Patent Application
20140088856
When visiting a neighborhood or a city, visitors will often remember that one or more friends have visited in the past and the visitors wished they could ask the one or more friends about favorite restaurants, sites, bars, etc. Similarly, when dining in a restaurant, the diner may recollect a friend talking about a restaurant reminiscent of the current restaurant and may wonder whether this is the restaurant that the friend was talking about all the time. Another instance may occur when someone is walking down a street in a neighborhood and wonders what kind of people eat, visit, live or work there. Further, a property owner may need to understand and evaluate the potential of their property and what kind of business they can start at a specific location. However, such information is difficult to obtain and cannot be obtained instantly.
In today's world, a number of social networking applications, e.g., Sonar, Twitter®, Facebook®, and Foursquare®, are enabling users to track location, and using this information, to capture, hold, and propagate contextual information. However, the association of the contextual information with the location is always one way in terms of association. More specifically, locations visited are attached to the person and not vice versa. As a result, the usages are centered around offering recommendations of places to visit to a user based on previous places they have visited.
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.
a-b illustrate cloud tags for profiles or identities of users created by the location metadata system (LMS) according to an embodiment;
By turning the usage the other way around, i.e., using data to center the contextual information on the location rather than the person, identities are associated with locations rather than persons. As a result, these identities may be exposed to users and applications in order to create different views whether they are global or specific to a user's interests or social network. Historical and archival records regarding people and objects are integral to implementing the embodiments described herein.
Accordingly, a location profile may be created that provides an identity and different views of the identity for a location based on the people who visit and interact with this location. An interaction may be defined by spending time in the location or even driving on the street at a particular location. In fact, the data could be saved regarding a user's interaction with a location irrespective of the length of the visit. Thus, location identity is derived from the correlation of metadata from, and about, individuals that interact with the location. The length of time and frequency of the interaction, though, might affect how much the individual is affecting the location metadata. Data from individuals can either be profile information based on existing social media site content, or context information based on virtual association, e.g., social media site interaction, or physical association, e.g., visiting a location. The accumulation of correlated metadata from many individuals over time strengthens the location profile.
Users usually have an account at one or more social networking services, such as Facebook®, Twitter®, and Foursquare®. Each of these accounts contains information related to users' profiles, preferences and behavior. Some of this information has been explicitly entered by users, such as employer, education, name, gender, marital status, likes and dislikes. Other information has been contributed by people in their social networks such as communication on the common social wall or tweets sent to a person. In addition, these sites have a list of friends and acquaintances in a person's social network.
As for locations, in today's world most businesses and landmarks have an online identity. These identities are often instrumental to businesses being located and include information such as the name of the location and its function. An example how a an online identity could be used to provide information specifying that “That Elephant” is a That Restaurant. However, there is no way to know more contextual information about the location. For example, user may be confused because this location is identified as the favorite lunch spot of the local IP industry, but is identified as the preferred dinner location for hipsters and artists. Some sites, such as Yelp, attempt to create an identity for some locations, mainly restaurants, but this identity relies on predefined categories. An example of such identities may include categories such as “good for kids,” “accepts credit cards,” “romantic,” etc. In other cases, sites like Yelp also try to highlight quotes from reviews.
A first user 220 signs up for the LMS service. However, some users might not sign up to the service or even be aware of it. These users are active members of another service where they publish their visits to the locations. Thus, a second user 230 may check-in to a location 232 using, for example, Foursquare® or another social networking service 234. Other users, e.g., third user 240, are active members of the LMS 210 by having earlier signed-up for the service. When the second user 230 and third user 240 visit the location 232, their visits are logged directly to the LMS 210.
The LMS 210 will then use the information of the first user 220 and the second user 230, based on their identities, to create an identity of people who visit this location 232. The combination of the identities may be normalized or the frequency of their visits could be analyzed. Other factors to include could also include the time of day and day of the week of the visits.
a-b illustrate cloud tags 300, 350 for profiles or identities of users created by the LMS (such as LMS 210 of
For example, in
Referring again to
Businesses may also be helped through different use cases.
One or more of the techniques (e.g., methodologies) discussed herein may be performed using the location metadata system (LMS) 1100 of
Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms. Modules are tangible entities (e.g., hardware) capable of performing specified operations and may be configured or arranged in a certain manner. In an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module. In an example, the whole or part of one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors may be configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations. In an example, the software may reside on a machine readable medium. In an example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operations.
Accordingly, the term “module” is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operation described herein. Considering examples in which modules are temporarily configured, each of the modules need not be instantiated at any one moment in time. For example, where the modules comprise a general-purpose processor 1140 configured using software, the general-purpose processor 1140 may be configured as respective different modules at different times. Software may accordingly configure processor 1140, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time. As described above, location metadata system (LMS) 1100 (e.g., computer system) may include a processor 1140, which may be a hardware processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof. The LMS 1100 may further include a display device 1160, such as a touchscreen display.
The data storage subsystem 1110 for storing collected data may include a machine readable medium 1112 on which is stored one or more sets of data structures or instructions 1114 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 1114 may instead, or also, reside, completely or at least partially, within the data storage subsystem 1110, within static memory 1150, or within the processor 1140 during execution thereof by the LMS 1100. In an example, one or any combination of the processor 1140, data storage subsystem 1110, or the static memory 1150 may constitute machine readable media.
While the machine readable medium 1112 is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that configured to store the one or more instructions 1114.
The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the LMS 1100 and that cause LMS 1100 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a massed machine readable medium comprises a machine readable medium with a plurality of particles having resting mass. Specific examples of massed machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The instructions 1114 may further be transmitted or received over a communications network using a transmission medium via the network interface 1120 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks such as Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), CDMA 2000 1x* standards and Long Term Evolution (LTE)). Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), peer-to-peer (P2P) networks, or other protocols now known or later developed.
In an example, the network interface 1120 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network. In an example, the network interface 1120 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by LMS 1100, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Example 1 includes subject matter (such as a location metadata system, apparatus or network interface device for providing location-based metadata) comprising a data storage subsystem for storing collected data associated with locations and users. The subject matter may also include a network interface, coupled to the data storage subsystem, for managing communication with devices of users to collect data associated with the locations and users. The subject matter may also include a data analysis system including a processor, the processor adapted for obtaining the collected data from the data storage subsystem and analyzing the collected data to create a first location identity associated with interaction of users with a first location.
Example 2 may optionally include the subject matter of Example 1 wherein the collected data include locations visited by the user and profiles associated with the users.
Example 3 may optionally include the subject matter of any one or more of Examples 1 and 2, wherein the network interface includes a transceiver for receiving messages from and transmitting messages to users.
Example 4 may optionally include the subject matter of any one or more of Examples 1-3, wherein the collected data comprises profile information based on existing social media site content, context information about the location, interactions with social media sites and physical association with a location.
Example 5 may optionally include the subject matter of any one or more of Examples 1-4, wherein the data analysis system normalizes the location identities.
Example 6 may optionally include the subject matter of any one or more of Examples 1-5, wherein the location identity includes a collection of metadata and associated weights, the metadata represented by a tag cloud of terms associated with the location according to the associated weights corresponding to the frequency of a term or concept.
Example 7 may optionally include the subject matter of any one or more of Examples 1-6, wherein the collected data comprises traffic volume information regarding roads proximate to the location, the traffic volume information used to provide trip planning information with the location identity.
Example 8 may optionally include the subject matter of any one or more of Examples 1-7, wherein the data analysis system correlates the traffic volume data with data associated with public transportation services for provisioning with the trip planning information.
Example 9 may optionally include the subject matter of any one or more of Examples 1-8, wherein the collected information further comprises information regarding a second location associated with the individuals after leaving the first location, the first location identity providing data for making business decisions based on the second location associated with the individuals after leaving the first location.
Example 10 may optionally include the subject matter of any one or more of Examples 1-9, wherein the collected information further comprises information regarding a status associated with an object at the first location, the first location identity providing data for determining an action with respect to the first location based on the status associated with an object at the first location.
Example 11 may optionally include the subject matter of any one or more of Examples 1-10, wherein the collected information further comprises information regarding a plurality of additional locations in an area proximate the first location, the data analysis system creating a profile for each of the plurality of additional locations and processing each of the profiles for each of the plurality of additional locations to produce location identities for each of the plurality of additional locations, the data analysis system further aggregating all of the locations' identities to produce a location identity for the area.
Example 12 may include, or may optionally be combined with the subject matter of any one or more of Examples 1-11 to include, subject matter (such as means for performing acts or machine readable medium including instructions that, when executed by the machine, cause the machine to perform acts) including collecting information regarding objects at a first location, creating a profile associated with the objects based on the collected information, processing the created profiles for the objects to produce a first location identity and providing the first location identity to a subscriber.
Example 13 may optionally include the subject matter of any one or more of Examples 1-12, wherein the objects comprise individuals at the first location.
Example 14 may optionally include the subject matter of any one or more of Examples 1-13, wherein the objects are subscribers, the information being obtained directly from the subscribers.
Example 15 may optionally include the subject matter of any one or more of Examples 1-14, wherein the objects are non-subscribers, the information being obtained from network sources the non-subscribers have published the information on.
Example 16 may optionally include the subject matter of any one or more of Examples 1-15, further comprising receiving a request for a first location identity and providing the first location identity to the individual making the request.
Example 17 may optionally include the subject matter of any one or more of Examples 1-16, wherein the objects comprise individuals, the collecting information further comprising obtaining information regarding a second location associated with the individuals after leaving the first location, the first location identity providing data for making business decisions based on the second location associated with the individuals after leaving the first location.
Example 18 may optionally include the subject matter of any one or more of Examples 1-17, wherein the objects comprise tables, the collecting of information further comprising obtaining information regarding status associated with an object at the first location, the first location identity providing data for determining an action with respect to the first location based on the status associated with an object at the first location.
Example 19 may optionally include the subject matter of any one or more of Examples 1-18, further comprising collecting information regarding a plurality of additional locations in an area proximate the first location, creating a profile for each of the plurality of additional locations and processing each of the profiles for each of the plurality of additional locations to produce location identities for each of the plurality of additional locations, and aggregating all of the locations identities to produce a location identity for the area.
Example 20 may include, or may optionally be combined with the subject matter of any one or more of Examples 1-19 to include, subject matter (such as a method or means for performing acts) including collecting information regarding objects at a first location, creating a profile associated with the objects based on the collected information, processing the created profiles for the objects to produce a first location identity and providing the first location identity to a subscriber.
Example 21 may optionally include the subject matter of any one or more of Examples 1-20, wherein the objects comprise individuals at the first location.
Example 22 may optionally include the subject matter of any one or more of Examples 1-21, wherein the objects are subscribers, the information being obtained directly from the subscribers.
Example 23 may optionally include the subject matter of any one or more of Examples 1-22, wherein the objects are non-subscribers, the information being obtained from network sources the non-subscribers have published the information on.
Example 24 may optionally include the subject matter of any one or more of Examples 1-23, further comprising receiving a request for a first location identity and providing the first location identity to the individual making the request.
Example 25 may optionally include the subject matter of any one or more of Examples 1-24, wherein the objects comprise individuals, the collecting information further comprising obtaining information regarding a second location associated with the individuals after leaving the first location, the first location identity providing data for making business decisions based on the second location associated with the individuals after leaving the first location.
Example 26 may optionally include the subject matter of any one or more of Examples 1-25, wherein the objects comprise tables, the collecting of information further comprising obtaining information regarding status associated with an object at the first location, the first location identity providing data for determining an action with respect to the first location based on the status associated with an object at the first location.
Example 27 may optionally include the subject matter of any one or more of Examples 1-26, further comprising collecting information regarding a plurality of additional locations in an area proximate the first location, creating a profile for each of the plurality of additional locations and processing each of the profiles for each of the plurality of additional locations to produce location identities for each of the plurality of additional locations, and aggregating all of the locations identities to produce a location identity for the area.
The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments that may be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.
All publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls.
In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” “third,” etc., are used merely as labels, and are not intended to impose numerical requirements on their objects
The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure, for example, to comply with 37 C.F.R. §1.72(b) in the United States of America. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the disclosed embodiments should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
