 1-550 for acquiring a first context data indicative of a first social activity executed by the third party and a second context data indicative of a second social activity executed by the third party as depicted in
In some implementations, operation 1-548 may further include an operation 1-552 for acquiring a first context data indicative of a first work activity executed by the third party and a second context data indicative of a second work activity executed by the third party as depicted in
In various implementations, the objective context data acquisition operation 1-304 of
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-556 for acquiring a first context data indicative of a first external event and a second context data indicative of a second external event as depicted in
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-558 for acquiring a first context data indicative of a first location of the user and a second context data indicative of a second location of the user as depicted in
In various implementations, the objective context data acquisition operation 1-304 may include an operation 1-560 for acquiring a first time stamp associated with the first objective occurrence and a second time stamp associated with the second objective occurrence as depicted in
Referring back to
In some implementations, operation 1-602 may further include an operation 1-604 for determining at least an extent of time difference between the second subjective user state associated with the user and the second objective occurrence associated with the user as depicted in
In some implementations, operation 1-604 may further include an operation 1-606 for comparing the extent of time difference between the first subjective user state and the first objective occurrence with the extent of time difference between the second subjective user state and the second objective occurrence as depicted in
In various implementations, the correlation operation 1-306 may include an operation 1-608 for determining an extent of difference between the first subjective user state and the second subjective user state associated with the user as depicted in
In various implementations, the correlation operation 1-306 may include an operation 1-610 for determining an extent of difference between the first objective occurrence and the second objective occurrence associated with the user as depicted in
In various implementations, the correlation operation 1-306 may include an operation 1-612 for determining a strength of the correlation between the subjective user state data and the objective context data as depicted in
In some implementations, the correlation operation 1-306 may include an operation 1-614 for determining whether the first subjective user state occurred after occurrence of the first objective occurrence associated with the user as depicted in
In some implementations, the correlation operation 1-306 may include an operation 1-616 for determining whether the second subjective user state occurred after occurrence of the second objective occurrence associated with the user as depicted in
In some implementations, the correlation operation 1-306 may include an operation 1-618 for determining whether the first subjective user state occurred before occurrence of the first objective occurrence associated with the user as depicted in
In some implementations, the correlation operation 1-306 may include an operation 1-620 for determining whether the second subjective user state occurred before occurrence of the second objective occurrence associated with the user as depicted in
In some implementations, the correlation operation 1-306 may include an operation 1-622 for determining whether the first subjective user state occurred at least partially concurrently with occurrence of the first objective occurrence associated with the user as depicted in
In some implementations, the correlation operation 1-306 may include an operation 1-624 for determining whether the second subjective user state occurred at least partially concurrently with occurrence of the second objective occurrence associated with the user as depicted in
The presentation operation 1-702 may include one or more additional operations in various alternative implementations as illustrated in
In some implementations, the transmission operation 1-801 may include an operation 1-802 for transmitting the one or more results to the user as depicted in
In some implementations, the transmission operation 1-801 may include an operation 1-804 for transmitting the one or more results to one or more third parties as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-806 for displaying the one or more results to the user via a user interface as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-808 for presenting an indication of a sequential relationship between a subjective user state and an objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-810 for presenting a prediction of a future subjective user state resulting from a future occurrence associated with the user as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-811 for presenting a prediction of a future subjective user state resulting from a past occurrence associated with the user as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-812 for presenting a past subjective user state associated with a past occurrence associated with the user as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-814 for presenting a recommendation for a future action as depicted in
In particular implementations, operation 1-814 may further include an operation 1-816 for presenting a justification for the recommendation as depicted in
In some implementations, the presentation operation 1-708 may include an operation 1-818 for presenting an indication of a strength of correlation between the subjective user state data and the objective context data as depicted in
In various implementations, the presentation operation 1-708 may include an operation 1-820 for presenting one or more results of the correlating in response to a reporting of an occurrence of a third objective occurrence associated with the user as depicted in
In various implementations, operation 1-820 may include one or more additional operations. For example, in some implementations, operation 1-820 may include an operation 1-822 for presenting one or more results of the correlating in response to a reporting of an event that was executed by the user as depicted in
In some implementations, operation 1-820 may include an operation 1-824 for presenting one or more results of the correlating in response to a reporting of an event that was executed by a third party as depicted in
In some implementations, operation 1-820 may include an operation 1-826 for presenting one or more results of the correlating in response to a reporting of an occurrence of an external event as depicted in
In various implementations, the presentation operation 1-708 may include an operation 1-828 for presenting one or more results of the correlating in response to a reporting of an occurrence of a third subjective user state as depicted in
III. Correlating Data Indicating at Least One Subjective User State with Data Indicating at Least One Objective Occurrence Associated with a User
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post their thoughts and opinions on various topics, the latest news, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social network status reports in which a user may report or post for others to view the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted through microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, event, happening, or any other aspects associated with or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, the local weather, the stock market (which the microblogger may have an interest in), activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblogging entries include “subjective user states” of the microblogger. Subjective user states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., “I am feeling happy”), the subjective physical states of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and the subjective overall state of the microblogger (e.g., “I'm good” or “I'm well”). Note that the term “subjective overall state” as will be used herein refers to those subjective states that do not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). Although microblogs are being used to provide a wealth of personal information, they have only been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided for, among other things, correlating subjective user state data (e.g., data that indicate one or more subjective user states of a user) with objective occurrence data (e.g., data that indicate one or more objective occurrences associated with the user). In doing so, a causal relationship between one or more objective occurrences (e.g., cause) and one or more subjective user states (e.g., result) associated with a user (e.g., a blogger or microblogger) may be determined in various alternative embodiments. For example, determining that the last time a user ate a banana (e.g., objective occurrence), the user felt “good” (e.g., subjective user state) or determining whenever a user eats a banana the user always or sometimes feels good. Note that an objective occurrence does not need to occur prior to a corresponding subjective user state but instead, may occur subsequent or concurrently with the incidence of the subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence) or a person may become gloomy while (e.g., concurrently) it is raining.
As briefly described above, a “subjective user state” is in reference to any state or status associated with a user (e.g., a blogger or microblogger) at any moment or interval in time that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of the user (e.g., user is feeling sad), the subjective physical state (e.g., physical characteristic) of the user that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), and the subjective overall state of the user (e.g., user is “good”). Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be categorized as a subjective mental state or as a subjective physical state. Examples of overall states of a user that may be subjective user states include, for example, the user being good, bad, exhausted, lack of rest, wellness, and so forth.
In contrast, “objective occurrence data,” which may also be referred to as “objective context data,” may include data that indicate one or more objective occurrences associated with the user that occurred at particular intervals or points in time. An objective occurrence may be any physical characteristic, event, happenings, or any other aspect associated with or is of interest to a user that can be objectively reported by at least a third party or a sensor device. Note, however, that such objective occurrence data does not have to be actually provided by a sensor device or by a third party, but instead, may be reported by the user himself or herself (e.g., via microblog entries). Examples of objectively reported occurrences that could be indicated by the objective occurrence data include, for example, a user's food, medicine, or nutraceutical intake, the user's location at any given point in time, the user's exercise routine, user's blood pressure, the weather at user's location, activities associated with third parties, the stock market, and so forth.
The term “correlating” as will be used herein is in reference to a determination of one or more relationships between at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that represents at least one subjective user state of a user and the second variable is objective occurrence data that represents at least one objective occurrence associated with the user. In embodiments where the subjective user state data represents multiple subjective user states, each of the subjective user states represented by the subjective user state data may be the same or similar type of subjective user state (e.g., user being happy) at different intervals or points in time. In alternative embodiments, however, different types of subjective user state (e.g., user being happy and user being sad) may be represented by the subjective user state data. Similarly, in embodiments where multiple objective occurrences are represented by the objective occurrence data, each of the objective occurrences may represent the same or similar type of objective occurrence (e.g., user exercising) at different intervals or points in time, or, in alternative embodiments, different types of objective occurrence (e.g., user exercising and user resting).
Various techniques may be employed for correlating the subjective user state data with the objective occurrence data. For example, in some embodiments, correlating the objective occurrence data with the subjective user state data may be accomplished by determining a sequential pattern associated with at least one subjective user state indicated by the subjective user state data and at least one objective occurrence indicated by the objective occurrence data. In other embodiments, correlating of the objective occurrence data with the subjective user state data may involve determining multiple sequential patterns associated with multiple subjective user states and multiple objective occurrences.
As will be further described herein a sequential pattern, in some implementations, may merely indicate or represent the temporal relationship or relationships between at least one subjective user state and at least one objective occurrence (e.g., whether the incidence or occurrence of the at least one subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least one objective occurrence). In alternative implementations, and as will be further described herein, a sequential pattern may indicate a more specific time relationship between the incidences of one or more subjective user states and the incidences of one or more objective occurrences. For example, a sequential pattern may represent the specific pattern of events (e.g., one or more objective occurrences and one or more subjective user states) that occurs along a timeline.
The following illustrative example is provided to describe how a sequential pattern associated with at least one subjective user state and at least one objective occurrence may be determined based, at least in part, on the temporal relationship between the incidence of the at least one subjective user state and the incidence of the at least one objective occurrence in accordance with some embodiments. For these embodiments, the determination of a sequential pattern may initially involve determining whether the incidence of the at least one subjective user state occurred within some predefined time increments of the incidence of the one objective occurrence. That is, it may be possible to infer that those subjective user states that did not occur within a certain time period from the incidence of an objective occurrence are not related or are unlikely related to the incidence of that objective occurrence.
For example, suppose a user during the course of a day eats a banana and also has a stomach ache sometime during the course of the day. If the consumption of the banana occurred in the early morning hours but the stomach ache did not occur until late that night, then the stomach ache may be unrelated to the consumption of the banana and may be disregarded. On the other hand, if the stomach ache had occurred within some predefined time increment, such as within 2 hours of consumption of the banana, then it may be concluded that there is a correlation or link between the stomach ache and the consumption of the banana. If so, a temporal relationship between the consumption of the banana and the occurrence of the stomach ache may be determined. Such a temporal relationship may be represented by a sequential pattern. Such a sequential pattern may simply indicate that the stomach ache (e.g., a subjective user state) occurred after (rather than before or concurrently) the consumption of banana (e.g., an objective occurrence).
As will be further described herein, other factors may also be referenced and examined in order to determine a sequential pattern and whether there is a relationship (e.g., causal relationship) between an objective occurrence and a subjective user state. These factors may include, for example, historical data (e.g., historical medical data such as genetic data or past history of the user or historical data related to the general population regarding stomach aches and bananas). Alternatively, a sequential pattern may be determined for multiple subjective user states and multiple objective occurrences. Such a sequential pattern may particularly map the exact temporal or time sequencing of the various events (e.g., subjective user states and/or objective occurrences). The determined sequential pattern may then be used to provide useful information to the user and/or third parties.
The following is another illustrative example of how subjective user state data may be correlated with objective occurrence data by determining multiple sequential patterns and comparing the sequential patterns with each other. Suppose, for example, a user such as a microblogger reports that the user ate a banana on a Monday. The consumption of the banana, in this example, is a reported first objective occurrence associated with the user. The user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported first subjective user state. Thus, the reported incidence of the first objective occurrence (e.g., eating the banana) and the reported incidence of the first subjective user state (user felt very happy) on Monday may be represented by a first sequential pattern.
On Tuesday, the user reports that the user ate another banana (e.g., a second objective occurrence associated with the user). The user then reports that 20 minutes after eating the second banana, the user felt somewhat happy (e.g., a second subjective user state). Thus, the reported incidence of the second objective occurrence (e.g., eating the second banana) and the reported incidence of the second subjective user state (user felt somewhat happy) on Tuesday may be represented by a second sequential pattern. Note that in this example, the occurrences of the first subjective user state and the second subjective user state may be indicated by subjective user state data while the occurrences of the first objective occurrence and the second objective occurrence may be indicated by objective occurrence data.
By comparing the first sequential pattern with the second sequential pattern, the subjective user state data may be correlated with the objective occurrence data. In some implementations, the comparison of the first sequential pattern with the second sequential pattern may involve trying to match the first sequential pattern with the second sequential pattern by examining certain attributes and/or metrics. For example, comparing the first subjective user state (e.g., user felt very happy) of the first sequential pattern with the second subjective user state (e.g., user felt somewhat happy) of the second sequential pattern to see if they at least substantially match or are contrasting (e.g., being very happy in contrast to being slightly happy or being happy in contrast to being sad). Similarly, comparing the first objective occurrence (e.g., eating a banana) of the first sequential pattern may be compared to the second objective occurrence (e.g., eating of another banana) of the second sequential pattern to determine whether they at least substantially match or are contrasting.
A comparison may also be made to see if the extent of time difference (e.g., 15 minutes) between the first subjective user state (e.g., user being very happy) and the first objective occurrence (e.g., user eating a banana) matches or are at least similar to the extent of time difference (e.g., 20 minutes) between the second subjective user state (e.g., user being somewhat happy) and the second objective occurrence (e.g., user eating another banana). These comparisons may be made in order to determine whether the first sequential pattern matches the second sequential pattern. A match or substantial match would suggest, for example, that a subjective user state (e.g., happiness) is linked to an objective occurrence (e.g., consumption of banana).
As briefly described above, the comparison of the first sequential pattern with the second sequential pattern may include a determination as to whether, for example, the respective subjective user states and the respective objective occurrences of the sequential patterns are contrasting subjective user states and/or contrasting objective occurrences. For example, suppose in the above example the user had reported that the user had eaten a whole banana on Monday and felt very energetic (e.g., first subjective user state) after eating the whole banana (e.g., first objective occurrence). Suppose that the user also reported that on Tuesday he ate a half a banana instead of a whole banana and only felt slightly energetic (e.g., second subjective user state) after eating the half banana (e.g., second objective occurrence). In this scenario, the first sequential pattern (e.g., feeling very energetic after eating a whole banana) may be compared to the second sequential pattern (e.g., feeling slightly energetic after eating only a half of a banana) to at least determine whether the first subjective user state (e.g., being very energetic) and the second subjective user state (e.g., being slightly energetic) are contrasting subjective user states. Another determination may also be made during the comparison to determine whether the first objective occurrence (eating a whole banana) is in contrast with the second objective occurrence (e.g., eating a half of a banana).
In doing so, an inference may be made that eating a whole banana instead of eating only a half of a banana makes the user happier or eating more banana makes the user happier. Thus, the word “contrasting” as used here with respect to subjective user states refers to subjective user states that are the same type of subjective user states (e.g., the subjective user states being variations of a particular type of subjective user states such as variations of subjective mental states). Thus, for example, the first subjective user state and the second subjective user state in the previous illustrative example are merely variations of subjective mental states (e.g., happiness). Similarly, the use of the word “contrasting” as used here with respect to objective occurrences refers to objective states that are the same type of objective occurrences (e.g., consumption of food such as banana).
As those skilled in the art will recognize, a stronger correlation between the subjective user state data and the objective occurrence data could be obtained if a greater number of sequential patterns (e.g., if there was a third sequential pattern, a fourth sequential pattern, and so forth, that indicated that the user became happy or happier whenever the user ate bananas) are used as a basis for the correlation. Note that for ease of explanation and illustration, each of the exemplary sequential patterns to be described herein will be depicted as a sequential pattern of occurrence of a single subjective user state and occurrence of a single objective occurrence. However, those skilled in the art will recognize that a sequential pattern, as will be described herein, may also be associated with occurrences of multiple objective occurrences and/or multiple subjective user states. For example, suppose the user had reported that after eating a banana, he had gulped down a can of soda. The user then reported that he became happy but had an upset stomach. In this example, the sequential pattern associated with this scenario will be associated with two objective occurrences (e.g., eating a banana and drinking a can of soda) and two subjective user states (e.g., user having an upset stomach and feeling happy).
In some embodiments, and as briefly described earlier, the sequential patterns derived from subjective user state data and objective occurrence data may be based on temporal relationships between objective occurrences and subjective user states. For example, whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence. For instance, a plurality of sequential patterns derived from subjective user state data and objective occurrence data may indicate that a user always has a stomach ache (e.g., subjective user state) after eating a banana (e.g., first objective occurrence).
a and 2-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 2-100 may include at least a computing device 2-10 (see
In some embodiments, the computing device 2-10 may be a network server in which case the computing device 2-10 may communicate with a user 2-20a via a mobile device 2-30 and through a wireless and/or wired network 2-40. A network server, as will be described herein, may be in reference to a network server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 2-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 2-10. In alternative embodiments, the computing device 2-10 may be a local computing device that communicates directly with a user 2-20b. For these embodiments, the computing device 2-10 may be any type of handheld device such as a cellular telephone or a PDA, or other types of computing/communication devices such as a laptop computer, a desktop computer, and so forth. In certain embodiments, the computing device 2-10 may be a peer-to-peer network component device. In some embodiments, the computing device 2-10 may operate via a web 2.0 construct.
In embodiments where the computing device 2-10 is a server, the computing device 2-10 may obtain the subjective user state data 2-60 indirectly from a user 2-20a via a network interface 2-120. In alternative embodiments in which the computing device 2-10 is a local device, the subjective user state data 2-60 may be directly obtained from a user 2-20b via a user interface 2-122. As will be further described, the computing device 2-10 may acquire the objective occurrence data 2-70* from one or more sources.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 2-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 2-10 is a local device such as a handheld device that may communicate directly with a user 2-20b.
Assuming that the computing device 2-10 is a server, the computing device 2-10, in various implementations, may be configured to acquire subjective user state data 2-60 including data indicating at least one subjective user state 2-60a via the mobile device 2-30 and through wireless and/or wired networks 2-40. In some implementations, the subjective user state data 2-60 may further include additional data that may indicate one or more additional subjective user states (e.g., data indicating at least a second subjective user state 2-60b). In various embodiments, the data indicating the at least one subjective user state 2-60a, as well as the data indicating the at least second subjective user state 2-60b, may be in the form of blog entries, such as microblog entries, status reports (e.g., social networking status reports), electronic messages (email, text messages, instant messages, etc.) or other types of electronic messages or documents. The data indicating the at least one subjective user state 2-60a and the data indicating the at least second subjective user state 2-60b may, in some instances, indicate the same, contrasting, or completely different subjective user states. Examples of subjective user states that may be indicated by the subjective user state data 2-60 include, for example, subjective mental states of the user 2-20a (e.g., user 2-20a is sad or angry), subjective physical states of the user 2-20a (e.g., physical or physiological characteristic of the user 2-20a such as the presence or absence of a stomach ache or headache), subjective overall states of the user 2-20a (e.g., user is “well”), and/or other subjective user states that only the user 2-20a can typically indicate.
The computing device 2-10 may be further configured to acquire objective occurrence data 2-70* from one or more sources. In various embodiments, the objective occurrence data 2-70* acquired by the computing device 2-10 may include data indicative of at least one objective occurrence associated with the user 2-20a. The objective occurrence data 2-70* may additionally include, in some embodiments, data indicative of one or more additional objective occurrences associated with the user 2-20a including data indicating at least a second objective occurrence associated with the user 2-20a. In some embodiments, objective occurrence data 2-70a may be acquired from one or more third parties 2-50. Examples of third parties 2-50 include, for example, other users, a health care provider, a hospital, a place of employment, a content provider, and so forth.
In some embodiments, objective occurrence data 2-70b may be acquired from one or more sensors 2-35 for sensing or monitoring various aspects associated with the user 2-20a. For example, in some implementations, sensors 2-35 may include a global positioning system (GPS) device for determining the location of the user 2-20a or a physical activity sensor for measuring physical activities of the user 2-20a. Examples of a physical activity sensor include, for example, a pedometer for measuring physical activities of the user 2-20a. In certain implementations, the one or more sensors 2-35 may include one or more physiological sensor devices for measuring physiological characteristics of the user 2-20a. Examples of physiological sensor devices include, for example, a blood pressure monitor, a heart rate monitor, a glucometer, and so forth. In some implementations, the one or more sensors 2-35 may include one or more image capturing devices such as a video or digital camera.
In some embodiments, objective occurrence data 2-70c may be acquired from the user 2-20a via the mobile device 2-30. For these embodiments, the objective occurrence data 2-70c may be in the form of blog entries (e.g., microblog entries), status reports, or other types of electronic messages. In various implementations, the objective occurrence data 2-70c acquired from the user 2-20a may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by the user 2-20a, certain physical characteristics (e.g., blood pressure or location) associated with the user 2-20a, or other aspects associated with the user 2-20a that the user 2-20a can report objectively. In still other implementations, objective occurrence data 2-70d may be acquired from a memory 2-140.
After acquiring the subjective user state data 2-60 and the objective occurrence data 2-70*, the computing device 2-10 may be configured to correlate the acquired subjective user data 2-60 with the acquired objective occurrence data 2-70* by, for example, determining whether there is a sequential relationship between the one or more subjective user states as indicated by the acquired subjective user state data 2-60 and the one or more objective occurrences indicated by the acquired objective occurrence data 2-70*.
In some embodiments, and as will be further indicated in the operations and processes to be described herein, the computing device 2-10 may be further configured to present one or more results of correlation. In various embodiments, the one or more correlation results 2-80 may be presented to the user 2-20a and/or to one or more third parties 2-50 in various forms. The one or more third parties 2-50 may be other users 2-20* such as other microbloggers, a health care provider, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the subjective user state data acquisition module 2-102 of the computing device 2-10 of
In some implementations, the subjective user state data reception module 2-202 may further include a user interface data reception module 2-204, a network interface data reception module 2-206, a text entry data reception module 2-208, an audio entry data reception module 2-210, and/or an image entry data reception module 2-212. In brief, and as will be further described in the processes and operations to be described herein, the user interface data reception module 2-204 may be configured to acquire subjective user state data 2-60 via a user interface 2-122 (e.g., a display monitor, a keyboard, a touch screen, a mouse, a keypad, a microphone, a camera, and/or other interface devices) such as in the case where the computing device 2-10 is a local device to be used directly by a user 2-20b.
In contrast, the network interface data reception module 2-206 may be configured to acquire subjective user state data 2-60 via a network interface 2-120 (e.g., network interface card or NIC) such as in the case where the computing device 2-10 is a network server. The text entry data reception module 2-208 may be configured to receive data indicating at least one subjective user state 2-60a that was obtained based, at least in part, on one or more text entries provided by a user 2-20*. The audio entry data reception module 2-210 may be configured to receive data indicating at least one subjective user state 2-60a that was obtained, based, at least in part, on one or more audio entries provided by a user 2-20*. The image entry data reception module 2-212 may be configured to receive data indicating at least one subjective user state 2-60a that was obtained based, at least in part, on one or more image entries provided by a user 2-20*.
In some embodiments, the subjective user state data acquisition module 2-102 may include a subjective user state data solicitation module 2-214 for soliciting subjective user state data 2-60 from a user 2-20*. The subjective user state data solicitation module 2-214 may solicit the subjective user state data 2-60 from a user 2-20a via a network interface 2-120 (e.g., in the case where the computing device 2-10 is a network server) or from a user 2-20b via a user interface 2-122 (e.g., in the case where the computing device 2-10 is a local device used directly by a user 2-20b). The solicitation of the subjective user state data 2-60, in various embodiments, may involve requesting a user 2-20* to select one or more subjective user states from a list of alternative subjective user state options (e.g., user 2-20* can choose at least one from a choice of “I'm feeling alert,” “I'm feeling sad,” “My back is hurting,” “I have an upset stomach,” and so forth).
In some embodiments, the request to select from a list of alternative subjective user state options may simply involve requesting the user 2-20* to select one subjective user state from two contrasting and opposite subjective user state options (e.g., “I'm feeling good” or “I'm feeling bad”). The subjective user state data solicitation module 2-214 may be used in some circumstances in order to prompt a user 2-20* to provide useful data. For instance, if a user 2-20* reports a first subjective user state following the occurrence of a first objective occurrence, then the subjective user state data solicitation module 2-214 may solicit from the user 2-20* a second subjective user state following the occurrence of a second objective occurrence.
In some implementations, the subjective user state data solicitation module 2-214 may further include a transmission module 2-216 for transmitting to a user 2-20a, a request (e.g., solicitation) for a subjective user state. The request or solicitation for the subjective user state may be transmitted to the user 2-20a via a network interface 2-120 and may be in the form of an electronic message.
In some implementations, the subjective user state data solicitation module 2-214 may further include a display module 2-218 for displaying to a user 2-20b, a request (e.g., solicitation) for a subjective user state. The request or solicitation for the subjective user state may be displayed to the user 2-20b via a user interface 2-122 in the form of a text message, an audio message, or a visual message.
In various embodiments, the subjective user state data acquisition module 2-102 may include a time data acquisition module 2-220 for acquiring time and/or temporal elements associated with one or more subjective user states of a user 2-20*. For these embodiments, the time and/or temporal elements (e.g., time stamps, time interval indicators, and/or temporal relationship indicators) acquired by the time data acquisition module 2-220 may be useful for determining sequential patterns associated with subjective user states and objective occurrences as will be further described herein. In some implementations, the time data acquisition module 2-220 may include a time stamp acquisition module 2-222 for acquiring (e.g., either by receiving or generating) one or more time stamps associated with one or more subjective user states. In the same or different implementations, the time data acquisition module 2-220 may include a time interval acquisition module 2-223 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more subjective user states. In the same or different implementations, the time data acquisition module 2-220 may include a temporal relationship acquisition module 2-224 for acquiring indications of temporal relationships between subjective user states and objective occurrence (e.g., an indication that a subjective user state occurred before, after, or at least partially concurrently with incidence of an objective occurrence).
Referring now to
In the same or different embodiments, the objective occurrence data acquisition module 2-104 may include a time data acquisition module 2-228 configured to acquire time and/or temporal elements associated with one or more objective occurrences associated with a user 2-20*. For these embodiments, the time and/or temporal elements (e.g., time stamps, time intervals, and/or temporal relationships) may be useful for determining sequential patterns associated with objective occurrences and subjective user states. In some implementations, the time data acquisition module 2-228 may include a time stamp acquisition module 2-230 for acquiring (e.g., either by receiving or generating) one or more time stamps associated with one or more objective occurrences associated with a user 2-20*. In the same or different implementations, the time data acquisition module 2-228 may include a time interval acquisition module 2-231 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more objective occurrences associated with a user 2-20*. In the same or different implementations, the time data acquisition module 2-228 may include a temporal relationship acquisition module 2-232 for acquiring indications of temporal relationships between objective occurrences and subjective user states (e.g., an indication that an objective occurrence occurred before, after, or at least partially concurrently with incidence of a subjective user state).
In various embodiments, the objective occurrence data acquisition module 2-104 may include an objective occurrence data solicitation module 2-234 for soliciting objective occurrence data 2-70* from one or more sources (e.g., a user 2-20*, one or more third parties 2-50, one or more sensors 2-35, and/or other sources). In some embodiments, the objective occurrence data solicitation module 2-234 may be prompted to solicit objective occurrence data 2-70* including data indicating one or more objective occurrences in response to a reporting of one or more subjective user states or to a reporting of one or more other types of events. For example, if a user 2-20* reports that he or she is feeling ill, the objective occurrence data solicitation module 2-234 may request the user 2-20* to provide the user's blood sugar level (i.e., an objective occurrence).
Turning now to
The sequential pattern determination module 2-236, in various implementations, may include one or more sub-modules that may facilitate in the determination of one or more sequential patterns. As depicted, the one or more sub-modules that may be included in the sequential pattern determination module 2-236 may include, for example, a “within predefined time increment determination” module 2-238, a temporal relationship determination module 2-239, a subjective user state and objective occurrence time difference determination module 2-240, and/or a historical data referencing module 2-241. In brief, the within predefined time increment determination module 2-238 may be configured to determine whether at least one subjective user state of a user 2-20* occurred within a predefined time increment from an incidence of at least one objective occurrence. For example, determining whether a user 2-20* feeling “bad” (i.e., a subjective user state) occurred within ten hours (i.e., predefined time increment) of eating a large chocolate sundae (i.e., an objective occurrence). Such a process may be used in order to filter out events that are likely not related or to facilitate in determining the strength of correlation between subjective user state data 2-60 and objective occurrence data 2-70*.
The temporal relationship determination module 2-239 may be configured to determine the temporal relationships between one or more subjective user states and one or more objective occurrences. For example, this may entail determining whether a particular subjective user state (e.g., sore back) occurred before, after, or at least partially concurrently with incidence of an objective occurrence (e.g., sub-freezing temperature).
The subjective user state and objective occurrence time difference determination module 2-240 may be configured to determine the extent of time difference between the incidence of at least one subjective user state and the incidence of at least one objective occurrence. For example, determining how long after taking a particular brand of medication (e.g., objective occurrence) did a user 2-20* feel “good” (e.g., subjective user state).
The historical data referencing module 2-241 may be configured to reference historical data 2-72 in order to facilitate in determining sequential patterns. For example, in various implementations, the historical data 2-72 that may be referenced may include, for example, general population trends (e.g., people having a tendency to have a hangover after drinking or ibuprofen being more effective than aspirin for toothaches in the general population), medical information such as genetic, metabolome, or proteome information related to the user 2-20* (e.g., genetic information of the user 2-20* indicating that the user 2-20* is susceptible to a particular subjective user state in response to occurrence of a particular objective occurrence), or historical sequential patterns such as known sequential patterns of the general population or of the user 2-20* (e.g., people tending to have difficulty sleeping within five hours after consumption of coffee). In some instances, such historical data 2-72 may be useful in associating one or more subjective user states with one or more objective occurrences.
In some embodiments, the correlation module 2-106 may include a sequential pattern comparison module 2-242. As will be further described herein, the sequential pattern comparison module 2-242 may be configured to compare multiple sequential patterns with each other to determine, for example, whether the sequential patterns at least substantially match each other or to determine whether the sequential patterns are contrasting sequential patterns.
As depicted in
The subjective user state equivalence determination module 2-243 may be configured to determine whether subjective user states associated with different sequential patterns are equivalent. For example, the subjective user state equivalence determination module 2-243 determining whether a first subjective user state of a first sequential pattern is equivalent to a second subjective user state of a second sequential pattern. For instance, suppose a user 2-20* reports that on Monday he had a stomach ache (e.g., first subjective user state) after eating at a particular restaurant (e.g., a first objective occurrence), and suppose further that the user 2-20* again reports having a stomach ache (e.g., a second subjective user state) after eating at the same restaurant (e.g., a second objective occurrence) on Tuesday, then the subjective user state equivalence determination module 2-243 may be employed in order to compare the first subjective user state (e.g., stomach ache) with the second subjective user state (e.g., stomach ache) to determine whether they are equivalent.
In contrast, the objective occurrence equivalence determination module 2-244 may be configured to determine whether objective occurrences of different sequential patterns are equivalent. For example, the objective occurrence equivalence determination module 2-244 determining whether a first objective occurrence of a first sequential pattern is equivalent to a second objective occurrence of a second sequential pattern. For instance, for the above example the objective occurrence equivalence determination module 2-244 may compare eating at the particular restaurant on Monday (e.g., first objective occurrence) with eating at the same restaurant on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is equivalent to the second objective occurrence.
In some implementations, the sequential pattern comparison module 2-242 may include a subjective user state contrast determination module 2-245 that may be configured to determine whether subjective user states associated with different sequential patterns are contrasting subjective user states. For example, the subjective user state contrast determination module 2-245 may determine whether a first subjective user state of a first sequential pattern is a contrasting subjective user state from a second subjective user state of a second sequential pattern. For instance, suppose a user 2-20* reports that he felt very “good” (e.g., first subjective user state) after jogging for an hour (e.g., first objective occurrence) on Monday, but reports that he felt “bad” (e.g., second subjective user state) when he did not exercise (e.g., second objective occurrence) on Tuesday, then the subjective user state contrast determination module 2-245 may compare the first subjective user state (e.g., feeling good) with the second subjective user state (e.g., feeling bad) to determine that they are contrasting subjective user states.
In some implementations, the sequential pattern comparison module 2-242 may include an objective occurrence contrast determination module 2-246 that may be configured to determine whether objective occurrences of different sequential patterns are contrasting objective occurrences. For example, the objective occurrence contrast determination module 2-246 may determine whether a first objective occurrence of a first sequential pattern is a contrasting objective occurrence from a second objective occurrence of a second sequential pattern. For instance, for the above example, the objective occurrence contrast determination module 2-246 may compare the “jogging” on Monday (e.g., first objective occurrence) with the “no jogging” on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence. Based on the contrast determination, an inference may be made that the user 2-20* may feel better by jogging rather than by not jogging at all.
In some embodiments, the sequential pattern comparison module 2-242 may include a temporal relationship comparison module 2-247 that may be configured to make comparisons between different temporal relationships of different sequential patterns. For example, the temporal relationship comparison module 2-247 may compare a first temporal relationship between a first subjective user state and a first objective occurrence of a first sequential pattern with a second temporal relationship between a second subjective user state and a second objective occurrence of a second sequential pattern in order to determine whether the first temporal relationship at least substantially matches the second temporal relationship.
For example, suppose in the above example the user 2-20* eating at the particular restaurant (e.g., first objective occurrence) and the subsequent stomach ache (e.g., first subjective user state) on Monday represents a first sequential pattern while the user 2-20* eating at the same restaurant (e.g., second objective occurrence) and the subsequent stomach ache (e.g., second subjective user state) on Tuesday represents a second sequential pattern. In this example, the occurrence of the stomach ache after (rather than before or concurrently) eating at the particular restaurant on Monday represents a first temporal relationship associated with the first sequential pattern while the occurrence of a second stomach ache after (rather than before or concurrently) eating at the same restaurant on Tuesday represents a second temporal relationship associated with the second sequential pattern. Under such circumstances, the temporal relationship comparison module 2-247 may compare the first temporal relationship to the second temporal relationship in order to determine whether the first temporal relationship and the second temporal relationship at least substantially match (e.g., stomachaches in both temporal relationships occurring after eating at the restaurant). Such a match may result in the inference that a stomach ache is associated with eating at the particular restaurant.
In some implementations, the sequential pattern comparison module 2-242 may include an extent of time difference comparison module 2-248 that may be configured to compare the extent of time differences between incidences of subjective user states and incidences of objective occurrences of different sequential patterns. For example, the extent of time difference comparison module 2-248 may compare the extent of time difference between incidence of a first subjective user state and incidence of a first objective occurrence of a first sequential pattern with the extent of time difference between incidence of a second subjective user state and incidence of a second objective occurrence of a second sequential pattern. In some implementations, the comparisons may be made in order to determine that the extent of time differences of the different sequential patterns at least substantially or proximately match.
In some embodiments, the correlation module 2-106 may include a strength of correlation determination module 2-250 for determining a strength of correlation between subjective user state data 2-60 and objective occurrence data 2-70* associated with a user 2-20*. In some implementations, the strength of correlation may be determined based, at least in part, on the results provided by the other sub-modules of the correlation module 2-106 (e.g., the sequential pattern determination module 2-236, the sequential pattern comparison module 2-242, and their sub-modules).
d illustrates particular implementations of the presentation module 2-108 of the computing device 2-10 of
In various implementations, the presentation module 2-108 may include a transmission module 2-252 for transmitting one or more results of the correlation performed by the correlation module 2-106. For example, in the case where the computing device 2-10 is a server, the transmission module 2-252 may be configured to transmit to the user 2-20a or a third party 2-50 the one or more results of the correlation performed by the correlation module 2-106 via a network interface 2-120.
In the same or different implementations, the presentation module 2-108 may include a display module 2-254 for displaying the one or more results of the correlation operations performed by the correlation module 2-106. For example, in the case where the computing device 2-10 is a local device, the display module 2-254 may be configured to display to the user 2-20b the one or more results of the correlation performed by the correlation module 2-106 via a user interface 2-122.
In some implementations, the presentation module 2-108 may include a sequential relationship presentation module 2-256 configured to present an indication of a sequential relationship between at least one subjective user state of a user 2-20* and at least one objective occurrence associated with the user 2-20*. In some implementations, the presentation module 2-108 may include a prediction presentation module 2-258 configured to present a prediction of a future subjective user state of a user 2-20* resulting from a future objective occurrence associated with the user 2-20*. In the same or different implementations, the prediction presentation module 2-258 may also be designed to present a prediction of a future subjective user state of a user 2-20* resulting from a past objective occurrence associated with the user 2-20*. In some implementations, the presentation module 2-108 may include a past presentation module 2-260 that is designed to present a past subjective user state of a user 2-20* in connection with a past objective occurrence associated with the user 2-20*.
In some implementations, the presentation module 2-108 may include a recommendation module 2-262 that is configured to present a recommendation for a future action based, at least in part, on the results of a correlation of subjective user state data 2-60 with objective occurrence data 2-70* performed by the correlation module 2-106. In certain implementations, the recommendation module 2-262 may further include a justification module 2-264 for presenting a justification for the recommendation presented by the recommendation module 2-262. In some implementations, the presentation module 2-108 may include a strength of correlation presentation module 2-266 for presenting an indication of a strength of correlation between subjective user state data 2-60 and objective occurrence data 2-70*.
As will be further described herein, in some embodiments, the presentation module 2-108 may be prompted to present the one or more results of a correlation operation performed by the correlation module 2-106 in response to a reporting of one or more events, objective occurrences, and/or subjective user states.
As briefly described earlier, in various embodiments, the computing device 2-10 may include a network interface 2-120 that may facilitate in communicating with a user 2-20a and/or one or more third parties 2-50. For example, in embodiments whereby the computing device 2-10 is a server, the computing device 2-10 may include a network interface 2-120 that may be configured to receive from the user 2-20a subjective user state data 2-60. In some embodiments, objective occurrence data 2-70a, 2-70b, or 2-70c may also be received through the network interface 2-120. Examples of a network interface 2-120 includes, for example, a network interface card (NIC).
The computing device 2-10, in various embodiments, may also include a memory 2-140 for storing various data. For example, in some embodiments, memory 2-140 may be employed in order to store subjective user state data 2-61 of a user 2-20* that may indicate one or more past subjective user states of the user 2-20* and objective occurrence data 2-70* associated with the user 2-20* that may indicate one or more past objective occurrences. In some embodiments, memory 2-140 may store historical data 2-72 such as historical medical data of a user 2-20* (e.g., genetic, metoblome, proteome information), population trends, historical sequential patterns derived from general population, and so forth.
In various embodiments, the computing device 2-10 may include a user interface 2-122 to communicate directly with a user 2-20b. For example, in embodiments in which the computing device 2-10 is a local device, the user interface 2-122 may be configured to directly receive from the user 2-20b subjective user state data 2-60. The user interface 2-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a key pad, a mouse, an audio system, an imaging system including a digital or video camera, and/or other user interface devices.
e illustrates particular implementations of the one or more applications 2-126 of
In
Further, in
In any event, after a start operation, the operational flow 2-300 may move to a subjective user state data acquisition operation 2-302 for acquiring subjective user state data including data indicating at least one subjective user state associated with a user. For instance, the subjective user state data acquisition module 2-102 of the computing device 2-10 of
Operational flow 2-300 may also include an objective occurrence data acquisition operation 2-304 for acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user. For instance, the objective occurrence data acquisition module 2-104 of the computing device 2-10 acquiring, via the network interface 2-120 or via the user interface 2-122, objective occurrence data 2-70* including data indicating at least one objective occurrence (e.g., ingestion of a food, medicine, or nutraceutical) associated with the user 2-20*. Note that, and as those skilled in the art will recognize, the subjective user state data acquisition operation 2-302 does not have to be performed prior to the objective occurrence data acquisition operation 2-304 and may be performed subsequent to the performance of the objective occurrence data acquisition operation 2-304 or may be performed concurrently with the objective occurrence data acquisition operation 2-304.
Operational flow 2-300 may further include a correlation operation 2-306 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence. For instance, the correlation module 2-106 of the computing device 2-10 correlating the subjective user state data 2-60 with the objective occurrence data 2-70* based, at least in part, on a determination of at least one sequential pattern (e.g., time sequential pattern) associated with the at least one subjective user state (e.g., user feeling “tired”) and the at least one objective occurrence (e.g., high blood sugar level).
Finally, the operational flow 2-300 may include a presentation operation 2-308 for presenting one or more results of the correlating. For instance, the presentation module 2-108 of the computing device 2-10 presenting, via the network interface 2-120 or via the user interface 2-122, one or more results (e.g., in the form of a recommendation for a future action or in the form of a notification of a past event) of the correlating performed by the correlation operation 2-306.
In various implementations, the subjective user state data acquisition operation 2-302 may include one or more additional operations as illustrated in
The reception operation 2-402 may, in turn, further include one or more additional operations. For example, in some implementations, the reception operation 2-402 may include an operation 2-404 for receiving the subjective user state data via a user interface as depicted in
In some implementations, the reception operation 2-402 may include an operation 2-406 for receiving the subjective user state data via a network interface as depicted in
In various implementations, operation 2-406 may further include one or more operations. For example, in some implementations operation 2-406 may include an operation 2-408 for receiving data indicating the at least one subjective user state via an electronic message generated by the user as depicted in
In some implementations, operation 2-406 may include an operation 2-410 for receiving data indicating the at least one subjective user state via a blog entry generated by the user as depicted in
In some implementations, operation 2-406 may include an operation 2-412 for receiving data indicating the at least one subjective user state via a status report generated by the user as depicted in
In some implementations, the reception operation 2-402 may include an operation 2-414 for receiving subjective user state data including data indicating at least one subjective user state specified by a selection made by the user, the selection being a selection of a subjective user state from a plurality of alternative subjective user states as depicted in
Operation 2-414 may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 2-414 may include an operation 2-416 for receiving subjective user state data including data indicating at least one subjective user state specified by a selection made by the user, the selection being a selection of a subjective user state from two alternative contrasting subjective user states as depicted in
In some implementations, operation 2-414 may include an operation 2-417 for receiving the selection via a network interface as depicted in
In some implementations, operation 2-414 may include an operation 2-418 for receiving the selection via user interface as depicted in
In some implementations, the reception operation 2-402 may include an operation 2-420 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on a text entry provided by the user as depicted in
In some implementations, the reception operation 2-402 may include an operation 2-422 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an audio entry provided by the user as depicted in
In some implementations, the reception operation 2-402 may include an operation 2-424 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an image entry provided by the user as depicted in
Operation 2-424 may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 2-424 may include an operation 2-426 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an image entry showing a gesture made by the user as depicted in
In some implementations, operation 2-424 may include an operation 2-428 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an image entry showing an expression made by the user as depicted in
In some implementations, the reception operation 2-402 may include an operation 2-430 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on data provided through user interaction with a user interface as depicted in
In various implementations, the subjective user state data acquisition operation 2-302 may include an operation 2-432 for acquiring data indicating at least one subjective mental state of the user as depicted in
In some implementations, operation 2-432 may further include an operation 2-434 for acquiring data indicating at least a level of the one subjective mental state of the user as depicted in
In various implementations, the subjective user state data acquisition operation 2-302 may include an operation 2-436 for acquiring data indicating at least one subjective physical state of the user as depicted in
In some implementations, operation 2-436 may further include an operation 2-438 for acquiring data indicating at least a level of the one subjective physical state of the user as depicted in
In various implementations, the subjective user state data acquisition operation 2-302 may include an operation 2-440 for acquiring data indicating at least one subjective overall state of the user as depicted in
In some implementations, operation 2-440 may further include an operation 2-442 for acquiring data indicating at least a level of the one subjective overall state of the user as depicted in
In various implementations, the subjective user state data acquisition operation 2-302 may include an operation 2-444 for acquiring subjective user state data including data indicating at least a second subjective user state associated with the user as depicted in
In various alternative implementations, operation 2-444 may include one or more additional operations. For example, in some implementations, operation 2-444 includes an operation 2-446 for acquiring subjective user state data including data indicating at least a second subjective user state that is equivalent to the at least one subjective user state as depicted in
In some implementations, operation 2-446 may further include an operation 2-448 for acquiring subjective user state data including data indicating at least a second subjective user state that is at least proximately equivalent in meaning to the at least one subjective user state as depicted in
In some implementations, operation 2-444 includes an operation 2-450 for acquiring subjective user state data including data indicating at least a second subjective user state that is proximately equivalent to the at least one subjective user state as depicted in
In some implementations, operation 2-444 includes an operation 2-451 for acquiring subjective user state data including data indicating at least a second subjective user state that is a contrasting subjective user state from the at least one subjective user state as depicted in
In some implementations, operation 2-444 includes an operation 2-452 for acquiring subjective user state data including data indicating at least a second subjective user state that references the at least one subjective user state as depicted in
In some implementations, operation 2-452 may further include an operation 2-453 for acquiring subjective user state data including data indicating at least a second subjective user state that is one of modification, extension, improvement, or regression of the at least one subjective user state as depicted in
In some implementations the subjective user state data acquisition operation 2-302 of
Operation 2-454 may further include, in various implementations, an operation 2-455 for acquiring another time stamp associated with a second subjective user state indicated by the subjective user state data as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-456 for acquiring an indication of a time interval associated with the at least one subjective user state as depicted in
Operation 2-456 may further include, in various implementations, an operation 2-457 for acquiring another indication of another time interval associated with a second subjective user state indicated by the subjective user state data as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-458 for acquiring an indication of a temporal relationship between the at least one subjective user state and the at least one objective occurrence as depicted in
Operation 2-458 may further include, in various implementations, an operation 2-459 for acquiring an indication of a temporal relationship between the at least one subjective user state and a second subjective user state indicated by the subjective user state data as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-460 for soliciting from the user the at least one subjective user state as depicted in
Operation 2-460 may further include, in some implementations, an operation 2-462 for transmitting to the user a request for a subjective user state as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-463 for acquiring the subjective user state data at a server as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-464 for acquiring the subjective user state data at a handheld device as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-466 for acquiring the subjective user state data at a peer-to-peer network component device as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-468 for acquiring the subjective user state data via a Web 2.0 construct as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-470 for acquiring data indicating one subjective user state that occurred at least partially concurrently with an incidence of one objective occurrence associated with the user as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-472 for acquiring data indicating one subjective user state that occurred prior to an incidence of one objective occurrence associated with the user as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-474 for acquiring data indicating one subjective user state that occurred subsequent to an incidence of one objective occurrence associated with the user as depicted in
In some implementations the subjective user state data acquisition operation 2-302 may include an operation 2-476 for acquiring data that indicates one subjective user state that occurred within a predefined time period of an incidence of one objective occurrence associated with the user as depicted in
Referring back to
The reception operation 2-500 in various implementations may include one or more additional operations. For example, in some implementations the reception operation 2-500 may include an operation 2-501 for receiving the objective occurrence data from at least one of a wireless network or a wired network as depicted in
In some implementations, the reception operation 2-500 may include an operation 2-502 for receiving the objective occurrence data via one or more blog entries as depicted in
In some implementations, the reception operation 2-500 may include an operation 2-503 for receiving the objective occurrence data via one or more status reports as depicted in
In some implementations, the reception operation 2-500 may include an operation 2-504 for receiving the objective occurrence data via a Web 2.0 construct as depicted in
In some implementations, the reception operation 2-500 may include an operation 2-505 for receiving the objective occurrence data from one or more third party sources as depicted in
In some implementations, the reception operation 2-500 may include an operation 2-506 for receiving the objective occurrence data from one or more sensors configured to sense one or more objective occurrences associated with the user as depicted in
In some implementations, the reception operation 2-500 may include an operation 2-507 for receiving the objective occurrence data from the user as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-508 for acquiring objective occurrence data including data indicating at least a second objective occurrence associated with the user as depicted in
In various implementations, operation 2-508 may further include one or more additional operations. For example, in some implementations, operation 2-508 may include an operation 2-509 for acquiring objective occurrence data including data indicating one objective occurrence associated with a first point in time and data indicating a second objective occurrence associated with a second point in time as depicted in
In some implementations, operation 2-508 may include an operation 2-510 for acquiring objective occurrence data including data indicating one objective occurrence associated with a first time interval and data indicating a second objective occurrence associated with a second time interval as depicted in
In some implementations, operation 2-508 may include an operation 2-511 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is equivalent to the at least one objective occurrence as depicted in
Operation 2-511 in certain implementations may further include an operation 2-512 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is at least proximately equivalent in meaning to the at least one objective occurrence as depicted in
In some implementations, operation 2-508 may include an operation 2-513 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is proximately equivalent to the at least one objective occurrence as depicted in
In some implementations, operation 2-508 may include an operation 2-514 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is a contrasting objective occurrence from the at least one objective occurrence as depicted in
In some implementations, operation 2-508 may include an operation 2-515 for acquiring objective occurrence data including data indicating at least a second objective occurrence that references the at least one objective occurrence as depicted in
Operation 2-515 may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 2-515 may include an operation 2-516 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is a comparison to the at least one objective occurrence as depicted in
In some implementations, operation 2-515 may include an operation 2-517 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is a modification of the at least one objective occurrence as depicted in
In some implementations, operation 2-515 may include an operation 2-518 for acquiring objective occurrence data including data indicating at least a second objective occurrence that is an extension of the at least one objective occurrence as depicted in
In various implementations, the objective occurrence data acquisition operation 2-304 of
Operation 2-519 in some implementations may further include an operation 2-520 for acquiring another time stamp associated with a second objective occurrence indicated by the objective occurrence data as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-521 for acquiring an indication of a time interval associated with the at least one objective occurrence as depicted in
Operation 2-521 in some implementations may further include an operation 2-522 for acquiring another indication of another time interval associated with a second objective occurrence indicated by the objective occurrence data as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
In some implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-524 for acquiring data indicating at least one objective occurrence associated with the user and one or more attributes associated with the at least one objective occurrence as depicted in
In various implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-525 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a medicine as depicted in
Operation 2-525 may further include, in some implementations, an operation 2-526 for acquiring data indicating another objective occurrence of another ingestion by the user of another medicine as depicted in
Operation 2-526 may further include, in some implementations, an operation 2-527 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a medicine and data indicating another objective occurrence of another ingestion by the user of another medicine, the ingestions of the medicine and the another medicine being ingestions of same or similar type of medicine as depicted in
In some implementations, operation 2-527 may further include an operation 2-528 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a medicine and data indicating another objective occurrence of another ingestion by the user of another medicine, the ingestions of the medicine and the another medicine being ingestions of same or similar quantities of the same or similar type of medicine as depicted in
In some alternative implementations, operation 2-526 may include an operation 2-529 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a medicine and data indicating another objective occurrence of another ingestion by the user of another medicine, the ingestions of the medicine and the another medicine being ingestions of different types of medicine as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
Operation 2-530 may, in turn, include an operation 2-531 for acquiring data indicating another objective occurrence of another ingestion by the user of another food item as depicted in
In some implementations, operation 2-531 may further include an operation 2-532 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a food item and data indicating another objective occurrence of another ingestion by the user of another food item, the ingestions of the food item and the another food item being ingestions of same or similar type of food item as depicted in
In some implementations, operation 2-532 may further include an operation 2-533 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a food item and data indicating another objective occurrence of another ingestion by the user of another food item, the ingestions of the food item and the another food item being ingestions of same or similar quantities of the same or similar type of food item as depicted in
In some alternative implementations, operation 2-531 may include an operation 2-534 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a food item and data indicating another objective occurrence of another ingestion by the user of another food item, the ingestions of the food item and the another food item being ingestions of different types of food item as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
Operation 2-535 in certain implementations may further include an operation 2-536 for acquiring data indicating another objective occurrence of another ingestion by the user of another nutraceutical as depicted in
In some implementations, operation 2-536 may include an operation 2-537 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a nutraceutical and data indicating another objective occurrence of another ingestion by the user of another nutraceutical, the ingestions of the nutraceutical and the another nutraceutical being ingestions of same or similar type of nutraceutical as depicted in
Operation 2-537 may, in some instances, further include an operation 2-538 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a nutraceutical and data indicating another objective occurrence of another ingestion by the user of another nutraceutical, the ingestions of the nutraceutical and the another nutraceutical being ingestions of same or similar quantities of the same or similar type of nutraceutical as depicted in
In some alternative implementations, operation 2-536 may include an operation 2-539 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a nutraceutical and data indicating another objective occurrence of another ingestion by the user of another nutraceutical, the ingestions of the nutraceutical and the another nutraceutical being ingestions of different types of nutraceutical as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
In various implementations, operation 2-540 may further include an operation 2-541 for acquiring data indicating another objective occurrence of another exercise routine executed by the user as depicted in
In some implementations, operation 2-541 may further include an operation 2-542 for acquiring data indicating at least one objective occurrence of an exercise routine executed by the user and data indicating another objective occurrence of another exercise routine executed by the user, the exercise routines executed by the user being the same or similar type of exercise routine as depicted in
In some implementations, operation 2-542 may further include an operation 2-543 for acquiring data indicating at least one objective occurrence of an exercise routine executed by the user and data indicating another objective occurrence of another exercise routine executed by the user, the exercise routines executed by the user being the same or similar quantity of the same or similar type of exercise routine as depicted in
In some implementations, operation 2-541 may include an operation 2-544 for acquiring data indicating at least one objective occurrence of an exercise routine executed by the user and data indicating another objective occurrence of another exercise routine executed by the user, the exercise routines executed by the user being different types of exercise routine as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
In some implementations, operation 2-545 may further include an operation 2-546 acquiring data indicating another objective occurrence of another social activity executed by the user as depicted in
In some implementations, operation 2-546 may include an operation 2-547 for acquiring data indicating at least one objective occurrence of a social activity executed by the user and data indicating another objective occurrence of another social activity executed by the user, the social activities executed by the user being same or similar type of social activities as depicted in
In some implementations, operation 2-546 may include an operation 2-548 for acquiring data indicating at least one objective occurrence of a social activity executed by the user and data indicating another objective occurrence of another social activity executed by the user, the social activities executed by the user being different types of social activity as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
Operation 2-549, in some instances, may further include an operation 2-550 for acquiring data indicating another objective occurrence of another activity performed by the third party as depicted in
In some implementations, operation 2-550 may include an operation 2-551 for acquiring data indicating at least one objective occurrence of an activity performed by a third party and data indicating another objective occurrence of another activity performed by the third party, the activities performed by the third party being same or similar type of activities as depicted in
In some implementations, operation 2-550 may include an operation 2-552 for acquiring data indicating at least one objective occurrence of an activity performed by a third party and data indicating another objective occurrence of another activity performed by the third party, the activities performed by the third party being different types of activity as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
Operation 2-553, in some instances, may further include an operation 2-554 for acquiring data indicating another objective occurrence of another physical characteristic of the user as depicted in
In some implementations, operation 2-554 may include an operation 2-555 for acquiring data indicating at least one objective occurrence of a physical characteristic of the user and data indicating another objective occurrence of another physical characteristic of the user, the physical characteristics of the user being same or similar type of physical characteristic as depicted in
In some implementations, operation 2-554 may include an operation 2-556 for acquiring data indicating at least one objective occurrence of a physical characteristic of the user and data indicating another objective occurrence of another physical characteristic of the user, the physical characteristics of the user being different types of physical characteristic as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-557 for acquiring data indicating at least one objective occurrence of a resting, a learning, or a recreational activity by the user as depicted in
Operation 2-557, in some instances, may further include an operation 2-558 for acquiring data indicating another objective occurrence of another resting, another learning, or another recreational activity by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-559 for acquiring data indicating at least one objective occurrence of an external event as depicted in
Operation 2-559, in some instances, may further include an operation 2-560 for acquiring data indicating another objective occurrence of another external event as depicted in
In some implementations, operation 2-560 may include an operation 2-561 for acquiring data indicating at least one objective occurrence of an external event and data indicating another objective occurrence of another external event, the external events being same or similar type of external event as depicted in
In some implementations, operation 2-560 may include an operation 2-562 for acquiring data indicating at least one objective occurrence of an external event and data indicating another objective occurrence of another external event, the external events being different types of external event as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 of
Operation 2-563, in some instances, may further include an operation 2-564 for acquiring data indicating another objective occurrence related to another location of the user as depicted in
In some implementations, operation 2-564 may include an operation 2-565 for acquiring data indicating at least one objective occurrence related to a location of the user and data indicating another objective occurrence related to another location of the user, the locations being same or similar location as depicted in
In some implementations, operation 2-564 may include an operation 2-566 for acquiring data indicating at least one objective occurrence related to a location of the user and data indicating another objective occurrence related to another location of the user, the locations being different locations as depicted in
In some implementations, the objective occurrence data acquisition operation 2-304 may include an operation 2-569 for soliciting the objective occurrence data including data indicating at least one objective occurrence associated with the user as depicted in
In various implementations, operation 2-569 may include one or more additional operations. For instance, in some implementations, operation 2-569 may include an operation 2-570 for soliciting from the user the objective occurrence data as depicted in
In some implementations, operation 2-569 may include an operation 2-571 for soliciting from a third party source the objective occurrence data as depicted in
In some implementations, operation 2-569 may include an operation 2-572 for soliciting the objective occurrence data in response to a reporting of a subjective user state as depicted in
Referring back to
In some implementations, the correlation operation 2-306 may include an operation 2-608 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of whether the at least one subjective user state occurred before, after, or at least partially concurrently with incidence of the at least one objective occurrence as depicted in
In some implementations, the correlation operation 2-306 may include an operation 2-614 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing historical data as depicted in
In various implementations, operation 2-614 may include one or more operations. For example, in some implementations, operation 2-614 may include an operation 2-616 for correlating the subjective user state data with the objective occurrence data based, at least in part, on historical data indicative of a link between a subjective user state type and an objective occurrence type as depicted in
In some implementations, operation 2-616 may further include an operation 2-618 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a historical sequential pattern as depicted in
In some implementations, operation 2-614 may include an operation 2-620 for correlating the subjective user state data with the objective occurrence data based, at least in part, on historical medical data of the user as depicted in
In various implementations, the correlation operation 2-306 of
Operation 2-622, in some instances, may further include an operation 2-623 for comparing the one sequential pattern to the second sequential pattern to determine whether the first sequential pattern at least substantially matches the second sequential pattern as depicted in
In various alternative implementations, operation 2-623 may further include one or more additional operations. For example, in some implementations, operation 2-623 may include an operation 2-624 for determining whether the at least one subjective user state is equivalent to the at least a second subjective user state as depicted in
In some implementations, operation 2-623 may include an operation 2-626 for determining whether the at least one subjective user state is at least proximately equivalent in meaning to the at least a second subjective user state as depicted in
In some implementations, operation 2-623 may include an operation 2-628 for determining whether the at least one subjective user state is proximately equivalent to the at least a second subjective user state as depicted in
In some implementations, operation 2-623 may include an operation 2-630 for determining whether the at least one subjective user state is a contrasting subjective user state from the at least a second subjective user state as depicted in
In some implementations, operation 2-623 may include an operation 2-632 for determining whether the at least one objective occurrence is equivalent to the at least a second objective occurrence as depicted in
In some implementations, operation 2-623 may include an operation 2-634 for determining whether the at least one objective occurrence is at least proximately equivalent in meaning to the at least a second objective occurrence as depicted in
In some implementations, operation 2-623 may include an operation 2-636 for determining whether the at least one objective occurrence is proximately equivalent to the at least a second objective occurrence as depicted in
In some implementations, operation 2-623 may include an operation 2-638 for determining whether the at least one objective occurrence is a contrasting objective occurrence from the at least a second objective occurrence as depicted in
In some implementations, operation 2-623 may include an operation 2-640 for determining whether the at least one subjective user state occurred within a predefined time increment from incidence of the at least one objective occurrence as depicted in
Operation 2-640 may, in some instances, include an additional operation 2-642 for determining whether the at least a second subjective user state occurred within the predefined time increment from incidence of the at least a second objective occurrence as depicted in
In various implementations, operation 2-622 may include an operation 2-644 for determining a first sequential pattern by determining at least whether the at least one subjective user state occurred before, after, or at least partially concurrently with incidence of the at least one objective occurrence as depicted in
In some implementations, operation 2-644 may include an additional operation 2-646 for determining the second sequential pattern by determining at least whether the at least a second subjective user state occurred before, after, or at least partially concurrently with incidence of the at least a second objective occurrence as depicted in
In various implementations, operation 2-622 may include an operation 2-650 for determining the one sequential pattern by determining at least an extent of time difference between incidence of the at least one subjective user state and incidence of the at least one objective occurrence as depicted in
Operation 2-650 may, in some instances, include an additional operation 2-652 for determining the second sequential pattern by determining at least an extent of time difference between incidence of the at least a second subjective user state and incidence of the at least a second objective occurrence as depicted in
In some implementations, the correlation operation 2-306 of
In some implementations, the correlation operation 2-306 may include an operation 2-658 for correlating the subjective user state data with the objective occurrence data at a server as depicted in
In some implementations, the correlation operation 2-306 may include an operation 2-660 for correlating the subjective user state data with the objective occurrence data at a handheld device as depicted in
In some implementations, the correlation operation 2-306 may include an operation 2-662 for correlating the subjective user state data with the objective occurrence data at a peer-to-peer network component device as depicted in
Referring back to
In some implementations, the presentation operation 2-308 may include a transmission operation 2-704 for transmitting the one or more results via a network interface as depicted in
The transmission operation 2-704 may further include one or more additional operations. For example, in some implementations, the transmission operation 2-704 may include an operation 2-706 for transmitting the one or more results to the user as depicted in
In some implementations, the transmission operation 2-704 may include an operation 2-708 for transmitting the one or more results to one or more third parties as depicted in
In some implementations, the presentation operation 2-308 of
In some implementations, the presentation operation 2-308 may include an operation 2-714 for presenting a prediction of a future subjective user state resulting from a future objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 2-308 may include an operation 2-716 for presenting a prediction of a future subjective user state resulting from a past objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 2-308 may include an operation 2-718 for presenting a past subjective user state in connection with a past objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 2-308 may include an operation 2-720 for presenting a recommendation for a future action as depicted in
Operation 2-720 may, in some instances, include an additional operation 2-722 for presenting a justification for the recommendation as depicted in
In some implementations, the presentation operation 2-308 may include an operation 2-724 for presenting an indication of a strength of correlation between the subjective user state data and the objective occurrence data as depicted in
In some implementations, the presentation operation 2-308 may include an operation 2-726 for presenting one or more results of the correlating in response to a reporting of an occurrence of another objective occurrence associated with the user as depicted in
In various implementations, operation 2-726 may further include one or more additional operations. For example, in some implementations, operation 2-726 may include an operation 2-728 for presenting one or more results of the correlating in response to a reporting of an event executed by the user as depicted in
In some implementations, operation 2-726 may include an operation 2-730 for presenting one or more results of the correlating in response to a reporting of an event executed by one or more third parties as depicted in
In some implementations, operation 2-726 may include an operation 2-732 for presenting one or more results of the correlating in response to a reporting of an occurrence of an external event as depicted in
In some implementations, the presentation operation 2-308 of
In some implementations, the presentation operation 2-308 may include an operation 2-736 for presenting one or more results of the correlating in response to an inquiry made by the user as depicted in
In some implementations, the presentation operation 2-308 may include an operation 2-738 for presenting one or more results of the correlating in response to an inquiry made by a third party as depicted in
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post their thoughts and opinions on various topics, the latest news, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social network status reports in which a user may report or post for others to view the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted through microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, event, happening, or any other aspects associated with or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, external events that may not be directly related to the user such as the local weather or the performance of the stock market (which the microblogger may have an interest in), activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblogging entries include “subjective user states” of the microblogger. Subjective user states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., “I am feeling happy”), the subjective physical states of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and the subjective overall state of the microblogger (e.g., “I'm good” or “I'm well”). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). Although microblogs are being used to provide a wealth of personal information, they have thus far been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided for, among other things, acquiring subjective user state data including data indicative of at least one subjective user state associated with a user and soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating at least one objective occurrence. As will be further described herein, in some embodiments, the solicitation of the objective occurrence data may, in addition to be prompted by the acquisition of the subjective user state data, may be prompted by referencing historical data. Such historical data may be historical data that is associated with the user, associated with a group of users, associated with a segment of the general population, or associated with the general population.
The methods, systems, and computer program products may then correlate the subjective user state data (e.g., data that indicate one or more subjective user states of a user) with the objective occurrence data (e.g., data that indicate one or more objective occurrences associated with the user). By correlating the subjective user state data with the objective occurrence data, a causal relationship between one or more objective occurrences (e.g., cause) and one or more subjective user states (e.g., result) associated with a user (e.g., a blogger or microblogger) may be determined in various alternative embodiments. For example, determining that the last time a user ate a banana (e.g., objective occurrence), the user felt “good” (e.g., subjective user state) or determining whenever a user eats a banana the user always or sometimes feels good. Note that an objective occurrence does not need to occur prior to a corresponding subjective user state but instead, may occur subsequent or concurrently with the incidence of the subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence) or a person may become gloomy while (e.g., concurrently) it is raining.
As briefly described above, a “subjective user state” is in reference to any state or status associated with a user (e.g., a blogger or microblogger) at any moment or interval in time that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of the user (e.g., user is feeling sad), the subjective physical state (e.g., physical characteristic) of the user that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), and the subjective overall state of the user (e.g., user is “good”). Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be easily categorized as a subjective mental state or as a subjective physical state. Examples of overall states of a user that may be subjective user states include, for example, the user being good, bad, exhausted, lack of rest, wellness, and so forth.
In contrast, “objective occurrence data,” which may also be referred to as “objective context data,” may include data that indicate one or more objective occurrences associated with the user that occurred at particular intervals or points in time. An objective occurrence may be any physical characteristic, event, happenings, or any other aspect that may be associated with or is of interest to a user that can be objectively reported by at least a third party or a sensor device. Note, however, that such objective occurrence data does not have to be actually provided by a sensor device or by a third party, but instead, may be reported by the user himself or herself (e.g., via microblog entries). Examples of objectively reported occurrences that could be indicated by the objective occurrence data include, for example, a user's food, medicine, or nutraceutical intake, the user's location at any given point in time, a user's exercise routine, a user's physiological characteristics such as blood pressure, social or professional activities, the weather at a user's location, activities associated with third parties, occurrence of external events such as the performance of the stock market, and so forth.
The term “correlating” as will be used herein is in reference to a determination of one or more relationships between at least two variables. Alternatively, the term “correlating” may merely be in reference to the linking or associating of at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that represents at least one subjective user state of a user and the second variable is objective occurrence data that represents at least one objective occurrence. In embodiments where the subjective user state data includes data that indicates multiple subjective user states, each of the subjective user states represented by the subjective user state data may be the same or similar type of subjective user state (e.g., user being happy) at different intervals or points in time. Alternatively, different types of subjective user state (e.g., user being happy and user being sad) may be represented by the subjective user state data. Similarly, in embodiments where multiple objective occurrences are indicated by the objective occurrence data, each of the objective occurrences may represent the same or similar type of objective occurrence (e.g., user exercising) at different intervals or points in time, or alternatively, different types of objective occurrence (e.g., user exercising and user resting).
Various techniques may be employed for correlating subjective user state data with objective occurrence data in various alternative embodiments. For example, in some embodiments, correlating the objective occurrence data with the subjective user state data may be accomplished by determining a sequential pattern associated with at least one subjective user state indicated by the subjective user state data and at least one objective occurrence indicated by the objective occurrence data. In other embodiments, correlating of the objective occurrence data with the subjective user state data may involve determining multiple sequential patterns associated with multiple subjective user states and multiple objective occurrences.
A sequential pattern, as will be described herein, may define time and/or temporal relationships between two or more events (e.g., one or more subjective user states and one or more objective occurrences). In order to determine a sequential pattern, objective occurrence data including data indicating at least one objective occurrence may be solicited (e.g., from a user, from one or more third party sources, or from one or more sensor devices) in response to an acquisition of subjective user state data including data indicating at least one subjective user state.
For example, if a user reports that the user felt gloomy on a particular day (e.g., subjective user state) then a solicitation (e.g., from the user or from a third party source such as a content provider) may be made about the local weather (e.g., objective occurrence). Such solicitation of objective occurrence data may be prompted based, at least in part, on the reporting of the subjective user state and based on historical data such as historical data that indicates or suggests that the user tends to get gloomy when the weather is bad (e.g., cloudy) or based on historical data that indicates that people in the general population tend to get gloomy whenever the weather is bad. In some embodiments, such historical data may indicate or define one or more historical sequential patterns of the user or of the general population as they relate to subjective user states and objective occurrences.
As briefly described above, a sequential pattern may merely indicate or represent the temporal relationship or relationships between at least one subjective user state and at least one objective occurrence (e.g., whether the incidence or occurrence of the at least one subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least one objective occurrence). In alternative implementations, and as will be further described herein, a sequential pattern may indicate a more specific time relationship between the incidences of one or more subjective user states and the incidences of one or more objective occurrences. For example, a sequential pattern may represent the specific pattern of events (e.g., one or more objective occurrences and one or more subjective user states) that occurs along a timeline.
The following illustrative example is provided to describe how a sequential pattern associated with at least one subjective user state and at least one objective occurrence may be determined based, at least in part, on the temporal relationship between the incidence of the at least one subjective user state and the incidence of the at least one objective occurrence in accordance with some embodiments. For these embodiments, the determination of a sequential pattern may initially involve determining whether the incidence of the at least one subjective user state occurred within some predefined time increments of the incidence of the one objective occurrence. That is, it may be possible to infer that those subjective user states that did not occur within a certain time period from the incidence of an objective occurrence are not related or are unlikely related to the incidence of that objective occurrence.
For example, suppose a user during the course of a day eats a banana and also has a stomach ache sometime during the course of the day. If the consumption of the banana occurred in the early morning hours but the stomach ache did not occur until late that night, then the stomach ache may be unrelated to the consumption of the banana and may be disregarded. On the other hand, if the stomach ache had occurred within some predefined time increment, such as within 2 hours of consumption of the banana, then it may be concluded that there is a correlation or link between the stomach ache and the consumption of the banana. If so, a temporal relationship between the consumption of the banana and the occurrence of the stomach ache may be determined. Such a temporal relationship may be represented by a sequential pattern. Such a sequential pattern may simply indicate that the stomach ache (e.g., a subjective user state) occurred after (rather than before or concurrently) the consumption of banana (e.g., an objective occurrence).
As will be further described herein, other factors may also be referenced and examined in order to determine a sequential pattern and whether there is a relationship (e.g., causal relationship) between an objective occurrence and a subjective user state. These factors may include, for example, historical data (e.g., historical medical data such as genetic data or past history of the user or historical data related to the general population regarding, for example, stomach aches and bananas) as briefly described above. Alternatively, a sequential pattern may be determined for multiple subjective user states and multiple objective occurrences. Such a sequential pattern may particularly map the exact temporal or time sequencing of the various events (e.g., subjective user states and/or objective occurrences). The determined sequential pattern may then be used to provide useful information to the user and/or third parties.
The following is another illustrative example of how subjective user state data may be correlated with objective occurrence data by determining multiple sequential patterns and comparing the sequential patterns with each other. Suppose, for example, a user such as a microblogger reports that the user ate a banana on a Monday. The consumption of the banana, in this example, is a reported first objective occurrence associated with the user. The user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported first subjective user state. Thus, the reported incidence of the first objective occurrence (e.g., eating the banana) and the reported incidence of the first subjective user state (user felt very happy) on Monday may be represented by a first sequential pattern.
On Tuesday, the user reports that the user ate another banana (e.g., a second objective occurrence associated with the user). The user then reports that 20 minutes after eating the second banana, the user felt somewhat happy (e.g., a second subjective user state). Thus, the reported incidence of the second objective occurrence (e.g., eating the second banana) and the reported incidence of the second subjective user state (user felt somewhat happy) on Tuesday may be represented by a second sequential pattern. Note that in this example, the occurrences of the first subjective user state and the second subjective user state may be indicated by subjective user state data while the occurrences of the first objective occurrence and the second objective occurrence may be indicated by objective occurrence data.
In a slight variation of the above example, suppose the user had forgotten to report for Tuesday the consumption of the banana but does report feeling somewhat happy on Tuesday. This may result in the user being asked, based on the reporting of the user feeling somewhat happy on Tuesday, as to whether the user ate anything prior to feeling somewhat happy or whether the user ate a banana prior to feeling somewhat happy. Asking of such questions may be prompted both in response to the reporting of the user feeling somewhat happy on Tuesday and on referencing historical data (e.g., first sequential pattern derived from Monday's consumption of banana and feeling happy). Upon the user confirming the consumption of the banana on Tuesday, a second sequential pattern may be determined.
In any event, by comparing the first sequential pattern with the second sequential pattern, the subjective user state data may be correlated with the objective occurrence data. In some implementations, the comparison of the first sequential pattern with the second sequential pattern may involve trying to match the first sequential pattern with the second sequential pattern by examining certain attributes and/or metrics. For example, comparing the first subjective user state (e.g., user felt very happy) of the first sequential pattern with the second subjective user state (e.g., user felt somewhat happy) of the second sequential pattern to see if they at least substantially match or are contrasting (e.g., being very happy in contrast to being slightly happy or being happy in contrast to being sad). Similarly, comparing the first objective occurrence (e.g., eating a banana) of the first sequential pattern may be compared to the second objective occurrence (e.g., eating of another banana) of the second sequential pattern to determine whether they at least substantially match or are contrasting.
A comparison may also be made to determine if the extent of time difference (e.g., 15 minutes) between the first subjective user state (e.g., user being very happy) and the first objective occurrence (e.g., user eating a banana) matches or are at least similar to the extent of time difference (e.g., 20 minutes) between the second subjective user state (e.g., user being somewhat happy) and the second objective occurrence (e.g., user eating another banana). These comparisons may be made in order to determine whether the first sequential pattern matches the second sequential pattern. A match or substantial match would suggest, for example, that a subjective user state (e.g., happiness) is linked to a particular objective occurrence (e.g., consumption of banana).
As briefly described above, the comparison of the first sequential pattern with the second sequential pattern may include a determination as to whether, for example, the respective subjective user states and the respective objective occurrences of the sequential patterns are contrasting subjective user states and/or contrasting objective occurrences. For example, suppose in the above example the user had reported that the user had eaten a whole banana on Monday and felt very energetic (e.g., first subjective user state) after eating the whole banana (e.g., first objective occurrence). Suppose that the user also reported that on Tuesday he ate a half a banana instead of a whole banana and only felt slightly energetic (e.g., second subjective user state) after eating the half banana (e.g., second objective occurrence). In this scenario, the first sequential pattern (e.g., feeling very energetic after eating a whole banana) may be compared to the second sequential pattern (e.g., feeling slightly energetic after eating only a half of a banana) to at least determine whether the first subjective user state (e.g., being very energetic) and the second subjective user state (e.g., being slightly energetic) are contrasting subjective user states. Another determination may also be made during the comparison to determine whether the first objective occurrence (eating a whole banana) is in contrast with the second objective occurrence (e.g., eating a half of a banana).
In doing so, an inference may be made that eating a whole banana instead of eating only a half of a banana makes the user happier or eating more banana makes the user happier. Thus, the word “contrasting” as used here with respect to subjective user states refers to subjective user states that are the same type of subjective user states (e.g., the subjective user states being variations of a particular type of subjective user states such as variations of subjective mental states). Thus, for example, the first subjective user state and the second subjective user state in the previous illustrative example are merely variations of subjective mental states (e.g., happiness). Similarly, the use of the word “contrasting” as used here with respect to objective occurrences refers to objective states that are the same type of objective occurrences (e.g., consumption of food such as banana).
As those skilled in the art will recognize, a stronger correlation between the subjective user state data and the objective occurrence data could be obtained if a greater number of sequential patterns (e.g., if there was a third sequential pattern, a fourth sequential pattern, and so forth, that indicated that the user became happy or happier whenever the user ate bananas) are used as a basis for the correlation. Note that for ease of explanation and illustration, each of the exemplary sequential patterns to be described herein will be depicted as a sequential pattern of an occurrence of a single subjective user state and an occurrence of a single objective occurrence. However, those skilled in the art will recognize that a sequential pattern, as will be described herein, may also be associated with occurrences of multiple objective occurrences and/or multiple subjective user states. For example, suppose the user had reported that after eating a banana, he had gulped down a can of soda. The user then reported that he became happy but had an upset stomach. In this example, the sequential pattern associated with this scenario will be associated with two objective occurrences (e.g., eating a banana and drinking a can of soda) and two subjective user states (e.g., user having an upset stomach and feeling happy).
In some embodiments, and as briefly described earlier, the sequential patterns derived from subjective user state data and objective occurrence data may be based on temporal relationships between objective occurrences and subjective user states. For example, whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence. For instance, a plurality of sequential patterns derived from subjective user state data and objective occurrence data may indicate that a user always has a stomach ache (e.g., subjective user state) after eating a banana (e.g., first objective occurrence).
a and 3-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 3-100 may include at least a computing device 3-10 (see
In some embodiments, the computing device 3-10 may be a network server in which case the computing device 3-10 may communicate with a user 3-20a via a mobile device 3-30 and through a wireless and/or wired network 3-40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 3-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 3-10.
In alternative embodiments, the computing device 3-10 may be a local computing device that communicates directly with a user 3-20b. For these embodiments, the computing device 3-10 may be any type of handheld device such as a cellular telephone, a PDA, or other types of computing/communication devices such as a laptop computer, a desktop computer, and so forth. In various embodiments, the computing device 3-10 may be a peer-to-peer network component device. In some embodiments, the computing device 3-10 may operate via a web 2.0 construct.
In embodiments where the computing device 3-10 is a server, the computing device 3-10 may obtain the subjective user state data 3-60 indirectly from a user 3-20a via a network interface 3-120. In alternative embodiments in which the computing device 3-10 is a local device such as a handheld device (e.g., cellular telephone, personal digital assistant, etc.), the subjective user state data 3-60 may be directly obtained from a user 3-20b via a user interface 3-122. As will be further described, the computing device 3-10 may acquire the objective occurrence data 3-70* from one or more alternative sources.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 3-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 3-10 is a local device such as a handheld device that may communicate directly with a user 3-20b.
Assuming that the computing device 3-10 is a server, the computing device 3-10, in various implementations, may be configured to acquire subjective user state data 3-60 including data indicating at least one subjective user state 3-60a via the mobile device 3-30 and through wireless and/or wired networks 3-40. In some implementations, the subjective user state data 3-60 may further include additional data that may indicate one or more additional subjective user states (e.g., data indicating at least a second subjective user state 3-60b).
In various embodiments, the data indicating the at least one subjective user state 3-60a, as well as the data indicating the at least second subjective user state 3-60b, may be in the form of blog entries, such as microblog entries, status reports (e.g., social networking status reports), electronic messages (email, text messages, instant messages, etc.) or other types of electronic messages or documents. The data indicating the at least one subjective user state 3-60a and the data indicating the at least second subjective user state 3-60b may, in some instances, indicate the same, contrasting, or completely different subjective user states.
Examples of subjective user states that may be indicated by the subjective user state data 3-60 include, for example, subjective mental states of the user 3-20a (e.g., user 3-20a is sad or angry), subjective physical states of the user 3-20a (e.g., physical or physiological characteristic of the user 3-20a such as the presence, absence, elevating, or easing of a stomach ache or headache), subjective overall states of the user 3-20a (e.g., user 3-20a is “well”), and/or other subjective user states that only the user 3-20a can typically indicate.
The computing device 3-10 may also be configured to solicit objective occurrence data 3-70* including data indicating at least one objective occurrence. Such a solicitation of the objective occurrence data 3-70* may be prompted in response to the acquisition of subjective user state data 3-60 and/or in response to referencing of historical data 3-72 as will be further described herein. The solicitation of objective occurrence data 3-70* may be made through a network interface 3-120 or through the user interface 3-122. As will be further described, the solicitation of the objective occurrence data 3-70* from a source (e.g., the user 3-20*, one or more third party sources, or one or more sensors 3-35) may be accomplished in a number of ways depending on the specific circumstances (e.g., whether the computing device 3-10 is a server or a local device and whether the source is the user 3-20*, one or more third parties 3-50, or one or more sensors 3-35). Examples of how objective occurrence data 3-70* could be solicited include, for example, transmitting via a network interface 3-120 a request for objective occurrence data 3-70*, indicating via a user interface 3-122 a request for objective occurrence data 3-70*, configurating or activating one or more sensors 3-35 to collect and provide objective occurrence data 3-70b, and so forth.
After soliciting for the objective occurrence data 3-70*, the computing device 3-10 may be configured to acquire the objective occurrence data 3-70* from one or more sources. In various embodiments, the objective occurrence data 3-70* acquired by the computing device 3-10 may include data indicative of at least one objective occurrence associated with a user 3-20a (or with user 3-20b in the case where the computing device 3-10 is a local device). The objective occurrence data 3-70* may additionally include data indicative of one or more additional objective occurrences associated with the user 3-20a (or user 3-20b) including data indicating at least a second objective occurrence associated with the user 3-20a (or user 3-20b). In some embodiments, objective occurrence data 3-70a may be acquired from one or more third parties 3-50. Examples of third parties 3-50 include, for example, other users (not depicted), a healthcare provider, a hospital, a place of employment, a content provider, and so forth.
In some embodiments, objective occurrence data 3-70b may be acquired from one or more sensors 3-35 that may be designed for sensing or monitoring various aspects associated with the user 3-20a (or user 3-20b). For example, in some implementations, the one or more sensors 3-35 may include a global positioning system (GPS) device for determining the location of the user 3-20a and/or a physical activity sensor for measuring physical activities of the user 3-20a. Examples of a physical activity sensor include, for example, a pedometer for measuring physical activities of the user 3-20a. In certain implementations, the one or more sensors 3-35 may include one or more physiological sensor devices for measuring physiological characteristics of the user 3-20a. Examples of physiological sensor devices include, for example, a blood pressure monitor, a heart rate monitor, a glucometer, and so forth. In some implementations, the one or more sensors 3-35 may include one or more image capturing devices such as a video or digital camera.
In some embodiments, objective occurrence data 3-70c may be acquired from the user 3-20a via the mobile device 3-30 (or from user 3-20b via user interface 3-122). For these embodiments, the objective occurrence data 3-70c may be in the form of blog entries (e.g., microblog entries), status reports, or other types of electronic entries or messages. In various implementations, the objective occurrence data 3-70c acquired from the user 3-20a may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by the user 3-20a, certain physical characteristics (e.g., blood pressure or location) associated with the user 3-20a, or other aspects associated with the user 3-20a that the user 3-20a can report objectively. The objective occurrence data 3-70c may be in the form of a text data, audio or voice data, or image data.
After acquiring the subjective user state data 3-60 and the objective occurrence data 3-70*, the computing device 3-10 may be configured to correlate the acquired subjective user data 3-60 with the acquired objective occurrence data 3-70* by, for example, determining whether there is a sequential relationship between the one or more subjective user states as indicated by the acquired subjective user state data 3-60 and the one or more objective occurrences indicated by the acquired objective occurrence data 3-70*.
In some embodiments, and as will be further indicated in the operations and processes to be described herein, the computing device 3-10 may be further configured to present one or more results of correlation. In various embodiments, the one or more correlation results 3-80 may be presented to the user 3-20a and/or to one or more third parties 3-50 in various forms (e.g., in the form of an advisory, a warning, a prediction, and so forth). The one or more third parties 3-50 may be other users 3-20* such as other microbloggers, a health care provider, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the subjective user state data acquisition module 3-102 of the computing device 3-10 of
In some implementations, the subjective user state data reception module 3-202 may further include a user interface data reception module 3-204 and/or a network interface data reception module 3-206. In brief, and as will be further described in the processes and operations to be described herein, the user interface data reception module 3-204 may be configured to acquire subjective user state data 3-60 via a user interface 3-122 (e.g., a display monitor, a keyboard, a touch screen, a mouse, a keypad, a microphone, a camera, and/or other interface devices) such as in the case where the computing device 3-10 is a local device to be used directly by a user 3-20b. In contrast, the network interface data reception module 3-206 may be configured to acquire subjective user state data 3-60 from a wireless and/or wired network 3-40 via a network interface 3-120 (e.g., network interface card or NIC) such as in the case where the computing device 3-10 is a network server.
In various embodiments, the subjective user state data acquisition module 3-102 may include a time data acquisition module 3-208 for acquiring time and/or temporal elements associated with one or more subjective user states of a user 3-20*. For these embodiments, the time and/or temporal elements (e.g., time stamps, time interval indicators, and/or temporal relationship indicators) acquired by the time data acquisition module 3-208 may be useful for, among other things, determining one or more sequential patterns associated with subjective user states and objective occurrences as will be further described herein. In some implementations, the time data acquisition module 3-208 may include a time stamp acquisition module 3-210 for acquiring (e.g., either by receiving or generating) one or more time stamps associated with one or more subjective user states. In the same or different implementations, the time data acquisition module 3-208 may include a time interval acquisition module 3-212 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more subjective user states. In the same or different implementations, the time data acquisition module 3-208 may include a temporal relationship acquisition module 3-214 for acquiring, for example, indications of temporal relationships between subjective user states and objective occurrences. For example, acquiring an indication that a subjective user state such as a stomach ache occurred before, after, or at least partially concurrently with incidence of an objective occurrence such as eating lunch or the time being noon.
b illustrates particular implementations of the objective occurrence data solicitation module 3-103 of the computing device 3-10 of
In various embodiments, the objective occurrence data solicitation module 3-103 may be configured to solicit data indicating occurrence of at least one objective occurrence that occurred at a specified point in time or occurred at a specified time interval. In some implementations, the solicitation of the objective occurrence data 3-70* by the objective occurrence data solicitation module 3-103 may be prompted by the acquisition of subjective user state data 3-60 including data indicating at least one subjective user state 3-60a and/or as a result of referencing historical data 3-72 (which may be stored in memory 3-140). Historical data 3-72, in some instances, may prompt solicitation of particular data indicating occurrence of a particular or a particular type of objective occurrence. In some implementations, the historical data 3-72 to be referenced may be historical data 3-72 indicative of a link between a subjective user state type and an objective occurrence type. In the same or different implementations, the historical data 3-72 to be referenced may include one or more historical sequential patterns associated with the user 3-20*, a group of users, or the general population. In the same or different implementations, the historical data 3-72 to be referenced may include historical medical data associated with the user 3-20*, associated with other users, or associated with the general population. The relevance of the historical data 3-72 with respect to the solicitation operations performed by the objective occurrence data solicitation module 3-103 will be apparent in the processes and operations to be described herein.
In order to perform the various functions described herein, the objective occurrence data solicitation module 3-103 may include a network interface solicitation module 3-215, a user interface solicitation module 3-216, a requesting module 3-217, a configuration module 3-218, and/or a directing/instructing module 3-219. In brief, the network interface solicitation module 3-215 may be employed in order to solicit objective occurrence data 3-70* via a network interface 3-120. The user interface solicitation module 3-216 may be employed in order to, among other things, solicit objective occurrence data 3-70* via user interface 3-122 from, for example, a user 3-20b. The requesting module 3-217 may be employed in order to request the objective occurrence data 3-70a and 3-70b from a user 3-20* or from one or more third parties 3-50. The configuration module 3-218 may be employed in order to configure one or more sensors 3-35 to collect and provide objective occurrence data 3-70b. The directing/instructing module 3-219 may be employed in order to direct and/or instruct the one or more sensors 3-35 to collect and provide objective occurrence data 3-70b.
Referring now to
In various embodiments, the objective occurrence data acquisition module 3-104 may include a time data acquisition module 3-228 configured to acquire (e.g., receive or generate) time and/or temporal elements associated with one or more objective occurrences. For these embodiments, the time and/or temporal elements (e.g., time stamps, time intervals, and/or temporal relationships) may be useful for determining sequential patterns associated with objective occurrences and subjective user states.
In some implementations, the time data acquisition module 3-228 may include a time stamp acquisition module 3-230 for acquiring (e.g., either by receiving or by generating) one or more time stamps associated with one or more objective occurrences associated with a user 3-20*. In the same or different implementations, the time data acquisition module 3-228 may include a time interval acquisition module 3-231 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more objective occurrences. In the same or different implementations, the time data acquisition module 3-228 may include a temporal relationship acquisition module 3-232 for acquiring indications of temporal relationships between objective occurrences and subjective user states (e.g., an indication that an objective occurrence occurred before, after, or at least partially concurrently with incidence of a subjective user state).
Turning now to
The sequential pattern determination module 3-236, in various implementations, may include one or more sub-modules that may facilitate in the determination of one or more sequential patterns. As depicted, the one or more sub-modules that may be included in the sequential pattern determination module 3-236 may include, for example, a “within predefined time increment determination” module 3-238, a temporal relationship determination module 3-239, a subjective user state and objective occurrence time difference determination module 3-240, and/or a historical data referencing module 3-241. In brief, the within predefined time increment determination module 3-238 may be configured to determine whether at least one subjective user state of a user 3-20* occurred within a predefined time increment from an incidence of at least one objective occurrence. For example, determining whether a user 3-20* feeling “bad” (i.e., a subjective user state) occurred within ten hours (i.e., predefined time increment) of eating a large chocolate sundae (i.e., an objective occurrence). Such a process may be used in order to filter out events that are likely not related or to facilitate in determining the strength of correlation between subjective user state data 3-60 and objective occurrence data 3-70*.
The temporal relationship determination module 3-239 may be configured to determine the temporal relationships between one or more subjective user states and one or more objective occurrences. For example, this may entail determining whether a particular subjective user state (e.g., sore back) occurred before, after, or at least partially concurrently with incidence of an objective occurrence (e.g., sub-freezing temperature).
The subjective user state and objective occurrence time difference determination module 3-240 may be configured to determine the extent of time difference between the incidence of at least one subjective user state and the incidence of at least one objective occurrence. For example, determining how long after taking a particular brand of medication (e.g., objective occurrence) did a user 3-20* feel “good” (e.g., subjective user state).
The historical data referencing module 3-241 may be configured to reference historical data 3-72 in order to facilitate in determining sequential patterns. For example, in various implementations, the historical data 3-72 that may be referenced may include, for example, general population trends (e.g., people having a tendency to have a hangover after drinking or ibuprofen being more effective than aspirin for toothaches in the general population), medical information such as genetic, metabolome, or proteome information related to the user 3-20* (e.g., genetic information of the user 3-20* indicating that the user 3-20* is susceptible to a particular subjective user state in response to occurrence of a particular objective occurrence), or historical sequential patterns such as known sequential patterns of the general population or of the user 3-20* (e.g., people tending to have difficulty sleeping within five hours after consumption of coffee). In some instances, such historical data 3-72 may be useful in associating one or more subjective user states with one or more objective occurrences.
In some embodiments, the correlation module 3-106 may include a sequential pattern comparison module 3-242. As will be further described herein, the sequential pattern comparison module 3-242 may be configured to compare two or more sequential patterns with each other to determine, for example, whether the sequential patterns at least substantially match each other or to determine whether the sequential patterns are contrasting sequential patterns.
As depicted in
The subjective user state equivalence determination module 3-243 may be configured to determine whether subjective user states associated with different sequential patterns are equivalent. For example, the subjective user state equivalence determination module 3-243 may determine whether a first subjective user state of a first sequential pattern is equivalent to a second subjective user state of a second sequential pattern. For instance, suppose a user 3-20* reports that on Monday he had a stomach ache (e.g., first subjective user state) after eating at a particular restaurant (e.g., a first objective occurrence), and suppose further that the user 3-20* again reports having a stomach ache (e.g., a second subjective user state) after eating at the same restaurant (e.g., a second objective occurrence) on Tuesday, then the subjective user state equivalence determination module 3-243 may be employed in order to compare the first subjective user state (e.g., stomach ache) with the second subjective user state (e.g., stomach ache) to determine whether they are equivalent.
In contrast, the objective occurrence equivalence determination module 3-244 may be configured to determine whether objective occurrences of different sequential patterns are equivalent. For example, the objective occurrence equivalence determination module 3-244 may determine whether a first objective occurrence of a first sequential pattern is equivalent to a second objective occurrence of a second sequential pattern. For instance, for the above example the objective occurrence equivalence determination module 3-244 may compare eating at the particular restaurant on Monday (e.g., first objective occurrence) with eating at the same restaurant on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is equivalent to the second objective occurrence.
In some implementations, the sequential pattern comparison module 3-242 may include a subjective user state contrast determination module 3-245 that may be configured to determine whether subjective user states associated with different sequential patterns are contrasting subjective user states. For example, the subjective user state contrast determination module 3-245 may determine whether a first subjective user state of a first sequential pattern is a contrasting subjective user state from a second subjective user state of a second sequential pattern. To illustrate, suppose a user 3-20* reports that he felt very “good” (e.g., first subjective user state) after jogging for an hour (e.g., first objective occurrence) on Monday, but reports that he felt “bad” (e.g., second subjective user state) when he did not exercise (e.g., second objective occurrence) on Tuesday, then the subjective user state contrast determination module 3-245 may compare the first subjective user state (e.g., feeling good) with the second subjective user state (e.g., feeling bad) to determine that they are contrasting subjective user states.
In some implementations, the sequential pattern comparison module 3-242 may include an objective occurrence contrast determination module 3-246 that may be configured to determine whether objective occurrences of different sequential patterns are contrasting objective occurrences. For example, the objective occurrence contrast determination module 3-246 may determine whether a first objective occurrence of a first sequential pattern is a contrasting objective occurrence from a second objective occurrence of a second sequential pattern. For instance, for the above example, the objective occurrence contrast determination module 3-246 may compare the “jogging” on Monday (e.g., first objective occurrence) with the “no jogging” on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence. Based on the contrast determination, an inference may be made that the user 3-20* may feel better by jogging rather than by not jogging at all.
In some embodiments, the sequential pattern comparison module 3-242 may include a temporal relationship comparison module 3-247 that may be configured to make comparisons between different temporal relationships of different sequential patterns. For example, the temporal relationship comparison module 3-247 may compare a first temporal relationship between a first subjective user state and a first objective occurrence of a first sequential pattern with a second temporal relationship between a second subjective user state and a second objective occurrence of a second sequential pattern in order to determine whether the first temporal relationship at least substantially matches the second temporal relationship.
For example, suppose in the above example the user 3-20* eating at the particular restaurant (e.g., first objective occurrence) and the subsequent stomach ache (e.g., first subjective user state) on Monday represents a first sequential pattern while the user 3-20* eating at the same restaurant (e.g., second objective occurrence) and the subsequent stomach ache (e.g., second subjective user state) on Tuesday represents a second sequential pattern. In this example, the occurrence of the stomach ache after (rather than before or concurrently) eating at the particular restaurant on Monday represents a first temporal relationship associated with the first sequential pattern while the occurrence of a second stomach ache after (rather than before or concurrently) eating at the same restaurant on Tuesday represents a second temporal relationship associated with the second sequential pattern. Under such circumstances, the temporal relationship comparison module 3-247 may compare the first temporal relationship to the second temporal relationship in order to determine whether the first temporal relationship and the second temporal relationship at least substantially match (e.g., stomachaches in both temporal relationships occurring after eating at the restaurant). Such a match may result in the inference that a stomach ache is associated with eating at the particular restaurant.
In some implementations, the sequential pattern comparison module 3-242 may include an extent of time difference comparison module 3-248 that may be configured to compare the extent of time differences between incidences of subjective user states and incidences of objective occurrences of different sequential patterns. For example, the extent of time difference comparison module 3-248 may compare the extent of time difference between incidence of a first subjective user state and incidence of a first objective occurrence of a first sequential pattern with the extent of time difference between incidence of a second subjective user state and incidence of a second objective occurrence of a second sequential pattern. In some implementations, the comparisons may be made in order to determine that the extent of time differences of the different sequential patterns at least substantially or proximately match.
In some embodiments, the correlation module 3-106 may include a strength of correlation determination module 3-250 for determining a strength of correlation between subjective user state data 3-60 and objective occurrence data 3-70* associated with a user 3-20*. In some implementations, the strength of correlation may be determined based, at least in part, on the results provided by the other sub-modules of the correlation module 3-106 (e.g., the sequential pattern determination module 3-236, the sequential pattern comparison module 3-242, and their sub-modules).
e illustrates particular implementations of the presentation module 3-108 of the computing device 3-10 of
In various implementations, the presentation module 3-108 may include a network interface transmission module 3-252 for transmitting one or more results of the correlation performed by the correlation module 3-106 via network interface 3-120. For example, in the case where the computing device 3-10 is a server, the network interface transmission module 3-252 may be configured to transmit to the user 3-20a or a third party 3-50 the one or more results of the correlation performed by the correlation module 3-106 via a network interface 3-120.
In the same or different implementations, the presentation module 3-108 may include a user interface indication module 3-254 for indicating the one or more results of the correlation operations performed by the correlation module 3-106 via a user interface 3-122. For example, in the case where the computing device 3-10 is a local device, the user interface indication module 3-254 may be configured to indicate to a user 3-20b the one or more results of the correlation performed by the correlation module 3-106 via a user interface 3-122 (e.g., a display monitor, a touchscreen, an audio system including at least a speaker, and/or other interface devices).
The presentation module 3-108 may further include one or more sub-modules to present the one or more results of the correlation operations performed by the correlation module 3-106 in different forms. For example, in some implementations, the presentation module 3-108 may include a sequential relationship presentation module 3-256 configured to present an indication of a sequential relationship between at least one subjective user state of a user 3-20* and at least one objective occurrence. In the same or different implementations, the presentation module 3-108 may include a prediction presentation module 3-258 configured to present a prediction of a future subjective user state of a user 3-20* resulting from a future objective occurrence associated with the user 3-20*. In the same or different implementations, the prediction presentation module 3-258 may also be designed to present a prediction of a future subjective user state of a user 3-20* resulting from a past objective occurrence associated with the user 3-20*. In some implementations, the presentation module 3-108 may include a past presentation module 3-260 that is designed to present a past subjective user state of a user 3-20* in connection with a past objective occurrence associated with the user 3-20*.
In some implementations, the presentation module 3-108 may include a recommendation module 3-262 that is configured to present a recommendation for a future action based, at least in part, on the results of a correlation of subjective user state data 3-60 with objective occurrence data 3-70* performed by the correlation module 3-106. In certain implementations, the recommendation module 3-262 may further include a justification module 3-264 for presenting a justification for the recommendation presented by the recommendation module 3-262. In some implementations, the presentation module 3-108 may include a strength of correlation presentation module 3-266 for presenting an indication of a strength of correlation between subjective user state data 3-60 and objective occurrence data 3-70*.
In various embodiments, the computing device 3-10 may include a network interface 3-120 that may facilitate in communicating with a user 3-20a, one or more sensors 3-35, and/or one or more third parties 3-50. For example, in embodiments where the computing device 3-10 is a server, the computing device 3-10 may include a network interface 3-120 that may be configured to receive from the user 3-20a subjective user state data 3-60. In some embodiments, objective occurrence data 3-70a, 3-70b, and/or 3-70c may also be received through the network interface 3-120. Examples of a network interface 3-120 includes, for example, a network interface card (NIC).
The computing device 3-10, in various embodiments, may also include a memory 3-140 for storing various data. For example, in some embodiments, memory 3-140 may be employed in order to store historical data 3-72. In some implementations, the historical data 3-72 may include historical subjective user state data of a user 3-20* that may indicate one or more past subjective user states of the user 3-20* and historical objective occurrence data that may indicate one or more past objective occurrences. In same or different implementations, the historical data 3-72 may include historical medical data of a user 3-20* (e.g., genetic, metoblome, proteome information), population trends, historical sequential patterns derived from general population, and so forth.
In various embodiments, the computing device 3-10 may include a user interface 3-122 to communicate directly with a user 3-20b. For example, in embodiments in which the computing device 3-10 is a local device such as a handheld device (e.g., cellular telephone, PDA, and so forth), the user interface 3-122 may be configured to directly receive from the user 3-20b subjective user state data 3-60. The user interface 3-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a key pad, a mouse, an audio system, an imaging system including a digital or video camera, and/or other user interface devices.
e illustrates particular implementations of the one or more applications 3-126 of
In
Further, in
In any event, after a start operation, the operational flow 3-300 may move to a subjective user state data acquisition operation 3-302 for acquiring subjective user state data including data indicating at least one subjective user state associated with a user. For instance, the subjective user state data acquisition module 3-102 of the computing device 3-10 of
Operational flow 3-300 may also include an objective occurrence data solicitation operation 3-304 for soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating occurrence of at least one objective occurrence. For instance, the objective occurrence data solicitation module 3-103 of the computing device 3-10 soliciting (e.g., from the user 3-20*, from one or more third parties 3-50, or from one or more sensors 3-35), in response to the acquisition of the subjective user state data 3-60, objective occurrence data 3-70* including data indicating occurrence of at least one objective occurrence 3-60a (e.g., ingestion of a food, medicine, or nutraceutical). Note that the solicitation of the objective occurrence data as described above does not necessarily mean, although it may in some cases, to solicitation of particular data that indicates occurrence of a particular or particular type of objective occurrence.
The term “soliciting” as used above may be in reference to direct or indirect solicitation of objective occurrence data 3-70* from one or more sources (e.g., user 3-20*, one or more sensors 3-35, or one or more third parties 3-50). For example, if the computing device 3-10 is a server, then the computing device 3-10 may indirectly solicit the objective occurrence data 3-70* from, for example, a user 3-20a by transmitting the solicitation (e.g., a request or inquiry) for the objective occurrence data 3-70* to the mobile device 3-30, which may then actually solicit the objective occurrence data 3-70* from the user 3-20a.
Operational flow 3-300 may further include an objective occurrence data acquisition operation 3-306 for acquiring the objective occurrence data. For instance, the objective occurrence data acquisition module 3-104 of the computing device 3-10 acquiring (e.g., receiving via user interface 3-122 or via the network interface 3-120) the objective occurrence data 3-70*.
Finally, operational flow 3-300 may include a correlation operation 3-308 for correlating the subjective user state data with the objective occurrence data. For instance, the correlation module 3-106 of the computing device 3-10 correlating the subjective user state data 3-60 with the objective occurrence data 3-70* by determining, for example, at least one sequential pattern (e.g., time sequential pattern) associated with the at least one subjective user state (e.g., user feeling “tired”) and the at least one objective occurrence (e.g., high blood sugar level).
In various implementations, the subjective user state data acquisition operation 3-302 may include one or more additional operations as illustrated in
The reception operation 3-402 may, in turn, further include one or more additional operations. For example, in some implementations, the reception operation 3-402 may include an operation 3-404 for receiving the subjective user state data via a user interface as depicted in
In some implementations, the reception operation 3-402 may include an operation 3-406 for receiving the subjective user state data via a network interface as depicted in
In various implementations, operation 3-406 may further include one or more additional operations. For example, in some implementations operation 3-406 may include an operation 3-408 for receiving data indicating the at least one subjective user state via an electronic message generated by the user as depicted in
In some implementations, operation 3-406 may include an operation 3-410 for receiving data indicating the at least one subjective user state via a blog entry generated by the user as depicted in
In some implementations, operation 3-406 may include an operation 3-412 for receiving data indicating the at least one subjective user state via a status report generated by the user as depicted in
In some implementations, the reception operation 3-402 may include an operation 3-414 for receiving subjective user state data including data indicating at least one subjective user state specified by a selection made by the user, the selection being a selection of a subjective user state from a plurality of indicated alternative subjective user states as depicted in
Operation 3-414 may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 3-414 may include an operation 3-416 for receiving subjective user state data including data indicating at least one subjective user state specified by a selection made by the user, the selection being a selection of a subjective user state from at least two indicated alternative contrasting subjective user states as depicted in
In some implementations, operation 3-414 may include an operation 3-417 for receiving the selection via a network interface as depicted in
In some implementations, operation 3-414 may include an operation 3-418 for receiving the selection via user interface as depicted in
In some implementations, the reception operation 3-402 may include an operation 3-420 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on a text entry provided by the user as depicted in
In some implementations, the reception operation 3-402 may include an operation 3-422 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an audio entry provided by the user as depicted in
In some implementations, the reception operation 3-402 may include an operation 3-424 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an image entry provided by the user as depicted in
Operation 3-424 may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 3-424 may include an operation 3-426 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an image entry showing a gesture made by the user as depicted in
In some implementations, operation 3-424 may include an operation 3-428 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on an image entry showing an expression made by the user as depicted in
In some implementations, the reception operation 3-402 may include an operation 3-430 for receiving data indicating at least one subjective user state associated with the user that was obtained based, at least in part, on data provided through user interaction with a user interface as depicted in
In various implementations, the subjective user state data acquisition operation 3-302 may include an operation 3-432 for acquiring data indicating at least one subjective mental state of the user as depicted in
In some implementations, operation 3-432 may further include an operation 3-434 for acquiring data indicating at least a level of the one subjective mental state of the user as depicted in
In various implementations, the subjective user state data acquisition operation 3-302 may include an operation 3-436 for acquiring data indicating at least one subjective physical state of the user as depicted in
In some implementations, operation 3-436 may further include an operation 3-438 for acquiring data indicating at least a level of the one subjective physical state of the user as depicted in
In various implementations, the subjective user state data acquisition operation 3-302 may include an operation 3-440 for acquiring data indicating at least one subjective overall state of the user as depicted in
In some implementations, operation 3-440 may further include an operation 3-442 for acquiring data indicating at least a level of the one subjective overall state of the user as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-444 for acquiring a time stamp associated with occurrence of the at least one subjective user state as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-446 for acquiring an indication of a time interval associated with occurrence of the at least one subjective user state as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-448 for acquiring an indication of a temporal relationship between occurrence of the at least one subjective user state and occurrence of the at least one objective occurrence as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-450 for acquiring the subjective user state data at a server as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-452 for acquiring the subjective user state data at a handheld device as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-454 for acquiring the subjective user state data at a peer-to-peer network component device as depicted in
In some implementations the subjective user state data acquisition operation 3-302 may include an operation 3-456 for acquiring the subjective user state data via a Web 2.0 construct as depicted in
Referring back to
Operation 3-500 may also further include one or more additional operations. For example, in some implementations, operation 3-500 may include an operation 3-502 for soliciting the data indicating an occurrence of at least one objective occurrence via user interface as depicted in
Operation 3-502, in turn, may include one or more additional operations. For example, in some implementations, operation 3-502 may include an operation 3-504 for soliciting the data indicating an occurrence of at least one objective occurrence through at least one of a display monitor or a touchscreen as depicted in
In some implementations, operation 3-502 may include an operation 3-506 for soliciting the data indicating an occurrence of at least one objective occurrence through at least an audio system as depicted in
In various implementations, operation 3-500 may include an operation 3-508 for soliciting the data indicating an occurrence of at least one objective occurrence via a network interface as depicted in
In some implementations, operation 3-500 may include an operation 3-510 for requesting the user to confirm occurrence of at least one objective occurrence as depicted in
In some implementations, operation 3-500 may include an operation 3-512 for requesting the user to select at least one objective occurrence from a plurality of indicated alternative objective occurrences as depicted in
Operation 3-512, in various implementations, may in turn include an operation 3-514 for requesting the user to select one objective occurrence from at least two indicated alternative contrasting objective occurrences as depicted in
In some implementations, operation 3-500 may include an operation 3-516 for requesting the user to provide an indication of occurrence of at least one objective occurrence with respect to occurrence of the at least one subjective user state as depicted in
In some implementations, operation 3-500 may include an operation 3-518 for requesting the user to provide an indication of occurrence of at least one objective occurrence associated with a particular type of objective occurrences as depicted in
In some implementations, operation 3-500 may include an operation 3-520 for requesting the user to provide an indication of a time or temporal element associated with occurrence of the at least one objective occurrence as depicted in
Operation 3-520 in various implementations may further include one or more additional operations. For example, in some implementations, operation 3-520 may include an operation 3-522 for requesting the user to provide an indication of a point in time associated with the occurrence of the at least one objective occurrence as depicted in
In some implementations, operation 3-520 may include an operation 3-524 for requesting the user to provide an indication of a time interval associated with the occurrence of the at least one objective occurrence as depicted in
In some implementations, operation 3-500 may include an operation 3-526 for requesting the user to provide an indication of temporal relationship between occurrence of the at least one objective occurrence and occurrence of the at least one subjective user state as depicted in
In various implementations, the solicitation operation 3-304 of
Operation 3-528 may, in turn, include one or more additional operations in various alternative implementations. For example, in some implementations, operation 3-528 may include an operation 3-530 for requesting from one or more other users the data indicating occurrence of at least one objective occurrence as depicted in
In some implementations, operation 3-528 may include an operation 3-532 for requesting from one or more healthcare entities the data indicating occurrence of at least one objective occurrence as depicted in
In some implementations, operation 3-528 may include an operation 3-533 for requesting from one or more content providers the data indicating occurrence of at least one objective occurrence as depicted in
In some implementations, operation 3-528 may include an operation 3-534 for requesting from one or more third party sources the data indicating occurrence of at least one objective occurrence that occurred at a specified point in time as depicted in
In some implementations, operation 3-528 may include an operation 3-535 for requesting from one or more third party sources the data indicating occurrence of at least one objective occurrence that occurred during a specified time interval as depicted in
In some implementations, the solicitation operation 3-304 of
Operation 3-536 may include, in various implementations, one or more additional operations. For example, in some implementations, operation 3-536 may include an operation 3-538 for configuring the one or more sensors to collect and provide the data indicating occurrence of at least one objective occurrence as depicted in
In some implementations, operation 3-536 may include an operation 3-540 for directing or instructing the one or more sensors to collect and provide the data indicating occurrence of at least one objective occurrence as depicted in
The solicitation operation 3-304 of
In various implementations, operation 3-542 may further include one or more additional operations. For example, in some implementations, operation 3-542 may include an operation 3-544 for soliciting the data indicating occurrence of at least one objective occurrence based, at least in part, on one or more historical sequential patterns as depicted in
In some implementations, operation 3-542 may include an operation 3-546 for soliciting the data indicating occurrence of at least one objective occurrence based, at least in part, on medical data of the user as depicted in
In some implementations, operation 3-542 may include an operation 3-547 for soliciting the data indicating occurrence of at least one objective occurrence based, at least in part, on historical data indicative of a link between a subjective user state type and an objective occurrence type as depicted in
In some implementations, operation 3-542 may include an operation 3-548 for soliciting the data indicating occurrence of at least one objective occurrence, the soliciting prompted, at least in part, by the historical data as depicted in
In some implementations, operation 3-542 may include an operation 3-549 for soliciting data indicating occurrence of a particular or a particular type of objective occurrence based on the historical data as depicted in
In some implementations, the solicitation operation 3-304 of
In some implementations, the solicitation operation 3-304 may include an operation 3-551 for soliciting the data indicating occurrence of at least one objective occurrence by requesting access to the data indicating occurrence of the at least one objective occurrence as depicted in
In various embodiments, the objective occurrence data acquisition operation 3-306 of
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-604 for receiving the objective occurrence data from at least one of a wireless network or a wired network as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-606 for receiving the objective occurrence data via one or more blog entries as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-608 for receiving the objective occurrence data via one or more status reports as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-610 for receiving the objective occurrence data from the user as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-612 for receiving the objective occurrence data from one or more third party sources as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-614 for receiving the objective occurrence data from one or more sensors configured to sense one or more objective occurrences as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-616 for acquiring at least one time stamp associated with occurrence of at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-618 for acquiring an indication of at least one time interval associated with occurrence of at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-619 for acquiring an indication of at least a temporal relationship between the at least one objective occurrence and occurrence of the at least one subjective user state as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-620 for acquiring data indicating at least one objective occurrence and one or more attributes associated with the at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-622 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a medicine as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-624 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a food item as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-626 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a nutraceutical as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-628 for acquiring data indicating at least one objective occurrence of an exercise routine executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-630 for acquiring data indicating at least one objective occurrence of a social activity executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-632 for acquiring data indicating at least one objective occurrence of an activity performed by a third party as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-634 for acquiring data indicating at least one objective occurrence of a physical characteristic of the user as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-636 for acquiring data indicating at least one objective occurrence of a resting, a learning or a recreational activity by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-638 for acquiring data indicating at least one objective occurrence of an external event as depicted in
In some implementations, the objective occurrence data acquisition operation 3-306 may include an operation 3-640 for acquiring data indicating at least one objective occurrence related to a location of the user as depicted in
Referring back to
In various alternative implementations, operation 3-702 may include one or more additional operations. For example, in some implementations, operation 3-702 may include an operation 3-704 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of whether the at least one subjective user state occurred within a predefined time increment from incidence of the at least one objective occurrence as depicted in
In some implementations, operation 3-702 may include an operation 3-706 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of whether the at least one subjective user state occurred before, after, or at least partially concurrently with incidence of the at least one objective occurrence as depicted in
In some implementations, operation 3-702 may include an operation 3-708 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing of historical data as depicted in
In various implementations, operation 3-708 may include one or more additional operations. For example, in some implementations, operation 3-708 may include an operation 3-710 for correlating the subjective user state data with the objective occurrence data based, at least in part, on the historical data indicative of a link between a subjective user state type and an objective occurrence type as depicted in
In some instances, operation 3-710 may further include an operation 3-712 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a historical sequential pattern as depicted in
In some implementations, operation 3-708 may include an operation 3-714 for correlating the subjective user state data with the objective occurrence data based, at least in part, on historical medical data associated with the user as depicted in
In some implementations, operation 3-702 may include an operation 3-716 for comparing the at least one sequential pattern to a second sequential pattern to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
In various implementations, operation 3-716 may further include an operation 3-718 for comparing the at least one sequential pattern to a second sequential pattern related to at least a second subjective user state associated with the user and a second objective occurrence to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
In some implementations, the correlation operation 3-308 of
In some implementations, the correlation operation 3-308 may include an operation 3-722 for correlating the subjective user state data with the objective occurrence data at a handheld device as depicted in
In some implementations, the correlation operation 3-308 may include an operation 3-724 for correlating the subjective user state data with the objective occurrence data at a peer-to-peer network component device as depicted in
Referring to
In addition, operational flow 3-800 includes a presentation operation 3-810 for presenting one or more results of the correlating as depicted in
In various embodiments, the presentation operation 3-810 may include one or more additional operations as depicted in
In some implementations, the presentation operation 3-810 may include an operation 3-904 for transmitting the one or more results of the correlating via a network interface. For instance, the network interface transmission module 3-252 (see
In some implementations, the presentation operation 3-810 may include an operation 3-906 for presenting an indication of a sequential relationship between the at least one subjective user state and the at least one objective occurrence. For instance, the sequential relationship presentation module 3-256 of the computing device 3-10 presenting (e.g., transmitting via the network interface 3-120 or indicating via user interface 3-122) an indication of a sequential relationship between the at least one subjective user state (e.g., headache) and the at least one objective occurrence (e.g., drinking beer).
In some implementations, the presentation operation 3-810 may include an operation 3-908 for presenting a prediction of a future subjective user state associated with the user resulting from a future objective occurrence. For instance, the prediction presentation module 3-258 of the computing device 3-10 a prediction of a future subjective user state associated with the user 3-20* resulting from a future objective occurrence. An example prediction might state that “if the user drinks five shots of whiskey tonight, the user will have a hangover tomorrow.”
In some implementations, the presentation operation 3-810 may include an operation 3-910 for presenting a prediction of a future subjective user state associated with the user resulting from a past objective occurrence. For instance, the prediction presentation module 3-258 of the computing device 3-10 presenting a prediction of a future subjective user state associated with the user 3-20* resulting from a past objective occurrence. An example prediction might state that “the user will have a hangover tomorrow since the user drank five shots of whiskey tonight.”
In some implementations, the presentation operation 3-810 may include an operation 3-912 for presenting a past subjective user state associated with the user in connection with a past objective occurrence. For instance, the past presentation module 3-260 of the computing device 3-10 presenting a past subjective user state associated with the user 3-20* in connection with a past objective occurrence. An example of such a presentation might state that “the user got depressed the last time it rained.”
In some implementations, the presentation operation 3-810 may include an operation 3-914 for presenting a recommendation for a future action. For instance, the recommendation module 3-262 of the computing device 3-10 presenting a recommendation for a future action. An example recommendation might state that “the user should not drink five shots of whiskey.”
Operation 3-914 may, in some instances, include an additional operation 3-916 for presenting a justification for the recommendation. For instance, the justification module 3-264 of the computing device 3-10 presenting a justification for the recommendation. An example justification might state that “the user should not drink five shots of whiskey because the last time the user drank five shots of whiskey, the user got a hangover.”
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post their thoughts and opinions on various topics, latest news, current events, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social network status reports in which a user may report or post for others to view the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted through microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, event, happening, or any other aspects associated with or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, external events that may not be directly related to the user such as the local weather or the performance of the stock market (which the microblogger may have an interest in), activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblogging entries include “subjective user states” of the microblogger. Subjective user states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., “I am feeling happy”), the subjective physical states of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and the subjective overall state of the microblogger (e.g., “I'm good” or “I'm well”). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). Although microblogs are being used to provide a wealth of personal information, they have thus far been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided for, among other things, soliciting and acquiring subjective user state data including data indicative of at least one subjective user state associated with a user in response to acquisition of objective occurrence data including data indicating at least one objective occurrence. As will be further described herein, in some embodiments, the solicitation of the subjective user state data may, in addition to being prompted by the acquisition of the objective occurrence data, may be prompted based on historical data. Such historical data may be historical data that is associated with the user, associated with a group of users, associated with a segment of the general population, or associated with the general population.
The methods, systems, and computer program products may then correlate the subjective user state data (e.g., data that indicate one or more subjective user states of a user) with the objective occurrence data (e.g., data that indicate one or more objective occurrences associated with the user). By correlating the subjective user state data with the objective occurrence data, a causal relationship between one or more objective occurrences (e.g., cause) and one or more subjective user states (e.g., result) associated with a user (e.g., a blogger or microblogger) may be determined in various alternative embodiments. For example, determining that the last time a user ate a banana (e.g., objective occurrence), the user felt “good” (e.g., subjective user state) or determining whenever a user eats a banana the user always or sometimes feels good. Note that an objective occurrence does not need to occur prior to a corresponding subjective user state but instead, may occur subsequent or concurrently with the incidence of the subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence) or a person may become gloomy while (e.g., concurrently) it is raining
In various embodiments, subjective user state data may include data that indicate the occurrence of one or more subjective user states associated with a user. As briefly described above, a “subjective user state” is in reference to any state or status associated with a user (e.g., a blogger or microblogger) at any moment or interval in time that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of the user (e.g., user is feeling sad), the subjective physical state (e.g., physical characteristic) of the user that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), and the subjective overall state of the user (e.g., user is “good”).
Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be easily categorized as a subjective mental state or as a subjective physical state. Examples of overall states of a user that may be subjective user states include, for example, the user being good, bad, exhausted, lack of rest, wellness, and so forth.
In contrast, “objective occurrence data,” which may also be referred to as “objective context data,” may include data that indicate one or more objective occurrences associated with the user that occurred at particular intervals or points in time. In some embodiments, an objective occurrence may be any physical characteristic, event, happenings, or any other aspect that may be associated with, is of interest to, or may somehow impact a user that can be objectively reported by at least a third party or a sensor device. Note, however, that such objective occurrence data does not have to be actually provided by a sensor device or by a third party, but instead, may be reported by the user himself or herself (e.g., via microblog entries). Examples of objectively reported occurrences that could be indicated by the objective occurrence data include, for example, a user's food, medicine, or nutraceutical intake, the user's location at any given point in time, a user's exercise routine, a user's physiological characteristics such as blood pressure, social or professional activities, the weather at a user's location, activities associated with third parties, occurrence of external events such as the performance of the stock market, and so forth.
The term “correlating” as will be used herein may be in reference to a determination of one or more relationships between at least two variables. Alternatively, the term “correlating” may merely be in reference to the linking or associating of at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that represents at least one subjective user state of a user and the second variable is objective occurrence data that represents at least one objective occurrence. In embodiments where the subjective user state data includes data that indicates multiple subjective user states, each of the subjective user states represented by the subjective user state data may be the same or similar type of subjective user state (e.g., user being happy) at different intervals or points in time. Alternatively, different types of subjective user state (e.g., user being happy and user being sad) may be represented by the subjective user state data. Similarly, in embodiments where multiple objective occurrences are indicated by the objective occurrence data, each of the objective occurrences may represent the same or similar type of objective occurrence (e.g., user exercising) at different intervals or points in time, or alternatively, different types of objective occurrence (e.g., user exercising and user resting).
Various techniques may be employed for correlating subjective user state data with objective occurrence data in various alternative embodiments. For example, in some embodiments, correlating the objective occurrence data with the subjective user state data may be accomplished by determining a sequential pattern associated with at least one subjective user state indicated by the subjective user state data and at least one objective occurrence indicated by the objective occurrence data. In other embodiments, correlating of the objective occurrence data with the subjective user state data may involve determining multiple sequential patterns associated with multiple subjective user states and multiple objective occurrences.
A sequential pattern, as will be described herein, may define time and/or temporal relationships between two or more events (e.g., one or more subjective user states and one or more objective occurrences). In order to determine a sequential pattern, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user may be solicited in response to an acquisition of objective occurrence data including data indicating occurrence of at least one objective occurrence.
For example, if a user (or a third party source such as a content provider or another user) reports that the weather on a particular day (e.g., objective occurrence) was bad (e.g., cloudy weather) then a solicitation for subjective user state data including data indicating occurrence of at least one subjective user state associated with the user on that particular day may be made. Such solicitation of subjective user state data may be prompted based, at least in part, on the reporting of the objective occurrence (e.g., cloudy weather) and based on historical data such as historical data that indicates or suggests that the user tends to get gloomy when the weather is bad (e.g., cloudy) or based on historical data that indicates that people in the general population tend to get gloomy whenever the weather is bad. In some embodiments, such historical data may indicate or define one or more historical sequential patterns of the user or of the general population as they relate to subjective user states and objective occurrences.
As briefly described above, a sequential pattern may merely indicate or represent the temporal relationship or relationships between at least one subjective user state and at least one objective occurrence (e.g., whether the incidence or occurrence of the at least one subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least one objective occurrence). In alternative implementations, and as will be further described herein, a sequential pattern may indicate a more specific time relationship between the incidences of one or more subjective user states and the incidences of one or more objective occurrences. For example, a sequential pattern may represent the specific pattern of events (e.g., one or more objective occurrences and one or more subjective user states) that occurs along a timeline.
The following illustrative example is provided to describe how a sequential pattern associated with at least one subjective user state and at least one objective occurrence may be determined based, at least in part, on the temporal relationship between the incidence of the at least one subjective user state and the incidence of the at least one objective occurrence in accordance with some embodiments. For these embodiments, the determination of a sequential pattern may initially involve determining whether the incidence of the at least one subjective user state occurred within some predefined time increments of the incidence of the one objective occurrence. That is, it may be possible to infer that those subjective user states that did not occur within a certain time period from the incidence of an objective occurrence are not related or are unlikely related to the incidence of that objective occurrence.
For example, suppose a user during the course of a day eats a banana and also has a stomach ache sometime during the course of the day. If the consumption of the banana occurred in the early morning hours but the stomach ache did not occur until late that night, then the stomach ache may be unrelated to the consumption of the banana and may be disregarded. On the other hand, if the stomach ache had occurred within some predefined time increment, such as within 2 hours of consumption of the banana, then it may be concluded that there is a correlation or link between the stomach ache and the consumption of the banana. If so, a temporal relationship between the consumption of the banana and the occurrence of the stomach ache may be determined. Such a temporal relationship may be represented by a sequential pattern. Such a sequential pattern may simply indicate that the stomach ache (e.g., a subjective user state) occurred after (rather than before or concurrently) the consumption of banana (e.g., an objective occurrence).
Other factors may also be referenced and examined in order to determine a sequential pattern and whether there is a relationship (e.g., causal relationship) between an objective occurrence and a subjective user state. These factors may include, for example, historical data (e.g., historical medical data such as genetic data or past history of the user or historical data related to the general population regarding, for example, stomach aches and bananas) as briefly described above. Alternatively, a sequential pattern may be determined for multiple subjective user states and multiple objective occurrences. Such a sequential pattern may particularly map the exact temporal or time sequencing of the various events (e.g., subjective user states and/or objective occurrences). The determined sequential pattern may then be used to provide useful information to the user and/or third parties.
The following is another illustrative example of how subjective user state data may be correlated with objective occurrence data by determining multiple sequential patterns and comparing the sequential patterns with each other. Suppose, for example, a user such as a microblogger reports that the user ate a banana on a Monday. The consumption of the banana, in this example, is a reported first objective occurrence associated with the user. The user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported first subjective user state. Thus, the reported incidence of the first objective occurrence (e.g., eating the banana) and the reported incidence of the first subjective user state (user felt very happy) on Monday may be represented by a first sequential pattern.
On Tuesday, the user reports that the user ate another banana (e.g., a second objective occurrence associated with the user). The user then reports that 20 minutes after eating the second banana, the user felt somewhat happy (e.g., a second subjective user state). Thus, the reported incidence of the second objective occurrence (e.g., eating the second banana) and the reported incidence of the second subjective user state (user felt somewhat happy) on Tuesday may be represented by a second sequential pattern. Note that in this example, the occurrences of the first subjective user state and the second subjective user state may be indicated by subjective user state data while the occurrences of the first objective occurrence and the second objective occurrence may be indicated by objective occurrence data.
In a slight variation of the above example, suppose the user had forgotten to report for Tuesday the feeling of being somewhat happy but does report consuming the second banana on Tuesday. This may result in the user being asked, based on the reporting of the user consuming the banana on Tuesday, as to how the user felt on Tuesday or how the user felt after eating the banana on Tuesday. Asking such questions may be prompted both in response to the reporting of the consumption of the second banana on Tuesday (e.g., an objective occurrence) and on referencing historical data (e.g., first sequential pattern derived from Monday's consumption of banana and feeling happy). Upon the user indicating feeling somewhat happy on Tuesday, a second sequential pattern may be determined.
In any event, by comparing the first sequential pattern with the second sequential pattern, the subjective user state data may be correlated with the objective occurrence data. In some implementations, the comparison of the first sequential pattern with the second sequential pattern may involve trying to match the first sequential pattern with the second sequential pattern by examining certain attributes and/or metrics. For example, comparing the first subjective user state (e.g., user felt very happy) of the first sequential pattern with the second subjective user state (e.g., user felt somewhat happy) of the second sequential pattern to see if they at least substantially match or are contrasting (e.g., being very happy in contrast to being slightly happy or being happy in contrast to being sad). Similarly, comparing the first objective occurrence (e.g., eating a banana) of the first sequential pattern may be compared to the second objective occurrence (e.g., eating of another banana) of the second sequential pattern to determine whether they at least substantially match or are contrasting.
A comparison may also be made to determine if the extent of time difference (e.g., 15 minutes) between the first subjective user state (e.g., user being very happy) and the first objective occurrence (e.g., user eating a banana) matches or are at least similar to the extent of time difference (e.g., 20 minutes) between the second subjective user state (e.g., user being somewhat happy) and the second objective occurrence (e.g., user eating another banana). These comparisons may be made in order to determine whether the first sequential pattern matches the second sequential pattern. A match or substantial match would suggest, for example, that a subjective user state (e.g., happiness) is linked to a particular objective occurrence (e.g., consumption of banana).
As briefly described above, the comparison of the first sequential pattern with the second sequential pattern may include a determination as to whether, for example, the respective subjective user states and the respective objective occurrences of the sequential patterns are contrasting subjective user states and/or contrasting objective occurrences. For example, suppose in the above example the user had reported that the user had eaten a whole banana on Monday and felt very energetic (e.g., first subjective user state) after eating the whole banana (e.g., first objective occurrence). Suppose that the user also reported that on Tuesday he ate a half a banana instead of a whole banana and only felt slightly energetic (e.g., second subjective user state) after eating the half banana (e.g., second objective occurrence). In this scenario, the first sequential pattern (e.g., feeling very energetic after eating a whole banana) may be compared to the second sequential pattern (e.g., feeling slightly energetic after eating only a half of a banana) to at least determine whether the first subjective user state (e.g., being very energetic) and the second subjective user state (e.g., being slightly energetic) are contrasting subjective user states. Another determination may also be made during the comparison to determine whether the first objective occurrence (eating a whole banana) is in contrast with the second objective occurrence (e.g., eating a half of a banana).
In doing so, an inference may be made that eating a whole banana instead of eating only a half of a banana makes the user happier or eating more banana makes the user happier. Thus, the word “contrasting” as used here with respect to subjective user states refers to subjective user states that are the same type of subjective user states (e.g., the subjective user states being variations of a particular type of subjective user states such as variations of subjective mental states). Thus, for example, the first subjective user state and the second subjective user state in the previous illustrative example are merely variations of subjective mental states (e.g., happiness). Similarly, the use of the word “contrasting” as used here with respect to objective occurrences refers to objective states that are the same type of objective occurrences (e.g., consumption of food such as banana).
As those skilled in the art will recognize, a stronger correlation between the subjective user state data and the objective occurrence data could be obtained if a greater number of sequential patterns (e.g., if there was a third sequential pattern, a fourth sequential pattern, and so forth, that indicated that the user became happy or happier whenever the user ate bananas) are used as a basis for the correlation. Note that for ease of explanation and illustration, each of the exemplary sequential patterns to be described herein will be depicted as a sequential pattern of an occurrence of a single subjective user state and an occurrence of a single objective occurrence. However, those skilled in the art will recognize that a sequential pattern, as will be described herein, may also be associated with occurrences of multiple objective occurrences and/or multiple subjective user states. For example, suppose the user had reported that after eating a banana, he had gulped down a can of soda. The user then reported that he became happy but had an upset stomach. In this example, the sequential pattern associated with this scenario will be associated with two objective occurrences (e.g., eating a banana and drinking a can of soda) and two subjective user states (e.g., user having an upset stomach and feeling happy).
In some embodiments, and as briefly described earlier, the sequential patterns derived from subjective user state data and objective occurrence data may be based on temporal relationships between objective occurrences and subjective user states. For example, whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence. For instance, a plurality of sequential patterns derived from subjective user state data and objective occurrence data may indicate that a user always has a stomach ache (e.g., subjective user state) after eating a banana (e.g., first objective occurrence).
a and 4-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 4-100 may include at least a computing device 4-10 (see
In some embodiments, the computing device 4-10 may be a network server in which case the computing device 4-10 may communicate with a user 4-20a via a mobile device 4-30 and through a wireless and/or wired network 4-40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 4-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 4-10.
In alternative embodiments, the computing device 4-10 may be a local computing device that communicates directly with a user 4-20b. For these embodiments, the computing device 4-10 may be any type of handheld device such as a cellular telephone, a PDA, or other types of computing/communication devices such as a laptop computer, a desktop computer, and so forth. In various embodiments, the computing device 4-10 may be a peer-to-peer network component device. In some embodiments, the computing device 4-10 may operate via a web 2.0 construct.
In embodiments where the computing device 4-10 is a server, the computing device 4-10 may obtain the subjective user state data 4-60 indirectly from a user 4-20a via a network interface 4-120. In alternative embodiments in which the computing device 4-10 is a local device such as a handheld device (e.g., cellular telephone, personal digital assistant, etc.), the subjective user state data 4-60 may be directly obtained from a user 4-20b via a user interface 4-122. As will be further described, the computing device 10 may acquire the objective occurrence data 4-70* from one or more alternative sources.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 4-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 4-10 is a local device such as a handheld device that may communicate directly with a user 4-20b.
Assuming that the computing device 4-10 is a server, the computing device 4-10, in various implementations, may be configured to acquire objective occurrence data 4-70* including data indicating incidence or occurrence of at least one objective occurrence via a network interface 4-120 or via a user interface 4-122. In some implementations, the objective occurrence data 4-70* may further include additional data that may indicate occurrences of one or more additional objective occurrences (e.g., data indicating occurrence of at least a second objective occurrence). The objective occurrence data 4-70* may be provided by a user 4-20*, by one or more third parties 4-50 (e.g., third party sources), or by one or more sensors 4-35.
For example, in some embodiments, objective occurrence data 4-70a may be acquired from one or more third parties 4-50. Examples of third parties 4-50 include, for example, other users, medical entities such as medical or dental clinics and hospitals, content providers, employers, fitness centers, social organizations, and so forth.
In some embodiments, objective occurrence data 4-70b may be acquired from one or more sensors 4-35 that may be designed for sensing or monitoring various aspects associated with the user 4-20a (or user 4-20b). For example, in some implementations, the one or more sensors 4-35 may include a global positioning system (GPS) device for determining the location of the user 4-20a and/or a physical activity sensor for measuring physical activities of the user 4-20a. Examples of a physical activity sensor include, for example, a pedometer for measuring physical activities of the user 4-20a. In certain implementations, the one or more sensors 4-35 may include one or more physiological sensor devices for measuring physiological characteristics of the user 4-20a. Examples of physiological sensor devices include, for example, a blood pressure monitor, a heart rate monitor, a glucometer, and so forth. In some implementations, the one or more sensors 4-35 may include one or more image capturing devices such as a video or digital camera.
In some embodiments, objective occurrence data 4-70c may be acquired from a user 4-20a via the mobile device 4-30 (or from user 4-20b via user interface 4-122). For these embodiments, the objective occurrence data 4-70c may be in the form of blog entries (e.g., microblog entries), status reports, or other types of electronic entries (e.g., diary or calendar entries) or messages. In various implementations, the objective occurrence data 4-70c acquired from the user 4-20a may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by the user 4-20a, certain physical characteristics (e.g., blood pressure or location) associated with the user 4-20a, or other aspects associated with the user 4-20a that the user 4-20a can report objectively. The objective occurrence data 4-70c may be in the form of a text data, audio or voice data, or image data.
The computing device 4-10 may also be configured to solicit subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a. Such a solicitation of the subjective user state data 4-60 may be prompted in response to the acquisition of objective occurrence data 4-70* and/or in response to referencing of historical data 4-72. The solicitation of the subjective user state 4-60 (e.g., the data indicating the occurrence of the at least one subjective user state 4-60a) may be made through a network interface 4-120 or through the user interface 4-122. As will be further described, the data indicating the occurrence of the at least one subjective user state 4-60a may be solicited from a user 4-20*, from a mobile device 4-30 (which may already have been provided with such data from the user 4-20*), or from one or more network servers (not depicted). Such a solicitation may be accomplished in a number of ways depending on the specific circumstances (e.g., whether the computing device 4-10 is a server or a local device). Examples of how subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a could be solicited include, for example, transmitting via a network interface 4-120 a request for subjective user state data 4-60, indicating via a user interface 4-122 a request for subjective user state data 4-60, configuring or activating a mobile device 4-30 or a network server to provide such data, and so forth.
After soliciting for the subjective user state data 4-60, the computing device 4-10 may be configured to acquire the subjective user state data 4-60 from one or more sources (e.g., user 4-20*, mobile device 4-30, and so forth). In various embodiments, the subjective user state data 4-60 acquired by the computing device 4-10 may include data indicating occurrence of at least one subjective user state 4-60a associated with a user 4-20a (or with user 4-20b in the case where the computing device 4-10 is a local device). The acquired subjective user state data 4-60 may additionally include data indicative of occurrence of one or more additional subjective user states associated with the user 4-20a (or user 4-20b) including data indicating occurrence of at least a second subjective user state 4-60b associated with the user 4-20a (or user 4-20b). Note that in various implementations, the data indicating occurrence of at least a second subjective user state 4-60b may or may not have been solicited.
In various embodiments, the data indicating occurrence of at least one subjective user state 4-60a, as well as the data indicating occurrence of at least a second subjective user state 4-60b, may be acquired in the form of blog entries (e.g., microblog entries), status reports (e.g., social networking status reports), electronic messages (email, text messages, instant messages, etc.) or other types of electronic messages or documents. The data indicating occurrence of at least one subjective user state 4-60a and the data indicating occurrence of at least a second subjective user state 4-60b may, in some instances, indicate the same, contrasting, or completely different subjective user states associated with a user 4-20*.
Examples of subjective user states that may be indicated by the subjective user state data 4-60 include, for example, subjective mental states of a user 4-20* (e.g., user 4-20* is sad or angry), subjective physical states of the user 4-20* (e.g., physical or physiological characteristic of the user 4-20* such as the presence, absence, elevating, or easing of a stomach ache or headache), subjective overall states of the user 4-20* (e.g., user 4-20* is “well”), and/or other subjective user states that only the user 4-20* can typically indicate.
After acquiring the subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a and the objective occurrence data 4-70* including data indicating occurrence of at least one objective occurrence, the computing device 4-10 may be configured to correlate the acquired subjective user data 4-60 with the acquired objective occurrence data 4-70* by, for example, determining whether there is a sequential relationship between the one or more subjective user states as indicated by the acquired subjective user state data 4-60 and the one or more objective occurrences indicated by the acquired objective occurrence data 4-70*.
In some embodiments, and as will be further explained in the operations and processes to be described herein, the computing device 4-10 may be further configured to present one or more results of correlation. In various embodiments, the one or more correlation results 4-80 may be presented to a user 4-20* and/or to one or more third parties 4-50 in various forms (e.g., in the form of an advisory, a warning, a prediction, and so forth). The one or more third parties 4-50 may be other users (e.g., microbloggers), health care providers, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the objective occurrence data acquisition module 4-102 of the computing device 4-10 of
In some implementations, the objective occurrence data reception module 4-202 may further include a user interface data reception module 4-204 and/or a network interface data reception module 4-206. In brief, and as will be further described in the processes and operations to be described herein, the user interface data reception module 4-204 may be configured to receive objective occurrence data 4-70* via a user interface 4-122 (e.g., a display monitor, a keyboard, a touch screen, a mouse, a keypad, a microphone, a camera, and/or other interface devices) such as in the case where the computing device 4-10 is a local device to be used directly by a user 4-20b. In contrast, the network interface data reception module 4-206 may be configured to receive objective occurrence data 4-70* from a wireless and/or wired network 4-40 via a network interface 4-120 (e.g., network interface card or NIC) such as in the case where the computing device 4-10 is a network server.
In various embodiments, the objective occurrence data acquisition module 4-102 may include a time data acquisition module 4-208 for acquiring time and/or temporal elements associated with one or more objective occurrences. For these embodiments, the time and/or temporal elements (e.g., time stamps, time interval indicators, and/or temporal relationship indicators) acquired by the time data acquisition module 4-208 may be useful for, among other things, determining one or more sequential patterns associated with subjective user states and objective occurrences as will be further described herein.
In some implementations, the time data acquisition module 4-208 may include a time stamp acquisition module 4-210 for acquiring (e.g., either by receiving or generating) one or more time stamps associated with one or more objective occurrences. In the same or different implementations, the time data acquisition module 4-208 may include a time interval acquisition module 4-212 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more objective occurrences. In the same or different implementations, the time data acquisition module 4-208 may include a temporal relationship acquisition module 4-214 for acquiring, for example, indications of temporal relationships between subjective user states and objective occurrences. For example, acquiring an indication that an objective occurrence such as “eating lunch” occurred before, after, or at least partially concurrently with incidence of a subjective user state such as a “stomach ache.”
b illustrates particular implementations of the subjective user state data solicitation module 4-103 of the computing device 4-10 of
In various embodiments, the subjective user state data solicitation module 4-103 may be configured to solicit data indicating occurrence of at least one subjective user state 4-60a associated with a user 4-20* that occurred at a specified point in time or occurred at a specified time interval. In some implementations, the solicitation of the subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a by the subjective user state data solicitation module 4-103 may be prompted by the acquisition of objective occurrence data 4-70* and/or as a result of referencing historical data 4-72 (which may be stored in memory 4-140).
In some implementations, referencing of the historical data 4-72 by the subjective user state data solicitation module 4-103 may prompt the solicitation of particular data indicating occurrence of a particular or a particular type of subjective user state associated with a user 4-20*. For example, in some implementations, the subjective user state data solicitation module 4-103 may solicit data indicating occurrence of a subjective mental state (e.g., soliciting data that indicates the happiness level of the user 4-20*), a subjective physical state (e.g., soliciting data that indicates the level of back pain of the user 4-20*), or a subjective overall state (e.g., soliciting data that indicates user status such as “good” or “bad”) of a user 4-20*.
In some implementations, the historical data 4-72 to be referenced may be data that may indicate a link between a subjective user state type and an objective occurrence type. In the same or different implementations, the historical data 4-72 to be referenced may include one or more historical sequential patterns associated with the user 4-20*, a group of users, or the general population. In the same or different implementations, the historical data 4-72 to be referenced may include historical medical data associated with the user 4-20*, associated with other users, or associated with the general population. The relevance of the historical data 4-72 with respect to the solicitation operations performed by the subjective user state data solicitation module 4-103 will be apparent in the processes and operations to be described herein.
In order to perform the various functions described herein, the subjective user state data solicitation module 4-103 may include, among other things, a network interface solicitation module 4-215, a user interface solicitation module 4-216, a requesting module 4-217, a configuration module 4-218, and/or a directing/instructing module 4-219. In brief, the network interface solicitation module 4-215 may be employed in order to solicit subjective user state data 4-60 via a network interface 4-120. In some implementations, the network interface solicitation module 4-215 may further include a transmission module 4-220 for transmitting a request for subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a.
In contrast, the user interface solicitation module 4-216 may be employed in order to, among other things, solicit subjective user state data 4-60 via user interface 4-122 from, for example, a user 4-20b. In some implementations, the user interface solicitation module 4-216 may further include an indication module 4-221 for, for example, audibly or visually indicating via a user interface 4-122 (e.g., an audio system including a speaker and/or a display system such as a display monitor) a request for subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a. The requesting module 4-217 may be employed in order to, among other things, request to be provided with or to have access to subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a associated with a user 4-20*. The configuration module 4-218 may be employed in order to configure, for example, a mobile device 4-30 or one or more network servers (not depicted) to provide the subjective user state data 4-60 including the data indicating occurrence of at least one subjective user state 4-60a. The directing/instructing module 4-219 may be employed in order to direct and/or instruct, for example, a mobile device 4-30 or one or more network servers (not depicted) to provide the subjective user state data 4-60 including the data indicating occurrence of at least one subjective user state 4-60a.
Referring now to
In various embodiments, the subjective user state data acquisition module 104 may include a time data acquisition module 4-228 configured to acquire (e.g., receive or generate) time and/or temporal elements associated with one or more subjective user states associated with a user 4-20*. For these embodiments, the time and/or temporal elements (e.g., time stamps, time intervals, and/or temporal relationships) may be useful for determining sequential patterns associated with objective occurrences and subjective user states.
In some implementations, the time data acquisition module 4-228 may include a time stamp acquisition module 4-230 for acquiring (e.g., either by receiving or by generating) one or more time stamps associated with one or more subjective user states associated with a user 4-20*. In the same or different implementations, the time data acquisition module 4-228 may include a time interval acquisition module 4-231 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more subjective user states associated with a user 4-20*. In the same or different implementations, the time data acquisition module 4-228 may include a temporal relationship acquisition module 4-232 for acquiring indications of temporal relationships between objective occurrences and subjective user states (e.g., an indication that a subjective user state associated with a user 4-20* occurred before, after, or at least partially concurrently with incidence of an objective occurrence).
Turning now to
The sequential pattern determination module 4-236, in various implementations, may include one or more sub-modules that may facilitate in the determination of one or more sequential patterns. As depicted, the one or more sub-modules that may be included in the sequential pattern determination module 4-236 may include, for example, a “within predefined time increment determination” module 4-238, a temporal relationship determination module 4-239, a subjective user state and objective occurrence time difference determination module 4-240, and/or a historical data referencing module 4-241. In brief, the within predefined time increment determination module 4-238 may be configured to determine whether at least one subjective user state of a user 4-20* occurred within a predefined time increment from an incidence of at least one objective occurrence. For example, determining whether a user 4-20* “feeling bad” (i.e., a subjective user state) occurred within ten hours (i.e., predefined time increment) of eating a large chocolate sundae (i.e., an objective occurrence). Such a process may be used in order to filter out events that are likely not related or to facilitate in determining the strength of correlation between subjective user state data 4-60 and objective occurrence data 4-70*. For example, if the user 4-20* “feeling bad” occurred more than 10 hours after eating the chocolate sundae, then this may indicate a weaker correlation between a subjective user state (e.g., feeling bad) and an objective occurrence (e.g., eating a chocolate sundae).
The temporal relationship determination module 4-239 of the sequential pattern determination module 4-236 may be configured to determine the temporal relationships between one or more subjective user states and one or more objective occurrences. For example, this may entail determining whether a particular subjective user state (e.g., sore back) occurred before, after, or at least partially concurrently with incidence of an objective occurrence (e.g., sub-freezing temperature).
The subjective user state and objective occurrence time difference determination module 4-240 of the sequential pattern determination module 4-236 may be configured to determine the extent of time difference between the incidence of at least one subjective user state and the incidence of at least one objective occurrence. For example, determining how long after taking a particular brand of medication (e.g., objective occurrence) did a user 4-20* feel “good” (e.g., subjective user state).
The historical data referencing module 4-241 of the sequential pattern determination module 4-236 may be configured to reference historical data 4-72 in order to facilitate in determining sequential patterns. For example, in various implementations, the historical data 4-72 that may be referenced may include, for example, general population trends (e.g., people having a tendency to have a hangover after drinking or ibuprofen being more effective than aspirin for toothaches in the general population), medical information such as genetic, metabolome, or proteome information related to the user 4-20* (e.g., genetic information of the user 4-20* indicating that the user 4-20* is susceptible to a particular subjective user state in response to occurrence of a particular objective occurrence), or historical sequential patterns such as known sequential patterns of the general population or of the user 4-20* (e.g., people tending to have difficulty sleeping within five hours after consumption of coffee). In some instances, such historical data 4-72 may be useful in associating one or more subjective user states with one or more objective occurrences.
In some embodiments, the correlation module 4-106 may include a sequential pattern comparison module 4-242. As will be further described herein, the sequential pattern comparison module 4-242 may be configured to compare two or more sequential patterns with each other to determine, for example, whether the sequential patterns at least substantially match each other or to determine whether the sequential patterns are contrasting sequential patterns.
As depicted in
The subjective user state equivalence determination module 4-243 of the sequential pattern comparison module 4-242 may be configured to determine whether subjective user states associated with different sequential patterns are equivalent. For example, the subjective user state equivalence determination module 4-243 may determine whether a first subjective user state of a first sequential pattern is equivalent to a second subjective user state of a second sequential pattern. For instance, suppose a user 4-20* reports that on Monday he had a stomach ache (e.g., first subjective user state) after eating at a particular restaurant (e.g., a first objective occurrence), and suppose further that the user 4-20* again reports having a stomach ache (e.g., a second subjective user state) after eating at the same restaurant (e.g., a second objective occurrence) on Tuesday, then the subjective user state equivalence determination module 4-243 may be employed in order to compare the first subjective user state (e.g., stomach ache) with the second subjective user state (e.g., stomach ache) to determine whether they are equivalent.
In contrast, the objective occurrence equivalence determination module 4-244 of the sequential pattern comparison module 4-242 may be configured to determine whether objective occurrences of different sequential patterns are equivalent. For example, the objective occurrence equivalence determination module 4-244 may determine whether a first objective occurrence of a first sequential pattern is equivalent to a second objective occurrence of a second sequential pattern. For instance, for the above example the objective occurrence equivalence determination module 4-244 may compare eating at the particular restaurant on Monday (e.g., first objective occurrence) with eating at the same restaurant on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is equivalent to the second objective occurrence.
In some implementations, the sequential pattern comparison module 4-242 may include a subjective user state contrast determination module 4-245 that may be configured to determine whether subjective user states associated with different sequential patterns are contrasting subjective user states. For example, the subjective user state contrast determination module 4-245 may determine whether a first subjective user state of a first sequential pattern is a contrasting subjective user state from a second subjective user state of a second sequential pattern. To illustrate, suppose a user 4-20* reports that he felt very “good” (e.g., first subjective user state) after jogging for an hour (e.g., first objective occurrence) on Monday, but reports that he felt “bad” (e.g., second subjective user state) when he did not exercise (e.g., second objective occurrence) on Tuesday, then the subjective user state contrast determination module 4-245 may compare the first subjective user state (e.g., feeling good) with the second subjective user state (e.g., feeling bad) to determine that they are contrasting subjective user states.
In some implementations, the sequential pattern comparison module 4-242 may include an objective occurrence contrast determination module 4-246 that may be configured to determine whether objective occurrences of different sequential patterns are contrasting objective occurrences. For example, the objective occurrence contrast determination module 4-246 may determine whether a first objective occurrence of a first sequential pattern is a contrasting objective occurrence from a second objective occurrence of a second sequential pattern. For instance, for the above example, the objective occurrence contrast determination module 4-246 may compare the “jogging” on Monday (e.g., first objective occurrence) with the “no jogging” on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence. Based on the contrast determination, an inference may be made that the user 4-20* may feel better by jogging rather than by not jogging at all.
In some embodiments, the sequential pattern comparison module 4-242 may include a temporal relationship comparison module 4-247 that may be configured to make comparisons between different temporal relationships of different sequential patterns. For example, the temporal relationship comparison module 4-247 may compare a first temporal relationship between a first subjective user state and a first objective occurrence of a first sequential pattern with a second temporal relationship between a second subjective user state and a second objective occurrence of a second sequential pattern in order to determine whether the first temporal relationship at least substantially matches the second temporal relationship.
For example, suppose in the above example the user 4-20* eating at the particular restaurant (e.g., first objective occurrence) and the subsequent stomach ache (e.g., first subjective user state) on Monday represents a first sequential pattern while the user 4-20* eating at the same restaurant (e.g., second objective occurrence) and the subsequent stomach ache (e.g., second subjective user state) on Tuesday represents a second sequential pattern. In this example, the occurrence of the stomach ache after (rather than before or concurrently) eating at the particular restaurant on Monday represents a first temporal relationship associated with the first sequential pattern while the occurrence of a second stomach ache after (rather than before or concurrently) eating at the same restaurant on Tuesday represents a second temporal relationship associated with the second sequential pattern. Under such circumstances, the temporal relationship comparison module 4-247 may compare the first temporal relationship to the second temporal relationship in order to determine whether the first temporal relationship and the second temporal relationship at least substantially match (e.g., stomachaches in both temporal relationships occurring after eating at the restaurant). Such a match may result in the inference that a stomach ache is associated with eating at the particular restaurant.
In some implementations, the sequential pattern comparison module 4-242 may include an extent of time difference comparison module 4-248 that may be configured to compare the extent of time differences between incidences of subjective user states and incidences of objective occurrences of different sequential patterns. For example, the extent of time difference comparison module 4-248 may compare the extent of time difference between incidence of a first subjective user state and incidence of a first objective occurrence of a first sequential pattern with the extent of time difference between incidence of a second subjective user state and incidence of a second objective occurrence of a second sequential pattern. In some implementations, the comparisons may be made in order to determine that the extent of time differences of the different sequential patterns at least substantially or proximately match.
In some embodiments, the correlation module 4-106 may include a strength of correlation determination module 4-250 for determining a strength of correlation between subjective user state data 4-60 and objective occurrence data 4-70* associated with a user 4-20*. In some implementations, the strength of correlation may be determined based, at least in part, on the results provided by the other sub-modules of the correlation module 4-106 (e.g., the sequential pattern determination module 4-236, the sequential pattern comparison module 4-242, and their sub-modules).
e illustrates particular implementations of the presentation module 4-108 of the computing device 4-10 of
In various implementations, the presentation module 4-108 may include a network interface transmission module 4-252 for transmitting one or more results of the correlation performed by the correlation module 4-106 via network interface 4-120. For example, in the case where the computing device 4-10 is a server, the network interface transmission module 4-252 may be configured to transmit to the user 4-20a or a third party 4-50 the one or more results of the correlation performed by the correlation module 4-106 via a network interface 4-120.
In the same or different implementations, the presentation module 4-108 may include a user interface indication module 4-254 for indicating the one or more results of the correlation operations performed by the correlation module 4-106 via a user interface 4-122. For example, in the case where the computing device 4-10 is a local device, the user interface indication module 4-254 may be configured to indicate to a user 4-20b the one or more results of the correlation performed by the correlation module 4-106 via a user interface 4-122 (e.g., a display monitor, a touchscreen, an audio system including at least a speaker, and/or other interface devices).
The presentation module 4-108 may further include one or more sub-modules to present the one or more results of the correlation operations performed by the correlation module 4-106 in different forms. For example, in some implementations, the presentation module 4-108 may include a sequential relationship presentation module 4-256 configured to present an indication of a sequential relationship between at least one subjective user state of a user 4-20* and at least one objective occurrence. In the same or different implementations, the presentation module 4-108 may include a prediction presentation module 4-258 configured to present a prediction of a future subjective user state of a user 4-20* resulting from a future objective occurrence associated with the user 4-20*. In the same or different implementations, the prediction presentation module 4-258 may also be designed to present a prediction of a future subjective user state of a user 4-20* resulting from a past objective occurrence associated with the user 4-20*. In some implementations, the presentation module 4-108 may include a past presentation module 4-260 that is designed to present a past subjective user state of a user 4-20* in connection with a past objective occurrence associated with the user 4-20*.
In some implementations, the presentation module 4-108 may include a recommendation module 4-262 configured to present a recommendation for a future action based, at least in part, on the results of a correlation of subjective user state data 4-60 with objective occurrence data 4-70* as performed by the correlation module 4-106. In certain implementations, the recommendation module 4-262 may further include a justification module 4-264 for presenting a justification for the recommendation presented by the recommendation module 4-262. In some implementations, the presentation module 4-108 may include a strength of correlation presentation module 4-266 for presenting an indication of a strength of correlation between subjective user state data 4-60 and objective occurrence data 4-70*.
In various embodiments, the computing device 4-10 of
The computing device 4-10 may also include a memory 4-140 for storing various data. For example, in some embodiments, memory 4-140 may be employed in order to store historical data 4-72. In some implementations, the historical data 4-72 may include historical subjective user state data of a user 4-20* that may indicate one or more past subjective user states of the user 4-20* and historical objective occurrence data that may indicate one or more past objective occurrences. In same or different implementations, the historical data 4-72 may include historical medical data of a user 4-20* (e.g., genetic, metoblome, proteome information), population trends, historical sequential patterns derived from general population, and so forth.
In various embodiments, the computing device 4-10 may include a user interface 4-122 to communicate directly with a user 4-20b. For example, in embodiments in which the computing device 4-10 is a local device such as a handheld device (e.g., cellular telephone, PDA, and so forth), the user interface 4-122 may be configured to directly receive from the user 4-20b subjective user state data 4-60 and/or objective occurrence data 4-70*. In some implementations, the user interface 4-122 may also be designed to visually or audibly present the results of correlating subjective user state data 4-60 and objective occurrence data 4-70*. The user interface 4-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a key pad, a mouse, an audio system including a microphone and/or one or more speakers, an imaging system including a digital or video camera, and/or other user interface devices.
e illustrates particular implementations of the one or more applications 4-126 of
In
Further, in
In any event, after a start operation, the operational flow 4-300 may move to an objective occurrence data acquisition operation 4-302 for acquiring objective occurrence data including data indicating occurrence of at least one objective occurrence. For instance, the objective occurrence data acquisition module 4-102 of the computing device 4-10 acquiring (e.g., receiving via network interface 4-120 or via user interface 4-122) objective occurrence data 4-70* including data indicating occurrence of at least one objective occurrence (e.g., an activity performed by a user 4-20*, an activity performed by another user (not depicted), a physical characteristic of the user 4-20*, an external event, and so forth).
Operational flow 4-300 may also include a subjective user state data solicitation operation 4-304 for soliciting, in response to the acquisition of the objective occurrence data, subjective user state data including data indicating occurrence of at least one subjective user state associated with a user. For instance, the subjective user state data solicitation module 4-103 of the computing device 4-10 soliciting (e.g., requesting from the user 4-20*, from the mobile device 4-30, or from a network server), in response to the acquisition of the objective occurrence data 4-70*, subjective user state data 4-60 including data indicating occurrence of at least one subjective user state 4-60a (e.g., a subjective mental state, a subjective physical state, or a subjective overall state) associated with a user 4-20*.
Note that the solicitation of the subjective user state data 4-60, as described above, may or may not be in reference to solicitation of particular data that indicates occurrence of a particular or particular type of subjective user state. That is, in some embodiments, the solicitation of the subjective user state data 4-60 may be in reference to solicitation for subjective user state data 4-60 including data indicating occurrence of any subjective user state, while in other embodiments, the solicitation of the subjective user state data 4-60 may involve solicitation for subjective user state data 4-60 including data indicating occurrence of a particular or particular type of subjective user state.
The term “soliciting” as described above may be in reference to direct or indirect solicitation of (e.g., requesting to be provided with, requesting to access, or other methods of being provided with, or being allowed access) subjective user state data 4-60 from one or more sources. The sources may be the user 4-20* him or herself, a mobile device 4-30, or one or more network servers (not depicted), which may have already been provided with such subjective user state data 4-60. For example, if the computing device 4-10 is a server, then the computing device 4-10 may indirectly solicit the objective occurrence data 4-70* from a user 4-20a by transmitting the solicitation (e.g., a request or inquiry) to the mobile device 4-30, which may then actually solicit the subjective user state data 4-60 from the user 4-20a. Alternatively, such subjective user state data 4-60 may have already been provided to the mobile device 4-30, in which case the mobile device 4-30 merely provides for or allows access to such data. In still other alternative implementations, such subjective user state data 4-60 may have been previously stored in a network server (not depicted), and such a network server may be solicited for the subjective user state data 4-60. In yet other implementations in which the computing device 4-10 is a local device such as a handheld device to be used directly by a user 4-20b, the computing device 4-10 may directly solicit the subjective user state data 4-60 from the user 4-20b.
Operational flow 4-300 may further include subjective user state data acquisition operation 4-306 for acquiring the subjective user state data. For instance, the subjective user state data acquisition module 4-104 of the computing device 4-10 acquiring (e.g., receiving via user interface 4-122 or via the network interface 4-120) the subjective user state data 4-60.
Finally, operational flow 4-300 may include a correlation operation 4-308 for correlating the subjective user state data with the objective occurrence data. For instance, the correlation module 4-106 of the computing device 4-10 correlating the subjective user state data 4-60 with the objective occurrence data 4-70* by determining, for example, at least one sequential pattern (e.g., time sequential pattern) associated with the occurrence of the at least one subjective user state (e.g., user feeling “tired”) and the occurrence of the at least one objective occurrence (e.g., elevated blood sugar level).
In various implementations, the objective occurrence data acquisition operation 4-302 of
The reception operation 4-402 in turn may further include one or more additional operations. For example, in some implementations, the reception operation 4-402 may include an operation 4-404 for receiving the objective occurrence data from at least one of a wireless network or a wired network as depicted in
In some implementations, the reception operation 4-402 may include an operation 4-406 for receiving the objective occurrence data via one or more blog entries as depicted in
In some implementations, the reception operation 4-402 may include an operation 4-408 for receiving the objective occurrence data via one or more status reports as depicted in
In some implementations, the reception operation 4-402 may include an operation 4-410 for receiving the objective occurrence data from one or more third party sources as depicted in
In some implementations, the reception operation 4-402 may include an operation 4-412 for receiving the objective occurrence data from one or more sensors configured to sense one or more objective occurrences as depicted in
In some implementations, the reception operation 4-402 may include an operation 4-414 for receiving the objective occurrence data from the user as depicted in
The objective occurrence data acquisition operation 4-302 of
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-418 for acquiring an indication of a time interval associated with occurrence of the at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-420 for acquiring an indication of a temporal relationship between occurrence of the at least one objective occurrence and occurrence of at least one subjective user state as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-422 for acquiring data indicating the at least one objective occurrence and one or more attributes associated with the at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-424 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a medicine as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-426 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a food item as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-428 for acquiring data indicating at least one objective occurrence of an ingestion by the user of a nutraceutical as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-430 for acquiring data indicating at least one objective occurrence of an exercise routine executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-432 for acquiring data indicating at least one objective occurrence of a social activity executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-434 for acquiring data indicating at least one objective occurrence of an activity performed by a third party as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-436 for acquiring data indicating at least one objective occurrence of a physical characteristic of the user as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-438 for acquiring data indicating at least one objective occurrence of a resting, a learning or a recreational activity by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-440 for acquiring data indicating at least one objective occurrence of an external event as depicted in
In some implementations, the objective occurrence data acquisition operation 4-302 may include an operation 4-442 for acquiring data indicating at least one objective occurrence related to a location of the user as depicted in
Referring back to
In some implementations, operation 4-500 may further include an operation 4-502 for requesting to be provided with the data indicating occurrence of at least one subjective user state associated with a user as depicted in
In some implementations, operation 4-500 may include an operation 4-504 for requesting to have access to the data indicating occurrence of at least one subjective user state associated with a user as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-506 for configuring to obtain the data indicating occurrence of at least one subjective user state associated with a user as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-508 for directing or instructing to obtain the data indicating occurrence of at least one subjective user state associated with a user as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-510 for soliciting from the user the data indicating occurrence of at least one subjective user state associated with the user as depicted in
Operation 4-510, in turn, may further include an operation 4-512 for soliciting the data indicating occurrence of at least one subjective user state associated with the user via a user interface as depicted in
In various implementations, operation 4-512 may include an operation 4-514 for indicating a request for the data indicating occurrence of at least one subjective user state associated with the user through at least one of a display monitor or a touchscreen as depicted in
In some implementations, operation 4-512 may include an operation 4-516 for indicating a request for the data indicating occurrence of at least one subjective user state associated with the user through at least an audio system as depicted in
In various implementations, operation 4-510 of
Operation 4-518, in some implementations, may further include an operation 4-520 for transmitting to the user a request for the data indicating occurrence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 4-510 may include an operation 4-522 for requesting the user to select a subjective user state from a plurality of indicated alternative subjective user states as depicted in
Operation 4-522, in turn, may further include an operation 4-524 for requesting the user to select a subjective user state from a plurality of indicated alternative contrasting subjective user states as depicted in
In various implementations, operation 4-510 may include an operation 4-526 for requesting the user to confirm occurrence of a subjective user state as depicted in
In some implementations, operation 4-510 may include an operation 4-528 for requesting the user to provide an indication of occurrence of the at least one subjective user state with respect to occurrence of the at least one objective occurrence as depicted in
In some implementations, operation 4-510 may include an operation 4-530 for requesting the user to provide an indication of a time or temporal element associated with occurrence of the at least one subjective user state as depicted in
Operation 4-530 may, in turn, include one or more additional operations. For example, in some implementations operation 4-530 may include an operation 4-532 for requesting the user to provide an indication of a point in time associated with the occurrence of the at least one subjective user state as depicted in
In some implementations, operation 4-530 may include an operation 4-534 for requesting the user to provide an indication of a time interval associated with the occurrence of the at least one subjective user state as depicted in
In some implementations, operation 4-530 may include an operation 4-536 for requesting the user to provide an indication of a temporal relationship between occurrence of the at least one subjective user state and occurrence of at least one objective occurrence as depicted in
In various implementations, the subjective user state data solicitation operation 4-304 of
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-540 for soliciting data indicating occurrence of at least one subjective physical state associated with the user as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-542 for soliciting data indicating occurrence of at least one subjective overall state associated with the user as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-544 for soliciting data indicating occurrence of at least one subjective user state during a specified point in time as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-546 for soliciting data indicating occurrence of at least one subjective user state during a specified time interval as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-548 for soliciting data indicating occurrence of the at least one subjective user state in response to the acquisition of the objective occurrence data and based on historical data as depicted in
For example, suppose the historical data 4-72 indicates that the last time the user 4-20* ate a chocolate sundae, the user 4-20* had a stomach ache. Suppose further that the user 4-20* again reports that the user 4-20* ate another chocolate sundae (e.g., objective occurrence) the next day but forgets to indicate the subjective user state of the user 4-20* after eating the chocolate sundae. Then, upon the reporting of the objective occurrence (e.g., eating a chocolate sundae), and based on historical data 4-72 (e.g., the previous reports of eating a chocolate sundae and having a stomach ache), the user 4-20* may be asked via the user interface 4-122 or via the mobile device 4-30 how the user 4-20* feels or whether the user 4-20* had a stomach ache after consuming the chocolate sundae.
Alternatively, a solicitation from the mobile device 4-30 or from a network server (not depicted) for data that indicates the subjective user state of the user 4-20a around the time of the consumption of the second chocolate sundae may be prompted based on the reporting of the consumption of the second chocolate sundae and based on historical data 4-72 without soliciting such data from the user 4-20a. That is, in some cases, such data may have already been received and/or recorded by the mobile device 4-30 or by the network server. In which case, there is no need to solicit the data from the user 4-20a and instead, the relevant data may only need to be accessed or be prompted to be released.
In various implementations, operation 4-548 may include one or more additional operations. For example, in some implementations, operation 4-548 may include an operation 4-550 for soliciting data indicating occurrence of the at least one subjective user state based, at least in part, on one or more historical sequential patterns as depicted in
In some implementations, operation 4-548 may include an operation 4-552 for soliciting data indicating occurrence of the at least one subjective user state based, at least in part, on medical data associated with the user as depicted in
In some implementations, operation 4-548 may include an operation 4-554 for soliciting data indicating occurrence of the at least one subjective user state based, at least in part, on the historical data indicating a link between a subjective user state type and an objective occurrence type as depicted in
In some implementations, operation 4-548 may include an operation 4-556 for soliciting data indicating occurrence of the at least one subjective user state, the soliciting prompted, at least in part, by the historical data as depicted in
In some implementations, operation 4-548 may include an operation 4-558 for soliciting data indicating occurrence of a particular or a particular type of subjective user state based on the historical data as depicted in
In some implementations, the subjective user state data solicitation operation 4-304 may include an operation 4-560 for soliciting data indicating one or more attributes associated with the at least one subjective user state as depicted in
In various embodiments, the subjective user state data acquisition operation 4-306 of
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-604 for receiving the subjective user state data via a network interface as depicted in
Operation 4-604 may, in turn, include one or more additional operations in various alternative implementations. For example, in some implementations, operation 4-604 may include an operation 4-606 for receiving data indicating the at least one subjective user state via an electronic message generated by the user as depicted in
In some implementations, operation 4-604 may include an operation 4-608 for receiving data indicating the at least one subjective user state via a blog entry generated by the user as depicted in
In some implementations, operation 4-604 may include an operation 4-610 for receiving data indicating the at least one subjective user state via a status report generated by the user as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-612 for receiving subjective user state data including data indicating at least one subjective user state specified by a selection made by the user, the selection being a selection of a subjective user state from a plurality of indicated alternative subjective user states as depicted in
In certain implementations, operation 4-612 may further include an operation 4-614 for receiving subjective user state data including data indicating at least one subjective user state specified by a selection made by the user, the selection being a selection of a subjective user state from at least two indicated alternative contrasting subjective user states as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-616 for acquiring data indicating occurrence of at least one subjective mental state of the user as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-618 for acquiring data indicating occurrence of at least one subjective physical state of the user as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-620 for acquiring data indicating occurrence of at least one subjective overall state of the user as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-622 for acquiring a time stamp associated with occurrence of at least one subjective user state as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-624 for acquiring an indication of a time interval associated with occurrence of at least one subjective user state as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-626 for acquiring an indication of a temporal relationship between occurrence of at least one subjective user state and occurrence of at least one objective occurrence as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-628 for acquiring the subjective user state data at a server as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-630 for acquiring the subjective user state data at a handheld device as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-632 for acquiring the subjective user state data at a peer-to-peer network component device as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-634 for acquiring the subjective user state data via a Web 2.0 construct as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-636 for acquiring data indicating at least one subjective user state that occurred at least partially concurrently with an incidence of the at least one objective occurrence as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-638 for acquiring data indicating at least one subjective user state that occurred prior to an incidence of the at least one objective occurrence as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-640 for acquiring data indicating at least one subjective user state that occurred subsequent to an incidence of the at least one objective occurrence as depicted in
In some embodiments, the subjective user state data acquisition operation 4-306 may include an operation 4-642 for acquiring data indicating at least one subjective user state that occurred within a predefined time period of an incidence of the at least one objective occurrence as depicted in
Referring back to
In various alternative implementations, operation 4-702 may include one or more additional operations. For example, in some implementations, operation 4-702 may include an operation 4-704 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of whether the at least one subjective user state occurred within a predefined time increment from incidence of the at least one objective occurrence as depicted in
In some implementations, operation 4-702 may include an operation 4-706 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of whether the at least one subjective user state occurred before, after, or at least partially concurrently with incidence of the at least one objective occurrence as depicted in
In some implementations, operation 4-702 may include an operation 4-708 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing of historical data as depicted in
In various implementations, operation 4-708 may include one or more additional operations. For example, in some implementations, operation 4-708 may include an operation 4-710 for correlating the subjective user state data with the objective occurrence data based, at least in part, on the historical data indicating a link between a subjective user state type and an objective occurrence type as depicted in
In some instances, operation 4-710 may further include an operation 4-712 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a historical sequential pattern as depicted in
For example, a previously determined historical sequential pattern associated with the user 4-20* may have been determined based on previously acquired data indicating occurrence of at least a second subjective user state 4-60b (see
In some implementations, operation 4-708 may include an operation 4-714 for correlating the subjective user state data with the objective occurrence data based, at least in part, on historical medical data as depicted in
In some implementations, operation 4-702 may include an operation 4-716 for comparing the at least one sequential pattern to a second sequential pattern to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
In various implementations, operation 4-716 may further include an operation 4-718 for comparing the at least one sequential pattern to a second sequential pattern related to at least a second subjective user state associated with the user and a second objective occurrence to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
In some implementations, the correlation operation 4-308 of
In some implementations, the correlation operation 4-308 may include an operation 4-722 for correlating the subjective user state data with the objective occurrence data at a handheld device as depicted in
In some implementations, the correlation operation 4-308 may include an operation 4-724 for correlating the subjective user state data with the objective occurrence data at a peer-to-peer network component device as depicted in
Referring to
In addition, operational flow 4-800 includes a presentation operation 4-810 for presenting one or more results of the correlating as depicted in
In various embodiments, the presentation operation 4-810 may include one or more additional operations as depicted in
In some implementations, the presentation operation 4-810 may include an operation 4-904 for transmitting the one or more results of the correlating via a network interface. For instance, the network interface transmission module 4-252 (see
In some implementations, the presentation operation 4-810 may include an operation 4-906 for presenting an indication of a sequential relationship between the at least one subjective user state and the at least one objective occurrence. For instance, the sequential relationship presentation module 4-256 of the computing device 4-10 presenting (e.g., transmitting via the network interface 4-120 or indicating via user interface 4-122) an indication of a sequential relationship between the at least one subjective user state (e.g., headache) and the at least one objective occurrence (e.g., drinking beer).
In some implementations, the presentation operation 4-810 may include an operation 4-908 for presenting a prediction of a future subjective user state resulting from a future objective occurrence associated with the user. For instance, the prediction presentation module 4-258 of the computing device 4-10 a prediction of a future subjective user state associated with the user 4-20* resulting from a future objective occurrence. An example prediction might state that “if the user drinks five shots of whiskey tonight, the user will have a hangover tomorrow.”
In some implementations, the presentation operation 4-810 may include an operation 4-910 for presenting a prediction of a future subjective user state resulting from a past objective occurrence associated with the user. For instance, the prediction presentation module 4-258 of the computing device 4-10 presenting a prediction of a future subjective user state associated with the user 4-20* resulting from a past objective occurrence. An example prediction might state that “the user will have a hangover tomorrow since the user drank five shots of whiskey tonight.”
In some implementations, the presentation operation 4-810 may include an operation 4-912 for presenting a past subjective user state in connection with a past objective occurrence associated with the user. For instance, the past presentation module 4-260 of the computing device 4-10 presenting a past subjective user state associated with the user 4-20* in connection with a past objective occurrence. An example of such a presentation might state that “the user got depressed the last time it rained.”
In some implementations, the presentation operation 4-810 may include an operation 4-914 for presenting a recommendation for a future action. For instance, the recommendation module 4-262 of the computing device 4-10 presenting a recommendation for a future action. An example recommendation might state that “the user should not drink five shots of whiskey.”
Operation 4-914 may, in some instances, include an additional operation 4-916 for presenting a justification for the recommendation. For instance, the justification module 4-264 of the computing device 4-10 presenting a justification for the recommendation. An example justification might state that “the user should not drink five shots of whiskey because the last time the user drank five shots of whiskey, the user got a hangover.”
VI: Correlating Data Indicating Subjective User States Associated with Multiple Users with Data Indicating Objective Occurrences
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post their thoughts and opinions on various topics, latest news, and various other aspects of users everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social network status reports in which a user may report or post, for others to view, the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted through microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” that may be directly or indirectly associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, event, happening, or any other aspect that may be directly or indirectly associated with or of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, the local weather, the stock market (which the microblogger may have an interest in), activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblogging entries include “subjective user states” of the microblogger. Subjective user states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., “I am feeling happy”), the subjective physical states of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and the subjective overall state of the microblogger (e.g., “I'm good” or “I'm well”). Note that the term “subjective overall state” as will be used herein refers to those subjective states that do not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). Although microblogs are being used to provide a wealth of personal information, they have only been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided for, among other things, correlating subjective user state data including data indicating incidences of one or more subjective user states of multiple users with objective occurrence data including data indicating incidences of one or more objective occurrences. In doing so, a causal relationship between one or more objective occurrences (e.g., cause) and one or more subjective user states (e.g., result) associated with multiple users (e.g., bloggers or microbloggers) may be determined in various alternative embodiments. For example, determining that eating a banana (e.g., objective occurrence) may result in a user feeling good (e.g., subjective user state) or determining that users will usually or always feel satisfied or good whenever they eat bananas. Note that an objective occurrence does not need to occur prior to a corresponding subjective user state but instead, may occur subsequent or concurrently with the incidence of the subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence) or a person may become gloomy while (e.g., concurrently) it is raining
In various embodiments, subjective user state data may include data indicating subjective user states of multiple users. A “subjective user state,” as will be used herein, may be in reference to any subjective state or status associated with a particular user (e.g., a particular blogger or microblogger) at any moment or interval in time that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of a user (e.g., user is feeling sad), the subjective physical state (e.g., physical characteristic) of a user that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), and the subjective overall state of a user (e.g., user is “good”). Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be categorized as a subjective mental state or as a subjective physical state. Examples of overall states of a user that may be subjective user states include, for example, the user being good, bad, exhausted, lack of rest, wellness, and so forth.
In contrast, “objective occurrence data,” which may also be referred to as “objective context data,” may include data that indicate one or more objective occurrences that may or may not be directly or indirectly associated with one or more users. In particular, an objective occurrence may be a physical characteristic, an event, one or more happenings, or any other aspect that may be associated with or is of interest to a user (or a group of users) that can be objectively reported by at least a third party or a sensor device. Note, however, that the occurrence or incidence of an objective occurrence does not have to be actually provided by a sensor device or by a third party, but instead, may be reported by a user or a group of users. Examples of an objective occurrence that could be indicated by the objective occurrence data include, for example, a user's food, medicine, or nutraceutical intake, a user's location at any given point in time, a user's exercise routine, a user's blood pressure, weather at a user's or a group of users' location, activities associated with third parties, the stock market, and so forth.
The term “correlating” as will be used herein is in reference to a determination of one or more relationships between at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that represents multiple subjective user states of multiple users and the second variable is objective occurrence data that represents one or more objective occurrences. Each of the subjective user states represented by the subjective user state data may be associated with a respective user and may or may not be the same or similar type of subjective user state. Similarly, when multiple objective occurrences are represented by the objective occurrence data, each of the objective occurrences indicated by the objective occurrence data may or may not represent the same or similar type of objective occurrence.
Various techniques may be employed for correlating the subjective user state data with the objective occurrence data. For example, in some embodiments, correlating the objective occurrence data with the subjective user state data may be accomplished by determining a first sequential pattern for a first user, the first sequential pattern being associated with at least a first subjective user state (e.g., upset stomach) associated with the first user and at least a first objective occurrence (e.g., first user eating spicy food).
A second sequential pattern may also be determined for a second user, the second sequential pattern being associated with at least a second subjective user state (e.g., upset stomach) associated the second user and at least a second objective occurrence (second user eating spicy food). The subjective user state data (which may indicate the subjective user states of the first and the second user) and the objective occurrence data (which may indicate the first and the second objective occurrence) may then be correlated by comparing the first sequential pattern with the second sequential pattern. In doing so, for example, a hypothesis may be determined indicating that, for example, eating spicy foods causes upset stomachs.
Note that in some cases, the first and second objective occurrences indicated by the objective occurrence data could actually be the same objective occurrence. For example, the first and second objective occurrence could be related to the weather at a particular location (and therefore, potentially affect multiple users). However, since a single objective occurrence event such as weather could be reported via different sources (e.g., different users or third party sources), a single objective occurrence event could be indicated multiple times by the objective occurrence data. In still other variations, the first and the second objective occurrences may be the same or similar types of objective occurrences (e.g., bad weather on different days or different locations). In still other variations, the first and the second objective occurrences could be different objective occurrences (e.g., sunny weather as opposed to stormy weather) or variations of each other (e.g., a blizzard as opposed to light snow).
Similarly, the first and the second subjective user states of the first and second users may, in some instances, be the same or similar type of subjective user states (e.g., the first and second both feeling happy). In other situations, they may not be the same or similar type of subjective user state. For example, the first user may have had a very bad upset stomach (e.g., first subjective user state) after eating spicy food while the second user may only have had a mild upset stomach or no upset stomach after eating spicy food. In such a scenario, this may indicate a weaker correlation between spicy foods and upset stomachs.
As will be further described herein a sequential pattern, in some implementations, may merely indicate or represent the temporal relationship or relationships between at least one subjective user state associated with a user and at least one objective occurrence (e.g., whether the incidence or occurrence of the at least one subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least one objective occurrence). In alternative implementations, and as will be further described herein, a sequential pattern may indicate a more specific time relationship between incidences of one or more subjective user states associated with a user and incidences of one or more objective occurrences. For example, a sequential pattern may represent the specific pattern of events (e.g., one or more objective occurrences and one or more subjective user states) that occurs along a timeline.
The following illustrative example is provided to describe how a sequential pattern associated with at least one subjective user state associated with a user and at least one objective occurrence may be determined based, at least in part, on the temporal relationship between the incidence of the at least one subjective user state and the incidence of the at least one objective occurrence in accordance with some embodiments. For these embodiments, the determination of a sequential pattern may initially involve determining whether the incidence of the at least one subjective user state occurred within some predefined time increments of the incidence of the one objective occurrence. That is, it may be possible to infer that those subjective user states that did not occur within a certain time period from the incidence of an objective occurrence are not related or are unlikely related to the incidence of that objective occurrence.
For example, suppose a user during the course of a day eats a banana and also has a stomach ache sometime during the course of the day. If the consumption of the banana occurred in the early morning hours but the stomach ache did not occur until late that night, then the stomach ache may be unrelated to the consumption of the banana and may be disregarded. On the other hand, if the stomach ache had occurred within some predefined time increment, such as within 2 hours of consumption of the banana, then it may be concluded that there may be a link between the stomach ache and the consumption of the banana. If so, a temporal relationship between the consumption of the banana and the occurrence of the stomach ache may be determined. Such a temporal relationship may be represented by a sequential pattern that may simply indicate that the stomach ache (e.g., a subjective user state) occurred after (rather than before or concurrently with) the consumption of banana (e.g., an objective occurrence).
As will be further described herein, other factors may also be referenced and examined in order to determine a sequential pattern and whether there is a relationship (e.g., causal relationship) between an objective occurrence and a subjective user state. These factors may include, for example, historical data (e.g., historical medical data such as genetic data or past history of the user or historical data related to the general population regarding stomach aches and bananas). Alternatively, a sequential pattern may be determined for multiple subjective user states associated with a single user and multiple objective occurrences. Such a sequential pattern may particularly map the exact temporal or time sequencing of various events (e.g., subjective user states and/or objective occurrences). The determined sequential pattern may then be used to provide useful information to the user and/or third parties.
The following is another illustrative example of how subjective user state data may be correlated with objective occurrence data by determining multiple sequential patterns and comparing the sequential patterns with each other. Suppose, for example, a first user such as a microblogger reports that the first user ate a banana. The consumption of the banana, in this example, is a reported first objective occurrence associated with the first user. The first user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported first subjective user state associated with the first user. Thus, the reported incidence of the first objective occurrence (e.g., eating the banana) and the reported incidence of the first subjective user state (user felt very happy) may be represented by a first sequential pattern.
A second user reports that the second user also ate a banana (e.g., a second objective occurrence). The second user then reports that 20 minutes after eating the banana, the user felt somewhat happy (e.g., a second subjective user state associated with the second user). Thus, the reported incidence of the second objective occurrence (e.g., eating the banana by the second user) and the reported incidence of the second subjective user state (second user felt somewhat happy) may then be represented by a second sequential pattern. Note that in this example, the occurrences of the first subjective user state associated with the first user and the second subjective user state associated with the second user may be indicated by subjective user state data while the occurrences of the first objective occurrence and the second objective occurrence may be indicated by objective occurrence data.
By comparing the first sequential pattern with the second sequential pattern, the subjective user state data may be correlated with the objective occurrence data. In some implementations, the comparison of the first sequential pattern with the second sequential pattern may involve trying to match the first sequential pattern with the second sequential pattern by examining certain attributes and/or metrics. For example, comparing the first subjective user state (e.g., the first user felt very happy) of the first sequential pattern with the second subjective user state (e.g., the second user felt somewhat happy) of the second sequential pattern to see if they at least substantially match or are contrasting (e.g., being very happy in contrast to being slightly happy or being happy in contrast to being sad). Similarly, comparing the first objective occurrence (e.g., the first user eating a banana) of the first sequential pattern may be compared to the second objective occurrence (e.g., the second user eating a banana) of the second sequential pattern to determine whether they at least substantially match or are contrasting.
A comparison may also be made to see if the extent of time difference (e.g., 15 minutes) between the first subjective user state (e.g., first user being very happy) and the first objective occurrence (e.g., first user eating a banana) matches or are at least similar to the extent of time difference (e.g., 20 minutes) between the second subjective user state (e.g., second user being somewhat happy) and the second objective occurrence (e.g., second user eating a banana). These comparisons may be made in order to determine whether the first sequential pattern matches the second sequential pattern. A match or substantial match would suggest, for example, that a subjective user state (e.g., happiness) is linked to an objective occurrence (e.g., consumption of banana).
As briefly described above, the comparison of the first sequential pattern with the second sequential pattern may include a determination as to whether, for example, the respective subjective user states and the respective objective occurrences of the sequential patterns are contrasting subjective user states and/or contrasting objective occurrences. For example, suppose in the above example the first user had reported that the first user had eaten a whole banana and felt very energetic (e.g., first subjective user state) after eating the whole banana (e.g., first objective occurrence). Suppose that the second user reports eating a half a banana instead of a whole banana and only felt slightly energetic (e.g., second subjective user state) after eating the half banana (e.g., second objective occurrence). In this scenario, the first sequential pattern (e.g., first user feeling very energetic after eating a whole banana) may be compared to the second sequential pattern (e.g., second user feeling slightly energetic after eating only a half of a banana) to at least determine whether the first subjective user state (e.g., first user being very energetic) and the second subjective user state (e.g., second user being slightly energetic) are contrasting subjective user states. Another determination may also be made during the comparison to determine whether the first objective occurrence (first user eating a whole banana) is in contrast with the second objective occurrence (e.g., second user eating a half of a banana).
In doing so, an inference may be made that eating a whole banana instead of eating only a half of a banana makes a user happier or eating more banana makes a user happier. Thus, the word “contrasting” as used here with respect to subjective user states refers to subjective user states that are the same type of subjective user states (e.g., the subjective user states being variations of a particular type of subjective user states such as variations of subjective mental states). Thus, for example, the first subjective user state and the second subjective user state in the previous illustrative example are merely variations of subjective mental states (e.g., happiness). Similarly, the use of the word “contrasting” as used here with respect to objective occurrences refers to objective states that are the same type of objective occurrences (e.g., consumption of a food item such as a banana).
As those skilled in the art will recognize, a stronger correlation between subjective user state data and objective occurrence data may be obtained if a greater number of sequential patterns (e.g., if there was a third sequential pattern associated with a third user, a fourth sequential pattern associated with a fourth user, and so forth) that indicated that a user becomes happy or happier whenever a user eats a banana) are used as a basis for the correlation. Note that for ease of explanation and illustration, each of the exemplary sequential patterns to be described herein will be depicted as a sequential pattern associated with incidence of a single subjective user state and incidence of a single objective occurrence. However, those skilled in the art will recognize that a sequential pattern, as will be described herein, may also be associated with incidences of multiple objective occurrences and/or multiple subjective user states. For example, suppose a user had reported that after eating a banana, he had gulped down a can of soda. The user then reports that he became happy but had an upset stomach. In this example, the sequential pattern associated with this scenario will be associated with two objective occurrences (e.g., eating a banana and drinking a can of soda) and two subjective user states (e.g., user having an upset stomach and feeling happy).
In some embodiments, and as briefly described earlier, the sequential patterns derived from subjective user state data and objective occurrence data may be based on temporal relationships between objective occurrences and subjective user states. For example, whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence. For instance, a plurality of sequential patterns derived from subjective user state data and objective occurrence data may indicate that a user always has a stomach ache (e.g., subjective user state) after eating a banana (e.g., first objective occurrence).
a and 5-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 5-100 may include at least a computing device 5-10 (see
In some embodiments, the computing device 5-10 may be a network server in which case the computing device 5-10 may communicate with a plurality of users 5-20* via mobile devices 5-30* and through a wireless and/or wired network 5-40. A network server, as will be described herein, may be in reference to a network server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. A mobile device 5-30* may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 5-10.
In alternative embodiments, the computing device 5-10 may be a local computing device such as a client device that communicates directly with one or more users 5-20* as indicated by ref 21 as illustrated in
In embodiments where the computing device 5-10 is a server, the computing device 5-10 may obtain subjective user state data 5-60 indirectly from one or more users 5-20* via a network interface 5-120. Alternatively, the subjective user state data 5-60 may be received from one or more third party sources 5-50 such as other network servers. In still other embodiments, subjective user state data 5-60 may be retrieved from a memory 5-140. In embodiments in which the computing device 5-10 is a local device rather than a server, the subjective user state data 5-60 may be directly obtained from one or more users 5-20* via a user interface 5-122. As will be further described herein, the computing device 5-10 may acquire the objective occurrence data 5-70* from one or more sources.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 5-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 5-10 is a local device such as a handheld device that may communicate directly with one or more users 5-20*.
Assuming that the computing device 5-10 is a server, the computing device 5-10, in some implementations, may be configured to acquire subjective user state data 5-60 including data indicating incidence of at least a first subjective user state 5-60a associated with a first user 5-20a and data indicating incidence of at least a second subjective user state 5-60b associated with a second user 5-20b via mobile devices 5-30a and 5-30b and through wireless and/or wired networks 5-40. In some embodiments, the subjective user state data 5-60 may further include data indicating incidence of at least a third subjective user state 5-60c associated with a third user 5-20c, data indicating incidence of at least a fourth subjective user state 5-60d associated with a fourth user 5-20d, and so forth.
In various embodiments, the data indicating incidence of at least a first subjective user state 5-60a associated with a first user 5-20a, as well as the data indicating incidence of at least a second subjective user state 5-60b associated with a second user 5-20b may be acquired in the form of blog entries, such as microblog entries, status reports (e.g., social networking status reports), electronic messages (email, text messages, instant messages, etc.) or other types of electronic messages or documents. The data indicating the incidence of at least a first subjective user state 5-60a and the data indicating the incidence of at least a second subjective user state 5-60b may, in some instances, indicate the same, contrasting, or completely different subjective user states. Examples of subjective user states that may be indicated by the subjective user state data 5-60 include, for example, subjective mental states of a user 5-20* (e.g., a user 5-20* is sad or angry), subjective physical states of a user 5-20* (e.g., physical or physiological characteristic of a user 5-20* such as the presence or absence of a stomach ache or headache), and/or subjective overall states of a user 5-20* (e.g., a user 5-20* is “well” or any other subjective states that may not be classified as a subjective physical state or a subjective mental state).
The computing device 5-10 may be further configured to acquire objective occurrence data 5-70* from one or more sources. In various embodiments, the objective occurrence data 5-70* acquired by the computing device 5-10 may include data indicative of at least one objective occurrence. In some embodiments, the objective occurrence data 5-70* may include at least data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence, wherein the first and the second objective occurrence may or may not be the same objective occurrence (e.g., stormy weather on a particular day that may affect multiple users 5-20*). In some embodiments, the first objective occurrence may be associated with the first user 5-20a (e.g., physical characteristic of the first user 5-20a) while the second objective occurrence may be associated with the second user 5-20b. (e.g., physical characteristic of the second user 5-20b).
The objective occurrence data 5-70* may be acquired from various sources. For example, in some embodiments, objective occurrence data 5-70a may be acquired from one or more third party sources 5-50 (e.g., one or more third parties). Examples of third party sources 5-50 include, for example, network servers and other network devices associated with third parties. Examples of third parties include, for example, other users 5-20*, a health care provider, a hospital, a place of employment, a content provider, and so forth.
In some embodiments, objective occurrence data 5-70b may be acquired from one or more sensors 5-35 for sensing or monitoring various aspects associated with one or more users 5-20*. For example, in some implementations, sensors 5-35 may include a global positioning system (GPS) device for determining the locations of one or more users 5-20* or a physical activity sensor for measuring physical activities of one or more users 5-20*. Examples of a physical activity sensor include, for example, a pedometer for measuring physical activities of one or more users 5-20*. In certain implementations, the one or more sensors 5-35 may include one or more physiological sensor devices for measuring physiological characteristics of one or more users 5-20*. Examples of physiological sensor devices include, for example, a blood pressure monitor, a heart rate monitor, a glucometer, and so forth. In some implementations, the one or more sensors 5-35 may include one or more image capturing devices such as a video or digital camera.
In some embodiments, objective occurrence data 5-70c may be acquired from one or more users 5-20* via one or more mobile devices 5-30*. For these embodiments, the objective occurrence data 5-70c may be in the form of blog entries (e.g., microblog entries), status reports, or other types of electronic messages that may be generated by one or more users 5-20*. In various implementations, the objective occurrence data 5-70c acquired from one or more users 5-20* may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by one or more users 5-20*, certain physical characteristics (e.g., blood pressure or location) associated with one or more users 5-20*, or other aspects associated with one or more users 5-20* that the one or more users 5-20* can report objectively. In still other implementations, objective occurrence data 5-70* may be acquired from a memory 5-140.
After acquiring the subjective user state data 5-60 and the objective occurrence data 5-70*, the computing device 5-10 may be configured to correlate the acquired subjective user data 5-60 with the acquired objective occurrence data 5-70* based, at least in part, on a determination of multiple sequential patterns including at least a first sequential pattern and a second sequential pattern. The first sequential pattern being a sequential pattern of at least the first subjective user state and at least the first objective occurrence, and the second sequential pattern being a sequential pattern of at least the second subjective user state and at least the second objective occurrence, the first subjective user state being associated with the first user 5-20a and the second subjective user state being associated with the second user 5-20b. The determined sequential patterns may then be compared to each other in order to correlate the subjective user state data 5-60 with the objective occurrence data 5-70*.
In some embodiments, and as will be further indicated in the operations and processes to be described herein, the computing device 5-10 may be further configured to present one or more results of the correlation operation. In various embodiments, one or more correlation results 5-80 may be presented to one or more users 5-20* and/or to one or more third parties (e.g., one or more third party sources 5-50) in various alternative forms. The one or more third parties may be other users 5-20* such as other microbloggers, health care providers, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the subjective user state data acquisition module 5-102 of the computing device 5-10 of
In some implementations, the reception module 5-202 may further include an electronic message reception module 5-204, a blog entry reception module 5-205, a status report reception module 5-206, a text entry reception module 5-207, an audio entry reception module 5-208, and/or an image entry reception module 5-209. In brief, and as will be further described in the processes and operations to be described herein, the electronic message reception module 5-204 may be configured to acquire subjective user state data 5-60 including one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b in the form of one or more electronic messages (e.g., text message, email, and so forth).
In contrast, the blog entry reception module 5-205 may be configured to receive subjective user state data 5-60 including one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b in the form of one or more blog entries (e.g., microblog entries). The status report reception module 5-206 may be configured to receive subjective user state data 5-60 including one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b via one or more status reports (e.g., social networking status reports).
The text entry reception module 5-207 may be configured to receive subjective user state data 5-60 including one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b via one or more text entries. The audio entry reception module 5-208 may be configured to receive subjective user state data 5-60 including one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b via one or more audio entries (e.g., audio recordings of user voice). The image entry reception module 5-209 may be configured to receive subjective user state data 5-60 including one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b via one or more image entries (e.g., digital still or motion images showing, for example, one or more gestures made by one or more users 5-20* and/or one or more facial expressions of one or more users 5-20*).
In some embodiments, the subjective user state data acquisition module 5-102 may include a time stamp acquisition module 5-210 designed to acquire (e.g., by receiving or by self-generating) one or more time stamps associated with incidences of one or more subjective user states associated with one or more users 5-20*. In some embodiments, the subjective user state data acquisition module 5-102 may include a time interval indication acquisition module 5-211 designed to acquire (e.g., by receiving or by self-generating) one or more indications of time intervals associated with incidences of one or more subjective user states associated with one or more users 5-20*. In some embodiments, the subjective user state data acquisition module 5-102 may include a temporal relationship indication acquisition module 5-212 designed to acquire (e.g., by receiving or by self-generating) one or more indications of temporal relationships associated with incidences of one or more subjective user states associated with one or more users 5-20*.
In some embodiments, the subjective user state data acquisition module 5-102 may include a solicitation module 5-213 configured to solicit subjective user state data 5-60 including soliciting at least one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and data indicating incidence of at least a second subjective user state 5-60b. In various embodiments, the solicitation module 5-213 may solicit the subjective user state data 5-60 from one or more users 5-20* via a network interface 5-120 (e.g., in the case where the computing device 5-10 is a network server) or via a user interface 5-122 (e.g., in the case where the computing device 5-10 is a local device used directly by a user 5-20b). In some alternative implementations, the solicitation module 5-213 may solicit the subjective user state data 5-60 from one or more third party sources 5-50 (e.g., network servers associated with third parties).
In some embodiments, the solicitation module 5-213 may include a request transmit/indicate module 5-214 configured to transmit (e.g., via network interface 5-120) and/or to indicate (e.g., via a user interface 5-122) a request for subjective user state data 5-60 including requesting for at least one, or both, of the data indicating incidence of at least a first subjective user state 5-60a and data indicating incidence of at least a second subjective user state 5-60b. In some implementations, the solicitation of the subjective user state data 5-60 may involve requesting a user 5-20* to select one or more subjective user states from a list of alternative subjective user state options (e.g., a user 5-20* may choose at least one from a choice of “I'm feeling alert,” “I'm feeling sad,” “My back is hurting,” “I have an upset stomach,” and so forth). In certain embodiments, the request to select from a list of alternative subjective user state options may mean requesting a user 5-20* to select one subjective user state from at least two contrasting subjective user state options (e.g., “I'm feeling good” or “I'm feeling bad”).
Referring now to
In various embodiments, the objective occurrence data reception module 5-215 may include a blog entry reception module 5-216 and/or a status report reception module 5-217. The blog entry reception module 5-216 may be designed to receive (e.g., via a network interface 5-120 or via a user interface 5-122) the objective occurrence data 5-70* including receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence in the form of one or more blog entries (e.g., microblog entries). Such blog entries may be generated by one or more users 5-20* or by one or more third party sources 5-50.
In contrast, the status report reception module 5-217 may be designed to receive (e.g., via a network interface 5-120 or via a user interface 5-122) the objective occurrence data 5-70* including receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence in the form of one or more status reports (e.g., social networking status reports). Such status reports may be provided by one or more users 5-20* or by one or more third party sources 5-50. Although not depicted, the objective occurrence data acquisition module 5-104 may additionally include an electronic message reception module for receiving the objective occurrence data 5-70* via one or more electronic messages (e.g., email, text message, and so forth).
In the same or different embodiments, the objective occurrence data acquisition module 5-104 may include a time stamp acquisition module 5-218 for acquiring (e.g., either by receiving or self-generating) one or more time stamps associated with one or more objective occurrences. In the same or different implementations, the objective occurrence data acquisition module 5-104 may include a time interval indication acquisition module 5-219 for acquiring (e.g., either by receiving or self-generating) indications of one or more time intervals associated with one or more objective occurrences. Although not depicted, in some implementations, the objective occurrence data acquisition module 5-104 may include a temporal relationship indication acquisition module for acquiring indications of temporal relationships associated with objective occurrences (e.g., indications that objective occurrences occurred before, after, or at least partially concurrently with incidences of subjective user states).
Turning now to
The sequential pattern determination module 5-220, in various implementations, may include one or more sub-modules that may facilitate in the determination of one or more sequential patterns. As depicted, the one or more sub-modules that may be included in the sequential pattern determination module 5-220 may include, for example, a “within predefined time increment determination” module 5-221 and/or a temporal relationship determination module 5-222. In brief, the within predefined time increment determination module 5-221 may be configured to determine whether, for example, a subjective user state associated with a user 5-20* occurred within a predefined time increment from an incidence of an objective occurrence. For example, determining whether a user 5-20* feeling “bad” (i.e., a subjective user state) occurred within ten hours (i.e., predefined time increment) of eating a large chocolate sundae (i.e., an objective occurrence). Such a process may be used in order to determine that reported events, such as objective occurrences and subjective user states, are not or likely not related to each other, or to facilitate in determining the strength of correlation between subjective user states as identified by subjective user state data 5-60 and objective occurrences as identified by objective occurrence data 5-70*.
The temporal relationship determination module 5-222 may be configured to determine the temporal relationships between one or more subjective user states and one or more objective occurrences. For example, this may entail determining whether a particular subjective user state (e.g., sore back) of a user 5-20* occurred before, after, or at least partially concurrently with incidence of an objective occurrence (e.g., sub-freezing temperature).
In various embodiments, the correlation module 5-106 may include a sequential pattern comparison module 5-224. As will be further described herein, the sequential pattern comparison module 5-224 may be configured to compare multiple sequential patterns with each other to determine, for example, whether the sequential patterns at least substantially match each other or to determine whether the sequential patterns are contrasting sequential patterns. In some embodiments, at least two of the sequential patterns to be compared may be associated with different users 5-20*. For example, the sequential pattern comparison module 5-224 may be designed to compare a first sequential pattern of incidence of at least a first subjective user state and incidence of at least a first objective occurrence to a second sequential pattern of incidence of at least a second subjective user state and incidence of at least a second objective occurrence. For these embodiments, the first subjective user state may be a subjective user state associated with a first user 5-20a and the second subjective user state may be a subjective user state associated with a second user 5-20b.
As depicted in
The subjective user state equivalence determination module 5-225 may be configured to determine whether subjective user states associated with different sequential patterns are equivalent. For example, the subjective user state equivalence determination module 5-225 may be designed to determine whether a first subjective user state associated with a first user 5-20a of a first sequential pattern is equivalent to a second subjective user state associated with a second user 5-20b of a second sequential pattern. For instance, suppose a first user 5-20a reports that he had a stomach ache (e.g., first subjective user state) after eating at a particular restaurant (e.g., a first objective occurrence), and suppose further a second user 5-20b also reports having a stomach ache (e.g., a second subjective user state) after eating at the same restaurant (e.g., a second objective occurrence, then the subjective user state equivalence determination module 5-225 may be employed in order to compare the first subjective user state (e.g., stomach ache) with the second subjective user state (e.g., stomach ache) to determine whether they are at least equivalent.
In contrast, the objective occurrence equivalence determination module 5-226 may be configured to determine whether objective occurrences of different sequential patterns are equivalent. For example, the objective occurrence equivalence determination module 5-226 may be designed to determine whether a first objective occurrence of a first sequential pattern is equivalent to a second objective occurrence of a second sequential pattern. For instance, for the above example the objective occurrence equivalence determination module 5-226 may compare eating at the particular restaurant by the first user 5-20a (e.g., first objective occurrence) with eating at the same restaurant (e.g., second objective occurrence) by the second user 5-20b in order to determine whether the first objective occurrence is equivalent to the second objective occurrence.
In some implementations, the sequential pattern comparison module 5-224 may include a subjective user state contrast determination module 5-227, which may be configured to determine whether subjective user states associated with different sequential patterns are contrasting subjective user states. For example, the subjective user state contrast determination module 5-227 may determine whether a first subjective user state associated with a first user 5-20a of a first sequential pattern is a contrasting subjective user state from a second subjective user state associated with a second user 5-20b of a second sequential pattern. For instance, suppose a first user 5-20a reports that he felt very “good” (e.g., first subjective user state) after jogging for an hour (e.g., first objective occurrence), while a second user 5-20b reports that he felt “bad” (e.g., second subjective user state) when he did not exercise (e.g., second objective occurrence), then the subjective user state contrast determination module 5-227 may compare the first subjective user state (e.g., feeling good) with the second subjective user state (e.g., feeling bad) to determine that they are contrasting subjective user states.
In some implementations, the sequential pattern comparison module 5-224 may include an objective occurrence contrast determination module 5-228 that may be configured to determine whether objective occurrences of different sequential patterns are contrasting objective occurrences. For example, the objective occurrence contrast determination module 5-228 may determine whether a first objective occurrence of a first sequential pattern is a contrasting objective occurrence from a second objective occurrence of a second sequential pattern. For instance, for the above example, the objective occurrence contrast determination module 5-228 may be configured to compare the first user 5-20a jogging (e.g., first objective occurrence) with the no jogging or exercise by the second user 5-20b (e.g., second objective occurrence) in order to determine whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence. Based on the contrast determination, an inference may be made that a user 5-20* may feel better by jogging rather than by not jogging at all.
In some embodiments, the sequential pattern comparison module 5-224 may include a temporal relationship comparison module 5-229, which may be configured to make comparisons between different temporal relationships of different sequential patterns. For example, the temporal relationship comparison module 5-229 may compare a first temporal relationship between a first subjective user state and a first objective occurrence of a first sequential pattern with a second temporal relationship between a second subjective user state and a second objective occurrence of a second sequential pattern in order to determine whether the first temporal relationship at least substantially matches the second temporal relationship.
For example, suppose in the above example the first user 5-20a eating at the particular restaurant (e.g., first objective occurrence) and the subsequent stomach ache (e.g., first subjective user state) represents a first sequential pattern while the second user 5-20b eating at the same restaurant (e.g., second objective occurrence) and the subsequent stomach ache (e.g., second subjective user state) represents a second sequential pattern. In this example, the occurrence of the stomach ache after (rather than before or concurrently) eating at the particular restaurant by the first user 5-20a represents a first temporal relationship associated with the first sequential pattern while the occurrence of a second stomach ache after (rather than before or concurrently) eating at the same restaurant by the second user 5-20b represents a second temporal relationship associated with the second sequential pattern. Under such circumstances, the temporal relationship comparison module 5-229 may compare the first temporal relationship to the second temporal relationship in order to determine whether the first temporal relationship and the second temporal relationship at least substantially match (e.g., stomach aches in both temporal relationships occurring after eating at the same restaurant). Such a match may result in the inference that a stomach ache is associated with eating at the particular restaurant.
In some embodiments, the correlation module 5-106 may include a historical data referencing module 5-230. For these embodiments, the historical data referencing module 5-230 may be employed in order to facilitate the correlation of the subjective user state data 5-60 with the objective occurrence data 5-70*. For example, in some implementations, the historical data referencing module 5-230 may be configured to reference historical data 5-72, which may be stored in a memory 5-140, in order to facilitate in determining sequential patterns.
For example, in various implementations, the historical data 5-72 that may be referenced may include, for example, general population trends (e.g., people having a tendency to have a hangover after drinking or ibuprofen being more effective than aspirin for toothaches in the general population), medical information such as genetic, metabolome, or proteome information related to a user 5-20* (e.g., genetic information of the user 5-20* indicating that the user 5-20* is susceptible to a particular subjective user state in response to occurrence of a particular objective occurrence), or historical sequential patterns such as known sequential patterns of the general population or of one or more users 5-20* (e.g., people tending to have difficulty sleeping within five hours after consumption of coffee). In some instances, such historical data 5-72 may be useful in associating one or more subjective user states with one or more objective occurrences as represented by, for example, a sequential pattern.
In some embodiments, the correlation module 5-106 may include a strength of correlation determination module 5-231 for determining a strength of correlation between subjective user state data 5-60 and objective occurrence data 5-70*. In some implementations, the strength of correlation may be determined based, at least in part, on the results provided by the other sub-modules of the correlation module 5-106 (e.g., the sequential pattern determination module 5-220, the sequential pattern comparison module 5-224, and their sub-modules).
d illustrates particular implementations of the presentation module 5-108 of the computing device 5-10 of
In various implementations, the presentation module 5-108 may include a network interface transmission module 5-232 for transmitting one or more results of the correlation performed by the correlation module 5-106. For example, in the case where the computing device 5-10 is a server, the network interface transmission module 5-232 may be configured to transmit to one or more users 5-20* or to a third party (e.g., third party sources 5-50) the one or more results of the correlation performed by the correlation module 5-106 via a network interface 5-120.
In the same or different implementations, the presentation module 5-108 may include a user interface indication module 5-233 for indicating via a user interface 5-122 the one or more results of the correlation operations performed by the correlation module 5-106. For example, in the case where the computing device 5-10 is a local device, the user interface indication module 5-233 may be configured to indicate, via user interface 5-122 such as a display monitor and/or an audio system, the one or more results of the correlation performed by the correlation module 5-106.
In some implementations, the presentation module 5-108 may include a sequential relationship presentation module 5-234 configured to present an indication of a sequential relationship between at least one subjective user state and at least one objective occurrence. In some implementations, the presentation module 5-108 may include a prediction presentation module 5-236 configured to present a prediction of a future subjective user state associated with a user 5-20* resulting from a future objective occurrence. In the same or different implementations, the prediction presentation module 5-236 may also be designed to present a prediction of a future subjective user state associated with a user 5-20* resulting from a past objective occurrence. In some implementations, the presentation module 5-108 may include a past presentation module 5-238 that is designed to present a past subjective user state associated with a user 5-20* in connection with a past objective occurrence.
In some implementations, the presentation module 5-108 may include a recommendation module 5-240 that is configured to present a recommendation for a future action based, at least in part, on the results of a correlation of the subjective user state data 5-60 with the objective occurrence data 5-70* performed by the correlation module 5-106. In certain implementations, the recommendation module 5-240 may further include a justification module 5-242 for presenting a justification for the recommendation presented by the recommendation module 5-240. In some implementations, the presentation module 5-108 may include a strength of correlation presentation module 5-244 for presenting an indication of a strength of correlation between subjective user state data 5-60 and objective occurrence data 5-70*.
As will be further described herein, in some embodiments, the presentation module 5-108 may be prompted to present the one or more results of a correlation operation performed by the correlation module 5-106 in response to a reporting of one or more events, objective occurrences, and/or subjective user states.
As briefly described earlier, in various embodiments, the computing device 5-10 may include a network interface 5-120 that may facilitate in communicating with a remotely located user 5-20* and/or one or more third parties. For example, in embodiments whereby the computing device 5-10 is a server, the computing device 5-10 may include a network interface 5-120 that may be configured to receive from a user 5-20* subjective user state data 5-60. In some embodiments, objective occurrence data 5-70a, 5-70b, or 5-70c may also be received through the network interface 5-120. Examples of a network interface 5-120 includes, for example, a network interface card (NIC).
The computing device 5-10, in various embodiments, may also include a memory 5-140 for storing various data. For example, in some embodiments, memory 5-140 may be employed in order to store subjective user state data 5-60 of one or more users 5-20* including data that may indicate one or more past subjective user states of one or more users 5-20* and objective occurrence data 5-70* including data that may indicate one or more past objective occurrences. In some embodiments, memory 5-140 may store historical data 5-72 such as historical medical data of one or more users 5-20* (e.g., genetic, metoblome, proteome information), population trends, historical sequential patterns derived from general population, and so forth.
In various embodiments, the computing device 5-10 may include a user interface 5-122 to communicate directly with a user 5-20b. For example, in embodiments in which the computing device 5-10 is a local device, the user interface 5-122 may be configured to directly receive from the user 5-20b subjective user state data 5-60. The user interface 5-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a key pad, a mouse, an audio system, an imaging system including a digital or video camera, and/or other user interface devices.
e illustrates particular implementations of the one or more applications 5-126 of
The functional roles of the various components, modules, and sub-modules of the computing device 5-10 presented thus far will be described in greater detail with respect to the processes and operations to be described herein. Note that the subjective user state data 5-60 may be in a variety of forms including, for example, text messages (e.g., blog entries, microblog entries, instant messages, email messages, and so forth), audio messages, and/or image files (e.g., an image capturing user's facial expression or user gestures).
In
Further, in
In any event, after a start operation, the operational flow 5-300 may move to a subjective user state data acquisition operation 5-302 for acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user. For instance, the subjective user state data acquisition module 5-102 of the computing device 5-10 of
Operational flow 5-300 may also include an objective occurrence data acquisition operation 5-304 for acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence. For instance, the objective occurrence data acquisition module 5-104 of the computing device 5-10 acquiring, via the network interface 5-120 or via the user interface 5-122, objective occurrence data 5-70* including data indicating incidence of at least one objective occurrence (e.g., ingestion of a food, medicine, or nutraceutical by the first user 5-20a) and data indicating incidence of at least a second objective occurrence (e.g., ingestion of a food, medicine, or nutraceutical by the second user 5-20b).
In various implementations, and as will be further described herein, the first objective occurrence and the second objective occurrence may be related to the same event (e.g., both the first and the second objective occurrence relating to the same “cloudy weather” in Seattle on Mar. 3, 2010), related to the same types of events (e.g., the first objective occurrence relating to “cloudy weather” in Seattle on Mar. 3, 2010 while the second objective occurrence relating to “cloudy weather” in Los Angeles on Feb. 20, 2010), or related to different types of events (e.g., the first objective occurrence relating to “cloudy” weather” in Seattle on Mar. 3, 2010 while the second objective occurrence relating to “sunny weather” in Los Angeles on Feb. 20, 2010).
Again, note that “*” represents a wildcard. Thus, in the above, objective occurrence data 5-70* may represent objective occurrence data 5-70a, objective occurrence data 5-70b, and/or objective occurrence data 5-70c. As those skilled in the art will recognize, the subjective user state data acquisition operation 5-302 does not have to be performed prior to the objective occurrence data acquisition operation 5-304 and may be performed subsequent to the performance of the objective occurrence data acquisition operation 5-304 or may be performed concurrently with the objective occurrence data acquisition operation 5-304.
Finally, operational flow 5-300 may further include a correlation operation 5-306 for correlating the subjective user state data with the objective occurrence data. For instance, the correlation module 5-106 of the computing device 5-10 correlating (e.g., linking or determining a relationship) the subjective user state data 5-60 with the objective occurrence data 5-70*.
In various implementations, the subjective user state data acquisition operation 5-302 may include one or more additional operations as illustrated in
The reception operation 5-402 may, in turn, further include one or more additional operations. For example, in some implementations, the reception operation 5-402 may include an operation 5-404 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via a user interface as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-406 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via a network interface as depicted in
The subjective user state data 5-60 including the data indicating incidence of at a least first subjective user state 5-60a and the data indicating incidence of at least a second subjective user state 5-60b may be received in various forms. For example, in some implementations, the reception operation 5-402 may include an operation 5-408 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via one or more electronic messages as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-410 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via one or more blog entries as depicted in
In some implementations, operation 5-402 may include an operation 5-412 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via one or more status reports as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-414 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via one or more text entries as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-416 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via one or more audio entries as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-418 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state via one or more image entries as depicted in
The subjective user state data 5-60 may be obtained from various alternative and/or complementary sources. For example, in some implementations, the reception operation 5-402 may include an operation 5-420 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state from one, or both, the first user and the second user as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-422 for receiving one, or both, of the data indicating incidence of at least a first subjective user state and the data indicating incidence of at least a second subjective user state from one or more third party sources as depicted in
In some implementations, the reception operation 5-402 may include an operation 5-424 for receiving data indicating a selection made by the first user, the selection indicating the first subjective user state selected from a plurality of indicated alternative subjective user states as depicted in
In some implementations, operation 5-424 may further include an operation 5-426 for receiving data indicating a selection made by the first user, the selection indicating the first subjective user state selected from a plurality of indicated alternative contrasting subjective user states as depicted in
In some implementations, operation 5-424 may further include an operation 5-428 for receiving data indicating a selection made by the second user, the selection indicating the second subjective user state selected from a plurality of indicated alternative subjective user states as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 of
In various alternative implementations, operation 5-430 may further include an operation 5-432 for acquiring data indicating incidence of a second subjective mental state associated with the second user as depicted in
Operation 5-432, in turn, may further include one or more additional operations in some implementations. For example, in some implementations, operation 5-432 may include an operation 5-434 for acquiring data indicating incidence of a second subjective mental state associated with the second user, the second subjective mental state of the second user being a subjective mental state that is similar or same as the first subjective mental state of the first user as depicted in
In some implementations, operation 5-432 may include an operation 5-436 for acquiring data indicating incidence of a second subjective mental state associated with the second user, the second subjective mental state of the second user being a contrasting subjective mental state from the first subjective mental state of the first user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 of
In various implementations, operation 5-438 may further include one or more additional operations. For example, in some implementations, operation 5-438 may include an operation 5-440 for acquiring data indicating incidence of a second subjective physical state associated with the second user as depicted in
In some implementations, operation 5-440 may further include an operation 5-442 for acquiring data indicating incidence of a second subjective physical state associated with the second user, the second subjective physical state of the second user being a subjective physical state that is similar or same as the first subjective physical state of the first user as depicted in
In some implementations, operation 5-440 may include an operation 5-444 for acquiring data indicating incidence of a second subjective physical state associated with the second user, the second subjective physical state of the second user being a contrasting subjective physical state from the first subjective physical state of the first user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 of
In various implementations, operation 5-446 may further include one or more additional operations. For example, in some implementations, operation 5-446 may include an operation 5-448 for acquiring data indicating incidence of a second subjective overall state associated with the second user as depicted in
In some implementations, operation 5-448 may further include an operation 5-450 for acquiring data indicating incidence of a second subjective overall state associated with the second user, the second subjective overall state of the second user being a subjective overall state that is similar or same as the first subjective overall state of the first user as depicted in
In some implementations, operation 5-448 may include an operation 5-452 for acquiring data indicating incidence of a second subjective overall state associated with the second user, the second subjective overall state of the second user being a contrasting subjective overall state from the first subjective overall state of the first user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 of
In various implementations, operation 5-454 may further include one or more additional operations. For example, in some implementations, operation 5-454 may include an operation 5-456 for acquiring data indicating a second subjective user state associated with the second user that is at least approximately equivalent in meaning to the first subjective user state associated with the first user as depicted in
In some implementations, operation 5-454 may include an operation 5-458 for acquiring data indicating a second subjective user state associated with the second user that is same as the first subjective user state associated with the first user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 of
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-462 for acquiring a time stamp associated with the at least first subjective user state associated with the first user as depicted in
Operation 5-462, in turn, may further include an operation 5-464 for acquiring another time stamp associated with the at least second subjective user state associated with the second user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-466 for acquiring an indication of a time interval associated with the at least first subjective user state associated with the first user as depicted in
Operation 5-466, in turn, may further include an operation 5-468 for acquiring another indication of a time interval associated with the at least second subjective user state associated with the second user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-470 for acquiring an indication of a temporal relationship between the at least first subjective user state and the at least first objective occurrence as depicted in
Operation 5-470, in turn, may further include an operation 5-472 for acquiring an indication of a temporal relationship between the at least second subjective user state and the at least second objective occurrence as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-474 for soliciting from the first user the data indicating incidence of at least a first subjective user state associated with the first user as depicted in
Operation 5-474, in turn, may further include an operation 5-476 for transmitting or indicating to the first user a request for the data indicating incidence of at least a first subjective user state associated with the first user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-478 for acquiring data indicating incidence of at least a third subjective user state associated with a third user as depicted in
Operation 5-478, in turn, may further include an operation 5-480 for acquiring data indicating incidence of at least a fourth subjective user state associated with a fourth user as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-482 for acquiring the subjective user state data at a server as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-484 for acquiring the subjective user state data at a handheld device as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-486 for acquiring the subjective user state data at a peer-to-peer network component device as depicted in
In some implementations, the subjective user state data acquisition operation 5-302 may include an operation 5-488 for acquiring the subjective user state data via a Web 2.0 construct as depicted in
Referring back to
In various implementations, the reception operation 5-502 may include one or more additional operations. For example, in some implementations the reception operation 5-502 may include an operation 5-504 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence via user interface as depicted in
In some implementations, the reception operation 5-502 may include an operation 5-506 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence from at least one of a wireless network or a wired network as depicted in
In some implementations, the reception operation 5-502 may include an operation 5-508 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence via one or more blog entries as depicted in
In some implementations, the reception operation 5-502 may include an operation 5-510 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence via one or more status reports as depicted in
In some implementations, the reception operation 5-502 may include an operation 5-512 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence via a Web 2.0 construct as depicted in
In some implementations, the reception operation 5-502 may include an operation 5-514 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence from one or more sensors as depicted in
In various implementations, the reception operation 5-502 may include an operation 5-516 for receiving the data indicating incidence of at least a first objective occurrence from the first user as depicted in
In some implementations, operation 5-516 may further include an operation 5-518 for receiving the data indicating incidence of at least a second objective occurrence from the second user as depicted in
In some implementations, the reception operation 5-502 may include an operation 5-520 for receiving one, or both, of the data indicating incidence of at least a first objective occurrence and the data indicating incidence of at least a second objective occurrence from one or more third party sources as depicted in
In various implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-522, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-522 may further include an operation 5-524 for acquiring data indicating a second objective occurrence that is at least proximately equivalent in meaning to the first objective occurrence as depicted in
In some implementations, operation 5-522 may include an operation 5-526 for acquiring data indicating a second objective occurrence that is same as the first objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
In some implementations, the objective occurrence data acquisition operation 5-304 may include an operation 5-530 for acquiring data indicating a second objective occurrence that references the first objective occurrence as depicted in
In various alternative implementations, operation 5-530 may further include one or more additional operations. For example, in some implementations, operation 5-530 may include an operation 5-532 for acquiring data indicating a second objective occurrence that is a comparison to the first objective occurrence as depicted in
In some implementations, operation 5-530 may include an operation 5-534 for acquiring data indicating a second objective occurrence that is a modification of the first objective occurrence as depicted in
In some implementations, operation 5-530 may include an operation 5-536 for acquiring data indicating a second objective occurrence that is an extension of the first objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 may include an operation 5-538 for acquiring a time stamp associated with the at least first objective occurrence as depicted in
Operation 5-538, in various implementations, may further include an operation 5-540 for acquiring another time stamp associated with the at least second objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-542, in various implementations, may further include an operation 5-544 for acquiring another indication of a time interval associated with the at least second objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 may include an operation 5-546 for acquiring data indicating one or more attributes associated with the first objective occurrence as depicted in
Operation 5-546, in turn, may further include an operation 5-548 for acquiring data indicating one or more attributes associated with the second objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-550, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-550 may include an operation 5-551 for acquiring data indicating at least an ingestion by the second user of a medicine as depicted in
In some implementations, operation 5-551 may further include an operation 5-552 for acquiring data indicating ingestions of same or similar types of medicine by the first user and the second user as depicted in
Operation 5-552, in turn, may further include an operation 5-553 for acquiring data indicating ingestions of same or similar quantities of the same or similar type of medicine by the first user and the second user as depicted in
In some implementations, operation 5-550 may include an operation 5-554 for acquiring data indicating at least an ingestion by the second user of another medicine, the another medicine ingested by the second user being a different type of medicine from the medicine ingested by the first user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-555, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-555 may include an operation 5-556 for acquiring data indicating at least an ingestion by the second user of a food item as depicted in
Operation 5-556, in turn, may further include an operation 5-557 for acquiring data indicating ingestions of same or similar types of food items by the first user and the second user as depicted in
In some implementations, operation 5-557 may include an operation 5-558 for acquiring data indicating ingestions of same or similar quantities of the same or similar types of food items by the first user and the second user as depicted in
In some implementations, operation 5-555 may include an operation 5-559 for acquiring data indicating at least an ingestion by the second user of another food item, the another food item ingested by the second user being a different food item from the food item ingested by the first user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-560, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-560 may include an operation 5-561 for acquiring data indicating at least an ingestion by the second user of a nutraceutical as depicted in
Operation 5-561, in turn, may further include an operation 5-562 for acquiring data indicating ingestions of same or similar type of nutraceutical by the first user and the second user as depicted in
In some implementations, operation 5-562 may further include an operation 5-563 for acquiring data indicating ingestions of same or similar quantity of the same or similar type of nutraceutical by the first user and the second user as depicted in
In some implementations, operation 5-560 may include an operation 5-564 for acquiring data indicating at least an ingestion by the second user of another nutraceutical, the another nutraceutical ingested by the second user being a different type of nutraceutical from the nutraceutical ingested by the first user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-565, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-565 may include an operation 5-566 for acquiring data indicating at least an exercise routine executed by the second user as depicted in
Operation 5-566, in turn, may further include an operation 5-567 for acquiring data indicating same or similar types of exercise routines executed by the first user and the second user as depicted in
In some implementations, operation 5-567 may further include an operation 5-568 for acquiring data indicating same or similar quantities of the same or similar types of exercise routines executed by the first user and the second user as depicted in
In some implementations, operation 5-565 may include an operation 5-569 for acquiring data indicating at least another exercise routine executed by the second user, the another exercise routine executed by the second user being a different type of exercise routine from the exercise routine executed by the first user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-570, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-570 may include an operation 5-571 for acquiring data indicating at least a social activity executed by the second user as depicted in
In some implementations, operation 5-571 may include an operation 5-572 for acquiring data indicating same or similar types of social activities executed by the first user and the second user as depicted in
In some implementations, operation 5-571 may include an operation 5-573 for acquiring data indicating different types of social activities executed by the first user and the second user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-574, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-574 may include an operation 5-575 for acquiring data indicating at least another activity executed by the third party or by another third party as depicted in
In some implementations, operation 5-575 may include an operation 5-576 for acquiring data indicating same or similar types of activities executed by the third party or by the another third party as depicted in
In some implementations, operation 5-575 may include an operation 5-577 for acquiring data indicating different types of activities executed by the third party or by the another third party as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-578, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-578 may include an operation 5-579 for acquiring data indicating at least a physical characteristic associated with the second user as depicted in
In some implementations, operation 5-579 may include an operation 5-580 for acquiring data indicating same or similar physical characteristics associated with the first user and the second user as depicted in
In some implementations, operation 5-579 may include an operation 5-581 for acquiring data indicating different physical characteristics associated with the first user and the second user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-582, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-582 may include an operation 5-583 for acquiring data indicating occurrence of at least another external event as depicted in
In some implementations, operation 5-583 may include an operation 5-584 for acquiring data indicating occurrences of same or similar external events as depicted in
In some implementations, operation 5-583 may include an operation 5-585 for acquiring data indicating occurrences of different external events as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
Operation 5-586, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 5-586 may include an operation 5-587 for acquiring data indicating at least a location associated with the second user as depicted in
In some implementations, operation 5-587 may include an operation 5-588 for acquiring data indicating the location associated with the first user that is same as the location associated with the second user as depicted in
In some implementations, operation 5-587 may include an operation 5-589 for acquiring data indicating the location associated with the first user that is different from the location associated with the second user as depicted in
In some implementations, the objective occurrence data acquisition operation 5-304 of
In some implementations, operation 5-590 may further include an operation 5-591 for acquiring data indicating incidence of at least a fourth objective occurrence as depicted in
In various implementations, the correlation operation 5-306 of
In various alternative implementations, operation 5-602 may include one or more additional operations. For example, in some implementations, operation 5-602 may include an operation 5-604 for determining the at least first sequential pattern based, at least in part, on a determination of whether the incidence of the at least first subjective user state occurred within a predefined time increment from the incidence of the at least first objective occurrence as depicted in
In some implementations, operation 5-602 may include an operation 5-606 for determining the first sequential pattern based, at least in part, on a determination of whether the incidence of the at least first subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least first objective occurrence as depicted in
In some implementations, operation 5-602 may include an operation 5-608 for correlating the subjective user state data with the objective occurrence data based, at least in part, on determining a second sequential pattern associated with the incidence of the at least second subjective user state and the incidence of the at least second objective occurrence as depicted in
In various alternative implementations, operation 5-608 may include one or more additional operations. For example, in some implementations, operation 5-608 may include an operation 5-610 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a comparison of the first sequential pattern to the second sequential pattern as depicted in
In various implementations, operation 5-610 may further include an operation 5-612 for correlating the subjective user state data with the objective occurrence data based, at least in part, on determining whether the first sequential pattern at least substantially matches with the second sequential pattern as depicted in
In some implementations, operation 5-612 may include an operation 5-614 for determining whether the first subjective user state is equivalent to the second subjective user state as depicted in
In some implementations, operation 5-612 may include an operation 5-616 for determining whether the first subjective user state is at least proximately equivalent to the second subjective user state as depicted in
In various implementations, operation 5-612 of
In some implementations, operation 5-612 may include an operation 5-620 for determining whether the first objective occurrence is equivalent to the second objective occurrence as depicted in
In some implementations, operation 5-612 may include an operation 5-622 for determining whether the first objective occurrence is at least proximately equivalent to the second objective occurrence as depicted in
In some implementations, operation 5-612 may include an operation 5-624 for determining whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence as depicted in
In various implementations, operation 5-610 of
In some implementations, operation 5-626 may include an operation 5-628 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a comparison between the first sequential pattern, the second sequential pattern, the third sequential pattern, and a fourth sequential pattern associated with incidence of at least a fourth subjective user state associated with a fourth user and incidence of at least a fourth objective occurrence as depicted in
In various implementations, operation 5-608 of
In some implementations, operation 5-630 may further include an operation 5-632 for determining the second sequential pattern based, at least in part, on determining whether the incidence of the at least second subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least second objective occurrence as depicted in
In various implementations, the correlation operation 5-306 of
In various implementations, operation 5-634 may include one or more additional operations. For example, in some implementations, operation 5-634 may include an operation 5-636 for correlating the subjective user state data with the objective occurrence data based, at least in part, on historical data indicative of a link between a subjective user state type and an objective occurrence type as depicted in
Operation 5-636, in turn, may further include an operation 5-638 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a historical sequential pattern as depicted in
In some implementations, operation 5-634 may further include an operation 5-640 for correlating the subjective user state data with the objective occurrence data based, at least in part, on historical medical data as depicted in
In some implementations, the correlation operation 5-306 of
In some implementations, the correlation operation 5-306 may include an operation 5-644 for correlating the subjective user state data with the objective occurrence data at a server as depicted in
In some implementations, the correlation operation 5-306 may include an operation 5-646 for correlating the subjective user state data with the objective occurrence data at a handheld device as depicted in
In some implementations, the correlation operation 5-306 may include an operation 5-648 for correlating the subjective user state data with the objective occurrence data at a peer-to-peer network component device as depicted in
Referring to
In addition, operational flow 5-700 includes a presentation operation 5-708 for presenting one or more results of the correlating as depicted in
In various implementations, the presentation operation 5-708 may include one or more additional operations as depicted in
In various implementations, the presentation operation 5-708 may include an operation 5-804 for transmitting the one or more results via a network interface. For instance, the network interface transmission module 5-232 of the computing device 5-10 transmitting the one or more results of the correlation operation performed by the correlation module 5-106 via a network interface 5-120.
In some implementations, operation 5-804 may further include an operation 5-806 for transmitting the one or more results to one, or both, the first user and the second user. For example, the network interface transmission module 5-232 of the computing device 5-10 transmitting the one or more results of the correlation operation performed by the correlation module 5-106 to one, or both, the first user 5-20a and the second user 5-20b.
In some implementations, operation 5-804 may further include an operation 5-808 for transmitting the one or more results to one or more third parties. For example, the network interface transmission module 5-232 of the computing device 5-10 transmitting the one or more results of the correlation operation performed by the correlation module 5-106 to one or more third parties (e.g., third party sources 5-50).
In some implementations, the presentation operation 5-708 may include an operation 5-810 for presenting a prediction of a future subjective user state resulting from a future objective occurrence as depicted in
In some implementations, the presentation operation 5-708 may include an operation 5-812 for presenting a prediction of a future subjective user state resulting from a past objective occurrence as depicted in
In some implementations, the presentation operation 5-708 may include an operation 5-814 for presenting a past subjective user state in connection with a past objective occurrence as depicted in
In various implementations, the presentation operation 5-708 may include an operation 5-816 for presenting a recommendation for a future action as depicted in
In some implementations, operation 5-816 may include an operation 5-818 for presenting a justification for the recommendation as depicted in
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post their thoughts and opinions on various topics, latest news, current events, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social network status reports in which a user may report or post for others to view the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted through microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, event, happening, or any other aspects associated with or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, performance of the stock market (which the microblogger may have an interest in), and so forth. In some cases, objective occurrences may not be at least directly associated with a microblogger. Examples of such objective occurrences include, for example, external events that may not be directly related to the microblogger such as the local weather, activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., “I am feeling happy”), the subjective physical state of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and the subjective overall state of the microblogger (e.g., “I'm good” or “I'm well”). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). Although microblogs are being used to provide a wealth of personal information, they have thus far been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided to, among other things, solicit and acquire subjective user state data including soliciting and acquiring data indicating incidence of at least one subjective user state associated with a user, the solicitation being indirectly or directly prompted based, at least in part on a hypothesis that links one or more subjective user states with one or more objective occurrences and in response to an incidence of at least one objective occurrence.
In various embodiments, a hypothesis may be defined by a sequential pattern that indicates or suggests a temporal or specific time sequencing relationship between one or more subjective user states and one or more objective occurrences. In some cases, the one or more subjective user states associated with the hypothesis may be based on past incidences of one or more subjective user states associated with a user, with multiple users, with a sub-group of the general population, or with the general population. Similarly, the one or more objective occurrences associated with the hypothesis may be based on past incidences of objective occurrences.
In some cases, a hypothesis may be formulated when it is determined that a particular pattern of events (e.g., incidences of one or more subjective user states and one or more objective occurrences) occurs repeatedly with respect to a particular user, a group of users, a subset of the general population, or the general population. For example, a hypothesis may be formulated that suggests or predicts that a person will likely have an upset stomach after eating a hot fudge sundae when it is determined that multiple users had reported having an upset stomach after eating a hot fudge sundae. In other cases, a hypothesis may be formulated based, at least in part, on a single pattern of events and historical data related to such events. For instance, a hypothesis may be formulated when a person reports that he had a stomach ache after eating a hot fudge sundae, and historical data suggests that a segment of the population may not be able to digest certain nutrients included in a hot fudge sundae (e.g., the hypothesis would suggest or indicate that the person may get stomach aches whenever the person eats a hot fudge sundae).
The subjective user state data to be acquired by the methods, systems, and the computer program products may include data indicating the incidence of at least one subjective user state associated with a user. Such subjective user state data together with objective occurrence data including data indicating incidence of at least one objective occurrence may then be correlated. The results of the correlation may be presented in a variety of different forms and may, in some cases, confirm the veracity of the hypothesis. The results of the correlation, in various embodiments, may be presented to the user, to other users, or to one or more third parties as will further described herein.
In some embodiments, the correlation of the acquired subjective user state data with the objective occurrence data may facilitate in determining a causal relationship between at least one objective occurrence (e.g., cause) and at least one subjective user state (e.g., result). For example, determining whenever a user eats a banana the user always or sometimes feels good. Note that an objective occurrence does not need to occur prior to a corresponding subjective user state but instead, may occur subsequent or at least partially concurrently with the incidence of the subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence) or a person may become gloomy while (e.g., concurrently) it is raining
As briefly described earlier, the subjective user state data to be acquired may include data that indicate the incidence or occurrence of at least one subjective user state associated with a user. In situations where the subjective user state data to be acquired indicates multiple subjective user states, each of the subjective user states indicated by the acquired subjective user state data may be solicited, while in other embodiments, only one or a subset of the subjective user states indicated by the acquired subjective user state data may be solicited. A “subjective user state” is in reference to any subjective user state or status associated with a user (e.g., a blogger or microblogger) at any moment or interval in time that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of the user (e.g., user is feeling sad), the subjective physical state (e.g., physical characteristic) of the user that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), and the subjective overall state of the user (e.g., user is “good”).
Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be easily categorized as a subjective mental state or as a subjective physical state. Examples of subjective overall states include, for example, the user “being good,” “bad,” “exhausted,” “lack of rest,” “wellness,” and so forth.
In contrast, “objective occurrence data,” as will be described herein, may include data that indicate incidence of at least one objective occurrence. In some embodiments, an objective occurrence may be any physical characteristic, event, happenings, or any other aspect that may be associated with, is of interest to, or may somehow impact a user that can be objectively reported by at least a third party or a sensor device. Note, however, that an objective occurrence does not have to be actually reported by a sensor device or by a third party, but instead, may be reported by the user himself or herself (e.g., via microblog entries). Examples of objectively reported occurrences that could be indicated by the objective occurrence data include, for example, a user's food, medicine, or nutraceutical intake, the user's location at any given point in time, a user's exercise routine, a user's physiological characteristics such as blood pressure, social or professional activities, the weather at a user's location, activities associated with third parties, occurrence of external events such as the performance of the stock market, and so forth.
The term “correlating” as will be used herein may be in reference to a determination of one or more relationships between at least two variables. Alternatively, the term “correlating” may merely be in reference to the linking or associating of the at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that indicates at least one subjective user state and the second variable is objective occurrence data that indicates at least one objective occurrence. In embodiments where the subjective user state indicates multiple subjective user states, each of the subjective user states indicated by the subjective user state data may represent different incidences of the same or similar type of subjective user state (e.g., happiness). Alternatively, the subjective user state data may indicate multiple subjective user states that represent different incidences of different types of subjective user states (e.g., happiness and sadness).
Similarly, in some embodiments where the objective occurrence data may indicate multiple objective occurrences, each of the objective occurrences indicated by the objective occurrence data may represent different incidences of the same or similar type of objective occurrence (e.g., exercising). In alternative embodiments, however, each of the objective occurrences indicated by the objective occurrence data may represent different incidences of different types of objective occurrence (e.g., user exercising and user resting).
Various techniques may be employed for correlating subjective user state data with objective occurrence data in various alternative embodiments. For example, in some embodiments, the correlation of the objective occurrence data with the subjective user state data may be accomplished by determining a sequential pattern associated with at least one subjective user state indicated by the subjective user state data and at least one objective occurrence indicated by the objective occurrence data. In other embodiments, the correlation of the objective occurrence data with the subjective user state data may involve determining multiple sequential patterns associated with multiple subjective user states and multiple objective occurrences.
A sequential pattern, as will be described herein, may define time and/or temporal relationships between two or more events (e.g., one or more subjective user states and one or more objective occurrences). In order to determine a sequential pattern, subjective user state data including data indicating incidence of at least one subjective user state associated with a user may be solicited, the solicitation being prompted based, at least in part, on a hypothesis linking one or more subjective user states with one or more objective occurrences and in response, at least in part, to an incidence of at least one objective occurrence.
For example, suppose a hypothesis suggests that a user or a group of users tend to be depressed whenever the weather is bad (e.g., cloudy or overcast weather), the hypothesis being formed, for example, based at least in part on reported past events (e.g., reported past subjective user states of a user or a group of users and reported past objective occurrences). Then upon the weather turning bad, and based at least in part on the hypothesis, subjective user state data including data indicating incidence of at least one subjective user state associated with a user may be solicited from, for example, the user (or from other sources such as third party sources). If, after soliciting for the subjective user state data, data indeed is acquired that indicates that the user felt depressed when the weather turned bad, this may confirm the veracity of the hypothesis. On the other hand, if the data that is acquired after the solicitation indicates that the user was happy when the weather turned bad, this may indicate that there is a weaker correlation or link between depression and bad weather.
As briefly described above, a hypothesis may be represented by a sequential pattern that may merely indicate or represent the temporal relationship or relationships between at least one subjective user state and at least one objective occurrence (e.g., whether the incidence or occurrence of at least one subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least one objective occurrence). In alternative implementations, and as will be further described herein, a sequential pattern may indicate a more specific time relationship between the incidences of one or more subjective user states and the incidences of one or more objective occurrences. For example, a sequential pattern may represent the specific pattern of events (e.g., one or more objective occurrences and one or more subjective user states) that occurs along a timeline.
The following illustrative example is provided to describe how a sequential pattern associated with at least one subjective user state and at least one objective occurrence may be determined based, at least in part, on the temporal relationship between the incidence of at least one subjective user state and the incidence of at least one objective occurrence in accordance with some embodiments. For these embodiments, the determination of a sequential pattern may initially involve determining whether the incidence of the at least one subjective user state occurred within some predefined time increment from the incidence of the one objective occurrence. That is, it may be possible to infer that those subjective user states that did not occur within a certain time period from the incidence of an objective occurrence are not related or are unlikely related to the incidence of that objective occurrence.
For example, suppose a user during the course of a day eats a banana and also has a stomach ache sometime during the course of the day. If the consumption of the banana occurred in the early morning hours but the stomach ache did not occur until late that night, then the stomach ache may be unrelated to the consumption of the banana and may be disregarded. On the other hand, if the stomach ache had occurred within some predefined time increment, such as within 2 hours of consumption of the banana, then it may be concluded that there is a link between the stomach ache and the consumption of the banana. If so, a temporal relationship between the consumption of the banana and the occurrence of the stomach ache may be established. Such a temporal relationship may be represented by a sequential pattern. Such a sequential pattern may simply indicate that the stomach ache (e.g., a subjective user state) occurred after (rather than before or concurrently) the consumption of banana (e.g., an objective occurrence).
Other factors may also be referenced and examined in order to determine a sequential pattern and whether there is a relationship (e.g., causal relationship) between an incidence of an objective occurrence and an incidence of a subjective user state. These factors may include, for example, historical data (e.g., historical medical data such as genetic data or past history of the user or historical data related to the general population regarding, for example, stomach aches and bananas) as briefly described above.
In some implementations, a sequential pattern may be determined for multiple subjective user states and multiple objective occurrences. Such a sequential pattern may particularly map the exact temporal or time sequencing of the various events (e.g., subjective user states and/or objective occurrences). The determined sequential pattern may then be used to provide useful information to the user and/or third parties.
The following is another illustrative example of how subjective user state data may be correlated with objective occurrence data by determining multiple sequential patterns and comparing the sequential patterns with each other. Suppose, for example, a user such as a microblogger reports that the user ate a banana on a Monday. The consumption of the banana, in this example, is a reported incidence of a first objective occurrence associated with the user. The user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported incidence of a first subjective user state. Thus, the reported incidence of the first objective occurrence (e.g., eating the banana) and the reported incidence of the first subjective user state (user felt very happy) on Monday may be represented by a first sequential pattern.
On Tuesday, the user reports that the user ate another banana (e.g., a second objective occurrence associated with the user). The user then reports that 20 minutes after eating the second banana, the user felt somewhat happy (e.g., a second subjective user state). Thus, the reported incidence of the second objective occurrence (e.g., eating the second banana) and the reported incidence of the second subjective user state (user felt somewhat happy) on Tuesday may be represented by a second sequential pattern. Under this scenario, the first sequential pattern may represent a hypothesis that links feeling happy or very happy (e.g., a subjective user state) with eating a banana (e.g., an objective occurrence). Alternatively, the first sequential pattern may merely represent historical data (e.g., historical sequential pattern). Note that in this example, the occurrences of the first subjective user state and the second subjective user state may be indicated by subjective user state data while the occurrences of the first objective occurrence and the second objective occurrence may be indicated by objective occurrence data.
In a slight variation of the above example, suppose the user had forgotten to report for Tuesday the feeling of being somewhat happy but does report consuming the second banana on Tuesday. This may result in the user being asked, based at least in part on the reporting of the user consuming the banana on Tuesday, and based at least in part on the hypothesis, as to how the user felt on Tuesday or how the user felt after eating the banana on Tuesday. Upon the user indicating feeling somewhat happy on Tuesday, a second sequential pattern may be determined.
In any event, by comparing the first sequential pattern with the second sequential pattern, the subjective user state data may be correlated with the objective occurrence data. Such a comparison may confirm the veracity of the hypothesis. In some implementations, the comparison of the first sequential pattern with the second sequential pattern may involve trying to match the first sequential pattern with the second sequential pattern by examining certain attributes and/or metrics. For example, comparing the first subjective user state (e.g., user felt very happy) of the first sequential pattern with the second subjective user state (e.g., user felt somewhat happy) of the second sequential pattern to see if they at least substantially match or are contrasting (e.g., being very happy in contrast to being slightly happy or being happy in contrast to being sad). Similarly, comparing the first objective occurrence (e.g., eating a banana) of the first sequential pattern may be compared to the second objective occurrence (e.g., eating of another banana) of the second sequential pattern to determine whether they at least substantially match or are contrasting.
A comparison may also be made to determine if the extent of time difference (e.g., 15 minutes) between the first subjective user state (e.g., user being very happy) and the first objective occurrence (e.g., user eating a banana) matches or are at least similar to the extent of time difference (e.g., 20 minutes) between the second subjective user state (e.g., user being somewhat happy) and the second objective occurrence (e.g., user eating another banana). These comparisons may be made in order to determine whether the first sequential pattern matches the second sequential pattern. A match or substantial match would suggest, for example, that a subjective user state (e.g., happiness) is linked to a particular objective occurrence (e.g., consumption of banana). In other words, confirming the hypothesis that happiness may be linked to the consumption of bananas.
As briefly described above, the comparison of the first sequential pattern with the second sequential pattern may include a determination as to whether, for example, the respective subjective user states and the respective objective occurrences of the sequential patterns are contrasting subjective user states and/or contrasting objective occurrences. For example, suppose in the above example the user had reported that the user had eaten a whole banana on Monday and felt very energetic (e.g., first subjective user state) after eating the whole banana (e.g., first objective occurrence). Suppose that the user also reported that on Tuesday he ate a half a banana instead of a whole banana and only felt slightly energetic (e.g., second subjective user state) after eating the half banana (e.g., second objective occurrence). In this scenario, the first sequential pattern (e.g., feeling very energetic after eating a whole banana) may be compared to the second sequential pattern (e.g., feeling slightly energetic after eating only a half of a banana) to at least determine whether the first subjective user state (e.g., being very energetic) and the second subjective user state (e.g., being slightly energetic) are contrasting subjective user states. Another determination may also be made during the comparison to determine whether the first objective occurrence (eating a whole banana) is in contrast with the second objective occurrence (e.g., eating a half of a banana).
In doing so, an inference may be made that eating a whole banana instead of eating only a half of a banana makes the user happier or eating more banana makes the user happier. Thus, the word “contrasting” as used here with respect to subjective user states refers to subjective user states that are the same type of subjective user states (e.g., the subjective user states being variations of a particular type of subjective user states such as variations of subjective mental states). Thus, for example, the first subjective user state and the second subjective user state in the previous illustrative example are merely variations of subjective mental states (e.g., happiness). Similarly, the use of the word “contrasting” as used here with respect to objective occurrences refers to objective states that are the same type of objective occurrences (e.g., consumption of food such as banana).
As those skilled in the art will recognize, a stronger correlation between the subjective user state data and the objective occurrence data could be obtained if a greater number of sequential patterns (e.g., if there was a third sequential pattern, a fourth sequential pattern, and so forth, that indicated that the user became happy or happier whenever the user ate bananas) are used as a basis for the correlation. Note that for ease of explanation and illustration, each of the exemplary sequential patterns to be described herein will be depicted as a sequential pattern of an incidence of a single subjective user state and an incidence of a single objective occurrence. However, those skilled in the art will recognize that a sequential pattern, as will be described herein, may also be associated with incidences or occurrences of multiple objective occurrences and/or multiple subjective user states. For example, suppose the user had reported that after eating a banana, he had gulped down a can of soda. The user then reported that he became happy but had an upset stomach. In this example, the sequential pattern associated with this scenario will be associated with two objective occurrences (e.g., eating a banana and drinking a can of soda) and two subjective user states (e.g., user having an upset stomach and feeling happy).
In some embodiments, and as briefly described earlier, the sequential patterns derived from subjective user state data and objective occurrence data may be based on temporal relationships between objective occurrences and subjective user states. For example, whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence. For instance, a plurality of sequential patterns derived from subjective user state data and objective occurrence data may indicate that a user always has a stomach ache (e.g., subjective user state) after eating a banana (e.g., first objective occurrence).
a and 6-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 6-100 may include at least a computing device 6-10 (see
As previously indicated, in some embodiments, the computing device 6-10 may be a network server in which case the computing device 6-10 may communicate with a user 6-20a via a mobile device 6-30 and through a wireless and/or wired network 6-40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 6-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 6-10.
In alternative embodiments, the computing device 6-10 may be a standalone computing device 6-10 (or simply “standalone device”) that communicates directly with a user 6-20b. For these embodiments, the computing device 6-10 may be any type of handheld device such as a cellular telephone, a PDA, or other types of computing/communication devices such as a laptop computer, a desktop computer, and so forth. In various embodiments, the computing device 6-10 (as well as the mobile device 6-30) may be a peer-to-peer network component device. In some embodiments, the computing device 6-10 may operate via a web 2.0 construct.
In embodiments where the computing device 6-10 is a server, the computing device 6-10 may solicit and acquire the subjective user state data 6-60 indirectly from a user 6-20a via a network interface 6-120 and via mobile device 6-30. In alternative embodiments in which the computing device 6-10 is a local device such as a handheld device (e.g., cellular telephone, personal digital assistant, etc.), the subjective user state data 6-60 may be directly obtained from a user 6-20b via a user interface 6-122. As will be further described, the computing device 6-10 may acquire the objective occurrence data 6-70* from one or more alternative sources.
In various embodiments, and regardless of whether the computing device 6-10 is a server or a standalone device, the computing device 6-10 may have access to at least one hypothesis 6-71. For example, in some situations, a hypothesis 6-71 may have been generated based on reported past events including past incidences of one or more subjective user states (which may be associated with a user 6-20*, a group of users 6-20*, a portion of the general population, or the general population) and past incidences of one or more objective occurrences. Such a hypothesis 6-71, in some instances, may be stored in a memory 6-140 to be easily accessible.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 6-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 6-10 is a standalone device such as a handheld device that may communicate directly with a user 6-20b.
The computing device 6-10, in various implementations, may be configured to solicit subjective user state data 6-60 including soliciting data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a from the user 6-20a via the mobile device 6-30. The solicitation of the data indicating incidence of at least one subjective user state 6-60a may be based, at least in part, on a hypothesis 6-71 and in response, at least in part, to an incidence of at least one objective occurrence. In the case where the computing device 6-10 is a server, the computing device, based at least in part, on the hypothesis 6-71 and in response to the incidence of the at least one objective occurrence, may generate and transmit a solicitation or a request for the data indicating incidence of at least one subjective user state 6-60a to the mobile device 6-30. The mobile device 6-30, in response, may either directly provide the data indicating incidence of at least one subjective user state 6-60a (if it already has such data) or may solicit such data from the user 6-20a in order to pass along such data to the computing device 6-10.
In the case where the computing device 6-10 is a standalone device, the computing device 6-10, may be configured to solicit subjective user state data 6-60 including soliciting data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20b directly from a user 6-20b via a user interface 6-122. After soliciting for the subjective user state data 6-60 including the data indicating incidence of at least one subjective user state 6-60a, the computing device 6-10 (e.g., either in the case where the computing device 6-10 is a server or in the case where the computing device 6-10 is a standalone device) may be further designed to acquire the data indicating incidence of at least one subjective user state 6-60a as well as to acquire other data indicating other incidences of subjective user states associated with a user 6-20* (e.g., data indicating incidence of at least a second subjective user state 6-60b, and so forth) from the user 6-20* via the mobile device 6-30 or via the user interface 6-122.
Examples of subjective user states that may be indicated by the subjective user state data 6-60 include, for example, subjective mental states of a user 6-20* (e.g., user 6-20* is sad or angry), subjective physical states of the user 6-20* (e.g., physical or physiological characteristic of the user 6-20* such as the presence, absence, elevating, or easing of a pain), subjective overall states of the user 6-20* (e.g., user 6-20* is “well”), and/or other subjective user states that only the user 6-20* can typically indicate.
In some implementations, the computing device 6-10 may also be configured to acquire objective occurrence data 6-70* including data indicating incidence of at least one objective occurrence via a network interface 6-120 or via user interface 6-122 (in the case where the computing device 6-10 is a standalone device). In some implementations, the objective occurrence data 6-70* to be acquired may further include additional data such as data indicating incidences of one or more additional objective occurrences (e.g., data indicating occurrence of at least a second objective occurrence). The objective occurrence data 6-70* may be provided by a user 6-20*, by one or more third party sources 6-50 (e.g., one or more third parties), or by one or more sensors 6-35.
For example, in some embodiments, objective occurrence data 6-70a may be acquired from one or more third party sources 6-50. Examples of third party sources 6-50 include, for example, other users, medical entities such as medical or dental clinics and hospitals, content providers, employers, fitness centers, social organizations, and so forth.
In some embodiments, objective occurrence data 6-70b may be acquired from one or more sensors 6-35 that may be designed for sensing or monitoring various aspects associated with the user 6-20a (or user 6-20b). For example, in some implementations, the one or more sensors 6-35 may include a global positioning system (GPS) device for determining the location of the user 6-20a and/or a physical activity sensor for measuring physical activities of the user 6-20a. Examples of a physical activity sensor include, for example, a pedometer for measuring physical activities of the user 6-20a. In certain implementations, the one or more sensors 6-35 may include one or more physiological sensor devices for measuring physiological characteristics of the user 6-20a. Examples of physiological sensor devices include, for example, a blood pressure monitor, a heart rate monitor, a glucometer, and so forth. In some implementations, the one or more sensors 6-35 may include one or more image capturing devices such as a video or digital camera.
In some embodiments, objective occurrence data 6-70c may be acquired from a user 6-20a via the mobile device 6-30 (or from user 6-20b via user interface 6-122). For these embodiments, the objective occurrence data 6-70c may be in the form of blog entries (e.g., microblog entries), status reports, or other types of electronic entries (e.g., diary or calendar entries) or messages. In various implementations, the objective occurrence data 6-70c acquired from a user 6-20* may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by the user 6-20*, certain physical characteristics (e.g., blood pressure or location) associated with the user 6-20*, or other aspects associated with the user 6-20* that the user 6-20* can report objectively. The objective occurrence data 6-70c may be in the form of a text data, audio or voice data, or image data.
In various embodiments, after acquiring the subjective user state data 6-60 including data indicating incidence of at least one subjective user state 6-60a and the objective occurrence data 6-70* including data indicating incidence of at least one objective occurrence, the computing device 6-10 may be configured to correlate the acquired subjective user state data 6-60 with the acquired objective occurrence data 6-70* by, for example, determining whether there is a sequential relationship between the one or more subjective user states as indicated by the acquired subjective user state data 6-60 and the one or more objective occurrences indicated by the acquired objective occurrence data 6-70*.
In some embodiments, and as will be further explained in the operations and processes to be described herein, the computing device 6-10 may be further configured to present one or more results of correlation. In various embodiments, the one or more correlation results 6-80 may be presented to a user 6-20* and/or to one or more third parties in various forms (e.g., in the form of an advisory, a warning, a prediction, and so forth). The one or more third parties may be other users 6-20* (e.g., microbloggers), health care providers, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the subjective user state data solicitation module 6-101 of the computing device 6-10 of
The subjective user state data solicitation module 6-101 may include one or more sub-modules in various alternative implementations. For example, in various implementations, the subjective user state data solicitation module 6-101 may include a requesting module 6-202 configured to request for data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20*. The requesting module 6-202 may further include one or more sub-modules. For example, in some implementations, such as when the computing device 6-10 is a standalone device, the requesting module 6-202 may include a user interface requesting module 6-204 configured to request for data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20b via a user interface 6-122. The user interface requesting module 6-204, in some cases, may further include a request indication module 6-205 configured to indicate a request for data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20b via the user interface 6-122 (e.g., indicating through at least a display system including a display monitor or touchscreen, or an audio system including a speaker).
In some implementations, such as when the computing device 6-10 is a server, the requesting module 6-202 may include a network interface requesting module 6-206 configured to request for data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a via a network interface 6-120. The network interface requesting module 6-206 may further include one or more sub-modules in various alternative implementations. For example, in some implementations, the network interface requesting module 6-206 may include a request transmission module 6-207 configured to transmit a request to be provided with data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a. Alternatively or in the same implementations, the network interface requesting module 6-206 may include a request access module 6-208 configured to transmit data indicating incidence of at least one subjective user state 6-60a associated with the user 6-20a a request to have access to data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a.
In the same or different implementations, the network interface requesting module 6-206 may include a configuration module 6-209 designed to configure (e.g., remotely configure) one or more remote devices (e.g., a remote network server, a mobile device 6-30, or some other network device) to provide data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a. In the same or different implementations, the network interface requesting module 6-206 may include a directing/instructing module 6-210 configured to direct or instruct a remote device (e.g., transmitting directions or instructions to the remote device such as a remote network server or the mobile device 6-30) to provide data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a.
The requesting module 6-202 may include other sub-modules in various alternative implementations. These sub-modules may be included with the requesting module 6-202 regardless of whether the computing device 6-10 is a server or a standalone device. For example, in some implementations, the requesting module 6-202 may include a motivation provision module 6-212 configured to provide, among other things, a motivation for requesting for data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20*. In the same or different implementations, the requesting module 6-202 may include a selection request module 6-214 configured to, among other things, request a user 6-20* for a selection of a subjective user state from a plurality of indicated alternative subjective user states (e.g., asking the user 6-20* through the user interface 6-122* to select from alternative choices of “happy,” “sad,” “in pain,” and “upset stomach”).
In the same or different implementations, the requesting module 6-202 may include a confirmation request module 6-216 configured to request confirmation of an incidence of at least one subjective user state (e.g., asking a user 6-20* through the user interface 6-122* whether the user feels “well”) associated with a user 6-20*. In the same or different implementations, the requesting module 6-202 may include a time/temporal element request module 6-218 configured to, among other things, request for an indication of a time or temporal element associated with an incidence of at least one subjective user state associated with the user 6-20* (e.g., asking the user 6-20* via the user interface 6-122 whether the user 6-20* felt tired after lunch?).
In various implementations, the subjective user state data solicitation module 6-101 of
b illustrates particular implementations of the subjective user state data acquisition module 6-102 of the computing device 6-10 of
The subjective user state data acquisition module 6-102, in various implementations, may include a time data acquisition module 6-228 configured to acquire (e.g., receive or generate) time and/or temporal elements associated with one or more subjective user states associated with a user 6-20*. In some implementations, the time data acquisition module 6-228 may include a time stamp acquisition module 6-230 for acquiring (e.g., acquiring either by receiving or by generating) one or more time stamps associated with one or more subjective user states associated with a user 6-20*. In the same or different implementations, the time data acquisition module 6-228 may include a time interval acquisition module 6-231 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more subjective user states associated with a user 6-20*. In the same or different implementations, the time data acquisition module 6-228 may include a temporal relationship acquisition module 6-232 for acquiring indications of temporal relationships between objective occurrences and subjective user states (e.g., an indication that a subjective user state associated with a user 6-20* occurred before, after, or at least partially concurrently with incidence of an objective occurrence).
c illustrates particular implementations of the objective occurrence data acquisition module 6-104 of the computing device 6-10 of
The objective occurrence data reception module 6-234, in turn, may further include one or more sub-modules. For example, in some implementations, such as when the computing device 6-10 is a standalone device, the objective occurrence data reception module 6-234 may include a user interface data reception module 6-235 configured to receive objective occurrence data 6-70c via a user interface 6-122 (e.g., a keyboard, a mouse, a touchscreen, a microphone, an image capturing device such as a digital camera, and so forth). In some cases, the objective occurrence data 6-70c to be received via the user interface 6-122 may be provided, at least in part, by a user 6-20b. In some implementations, such as when the computing device 6-10 is a server, the objective occurrence data reception module 6-234 may include a network interface data reception module 6-236 configured to, among other things, receive objective occurrence data 6-70* from at least one of a wireless network or a wired network 6-40.
The objective occurrence data acquisition module 6-104 may include other sub-modules in various implementations. For example, in some implementations, the objective occurrence data acquisition module 6-104 may include a time data acquisition module 6-238 configured to acquire time and/or temporal elements associated with one or more objective occurrences. For these embodiments, the time and/or temporal elements (e.g., time stamps, time interval indicators, and/or temporal relationship indicators) acquired by the time data acquisition module 6-238 may be useful for, among other things, determining one or more sequential patterns.
In some implementations, the time data acquisition module 6-238 may include a time stamp acquisition module 6-240 configured to acquire (e.g., acquire either by receiving or by generating) one or more time stamps associated with one or more objective occurrences. In the same or different implementations, the time data acquisition module 6-238 may include a time interval acquisition module 6-241 configured to acquire (e.g., acquire either by receiving or by generating) one or more indicators of time intervals associated with one or more objective occurrences.
Turning now to
The sequential pattern determination module 6-242, in various implementations, may include one or more sub-modules that may facilitate in the determination of one or more sequential patterns. As depicted, the one or more sub-modules that may be included in the sequential pattern determination module 6-242 may include, for example, a “within predefined time increment determination” module 6-244, a temporal relationship determination module 6-246, a subjective user state and objective occurrence time difference determination module 6-245, and/or a historical data referencing module 6-243. In brief, the within predefined time increment determination module 6-244 may be configured to determine whether an incidence of at least one subjective user state associated with a user 6-20* occurred within a predefined time increment from an incidence of at least one objective occurrence. For example, determining whether a user 6-20* “feeling bad” (i.e., a subjective user state) occurred within ten hours (i.e., predefined time increment) of eating a large chocolate sundae (i.e., an objective occurrence). Such a process may be used in order to filter out events that are likely not related or to facilitate in determining the strength of correlation between subjective user state data 6-60 and objective occurrence data 6-70*. For example, if the user 6-20* “feeling bad” occurred more than 10 hours after eating the chocolate sundae, then this may indicate a weaker correlation between a subjective user state (e.g., feeling bad) and an objective occurrence (e.g., eating a chocolate sundae).
The temporal relationship determination module 6-246 of the sequential pattern determination module 6-242 may be configured to determine the temporal relationships between one or more incidences of subjective user states associated with a user 6-20* and one or more incidences of objective occurrences. For example, this determination may entail determining whether an incidence of a particular subjective user state (e.g., sore back) occurred before, after, or at least partially concurrently with an incidence of a particular objective occurrence (e.g., sub-freezing temperature).
The subjective user state and objective occurrence time difference determination module 6-245 of the sequential pattern determination module 6-242 may be configured to determine the extent of time difference between an incidence of at least one subjective user state associated with a user 6-20* and an incidence of at least one objective occurrence. For example, determining how long after taking a particular brand of medication (e.g., objective occurrence) did a user 6-20* feel “good” (e.g., subjective user state).
The historical data referencing module 6-243 of the sequential pattern determination module 6-242 may be configured to reference historical data 6-72 in order to facilitate in determining sequential patterns. For example, in various implementations, the historical data 6-72 that may be referenced may include, for example, general population trends (e.g., people having a tendency to have a hangover after drinking or ibuprofen being more effective than aspirin for toothaches in the general population), medical information such as genetic, metabolome, or proteome information related to the user 6-20* (e.g., genetic information of the user 6-20* indicating that the user 6-20* is susceptible to a particular subjective user state in response to occurrence of a particular objective occurrence), or historical sequential patterns such as known sequential patterns of the general population or of the user 6-20* (e.g., people tending to have difficulty sleeping within five hours after consumption of coffee). In some instances, such historical data 6-72 may be useful in associating one or more incidences of subjective user states associated with a user 6-20* with one or more incidences of objective occurrences.
In some embodiments, the correlation module 6-106 may include a sequential pattern comparison module 6-248. As will be further described herein, the sequential pattern comparison module 6-248 may be configured to compare two or more sequential patterns with each other to determine, for example, whether the sequential patterns at least substantially match each other or to determine whether the sequential patterns are contrasting sequential patterns.
As depicted in
The subjective user state equivalence determination module 6-250 of the sequential pattern comparison module 6-248 may be configured to determine whether subjective user states associated with different sequential patterns are at least substantially equivalent. For example, the subjective user state equivalence determination module 6-250 may determine whether a first subjective user state of a first sequential pattern is equivalent to a second subjective user state of a second sequential pattern. For instance, suppose a user 6-20* reports that on Monday he had a stomach ache (e.g., first subjective user state) after eating at a particular restaurant (e.g., a first objective occurrence), and suppose further that the user 6-20* again reports having a stomach ache (e.g., a second subjective user state) after eating at the same restaurant (e.g., a second objective occurrence) on Tuesday, then the subjective user state equivalence determination module 6-250 may be employed in order to compare the first subjective user state (e.g., stomach ache) with the second subjective user state (e.g., stomach ache) to determine whether they are equivalent. Note that in this example, the first sequential pattern may represent a hypothesis 6-71 linking a subjective user state (e.g., stomach ache) to an objective occurrence (e.g., eating at a particular restaurant).
In contrast, the objective occurrence equivalence determination module 6-251 of the sequential pattern comparison module 6-248 may be configured to determine whether objective occurrences of different sequential patterns are at least substantially equivalent. For example, the objective occurrence equivalence determination module 6-251 may determine whether a first objective occurrence of a first sequential pattern is equivalent to a second objective occurrence of a second sequential pattern. For instance, in the above example, the objective occurrence equivalence determination module 6-251 may compare eating at the particular restaurant on Monday (e.g., first objective occurrence) with eating at the same restaurant on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is equivalent to the second objective occurrence.
In some implementations, the sequential pattern comparison module 6-248 may include a subjective user state contrast determination module 6-252 that may be configured to determine whether subjective user states associated with different sequential patterns are contrasting subjective user states. For example, the subjective user state contrast determination module 6-252 may determine whether a first subjective user state of a first sequential pattern is a contrasting subjective user state from a second subjective user state of a second sequential pattern. To illustrate, suppose a user 6-20* reports that he felt very “good” (e.g., first subjective user state) after jogging for an hour (e.g., first objective occurrence) on Monday, but reports that he felt “bad” (e.g., second subjective user state) when he did not exercise (e.g., second objective occurrence) on Tuesday, then the subjective user state contrast determination module 6-245 may compare the first subjective user state (e.g., feeling good) with the second subjective user state (e.g., feeling bad) to determine that they are contrasting subjective user states.
In some implementations, the sequential pattern comparison module 6-248 may include an objective occurrence contrast determination module 6-253 that may be configured to determine whether objective occurrences of different sequential patterns are contrasting objective occurrences. For example, the objective occurrence contrast determination module 6-253 may determine whether a first objective occurrence of a first sequential pattern is a contrasting objective occurrence from a second objective occurrence of a second sequential pattern. For instance, in the previous example, the objective occurrence contrast determination module 6-253 may compare the “jogging” on Monday (e.g., first objective occurrence) with the “no jogging” on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence. Based on the contrast determination, an inference may be made that the user 6-20* may feel better by jogging rather than by not jogging at all.
In some embodiments, the sequential pattern comparison module 6-248 may include a temporal relationship comparison module 6-254 that may be configured to make comparisons between different temporal relationships of different sequential patterns. For example, the temporal relationship comparison module 6-254 may compare a first temporal relationship between a first subjective user state and a first objective occurrence of a first sequential pattern with a second temporal relationship between a second subjective user state and a second objective occurrence of a second sequential pattern in order to determine whether the first temporal relationship at least substantially matches the second temporal relationship.
For example, referring back to the earlier example, suppose the user 6-20* eating at the particular restaurant (e.g., first objective occurrence) and the subsequent stomach ache (e.g., first subjective user state) on Monday represents a first sequential pattern while the user 6-20* eating at the same restaurant (e.g., second objective occurrence) and the subsequent stomach ache (e.g., second subjective user state) on Tuesday represents a second sequential pattern. In this example, the occurrence of the stomach ache after (rather than before or concurrently) eating at the particular restaurant on Monday represents a first temporal relationship associated with the first sequential pattern while the occurrence of a second stomach ache after (rather than before or concurrently) eating at the same restaurant on Tuesday represents a second temporal relationship associated with the second sequential pattern. Under such circumstances, the temporal relationship comparison module 6-254 may compare the first temporal relationship to the second temporal relationship in order to determine whether the first temporal relationship and the second temporal relationship at least substantially match (e.g., stomach aches in both temporal relationships occurring after eating at the restaurant). Such a match may result in the inference that a stomach ache is associated with eating at the particular restaurant and may, in some instances, confirm the veracity of a hypothesis 6-71.
In some implementations, the sequential pattern comparison module 6-248 may include an extent of time difference comparison module 6-255 that may be configured to compare the extent of time differences between incidences of subjective user states and incidences of objective occurrences of different sequential patterns. For example, the extent of time difference comparison module 6-255 may compare the extent of time difference between incidence of a first subjective user state and incidence of a first objective occurrence of a first sequential pattern with the extent of time difference between incidence of a second subjective user state and incidence of a second objective occurrence of a second sequential pattern. In some implementations, the comparisons may be made in order to determine that the extent of time differences of the different sequential patterns at least substantially or proximately match.
In some embodiments, the correlation module 6-106 may include a strength of correlation determination module 6-256 for determining a strength of correlation between subjective user state data 6-60 and objective occurrence data 6-70* associated with a user 6-20*. In some implementations, the strength of correlation may be determined based, at least in part, on the results provided by the other sub-modules of the correlation module 6-106 (e.g., the sequential pattern determination module 6-242, the sequential pattern comparison module 6-248, and their sub-modules).
e illustrates particular implementations of the presentation module 6-108 of the computing device 6-10 of
The one or more results of a correlation operation performed by the correlation module 6-106 may be presented in different forms in various alternative embodiments. For example, in some implementations, the presentation of the one or more results may entail the presentation module 6-108 presenting to the user 6-20* (or some other third party) an indication of a sequential relationship between a subjective user state and an objective occurrence associated with the user 6-20* (e.g., “whenever you eat a banana, you have a stomach ache”). In alternative implementations, other ways of presenting the results of the correlation may be employed. For example, in various alternative implementations, a notification may be provided to notify past tendencies or patterns associated with a user 6-20*. In some implementations, a notification of a possible future outcome may be provided. In other implementations, a recommendation for a future course of action based on past patterns may be provided. These and other ways of presenting the correlation results will be described in the processes and operations to be described herein.
In order to present the one or more results of a correlation operation performed by the correlation module 6-106, the presentation module 6-108 may include one or more sub-modules. For example, in some implementations, the presentation module 6-108 may include a sequential relationship presentation module 6-260 configured to present an indication of a sequential relationship between at least one subjective user state of a user 6-20* and at least one objective occurrence. In the same or different implementations, the presentation module 6-108 may include a prediction presentation module 6-261 configured to present a prediction of a future subjective user state of a user 6-20* resulting from a future objective occurrence associated with the user 6-20*. In the same or different implementations, the prediction presentation module 6-261 may also be designed to present a prediction of a future subjective user state of a user 6-20* resulting from a past objective occurrence associated with the user 6-20*. In some implementations, the presentation module 6-108 may include a past presentation module 6-262 that is designed to present a past subjective user state of a user 6-20* in connection with a past objective occurrence associated with the user 6-20*.
In some implementations, the presentation module 6-108 may include a recommendation module 6-263 configured to present a recommendation for a future action based, at least in part, on the results of a correlation of subjective user state data 6-60 with objective occurrence data 6-70* as performed by the correlation module 6-106. In certain implementations, the recommendation module 6-262 may further include a justification module 6-264 for presenting a justification for the recommendation presented by the recommendation module 6-263. In some implementations, the presentation module 6-108 may include a strength of correlation presentation module 6-266 for presenting an indication of a strength of correlation between subjective user state data 6-60 and objective occurrence data 6-70*.
In various embodiments, the computing device 6-10 of
The computing device 6-10 may also include a memory 6-140 for storing various data. For example, in some embodiments, memory 6-140 may be employed in order to store a hypothesis 6-71 and/or historical data 6-72. In some implementations, the historical data 6-72 may include historical subjective user state data of a user 6-20* that may indicate one or more past subjective user states of the user 6-20* and historical objective occurrence data that may indicate one or more past objective occurrences. In the same or different implementations, the historical data 6-72 may include historical medical data of a user 6-20* (e.g., genetic, metoblome, proteome information), population trends, historical sequential patterns derived from general population, and so forth.
In various embodiments, the computing device 6-10 may include a user interface 6-122 to communicate directly with a user 6-20b. For example, in embodiments in which the computing device 6-10 is a standalone device such as a handheld device (e.g., cellular telephone, PDA, and so forth), the user interface 6-122 may be configured to directly receive from the user 6-20b subjective user state data 6-60 and/or objective occurrence data 6-70*. In some implementations, the user interface 6-122 may also be designed to visually or audibly present the results of correlating subjective user state data 6-60 and objective occurrence data 6-70*. The user interface 6-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a key pad, a mouse, an audio system including a microphone and/or one or more speakers, an imaging system including a digital or video camera, and/or other user interface devices.
f illustrates particular implementations of the one or more applications 6-126 of
The various features and characteristics of the components, modules, and sub-modules of the computing device 6-10 presented thus far will be described in greater detail with respect to the processes and operations to be described herein. Note that the subjective user state data 6-60 may be in a variety of forms including, for example, text messages (e.g., blog entries, microblog entries, instant messages, text email messages, and so forth), audio messages, and/or images (e.g., an image capturing user's facial expression or gestures).
Referring to
In various implementations, in addition to these components, the mobile device 6-30 may include a subjective user state data transmission module 6-160 that is configured to transmit (e.g., transmit via a wireless and/or wired network 6-40) subjective user state data 6-60 including data indicating incidence of at least one subjective user state 6-60a. In some implementations, the subjective user state data 6-60 may be transmitted to a network server such as computing device 6-10. In the same or different implementations, the mobile device 6-30 may include a correlation results reception module 6-162 that may be configured to receive, via a wireless and/or wired network 6-40, results of correlation of subjective user state data 6-60 with objective occurrence data 6-70*. In some implementations, such a correlation may have been performed at a network server (e.g., computing device 6-10).
h illustrates particular implementations of the subjective user state data solicitation module 6-101′ of the mobile device 6-30 of
In addition, the subjective user state data solicitation module 6-101′ may include a request to solicit reception module 6-270 that may be configured to receive a request to solicit data indicating incidence of at least one subjective user state 6-60a associated with a user 6-20a. Such a request, in some implementations, may be remotely generated (e.g. remotely generated at the computing device 6-10) based, at least in part, on a hypothesis 6-71 and, in some cases, in response, at least in part, to an incidence of at least one objective occurrence.
i illustrates particular implementations of the subjective user state data acquisition module 6-102′ of the mobile device 6-30 of
Referring to
k illustrates particular implementations of the presentation module 6-108′ of the mobile device 6-30 of
A more detailed discussion of these components (e.g., modules and interfaces) that may be included in the mobile device 6-30 and those that may be included in the computing device 6-10 will be provided with respect to the processes and operations to be described herein.
In
Further, in
In any event, after a start operation, the operational flow 6-300 may move to a subjective user state data solicitation operation 6-302 for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one objective occurrence, subjective user state data including data indicating incidence of at least one subjective user state associated with a user. For instance, the subjective user state data solicitation module 6-101 of the computing device 6-10 or the subjective user state data solicitation module 6-101′ of the mobile device 6-30 soliciting, based at least in part on a hypothesis 6-71 (e.g., the computing device 6-10 referencing a hypothesis 6-71, or the mobile device 6-30 receiving a request for soliciting from the computing device 6-10, the request being remotely generated and sent to the mobile device 6-30 based at least in part on a hypothesis 6-71) that links one or more objective occurrences with one or more subjective user states (e.g., a group of users 6-20* ingesting a particular type of medicine such as aspirin, and the subsequent subjective physical states, such as pain relief, associated with the group of users 6-20*) and in response at least in part to an incidence of at least one objective occurrence (e.g., ingestion of a medicine by a user 6-20*), subjective user state data 6-60 including data indicating incidence of at least one subjective user state 6-60a (e.g., pain relief by user 6-20*) associated with a user 6-20*.
Note that the solicitation of the subjective user state data 6-60, as described above, may or may not be in reference to solicitation of particular data that indicates occurrence of a particular or particular type of subjective user state. That is, in some embodiments, the solicitation of the subjective user state data 6-60 may be in reference to solicitation for subjective user state data 6-60 including data indicating incidence of any subjective user state with respect to, for example, a particular point in time or time interval. While in other embodiments, the solicitation of the subjective user state data 6-60 may involve solicitation for subjective user state data including solicitation of particular data indicating occurrence of a particular or particular type of subjective user state.
The term “soliciting” as described above may be in reference to direct or indirect solicitation of (e.g., requesting to be provided with, requesting to access, gathering of, or other methods of being provided with, or being allowed access) subjective user state data 6-60 from one or more sources. The sources for the subjective user state data 6-60 may be a user 6-20*, a mobile device 6-30, or one or more network servers (not depicted), which may have already been provided with such subjective user state data 6-60. For example, if the computing device 6-10 is a server, then the computing device 6-10 may indirectly solicit the objective occurrence data 6-70* from a user 6-20a by transmitting the solicitation (e.g., a request or inquiry) to the mobile device 6-30, which may then actually solicit the subjective user state data 6-60 from the user 6-20a. Alternatively, such subjective user state data 6-60 may have already been provided to the mobile device 6-30, in which case the mobile device 6-30 merely provides for or allows access to such data.
In still other alternative implementations, such subjective user state data 6-60 may have been previously stored in a network server (not depicted), and such a network server may be solicited for the subjective user state data 6-60. In yet other implementations in which the computing device 6-10 is a standalone device, such as a handheld device to be used directly by a user 6-20b, the computing device 6-10 may directly solicit the subjective user state data 6-60 from the user 6-20b.
Operational flow 6-300 may further include a subjective user state data acquisition operation 6-304 for acquiring the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user. For instance, the subjective user state data acquisition module 6-102 of the computing device 6-10 or the subjective user state data acquisition module 6-102′ of the mobile device 6-30 acquiring (e.g., receiving by the computing device 6-10 or by the mobile device 6-30 from a user 6-20*) the subjective user state data 6-60.
In various implementations, the subjective user state data solicitation operation 6-302 of
In various implementations, the requesting operation 6-402 may further include one or more additional operations. For example, in some implementations, the requesting operation 6-402 may include an operation 6-404 for requesting for the data indicating incidence of at least one subjective user state associated with the user via a user interface as depicted in
Operation 6-404, in turn, may further include an operation 6-406 for requesting for the data indicating incidence of at least one subjective user state associated with the user from the user as depicted in
In some implementations, operation 6-406 may include an operation 6-408 for indicating the request for the data indicating incidence of at least one subjective user state associated with the user through at least a display system as depicted in
In some implementations, operation 6-406 may include an operation 6-410 for indicating the request for the data indicating incidence of at least one subjective user state associated with the user through at least an audio system as depicted in
In various implementations, the reception operation 6-402 may include an operation 6-412 for requesting for the data indicating incidence of at least one subjective user state associated with the user via network interface as depicted in
In some implementations, operation 6-412 may include an operation 6-414 for transmitting a request to be provided with the data indicating incidence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 6-412 may include an operation 6-416 for transmitting a request to have access to the data indicating incidence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 6-412 may include an operation 6-418 for configuring a remote device to provide the data indicating incidence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 6-412 may include an operation 6-420 for directing or instructing a remote device to provide the data indicating incidence of at least one subjective user state associated with the user as depicted in
In various implementations, the reception operation 6-402 may include an operation 6-422 for providing a motivation for requesting for the data indicating incidence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 6-422 may further include an operation 6-424 for providing a motivation for requesting for the data indicating incidence of at least one subjective user state associated with the user, the motivation to be provided relating to the link between the one or more objective occurrences with the one or more subjective user states as indicated by the hypothesis as depicted in
In some implementations, the solicitation operation 6-302 of
Operation 6-426, in turn, may include one or more additional operations in various implementations. For example, in some implementations, operation 6-426 may include an operation 6-428 for requesting the user to select a subjective user state from a plurality of indicated alternative subjective user states as depicted in
In some implementations, operation 6-428 may further include an operation 6-430 for requesting the user to select a subjective user state from a plurality of indicated alternative contrasting subjective user states as depicted in
In some implementations, operation 6-426 may include an operation 6-432 for requesting the user to confirm incidence of at least one subjective user state as depicted in
In some implementations, operation 6-426 may include an operation 6-434 for requesting the user to provide an indication of occurrence of at least one subjective user state with respect to the incidence of the at least one objective occurrence as depicted in
In some implementations, operation 6-426 may include an operation 6-436 for requesting the user to provide an indication of a time or temporal element associated with the incidence of at least one subjective user state associated with the user as depicted in
In various implementations, operation 6-436 may include one or more additional operations. For example, in some implementations, operation 6-436 may include an operation 6-438 for requesting the user to provide an indication of a point in time associated with the incidence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 6-436 may include an operation 6-440 for requesting the user to provide an indication of a time interval associated with the incidence of at least one subjective user state associated with the user as depicted in
In some implementations, operation 6-436 may include an operation 6-442 for requesting the user to provide an indication of a temporal relationship between the incidence of the at least one subjective user state associated with the user and the incidence of the at least one objective occurrence as depicted in
In some implementations, the solicitation operation 6-302 of
In some implementations, the solicitation operation 6-302 may include an operation 6-446 for soliciting data indicating incidence of at least one subjective physical state associated with the user as depicted in
In some implementations, the solicitation operation 6-302 may include an operation 6-448 for soliciting data indicating incidence of at least one subjective overall state associated with the user as depicted in
In some implementations, the solicitation operation 6-302 may include an operation 6-450 for soliciting data indicating incidence of at least one subjective user state that occurred during a specified point in time as depicted in
In some implementations, the solicitation operation 6-302 may include an operation 6-452 for soliciting data indicating incidence of at least one subjective user state that occurred during a specified time interval as depicted in
In various embodiments, the solicitation operation 6-302 may include operations that may be particular to the computing device 6-10, which may be a standalone device or a network server. For example, in some implementations, the solicitation operation 6-302 may include an operation 6-453 for soliciting the data indicating incidence of at least one subjective user state based, at least in part, on referencing the hypothesis as depicted in
In various implementations, operation 6-453 may further include one or more additional operations. For example, in some implementations, operation 6-453 may include an operation 6-454 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies one or more temporal relationships between the one or more objective occurrences and the one or more subjective user states as depicted in
In some implementations, operation 6-454 may include an operation 6-456 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies one or more time sequential relationships between the at least one objective occurrences and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-458 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between at least an ingestion of a medicine and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-460 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between at least an ingestion of a food item and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-462 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between at least an ingestion of a nutraceutical and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-463 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between execution of one or more exercise routines and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-464 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between execution of one or more social activities and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-465 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between one or more activities executed by a third party and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-466 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between one or more physical characteristics of the user and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-467 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between a resting, a learning, or a recreation activity performed by the user and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-468 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between one or more external activities and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-469 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between one or more locations of the user and the one or more subjective user states as depicted in
In some implementations, operation 6-453 may include an operation 6-470 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that links the at least one objective occurrence with one or more historical subjective user states associated with the user as depicted in
In some implementations, operation 6-453 may include an operation 6-471 for soliciting the data indicating incidence of the at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that links the at least one objective occurrence with one or more historical subjective user states associated with a plurality of users as depicted in
In some implementations, operation 6-453 may include an operation 6-472 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that links the at least one objective occurrence with one or more historical subjective user states associated with at least a subset of a general population as depicted in
In various implementations, the solicitation operation 6-302 may include one or more operations that may be performed by the mobile device 6-30 rather than by the computing device 6-10. For example, in some implementations, the solicitation operation 6-302 may include an operation 6-477 for soliciting the data indicating incidence of at least one subjective user state associated with the user in response to a reception of a request to solicit the data indicating incidence of at least one subjective user state associated with the user, the request to solicit being remotely generated based, at least in part, on the hypothesis as depicted in
In some implementations, operation 6-477 may further include an operation 6-478 for soliciting the data indicating incidence of at least one subjective user state associated with the user in response to a reception of a request to solicit the data indicating incidence of at least one subjective user state associated with the user, the request to solicit being remotely generated based, at least in part, on the hypothesis and in response to the incidence of the at least one objective occurrence as depicted in
In some implementations, operation 6-477 may further include an operation 6-479 for receiving the request to solicit the data indicating incidence of at least one subjective user state associated with the user via at least one of a wireless network or a wired network as depicted in
Operation 6-479, in turn, may further include an operation 6-480 for receiving the request to solicit the data indicating incidence of at least one subjective user state associated with the user from a network server as depicted in
In various implementations, the solicitation operation 6-302 of
In some implementations, the solicitation operation 6-302 may include an operation 6-484 for soliciting data indicating incidence of at least one subjective user state associated with the user at a particular point in time as depicted in
In some implementations, the solicitation operation 6-302 may include an operation 6-486 for soliciting data indicating incidence of at least one subjective user state associated with the user during a particular time interval as depicted in
Referring back to
In various implementations, the reception operation 6-502 may include one or more additional operations. For example, in some implementations, the reception operation 6-502 may include an operation 6-504 for receiving the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user via a user interface as depicted in
The reception operation 6-502, in some implementations, may include operations that may be particular to the computing device 6-10 (e.g., when the computing device is a network server) and may not be executed by the mobile device 6-30. For example, in some implementations, the reception operation 6-502 may include an operation 6-506 for receiving the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user from at least one of a wireless network or a wired network as depicted in
In some implementations, operation 6-506 may further include an operation 6-508 for receiving the subjective user state data including data indicating incidence of at least one subjective user state associated with the user via one or more electronic messages generated by the user as depicted in
In some implementations, operation 6-506 may include an operation 6-510 for receiving the subjective user state data including data indicating incidence of at least one subjective user state associated with the user via one or more blog entries generated by the user as depicted in
In some implementations, operation 6-506 may include an operation 6-512 for receiving the subjective user state data including data indicating incidence of at least one subjective user state associated with the user via one or more status reports generated by the user as depicted in
In certain implementations, the reception operation 6-502 may include an operation 6-514 for receiving a selection made by the user, the selection being a selection of a subjective user state from a plurality of indicated alternative subjective user states as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 of
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-518 for acquiring data indicating at least one subjective physical state associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-520 for acquiring data indicating at least one subjective overall state associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-522 for acquiring a time stamp associated with the at least one subjective user state as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-524 for acquiring an indication of a time interval associated with the at least one subjective user state as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-526 for acquiring an indication of a temporal relationship between the at least one subjective user state and the at least one objective occurrence as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-528 for acquiring the data indicating incidence of at least one subjective user state associated with the user at a server as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-530 for acquiring the data indicating incidence of at least one subjective user state associated with the user at a handheld device as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-532 for acquiring the data indicating incidence of at least one subjective user state associated with the user at a peer-to-peer network component device as depicted in
In some implementations, the subjective user state data acquisition operation 6-304 may include an operation 6-534 for acquiring the data indicating incidence of at least one subjective user state associated with the user via a Web 2.0 construct as depicted in
Referring to
In addition, operational flow 6-600 includes an objective occurrence data acquisition operation 6-606 for acquiring objective occurrence data including data indicating incidence of the at least one objective occurrence as depicted in
In various alternative implementations, the objective occurrence data acquisition operation 6-606 may include one or more additional operations. For example, in some implementations, operation 6-606 may include a reception operation 6-702 for receiving the objective occurrence data as depicted in
The reception operation 6-702, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, the reception operation 6-702 may include an operation 6-704 for receiving the objective occurrence data via a user interface as depicted in
In some implementations, the reception operation 6-702 may include an operation 6-706 for receiving the objective occurrence data from at least one of a wireless network or a wired network as depicted in
In some implementations, the reception operation 6-702 may include an operation 6-708 for receiving the objective occurrence data via one or more blog entries as depicted in
In some implementations, the reception operation 6-702 may include an operation 6-710 for receiving the objective occurrence data via one or more status reports as depicted in
In some implementations, the reception operation 6-702 may include an operation 6-712 for receiving the objective occurrence data from one or more third party sources as depicted in
In some implementations, the reception operation 6-702 may include an operation 6-714 for receiving the objective occurrence data from one or more sensors as depicted in
In some implementations, the reception operation 6-702 may include an operation 6-716 for receiving the objective occurrence data from the user as depicted in
In various implementations, the objective occurrence data acquisition operation 6-606 of
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-720 for acquiring an indication of a time interval associated with the incidence of the at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-722 for acquiring data indicating one or more attributes associated with the at least one objective occurrence as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-724 for acquiring data indicating an ingestion by the user of a medicine as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-726 for acquiring data indicating an ingestion by the user of a food item as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-728 for acquiring data indicating an ingestion by the user of a nutraceutical as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-730 for acquiring data indicating an exercise routine executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-732 for acquiring data indicating a social activity executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-734 for acquiring data indicating an activity performed by a third party as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-736 for acquiring data indicating a physical characteristic of the user as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-738 for acquiring data indicating a resting, a learning or a recreational activity by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-740 for acquiring data indicating occurrence of an external event as depicted in
In some implementations, the objective occurrence data acquisition operation 6-606 may include an operation 6-742 for acquiring data indicating a location of the user as depicted in
Referring now to
In addition, operational flow 6-800 may further include a correlation operation 6-808 for correlating the subjective user state data with the objective occurrence data and a presentation operation 6-810 for presenting one or more results of the correlating of the subjective user state data with the objective occurrence data as depicted in
In various alternative implementations, the correlation operation 6-808 may include one or more additional operations. For example, in some implementations, the correlation operation 6-808 may include an operation 6-902 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence as depicted in
Operation 6-902, in turn, may further include one or more additional operations. For example, in some implementations, operation 6-902 may include an operation 6-904 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing historical data as depicted in
In some implementations, operation 6-904 may include an operation 6-906 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a historical sequential pattern as depicted in
In some implementations, operation 6-904 may include an operation 6-908 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing historical medical data as depicted in
In various implementations, operation 6-902 may include an operation 6-910 for comparing the at least one sequential pattern to a second sequential pattern to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
Operation 6-910, in some implementations, may further include an operation 6-912 for comparing the at least one sequential pattern to a second sequential pattern related to at least a second subjective user state associated with the user and a second objective occurrence to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
For these implementations, the comparison of the first sequential pattern to the second sequential pattern may involve making certain comparisons, For example, comparing the first subjective user state to the second subjective user state to determine at least whether they are the same or different. Similarly, the first objective occurrence may be compared to the second objective occurrence to determine at least whether they are the same or different. The temporal relationship or the specific time sequencing between the incidence of the first subjective user state and the incidence of the first objective occurrence (e.g., as represented by the first sequential pattern) may then be compared to the temporal relationship or the specific time sequencing between the incidence of the second subjective user state and the incidence of the second objective occurrence (e.g., as represented by the second sequential pattern).
In some implementations, the correlation operation 6-808 of
In alternative implementations, the correlation operation 6-808 may include an operation 6-916 for correlating the subjective user state data with the objective occurrence data at a handheld device as depicted in
In some implementations, the correlation operation 6-808 may include an operation 6-918 for correlating the subjective user state data with the objective occurrence data at a peer-to-peer network component device as depicted in
Referring back to
In some implementations, the presentation operation 6-810 may include an operation 6-1004 for transmitting the one or more results of the correlating via a network interface as depicted in
In some implementations, the presentation operation 6-810 may include an operation 6-1006 for presenting an indication of a sequential relationship between the at least one subjective user state and the at least one objective occurrence as depicted in
In some implementations, the presentation operation 6-810 may include an operation 6-1008 for presenting a prediction of a future subjective user state associated with the user resulting from a future objective occurrence as depicted in
In some implementations, the presentation operation 6-810 may include an operation 6-1010 for presenting a prediction of a future subjective user state associated with the user resulting from a past objective occurrence as depicted in
In some implementations, the presentation operation 6-810 may include an operation 6-1012 for presenting a past subjective user state associated with the user in connection with a past objective occurrence as depicted in
In some implementations, the presentation operation 6-810 may include an operation 6-1014 for presenting a recommendation for a future action as depicted in
In some implementations, operation 6-1014 may further include an operation 6-1016 for presenting a justification for the recommendation as depicted in
In addition, operational flow 6-1100 may further include a subjective user state data transmission operation 6-1106 for transmitting the acquired subjective user state data including the data indicating incidence of at least one subjective user state associated with the user and a reception operation 6-1108 for receiving one or more results of correlation of the subjective user state data with objective occurrence data including data indicating the incidence of the at least one objective occurrence as depicted in
In various alternative implementations, the subjective user state data transmission operation 6-1106 may include one or more additional operations. For example, in some implementations, the subjective user state data transmission operation 6-1106 may include an operation 6-1202 for transmitting the acquired subjective user state data via at least one of a wireless network or a wired network as depicted in
In some implementations, operation 6-1202 may include an operation 6-1204 for transmitting the acquired subjective user state data via one or more blog entries as depicted in
In some implementations, operation 6-1202 may include an operation 6-1206 for transmitting the acquired subjective user state data via one or more status reports as depicted in
In some implementations, operation 6-1202 may include an operation 6-1208 for transmitting the acquired subjective user state data via one or more electronic messages as depicted in
In some implementations, operation 6-1202 may include an operation 6-1210 for transmitting the acquired subjective user state data to a network server as depicted in
Referring back to
In some implementations, the reception operation 6-1108 may include an operation 6-1304 for receiving a prediction of a future subjective user state associated with the user resulting from a future objective occurrence as depicted in
In some implementations, the reception operation 6-1108 may include an operation 6-1306 for receiving a prediction of a future subjective user state associated with the user resulting from a past objective occurrence as depicted in
In some implementations, the reception operation 6-1108 may include an operation 6-1308 for receiving a past subjective user state associated with the user in connection with a past objective occurrence as depicted in
In some implementations, the reception operation 6-1108 may include an operation 6-1310 for receiving a recommendation for a future action as depicted in
In certain implementations, operation 6-1310 may further include an operation 6-1312 for receiving a justification for the recommendation as depicted in
Referring back to
In some implementations, operation 6-1402 may further include an operation 6-1404 for indicating the one or more results of the correlation via a display device as depicted in
In some implementations, operation 6-1402 may include an operation 6-1406 for indicating the one or more results of the correlation via an audio device as depicted in
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where one or more users may report or post their thoughts and opinions on various topics, latest news, current events, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social network status reports in which a user may report or post for others to view the latest status or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life.
The various things that are typically posted through microblog entries may be categorized into one of at least two possible categories. The first category of things that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, event, happening, or any other aspects associated with or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. These things would include, for example, food, medicine, or nutraceutical intake of the microblogger, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, daily activities of the microblogger observable by others or by a device, performance of the stock market (which the microblogger may have an interest in), and so forth. In some cases, objective occurrences may not be at least directly associated with a microblogger. Examples of such objective occurrences include, for example, external events that may not be directly related to the microblogger such as the local weather, activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of things that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., “I am feeling happy”), the subjective physical state of the microblogger (e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “my vision is blurry”), and the subjective overall state of the microblogger (e.g., “I'm good” or “I'm well”). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). Although microblogs are being used to provide a wealth of personal information, they have thus far been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided to, among other things, solicit and acquire at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence, the solicitation being directly or indirectly prompted based, at least in part on a hypothesis that links one or more subjective user states with one or more objective occurrences and in response to an incidence of at least one subjective user state associated with a user.
In various embodiments, a “hypothesis” may define one or more relationships or links between one or more subjective user states and one or more objective occurrences. In some embodiments, a hypothesis may be defined by a sequential pattern that indicates or suggests a temporal or specific time sequencing relationship between one or more subjective user states and one or more objective occurrences. In some cases, the one or more subjective user states associated with the hypothesis may be based on past incidences of one or more subjective user states that are associated with a user, that are associated with multiple users, that are associated with a sub-group of the general population, or that are associated with the general population. Similarly, the one or more objective occurrences associated with the hypothesis may be based on past incidences of objective occurrences.
In some cases, a hypothesis may be formulated when it is determined that a particular pattern of events (e.g., incidences of one or more subjective user states and one or more objective occurrences) occurs repeatedly with respect to a particular user, a group of users, a subset of the general population, or the general population. For example, a hypothesis may be formulated that suggests or predicts that a person will likely have an upset stomach after eating a hot fudge sundae when it is determined that multiple users had reported having an upset stomach after eating a hot fudge sundae. In other cases, a hypothesis may be formulated based, at least in part, on a single pattern of events and historical data related to such events. For instance, a hypothesis may be formulated when a person reports that he had a stomach ache after eating a hot fudge sundae, and historical data suggests that a segment of the population may not be able to digest certain nutrients included in a hot fudge sundae (e.g., the hypothesis would suggest or indicate that the person may get stomach aches whenever the person eats a hot fudge sundae).
The subjective user state data to be acquired by the methods, systems, and the computer program products may include data indicating the incidence of at least one subjective user state associated with a user. Such subjective user state data together with objective occurrence data including data indicating incidence of at least one objective occurrence may then be correlated. The results of the correlation may be presented in a variety of different forms and may, in some cases, confirm the veracity of the hypothesis. The results of the correlation, in various embodiments, may be presented to the user, to other users, or to one or more third parties as will be further described herein.
In some embodiments, the correlation of the acquired subjective user state data with the objective occurrence data may facilitate in determining a causal relationship between at least one objective occurrence (e.g., cause) and at least one subjective user state (e.g., result). For example, determining whenever a user eats a banana the user always or sometimes feels good. Note that an objective occurrence does not need to occur prior to a corresponding subjective user state but instead, may occur subsequent or at least partially concurrently with the incidence of the subjective user state. For example, a person may become “gloomy” (e.g., subjective user state) whenever it is about to rain (e.g., objective occurrence) or a person may become gloomy while (e.g., concurrently) it is raining. Further, in some cases, subjective user states may actually be the “cause” while an objective occurrence may be the “result.” For instance, when a user is angry (e.g., subjective user state), the user's angry state may cause his blood pressure (e.g., objective occurrence) to rise. Thus, a more relevant point to determine between subjective user states and objective occurrences is whether there are any links or relationships between the two types of events (e.g., subjective user states and objective occurrences).
An “objective occurrence data,” as will be described herein, may include data that indicate incidence of at least one objective occurrence. In some embodiments, an objective occurrence may be any physical characteristic, event, happenings, or any other aspect that may be associated with, is of interest to, or may somehow impact a user that can be objectively reported by at least a third party or a sensor device. Note, however, that an objective occurrence does not have to be actually reported by a sensor device or by a third party, but instead, may be reported by the user himself or herself (e.g., via microblog entries). Examples of objectively reported occurrences that could be indicated by the objective occurrence data include, for example, a user's food, medicine, or nutraceutical intake, the user's location at any given point in time, a user's exercise routine, a user's physiological characteristics such as blood pressure, social or professional activities, the weather at a user's location, activities associated with third parties, occurrence of external events such as the performance of the stock market, and so forth.
As briefly described earlier, the objective occurrence data to be acquired may include data that indicate the incidence or occurrence of at least one objective occurrence. In situations where the objective occurrence data to be acquired indicates multiple objective occurrences, each of the objective occurrences indicated by the acquired objective occurrence data may be solicited, while in other embodiments, only one or a subset of the objective occurrences indicated by the acquired objective occurrence data may be solicited.
A “subjective user state,” in contrast, is in reference to any subjective user state or status associated with a user (e.g., a blogger or microblogger) at any moment or interval in time that only the user can typically indicate or describe. Such states include, for example, the subjective mental state of the user (e.g., user is feeling sad), the subjective physical state (e.g., physical characteristic) of the user that only the user can typically indicate (e.g., a backache or an easing of a backache as opposed to blood pressure which can be reported by a blood pressure device and/or a third party), and the subjective overall state of the user (e.g., user is “good”).
Examples of subjective mental states include, for example, happiness, sadness, depression, anger, frustration, elation, fear, alertness, sleepiness, and so forth. Examples of subjective physical states include, for example, the presence, easing, or absence of pain, blurry vision, hearing loss, upset stomach, physical exhaustion, and so forth. Subjective overall states may include any subjective user states that cannot be easily categorized as a subjective mental state or as a subjective physical state. Examples of subjective overall states include, for example, the user “being good,” “bad,” “exhausted,” “lack of rest,” “wellness,” and so forth.
The term “correlating” as will be used herein may be in reference to a determination of one or more relationships between at least two variables. Alternatively, the term “correlating” may merely be in reference to the linking or associating of the at least two variables. In the following exemplary embodiments, the first variable is subjective user state data that indicates at least one subjective user state and the second variable is objective occurrence data that indicates at least one objective occurrence. In embodiments where the subjective user state data indicates multiple subjective user states, each of the subjective user states indicated by the subjective user state data may represent different incidences of the same or similar type of subjective user state (e.g., happiness). Alternatively, the subjective user state data may indicate multiple subjective user states that represent different incidences of different types of subjective user states (e.g., happiness and sadness).
Similarly, in some embodiments where the objective occurrence data may indicate multiple objective occurrences, each of the objective occurrences indicated by the objective occurrence data may represent different incidences of the same or similar type of objective occurrence (e.g., exercising). In alternative embodiments, however, each of the objective occurrences indicated by the objective occurrence data may represent different incidences of different types of objective occurrence (e.g., user exercising and user resting).
Various techniques may be employed for correlating subjective user state data with objective occurrence data in various alternative embodiments. For example, in some embodiments, the correlation of the objective occurrence data with the subjective user state data may be accomplished by determining a sequential pattern associated with at least one subjective user state indicated by the subjective user state data and at least one objective occurrence indicated by the objective occurrence data. In other embodiments, the correlation of the objective occurrence data with the subjective user state data may involve determining multiple sequential patterns associated with multiple subjective user states and multiple objective occurrences.
A sequential pattern, as will be described herein, may define time and/or temporal relationships between two or more events (e.g., one or more subjective user states and one or more objective occurrences). In order to determine a sequential pattern, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence may be solicited, the solicitation being prompted based, at least in part, on a hypothesis linking one or more subjective user states with one or more objective occurrences and in response, at least in part, to an incidence of at least one subjective user state associated with a user.
For example, suppose a hypothesis suggests that a user or a group of users tend to be depressed whenever the weather is bad (e.g., cloudy or overcast weather). In some implementations, such a hypothesis may have been derived based on, for example, reported past events (e.g., reported past subjective user states of a user or a group of users and reported past objective occurrences). Based at least in part on the hypothesis and upon a user reporting being emotionally depressed, objective occurrence data including data indicating incidence of at least one objective occurrence may be solicited from, for example, the user or from one or more third party sources such as a weather reporting service. If the solicitation for the objective occurrence data is successful then the objective occurrence data may be acquired from the source (e.g., a user, one or more third party sources, or one or more sensors). If the acquired objective occurrence data indicates that the weather was indeed bad when the user felt depressed, then this may confirm the veracity of the hypothesis. On the other hand, if the data that is acquired after the solicitation indicates that the weather was good when the user was depressed, this may indicate that there is a weaker correlation or link between depression and bad weather.
As briefly described above, a hypothesis may be represented by a sequential pattern that may merely indicate or represent the temporal relationship or relationships between at least one subjective user state and at least one objective occurrence (e.g., whether the incidence or occurrence of at least one subjective user state occurred before, after, or at least partially concurrently with the incidence of the at least one objective occurrence). In alternative implementations, and as will be further described herein, a sequential pattern may indicate a more specific time relationship between the incidences of one or more subjective user states and the incidences of one or more objective occurrences. For example, a sequential pattern may represent the specific pattern of events (e.g., one or more objective occurrences and one or more subjective user states) that occurs along a timeline.
The following illustrative example is provided to describe how a sequential pattern associated with at least one subjective user state and at least one objective occurrence may be determined based, at least in part, on the temporal relationship between the incidence of at least one subjective user state and the incidence of at least one objective occurrence in accordance with some embodiments. For these embodiments, the determination of a sequential pattern may initially involve determining whether the incidence of the at least one subjective user state occurred within some predefined time increment from the incidence of the one objective occurrence. That is, it may be possible to infer that those subjective user states that did not occur within a certain time period from the incidence of an objective occurrence are not related or are unlikely related to the incidence of that objective occurrence.
For example, suppose a user during the course of a day eats a banana and also has a stomach ache sometime during the course of the day. If the consumption of the banana occurred in the early morning hours but the stomach ache did not occur until late that night, then the stomach ache may be unrelated to the consumption of the banana and may be disregarded. On the other hand, if the stomach ache had occurred within some predefined time increment, such as within 2 hours of consumption of the banana, then it may be concluded that there is a link between the stomach ache and the consumption of the banana. If so, a temporal relationship between the consumption of the banana and the occurrence of the stomach ache may be established. Such a temporal relationship may be represented by a sequential pattern. Such a sequential pattern may simply indicate that the stomach ache (e.g., a subjective user state) occurred after (rather than before or concurrently) the consumption of banana (e.g., an objective occurrence).
Other factors may also be referenced and examined in order to determine a sequential pattern and whether there is a relationship (e.g., causal relationship) between an incidence of an objective occurrence and an incidence of a subjective user state. These factors may include, for example, historical data (e.g., historical medical data such as genetic data or past history of the user or historical data related to the general population regarding, for example, stomach aches and bananas) as briefly described above.
In some implementations, a sequential pattern may be determined for multiple subjective user states and multiple objective occurrences. Such a sequential pattern may particularly map the exact temporal or time sequencing of the various events (e.g., subjective user states and objective occurrences). The determined sequential pattern may then be used to provide useful information to the user and/or third parties.
The following is another illustrative example of how subjective user state data may be correlated with objective occurrence data by determining multiple sequential patterns and comparing the sequential patterns with each other. Suppose, for example, a user such as a microblogger reports that the user ate a banana on a Monday. The consumption of the banana, in this example, is a reported incidence of a first objective occurrence associated with the user. The user then reports that 15 minutes after eating the banana, the user felt very happy. The reporting of the emotional state (e.g., felt very happy) is, in this example, a reported incidence of a first subjective user state. Thus, the reported incidence of the first objective occurrence (e.g., eating the banana) and the reported incidence of the first subjective user state (user felt very happy) on Monday may be represented by a first sequential pattern.
On Tuesday, the user reports that the user ate another banana (e.g., a second objective occurrence associated with the user). The user then reports that 20 minutes after eating the second banana, the user felt somewhat happy (e.g., a second subjective user state). Thus, the reported incidence of the second objective occurrence (e.g., eating the second banana) and the reported incidence of the second subjective user state (user felt somewhat happy) on Tuesday may be represented by a second sequential pattern. Under this scenario, the first sequential pattern may represent a hypothesis that links feeling happy or very happy (e.g., a subjective user state) with eating a banana (e.g., an objective occurrence). Alternatively, the first sequential pattern may merely represent historical data (e.g., historical sequential pattern). Note that in this example, the occurrences of the first subjective user state and the second subjective user state may be indicated by subjective user state data while the occurrences of the first objective occurrence and the second objective occurrence may be indicated by objective occurrence data.
In a slight variation of the above example, suppose the user had forgotten to report the consumption of the second banana on Tuesday but does report feeling somewhat happy on Tuesday. This may result in the user being asked, based at least in part on the reporting of the user feeling somewhat happy on Tuesday, and based at least in part on the hypothesis, as to whether the user ate anything around the time that the user felt happy on Tuesday. Upon the user indicating that the user ate a banana on Tuesday, a second sequential pattern may be determined based on the reported events of Tuesday.
In any event, by comparing the first sequential pattern with the second sequential pattern, the subjective user state data may be correlated with the objective occurrence data. Such a comparison may confirm the veracity of the hypothesis. In some implementations, the comparison of the first sequential pattern with the second sequential pattern may involve trying to match the first sequential pattern with the second sequential pattern by examining certain attributes and/or metrics. For example, comparing the first subjective user state (e.g., user felt very happy) of the first sequential pattern with the second subjective user state (e.g., user felt somewhat happy) of the second sequential pattern to see if they at least substantially match or are contrasting (e.g., being very happy in contrast to being slightly happy or being happy in contrast to being sad). Similarly, comparing the first objective occurrence (e.g., eating a banana) of the first sequential pattern may be compared to the second objective occurrence (e.g., eating of another banana) of the second sequential pattern to determine whether they at least substantially match or are contrasting.
A comparison may also be made to determine if the extent of time difference (e.g., 15 minutes) between the first subjective user state (e.g., user being very happy) and the first objective occurrence (e.g., user eating a banana) matches or are at least similar to the extent of time difference (e.g., 20 minutes) between the second subjective user state (e.g., user being somewhat happy) and the second objective occurrence (e.g., user eating another banana). These comparisons may be made in order to determine whether the first sequential pattern matches the second sequential pattern. A match or substantial match would suggest, for example, that a subjective user state (e.g., happiness) is linked to a particular objective occurrence (e.g., consumption of banana). In other words, confirming the hypothesis that happiness may be linked to the consumption of bananas.
As briefly described above, the comparison of the first sequential pattern with the second sequential pattern may include a determination as to whether, for example, the respective subjective user states and the respective objective occurrences of the sequential patterns are contrasting subjective user states and/or contrasting objective occurrences. For example, suppose in the above example the user had reported that the user had eaten a whole banana on Monday and felt very energetic (e.g., first subjective user state) after eating the whole banana (e.g., first objective occurrence). Suppose that the user also reported that on Tuesday he ate a half a banana instead of a whole banana and only felt slightly energetic (e.g., second subjective user state) after eating the half banana (e.g., second objective occurrence). In this scenario, the first sequential pattern (e.g., feeling very energetic after eating a whole banana) may be compared to the second sequential pattern (e.g., feeling slightly energetic after eating only a half of a banana) to at least determine whether the first subjective user state (e.g., being very energetic) and the second subjective user state (e.g., being slightly energetic) are contrasting subjective user states. Another determination may also be made during the comparison to determine whether the first objective occurrence (eating a whole banana) is in contrast with the second objective occurrence (e.g., eating a half of a banana).
In doing so, an inference may be made that eating a whole banana instead of eating only a half of a banana makes the user happier or eating more banana makes the user happier. Thus, the word “contrasting” as used here with respect to subjective user states refers to subjective user states that are the same type of subjective user states (e.g., the subjective user states being variations of a particular type of subjective user states such as variations of subjective mental states). Thus, for example, the first subjective user state and the second subjective user state in the previous illustrative example are merely variations of subjective mental states (e.g., happiness). Similarly, the use of the word “contrasting” as used here with respect to objective occurrences refers to objective states that are the same type of objective occurrences (e.g., consumption of food such as banana).
As those skilled in the art will recognize, a stronger correlation between the subjective user state data and the objective occurrence data could be obtained if a greater number of sequential patterns (e.g., if there was a third sequential pattern, a fourth sequential pattern, and so forth, that indicated that the user became happy or happier whenever the user ate bananas) are used as a basis for the correlation. Note that for ease of explanation and illustration, each of the exemplary sequential patterns to be described herein will be depicted as a sequential pattern of an incidence of a single subjective user state and an incidence of a single objective occurrence. However, those skilled in the art will recognize that a sequential pattern, as will be described herein, may also be associated with incidences or occurrences of multiple objective occurrences and/or multiple subjective user states. For example, suppose the user had reported that after eating a banana, he had gulped down a can of soda. The user then reported that he became happy but had an upset stomach. In this example, the sequential pattern associated with this scenario will be associated with two objective occurrences (e.g., eating a banana and drinking a can of soda) and two subjective user states (e.g., user having an upset stomach and feeling happy).
In some embodiments, and as briefly described earlier, the sequential patterns derived from subjective user state data and objective occurrence data may be based on temporal relationships between objective occurrences and subjective user states. For example, whether a subjective user state occurred before, after, or at least partially concurrently with an objective occurrence. For instance, a plurality of sequential patterns derived from subjective user state data and objective occurrence data may indicate that a user always has a stomach ache (e.g., subjective user state) after eating a banana (e.g., first objective occurrence).
a and 7-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 7-100 may include at least a computing device 7-10 (see
The term “standalone device” as referred to herein may be in reference to a device or system that is configured to acquire the subjective user state data 7-60* and the objective occurrence data 7-70* and performs a correlation operation to at least substantially correlate the subjective user state data 7-60* with the objective occurrence data 7-70*. In contrast, a mobile device 7-30, although may acquire both the subjective user state data 7-60* and the objective occurrence data 7-70* like a standalone device, the mobile device 7-30 does not perform a correlation operation in order to substantially correlate the subjective user state data 7-60* with the objective occurrence data 7-70*.
As previously indicated, in some embodiments, the computing device 7-10 may be a network server in which case the computing device 7-10 may communicate with a user 7-20a via a mobile device 7-30 and through a wireless and/or wired network 7-40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 7-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 7-10. In some embodiments, the mobile device 7-30 may be a handheld device such as a cellular telephone, a smartphone, a Mobile Internet Device (MID), an Ultra Mobile Personal Computer (UMPC), a convergent device such as a personal digital assistant (PDA), and so forth.
In alternative embodiments, the computing device 7-10 may be a standalone computing device 7-10 (or simply “standalone device”) that communicates directly with a user 7-20b. For these embodiments, the computing device 7-10 may be any type of handheld device. In various embodiments, the computing device 7-10 (as well as the mobile device 7-30) may be a peer-to-peer network component device. In some embodiments, the computing device 7-10 and/or the mobile device 7-30 may operate via a Web 2.0 construct (e.g., Web 2.0 application 7-268).
In embodiments where the computing device 7-10 is a server, the computing device 7-10 may acquire the subjective user state data 7-60* indirectly from a user 7-20a via a network interface 7-120 and via mobile device 7-30. In alternative embodiments in which the computing device 7-10 is a standalone device such as a handheld device (e.g., cellular telephone, a smartphone, a MID, a UMPC, a PDA, and so forth), the subjective user state data 7-60* may be directly obtained from a user 7-20b via a user interface 7-122. As will be further described, the computing device 7-10 may solicit and acquire at least a portion of the objective occurrence data 7-70* (e.g., objective occurrence data 7-70a, objective occurrence data 7-70b, and/or objective occurrence data 7-70c) from one or more alternative sources. For example, in some situations, the computing device 7-10 may obtain objective occurrence data 7-70a from one or more third party sources 7-50 (e.g., content providers, other users, health care entities, businesses such as retail businesses, health fitness centers, social organizations, and so forth). In some situations, the computing device 7-10 may obtain objective occurrence data 7-70b from one or more sensors 7-35 (e.g., blood pressure sensors, glucometers, global positioning system (GPS), heart rate monitor, and so forth). In other situations, the computing device 7-10 (in the case where the computing device 7-10 is a server) may obtain objective occurrence data 7-70c from a user 7-20a via the mobile device 7-30 and through the wireless and/or wired network 7-40 or from a user 7-20b via user interface 7-122 (when the computing device 7-10 is a standalone device).
Note that in embodiments where the computing device 7-10 is a server, the computing device 7-10 may acquire the objective occurrence data 7-70a (e.g., from the one or more third party sources 7-50) and the objective occurrence data 7-70b (e.g. from the one or more sensors 7-35) via the mobile device 7-30. That is, in certain scenarios, only the user 7-20a (and the mobile device 7-30) may have access to such data in which case the computing device 7-10 may have to rely on the user 7-20a via the mobile device 7-30 in order to acquire the objective occurrence data 7-70a and 7-70b.
In order to acquire the objective occurrence data 7-70*, the computing device 7-10 may solicit at least a portion of the objective occurrence data 7-70* from one or more of the sources (e.g., user 7-20*, one or more third party sources 7-50, and/or one or more remote devices including one or more sensors 7-35). For example, in order to solicit at least a portion of the objective occurrence data 7-70a including soliciting data indicating incidence of at least one objective occurrence 7-71a, the computing device 7-10 may transmit a solicitation for objective occurrence data 7-75a to the one or more third party sources 7-50 via wireless and/or wired networks 7-40. In order to solicit at least a portion of the objective occurrence data 7-70b including soliciting data indicating incidence of at least one objective occurrence 7-71b, the computing device 7-10 may transmit a solicitation for objective occurrence data 7-75b to the one or more sensors 7-35. Finally, in order to solicit at least a portion of the objective occurrence data 7-70c including soliciting data indicating incidence of at least one objective occurrence 7-71c, the computing device 7-10 may transmit or indicate a solicitation for objective occurrence data 7-75c to a user 7-20*.
Note that an objective occurrence data 7-70* (e.g., objective occurrence data 7-70a, 7-70b, or 7-70c) may include data that indicates multiple incidences of objective occurrences. For ease of understanding and simplicity, however, each of the objective occurrence data 7-70* illustrated in
In various embodiments, and regardless of whether the computing device 7-10 is a server or a standalone device, the computing device 7-10 may have access to at least one hypothesis 7-77. For example, in some situations, a hypothesis 7-77 may have been generated based on reported past events including past incidences of one or more subjective user states (which may be associated with a user 7-20*, a group of users 7-20*, a portion of the general population, or the general population) and past incidences of one or more objective occurrences. Such a hypothesis 7-77, in some instances, may be stored in a memory 7-140 to be easily accessible.
For ease of illustration and explanation, the following systems and operations to be described herein will be generally described in the context of the computing device 7-10 being a network server. However, those skilled in the art will recognize that these systems and operations may also be implemented when the computing device 7-10 is a standalone device such as a handheld device that may communicate directly with a user 7-20b.
The computing device 7-10, in various implementations, may be configured to solicit at least a portion of objective occurrence data 7-70* including soliciting data indicating incidence of at least one objective occurrence 7-71*. The solicitation of the data indicating incidence of at least one objective occurrence data 7-71* may be based, at least in part, on a hypothesis 7-77 that links one or more subjective user states with one or more objective occurrences and in response, at least in part, to an incidence of at least one subjective user state associated with a user 7-20*. In the case where the computing device 7-10 is a server, the computing device 7-10, based at least in part, on the hypothesis 7-77 and in response to the incidence of the at least one subjective user state associated with a user 7-20a, may transmit a solicitation or a request for the data indicating incidence of at least one objective occurrence 7-71* to the user 7-20a via a mobile device 7-30, to one or more remote devices including one or more sensors 7-35, and/or to one or more third party sources 7-50. Note that in some situations, the mobile device 7-30 may be solicited for the data indicating incidence of at least one objective occurrence 7-71c rather than soliciting from the user 7-20a. That is, in some situations, the solicited data may already have been provided to the mobile device 7-30 by the user 7-20a.
In the case where the computing device 7-10 is a standalone device, the computing device 7-10, may be configured to solicit objective occurrence data 7-70* including soliciting data indicating incidence of at least one objective occurrence 7-70c directly from a user 7-20b via a user interface 7-122, from one or more remote devices (e.g., one or more remote network servers or one or more sensors 7-35), and/or from one or more third party sources 7-50 via at least one of a wireless or wired network 7-40. After soliciting for the data indicating incidence of at least one objective occurrence 7-71*, the computing device 7-10 (e.g., either in the case where the computing device 7-10 is a server or in the case where the computing device 7-10 is a standalone device) may be further designed to acquire the data indicating incidence of at least one objective occurrence 7-71* as well as to acquire other data indicating other incidences of objective occurrences (e.g., data indicating incidence of at least a second objective occurrence 7-72*, and so forth). Examples of the types of objective occurrences that may be indicated by the objective occurrence data 7-70* include, for example, ingestions of food items, medicines, or nutraceutical by a user 7-20*, exercise routines executed a user 7-20*, social or recreational activities of a user 7-20*, activities performed by third parties, geographical locations of a user 7-20*, external events, physical characteristics of a user 7-20* at any given moment in time, and so forth.
In some embodiments, the computing device 7-10 may be configured to acquire subjective user state data 7-60* including data indicating incidence of at least one subjective user state 7-61* associated with a user 7-20*. For example, in embodiments where the computing device 7-10 is a server, the computing device 7-10 may acquire subjective user state data 7-60a including data indicating incidence of at least one subjective user state 7-61a associated with a user 7-20a. Such data may be acquired from the user 7-20a via a mobile device 7-30 or from other sources such as other network servers that may have previously stored such data and through at least one of a wireless network or a wired network 7-40. In embodiments where the computing device 7-10 is a standalone device, the computing device 7-10 may acquire subjective user state data 7-60b including data indicating incidence of at least one subjective user state 7-61b associated with a user 7-20b. Such data may be acquired from the user 7-20b via a user interface 7-122.
Note that in various alternative implementations, the subjective user state data 7-60* may include data that indicates multiple subjective user states associated with a user 7-20*. For ease of illustration and explanation, each of the subjective user state data 7-60a and the subjective user state data 7-60b illustrated in
Examples of subjective user states that may be indicated by the subjective user state data 7-60* include, for example, subjective mental states of a user 7-20* (e.g., user 7-20* is sad or angry), subjective physical states of the user 7-20* (e.g., physical or physiological characteristic of the user 7-20* such as the presence, absence, elevating, or easing of a pain), subjective overall states of the user 7-20* (e.g., user 7-20* is “well”), and/or other subjective user states that only the user 7-20* can typically indicate.
The one or more sensors 7-35 illustrated in
In some embodiments, objective occurrence data 7-70c that may be acquired from a user 7-20a via the mobile device 7-30 (or from user 7-20b via user interface 7-122) may be acquired in various forms. For these embodiments, the objective occurrence data 7-70c may be in the form of blog entries (e.g., microblog entries), status reports, or other types of electronic entries (e.g., diary or calendar entries) or messages. In various implementations, the objective occurrence data 7-70c acquired from a user 7-20* may indicate, for example, activities (e.g., exercise or food or medicine intake) performed by the user 7-20*, certain physical characteristics (e.g., blood pressure or location) associated with the user 7-20*, or other aspects associated with the user 7-20* that the user 7-20* can report objectively. The objective occurrence data 7-70c may be in the form of a text data, audio or voice data, or image data.
In various embodiments, after acquiring the subjective user state data 7-60* including data indicating incidence of at least one subjective user state 7-61* and the objective occurrence data 7-70* including data indicating incidence of at least one objective occurrence 7-71*, the computing device 7-10 may be configured to correlate the acquired subjective user state data 7-60* with the acquired objective occurrence data 7-70* by, for example, determining whether there is a sequential relationship between the one or more subjective user states as indicated by the acquired subjective user state data 7-60* and the one or more objective occurrences indicated by the acquired objective occurrence data 7-70*.
In some embodiments, and as will be further explained in the operations and processes to be described herein, the computing device 7-10 may be further configured to present one or more results of the correlation. In various embodiments, the one or more correlation results 7-80 may be presented to a user 7-20* and/or to one or more third parties in various forms (e.g., in the form of an advisory, a warning, a prediction, and so forth). The one or more third parties may be other users 7-20* (e.g., microbloggers), health care providers, advertisers, and/or content providers.
As illustrated in
a illustrates particular implementations of the objective occurrence data solicitation module 7-101 of the computing device 7-10 of
The objective occurrence data solicitation module 7-101 may include one or more sub-modules in various alternative implementations. For example, in various implementations, the objective occurrence data solicitation module 7-101 may include a requesting module 7-202 configured to request for at least a portion of objective occurrence data 7-70* including requesting for data indicating incidence of at least one objective occurrence 7-71*. The requesting module 7-202 may further include one or more sub-modules. For example, in some implementations, such as when the computing device 7-10 is a standalone device, the requesting module 7-202 may include a user interface requesting module 7-204 configured to request for data indicating incidence of at least one objective occurrence 7-71* via a user interface 7-122. The user interface requesting module 7-204, in some cases, may further include a request indication module 7-205 configured to indicate a request for data indicating incidence of at least one objective occurrence 7-71* via the user interface 7-122 (e.g., indicating through at least a display system including a display monitor or touchscreen, or indicating via an audio system including a speaker).
In some implementations, such as when the computing device 7-10 is a server, the requesting module 7-202 may include a network interface requesting module 7-206 configured to request for at least data indicating incidence of at least one objective occurrence 7-71* via a network interface 7-120. The requesting module 7-202 may include other sub-modules in various alternative implementations. For example, in some implementations, the requesting module 7-202 may include a request transmission module 7-207 configured to transmit a request to be provided with at least data indicating incidence of at least one objective occurrence 7-71*. Alternatively or in the same implementations, the requesting module 7-202 may include a request access module 7-208 configured to transmit a request to have access to at least data indicating incidence of at least one objective occurrence 7-71*.
In the same or different implementations, the network interface requesting module 7-206 may include a configuration module 7-209 designed to configure (e.g., remotely configure) one or more remote devices (e.g., a remote network server, a mobile device 7-30, or some other network device) to provide at least data indicating incidence of at least one objective occurrence 7-71*. In the same or different implementations, the requesting module 7-202 may include a directing/instructing module 7-210 configured to direct or instruct a remote device (e.g., transmitting directions or instructions to the remote device such as a remote network server or the mobile device 7-30) to provide at least data indicating incidence of at least one objective occurrence 7-71*.
The requesting module 7-202 may include other sub-modules in various alternative implementations. These sub-modules may be included with the requesting module 7-202 regardless of whether the computing device 7-10 is a server or a standalone device. For example, in some implementations, the requesting module 7-202 may include a motivation provision module 7-212 configured to provide, among other things, a motivation for requesting for the data indicating incidence of at least one objective occurrence 7-71*. In the same or different implementations, the requesting module 7-202 may include a selection request module 7-214 configured to, among other things, request a user 7-20* for a selection of an objective occurrence from a plurality of indicated alternative objective occurrences (e.g., asking the user 7-20* through the user interface 7-122* to select from alternative choices of “bad weather,” “good weather,” “consumed alcohol,” “jogging for one hour,” and so forth).
In the same or different implementations, the requesting module 7-202 may include a confirmation request module 7-216 configured to request confirmation of an incidence of at least one objective occurrence (e.g., asking a user 7-20* through the user interface 7-122* whether the user 7-20* ate spicy foods for dinner). In the same or different implementations, the requesting module 7-202 may include a time/temporal element request module 7-218 configured to, among other things, request for an indication of a time or temporal element associated with an incidence of at least one objective occurrence (e.g., asking the user 7-20* via the user interface 7-122* whether the user 7-20* ate lunch before, after, or during when the user 7-20* felt tired?).
In various implementations, the objective occurrence data solicitation module 7-101 of
b illustrates particular implementations of the subjective user state data acquisition module 7-102 of the computing device 7-10 of
The subjective user state data acquisition module 7-102, in various implementations, may include a time data acquisition module 7-228 configured to acquire (e.g., receive or generate) time and/or temporal elements associated with one or more objective occurrences. In some implementations, the time data acquisition module 7-228 may include a time stamp acquisition module 7-230 for acquiring (e.g., acquiring either by receiving or by generating) one or more time stamps associated with one or more objective occurrences In the same or different implementations, the time data acquisition module 7-228 may include a time interval acquisition module 7-231 for acquiring (e.g., either by receiving or generating) indications of one or more time intervals associated with one or more objective occurrences.
c illustrates particular implementations of the objective occurrence data acquisition module 7-104 of the computing device 7-10 of
The objective occurrence data reception module 7-234, in turn, may further include one or more sub-modules. For example, in some implementations, such as when the computing device 7-10 is a standalone device, the objective occurrence data reception module 7-234 may include a user interface data reception module 7-235 configured to receive objective occurrence data 7-70* via a user interface 7-122 (e.g., a keyboard, a mouse, a touchscreen, a microphone, an image capturing device such as a digital camera, and so forth). In some cases, the objective occurrence data 7-70* (e.g., objective occurrence data 7-70c) to be received via the user interface 7-122 may have been provided by and originate from a user 7-20b. In other cases, the objective occurrence data 7-70* to be received via the user interface 7-122 may have originated from one or more third party sources 7-50 or from one or more remote sensors 7-35 and provided by user 7-20b. In some implementations, such as when the computing device 7-10 is a server, the objective occurrence data reception module 7-234 may include a network interface data reception module 7-236 configured to, among other things, receive objective occurrence data 7-70* from at least one of a wireless network or a wired network 7-40. The network interface data reception module 7-236 may directly or indirectly receive the objective occurrence data 7-70* from a user 7-20a, from one or more third party sources 7-50, or from one or more remote devices such as one or more sensors 7-35.
Turning now to
The sequential pattern determination module 7-242, in various implementations, may include one or more sub-modules that may facilitate in the determination of one or more sequential patterns. As depicted, the one or more sub-modules that may be included in the sequential pattern determination module 7-242 may include, for example, a “within predefined time increment determination” module 7-244, a temporal relationship determination module 7-246, a subjective user state and objective occurrence time difference determination module 7-245, and/or a historical data referencing module 7-243. In brief, the within predefined time increment determination module 7-244 may be configured to determine whether an incidence of at least one subjective user state associated with a user 7-20* occurred within a predefined time increment from an incidence of at least one objective occurrence. For example, determining whether a user 7-20* “feeling bad” (i.e., a subjective user state) occurred within ten hours (i.e., predefined time increment) of eating a large chocolate sundae (i.e., an objective occurrence). Such a process may be used in order to filter out events that are likely not related or to facilitate in determining the strength of correlation between subjective user state data 7-60* and objective occurrence data 7-70*. For example, if the user 7-20* “feeling bad” occurred more than 10 hours after eating the chocolate sundae, then this may indicate a weaker correlation between a subjective user state (e.g., feeling bad) and an objective occurrence (e.g., eating a chocolate sundae).
The temporal relationship determination module 7-246 of the sequential pattern determination module 7-242 may be configured to determine the temporal relationships between one or more incidences of subjective user states associated with a user 7-20* and one or more incidences of objective occurrences. For example, this determination may entail determining whether an incidence of a particular subjective user state (e.g., sore back) occurred before, after, or at least partially concurrently with an incidence of a particular objective occurrence (e.g., sub-freezing temperature).
The subjective user state and objective occurrence time difference determination module 7-245 of the sequential pattern determination module 7-242 may be configured to determine the extent of time difference between an incidence of at least one subjective user state associated with a user 7-20* and an incidence of at least one objective occurrence. For example, determining how long after taking a particular brand of medication (e.g., objective occurrence) did a user 7-20* feel “good” (e.g., subjective user state).
The historical data referencing module 7-243 of the sequential pattern determination module 7-242 may be configured to reference historical data 7-78 in order to facilitate in determining sequential patterns. For example, in various implementations, the historical data 7-78 that may be referenced may include, for example, general population trends (e.g., people having a tendency to have a hangover after drinking or ibuprofen being more effective than aspirin for toothaches in the general population), medical information such as genetic, metabolome, or proteome information related to the user 7-20* (e.g., genetic information of the user 7-20* indicating that the user 7-20* is susceptible to a particular subjective user state in response to occurrence of a particular objective occurrence), or historical sequential patterns such as known sequential patterns of the general population or of the user 7-20* (e.g., people tending to have difficulty sleeping within five hours after consumption of coffee). In some instances, such historical data 7-78 may be useful in associating one or more incidences of subjective user states associated with a user 7-20* with one or more incidences of objective occurrences.
In some embodiments, the correlation module 7-106 may include a sequential pattern comparison module 7-248. As will be further described herein, the sequential pattern comparison module 7-248 may be configured to compare two or more sequential patterns with respect to each other to determine, for example, whether the sequential patterns at least substantially match each other or to determine whether the sequential patterns are contrasting sequential patterns.
As depicted in
The subjective user state equivalence determination module 7-250 of the sequential pattern comparison module 7-248 may be configured to determine whether subjective user states associated with different sequential patterns are at least substantially equivalent. For example, the subjective user state equivalence determination module 7-250 may determine whether a first subjective user state of a first sequential pattern is equivalent to a second subjective user state of a second sequential pattern. For instance, suppose a user 7-20* reports that on Monday he had a stomach ache (e.g., first subjective user state) after eating at a particular restaurant (e.g., a first objective occurrence), and suppose further that the user 7-20* again reports having a stomach ache (e.g., a second subjective user state) after eating at the same restaurant (e.g., a second objective occurrence) on Tuesday, then the subjective user state equivalence determination module 7-250 may be employed in order to compare the first subjective user state (e.g., stomach ache) with the second subjective user state (e.g., stomach ache) to determine whether they are equivalent. Note that in this example, the first sequential pattern may represent a hypothesis 7-77 linking a subjective user state (e.g., stomach ache) to an objective occurrence (e.g., eating at a particular restaurant).
In contrast, the objective occurrence equivalence determination module 7-251 of the sequential pattern comparison module 7-248 may be configured to determine whether objective occurrences of different sequential patterns are at least substantially equivalent. For example, the objective occurrence equivalence determination module 7-251 may determine whether a first objective occurrence of a first sequential pattern is equivalent to a second objective occurrence of a second sequential pattern. For instance, in the above example, the objective occurrence equivalence determination module 7-251 may compare eating at the particular restaurant on Monday (e.g., first objective occurrence) with eating at the same restaurant on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is equivalent to the second objective occurrence.
In some implementations, the sequential pattern comparison module 7-248 may include a subjective user state contrast determination module 7-252 that may be configured to determine whether subjective user states associated with different sequential patterns are contrasting subjective user states. For example, the subjective user state contrast determination module 7-252 may determine whether a first subjective user state of a first sequential pattern is a contrasting subjective user state from a second subjective user state of a second sequential pattern. To illustrate, suppose a user 7-20* reports that he felt very “good” (e.g., first subjective user state) after jogging for an hour (e.g., first objective occurrence) on Monday, but reports that he felt “bad” (e.g., second subjective user state) when he did not exercise (e.g., second objective occurrence) on Tuesday, then the subjective user state contrast determination module 7-252 may compare the first subjective user state (e.g., feeling good) with the second subjective user state (e.g., feeling bad) to determine that they are contrasting subjective user states.
In some implementations, the sequential pattern comparison module 7-248 may include an objective occurrence contrast determination module 7-253 that may be configured to determine whether objective occurrences of different sequential patterns are contrasting objective occurrences. For example, the objective occurrence contrast determination module 7-253 may determine whether a first objective occurrence of a first sequential pattern is a contrasting objective occurrence from a second objective occurrence of a second sequential pattern. For instance, in the previous example, the objective occurrence contrast determination module 7-253 may compare the “jogging” on Monday (e.g., first objective occurrence) with the “no jogging” on Tuesday (e.g., second objective occurrence) in order to determine whether the first objective occurrence is a contrasting objective occurrence from the second objective occurrence. Based on the contrast determination, an inference may be made that the user 7-20* may feel better by jogging rather than by not jogging at all.
In some embodiments, the sequential pattern comparison module 7-248 may include a temporal relationship comparison module 7-254 that may be configured to make comparisons between different temporal relationships of different sequential patterns. For example, the temporal relationship comparison module 7-254 may compare a first temporal relationship between a first subjective user state and a first objective occurrence of a first sequential pattern with a second temporal relationship between a second subjective user state and a second objective occurrence of a second sequential pattern in order to determine whether the first temporal relationship at least substantially matches the second temporal relationship.
For example, referring back to the earlier restaurant example, suppose the user 7-20* eating at the particular restaurant (e.g., first objective occurrence) and the subsequent stomach ache (e.g., first subjective user state) on Monday represents a first sequential pattern while the user 7-20* eating at the same restaurant (e.g., second objective occurrence) and the subsequent stomach ache (e.g., second subjective user state) on Tuesday represents a second sequential pattern. In this example, the occurrence of the stomach ache after (rather than before or concurrently) eating at the particular restaurant on Monday represents a first temporal relationship associated with the first sequential pattern while the occurrence of a second stomach ache after (rather than before or concurrently) eating at the same restaurant on Tuesday represents a second temporal relationship associated with the second sequential pattern.
Under such circumstances, the temporal relationship comparison module 7-254 may compare the first temporal relationship to the second temporal relationship in order to determine whether the first temporal relationship and the second temporal relationship at least substantially match (e.g., stomach aches in both temporal relationships occurring after eating at the restaurant). Such a match may result in the inference that a stomach ache is associated with eating at the particular restaurant and may, in some instances, confirm the veracity of a hypothesis 7-77.
In some implementations, the sequential pattern comparison module 7-248 may include an extent of time difference comparison module 7-255 that may be configured to compare the extent of time differences between incidences of subjective user states and incidences of objective occurrences of different sequential patterns. For example, the extent of time difference comparison module 7-255 may compare the extent of time difference between incidence of a first subjective user state and incidence of a first objective occurrence of a first sequential pattern with the extent of time difference between incidence of a second subjective user state and incidence of a second objective occurrence of a second sequential pattern. In some implementations, the comparisons may be made in order to determine that the extent of time differences of the different sequential patterns at least substantially or proximately match.
In some embodiments, the correlation module 7-106 may include a strength of correlation determination module 7-256 for determining a strength of correlation between subjective user state data 7-60* and objective occurrence data 7-70*. In some implementations, the strength of correlation may be determined based, at least in part, on the results provided by the other sub-modules of the correlation module 7-106 (e.g., the sequential pattern determination module 7-242, the sequential pattern comparison module 7-248, and their sub-modules).
e illustrates particular implementations of the presentation module 7-108 of the computing device 7-10 of
The presentation module 7-108 may be particularly designed to present one or more results of a correlation operation performed by the correlation module 7-106 in a variety of different forms in various alternative embodiments. For example, in some implementations, the presentation of the one or more results may entail the presentation module 7-108 presenting to the user 7-20* (or some other third party) an indication of a sequential relationship between a subjective user state and an objective occurrence associated with the user 7-20* (e.g., “whenever you eat a banana, you have a stomach ache”). In alternative implementations, other ways of presenting the results of the correlation may be employed. For example, in various alternative implementations, a notification may be provided to notify past tendencies or patterns associated with a user 7-20*. In some implementations, a notification of a possible future outcome may be provided. In other implementations, a recommendation for a future course of action based on past patterns may be provided. These and other ways of presenting the correlation results will be described in the processes and operations to be described herein.
In order to present the one or more results of a correlation operation performed by the correlation module 7-106, the presentation module 7-108 may include one or more sub-modules. For example, in some implementations, the presentation module 7-108 may include a sequential relationship presentation module 7-260 configured to present an indication of a sequential relationship between at least one subjective user state of a user 7-20* and at least one objective occurrence. In the same or different implementations, the presentation module 7-108 may include a prediction presentation module 7-261 configured to present a prediction of a future subjective user state of a user 7-20* resulting from a future objective occurrence associated with the user 7-20*. In the same or different implementations, the prediction presentation module 7-261 may also be designed to present a prediction of a future subjective user state of a user 7-20* resulting from a past objective occurrence associated with the user 7-20*. In some implementations, the presentation module 7-108 may include a past presentation module 7-262 that is designed to present a past subjective user state of a user 7-20* in connection with a past objective occurrence associated with the user 7-20*.
In some implementations, the presentation module 7-108 may include a recommendation module 7-263 configured to present a recommendation for a future action based, at least in part, on the results of a correlation of subjective user state data 7-60* with objective occurrence data 7-70* as performed by the correlation module 7-106. In certain implementations, the recommendation module 7-263 may further include a justification module 7-264 for presenting a justification for the recommendation presented by the recommendation module 7-263. In some implementations, the presentation module 7-108 may include a strength of correlation presentation module 7-266 for presenting an indication of a strength of correlation between subjective user state data 7-60* and objective occurrence data 7-70*.
In various embodiments, the computing device 7-10 of
The computing device 7-10 may also include a memory 7-140 for storing various data. For example, in some embodiments, memory 7-140 may be employed in order to store a hypothesis 7-77 and/or historical data 7-78. In some implementations, the historical data 7-78 may include historical subjective user state data of a user 7-20* that may indicate one or more past subjective user states of the user 7-20*, and historical objective occurrence data that may indicate one or more past objective occurrences. In the same or different implementations, the historical data 7-78 may include historical medical data of a user 7-20* (e.g., genetic, metoblome, proteome information), population trends, historical sequential patterns derived from general population, and so forth. Examples of a memory 7-140 include, for example, a mass storage device, read only memory (ROM), programmable read only memory (PROM), erasable programmable read-only memory (EPROM), random access memory (RAM), flash memory, synchronous random access memory (SRAM), dynamic random access memory (DRAM), and so forth.
In various embodiments, the computing device 7-10 may include a user interface 7-122 to communicate directly with a user 7-20b. For example, in embodiments in which the computing device 7-10 is a standalone device such as a handheld device (e.g., cellular telephone, smartphone, PDA, and so forth), the user interface 7-122 may be configured to directly receive from the user 7-20b subjective user state data 7-60* and/or objective occurrence data 7-70*. In some implementations, the user interface 7-122 may also be designed to visually or audibly present the results of correlating subjective user state data 7-60* with objective occurrence data 7-70*. The user interface 7-122 may include, for example, one or more of a display monitor, a touch screen, a key board, a key pad, a mouse, an audio system including a microphone and/or one or more speakers, an imaging system including a digital or video camera, and/or other user interface devices.
f illustrates particular implementations of the one or more applications 7-126 of
The various features and characteristics of the components, modules, and sub-modules of the computing device 7-10 presented thus far will be described in greater detail with respect to the processes and operations to be described herein. Note that the subjective user state data 7-60* may be in a variety of forms including, for example, text messages (e.g., blog entries, microblog entries, instant messages, text email messages, and so forth), audio messages, and/or images (e.g., an image capturing user's facial expression or gestures).
Referring to
In various implementations, in addition to these components, the mobile device 7-30 may include an objective occurrence data transmission module 7-160 that is configured to transmit (e.g., transmit via a wireless and/or wired network 7-40) at least a portion of objective occurrence data 7-70* including data indicating incidence of at least one objective occurrence 7-71*. In some implementations, the subjective user state data 7-60a and/or at least a portion of the objective occurrence data 7-70* may be transmitted to a network server such as computing device 7-10. In the same or different implementations, the mobile device 7-30 may include a correlation results reception module 7-162 that may be configured to receive, via a wireless and/or wired network 7-40, results of correlation of subjective user state data 7-60* with objective occurrence data 7-70*. In some implementations, such a correlation may have been performed at a network server (e.g., computing device 7-10).
h illustrates particular implementations of the objective occurrence data solicitation module 7-101′ of the mobile device 7-30 of
In addition, and unlike the computing device 7-10, the objective occurrence data solicitation module 7-101′ of the mobile device 7-30 may include a request to solicit reception module 7-270 that may be configured to receive a request to solicit data indicating incidence of at least one objective occurrence 7-71*. Such a request, in some implementations, may be remotely generated (e.g. remotely generated at the computing device 7-10) based, at least in part, on a hypothesis 7-77 and, in some cases, in response, at least in part, to an incidence of at least one objective occurrence.
i illustrates particular implementations of the subjective user state data acquisition module 7-102′ of the mobile device 7-30 of
Referring to
k illustrates particular implementations of the presentation module 7-108′ of the mobile device 7-30 of
l illustrates particular implementations of the one or more applications 7-126′ of the mobile device 7-30 of
A more detailed discussion of these components (e.g., modules and interfaces) that may be included in the mobile device 7-30 and those that may be included in the computing device 7-10 will be provided with respect to the processes and operations to be described herein.
In
Further, in
In any event, after a start operation, the operational flow 7-300 may move to an objective occurrence data solicitation operation 7-302 for soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence. For instance, the objective occurrence data solicitation module 7-101 of the computing device 7-10 or the objective occurrence data solicitation module 7-101′ of the mobile device 7-30 soliciting, based at least in part on a hypothesis 7-77 (e.g., the computing device 7-10 referencing a hypothesis 7-77, or the mobile device 7-30 receiving a request for soliciting at least a portion of objective occurrence data from the computing device 7-10, the request being remotely generated by the computing device 7-10 and sent to the mobile device 7-30 based at least in part on a hypothesis 7-77) that links one or more objective occurrences with one or more subjective user states (e.g., a group of users 7-20* ingesting a particular type of medicine such as aspirin, and the subsequent subjective physical states, such as pain relief, associated with the group of users 7-20*) and in response at least in part to an incidence of at least one subjective user state (e.g., pain relief by a user 7-20*) associated with a user 7-20*, at least a portion of objective occurrence data 7-70* including data indicating incidence of at least one objective occurrence 7-71* (e.g., ingestion of aspirin by user 7-20*).
Note that the solicitation of at least a portion of the objective occurrence data 7-70*, as described above, may or may not be in reference to solicitation of particular data that indicates an incidence or occurrence of a particular or particular type of objective occurrence. That is, in some embodiments, the solicitation of at least a portion of the objective occurrence data 7-70* may be in reference to solicitation for objective occurrence data 7-70* including data indicating incidence of any objective occurrence with respect to, for example, a particular point in time or time interval or with respect to a incidence of a particular subjective user state associated with the user 7-20*. While in other embodiments, the solicitation of at least a portion of the objective occurrence data 7-70* may involve soliciting for data indicating occurrence of a particular or particular type of objective occurrence.
The term “soliciting,” as will be used herein, may be in reference to direct or indirect solicitation of (e.g., requesting to be provided with, requesting to access, gathering of, or other methods of being provided with or being allowed access to) at least a portion of objective occurrence data 7-70* from one or more sources. The sources for at least a portion of the objective occurrence data 7-70* may be a user 7-20* (e.g., providing objective occurrence data 7-70c via mobile device 7-30), a mobile device 7-30 (e.g., mobile device 7-30 may have previously obtained the objective occurrence data 7-70c from the user 7-20a or from other sources), one or more network servers (not depicted), one or more third party sources 7-50 (e.g., providing objective occurrence data 7-70a), or one or more sensors 7-35 (e.g., providing objective occurrence data 7-70b).
For example, if the computing device 7-10 is a server, then the computing device 7-10 may indirectly solicit at least a portion of objective occurrence data 7-70c from a user 7-20a by transmitting, for example, a request for at least the portion of the objective occurrence data 7-70c to the mobile device 7-30, which in turn may solicit at least the portion of the objective occurrence data 7-70c from the user 7-20a. Alternatively, such data may have already been provided to the mobile device 7-30, in which case the mobile device 7-30 merely provides for or allows access to such data. Note that the objective occurrence data 7-70c that may be provided by the mobile device 7-30 may have originally been obtained from the user 7-20a, from one or more third party sources 7-50, and/or from one or more remote network devices (e.g., sensors 7-35 or network servers).
In some situations, at least a portion of objective occurrence data 7-70* may be stored in a network server (not depicted), and such a network server may be solicited for at least portion of the objective occurrence data 7-70*. In other implementations, objective occurrence data 7-70a or 7-70b may be solicited from one or more third party sources 7-50 (e.g., one or more third parties or one or more network devices such as servers that are associated with one or more third parties) or from one or more sensors 7-35. In yet other implementations in which the computing device 7-10 is a standalone device, such as a handheld device to be used directly by a user 7-20b, the computing device 7-10 may directly solicit, for example, the objective occurrence data 7-70c from the user 7-20b.
Operational flow 7-300 may further include an objective occurrence data acquisition operation 7-304 for acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. For instance, the objective occurrence data acquisition module 7-104* of the computing device 7-10 or the mobile device 7-30 acquiring (e.g., receiving or accessing by the computing device 7-10 or by the mobile device 7-30) the objective occurrence data 7-70* including the data indicating incidence of at least one objective occurrence 7-71*.
In various implementations, the objective occurrence data solicitation operation 7-302 of
In various implementations, the requesting operation 7-402 may further include one or more additional operations. For example, in some implementations, the requesting operation 7-402 may include an operation 7-403 for requesting for the data indicating incidence of at least one objective occurrence via a user interface as depicted in
Operation 7-403, in turn, may further include an operation 7-404 for indicating a request for the data indicating incidence of at least one objective occurrence through at least a display device as depicted in
In the same or different implementations, operation 7-403 may include an operation 7-405 for indicating a request for the data indicating incidence of at least one objective occurrence through at least an audio device as depicted in
In some implementations, the requesting operation 7-402 may include an operation 7-406 for requesting for the data indicating incidence of at least one objective occurrence via at least one of a wireless network or a wired network as depicted in
In various implementations, the requesting operation 7-402 may include an operation 7-407 for requesting the user to select an objective occurrence from a plurality of indicated alternative objective occurrences as depicted in
In some implementations, operation 7-407 may further include an operation 7-408 for requesting the user to select an objective occurrence from a plurality of indicated alternative contrasting objective occurrences as depicted in
In some implementations, the requesting operation 7-402 may include an operation 7-409 for requesting the user to confirm incidence of the at least one objective occurrence as depicted in
In some implementations, the requesting operation 7-402 may include an operation 7-410 for requesting the user to provide an indication of an incidence of at least one objective occurrence that occurred during a specified point in time as depicted in
In some implementations, the requesting operation 7-402 may include an operation 7-411 for requesting the user to provide an indication of an incidence of at least one objective occurrence that occurred during a specified time interval as depicted in
In some implementations, the requesting operation 7-402 may include an operation 7-412 for requesting the user to indicate an incidence of at least one objective occurrence with respect to the incidence of the at least one subjective user state associated with the user as depicted in
In various implementations, the requesting operation 7-402 may include an operation 7-413 for providing a motivation for requesting for the data indicating incidence of at least one objective occurrence as depicted in
In some implementations, operation 7-413 may include an operation 7-414 for providing a motivation for requesting for the data indicating incidence of at least one objective occurrence, the motivation relating to the link between the one or more objective occurrences with the one or more subjective user states as provided by the hypothesis as depicted in
In various implementations, the solicitation operation 7-302 of
In various implementations, the requesting operation 7-415 may include one or more additional operations. For example, in some implementations, the requesting operation 7-415 may include an operation 7-416 for requesting for the data indicating incidence of at least one objective occurrence from one or more third party sources via at least one of a wireless network or a wired network as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-417 for requesting the one or more third party sources to confirm incidence of the at least one objective occurrence as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-418 for requesting the one or more third party sources to provide an indication of an incidence of at least one objective occurrence that occurred during a specified point in time as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-419 for requesting the one or more third party sources to provide an indication of an incidence of at least one objective occurrence that occurred during a specified time interval as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-420 for requesting the one or more third party sources to provide an indication of an incidence of at least one objective occurrence that occurred with respect to the incidence of the at least one subjective user state associated with the user as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-421 for requesting for the data indicating incidence of at least one objective occurrence from one or more content providers as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-422 for requesting for the data indicating incidence of at least one objective occurrence from one or more other users as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-423 for requesting for the data indicating incidence of at least one objective occurrence from one or more health care entities as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-424 for requesting for the data indicating incidence of at least one objective occurrence from one or more health fitness entities as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-425 for requesting for the data indicating incidence of at least one objective occurrence from one or more business entities as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-426 for requesting for the data indicating incidence of at least one objective occurrence from one or more social groups as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-427 for requesting for the data indicating incidence of at least one objective occurrence from one or more third party sources via a network interface as depicted in
In some implementations, the requesting operation 7-415 may include an operation 7-428 for requesting for the data indicating incidence of at least one objective occurrence from one or more third party sources through at least one of a wireless network or a wired network as depicted in
In various implementations, the solicitation operation 7-302 of
Operation 7-429, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 7-429 may include an operation 7-430 for transmitting a request to be provided with the data indicating incidence of at least one objective occurrence to the one or more remote devices as depicted in
In some implementations, operation 7-429 may include an operation 7-431 for transmitting a request to have access to the data indicating incidence of at least one objective occurrence to the one or more remote devices as depicted in
In some implementations, operation 7-429 may include an operation 7-432 for configuring one or more remote devices to provide the data indicating incidence of at least one objective occurrence as depicted in
In some implementations, operation 7-429 may include an operation 7-433 for directing or instructing the one or more remote devices to provide the data indicating incidence of at least one objective occurrence as depicted in
In some implementations, operation 7-429 may include an operation 7-434 for requesting for the data indicating incidence of at least one objective occurrence from one or more sensors as depicted in
In some implementations, operation 7-429 may include an operation 7-435 for requesting for the data indicating incidence of at least one objective occurrence from one or more network servers as depicted in
In some implementations, operation 7-429 may include an operation 7-436 for requesting for the data indicating incidence of at least one objective occurrence from one or more mobile devices as depicted in
In some implementations, operation 7-429 may include an operation 7-437 for requesting for the data indicating incidence of at least one objective occurrence from one or more remote devices through at least one of a wireless network or a wired network as depicted in
In some implementations, operation 7-429 may include an operation 7-438 for requesting for the data indicating incidence of at least one objective occurrence from one or more remote devices via a network interface as depicted in
In various implementations, the solicitation operation 7-302 of
In some implementations, the solicitation operation 7-302 may include an operation 7-440 for requesting to be provided with an indication of a time interval associated with the incidence of at least one objective occurrence as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-441 for requesting to be provided with an indication of a temporal relationship between the incidence of the at least one subjective user state associated with the user and the incidence of the at least one objective occurrence as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-442 for soliciting data indicating an ingestion by the user of a medicine as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-443 for soliciting data indicating an ingestion by the user of a food item as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-444 for soliciting data indicating an ingestion by the user of a nutraceutical as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-445 for soliciting data indicating an exercise routine executed by the user as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-446 for soliciting data indicating a social activity executed by the user as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-447 for soliciting data indicating an activity performed by one or more third parties as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-448 for soliciting data indicating one or more physical characteristics of the user as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-449 for soliciting data indicating a resting, a learning, or a recreational activity by the user as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-450 for soliciting data indicating occurrence of one or more external events as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-451 for soliciting data indicating one or more locations of the user as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-452 for soliciting data indicating incidence of at least one objective occurrence that occurred during a specified point in time as depicted in
In some implementations, the solicitation operation 7-302 may include an operation 7-453 for soliciting data indicating incidence of at least one objective occurrence that occurred during a specified time interval as depicted in
In various implementations, the solicitation operation 7-302 of
Operation 7-454, in various implementations, may further include one or more additional operations. For example, in some implementations, operation 7-454 may include an operation 7-455 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies one or more temporal relationships between the one or more objective occurrences and the one or more subjective user states as depicted in
In some cases, operation 7-455 may further include an operation 7-456 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies one or more time sequential relationships between the at least one subjective user state and the one or more objective occurrences as depicted in
In some implementations, operation 7-454 may include an operation 7-457 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between at least an ingestion of a medicine and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-458 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between at least an ingestion of a food item and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-459 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between at least an ingestion of a nutraceutical and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-460 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between execution of one or more exercise routines and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-461 for soliciting the data indicating incidence of at least one subjective user state associated with the user based, at least in part, on referencing a hypothesis that identifies a relationship between execution of one or more social activities and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-462 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between one or more activities executed by a third party and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-463 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between one or more physical characteristics of the user and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-464 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between a resting, a learning, or a recreational activity performed by the user and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-465 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between one or more external activities and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-466 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that identifies a relationship between one or more locations of the user and the one or more subjective user states as depicted in
In some implementations, operation 7-454 may include an operation 7-467 for soliciting the data indicating incidence of at least one objective occurrence based, at least in part, on referencing a hypothesis that links the at least one subjective user state with one or more historical objective occurrences as depicted in
In various implementations, the solicitation operation 7-302 of
Operation 7-468, in turn, may further include one or more additional operations. For example, in some implementations, operation 7-468 may include an operation 7-469 for soliciting the data indicating incidence of at least one objective occurrence in response to a reception of a request to solicit the data indicating incidence of at least one objective occurrence, the request to solicit being remotely generated based, at least in part, on the hypothesis and in response to the incidence of the at least one subjective user state associated with the user as depicted in
In some implementations, operation 7-468 may include an operation 7-470 for receiving the request to solicit the data indicating incidence of at least one objective occurrence via at least one of a wireless network or a wired network as depicted by
Operation 7-470, in turn, may include an operation 7-471 for receiving the request to solicit the data indicating incidence of at least one objective occurrence from a network server as depicted by
In various implementations, the solicitation operation 7-302 of
In some implementations, operation 7-472 may further include an operation 7-473 for soliciting the data indicating incidence of at least one objective occurrence in response, at least in part, to receiving data indicating incidence of the at least one subjective user state associated with the user via a user interface as depicted in
In some implementations, operation 7-472 may include an operation 7-474 for soliciting the data indicating incidence of at least one objective occurrence in response, at least in part, to receiving data indicating incidence of the at least one subjective user state associated with the user via a network interface as depicted in
In some implementations, operation 7-472 may include an operation 7-475 for soliciting the data indicating incidence of at least one objective occurrence in response, at least in part, to receiving data indicating incidence of the at least one subjective user state associated with the user via one or more blog entries as depicted in
In some implementations, operation 7-472 may include an operation 7-476 for soliciting the data indicating incidence of at least one objective occurrence in response, at least in part, to receiving data indicating incidence of the at least one subjective user state associated with the user via one or more status reports as depicted in
In some implementations, operation 7-472 may include an operation 7-477 for soliciting the data indicating incidence of at least one objective occurrence in response, at least in part, to receiving data indicating incidence of the at least one subjective user state associated with the user via one or more electronic messages as depicted in
In some implementations, operation 7-472 may include an operation 7-478 for soliciting the data indicating incidence of at least one objective occurrence in response, at least in part, to receiving data indicating incidence of the at least one subjective user state associated with the user from the user as depicted in
Referring back to
In various alternative implementations, the reception module 7-502 may include one or more additional operations. For example, in some implementations, the reception operation 7-502 may include an operation 7-504 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence via a user interface as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-506 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence from at least one of a wireless network or a wired network as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-510 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence via one or more blog entries as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-512 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence via one or more status reports as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-514 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence via one or more electronic messages as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-516 for receiving a selection made by the user, the selection being a selection of an objective occurrence from a plurality of indicated alternative objective occurrences as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-518 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence from the user as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-520 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence from one or more third party sources as depicted in
In some implementations, the reception operation 7-502 may include an operation 7-522 for receiving the objective occurrence data including the data indicating incidence of at least one objective occurrence from one or more remote devices as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 of
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-526 for acquiring data indicating an ingestion by the user of a food item as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-528 for acquiring data indicating an ingestion by the user of a nutraceutical as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-530 for acquiring data indicating an exercise routine executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-532 for acquiring data indicating a social activity executed by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-534 for acquiring data indicating an activity performed by one or more third parties as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-536 for acquiring data indicating one or more physical characteristics of the user as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-538 for acquiring data indicating a resting, a learning, or a recreational activity by the user as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-540 for acquiring data indicating occurrence of one or more external events as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-542 for acquiring data indicating one or more locations of the user as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-544 for acquiring data indicating incidence of at least one objective occurrence that occurred during a specified point in time as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-546 for acquiring data indicating incidence of at least one objective occurrence that occurred during a specified time interval as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-548 for acquiring data indicating incidence of at least one objective occurrence at a server as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-550 for acquiring data indicating incidence of at least one objective occurrence at a handheld device as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-552 for acquiring data indicating incidence of at least one objective occurrence at a peer-to-peer network component device as depicted in
In some implementations, the objective occurrence data acquisition operation 7-304 may include an operation 7-554 for acquiring data indicating incidence of at least one objective occurrence via a Web 2.0 construct as depicted in
Referring to
In addition, and unlike operation 7-300 of
In various alternative implementations, the subjective user state data acquisition operation 7-606 may include one or more additional operations. For example, in some implementations, the subjective user state data acquisition operation 7-606 may include a reception operation 7-702 for receiving the subjective user state data as depicted in
The reception operation 7-702, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, the reception operation 7-702 may include an operation 7-704 for receiving the subjective user state data via a user interface as depicted in
In some implementations, the reception operation 7-702 may include an operation 7-706 for receiving the subjective user state data from at least one of a wireless network or a wired network as depicted in
In some implementations, the reception operation 7-702 may include an operation 7-708 for receiving the subjective user state data via one or more blog entries as depicted in
In some implementations, the reception operation 7-702 may include an operation 7-710 for receiving the subjective user state data via one or more status reports as depicted in
In some implementations, the reception operation 7-702 may include an operation 7-712 for receiving the subjective user state data via one or more electronic messages as depicted in
In some implementations, the reception operation 7-702 may include an operation 7-714 for receiving the subjective user state data from the user as depicted in
Operation 7-714, in turn, may further include an operation 7-716 for receiving the subjective user state data from the user via one or more remote network devices as depicted in
In some implementations, the reception operation 7-702 may include an operation 7-718 for receiving a selection made by the user, the selection being a selection of a subjective user state from a plurality of indicated alternative subjective user states as depicted in
In various implementations, the subjective user state data acquisition operation 7-606 of
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-722 for acquiring data indicating at least one subjective physical state associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-724 for acquiring data indicating at least one subjective overall state associated with the user as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-726 for acquiring a time stamp associated with the incidence of the at least one subjective user state as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-728 for acquiring an indication of a time interval associated with the incidence of the at least one subjective user state as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-730 for acquiring the subjective user state data at a server as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-732 for acquiring the subjective user state data at a handheld device as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-734 for acquiring the subjective user state data at a peer-to-peer network component device as depicted in
In some implementations, the subjective user state data acquisition operation 7-606 may include an operation 7-736 for acquiring the subjective user state data via a Web 2.0 construct as depicted in
Referring now to
In addition, and unlike operational flow 7-600, operational flow 7-800 may further include a correlation operation 7-808 for correlating the subjective user state data with the objective occurrence data and a presentation operation 7-810 for presenting one or more results of the correlating of the subjective user state data with the objective occurrence data as depicted in
In various alternative implementations, the correlation operation 7-808 may include one or more additional operations. For example, in some implementations, the correlation operation 7-808 may include an operation 7-902 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence as depicted in
Operation 7-902, in turn, may further include one or more additional operations. For example, in some implementations, operation 7-902 may include an operation 7-904 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing historical data as depicted in
In some implementations, operation 7-904 may include an operation 7-906 for correlating the subjective user state data with the objective occurrence data based, at least in part, on a historical sequential pattern as further depicted in
In some implementations, operation 7-904 may include an operation 7-908 for correlating the subjective user state data with the objective occurrence data based, at least in part, on referencing historical medical data as depicted in
In various implementations, operation 7-902 may include an operation 7-910 for comparing the at least one sequential pattern to a second sequential pattern to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
Operation 7-910, in some implementations, may further include an operation 7-912 for comparing the at least one sequential pattern to a second sequential pattern related to at least a second subjective user state associated with the user and a second objective occurrence to determine whether the at least one sequential pattern at least substantially matches with the second sequential pattern as depicted in
For these implementations, the comparison of the first sequential pattern to the second sequential pattern may involve making certain comparisons, For example, comparing the first subjective user state to the second subjective user state to determine at least whether they are the same or different types of subjective user states. Similarly, the first objective occurrence may be compared to the second objective occurrence to determine at least whether they are the same or different types of objective occurrences. The temporal relationship or the specific time sequencing between the incidence of the first subjective user state and the incidence of the first objective occurrence (e.g., as represented by the first sequential pattern) may then be compared to the temporal relationship or the specific time sequencing between the incidence of the second subjective user state and the incidence of the second objective occurrence (e.g., as represented by the second sequential pattern).
In some implementations, the correlation operation 7-808 of
In alternative implementations, the correlation operation 7-808 may include an operation 7-916 for correlating the subjective user state data with the objective occurrence data at a handheld device as depicted in
In some implementations, the correlation operation 7-808 may include an operation 7-918 for correlating the subjective user state data with the objective occurrence data at a peer-to-peer network component device as depicted in
Referring back to
In some implementations, the presentation operation 7-810 may include an operation 7-1004 for transmitting the one or more results of the correlating via a network interface as depicted in
In some implementations, the presentation operation 7-810 may include an operation 7-1006 for presenting an indication of a sequential relationship between the at least one subjective user state and the at least one objective occurrence as depicted in
In some implementations, the presentation operation 7-810 may include an operation 7-1008 for presenting a prediction of a future subjective user state resulting from a future objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 7-810 may include an operation 7-1010 for presenting a prediction of a future subjective user state resulting from a past objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 7-810 may include an operation 7-1012 for presenting a past subjective user state in connection with a past objective occurrence associated with the user as depicted in
In some implementations, the presentation operation 7-810 may include an operation 7-1014 for presenting a recommendation for a future action as depicted in
In some implementations, operation 7-1014 may further include an operation 7-1016 for presenting a justification for the recommendation as depicted in
In some implementations, the presentation operation 7-810 may include an operation 7-1018 for presenting the hypothesis as depicted in
In addition, and unlike operational flow 7-800, operational flow 7-1100 may further include an objective occurrence data transmission operation 7-1106 for transmitting the acquired objective occurrence data including the data indicating incidence of at least one objective occurrence and a reception operation 7-1108 for receiving one or more results of correlation of the objective occurrence data with subjective user state data including data indicating the incidence of the at least one subjective user state associated with the user as depicted in
The correlation results reception module 7-162 of the mobile device 7-30 may then receive (e.g., receive from the computing device 7-10) one or more results of correlation of the subjective user state data 7-60a with objective occurrence data 7-70* including data indicating the incidence of the at least one objective occurrence 7-71*.
In various alternative implementations, the objective occurrence data transmission operation 7-1106 may include one or more additional operations. For example, in some implementations, the objective occurrence data transmission operation 7-1106 may include an operation 7-1202 for transmitting the acquired objective occurrence data via at least a wireless network or a wired network as depicted in
In some implementations, operation 7-1202 may further include an operation 7-1204 for transmitting the acquired objective occurrence data via one or more blog entries as depicted in
In some implementations, operation 7-1202 may include an operation 7-1206 for transmitting the acquired objective occurrence data via one or more status reports as depicted in
In some implementations, operation 7-1202 may include an operation 7-1208 for transmitting the acquired objective occurrence data via one or more electronic messages as depicted in
In some implementations, operation 7-1202 may include an operation 7-1210 for transmitting the acquired objective occurrence data to a network server as depicted in
Referring back to
In some implementations, the reception operation 7-1108 may include an operation 7-1304 for receiving a prediction of a future subjective user state resulting from a future objective occurrence associated with the user as depicted in
In some implementations, the reception operation 7-1108 may include an operation 7-1306 for receiving a prediction of a future subjective user state resulting from a past objective occurrence associated with the user as depicted in
In some implementations, the reception operation 7-1108 may include an operation 7-1308 for receiving a past subjective user state in connection with a past objective occurrence as depicted in
In some implementations, the reception operation 7-1108 may include an operation 7-1310 for receiving a recommendation for a future action as depicted in FIG. 7-13. For instance, the correlation results reception module 7-162 of the mobile device 7-30 receiving (e.g., via wireless network and/or wired network 7-40) at least a recommendation for a future action (e.g., “you should go to sleep early”).
In certain implementations, operation 7-1310 may further include an operation 7-1312 for receiving a justification for the recommendation as depicted in
In some implementations, the reception operation 7-1108 may include an operation 7-1314 for receiving an indication of the hypothesis as depicted in
Referring back to
In some implementations, operation 7-1402 may further include an operation 7-1404 for indicating the one or more results of the correlation via at least a display device as depicted in
In some implementations, operation 7-1402 may include an operation 7-1406 for indicating the one or more results of the correlation via at least an audio device as depicted in
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where users may report or post their latest status, personal activities, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social networking status reports in which a user may report or post for others to view their current status, activities, and/or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life. Typically, such microblog entries will describe the various “events” associated with or are of interest to the microblogger that occurs during a course of a typical day. The microblog entries are often continuously posted during the course of a typical day, and thus, by the end of a normal day, a substantial number of events may have been reported and posted.
Each of the reported events that may be posted through microblog entries may be categorized into one of at least three possible categories. The first category of events that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, incident, happening, or any other event that occurs with respect to the microblogger or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. Such events would include, for example, intake of food, medicine, or nutraceutical, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, activities of the microblogger observable by others or by a device, activities of others that may or may not be of interest to the microblogger, external events such as performance of the stock market (which the microblogger may have an interest in), performance of a favorite sports team, and so forth. In some cases, objective occurrences may not be at least directly associated with a microblogger. Examples of such objective occurrences include, for example, external events that may not be directly related to the microblogger such as the local weather, activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of events that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger may include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be directly reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., happiness, sadness, anger, tension, state of alertness, state of mental fatigue, jealousy, envy, and so forth), the subjective physical state of the microblogger (e.g., upset stomach, state of vision, state of hearing, pain, and so forth), and the subjective overall state of the microblogger (e.g., “good,” “bad,” state of overall wellness, overall fatigue, and so forth). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states).
A third category of events that may be reported or posted through microblog entries include “subjective observations” made by the microblogger. A subjective observation is any subjective opinion, thought, or evaluation relating to any incidence. Examples include, for example, a microblogger's perception about the subjective user state of another person (e.g., “he seems tired”), a microblogger's perception about another person's activities (e.g., “he drank too much yesterday”), a microblogger's perception about an external event (e.g., “it was a nice day today”), and so forth. Although microblogs are being used to provide a wealth of personal information, thus far they have been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided to, among other things, develop one or more hypotheses that may be specific to a particular person (e.g. a microblogger) based on selective reported events. The methods, systems, and computer program products may be employed in a variety of environments including, for example, social networking environments, blogging or microblogging environments, instant messaging (IM) environments, or any other type of environment that allows a user to maintain a diary. A “hypothesis,” as referred to herein, may define one or more relationships or links between a first one or more event types (e.g., a type of event such as a particular type of subjective user state, for example, “happy”) and a second one or more event types (e.g., another type of event such as particular type of objective occurrence, for example, favorite sports team winning). In some embodiments, a hypothesis may, at least in part, be defined or represented by an events pattern that indicates or suggests a spatial or a sequential (e.g., time/temporal) relationship between different event types. Such a hypothesis, in some cases, may also indicate the strength or weakness of the link between the different event types. That is, the strength (e.g., soundness) or weakness of the correlation between different event types may depend upon, for example, whether the events pattern repeatedly occurs.
In various embodiments, the development of such a hypothesis may be particularly useful to the user that the hypothesis is associated with. That is, in some cases, the hypothesis may assist the user in modifying his/her future behavior, while in other cases; such a hypothesis may simply alert or notify the user that a pattern of events are repeatedly occurring. In other situations, such a hypothesis may be useful to third parties such as advertisers in order to assist the advertisers in developing a more targeted marketing scheme. In still other situations, such a hypothesis may help in the treatment of ailments associated with the user.
In the case where a hypothesis is being developed for a particular user, such as a microblogger, the methods, systems, and computer program products may be able to disregard or ignore reported events that may not be relevant to the development of the hypothesis. In particular, during a course of a typical day, a user such as microblogger may post a large volume of data that indicates numerous events that may have occurred during the course of the day. It is likely that a vast majority of these reported events may not be relevant to the development of a particular hypothesis. Thus, these methods, systems, and computer program products may distinguish between relevant and non-relevant data. In other words, to disregard or ignore those reported events that may not be relevant to the development of the hypothesis and use only selective reported events for developing the hypothesis. Note that the hypothesis to be developed may or may not determine a causal relationship between multiple events. Instead, the developed hypothesis may merely indicate that there is some sort of relationship (e.g., spatial or time/temporal sequential relationship) between multiple events.
As briefly described above, a hypothesis may be represented by an events pattern that may indicate spatial or sequential (e.g., time or temporal) relationship or relationships between multiple event types. In some implementations, a hypothesis may indicate temporal sequential relationships between multiple event types that merely indicate the temporal relationships between multiple event types. In alternative implementations a hypothesis may indicate a more specific time relationship between multiple event types. For example, a sequential pattern may represent the specific pattern of events that occurs along a timeline that may indicate the specific time intervals between event types.
a and 8-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 8-100 may include at least a computing device 8-10 (see
As indicated earlier, in some embodiments, the computing device 8-10 may be a server while in other embodiments, the computing device 8-10 may be a standalone device. In the case where the computing device 8-10 is a network server, the computing device 8-10 may communicate indirectly with a user 8-20a via wireless and/or wired network 8-40. In contrast, when the computing device 8-10 is a standalone device, it may communicate directly with a user 8-20b via a user interface 8-122 (see
In embodiments in which the computing device 8-10 is a network server, the computing device 8-10 may communicate with a user 8-20a via a mobile device 8-30 and through a wireless and/or wired network 8-40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 8-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device that can communicate with the computing device 8-10. In some embodiments, the mobile device 8-30 may be a handheld device such as a cellular telephone, a smartphone, a Mobile Internet Device (MID), an Ultra Mobile Personal Computer (UMPC), a convergent device such as a personal digital assistant (PDA), and so forth.
In embodiments in which the computing device 8-10 is a standalone computing device 8-10 (or simply “standalone device”) that communicates directly with a user 8-20b, the computing device 8-10 may be any type of mobile device 8-30 (e.g., a handheld device) or stationary device (e.g., desktop computer or workstation). For these embodiments, the computing device 8-10 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication device. In some embodiments, in which the computing device 8-10 is a handheld device, the computing device 8-10 may be a cellular telephone, a smartphone, an MID, an UMPC, a convergent device such as a PDA, and so forth. In various embodiments, the computing device 8-10 may be a peer-to-peer network component device. In some embodiments, the computing device 8-10 and/or the mobile device 8-30 may operate via a Web 2.0 construct (e.g., Web 2.0 application 8-268).
In various embodiments, the computing device 8-10 may be configured to acquire events data 8-60* from one or more sources. Events data 8-60*, as will be described herein, may indicate the occurrences of multiple reported events. Each of the reported events may or may not be associated with a user 8-20*. In some embodiments, a reported event may be associated with the user 8-20* if it is reported by the user 8-20* or it is related to some aspect about the user 8-20* (e.g., the location of the user 8-20*, the local weather of the user 8-20*, activities performed by the user 8-20*, physical characteristics of the user 8-20* as detected by a sensor 8-35, subjective user state of the user 8-20*, and so forth). At least three different types of reported events may be indicated by the events data 8-60*, subjective user states associated with a user 8-20*, objective occurrences, and subjective observations made by the user 8-20* or by others (e.g., third party sources 8-50).
The events data 8-60*, in various embodiments and as illustrated in
As will be further described herein, in the following examples and illustrations, the first one or more reported events and the second one or more reported events may form the basis for developing a hypothesis. In contrast, the third one or more reported events may represent events that may not be relevant to the development of the hypothesis. In other words, the third one or more reported events may represent “noise” and may be ignored in the development of a hypothesis. That is, noise data must be accounted for particularly in, for example, the microblogging and social networking environments where much of the reported events posted through microblog entries and status reports may not be relevant to the development of a hypothesis. Such noise data may be filtered out prior to developing a useful hypothesis.
The events data 8-60* including the data indicating incidence of a first one or more reported events 8-61* and the data indicating incidence of a second one or more reported events 8-62* may be obtained from one or more distinct sources (e.g., the original sources for data). For example, in some implementations, a user 8-20* may provide at least a portion of the events data 8-60* (e.g., events data 8-60a that may include data indicating incidence of a first one or more reported events 8-61a, data indicating incidence of a second one or more reported events 8-62a, and/or data indicating incidence of a third one or more reported events 8-63a).
In the same or different embodiments, one or more remote network devices including one or more sensors 8-35 and/or one or more network servers 8-36 may provide at least a portion of the events data 8-60* (e.g., events data 8-60b that may include data indicating incidence of a first one or more reported events 8-61b, data indicating incidence of a second one or more reported events 8-62b, and/or data indicating incidence of a third one or more reported events 8-63b). In same or different embodiments, one or more third party sources may provide at least a portion of the events data 8-60* (e.g., events data 8-60c that may include data indicating incidence of a first one or more reported events 8-61c, data indicating incidence of a second one or more reported events 8-62c, and/or data indicating incidence of a third one or more reported events 8-63c). In still other embodiments, at least a portion of the events data 8-60* may be retrieved from a memory 8-140 in the form of historical data 8-82.
The one or more sensors 8-35 illustrated in
The one or more third party sources 8-50 illustrated in
In brief, after acquiring the events data 8-60* from one or more sources, the computing device 8-10 may determine an events pattern based selectively (e.g., by disregarding the third one or more reported events or other noise data) on the incidences of the first one or more reported events and the second one or more reported events as indicated by the events data 8-60*. The events pattern may at least identify the link or relationship (e.g., spatial or sequential relationship) between the first one or more reported events and the second one or more reported events.
After determining the events pattern, the computing device 8-10 may be configured to develop a hypothesis associated with the user 8-20* based, at least in part, on the determined events pattern. The development of the hypothesis may involve creation of a new hypothesis in some cases while in other cases; it may involve the refinement of an already existing hypothesis 8-80. The creation of the hypothesis may be based, in addition to the events pattern, on historical data 8-82 that may be particularly associated with the user 8-20* or with a subgroup of the general population that the user 8-20* belongs to. In some embodiments, the historical data 8-82 may be historical medical data specific to the user 8-20* or to the subgroup of the general population, or may be events data 8-60* that indicate past reported events (that may or may not be associated with the user 8-20*). Other types of past data may also be included in the historical data 8-82 in various alternative embodiments.
After developing the hypothesis, in some implementations, the computing device 8-10 may be designed to execute one or more actions. One such action that may be executed is to present one or more results 8-90 of the hypothesis development operations. For example, the computing device 8-10 may present the results 8-90 to the user 8-20* (e.g., by transmitting the results to the user 8-20a or indicating the results 8-90 to the user 8-20b via a user interface 8-122), to one or more third parties (e.g., one or more third party sources 8-50), and/or to one or more remote network devices (e.g., network servers 8-36). The results 8-90 to be presented may include the developed hypothesis, an advisory based on the hypothesis, a recommendation based on the hypothesis, or other types of results.
As illustrated in
The events data acquisition module 8-102 may be configured to, among other things, acquire events data 8-60* from one or more distinct sources. The events data 8-60* to be acquired by the events data acquisition module 8-102 may include at least data indicating incidence of a first one or more reported events 8-61* and data indicating incidence of a second one or more reported events 8-62*. At least one of the first one or more reported events 8-61* and the second one or more reported events 8-62* may be associated with a user 8-20*. The events data acquisition module 8-102 may also be designed to acquire data indicating incidence of a third one or more reported events 8-63* and other data indicating additional reported events from various sources.
Referring now to
The events pattern determination module 8-104 of the computing device 8-10 of
b illustrates particular implementations of the events pattern determination module 8-104 of
The hypothesis development module 8-106 of the computing device 8-10 of
c illustrates particular implementations of the hypothesis development module 8-106 of
In various implementations, the hypothesis development module 8-106 may include a determination module 8-222 to facilitate in the further development of an existing hypothesis 8-80. In particular, the determination module 8-222 may be configured to determine whether the events pattern determined by, for example, the events pattern determination module 8-104 supports an existing hypothesis 8-82 associated with the user 8-20*. The determination module 8-222 may further include a comparison module 8-224 designed to compare the events pattern determined by, for example, the events pattern determination module 8-104 to an events pattern associated with the existing hypothesis 8-80 (e.g., an events pattern that links a first one or more event types with a second one or more event types) to determine whether the determined events pattern supports the existing hypothesis 8-80.
The comparison module 8-224 may also include a strength determination module 8-226 and/or a weakness determination module 8-228. In various implementations, the strength determination module 8-226 may be designed to determine the strength (e.g., soundness) of the existing hypothesis 8-80 associated with the user 8-20* based, at least in part on the comparison made by the comparison module 8-224. In particular, the strength determination module 8-226 may determine the strength of the relationship (or link) between a first one or more event types and a second one or more event types identified by the existing hypothesis 8-80 based on the comparison made by the comparison module 8-224. Note that if the determined events pattern exactly or substantially matches the events pattern associated with the existing hypothesis 8-80, then that may lead to the conclusion that the existing hypothesis 8-80 is relatively sound.
In contrast, the weakness determination module 8-228 may be designed to determine the weakness of the existing hypothesis 8-80 associated with the user 8-20* based, at least in part on the comparison made by the comparison module 8-224. In particular, the weakness determination module 8-228 may determine the weakness of the relationship (or link) between a first one or more event types and a second one or more event types identified by the existing hypothesis 8-82 based on the comparison made by the comparison module 8-224. Note that if the determined events pattern is completely or substantially dissimilar to the events pattern associated with the existing hypothesis 8-80, then that may lead to the conclusion that the existing hypothesis 8-80 is relatively weak. The strength or weakness relating the existing hypothesis 8-80, as determined by the strength determination module 8-226 or the weakness determination module 8-228, may be added to the existing hypothesis 8-80 to further develop or refine the existing hypothesis 8-80.
In various implementations, the hypothesis development module 8-106 may include a determined events pattern referencing module 8-230 configured to reference events pattern that have been determined by, for example, the events pattern determination module 8-104. Such referencing of the determined events pattern may facilitate the hypothesis development module 8-106 in developing a hypothesis associated with the user 8-20*.
The action execution module 8-108 of the computing device 8-10 may be configured to execute one or more actions in response to, for example, the hypothesis development module 8-106 developing the hypothesis. Referring now to
The transmission module 8-234 may be designed to, for example, transmit the one or more results of the developing of the hypothesis via a wireless and/or wired network 8-40. In various implementations, the one or more results 8-90 may be transmitted to the user 8-20*, one or more third parties (e.g., one or more third party sources 8-50), and/or to one or more remote network devices such as one or more network servers 8-36. In contrast, the indication module 8-236 may be designed to, for example, indicate the one or more results 8-90 via a user interface 8-122. The hypothesis presentation module 8-238 may be configured to present (e.g., transmit or indicate) the hypothesis developed by, for example, the hypothesis development module 8-106. In contrast, the hypothesis confirmation presentation module 8-240 may be configured to present (e.g., transmit or indicate) an indication of a confirmation of the hypothesis (e.g., existing hypothesis 8-80).
The hypothesis soundness/weakness presentation module 8-242 may be configured to present (e.g., transmit or indicate) an indication of the soundness or weakness of the hypothesis. Note that the words “soundness” and “strength” have been used interchangeably in reference to a hypothesis and therefore, are synonymous unless indicated otherwise. The advisory presentation module 8-244 may be configured to, among other things, presenting (e.g., transmit or indicate) an advisory of one or more past events. The recommendation presentation module 8-246 may be configured to present a recommendation for a future action based, for example, on the hypothesis.
In various implementations, the action execution module 8-108 may include a monitoring module 8-250 that may be configured to, among other things, monitor reported events. The monitoring of the reported events may involve determining whether the reported events include events that match or substantially match the types of events identified by the hypothesis. Upon detecting such events, additional actions may be taken such as soliciting for additional events data 8-60* in order to confirm, for example, the veracity of the hypothesis or generating an advisory to the user 8-20* or to one or more third party sources 8-50 regarding, for example, the possibility of the pattern of events identified by the hypothesis occurring.
e depicts particular implementations of the one or more applications 8-126 of the computing device 8-10 of
The network interface 8-120 of the computing device 8-10 may be a device designed to interface with a wireless and/or wired network 8-40. Examples of such devices include, for example, a network interface card (NIC) or other interface devices or systems for communicating through at least one of a wireless network or wired network 8-40. The user interface 8-122 of the computing device 8-10 may comprise any device that may interface with a user 8-20b. Examples of such devices include, for example, a keyboard, a display monitor, a touchscreen, a microphone, a speaker, an image capturing device such as a digital or video camera, a mouse, and so forth.
The memory 8-140 of the computing device 8-10 may include any type of volatile or non-volatile device used to store data. Examples of a memory 8-140 include, for example, a mass storage device, read only memory (ROM), programmable read only memory (PROM), erasable programmable read-only memory (EPROM), random access memory (RAM), flash memory, synchronous random access memory (SRAM), dynamic random access memory (DRAM), and so forth.
The various features and characteristics of the components, modules, and sub-modules of the computing device 8-10 presented thus far will be described in greater detail with respect to the processes and operations to be described herein.
In
Further, in the following figures that depict various flow processes, various operations may be depicted in a box-within-a-box manner. Such depictions may indicate that an operation in an internal box may comprise an optional example embodiment of the operational step illustrated in one or more external boxes. However, it should be understood that internal box operations may be viewed as independent operations separate from any associated external boxes and may be performed in any sequence with respect to all other illustrated operations, or may be performed concurrently.
In any event, after a start operation, the operational flow 8-300 may move to an events data acquisition operation 8-302 for acquiring events data including data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events, at least one of the first one or more reported events and the second one or more reported events being associated with a user. For instance, the events data acquisition module 8-102 of the computing device 8-10 acquiring (e.g., acquiring from a user 8-20*, from one or more third party sources 8-50, from one or more sensors 8-35, and/or from memory 8-140) events data 8-60* including data indicating incidence of a first one or more reported events 8-61* and data indicating incidence of a second one or more reported events 8-62*, at least one of the first one or more reported events (e.g., subjective user states such as fatigue) and the second one or more reported events (e.g., objective occurrences such as going to sleep after midnight) being associated with a user 8-20*.
Next, operational flow 8-300 may include an events pattern determination operation 8-304 for determining an events pattern based selectively on the incidences of the first one or more reported events and the second one or more reported events. For instance, the events pattern determination module 8-104 of the computing device 8-10 determining an events pattern (e.g., a spatial events pattern or a time/temporal sequential events pattern) based selectively (e.g., by disregarding or filtering out non-relevant events data) on the incidences of the first one or more reported events (e.g., objective occurrences such as a user 8-20* meeting with the boss) and the second one or more reported events (e.g., subjective observations such as a third party observing that the user 8-20* appears to be angry).
Finally, operational flow 8-300 may include a hypothesis development operation 8-306 for developing a hypothesis associated with the user based, at least in part, on the determined events pattern. For instance, the hypothesis development module 8-106 of the computing device 8-10 developing a hypothesis (e.g., creating a new hypothesis or further developing an existing hypothesis 8-80) associated with the user 8-20* based, at least in part, on the events pattern determined, for example, by the events pattern determination module 8-104.
In various implementations, the events data acquisition operation 8-302 of
In various implementations, the reception operation 8-402 may be performed in a number of different ways depending on the particular circumstances. For example, in some implementations, the reception operation 8-402 may include an operation 8-403 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events via a user interface as depicted in
Operation 8-403, in turn, may further include an operation 8-404 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from the user as depicted in
In the same or different implementations, the reception operation 8-402 may include an operation 8-405 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events via at least one of a wireless network or a wired network as depicted in
Depending upon circumstances, the data indicating incidence of a first one or more reported events 8-61* and/or the data indicating incidence of a second one or more reported events 8-62* received via the wireless and/or a wired network 8-40 may be provided by one or more different sources. For example, in some implementations, operation 8-405 may include an operation 8-406 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from the user as depicted in
In the same or different implementations, operation 8-405 may include an operation 8-407 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more remote network devices as depicted in
In the same or different implementations, operation 8-405 may include an operation 8-408 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more third party sources as depicted in
The one or more third party sources 8-50, as referred to above, may be in reference to various third parties (and/or the network devices that are associated with such parties). For example, in some implementations, operation 8-408 may include an operation 8-409 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more content providers as depicted in
In some implementations, operation 8-408 may include an operation 8-410 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more other users as depicted in
In some implementations, operation 8-408 may include an operation 8-411 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more health care entities as depicted in
In some implementations, operation 8-408 may include an operation 8-412 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more business entities as depicted in
In some implementations, operation 8-408 may include an operation 8-413 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events from one or more social or athletic groups as depicted in
The data received during the reception operation 8-402 may be received in a variety of different forms. For example, in some implementations, the reception operation 8-402 may include an operation 8-414 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events via one or more blog entries as depicted in
In the same or different implementations, the reception operation 8-402 may include an operation 8-415 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events via one or more status reports as depicted in
In the same or different implementations, the reception operation 8-402 may include an operation 8-416 for receiving at least one of the data indicating incidence of a first one or more reported events and the data indicating incidence of a second one or more reported events via one or more electronic messages as depicted in
In various implementations, the data acquired through the events data acquisition operation 8-302 of
One or more types of subjective user states may be indicated by the data acquired through operation 8-417. For example, in some implementations, operation 8-417 may include an operation 8-418 for acquiring data indicating at least one subjective mental state associated with the user as depicted in
In the same or different implementations, operation 8-417 may include an operation 8-419 for acquiring data indicating at least one subjective physical state associated with the user as depicted in
In the same or different implementations, operation 8-417 may include an operation 8-420 for acquiring data indicating at least one subjective overall state associated with the user as depicted in
In some implementations, operation 8-417 may include an operation 8-421 for acquiring data indicating at least a second subjective user state associated with the user as provided by the user as depicted in
In some implementations, operation 8-421 may further include an operation 8-422 for acquiring data indicating one subjective user state associated with a first point or interval in time and data indicating a second subjective user state associated with a second point or interval in time as depicted in
In various implementations, the data acquired through the events data acquisition operation 8-302 of
One or more types of objective occurrences may be indicated by the data acquired through operation 8-423. For example, in some implementations, operation 8-423 may include an operation 8-424 for acquiring data indicating at least an ingestion by the user of a medicine as depicted in
In some implementations, operation 8-423 may include an operation 8-425 for acquiring data indicating at least an ingestion by the user of a food item as depicted in
In some implementations, operation 8-423 may include an operation 8-426 for acquiring data indicating at least an ingestion by the user of a nutraceutical as depicted in
Other types of activities executed by the user 8-20* or by one or more third parties (e.g., third party sources 8-50) may be indicated by data acquired during operation 8-423. For example, in some implementations, operation 8-423 may include an operation 8-427 for acquiring data indicating at least an exercise routine executed by the user as depicted in
In some implementations, operation 8-423 may include an operation 8-428 for acquiring data indicating at least a social activity routine executed by the user as depicted in
In some implementations, operation 8-423 may include an operation 8-429 for acquiring data indicating at least an activity performed by one or more third parties as depicted in
In some implementations, operation 8-423 may include an operation 8-430 for acquiring data indicating one or more physical characteristics associated with the user as depicted in
In some implementations, operation 8-423 may include an operation 8-431 for acquiring data indicating a resting, a learning, or a recreational activity by the user as depicted in
In some implementations, operation 8-423 may include an operation 8-432 for acquiring data indicating occurrence of one or more external events as depicted in
In some implementations, operation 8-423 may include an operation 8-433 for acquiring data indicating one or more locations associated with the user as depicted in
In some implementations, operation 8-423 may include an operation 8-434 for acquiring data indicating at least a second objective occurrence as depicted in
In various implementations, operation 8-434 may comprise of an operation 8-435 for acquiring data indicating one objective occurrence associated with a first point or interval in time and data indicating a second objective occurrence associated with a second point or interval in time as depicted in
The data acquired in the events data acquisition operation 8-302 of
Note that although a subjective observation may be made by a particular person such as user 8-20*, the data that indicates the subjective observation may be provided by the user 8-20*, by one or more third party sources 8-50 (e.g., other users), by one or more remote network devices such as network servers 8-36, or by any other entities that may have access to such data. In other words, the user 8-20* who may have made the actual subjective observation may provide indication of his/her observation to other parties/entities that may ultimately disseminate such information.
In various implementations, operation 8-436 may include one or more additional operations. For example, in some implementations, operation 8-436 may include an operation 8-437 for acquiring data indicating at least one subjective observation made by a second user regarding the user as depicted in
Operation 8-437, in turn, may further include one or more additional operations. For example, in some implementations, operation 8-437 may include an operation 8-438 for acquiring data indicating at least one subjective observation, as made by the second user, regarding a perceived subjective user state of the user as depicted in
In various implementations, operation 8-438 may further comprise one or more operations. For example, in some implementations, operation 8-438 may include an operation 8-439 for acquiring data indicating at least one subjective observation, as made by the second user, regarding a perceived subjective mental state of the user as depicted in
In some implementations, operation 8-438 may include an operation 8-440 for acquiring data indicating at least one subjective observation, as made by the second user, regarding a perceived subjective physical state of the user as depicted in
In some implementations, operation 8-438 may include an operation 8-441 for acquiring data indicating at least one subjective observation made by the second user regarding a perceived subjective overall state of the user as depicted in
In various implementations, operation 8-437 may include an operation 8-442 for acquiring data indicating at least one subjective observation made by the second user regarding an activity performed by the user as depicted in
In various implementations, operation 8-436 may include an operation 8-443 for acquiring data indicating at least one subjective observation of an occurrence of an external event as depicted in
In some implementations, operation 8-436 may include an operation 8-444 for acquiring data indicating at least one subjective observation made by the user regarding a second user as depicted in
For example, in some implementations, operation 8-444 may include an operation 8-445 for acquiring data indicating at least one subjective observation made by the user regarding a perceived subjective mental state of the second user as depicted in
In the same or different implementations, operation 8-444 may include an operation 8-446 for acquiring data indicating at least one subjective observation made by the user regarding a perceived subjective physical state of the second user as depicted in
In the same or different implementations, operation 8-444 may include an operation 8-447 for acquiring data indicating at least one subjective observation made by the user regarding a perceived subjective overall state of the second user as depicted in
In the same or different implementations, operation 8-444 may include an operation 8-448 for acquiring data indicating at least one subjective observation made by the user regarding an activity performed by the second user as depicted in
In various implementations, operation 8-436 may include an operation 8-449 for acquiring data indicating a second subjective observation as depicted in
In some implementations, operation 8-449 may include an operation 8-450 for acquiring data indicating one subjective observation associated with a first point or interval in time and a second subjective observation associated with a second point or interval in time as depicted in
In some implementations, operation 8-449 may include an operation 8-451 for acquiring data indicating one subjective observation made by the user and data indicating a second subjective observation made by a second user as depicted in
Referring back to the events data acquisition operation 8-302 of
In some implementations, the events data acquisition operation 8-302 may include an operation 8-453 for acquiring data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events that includes data indicating at least one subjective user state associated with the user and data indicating at least one subjective observation as depicted in
In some implementations, the events data acquisition operation 8-302 may include an operation 8-454 for acquiring data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events that includes data indicating at least one objective occurrence and data indicating at least one subjective observation as depicted in
In some implementations, the events data acquisition operation 8-302 may include an operation 8-455 for acquiring data indicating incidence of a first one or more reported events and data indicating incidence of a second one or more reported events that includes data indicating a first reported event associated with a first point or interval in time and data indicating a second reported event associated with a second point or interval in time as depicted in
In some implementations, the events data acquisition operation 8-302 may include an operation 8-456 for acquiring data indicating incidence of a third one or more reported events as depicted in
Referring back to
In various implementations, operation 8-502 may include an operation 8-504 for filtering the events data to filter out data indicating incidence of the third one or more reported events as depicted in
Operation 8-504, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 8-504 may include an operation 8-506 for filtering the events data based, at least in part, on historical data identifying and linking at least two event types as depicted in
Operation 8-506, in various implementations, may further include an operation 8-508 for filtering the events data by filtering out data that indicates events that are not identified by the historical data as depicted in
In some implementations, operation 8-504 may include an operation 8-510 for filtering the events data based, at least in part, on an existing hypothesis as depicted in
Operation 8-510, in turn, may include one or more additional operations in various alternative implementations. For example, in some implementations, operation 8-510 may include an operation 8-512 for filtering the events data based, at least in part, on an existing hypothesis that is specific to the user as depicted in
In some implementations, operation 8-510 may include an operation 8-514 for filtering the events data based, at least in part, on an existing hypothesis that is associated with at least a subgroup of a general population, the user included in the subgroup as depicted in
Referring back to the events pattern determination operation 8-304 of
In some implementations, the events pattern determination operation 8-304 may include an operation 8-518 for determining a spatial pattern based selectively on the incidences of the first one or more reported events and the second one or more reported events as depicted in
Referring back to the hypothesis development operation 8-306 of
In some implementations, the creation operation 8-602 may include an operation 8-604 for creating the hypothesis based, at least in part, on historical data that is particular to the user as depicted in
In some implementations, the creation operation 8-602 may include an operation 8-606 for creating the hypothesis based, at least in part, on historical data that is associated with at least a subgroup of a general population, the subgroup including the user as depicted in
The hypothesis development operation 8-306 of
In various implementations, the determination operation 8-608 may be executed in a number of different ways depending upon circumstances. For example, in various implementations, the determination operation 8-608 may include a comparison operation 8-610 for comparing the determined events pattern to an events pattern associated with the existing hypothesis to determine whether the determined events pattern supports the existing hypothesis as depicted in
In some implementations, the comparison operation 8-610 may include an operation 8-612 for determining strength of the existing hypothesis associated with the user based, at least in part, on the comparison as depicted in
In some implementations, the comparison operation 8-610 may include an operation 8-616 for determining weakness of the existing hypothesis associated with the user based, at least in part, on the comparison as depicted in
In various implementations, the determination operation 8-608 may include an operation 8-618 for determining whether the determined events pattern supports an existing hypothesis that links a first event type with a second event type as depicted in
In some implementations, operation 8-618 may include an operation 8-620 for determining whether the determined events pattern supports an existing hypothesis that time or temporally links a first event type with a second event type as depicted in
In some implementations, operation 8-618 may include an operation 8-622 for determining whether the determined events pattern supports an existing hypothesis that spatially links a first event type with a second event type as depicted in
In various implementations, the hypothesis development operation 8-306 of
In some implementations, the hypothesis development operation 8-306 may include an operation 8-626 for developing a hypothesis that links a first objective occurrence type with a second objective occurrence type based, at least in part, on the determined events pattern as depicted in
In some implementations, the hypothesis development operation 8-306 may include an operation 8-628 for developing a hypothesis that links a first subjective observation type with a second subjective observation type based, at least in part, on the determined events pattern as depicted in
In some implementations, the hypothesis development operation 8-306 may include an operation 8-630 for developing a hypothesis that associates one or more subjective user state types with one or more objective occurrence types based, at least in part, on the determined events pattern as depicted in
In some implementations, the hypothesis development operation 8-306 may include an operation 8-632 for developing a hypothesis that associates one or more subjective user state types with one or more subjective observation types based, at least in part, on the determined events pattern as depicted in
In some implementations, the hypothesis development operation 8-306 may include an operation 8-634 for developing a hypothesis that associates one or more objective occurrence types with one or more subjective observation types based, at least in part, on the determined events pattern as depicted in
Referring now to
In addition, and unlike operational flow 8-300, operational flow 8-700 may further include an action execution operation 8-708 for executing one or more actions in response to the developing as depicted in
In various implementations, the action execution operation 8-708 may be performed in a number of different ways depending upon the particular circumstances. For example, in some implementations, the action execution operation 8-708 may include a presentation operation 8-802 for presenting one or more results of the developing as depicted in
In various implementations, the presentation operation 8-802 may include one or more additional operations. For example, in some implementations, the presentation operation 8-802 may include an operation 8-804 for transmitting the one or more results of the developing via at least one of a wireless network and a wired network as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-806 for transmitting the one or more results to the user as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-808 for transmitting the one or more results to one or more third parties as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-810 for indicating the one or more results via a user interface as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-812 for presenting the hypothesis as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-814 for presenting an indication of a confirmation of the hypothesis as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-816 for presenting an indication of soundness or weakness of the hypothesis as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-818 for presenting an advisory of one or more past events as depicted in
In some implementations, the presentation operation 8-802 may include an operation 8-820 for presenting a recommendation for a future action as depicted in
In various implementations, the action execution operation 8-708 of
In some implementations, the monitoring operation 8-822 may include an operation 8-824 for monitoring of reported events to determine whether the reported events include events identified by the hypothesis as depicted in
In some implementations, the monitoring operation 8-822 may include an operation 8-826 for monitoring of reported events being reported by the user as depicted in
In some implementations, the monitoring operation 8-822 may include an operation 8-828 for monitoring of reported events being reported by one or more remote network devices as depicted in
In some implementations, the monitoring operation 8-822 may include an operation 8-830 for monitoring of reported events being reported by one or more third party sources as depicted in
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where users may report or post their latest status, personal activities, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social networking status reports in which a user may report or post for others to view their current status, activities, and/or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life. Typically, such microblog entries will describe the various “events” associated with or are of interest to the microblogger that occurs during a course of a typical day. The microblog entries are often continuously posted during the course of a typical day, and thus, by the end of a normal day, a substantial number of events may have been reported and posted.
Each of the reported events that may be posted through microblog entries may be categorized into one of at least three possible categories. The first category of events that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, incident, happening, or any other event that occurs with respect to the microblogger or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. Such events would include, for example, intake of food, medicine, or nutraceutical, certain physical characteristics of the microblogger such as blood sugar level or blood pressure that can be objectively measured, activities of the microblogger observable by others or by a device, activities of others that may or may not be of interest to the microblogger, external events such as performance of the stock market (which the microblogger may have an interest in), performance of a favorite sports team, and so forth. In some cases, objective occurrences may not be at least directly associated with a microblogger. Examples of such objective occurrences include, for example, external events that may not be directly related to the microblogger such as the local weather, activities of others (e.g., spouse or boss) that may directly or indirectly affect the microblogger, and so forth.
A second category of events that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger may include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be directly reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., happiness, sadness, anger, tension, state of alertness, state of mental fatigue, jealousy, envy, and so forth), the subjective physical state of the microblogger (e.g., upset stomach, state of vision, state of hearing, pain, and so forth), and the subjective overall state of the microblogger (e.g., “good,” “bad,” state of overall wellness, overall fatigue, and so forth). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states).
A third category of events that may be reported or posted through microblog entries include “subjective observations” made by the microblogger. A subjective observation is similar to subjective user states and may be any subjective opinion, thought, or evaluation relating to any external incidence. Thus, the difference between subjective user states and subjective observations is that subjective user states relates to self-described subjective descriptions of the user states of one's self while subjective observations relates to subjective descriptions or opinions regarding external events. Examples of subjective observations include, for example, a microblogger's perception about the subjective user state of another person (e.g., “he seems tired”), a microblogger's perception about another person's activities (e.g., “he drank too much yesterday”), a microblogger's perception about an external event (e.g., “it was a nice day today”), and so forth. Although microblogs are being used to provide a wealth of personal information, thus far they have been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
In accordance with various embodiments, methods, systems, and computer program products are provided to, among other things, select a hypothesis from a plurality of hypotheses based on at least one reported event associated with a user, the selected hypothesis being a hypothesis that may link together (e.g., correlate) a plurality of different types of events (i.e., event types). In some embodiments, the selected hypothesis (as well as, in some cases, the plurality of hypotheses) may be relevant to the user. After making the selection, the methods, systems, and computer program products may present one or more advisories related to the selected hypothesis. The methods, systems, and computer program products may be employed in a variety of environments including, for example, social networking environments, blogging or microblogging environments, instant messaging (IM) environments, or any other type of environment that allows a user to, for example, maintain a diary.
In various implementations, a “hypothesis,” as referred to herein, may define one or more relationships or links between different types of events (i.e., event types) including at least a first event type (e.g., a type of event such as a particular type of subjective user state, for example, an emotional state such as “happy”) and a second event type (e.g., another type of event such as particular type of objective occurrence, for example, favorite sports team winning a game). In some cases, a hypothesis may be represented by an events pattern that may indicate spatial or sequential relationships between different event types (e.g., different types of events such as subjective user states and objective occurrences). Note that for ease of explanation and illustration, the following description will describe a hypothesis as defining, for example, the sequential or spatial relationship between two different event types, a first event type and a second event type. However, those skilled in the art will recognize that such a hypothesis could also identify the relationships between three or more event types (e.g., a first event type, a second event type, a third event type, and so forth).
In some embodiments, a hypothesis may, at least in part, be defined or represented by an events pattern that indicates or suggests a spatial or a sequential (e.g., time/temporal) relationship between different event types. Such a hypothesis, in some cases, may also indicate the strength or weakness of the link between the different event types. That is, the strength or weakness (e.g., soundness) of the correlation between different event types may depend upon, for example, whether the events pattern repeatedly occurs and/or whether a contrasting events pattern has occurred that may contradict the hypothesis and therefore, weaken the hypothesis (e.g., an events pattern that indicates a person becoming tired after jogging for thirty minutes when a hypothesis suggests that a person will be energized after jogging for thirty minutes).
As briefly described above, a hypothesis may be represented by an events pattern that may indicate spatial or sequential (e.g., time or temporal) relationship or relationships between multiple event types. In some implementations, a hypothesis may merely indicate temporal sequential relationships between multiple event types that indicate the temporal relationships between multiple event types. In alternative implementations a hypothesis may indicate a more specific time relationship between multiple event types. For example, a sequential pattern may represent the specific pattern of events that occurs along a timeline that may indicate the specific time intervals between event types. In still other implementations, a hypothesis may indicate the spatial (e.g., geographical) relationships between multiple event types.
In various embodiments, the development of a hypothesis may be particularly useful to a user (e.g., a microblogger or a social networking user) that the hypothesis may be associated with. That is, in some instances a hypothesis may be developed for a user that may assist the user in modifying his/her future behavior, while in other instances such a hypothesis may simply alert or notify the user that a pattern of events are repeatedly occurring. In other situations, such a hypothesis may be useful to third parties such as advertisers in order to assist the advertisers in developing a more targeted marketing scheme. In still other situations, such a hypothesis may help in the treatment of ailments associated with the user.
One way to develop a hypothesis (e.g., creation of and/or further development of a hypothesis) is to determine a pattern of reported events that repeatedly occurs with respect to a particular user and/or to compare similar or dissimilar reported pattern of events that occurs with respect to a user. For example, if a user such as a microblogger reports repeatedly that after each visit to a particular restaurant, the user always has an upset stomach, then a hypothesis may be created and developed that suggests that the user will get an upset stomach after visiting the particular restaurant. If, on the other hand, after developing such a hypothesis, the user reports that the last time he ate at the restaurant, he did not get an upset stomach, then such a report may result in the weakening of the hypothesis. Alternatively, if after developing such a hypothesis, the user reports that the last time he ate at the restaurant, he again got an upset stomach, then such a report may result in a confirmation of the soundness of the hypothesis. Note that the soundness of a hypothesis (e.g., strength or weakness of the hypothesis) may depend upon how much the historical data supports such a hypothesis.
Numerous hypotheses may be developed and may be associated with a particular user. For example, in the case of a microblogger, given the amount of “events data” (and the large amounts of reported events indicated by the events data) that may be provided by the microblogger via microblog entries, a large number of hypotheses associated with the microblogger may eventually be developed based on the reported events indicated by the events data. Alternatively, hypotheses may also be provided by one or more third party sources. For example, a number of hypotheses may be provided by other users or by one or more network service providers.
Thus, in accordance with various embodiments, methods, systems, and computer program products are provided to, among other things, select a hypothesis from a plurality of hypotheses that may be associated with a particular user (e.g., a microblogger), where the selected hypothesis may link or correlate a plurality of different types of events (i.e., event types). After making the selection, the methods, systems, and computer program products may present one or more advisories related to the selected hypothesis.
a and 9-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 9-100 may include at least a computing device 9-10 (see
As indicated earlier, in some embodiments, the computing device 9-10 may be a server while in other embodiments the computing device 9-10 may be a standalone device. In the case where the computing device 9-10 is a network server, the computing device 9-10 may communicate indirectly with a user 9-20a via wireless and/or wired network 9-40. In contrast, in embodiments where the computing device 9-10 is a standalone device, it may communicate directly with a user 9-20b via a user interface 9-122 (see
In embodiments in which the computing device 9-10 is a network server, the computing device 9-10 may communicate with a user 9-20a via a mobile device 9-30 and through a wireless and/or wired network 9-40. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 9-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices that can communicate with the computing device 9-10. In some embodiments, the mobile device 9-30 may be a handheld device such as a cellular telephone, a smartphone, a Mobile Internet Device (MID), an Ultra Mobile Personal Computer (UMPC), a convergent device such as a personal digital assistant (PDA), and so forth.
In embodiments in which the computing device 9-10 is a standalone computing device 9-10 (or simply “standalone device”) that communicates directly with a user 9-20b, the computing device 9-10 may be any type of portable device (e.g., a handheld device) or stationary device (e.g., desktop computer or workstation). For these embodiments, the computing device 9-10 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices. In some embodiments, in which the computing device 9-10 is a handheld device, the computing device 9-10 may be a cellular telephone, a smartphone, an MID, an UMPC, a convergent device such as a PDA, and so forth. In various embodiments, the computing device 9-10 may be a peer-to-peer network component device. In some embodiments, the computing device 9-10 and/or the mobile device 9-30 may operate via a Web 2.0 construct (e.g., Web 2.0 application 9-268).
In various embodiments, the computing device 9-10 may be configured to acquire events data 9-60* from one or more sources. Events data 9-60*, as will be described herein, may indicate the occurrences of one or more reported events. Each of the reported events indicated by the events data 9-60* may or may not be associated with a user 9-20*. In some embodiments, a reported event may be associated with the user 9-20* if it is reported by the user 9-20* or it is related to some aspect about the user 9-20* (e.g., the location of the user 9-20*, the local weather of the user 9-20*, activities performed by the user 9-20*, physical characteristics of the user 9-20* as detected by a sensor 9-35, subjective user state of the user 9-20*, and so forth). At least three different types of reported events may be indicated by the events data 9-60*, subjective user states associated with a user 9-20*, objective occurrences, and subjective observations made by the user 9-20* or by others (e.g., one or more third parties 9-50).
The events data 9-60* that may be acquired by the computing device 9-10 may include at least data indicating at least one reported event 9-61* and/or data indicating at least a second reported event 9-62*. Though not depicted, the events data 9-60* may further include data indicating incidences of a third reported event, a fourth reported event, and so forth (as indicated by the dots). The events data 9-60* including the data indicating at least one reported event 9-61* and/or the data indicating at least a second reported event 9-62* may be obtained from one or more distinct sources (e.g., the original sources for the data). For example, in some implementations, a user 9-20* may provide at least a portion of the events data 9-60* (e.g., events data 9-60a that may include the data indicating at least one reported event 9-61a and/or the data indicating at least a second reported event 9-62a).
In the same or different embodiments, one or more remote network devices including one or more sensors 9-35 and/or one or more network servers 9-36 may provide at least a portion of the events data 9-60* (e.g., events data 9-60b that may include the data indicating at least one reported event 9-61b and/or the data indicating at least a second reported event 9-62b). In same or different embodiments, one or more third party sources may provide at least a portion of the events data 9-60* (e.g., events data 9-60c that may include the data indicating at least one reported event 9-61c and/or the data indicating at least a second reported event 9-62c). In still other embodiments, at least a portion of the events data 9-60* may be retrieved from a memory 9-140 in the form of historical data. Thus, to summarize, each of the data indicating at least one reported event 9-61* and the data indicating at least a second reported event 9-62* may be obtained from the same or different sources.
The one or more sensors 9-35 illustrated in
The one or more third parties 9-50 illustrated in
In brief, after acquiring the events data 9-60* including data indicating at least one reported event 9-61* and/or data indicating at least a second reported event 9-62* from one or more sources, the computing device 9-10 may be designed to select at least one hypothesis 9-81* from a plurality of hypotheses 9-80 based, at least in part, on at least one reported event associated with a user 9-20*. In some cases, the selected hypothesis 9-81* as well as the plurality of hypotheses 9-80 may be relevant to the user 9-20*. In various embodiments, each of the plurality of hypotheses 9-80 may have been created and/or may have been at least initially provided (e.g., pre-installed) by a third party (e.g., network service providers, computing device manufacturer, and so forth) and/or may have been further refined by the computing device 9-10.
After selecting the at least one hypothesis 9-81*, the computing device 9-10 may be designed to execute one or more actions. One such action that may be executed is to present one or more advisories 9-90 associated with the at least one hypothesis 9-81* that was selected. For example, the computing device 9-10 may present the one or more advisories 9-90 to a user 9-20* (e.g., by transmitting the one or more advisories 9-90 to a user 9-20a or indicating the one or more advisories 9-90 to a user 9-20b via a user interface 9-122), to one or more third parties 9-50, and/or to one or more remote network devices (e.g., network servers 9-36). The one or more advisories 9-90 to be presented may include at least a presentation of the selected hypothesis 9-81*, an alert regarding past events related to the hypothesis 9-81* (e.g., past events that the hypothesis 9-81* may have been based on), a recommendation for a future action based on the selected hypothesis 9-81*, a prediction of an occurrence of a future event based on the selected hypothesis 9-81*, or other types of advisories.
As illustrated in
The events data acquisition module 9-102 may be configured to, among other things, acquire events data 9-60* from one or more distinct sources (e.g., from a user 9-20*, from one or more third parties 9-50, from one or more network devices such as one or more sensors 9-35 and/or one or more network servers 9-36, from memory 9-140 and/or from other sources). The events data 9-60* to be acquired by the events data acquisition module 9-102 may include one, or both, of data indicating at least one reported event 9-61* and data indicating at least a second reported event 9-62*. Each of the data indicating at least one reported event 9-61* and the data indicating at least a second reported event 9-62* may be acquired from the same source or different sources. The events data acquisition module 9-102 may also be designed to acquire additional data indicating a third reported event, a fourth reported event, and so forth. The events data 9-60* may be acquired in the form of one or more electronic entries such as blog (e.g., microblog) entries, status report entries, electronic message entries, diary entries, and so forth.
Referring now to
The hypothesis selection module 9-104 of the computing device 9-10 of
In brief, the reported event referencing module 9-208 may be designed to reference one or more reported events that may have been indicated by the events data 9-60* acquired by the events data acquisition module 9-102. The referencing of the one or more reported events may facilitate the hypothesis selection module 9-104 in the selection of the at least one hypothesis 9-81*. In contrast, the comparison module 9-210 may be configured to compare the at least one reported event (e.g., as referenced by the reported event referencing module 9-208) to one, or both, of at least a first event type and a second event type that may be linked together by the at least one hypothesis 9-81*.
The matching module 9-212 may be configured to determine whether the at least one reported event at least substantially matches with the first event type and/or the second event type that may be indicated by the at least one hypothesis 9-81*. On the other hand, the contrasting module 9-214 may be configured to determine whether a second reported event (e.g., as indicated by the acquired events data 9-60*) is a contrasting event from the at least first event type and/or the second event type that may be indicated by the at least one hypothesis 9-81*.
The relationship determination module 9-216 may be configured to determine a relationship between a first reported event and a second reported event (e.g., as indicated by the acquired events data 9-60*). The sequential link determination module 9-218 may facilitate the relationship determination module 9-216 to determine a relationship between the first reported event and the second reported event by determining a sequential link (e.g., a temporal or a more specific time relationship) between the first reported event and the second reported event. The spatial link determination module 9-220 may facilitate the relationship determination module 9-216 to determine a relationship between the first reported event and the second reported event by determining a spatial link (e.g., a geographical relationship) between the first reported event and the second reported event.
c illustrates particular implementations of the presentation module 9-106 of
In various implementations, the presentation module 9-106 may include a hypothesis presentation module 9-226 configured to present (e.g., transmit via a wireless and/or wired network 9-40 or indicate via a user interface 9-122) at least one form of the at least one hypothesis 9-81* selected by the hypothesis selection module 9-104. The at least one hypothesis 9-81* may be presented in a number of different formats. For example, the hypothesis 9-81* may be presented in a graphical or iconic form, in audio form, or in textual form. Further, with respect to presenting the at least one hypothesis 9-81* in textual form, the hypothesis 9-81* may be presented in many different ways as there may be many different ways to describe a hypothesis 9-81* (this is also true when the hypothesis 9-81* is presented graphically or audibly). The hypothesis presentation module 9-226, in various implementations, may further include an event types relationship presentation module 9-228 that is configured to present an indication of a relationship (e.g., spatial or sequential relationship) between at least a first event type and at least a second event type as referenced by the at least one hypothesis 9-81* selected by the hypothesis selection module 9-104.
In various implementations, the event types relationship presentation module 9-228 may further include a soundness presentation module 9-230 configured to present an indication of the soundness of the at least hypothesis 9-81* selected by the hypothesis selection module 9-104. In some implementations, the soundness presentation module 9-230 may further include a strength/weakness presentation module 9-232 configured to present an indication of strength or weakness of correlation between the at least first event type and the at least second event type that may be linked together by the at least one hypothesis 9-81*, the at least one hypothesis 9-81* being selected by the hypothesis selection module 9-104.
The event types relationship presentation module 9-228, in various alternative implementations, may include a time/temporal relationship presentation module 9-234 that is configured to present an indication of a time or temporal relationship between the at least first event type and the at least second event type linked together by the at least one hypothesis 9-81*. In some implementations, the event types relationship presentation module 9-228 may be configured to present an indication of a spatial relationship between the at least first event type and the at least second event type linked together by the at least one hypothesis 9-81*.
In some implementations, the presentation module 9-106 may include a prediction presentation module 9-238 that is configured to present (e.g., transmit via a wireless and/or wired network 9-40 or indicate via a user interface 9-122) an advisory relating to a prediction of a future event. Such an advisory may be based on the at least one hypothesis 9-81* selected by the hypothesis selection module 9-104. For example, suppose the at least one hypothesis 9-81* suggests that there is a link between jogging and sore ankles, then upon the events data acquisition module 9-102 acquiring data indicating that a user 9-20* went jogging, then the predication presentation module 9-238 may present an indication that the user 9-20* will subsequently have sore ankles
In the same or different implementations, the presentation module 9-106 may include a recommendation presentation module 9-240 that may be configured to present (e.g., transmit via a wireless and/or wired network 9-40 or indicate via a user interface 9-122) a recommendation for a future course of action. Such a recommendation may be based, at least in part, on the at least one hypothesis 9-81* selected by the hypothesis selection module 9-104. For example, referring back to the above jogging/sore ankle example, the recommendation presentation module 9-240 may recommend that the user 9-20* ingest aspirin.
In some implementations, the recommendation presentation module 9-240 may include a justification presentation module 9-242 that may be configured to present a justification for the recommendation presented by the recommendation presentation module 9-240. For example, in the above jogging/sore ankle example, the justification presentation module 9-242 may present an indication that the user 9-20* should ingest the aspirin because her ankles will be sore as a result of jogging.
In various alternative implementations, the presentation module 9-106 may include a past events presentation module 9-244 that may be configured to present (e.g., transmit via a wireless and/or wired network 9-40 or indicate via a user interface 9-122) an indication of one or more past events. Such a presentation of past events may be based, at least in part, on the at least one hypothesis 9-81* selected by the hypothesis selection module 9-104. For example, in the above jogging/sore ankle example, the past events presentation module 9-244 may be designed to present an indication that the user 9-20* in the past seems to always have sore ankles after going jogging.
In various implementations, the computing device 9-10 may include a hypothesis development module 9-108 that may be configured to develop one or more hypothesis 9-81* (e.g., create new hypotheses or to further refine hypotheses). In various implementations, the development of a hypothesis 9-81* may be based, at least in part, on events data 9-60* that indicate one or more reported events. In some cases, the development of a hypothesis 9-81* may be further based on historical data such as historical medical data, population data, past user data (e.g., past user data indicating past reported events associated with a user 9-20*), and so forth.
In various implementations, the computing device 9-10 of
In various implementations, the computing device 9-10 may include a network interface 9-120, which may be a device designed to interface with a wireless and/or wired network 9-40. Examples of such devices include, for example, a network interface card (NIC) or other interface devices or systems for communicating through at least one of a wireless network or wired network 9-40. In some implementations, the computing device 9-10 may include a user interface 9-122. The user interface 9-122 may comprise any device that may interface with a user 9-20b. Examples of such devices include, for example, a keyboard, a display monitor, a touchscreen, a microphone, a speaker, an image capturing device such as a digital or video camera, a mouse, and so forth.
The computing device 9-10 may include a memory 9-140. The memory 9-140 may include any type of volatile and/or non-volatile devices used to store data. In various implementations, the memory 9-140 may include, for example, a mass storage device, read only memory (ROM), programmable read only memory (PROM), erasable programmable read-only memory (EPROM), random access memory (RAM), flash memory, synchronous random access memory (SRAM), dynamic random access memory (DRAM), and/or other memory devices. In various implementations, the memory 9-140 may store a plurality of hypotheses 9-80.
The various features and characteristics of the components, modules, and sub-modules of the computing device 9-10 presented thus far will be described in greater detail with respect to the processes and operations to be described herein.
In
Further, in the following figures that depict various flow processes, various operations may be depicted in a box-within-a-box manner. Such depictions may indicate that an operation in an internal box may comprise an optional example embodiment of the operational step illustrated in one or more external boxes. However, it should be understood that internal box operations may be viewed as independent operations separate from any associated external boxes and may be performed in any sequence with respect to all other illustrated operations, or may be performed concurrently.
In any event, after a start operation, the operational flow 9-300 may move to a hypothesis selection operation 9-302 for selecting at least one hypothesis from a plurality of hypotheses relevant to a user, the selection of the at least one hypothesis being based, at least in part, on at least one reported event associated with the user. For instance, the hypothesis selection module 9-104 of the computing device 9-10 selecting at least one hypothesis 9-81* (e.g., a hypothesis that correlates or links a first event type with a second event type) from a plurality of hypotheses 9-80 relevant to a user 9-20* (e.g., hypotheses 9-80 that may be specifically relevant to the user 9-20* or at least to a sub-group of the population that the user 9-20* belongs to), the selection of the at least one hypothesis 9-81* being based, at least in part, on at least one reported event associated with the user 9-20*. Note that in the following description and for ease of illustration and understanding the hypothesis 9-81* to be selected through the hypothesis selection operation 9-302 may be described as a hypothesis that links together or associates two types of events (i.e., event types). However, those skilled in the art will recognize that such a hypothesis 9-81* may actually relate to the linking together of three or more types of events in various alternative implementations.
Next, operational flow 9-300 may include an advisory presentation operation 9-304 for presenting one or more advisories related to the hypothesis. For instance, the presentation module 9-106 of the computing device 9-10 presenting (e.g., transmitting through a wireless and/or wired network 9-40, or indicating via a user interface 9-122) one or more advisories 9-90 (e.g., an advisory relating to one or more past events, a recommendation for a future action, and so forth) related to the hypothesis 9-81*.
The at least one hypothesis 9-81* to be selected during the hypothesis selection operation 9-302 of
In various implementations, the at least one hypothesis 9-81* to be selected through operation 9-402 may be directed to any one or more of a number of different types of subjective user states. For example, in some implementations, operation 9-402 may include an operation 9-403 for selecting at least one hypothesis that relates to at least one subjective mental state type as depicted in
In the same or different implementations, operation 9-402 may include an operation 9-404 for selecting at least one hypothesis that relates to at least one subjective physical state type as depicted in
In the same or different implementations, operation 9-402 may include an operation 9-405 for selecting at least one hypothesis that relates to at least one subjective overall state type as depicted in
In various implementations, the at least one hypothesis 9-81* to be selected through the hypothesis selection operation 9-302 may be related to at least one type of objective occurrence (i.e., objective occurrence type). For example, in some implementations, the hypothesis selection operation 9-302 may include an operation 9-406 for selecting at least one hypothesis that relates to at least one objective occurrence type as depicted in
In various implementations, operation 9-406 may include one or more additional operations. For example, in some implementations, operation 9-406 may include an operation 9-407 for selecting at least one hypothesis that relates to at least a type of user activity as depicted in
In some implementations, operation 9-407 may include an operation 9-408 for selecting at least one hypothesis that relates to at least a consumption of an item as depicted in
Operation 9-408, in turn, may further include one or more operations in various alternative implementations. For example, in some implementations, operation 9-408 may include an operation 9-409 for selecting at least one hypothesis that relates to at least a consumption of a type of food item as depicted in
In the same or different implementations, operation 9-408 may include an operation 9-410 for selecting at least one hypothesis that relates to at least a consumption of a type of medicine as depicted in
In the same or different implementations, operation 9-408 may include an operation 9-411 for selecting at least one hypothesis that relates to at least a consumption of a type of nutraceutical as depicted in
In some implementations, operation 9-407 may include an operation 9-412 for selecting at least one hypothesis that relates to a type of exercise activity as depicted in
In some implementations, operation 9-407 may include an operation 9-413 for selecting at least one hypothesis that relates to a type of social activity as depicted in
In some implementations, operation 9-407 may include an operation 9-414 for selecting at least one hypothesis that relates to a type of recreational activity as depicted in
In some implementations, operation 9-407 may include an operation 9-415 for selecting at least one hypothesis that relates to a type of learning or type of educational activity as depicted in
In various implementations, operation 9-406 of
In the same or different implementations, operation 9-406 may include an operation 9-417 for selecting at least one hypothesis that relates to one or more types of user physical characteristics as depicted in
In the same or different implementations, operation 9-406 may include an operation 9-418 for selecting at least one hypothesis that relates to one or more types of external activities as depicted in
In the same or different implementations, operation 9-406 may include an operation 9-419 for selecting at least one hypothesis that relates to one or more locations as depicted in
In various implementations, the hypothesis selection operation 9-302 may include an operation 9-420 for selecting at least one hypothesis that relates to at least one subjective observation type as depicted in
Operation 9-420, in turn, may further include one or more additional operations in various alternative implementations. For example, in some implementations, operation 9-420 may include an operation 9-421 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to a person as depicted in
In some implementations, operation 9-421 may further include an operation 9-422 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to a subjective user state of the person as depicted in
Operation 9-422, in turn, may include one or more additional operations. For example, in some implementations, operation 9-422 may include an operation 9-423 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to a subjective mental state of the person as depicted in
In the same or different implementations, operation 9-422 may include an operation 9-424 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to a subjective physical state of the person as depicted in
In the same or different implementations, operation 9-422 may include an operation 9-425 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to a subjective overall state of the person as depicted in
In some implementations, operation 9-420 may include an operation 9-426 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to a type of activity performed by a person as depicted in
In some implementations, operation 9-420 may include an operation 9-427 for selecting at least one hypothesis that relates to at least one type of subjective observation relating to an occurrence of an external event as depicted in
Referring back to the hypothesis selection operation 9-302 of
Thus, in various implementations, operation 9-428 may involve selecting a hypothesis 9-81* that links similar or different types of events. For example, in some implementations, operation 9-428 may include an operation 9-429 for selecting at least one hypothesis that links at least a first subjective user state type with at least a second subjective user state type as depicted in
In some implementations, operation 9-428 may include an operation 9-430 for selecting at least one hypothesis that links at least one subjective user state type with at least one objective occurrence type as depicted in
In some implementations, operation 9-428 may include an operation 9-431 for selecting at least one hypothesis that links at least one subjective user state type with at least one subjective observation type as depicted in
In some implementations, operation 9-428 may include an operation 9-432 for selecting at least one hypothesis that links at least a first objective occurrence type with at least a second objective occurrence type as depicted in
In some implementations, operation 9-428 may include an operation 9-433 for selecting at least one hypothesis that links at least one objective occurrence type with at least one subjective observation type as depicted in
In some implementations, operation 9-428 may include an operation 9-434 for selecting at least one hypothesis that links at least a first subjective observation type with at least a second subjective observation type as depicted in
In some implementations, operation 9-428 may include an operation 9-435 for selecting at least one hypothesis that at least sequentially links at least a first event type with at least a second event type as depicted in
In some implementations, operation 9-428 may include an operation 9-436 for selecting at least one hypothesis that at least spatially links at least a first event type with at least a second event type as depicted in
In various implementations, the at least one hypothesis 9-81* (as well as, in some cases, the plurality of hypotheses 9-80), may have been originally developed based on historical data specifically associated with the user 9-20* or on historical data specifically associated with at least a sub-group of the general population that the user 9-20* belongs to. For example, in some implementations, the hypothesis selection operation 9-302 of
In some implementations, operation 9-437 may further include an operation 9-438 for selecting at least one hypothesis that was developed based, at least in part, on a historical events pattern specifically associated with the user as depicted in
In various implementations, the hypothesis selection operation 9-302 of
In some implementations, operation 9-439 may include an operation 9-440 for selecting at least one hypothesis that was developed based, at least in part, on a historical events pattern associated with at least the sub-group of the population as depicted in
In some implementations, the hypothesis selection operation 9-302 may include an operation 9-441 for selecting at least one hypothesis from a plurality of hypotheses, the plurality of hypotheses being specifically associated with the user as depicted in
In various implementations, the hypothesis selection operation 9-302 may include an operation 9-442 for selecting at least one hypothesis from a plurality of hypotheses, the plurality of hypotheses being specifically associated with at least a sub-group of a population, the user being a member of the sub-group as depicted in
The selection of the at least one hypothesis 9-81* in the hypothesis selection operation 9-302 of
In particular, operation 9-443 may include an operation 9-444 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported via one or more blog entries in various implementations and as depicted in
In some implementations, operation 9-443 may include an operation 9-445 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported via one or more status reports as depicted in
In some implementations, operation 9-443 may include an operation 9-446 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported via one or more electronic messages as depicted in
In some implementations, operation 9-443 may include an operation 9-447 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported through one or more electronic entries composed by the user as depicted in
In some implementations, operation 9-443 may include an operation 9-448 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported through one or more electronic entries composed by one or more third parties as depicted in
In some implementations, operation 9-443 may include an operation 9-449 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported through one or more electronic entries generated by one or more remote network devices as depicted in
In some implementations, operation 9-449 may further include an operation 9-450 for selecting at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event reported through one or more electronic entries generated by one or more sensors as depicted in
In various implementations, the hypothesis selection operation 9-302 of
In some implementations, operation 9-451 may include an operation 9-452 for selecting the at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event of a first event type and a second reported event of a second event type as depicted in
In some implementations, operation 9-451 may include an operation 9-453 for selecting the at least one hypothesis from the plurality of hypotheses based, at least in part, on at least one reported event that originates from a first source and a second reported event that originates from a second source as depicted in
Various approaches may be employed in the hypothesis selection operation 9-302 of
In some implementations, operation 9-454 may further include an operation 9-455 for selecting the at least one hypothesis based, at least in part, on determining whether the at least one reported event at least substantially matches with the first event type or the second event type as depicted in
In some implementations, operation 9-454 may include an operation 9-456 for selecting the at least one hypothesis based, at least in part, on a comparison of a second reported event to one, or both, of the first event type and the second event type as depicted in
In various implementations, operation 9-456 may further include an operation 9-457 for selecting the at least one hypothesis based, at least in part, on determining whether the second reported event at least substantially matches with the first event type or the second event type as depicted in
In some implementations, operation 9-456 may include an operation 9-458 for selecting the at least one hypothesis based, at least in part, on determining whether the second reported event is a contrasting event from the first event type or the second event type as depicted in
In some implementations, operation 9-456 may include an operation 9-459 for selecting the at least one hypothesis based, at least in part, on determining a relationship between the first reported event and the second reported event as depicted in
Operation 9-459, in some implementations, may include an operation 9-460 for selecting the at least one hypothesis based, at least in part, on determining a sequential link between the first reported event and the second reported event as depicted in
In some implementations, operation 9-459 may include an operation 9-461 for selecting the at least one hypothesis based, at least in part, on determining a spatial link between the first reported event and the second reported event as depicted in
In some implementations, operation 9-459 may include an operation 9-462 for selecting the at least one hypothesis based, at least in part, on comparing the relationship between the first reported event and the second reported event to a relationship between the first event type and the second event type of the at least one hypothesis as depicted in
The hypothesis selection operation 9-302 of
In other alternative implementations, the hypothesis selection operation 9-302 may include an operation 9-464 for selecting the at least one hypothesis at a standalone device as depicted in
In some implementations, operation 9-464 may further include an operation 9-465 for selecting the at least one hypothesis at a handheld device as depicted in
In some implementations, the hypothesis selection operation 9-302 may include an operation 9-466 for selecting the at least one hypothesis at a peer-to-peer network component device as depicted in
In some implementations, the hypothesis selection operation 9-302 may include an operation 9-467 for selecting the at least one hypothesis via a Web 2.0 construct as depicted in
Referring back to the operational flow 9-300 of
In various implementations, the advisory presentation operation 9-304 may include a transmission operation 9-504 for transmitting the one or more advisories related to the hypothesis via at least one of a wireless network or a wired network as depicted in
In some implementations, the transmission operation 9-504 may include an operation 9-506 for transmitting the one or more advisories related to the hypothesis to the user as depicted in
In some implementations, the transmission operation 9-504 may include an operation 9-508 for transmitting the one or more advisories related to the hypothesis to one or more third parties as depicted in
In some implementations, the advisory presentation operation 9-304 may include a hypothesis presentation operation 9-510 for presenting at least one form of the hypothesis as depicted in
In various implementations, the hypothesis presentation operation 9-510 may include an operation 9-512 for presenting an indication of a relationship between at least a first event type and at least a second event type as referenced by the hypothesis as depicted in
In various implementations, operation 9-512 may include an operation 9-514 for presenting an indication of soundness of the hypothesis as depicted in
In some implementations, operation 9-514 may further include an operation 9-516 for presenting an indication of strength or weakness of correlation between the at least first event type and the at least second event type linked together by the hypothesis as depicted in
In some implementations, operation 9-512 may include an operation 9-518 for presenting an indication of a time or temporal relationship between the at least first event type and the at least second event type as depicted in
In some implementations, operation 9-512 may include an operation 9-520 for presenting an indication of a spatial relationship between the at least first event type and the at least second event type as depicted in
In various implementations, operation 9-512 of
In some implementations, operation 9-512 may include an operation 9-524 for presenting an indication of a relationship between at least a first objective occurrence type and at least a second objective occurrence type as indicated by the hypothesis as depicted in
In some implementations, operation 9-512 may include an operation 9-526 for presenting an indication of a relationship between at least a first subjective observation type and at least a second subjective observation type as indicated by the hypothesis as depicted in
In some implementations, operation 9-512 may include an operation 9-528 for presenting an indication of a relationship between at least a subjective user state type and at least an objective occurrence type as indicated by the hypothesis as depicted in
In some implementations, operation 9-512 may include an operation 9-530 for presenting an indication of a relationship between at least a subjective user state type and at least a subjective observation type as indicated by the hypothesis as depicted in
In some implementations, operation 9-512 may include an operation 9-532 for presenting an indication of a relationship between at least an objective occurrence type and at least a subjective observation type as indicated by the hypothesis as depicted in
In various implementations, the advisory presentation operation 9-304 of
In various implementations, the advisory presentation operation 9-304 may include an operation 9-536 for presenting a recommendation for a future course of action as depicted in
In some implementations, operation 9-536 may include an operation 9-538 for presenting a justification for the recommendation as depicted in
In some implementations, the advisory presentation operation 9-304 may include an operation 9-540 for presenting an indication of one or more past events as depicted in
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one's feelings, thoughts, and other aspects of the person's everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where users may report or post their latest status, personal activities, and various other aspects of the users' everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social networking status reports in which a user may report or post for others to view their current status, activities, and/or other aspects of the user.
A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger's daily life. Typically, such microblog entries will describe the various “events” associated with or are of interest to the microblogger that occurs during a course of a typical day. The microblog entries are often continuously posted during the course of a typical day, and thus, by the end of a normal day, a substantial number of events may have been reported and posted.
Each of the reported events that may be posted through microblog entries may be categorized into one of at least three possible categories. The first category of events that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, incident, happening, or any other event that occurs with respect to the microblogger or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. Such events would include, for example, intake of food, medicine, or nutraceutical, certain physical characteristics of the microblogger or by others such as blood sugar level or blood pressure that can be objectively measured, activities of the microblogger objectively observable by the microblogger, by others, or by a device, activities of others that may be objectively observed by the microblogger, by others, or by a device, external events such as performance of the stock market (which the microblogger may have an interest in), performance of a favorite sports team, and so forth.
In some cases, objective occurrences may not be at least directly associated with a microblogger. Examples of such objective occurrences include, for example, external events such as the local weather, activities of others (e.g., spouse or boss), the behavior or activities of a pet or livestock, the characteristics or performances of mechanical or electronic devices such as automobiles, appliances, and computing devices, and other events that may directly or indirectly affect the microblogger.
A second category of events that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger may include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be directly reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., happiness, sadness, anger, tension, state of alertness, state of mental fatigue, jealousy, envy, and so forth), the subjective physical state of the microblogger (e.g., upset stomach, state of vision, state of hearing, pain, and so forth), and the subjective overall state of the microblogger (e.g., “good,” “bad,” state of overall wellness, overall fatigue, and so forth). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states).
A third category of events that may be reported or posted through microblog entries include “subjective observations” made by the microblogger. A subjective observation is similar to subjective user states and may be any subjective opinion, thought, or evaluation relating to any external incidence (e.g., outward looking instead of inward looking as in the case of subjective user states). Thus, the difference between subjective user states and subjective observations is that subjective user states relates to self-described subjective descriptions of the user states of one's self while subjective observations relates to subjective descriptions or opinions regarding external events. Examples of subjective observations include, for example, a microblogger's perception about the subjective user state of another person (e.g., “he seems tired”), a microblogger's perception about another person's activities (e.g., “he drank too much yesterday”), a microblogger's perception about an external event (e.g., “it was a nice day today”), and so forth. Although microblogs are being used to provide a wealth of personal information, thus far they have been primarily limited to their use as a means for providing commentaries and for maintaining open diaries.
Another potential source for valuable but not yet fully exploited data is the data provided by sensing devices that are used to sense and/or monitor various aspects of everyday life. Currently there are a number of sensing devices that can detect and/or monitor various user related and nonuser related events. For example, there are presently a number of sensing devices that can sense various physical or physiological characteristics of a person or an animal (e.g., a pet or a livestock). Examples of such devices include commonly known and used monitoring devices such as blood pressure devices, heart rate monitors, blood glucose sensors (e.g., glucometers), respiration sensor devices, temperature sensors, and so forth. Other examples of devices that can monitor physical or physiological characteristics include more exotic and sophisticated devices such as functional magnetic resonance imaging (fMRI) device, functional Near Infrared (fNIR) devices, blood cell-sorting sensing device, and so forth. Many of these devices are becoming more compact and less expensive such that they are becoming increasingly accessible for purchase and/or self-use by the general public.
Other sensing devices may be used in order to sense and monitor activities of a person or an animal. These would include, for example, global positioning systems (GPS), pedometers, accelerometers, and so forth. Such devices are compact and can even be incorporated into, for example, a mobile communication device such a cellular telephone or on the collar of a pet. Other sensing devices for monitoring activities of individuals (e.g., users) may be incorporated into larger machines and may be used in order to monitor the usage of the machines by the individuals. These would include, for example, sensors that are incorporated into exercise machines, automobiles, bicycles, and so forth. Today there are even toilet monitoring devices that are available to monitor the toilet usage of individuals.
Other sensing devices are also available that can monitor general environmental conditions such as environmental temperature sensor devices, humidity sensor devices, barometers, wind speed monitors, water monitoring sensors, air pollution sensor devices (e.g., devices that can measure the amount of particulates in the air such as pollen, those that measure CO2 levels, those that measure ozone levels, and so forth). Other sensing devices may be employed in order to monitor the performance or characteristics of mechanical and/or electronic devices. All the above described sensing devices may provide useful data that may indicate objectively observable events (e.g., objective occurrences).
In accordance with various embodiments, robust methods, systems, and computer program products are provided to, among other things, acquiring events data indicating multiple events as originally reported by multiple sources including acquiring at least a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices. The methods, systems, and computer program products may then develop a hypothesis based, at least in part, on the first data and the second data. In some embodiments, one or more actions may be executed based, at least in part, on the developed hypothesis. Examples of the types of actions that may be executed include, for example, the presentation of the developed hypothesis or advisories relating to the developed hypothesis. Other actions that may be executed include the prompting of mechanical and/or electronic devices to execute one or more operations based, at least in part, on the developed hypothesis.
The robust methods, systems, and computer program products may be employed in a variety of environments including, for example, social networking environments, blogging or microblogging environments, instant messaging (IM) environments, or any other type of environment that allows a user to, for example, maintain a diary.
In various implementations, a “hypothesis,” as referred to herein, may define one or more relationships or links between different types of events (i.e., event types) including at least a first event type (e.g., a type of event such as a particular type of subjective user state including, for example, a subjective mental state such as “happy”) and a second event type (e.g., another type of event such as a particular type of objective occurrence, for example, favorite sports team winning a game). In some cases, a hypothesis may be represented by an events pattern that may indicate spatial or sequential relationships between different event types (e.g., different types of events such as subjective user states and objective occurrences). In some embodiments, a hypothesis may be further defined by an indication of the soundness (e.g., strength) of the hypothesis.
Note that for ease of explanation and illustration, the following description will describe a hypothesis as defining, for example, the sequential or spatial relationship between two different event types, for example, a first event type and a second event type. However, those skilled in the art will recognize that such a hypothesis may also identify the relationships between three or more event types (e.g., a first event type, a second event type, a third event type, and so forth).
In some embodiments, a hypothesis may, at least in part, be defined or represented by an events pattern that indicates or suggests a spatial or a sequential (e.g., time/temporal) relationship between different event types. Such a hypothesis, in some cases, may also indicate the strength or weakness of the link between the different event types. That is, the strength or weakness (e.g., soundness) of the correlation between different event types may depend upon, for example, whether the events pattern repeatedly occurs and/or whether a contrasting events pattern has occurred that may contradict the hypothesis and therefore, weaken the hypothesis (e.g., an events pattern that indicates a person becoming tired after jogging for thirty minutes when a hypothesis suggests that a person will be energized after jogging for thirty minutes).
As briefly described above, a hypothesis may be represented by an events pattern that may indicate spatial or sequential (e.g., time or temporal) relationship or relationships between multiple event types. In some implementations, a hypothesis may merely indicate temporal sequential relationships between multiple event types that indicate the temporal relationships between multiple event types. In alternative implementations a hypothesis may indicate a more specific time relationship between multiple event types. For example, a sequential pattern may represent the specific pattern of events that occurs along a timeline that may indicate the specific time intervals between event types. In still other implementations, a hypothesis may indicate the spatial (e.g., geographical) relationships between multiple event types.
In various embodiments, the development of a hypothesis may be particularly useful to a user (e.g., a microblogger or a social networking user) that the hypothesis may or may not be directly associated with. That is, in some embodiments, a hypothesis may be developed that directly relates to a user. Such a hypothesis may relate to, for example, one or more subjective user states associated with the user, one or more activities associated with the user, or one or more characteristics associated with the user. In other embodiments, however, a hypothesis may be developed that may not be directly associated with a user. For example, a hypothesis may be developed that may be particularly associated with an acquaintance of the user, a pet, or a device operated or used by the user.
In some embodiments, the development of a hypothesis may assist a user in modifying his/her future behavior, while in other embodiments, such a hypothesis may be useful to third parties such as other users or nonusers, or even to advertisers in order to assist the advertisers in developing a more targeted marketing scheme. In still other situations, the development of a hypothesis relating to a user may help in the treatment of ailments associated with the user.
In some embodiments, a hypothesis may be developed (e.g., creating and/or further refinement of a hypothesis) by determining a pattern of reported events that repeatedly occurs and/or to compare similar or dissimilar reported pattern of events. For example, if a user such as a microblogger reports repeatedly that after each visit to a particular restaurant, the user always has an upset stomach, then a hypothesis may be created and developed that suggests that the user will get an upset stomach after visiting the particular restaurant. Note that such events may be based on reported data originally provided by two different sources, the user who reports having a stomach ache, and a sensing device such as a GPS device that reports data that indicates the user's visit to the restaurant just prior to the user reporting the occurrence of the stomach ache.
If, on the other hand, after developing such a hypothesis, the GPS device reports data that indicates that the user visited the same restaurant again but after the second visit the user reports feeling fine, then the reported data provided by the GPS device and the data provided by the user during and/or after the second visit may result in the weakening of the hypothesis (e.g., the second visit contradicts the hypothesis that a stomach ache is associated with visiting the restaurant). Alternatively, if after developing such a hypothesis, the GPS device and the user reports that in a subsequent visit to the restaurant, the user again got an upset stomach, then such reporting, as provided by both the user and the GPS device, may result in a confirmation of the soundness of the hypothesis.
In various embodiments, other types of hypothesis may be developed that may not be directly related to a user. For instance, a user (e.g., a person) and one or more sensing devices may report on the various characteristics, activities, and/or behaviors of a friend, a spouse, a pet, or even a mechanical or electronic device that the user may have an interest in. Based on such reported data, one or more hypothesis may be developed that may not be directly related to the user.
Thus, in accordance with various embodiments, robust methods, systems, and computer program products are provided that may be designed to, among other things, acquire events data indicating multiple events as originally reported by multiple sources including at least a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event originally reported by one or more sensing devices. Based on the at least one reported event as indicated by the acquired first data and the at least second reported event as indicated by the second data, a hypothesis may be developed. In various embodiments, such a hypothesis may be related to, for example, the user, a third party (e.g., another user or nonuser, or a nonhuman living organism such as a pet or livestock), a mechanical and/or electronic device, the environment, or any other entity or item that may be relevant to the user. Note that the phrase “as originally reported” is used herein since the first data and the second data indicating the at least one reported event and the at least second reported event may be obtained from other sources other than their original sources (e.g., the user and the one or more sensing devices).
a and 10-1b illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system 10-100 may include at least a computing device 10-10 (see
Based at least on the reported events as indicated by the acquired first data 10-60 and the second data 10-61 (and in some cases, based further on the reported events indicated by the third data 10-62 and/or the fourth data 10-63), a hypothesis may be developed by the computing device 10-10. In some embodiments, one or more actions may be executed by the computing device 10-10 in response at least in part to the development of the hypothesis. In the following, “*” indicates a wildcard. Thus, references to user 10-20* may indicate a user 10-20a or a user 10-20b of
As indicated earlier, in some embodiments, the computing device 10-10 may be a server while in other embodiments the computing device 10-10 may be a standalone device. In the case where the computing device 10-10 is a network server, the computing device 10-10 may communicate indirectly with a user 10-20a, one or more third parties 10-50, and one or more sensing devices 10-35a via wireless and/or wired network 10-40. The wireless and/or wired network 10-40 may comprise of, for example, a local area network (LAN), a wireless local area network (WLAN), personal area network (PAN), Worldwide Interoperability for Microwave Access (WiMAX), public switched telephone network (PTSN), general packet radio service (GPRS), cellular networks, and/or other types of wires or wired networks. In contrast, in embodiments where the computing device 10-10 is a standalone device, the computing device 10-10 may communicate directly at least with a user 10-20b (e.g., via a user interface 10-122) and one or more sensing devices 10-35b. In embodiments in which the computing device 10-10 is a standalone device, the computing device 10-10 may also communicate indirectly with one or more third parties 10-50 and one or more sensing devices 10-35a via a wireless and/or wired network 10-40.
In embodiments in which the computing device 10-10 is a network server (or simply “server”); the computing device 10-10 may communicate with a user 10-20a through a wireless and/or wired network 10-40 and via a mobile device 10-30. A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device 10-30 may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices that can communicate with the computing device 10-10. In some embodiments, the mobile device 10-30 may be a handheld device such as a cellular telephone, a smartphone, a Mobile Internet Device (MID), an Ultra Mobile Personal Computer (UMPC), a convergent device such as a personal digital assistant (PDA), and so forth.
In embodiments in which the computing device 10-10 is a standalone device that may communicate directly with a user 10-20b, the computing device 10-10 may be any type of portable device (e.g., a handheld device) or non-portable device (e.g., desktop computer or workstation). For these embodiments, the computing device 10-10 may be any one of a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices. In some embodiments, in which the computing device 10-10 is a handheld device, the computing device 10-10 may be a cellular telephone, a smartphone, an MID, an UMPC, a convergent device such as a PDA, and so forth. In various embodiments, the computing device 10-10 may be a peer-to-peer network component device. In some embodiments, the computing device 10-10 and/or the mobile device 10-30 may operate via a Web 2.0 construct (e.g., Web 2.0 application 10-268).
In some implementations, in order to acquire the first data 10-60 and/or the second data 10-61, the computing device 10-10 may be designed to prompt the user 10-20* and/or the one or more sensing devices 10-35* (e.g., transmitting or indicating a request or an inquiry to the user 10-20* and/or the one or more sensing device 10-35*) to report occurrences of the first reported event and/or the second reported event as indicated by refs. 22 and 23. In alternative implementations, however, the computing device 10-10 may be designed to, rather than prompting the user 10-20* and/or the one or more sensors 10-35*, prompt one or more network devices such as the mobile device 10-30 and/or one or more network servers 10-36 in order to acquire the first data 10-60 and/or the second data 10-61. That is, in some cases, the user 10-20* and/or the one or more sensors 10-35* may already have previously provided the first data 10-60 and/or the second data 10-61 to one or more of the network devices (e.g., mobile device 10-30 and/or network servers 10-36).
Each of the reported events indicated by the first data 10-60 and/or the second data 10-61 may or may not be directly associated with a user 10-20*. For example, although each of the reported events may have been originally reported by the user 10-20* or by the one or more sensing devices 10-35*, the reported events (e.g., at least the one reported event as indicated by the first data 10-60 and the at least second reported event as indicated by the second data 10-61) may be, in some implementations, related or associated with one or more third parties (e.g., another user, a nonuser, or a nonhuman living organism such as a pet dog or livestock), one or more devices 10-55 (e.g., electronic and/or mechanical devices), or one or more aspects of the environmental (e.g., the quality of the local drinking water, local weather conditions, and/or atmospheric conditions). For example, when providing the first data 10-60, a user 10-20* may report on the perceptions made by the user 10-20* regarding the behavior or activities of a third party (e.g., another user or a pet) rather than the behavior or activities of the user 10-20* him or herself.
As previously described, a user 10-20* may at least be the original source for the at least one reported event as indicated by the first data 10-60. The at least one reported event as indicated by the first data 10-60 may indicate any one or more of a variety of possible events that may be reported by the user 10-20*. For example, and as will be explained in greater detail herein, the at least one reported event as indicated by the first data 10-60 may relate to at least a subjective user state (e.g., a subjective mental state, a subjective physical state, or a subjective overall state) of the user 10-20*, a subjective observation (e.g., the perceived subjective user state of a third party 10-50 as perceived by user 10-20*, the perceived activity of a third party 10-50 or the user 10-20* as perceived by the user 10-20*, the perceived performance or characteristic of a device 10-55 as perceived by the user 10-20*, the perceived occurrence of an external event as perceived by the user 10-20* such as the weather, and so forth), or an objective occurrence (e.g., objectively observable activities of the user 10-20*, a third party 10-50, or a device 10-55; objectively observable physical or physiological characteristics of the user 10-20* or a third party 10-50; objective observable external events including environmental events or characteristics of a device 10-55; and so forth).
In contrast, the at least second reported event as originally reported by one or more sensing devices 10-35* and indicated by the second data 10-61 may be related to an objective occurrence that may be objectively observed by the one or more sensing devices 10-35*. Examples of the type of objective occurrences that may be indicated by the second data 10-61 includes, for example, physical or physiological characteristics of the user 10-20* or a third party 10-50, selective activities of the user 10-20* or a third party 10-50, some external events such as environmental conditions (e.g., atmospheric temperature and humidity, air quality, and so forth), characteristics and/or operational activities of a device 10-35, geographic location of the user 10-20* or a third party 10-50, and so forth.
After acquiring the events data including the first data 10-60 indicating the at least one reported event as originally reported by a user 10-20* and the second data 10-61 indicating the at least second reported event as originally reported by one or more sensing devices 10-35*, the computing device may be designed to develop a hypothesis. In various embodiments, the computing device 10-10 may develop a hypothesis by creating a new hypothesis based on the acquired events data and/or by refining an already existing hypothesis 10-80, which in some cases, may be stored in a memory 10-140.
After developing a hypothesis, the computing device 10-10 may be designed to execute one or more actions in response, at least in part, to the development of the hypothesis. One such action that may be executed is to present (e.g., transmit via a wireless and/or wired network 10-40 and/or indicate via user interface 10-122) one or more advisories 10-90 that may be related to the developed hypothesis. For example, in some implementations, the computing device 10-10 may present the developed hypothesis itself, or present an advisory such as an alert regarding reported past events or a recommendation for a future action to a user 10-20*, to one or more third parties 10-50, and/or to one or more remote network devices (e.g., network servers 10-36). In other implementations, or in the same implementations, the computing device 10-10 may prompt (e.g., as indicated by ref 25) one or more devices 10-55 (e.g., an automobile or a portion thereof, a household appliance or a portion thereof, a computing or communication device or a portion thereof, and so forth) to execute one or more operations.
Turning now to
The events data acquisition module 10-102 of
Referring now to
In various implementations, the first data acquisition module 10-201 may include one or more sub-modules. For example, in some implementations, such as in the case where the computing device 10-10 is a server, the first data acquisition module 10-201 may include a network interface reception module 10-202 configured to interface with a wireless and/or wired network 10-40 in order to receive the first data from a wireless and/or a wired network 10-40. In some implementations, such as when the computing device 10-10 is a standalone device, the first data acquisition module 10-201 may include a user interface reception module 10-204 configured to receive the first data 10-60 through a user interface 10-122.
In some instances, the first data acquisition module 10-201 may include a user prompting module 10-206 configured to prompt a user 10-20* to report occurrence of an event. Such an operation may be needed in some cases when, for example, the computing device 10-10 is missing data (e.g., first data 10-60 indicating the at least one reported event) that may be needed in order to develop a hypothesis (e.g., refining an existing hypothesis 10-80). In order to implement its operations, the user prompting module 10-206 may include a requesting module 10-208 that may be configured to indicate (e.g., via a user interface 10-122) or transmit (e.g., via a wireless and/or wired network 10-40) a request to a user 10-20* to report the occurrence of the event. The requesting module 10-208 may, in turn, include an audio requesting module 10-210 configured to audibly request (e.g., via one or more speakers) the user 10-20* to report the occurrence of the event and/or a visual requesting module 10-212 configured to visually request (e.g., via a display monitor) the user 10-20* to report the occurrence of the event. In some implementations, the first data acquisition module 10-201 may include a device prompting module 10-214 configured to, among other things, prompt a network device (e.g., a mobile device 10-30 or a network server 10-36) to provide the first data 10-60.
Turning now to the second data acquisition module, 10-215, the second data acquisition module 10-215 in various implementations may include one or more sub-modules. For example, in some implementations, the second data acquisition module 10-215 may include a network interface reception module 10-216 configured to interface with a wireless and/or wired network 10-40 in order to, for example, receive the second data 10-61 from at least one of a wireless and/or a wired network 10-40 and/or a sensing device reception module 10-218 configured to receive the second data 10-61 directly from the one or more sensing devices 10-35b. In various implementations, the second data acquisition module 10-215 may include a device prompting module 10-220 configured to prompt the one or more sensing devices 10-35* to provide the second data 10-61 (e.g., to report the second reported event).
In order to implement its functional operations, the device prompting module 10-220 in some implementations may further include one or more sub-modules including a sensing device directing/instructing module 10-222 configured to direct or instruct the one or more sensing devices 10-35* to provide the second data 10-61 (e.g., to report the second reported event). In the same or different implementations, the device prompting module 10-220 may include a sensing device configuration module 10-224 designed to configure the one or more sensing devices 10-35* to provide the second data 10-61 (e.g., to report the second reported event). In the same or different implementations, the device prompting module 10-220 may include a sensing device requesting module 10-226 configured to request the one or more sensing devices 10-35* to provide the second data 10-61 (e.g., to report the second reported event).
In various implementations, the time element acquisition module 10-228 of the events data acquisition module 10-102 may include one or more sub-modules. For example, in some implementations, the time element acquisition module 10-228 may include a time stamp acquisition module 10-230 configured to acquire a first time stamp associated with the at least one reported event and a second time stamp associated with the at least second reported event. In the same or different implementations, the time element acquisition module 10-228 may include a time interval indication acquisition module 10-232 configured to acquire an indication of a first time interval associated with the at least one reported event and an indication of second time interval associated with the at least second reported event.
Referring back to
b illustrates particular implementations of the hypothesis development module 10-104 of
The hypothesis creation module 10-236 may include one or more sub-modules in various implementations. For example, in some implementations, the hypothesis creation module 10-236 may include an events pattern determination module 10-238 configured to determine an events pattern based, at least in part, on occurrence of the first reported event and occurrence of the second reported event. The determined events pattern may then facilitate the hypothesis creation module 10-236 in creating a hypothesis. In some implementations, the events pattern determination module 10-238, in order to for example facilitate the hypothesis creation module 10-236 to create a hypothesis, may further include a sequential events pattern determination module 10-240 configured to determine a sequential events pattern based, at least in part, on the time or temporal occurrence of the at least one reported event and the time or temporal occurrence of the at least second reported event and/or a spatial events pattern determination module 10-242 configured to determine a spatial events pattern based, at least in part, on the spatial occurrence of the at least one reported event and the spatial occurrence of the at least second reported event.
The existing hypothesis refinement module 10-244, in various implementations, may also include one or more sub-modules. For example, in various implementations, the existing hypothesis refinement module 10-244 may include an events pattern determination module 10-246 configured to, for example, facilitate the existing hypothesis refinement module 10-244 in refining the existing hypothesis 10-80 by determining at least an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event. In some implementations, the events pattern determination module 10-246 may further include a sequential events pattern determination module 10-248 configured to determine a sequential events pattern based, at least in part, on the time or temporal occurrence of the at least one reported event and the time or temporal occurrence of the at least second reported event and/or a spatial events pattern determination module 10-250 configured to determine a spatial events pattern based, at least in part, on the spatial occurrence of the at least one reported event and the spatial occurrence of the at least second reported event. Note that in cases where both the hypothesis creation module 10-236 and the existing hypothesis refinement module 10-244 are present in the hypothesis development module 10-104, one or more of the events pattern determination module 10-246, the sequential events pattern determination module 10-248, and the spatial events pattern determination module 10-250 of the existing hypothesis refinement module 10-244 may be the same modules as the events pattern determination module 10-238, the sequential events pattern determination module 10-240, and the spatial events pattern determination module 10-242, respectively, of the hypothesis creation module 10-236.
In some cases, the existing hypothesis refinement module 10-244 may include a support determination module 10-252 configured to determine whether an events pattern, as determined by the events pattern determination module 10-246, supports an existing hypothesis 10-80. In some implementations, the support determination module may further include a comparison module 10-254 configured to compare the determined events pattern (e.g., as determined by the events pattern determination module 10-246) with an events pattern associated with the existing hypothesis 10-80 to facilitate in the determination as to whether the determined events pattern supports the existing hypothesis 10-80.
In some cases, the existing hypothesis refinement module 10-244 may include a soundness determination module 10-256 configured to determine soundness of an existing hypothesis 10-80 based, at least in part, on a comparison made by the comparison module 10-254. In some cases, the existing hypothesis refinement module 10-244 may include a modification module 10-258 configured to modify an existing hypothesis 10-80 based, at least in part, on a comparison made by the comparison module 10-254.
Referring back to
Referring now to
The advisory presentation module 10-260, in turn, may further include one or more additional sub-modules. For instance, in some implementations, the advisory presentation module 10-260 may include an advisory indication module 10-262 configured to indicate, via a user interface 10-122, the one or more advisories related to the hypothesis developed by, for example, the hypothesis development module 10-104. In the same or different implementations, the advisory presentation module 10-260 may include an advisory transmission module 10-264 configured to transmit, via at least one of a wireless network or a wired network, the one or more advisories related to the hypothesis developed by, for example, the hypothesis development module 10-104.
In the same or different implementations, the advisory presentation module 10-260 may include a hypothesis presentation module 10-266 configured to, among other things, present (e.g., either transmit or indicate) at least a form of a hypothesis developed by, for example, the hypothesis development module 10-104. In various implementations, the hypothesis presentation module 10-266 may include one or more additional sub-modules. For example, in some implementations, the hypothesis presentation module 10-266 may include an event types relationship presentation module 10-268 configured to present an indication of a relationship between at least a first event type and at least a second event type as referenced by the hypothesis developed by, for example, the hypothesis development module 10-104.
In the same or different implementations, the hypothesis presentation module 10-266 may include a hypothesis soundness presentation module 10-270 configured to present an indication of soundness of the hypothesis developed by, for example, the hypothesis development module 10-104. In the same or different implementations, the hypothesis presentation module 10-266 may include a temporal/specific time relationship presentation module 10-271 configured to present an indication of a temporal or specific time relationship between the at least first event type and the at least second event type as referenced by the hypothesis developed by, for example, the hypothesis development module 10-104. In the same or different implementations, the hypothesis presentation module 10-266 may include a spatial relationship presentation module 10-272 configured to present an indication of a spatial relationship between the at least first event type and the at least second event type as referenced by the hypothesis developed by, for example, the hypothesis development module 10-104.
In various implementations, the advisory presentation module 10-260 may include a prediction presentation module 10-273 configured to present an advisory relating to a predication of one or more future events based, at least in part, on the hypothesis developed by, for example, the hypothesis development module 10-104. In the same or different implementations, the advisory presentation module 10-260 may include a recommendation presentation module 10-274 configured to present a recommendation for a future course of action based, at least in part, on the hypothesis developed by, for example, the hypothesis development module 10-104. In some implementations, the recommendation presentation module 10-274 may further include a justification presentation module 10-275 configured to present a justification for the recommendation presented by the recommendation presentation module 10-274.
In various implementations, the advisory presentation module 10-260 may include a past events presentation module 10-276 configured to present an indication of one or more past events based, at least in part, on the hypothesis developed by, for example, the hypothesis development module 10-104.
The device prompting module 10-277 in various embodiments may include one or more sub-modules. For example, in some implementations, the device prompting module 10-277 may include a device instruction module 10-278 configured to instruct one or more devices 10-55 to execute one or more operations (e.g., actions) based, at least in part, on the hypothesis developed by, for example, the hypothesis development module 10-104. In the same or different implementations, the device prompting module 10-277 may include a device activation module 10-279 configured to activate one or more devices 10-55 to execute one or more operations (e.g., actions) based, at least in part, on the hypothesis developed by, for example, the hypothesis development module 10-104. In the same or different implementations, the device prompting module 10-277 may include a device configuration module 10-280 designed to configure one or more devices 10-55 to execute one or more operations (e.g., actions) based, at least in part, on the hypothesis developed by, for example, the hypothesis development module 10-104.
Turning now to
In the same or different implementations, the one or more sensing devices 10-35* may include one or more imaging system devices 10-290 for capturing various types of images of a subject (e.g., a user 10-20* or a third party 10-50). Examples of such imaging system devices 10-290 include, for example, a digital or video camera, an x-ray machine, an ultrasound device, and so forth. Note that in some instances, the one or more imaging system devices 10-290 may also include an fMRI device 10-285 and/or an fNIR device 10-286.
In the same or different implementations, the one or more sensing devices 10-35* may include one or more user activity sensing devices 10-291 designed to sense or monitor one or more user activities of a subject (e.g., a user 10-20* or a third party 10-50 such as another person or a pet or livestock). For example, in some implementations, the user activity sensing devices 10-291 may include a pedometer 10-292, an accelerometer 10-293, an image capturing device 10-294 (e.g., digital or video camera), a toilet monitoring device 10-295, an exercise machine sensor device 10-296, and/or other types of sensing devices capable of sensing a subject's activities.
In the same or different implementations, the one or more sensing devices 10-35* may include a global position system (GPS) 10-297 to determine one or more locations of a subject (e.g., a user 10-20* or a third party 10-50 such as another user or an animal), an environmental temperature sensor device 10-298 designed to sense or measure environmental (e.g. atmospheric) temperature, an environmental humidity sensor device 10-299 designed to sense or measure environmental (e.g. atmospheric) humidity level, an environmental air pollution sensor device 10-320 to measure or sense various gases such as CO2, ozone, xenon, and so forth in the atmosphere or to measure particulates (e.g., pollen) in the atmosphere, and/or other devices for measuring or sensing various other characteristics of the environment (e.g., a barometer, a wind speed sensor, a water quality sensing device, and so forth).
In various implementations, the computing device 10-10 of
In various implementations, the computing device 10-10 may include a network interface 10-120, which may be a device designed to interface with a wireless and/or wired network 10-40. Examples of such devices include, for example, a network interface card (NIC) or other interface devices or systems for communicating through at least one of a wireless network or wired network 10-40. In some implementations, the computing device 10-10 may include a user interface 10-122. The user interface 10-122 may comprise any device that may interface with a user 10-20b. Examples of such devices include, for example, a keyboard, a display monitor, a touchscreen, a microphone, a speaker, an image capturing device such as a digital or video camera, a mouse, and so forth.
The computing device 10-10 may include a memory 10-140. The memory 10-140 may include any type of volatile and/or non-volatile devices used to store data. In various implementations, the memory 10-140 may comprise, for example, a mass storage device, a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read-only memory (EPROM), random access memory (RAM), a flash memory, a synchronous random access memory (SRAM), a dynamic random access memory (DRAM), and/or other memory devices. In various implementations, the memory 10-140 may store an existing hypotheses 10-80 and/or historical data 10-81 (e.g., historical data including, for example, past events data or historical events patterns related to a user 10-20*, related to a subgroup of the general population that the user 10-20 belongs to, or related to the general population).
The various features and characteristics of the components, modules, and sub-modules of the computing device 10-10 presented thus far will be described in greater detail with respect to the processes and operations to be described herein.
In
Further, in the following figures that depict various flow processes, various operations may be depicted in a box-within-a-box manner. Such depictions may indicate that an operation in an internal box may comprise an optional example embodiment of the operational step illustrated in one or more external boxes. However, it should be understood that internal box operations may be viewed as independent operations separate from any associated external boxes and may be performed in any sequence with respect to all other illustrated operations, or may be performed concurrently.
In any event, after a start operation, the operational flow 10-300 may move to a data acquisition operation 10-302 for acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices. For instance, the events data acquisition module 10-102 of the computing device 10-10 acquiring a first data 10-60 (e.g., in the form of a blog entry, a status report, an electronic message, or a diary entry) indicating at least one reported event (e.g., a subjective user state, a subjective observation, or an objective occurrence) as originally reported by a user 10-20* and a second data 10-61 indicating at least a second reported event (e.g., objective occurrence) as originally reported by one or more sensing devices 10-35*.
Next, operational flow 10-300 may include hypothesis development operation 10-304 for developing a hypothesis based, at least in part, on the first data and the second data. For instance, the hypothesis development module 10-104 of the computing device 10-10 developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis) based, at least in part, on the first data 10-60 and the second data 10-61. Note that in the following description and for ease of illustration and understanding the hypothesis to be developed through the hypothesis development operation 10-304 may be described as linking together two types of events (i.e., event types). However, those skilled in the art will recognize that such a hypothesis 10-80 may alternatively relate to the association of three or more types of events in various implementations.
In various implementations, the first data 10-60 to be acquired during the data acquisition operation 10-302 of
In some alternative implementations, the data acquisition operation 10-302 may include an operation 10-403 for receiving the first data via a user interface as depicted in
In the same or different implementations, the data acquisition operation 10-302 may include an operation 10-404 for prompting the user to report an occurrence of an event as depicted in
In various implementations, operation 10-404 may comprise an operation 10-405 for requesting the user to report the occurrence of the event as depicted in
In some implementations, operation 10-405 may further comprise an operation 10-406 for requesting audibly the user to report the occurrence of the event as depicted in
In some implementations, operation 10-405 may further comprise an operation 10-407 for requesting visually the user to report the occurrence of the event as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-408 for prompting a network device to provide the first data as depicted in
The first data 10-60 to be acquired through the data acquisition operation 10-302 may be in a variety of different forms. For example, in some implementations, the data acquisition operation 10-302 may include an operation 10-409 for acquiring, via one or more electronic entries, a first data indicating at least one reported event as originally reported by the user as depicted in
In some implementations, operation 10-409 may comprise an operation 10-410 for acquiring, via one or more blog entries, a first data indicating at least one reported event as originally reported by the user as depicted in
In some implementations, operation 10-409 may include an operation 10-411 for acquiring, via one or more status report entries, a first data indicating at least one reported event as originally reported by the user as depicted in
In some implementations, operation 10-409 may include an operation 10-412 for acquiring, via one or more electronic messages, a first data indicating at least one reported event originally reported by the user as depicted in
In some implementations, operation 10-409 may include an operation 10-413 for acquiring via one or more diary entries, a first data indicating at least one reported event originally reported by the user as depicted in
As will be further described herein, the first data 10-60 acquired during the data acquisition operation 10-302 of
Various types of subjective user states may be indicated by the first data 10-60 acquired through operation 10-414. For example, in some implementations, operation 10-414 may include an operation 10-415 for acquiring a first data indicating at least one subjective mental state of the user as originally reported by the user as depicted in
In some implementations, operation 10-414 may include an operation 10-416 for acquiring a first data indicating at least one subjective physical state of the user as originally reported by the user as depicted in
In some implementations, operation 10-414 may include an operation 10-417 for acquiring a first data indicating at least one subjective overall state of the user as originally reported by the user as depicted in
In various alternative implementations, the first data 10-60 acquired during the data acquisition operation 10-302 of
A variety of subjective observations may be indicated by the first data 10-60 acquired during operation 10-418. For example, in various implementations, operation 10-418 may include an operation 10-419 for acquiring a first data indicating at least one subjective observation made by the user regarding a third party as depicted in
As will be further described herein, various types of subjective observations may be made by the user 10-20* regarding a third party. For example, in various implementations, operation 10-419 may include an operation 10-420 for acquiring a first data indicating at least one subjective observation made by the user regarding subjective user state of the third party as perceived by the user as depicted in
In some implementations, operation 10-420 may include an operation 10-421 for acquiring a first data indicating at least one subjective observation made by the user regarding subjective mental state of the third party as perceived by the user as depicted in
In some implementations, operation 10-420 may include an operation 10-422 for acquiring a first data indicating at least one subjective observation made by the user regarding subjective physical state of the third party as perceived by the user as depicted in
In some implementations, operation 10-420 may include an operation 10-423 for acquiring a first data indicating at least one subjective observation made by the user regarding subjective overall state of the third party as perceived by the user as depicted in
In various implementations, operation 10-419 of
In various implementations, operation 10-418 of
In some implementations, operation 10-418 may include an operation 10-426 for acquiring a first data indicating at least one subjective observation made by the user relating to an external event as depicted in
The data acquisition operation 10-302 of
In some cases, operation 10-427 may involve acquiring a first data 10-60 that indicates an objective occurrence related to the user 10-20*. For example, in various implementations, operation 10-427 may include an operation 10-428 for acquiring a first data indicating at least one activity executed by the user as originally reported by the user as depicted in
In some instances, the first data 10-60 to be acquired may indicate an activity involving the consumption of an item by the user 10-20*. For example, in some implementations, operation 10-428 may comprise an operation 10-429 for acquiring a first data indicating at least a consumption of an item by the user as originally reported by the user as depicted in
In these implementations, the first data 10-60 to be acquired may indicate the user 10-20* consuming any one of a variety of items. For example, in some implementations, operation 10-429 may include an operation 10-430 for acquiring a first data indicating at least a consumption of a food item by the user as originally reported by the user as depicted in
In some implementations, operation 10-429 may include an operation 10-431 for acquiring a first data indicating at least a consumption of a medicine by the user as originally reported by the user as depicted in
In some implementations, operation 10-429 may include an operation 10-432 for acquiring a first data indicating at least a consumption of a nutraceutical by the user as originally reported by the user as depicted in
The first data 10-60 acquired in operation 10-428 may indicate other types of activities executed by the user 10-20* in various alternative implementations. For example, in some implementations, operation 10-428 may include an operation 10-433 for acquiring a first data indicating at least a social or leisure activity executed by the user as originally reported by the user as depicted in
In some implementations, operation 10-428 may include an operation 10-434 for acquiring a first data indicating at least a work activity executed by the user as originally reported by the user as depicted in
In some implementations, operation 10-428 may include an operation 10-435 for acquiring a first data indicating at least an exercise activity executed by the user as originally reported by the user as depicted in
In some implementations, operation 10-428 may include an operation 10-436 for acquiring a first data indicating at least a learning or educational activity executed by the user as originally reported by the user as depicted in
In various implementations, the first data 10-60 that may be acquired through operation 10-427 of
Various types of activities executed by the third party 10-50 may be indicated by the first data 10-60 acquired through operation 10-437. For example, in some implementations, operation 10-437 may further include an operation 10-438 for acquiring a first data indicating at least a consumption of an item by the third party as originally reported by the user as depicted in
For these implementations, the first data 10-60 acquired through operation 10-438 may indicate the third party 10-50 consuming at least one item from a variety of edible items. For example, in some implementations, operation 10-438 may include an operation 10-439 for acquiring a first data indicating at least a consumption of a food item by the third party as originally reported by the user as depicted in
In alternative implementations, however, operation 10-438 may include an operation 10-440 for acquiring a first data indicating at least a consumption of a medicine by the third party as originally reported by the user as depicted in
In still other alternative implementations, operation 10-438 may include an operation 10-441 for acquiring a first data indicating at least a consumption of a nutraceutical by the third party as originally reported by the user as depicted in
The first data 10-60 acquired through operation 10-437 may indicate other types of activities associated with a third party 10-50 other than a consumption of an item in various alternative implementations. For example, in some implementations, operation 10-437 may include an operation 10-442 for acquiring a first data indicating at least a social or leisure activity executed by the third party as originally reported by the user as depicted in
In some implementations, operation 10-437 may include an operation 10-443 for acquiring a first data indicating at least a work activity executed by the third party as originally reported by the user as depicted in
In some implementations, operation 10-437 may include an operation 10-444 for acquiring a first data indicating at least an exercise activity executed by the third party as originally reported by the user as depicted in
In some implementations, operation 10-437 may include an operation 10-445 for acquiring a first data indicating at least a learning or educational activity executed by the third party as originally reported by the user as depicted in
Referring back to
In some implementations, operation 10-427 may include an operation 10-447 for acquiring a first data indicating at least a location associated with a third party as originally reported by the user as depicted in
In some implementations, operation 10-427 may include an operation 10-448 for acquiring a first data indicating at least an external event as originally reported by the user as depicted in
In some implementations, operation 10-427 may include an operation 10-449 for acquiring a first data indicating one or more physical characteristics of the user as originally reported by the user as depicted in
In some implementations, operation 10-427 may include an operation 10-450 for acquiring a first data indicating one or more physical characteristics of a third party as originally reported by the user as depicted in
Referring back to the data acquisition operation 10-302 of
Alternatively, in some implementations, the data acquisition operation 10-302 may include an operation 10-452 for receiving the second data directly from the one or more sensing devices as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-453 for acquiring the second data by prompting the one or more sensing devices to provide the second data as depicted in
Various approaches may be employed in operation 10-453 in order to prompt the one or more sensing devices 10-35 to provide the second data 10-61. For example, in some implementations, operation 10-453 may include an operation 10-454 for acquiring the second data by directing or instructing the one or more sensing devices to provide the second data as depicted in
In some implementations, operation 10-453 may include an operation 10-455 for acquiring the second data by configuring the one or more sensing devices to provide the second data as depicted in
In some implementations, operation 10-453 may include an operation 10-456 for acquiring the second data by requesting the one or more sensing devices to provide the second data as depicted in
The second data 10-61 acquired through the data acquisition operation 10-302 of
In some implementations, operation 10-457 may include an operation 10-458 for acquiring the second data including data indicating one or more physiological characteristics of the user as originally reported by the one or more sensing devices as depicted in
Various types of physiological characteristics of the user 10-20* may be indicated by the second data 10-61 acquired through operation 10-458 in various alternative implementations. For example, in some implementations, operation 10-458 may include an operation 10-459 for acquiring the second data including heart rate sensor data relating to the user as depicted in
In some implementations, operation 10-458 may include an operation 10-460 for acquiring the second data including blood pressure sensor data relating to the user as depicted in
In some implementations, operation 10-458 may include an operation 10-461 for acquiring the second data including glucose sensor data relating to the user as depicted in
In some implementations, operation 10-458 may include an operation 10-462 for acquiring the second data including blood cell-sorting sensor data relating to the user as depicted in
In some implementations, operation 10-458 may include an operation 10-463 for acquiring the second data including sensor data relating to blood oxygen or blood volume changes of a brain of the user as depicted in
In some implementations, operation 10-458 may include an operation 10-464 for acquiring the second data including blood alcohol sensor data relating to the user as depicted in
In some implementations, operation 10-458 may include an operation 10-465 for acquiring the second data including temperature sensor data relating to the user as depicted in
In some implementations, operation 10-458 may include an operation 10-466 for acquiring the second data including respiration sensor data relating to the user as depicted in
In various implementations, operation 10-457 of
Referring back to the data acquisition operation 10-302 of
The data indicating the one or more activities of the user 10-20* acquired through operation 10-468 may be acquired from any one or more of a variety of different sensing devices 10-35* capable of sensing the activities of the user 10-20*. For example, in some implementations, operation 10-468 may include an operation 10-469 for acquiring the second data including pedometer data relating to the user as depicted in
In some implementations, operation 10-468 may include an operation 10-470 for acquiring the second data including accelerometer device data relating to the user as depicted in
In some implementations, operation 10-468 may include an operation 10-471 for acquiring the second data including image capturing device data relating to the user as depicted in
In some implementations, operation 10-468 may include an operation 10-472 for acquiring the second data including toilet monitoring device data relating to the user as depicted in
In some implementations, operation 10-468 may include an operation 10-473 for acquiring the second data including exercising machine sensor data relating to the user as depicted in
Various other types of events related to the user 10-20*, as originally reported by one or more sensing devices 10-35*, may be indicated by the second data 10-61 acquired in the data acquisition operation 10-302. For example, in some implementations, the data acquisition operation 10-302 may include an operation 10-474 for acquiring the second data including global positioning system (GPS) data indicating at least one location of the user as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-475 for acquiring the second data including temperature sensor data indicating at least one environmental temperature associated with a location of the user as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-476 for acquiring the second data including humidity sensor data indicating at least one environmental humidity level associated with a location of the user as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-477 for acquiring the second data including air pollution sensor data indicating at least one air pollution level associated with a location of the user as depicted in
In various implementations, the second data 10-61 acquired through the data acquisition operation 10-302 of
In various implementations, operation 10-478 may further include an operation 10-479 for acquiring the second data including data indicating one or more physiological characteristics of the third party as originally reported by the one or more sensing devices as depicted in
In various implementations, the second data 10-61 acquired through operation 10-479 may indicate at least one of a variety of physiological characteristics that may be associated with the third party 10-50*. For example, in some implementations, operation 10-479 may include an operation 10-480 for acquiring the second data including heart rate sensor data relating to the third party as depicted in FIG. 10-4j. For instance, the second data acquisition module 10-215 of the computing device 10-10 acquiring the second data 10-61 including heart rate sensor data relating to the third party 10-50 as at least originally provided by, for example, a heart rate sensor device 10-282.
In some implementations, operation 10-479 may include an operation 10-481 for acquiring the second data including blood pressure sensor data relating to the third party as depicted in
In some implementations, operation 10-479 may include an operation 10-482 for acquiring the second data including glucose sensor data relating to the third party as depicted in
In some implementations, operation 10-479 may include an operation 10-483 for acquiring the second data including blood cell-sorting sensor data relating to the third party as depicted in
In some implementations, operation 10-479 may include an operation 10-484 for acquiring the second data including sensor data relating to blood oxygen or blood volume changes of a brain of the third party as depicted in
In some implementations, operation 10-479 may include an operation 10-485 for acquiring the second data including blood alcohol sensor data relating to the third party as depicted in
In some implementations, operation 10-479 may include an operation 10-486 for acquiring the second data including temperature sensor data relating to the third party as depicted in
In some implementations, operation 10-479 may include an operation 10-487 for acquiring the second data including respiration sensor data relating to the third party as depicted in
In various implementations, operation 10-478 of
Referring back to the data acquisition operation 10-302 of
The data indicating the one or more activities of the third party 10-50 acquired through operation 10-489 may be acquired from any one or more of a variety of different sensing devices 10-35* capable of sensing the activities of the user 10-20*. For example, in some implementations, operation 10-489 may include an operation 10-490 for acquiring the second data including pedometer data relating to the third party as depicted in
In some implementations, operation 10-489 may include an operation 10-491 for acquiring the second data including accelerometer device data relating to the third party as depicted in
In some implementations, operation 10-489 may include an operation 10-492 for acquiring the second data including image capturing device data relating to the third party as depicted in
In some implementations, operation 10-489 may include an operation 10-493 for acquiring the second data including toilet monitoring sensor data relating to the third party as depicted in
In some implementations, operation 10-489 may include an operation 10-494 for acquiring the second data including exercising machine sensor data relating to the third party as depicted in
Various other types of events related to a third party 10-50, as originally reported by one or more sensing devices 10-35*, may be indicated by the second data 10-61 acquired in the data acquisition operation 10-302. For example, in some implementations, the data acquisition operation 10-302 may include an operation 10-495 for acquiring the second data including global positioning system (GPS) data indicating at least one location of a third party as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-496 for acquiring the second data including temperature sensor data indicating at least one environmental temperature associated with a location of a third party as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-497 for acquiring the second data including humidity sensor data indicating at least one environmental humidity level associated with a location of a third party as depicted in
In some implementations, the data acquisition operation 10-302 may include an operation 10-498 for acquiring the second data including air pollution sensor data indicating at least one air pollution level associated with a location of the third party as depicted in
In various alternative implementations, the second data 10-61 acquired through the data acquisition operation 10-302 of
In some alternative implementations, the data acquisition operation 10-302 may include an operation 10-500 for acquiring the second data including device characteristic sensor data indicating at least one characteristic of a device as depicted in
In some alternative implementations, the data acquisition operation 10-302 may include an operation 10-501 for acquiring the second data including environmental characteristic sensor data indicating at least one environmental characteristic as depicted in
In some implementations, the data acquisition operation 10-302 of
In alternative implementations, the data acquisition operation 10-302 may include an operation 10-503 for acquiring a third data indicating a third reported event as originally reported by another one or more sensing devices as depicted in
In still other alternative implementations, the data acquisition operation 10-302 may include an operation 10-504 for acquiring a third data indicating a third reported event as originally reported by a third party and a fourth data indicating a fourth reported event as originally reported by another one or more sensing devices as depicted in
In order to facilitate the development of a hypothesis, the data acquisition operation 10-302 of
In some implementations, operation 10-505 may comprise an operation 10-506 for acquiring a first time stamp associated with the at least one reported event and a second time stamp associated with the at least second reported event as depicted in
In some implementations, operation 10-505 may comprise an operation 10-507 for acquiring an indication of a first time interval associated with the at least one reported event and an indication of second time interval associated with the at least second reported event as depicted in
In some implementations, the data acquisition operation 10-302 may comprise an operation 10-508 for acquiring an indication of a first spatial location associated with the at least one reported event and an indication of a second spatial location associated with the at least second reported event as depicted in
Referring back to
In some instances, operation 10-509 may include an operation 10-510 for creating the hypothesis based, at least in part, on the at least one reported event, the at least second reported event, and historical data as depicted in
In some implementations, operation 10-510 may further include an operation 10-511 for creating the hypothesis based, at least in part, on the at least one reported event, the at least second reported event, and historical data that is particular to the user or a sub-group of a general population that the user belongs to as depicted in
In various implementations, the hypothesis created through operation 10-509 may be implemented by determining an events pattern. For example, in some instances, operation 10-509 may include an operation 10-512 for creating the hypothesis by determining an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event as depicted in
In some implementations, operation 10-512 may include an operation 10-513 for creating the hypothesis by determining a sequential events pattern based at least in part on time occurrence of the at least one reported event and time occurrence of the at least second reported event as depicted in
In some implementations, operation 10-512 may include an operation 10-514 for creating the hypothesis by determining a spatial events pattern based at least in part on spatial occurrence of the at least one reported event and spatial occurrence of the at least second reported event as depicted in
In various implementations, the hypothesis development operation 10-302 of
Various approaches may be employed in order to refine an existing hypothesis 10-80 in operation 10-515. For example, in some implementations, operation 10-515 may include an operation 10-516 for refining the existing hypothesis by at least determining an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event as depicted in
Operation 10-516, in turn, may further comprise an operation 10-517 for refining the existing hypothesis by at least determining a sequential events pattern based, at least in part, on time occurrence of the at least one reported event and time occurrence of the at least second reported event as depicted in
In some alternative implementations, operation 10-516 may include an operation 10-518 for refining the existing hypothesis by at least determining a spatial events pattern based, at least in part, on spatial occurrence of the at least one reported event and spatial occurrence of the at least second reported event as depicted in
In some implementations, operation 10-516 may include an operation 10-519 for refining the existing hypothesis by determining whether the determined events pattern supports the existing hypothesis as depicted in
In various implementations, operation 10-519, in turn, may include an operation 10-520 for comparing the determined events pattern with an events pattern associated with the existing hypothesis to determine whether the determined events pattern supports the existing hypothesis as depicted in
In some implementations, operation 10-520 may further include an operation 10-521 for determining soundness of the existing hypothesis based on the comparison as depicted in
In some implementations, operation 10-520 may further include an operation 10-522 for modifying the existing hypothesis based on the comparison as depicted in
In various implementations, the hypothesis to be developed in the hypothesis development operation 10-304 of
In some alternative implementations, the hypothesis development operation 10-304 may include an operation 10-524 for developing a hypothesis that relates to a third party as depicted in
In some implementations, operation 10-524 may include an operation 10-525 for developing a hypothesis that relates to a person as depicted in
In some implementations, operation 10-524 may include an operation 10-526 for developing a hypothesis that relates to a non-human living organism as depicted in
In various implementations, the hypothesis development operation 10-304 may include an operation 10-527 for developing a hypothesis that relates to a device as depicted in
In some implementations, the hypothesis development operation 10-304 may include an operation 10-528 for developing a hypothesis that relates to an environmental characteristic as depicted in
Referring now to
In addition, and unlike operational flow 10-300, operational flow 10-600 may further include an action execution operation 10-606 for executing one or more actions in response at least in part to the developing (e.g., developing of a hypothesis performed in the hypothesis development operation 10-604 of operational flow 10-600). For instance, the action execution module 10-106 of the computing device 10-10 executing one or more actions in response at least in part to the developing of the hypothesis (e.g., developing of the hypothesis as in the hypothesis development operation 10-604).
Various types of actions may be executed in the action execution operation 10-606 in various alternative implementations. For example, in some implementations, the action execution operation 10-606 may include an operation 10-730 for presenting one or more advisories relating to the hypothesis as depicted in
The presentation of the one or more advisories in operation 10-730 may be performed in various ways. For example, in some implementations, operation 10-730 may include an operation 10-731 for indicating the one or more advisories related to the hypothesis via a user interface as depicted in
In same or different implementations, operation 10-730 may include an operation 10-732 for transmitting the one or more advisories related to the hypothesis via at least one of a wireless network or a wired network as depicted in
In some implementations, operation 10-732 may further include an operation 10-733 for transmitting the one or more advisories related to the hypothesis to the user as depicted in
In the same or different implementations, operation 10-732 may include an operation 10-734 for transmitting the one or more advisories related to the hypothesis to one or more third parties as depicted in
In operation 10-730 of
In various instances, operation 10-735 may further comprise an operation 10-736 for presenting an indication of a relationship between at least a first event type and at least a second event type as referenced by the hypothesis as depicted in
In some implementations, operation 10-736 may include an operation 10-737 for presenting an indication of soundness of the hypothesis as depicted in
In some implementations, operation 10-736 may include an operation 10-738 for presenting an indication of a temporal or specific time relationship between the at least first event type and the at least second event type as depicted in
In some implementations, operation 10-736 may include an operation 10-739 for presenting an indication of a spatial relationship between the at least first event type and the at least second event type as depicted in
Various types of events may be linked together by the hypothesis to be presented through operation 10-736 of
In some implementations, operation 10-736 may include an operation 10-741 for presenting an indication of a relationship between at least a first objective occurrence type and at least a second objective occurrence type as indicated by the hypothesis as depicted in
In some implementations, operation 10-736 may include an operation 10-742 for presenting an indication of a relationship between at least a subjective observation type and at least an objective occurrence type as indicated by the hypothesis as depicted in
Other types of advisories other than the hypothesis itself may also be presented through operation 10-730 of
In various implementations, operation 10-730 may include an operation 10-744 for presenting a recommendation for a future course of action based, at least in part, on the hypothesis as depicted in
In some implementations, operation 10-744 may further include an operation 10-745 for presenting a justification for the recommendation as depicted in
In some implementations, operation 10-730 may include an operation 10-746 for presenting an indication of one or more past events based, at least in part, on the hypothesis as depicted in
Referring back to the action execution operation 10-606 of
In some implementations, operation 10-747 may include an operation 10-748 for instructing the one or more devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-749 for activating the one or more devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-750 for configuring the one or more devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-751 for prompting one or more environmental devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-752 for prompting one or more household devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-753 for prompting one or more of the sensing devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-754 for prompting a second one or more sensing devices to execute one or more actions as depicted in
In some implementations, operation 10-747 may include an operation 10-755 for prompting the one or more devices including one or more network devices to execute one or more actions as depicted in
The present application is related to and claims the benefit of the earliest available effective filing date(s) from the following listed application(s) (the “Related Applications”) (e.g., claims earliest available priority dates for other than provisional patent applications or claims benefits under 35 USC §119(e) for provisional patent applications, for any and all parent, grandparent, great-grandparent, etc. applications of the Related Application(s)). All subject matter of the Related Applications and of any and all parent, grandparent, great-grandparent, etc. applications of the Related Applications is incorporated herein by reference to the extent such subject matter is not inconsistent herewith. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/462,128, entitled ACTION EXECUTION BASED ON USER MODIFIED HYPOTHESIS, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 28 Jul. 2009, which is currently co-pending or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation of U.S. patent application Ser. No. 12/462,201, entitled ACTION EXECUTION BASED ON USER MODIFIED HYPOTHESIS, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 29 Jul. 2009, which is currently co-pending or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/313,659, entitled CORRELATING SUBJECTIVE USER STATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 21 Nov. 2008, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/315,083, entitled CORRELATING SUBJECTIVE USER STATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 26 Nov. 2008, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/319,135, entitled CORRELATING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 31 Dec. 2008, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/319,134, entitled CORRELATING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 31 Dec. 2008, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/378,162, entitled SOLICITING DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 9 Feb. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/378,288, entitled SOLICITING DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 11 Feb. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/380,409, entitled SOLICITING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 25 Feb. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/380,573, entitled SOLICITING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 26 Feb. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/383,581, entitled CORRELATING DATA INDICATING SUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATA INDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 24 Mar. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/383,817, entitled CORRELATING DATA INDICATING SUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATA INDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 25 Mar. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/384,660, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 6 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/384,779, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 7 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/387,487, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 30 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/387,465, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 30 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/455,309, entitled HYPOTHESIS DEVELOPMENT BASED ON SELECTIVE REPORTED EVENTS, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 28 May 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/455,317, entitled HYPOTHESIS DEVELOPMENT BASED ON SELECTIVE REPORTED EVENTS, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 29 May 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/456,249, entitled HYPOTHESIS SELECTION AND PRESENTATION OF ONE OR MORE ADVISORIES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 12 Jun. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/456,433, entitled HYPOTHESIS SELECTION AND PRESENTATION OF ONE OR MORE ADVISORIES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 15 Jun. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/459,775, entitled HYPOTHESIS DEVELOPMENT BASED ON USER AND SENSING DEVICE DATA, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 6 Jul. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/459,854, entitled HYPOTHESIS DEVELOPMENT BASED ON USER AND SENSING DEVICE DATA, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 7 Jul. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. The United States Patent Office (USPTO) has published a notice to the effect that the USPTO's computer programs require that patent applicants reference both a serial number and indicate whether an application is a continuation or continuation-in-part. Stephen G. Kunin, Benefit of Prior-Filed Application, USPTO Official Gazette Mar. 18, 2003, available at http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm. The present Applicant Entity (hereinafter “Applicant”) has provided above a specific reference to the application(s) from which priority is being claimed as recited by statute. Applicant understands that the statute is unambiguous in its specific reference language and does not require either a serial number or any characterization, such as “continuation” or “continuation-in-part,” for claiming priority to U.S. patent applications. Notwithstanding the foregoing, Applicant understands that the USPTO's computer programs have certain data entry requirements, and hence Applicant is designating the present application as a continuation-in-part of its parent applications as set forth above, but expressly points out that such designations are not to be construed in any way as any type of commentary and/or admission as to whether or not the present application contains any new matter in addition to the matter of its parent application(s). All subject matter of the Related Applications and of any and all parent, grandparent, great-grandparent, etc. applications of the Related Applications is incorporated herein by reference to the extent such subject matter is not inconsistent herewith.
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