Patent Application
20200042949
Electronic calendars have become ubiquitous in everyday work and home life. Users are able to coordinate their own schedules using electronic calendars, and with internet connectivity, they are able to interact with other users' calendars via the sending and receiving of electronic calendar event invitations. Although electronic calendars have made keeping track of events and schedules a much easier task than was previously possible, creating new events in those calendars and identifying content from similar calendar events that would be useful in creation of those new events, can still be an arduous task, especially at the enterprise level.
It is with respect to this general technical environment that aspects of the present technology disclosed herein have been contemplated. Furthermore, although a general environment has been discussed, it should be understood that the examples described herein should not be limited to the general environment identified in the background.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description section. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. Additional aspects, features, and/or advantages of examples will be set forth in part in the description which follows and, in part, will be apparent from the description or may be learned by practice of the disclosure.
Non-limiting examples of the present disclosure describe systems, methods and devices for automatically linking calendar events, and utilizing properties of those linked calendar events to assist users with future event scheduling and time management. An event scheduling service and/or a digital assistant service may identify various signals associated with calendar events and determine based on that analysis that certain groups of calendar events are associated with one another and that they should be linked or otherwise electronically associated. In some examples, the signals may be processed by one or more machine learning models for determining whether the calendar events are associated with one another and/or whether they should be linked. In other examples, the signals may be associated with values, which in some cases may be weighted, and a determination of associated event relationships may be determined based on those values and/or weights. Examples of signals that may be indicative of calendar events be associated with one another include: the same or similar event times; the same or similar event dates; the same or similar invitees; the same or similar event invite subject lines; the same or similar event invite subject lines; and/or other signals described herein.
In some examples, linked events may be categorized into one or more searchable indices. The linked indices may be searchable by keyword or phrases associated with words or phrases in corresponding event invitations and/or scheduled events. The linked indices may additionally or alternatively be searchable based on topographical categories that linked calendar events have been categorized in. According to additional examples, an event scheduling service and/or digital assistant service may generate metrics associated with one or more linked calendar events (e.g., how many meeting hours in a linked calendar event chain were required to accomplish project A, which required a plurality of meetings to complete; how many users/persons were present in linked calendar event chain meetings associated with accomplishing project B, which required a plurality of meetings to complete). In other examples, an event scheduling service and/or digital assistant service may provide comparative analysis associated with similar linked calendar events.
Non-limiting and non-exhaustive examples are described with reference to the following figures:
Various embodiments will be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims.
The various embodiments and examples described above are provided by way of illustration only and should not be construed to limit the claims attached hereto. Those skilled in the art will readily recognize various modifications and changes that may be made without following the example embodiments and applications illustrated and described herein, and without departing from the true spirit and scope of the claims.
Examples of the disclosure provide systems, methods, and devices for automatically associating/linking calendar events that have common properties and utilizing that association for generating metrics and telemetry which can be beneficial to users in managing their time and resources. Additional aspects described herein utilize the information from the linking of calendar events to provide enhanced digital assistant scheduling services, including automatically sending out event invitations based on information associated with previous events in a linked set.
Computing device 102A is displaying a user's electronic calendar/scheduling application, which includes a plurality of calendar events associated with that user. The plurality of calendar events are indicated on the display of computing device 102A by the blocks filled in with a diagonal line pattern. One or more computing devices associated with the event scheduling service (e.g., server computing device 108) may determine whether one or more calendar events associated with the user's electronic calendar should be associated/linked with one another based on one or more signals in service store 120. In some examples, the event scheduling service and/or a digital assistant (e.g., Cortana, Siri, Alexa) associated with the event scheduling service, may determine that calendar events should be associated with one another if, for example, those calendar events are associated with the same or similar subject matter (e.g., monthly progress report meetings, employee candidate hiring meetings, etc.), or if the calendar events are derived from email threads that have common ancestry (e.g., emails stamped with a common thread ID). In some examples, the event scheduling service and/or digital assistant may determine that calendar events should be associated with one another based on analyzing one or more signals in service store 120, comprising event information indicating: events that have the same or similar invitees/attendees, events that are scheduled for the same or similar times, events that are scheduled for the same or similar days of the week, events that have the same or similar durations, event invitations that have the same or similar attachments, event invitations that have the same or similar content in their subject lines, events that are scheduled to be held in the same or similar location, events that have the same or similar communication type (e.g., telephone, video conference), and/or event invitations that have the same or similar content in their bodies.
In some examples, one or more machine learning models may be applied to one or more of the above-described signals to determine whether events should be linked. The machine learning models may be trained with user interaction (e.g., users may verify that the event scheduling service has appropriately linked events and/or inappropriately linked events). For example, if a first machine learning model has been applied to a plurality of events that are then linked by the event scheduling service, but users have manually indicated that those links were inappropriately created, that machine learning model may be indicated as not performing well, and thus it may be modified or applied to other event types. In another example, if a second machine learning model has been applied to a plurality of events that are then linked by the event scheduling service, and users have manually indicated that those links have been appropriately created, that machine learning model may be indicated as performing sufficiently, and it may be applied more frequently and/or to similar types of event data. In additional examples, users may indicate that one or more events in their electronic calendar should be associated with one another, and machine learning models may be trained to recognize appropriate events to associate with one another based on that past user input. In other examples, each of the analyzed signals may be assigned a value, and determinations may be made as to whether one or more calendar events should be associated based on values of the signals. In additional examples, the values may be weighted (e.g., more weight may be assigned to “important” signals and less weight may be assigned to less “important” signals). In some examples, the relative importance of each signal may be determined via application of one or more machine learning models. In other examples, the relative importance of each signal may be manually assigned.
Once a determination has been made that one or more events should be linked, the event scheduling service and/or a digital assistant associated with the event scheduling service may tag the one or more events as being associated with one another. The tag may comprise indicia of one or more topical categories in which the event scheduling service has determined the one or more events belong to based on their one or more associated signals. In some examples, the tag may be included in one or more searchable indices. The tag may be searchable, such that the event scheduling service, a digital assistant, and/or a user, may query the event scheduling service with keywords or phrases indicative of the tag, and identify linked calendar events corresponding to the tag. For example, if a user provides a tag query to the event scheduling service of “hiring”, the event scheduling service may return information associated with one or more linked events related to the hiring of individuals associated with the querying user or the querying user's enterprise. In another example, if a user provides a tag query to the event scheduling service of “project A”, the event scheduling service may return information associated one or more linked events related to “project A”. In other examples, natural language understanding may be applied to user queries such that the event scheduling service may recognize that queries match one or more categorized event linkages, despite the queries and/or the content of the linked events not directly matching. For example, a user may query the service with the keyword “call” and the service may surface linked events related to sales phone calls because phone numbers and sales content may have been included in one or more of the linked events and/or their subsequent categorization.
Linking and tagging events allows another system (e.g., a user query system, a digital assistant service), whether visual or conversational, to query and return a set of event requests that are determined to be related by their implicit tags either in isolation or in combination with one another. For example, if there is a project code names ‘Moneypenny” within an organization, and event information related to the development and delivery of the project are requested through a digital assistant/virtual scheduling assistant, the organizational leader may query the system and review all events/meetings that have taken place as related to the Moneypenny project and aggregate data such as who has attended meetings related to Moneypenny, total number of meeting hours, and the time range when those meetings took place.
In the example shown in
The event scheduling service may match the user-provided tag query to one or more linked events based on keyword and/or phrase matching, including synonym matching. In this example, the event scheduling application has determined that one set of linked events match the query input into tag query user interface element 206. That tag query is therefore displayed on computing device 202B (which may be the same or a different computing device as computing device 202A) in matched tag query user interface element 206 in association with information related to the set of linked events that were determined to match the tag query.
In this example, the displayed information related to the set of linked events that were determined to match the tag query includes the calendar dates corresponding to each of the linked events that are related to the tag query (January 10 [212], February 7 [214], March 7 [216], and one or more additional calendar dates N [218]). In examples, the displayed information may include additional content related to the set of linked calendar events. In this particular example, user interface element 220 provides a total number of the linked calendar events that match the tag query (i.e., 7 meetings), user interface element 226 provides a list of users that attended and/or were invited to each of the linked meetings (i.e., Charles Lee, Warren Johnson, Tracy Western, Tristan Barnes, Jonathan Carrier), user interface element 222 provides an average meeting time for the set of linked calendar events (i.e., 45 minutes), and user interface element 224 provides a selectable option to have a digital assistant utilize information, from the set of linked calendar events, to automatically schedule the next calendar event in the set of linked calendar events.
If a user selects “Yes” in user interface element 224 (i.e., the user would like the digital assistant to automatically schedule the next calendar event in the set of linked calendar events), the digital assistant may analyze information related to each of the set of linked calendar events, and use that information to intelligently create/send out an event request for the next calendar event in the set. For example, if each of the linked calendar events takes place on a same day of the week and/or a same time, the digital assistant service may attempt to schedule the next calendar event in the set for the same day of the week and/or time of day. Similarly, if each of the linked calendar events includes the same group of invitees/attendees, the digital assistant service may invite each of those users to the next event in the set. The digital assistant service may also generate a title of the next event from information that was provided in one or more of the previous calendar events, include attachments and/or content in the body of the invite that was included in one or more of the previous calendar event invites, and/or schedule the next event in the set for a same or similar duration as one or more of the previous calendar events.
The digital assistant may provide a query to the digital assistant service that is matched to one or more linked calendar event tags in one or more searchable indices. For example, the digital assistant may utilize the subject line in the email sent from the meeting requester to determine whether a set of linked events correspond to the email, and/or the digital assistant may match the name of the sending requester to one or more previous meeting invites that the sending requester sent out to determine how to handle the current request. Once a set of linked calendar events are identified as a match, the digital assistant may identify shared properties amongst those linked calendar events, which the digital assistant can use to generate the new meeting request.
Computing device 302B displays a meeting invite 304 that the digital assistant service has sent in response to the email message sent from the meeting requester. In this example, the “to” field in meeting invite 304 has been populated by the digital assistant with “[Team Name],” which is the alias for a set of user email addresses that the digital assistant has sent the meeting invite to. The users corresponding to [Team Name] may have been identified from one or more linked calendar events that included those same or similar users. The digital assistant has also included attachment A and attachment B with the meeting invite, as is shown at display element 308 in meeting invite 304. Attachments A and B may have been included in the one or more linked calendar events that the digital assistant identified in generating meeting invite 304. For example, Attachment A may comprise a meeting agenda that was attached to one or more of the linked calendar events, and Attachment B may comprise a status report that was attached to one or more of the linked calendar events. Meeting invite 304 also includes an event time and date (2 pm-3 pm on Tuesday July 10) and a Location (Redmond Conference Room 32/31). The digital assistant may have selected the event time, date and location based on information associated with one or more of the linked calendar events that it identified.
The event scheduling service may identify one or more properties from the set of linked events corresponding to the “# HiringMeetings” tag 406, and displayably present them on user interface 404. In this example, the event scheduling service has identified from content associated with the linked events corresponding to the “# HiringMeetings” tag 406 that: there were five new employees hired as a result of meetings that took place in the set of linked events corresponding to the “# HiringMeetings” tag 406, as indicated at user interface element 410; there were five attendees that each attended a majority and/or each of the meetings (i.e., HR person A, Manager A, Manager B, Colleague A, Colleague B), as indicated at user interface element 412; the five employees that were hired (and that were the subject of the meetings) were Candidate A, Candidate B, Candidate C, Candidate D, and Candidate E, as indicated at user interface element 408; and the average amount of time spent in meetings by interviewers prior to hiring a candidate was 6.8 hours, as indicated at user interface element 414. User interface 404 also includes a selectable element 416 for exporting linked meeting metrics to a spreadsheet.
Computing device 402A displays exemplary exported linked meeting metrics on spreadsheet 418. Specifically, information corresponding to an amount of meeting time that was spent in relation to hiring each of candidates A-E has been identified from the linked meetings, and provided in column A 420 (the name of each candidate), and column B 422 (the number of hours spent in meetings before hiring a corresponding candidate). The name and hour information extracted from the linked meetings has also been used to generate table 424, which displays each hired candidate in relation to the number of hours in meetings that were spent prior to each candidate being hired. Other metrics may be identified from linked calendar events and similarly provided via user interface elements such as those shown in relation to
From operation 502 flow continues to operation 504 where a determination is made, based on the analysis performed at operation 502, that the plurality of calendar events are related. In some examples, one or more machine learning models may be applied to one or more of the above-described signals to determine whether events should be linked. The machine learning models may be trained with user interaction (e.g., users may verify that the event scheduling service has appropriately linked events and/or inappropriately linked events). For example, if a first machine learning model has been applied to a plurality of events that are then linked by the event scheduling service, but users have manually indicated that those links were inappropriately created, that machine learning model may be indicated as not performing well, and thus it may be modified or applied to other event types. In another example, if a second machine learning model has been applied to a plurality of events that are then linked by the event scheduling service, and users have manually indicated that those links have been appropriately created, that machine learning model may be indicated as performing sufficiently, and it may be applied more frequently and/or to similar types of event data. In other examples, a determination that the plurality of calendar events are related may be made based on one or more values associated with one or more event signals and/or event invitation signals.
From operation 504 flow continues to operation 506 where the plurality of calendar events are linked into a searchable entity with grouped properties. In some examples, the linking of the searchable entities may comprise applying one or more searchable tags associated with one or more properties of the linked calendar events and/or one or more searchable tags associated with one or more topical categories that one or more of the calendar events has been classified into. In some examples, the tags may be associated with one or more searchable indices. The grouped properties may comprise common content in the body of calendar invites, common content in the title of calendar invites, common content in the subject of calendar invites, common invitees, common senders, common dates, common times, and/or other overlapping calendar event signals amongst one or more calendar events.
From operation 506 flow continues to operation 508 where a search that indicates at least one of the grouped properties is received. The search may comprise a keyword or phrase that corresponds to one or more of the grouped properties, a keyword or phrase that corresponds to one or more tags that have been applied to one or more of the calendar events, and/or synonyms of the same.
From operation 508 flow continues to operation 510 where information associated with the searchable entity is caused to be displayed. In some examples, the information may comprise one or more calendar dates and events corresponding to the linked calendar events. In additional examples, the information may comprise metrics and/or analytics corresponding to one or more of the linked calendar events (e.g., total number of events in a linked group or sub-group, total amount of invitee time spent in meetings associated with a linked group or sub-group, identity of common properties amongst a linked group or sub-group). In some examples, the information may be drilled down into for more detailed information and/or compared to metrics from other linked calendar events (e.g., compare the number of calendar event hours spent to complete Project A with the number of calendar event hours spent to complete Project B; identify the cost of management hours spent in hiring meetings for candidates in one group in an enterprise compared with the cost of management hours spent in hiring meetings for candidates in a second group in the enterprise).
From operation 510 flow continues to an end operation, and the method 500 ends.
One or more application programs 766 may be loaded into the memory 762 and run on or in association with the operating system 864. Examples of the application programs include phone dialer programs, e-mail programs, personal information management (PIM) programs, word processing programs, spreadsheet programs, Internet browser programs, messaging programs, and so forth. The system 702 also includes a non-volatile storage area 768 within the memory 762. The non-volatile storage area 768 may be used to store persistent information that should not be lost if the system 702 is powered down. The application programs 766 may use and store information in the non-volatile storage area 768, such as e-mail or other messages used by an e-mail application, and the like. A synchronization application (not shown) also resides on the system 702 and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in the non-volatile storage area 768 synchronized with corresponding information stored at the host computer. As should be appreciated, other applications may be loaded into the memory 762 and run on the mobile computing device 700, including instructions for providing and operating a digital assistant computing platform.
The system 702 has a power supply 770, which may be implemented as one or more batteries. The power supply 770 might further include an external power source, such as an AC adapter or a powered docking cradle that supplements or recharges the batteries.
The system 702 may also include a radio interface layer 772 that performs the function of transmitting and receiving radio frequency communications. The radio interface layer 772 facilitates wireless connectivity between the system 702 and the “outside world,” via a communications carrier or service provider. Transmissions to and from the radio interface layer 772 are conducted under control of the operating system 764. In other words, communications received by the radio interface layer 772 may be disseminated to the application programs 766 via the operating system 764, and vice versa.
The visual indicator 620 may be used to provide visual notifications, and/or an audio interface 774 may be used for producing audible notifications via the audio transducer 625. In the illustrated embodiment, the visual indicator 620 is a light emitting diode (LED) and the audio transducer 625 is a speaker. These devices may be directly coupled to the power supply 770 so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor 760 and other components might shut down for conserving battery power. The LED may be programmed to remain on indefinitely until the user takes action to indicate the powered-on status of the device. The audio interface 774 is used to provide audible signals to and receive audible signals from the user. For example, in addition to being coupled to the audio transducer 625, the audio interface 774 may also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. In accordance with embodiments of the present disclosure, the microphone may also serve as an audio sensor to facilitate control of notifications, as will be described below. The system 702 may further include a video interface 776 that enables an operation of an on-board camera 630 to record still images, video stream, and the like.
A mobile computing device 700 implementing the system 702 may have additional features or functionality. For example, the mobile computing device 700 may also include additional data storage devices (removable and/or non-removable) such as, magnetic disks, optical disks, or tape. Such additional storage is illustrated in
Data/information generated or captured by the mobile computing device 700 and stored via the system 702 may be stored locally on the mobile computing device 700, as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio interface layer 772 or via a wired connection between the mobile computing device 700 and a separate computing device associated with the mobile computing device 700, for example, a server computer in a distributed computing network, such as the Internet. As should be appreciated such data/information may be accessed via the mobile computing device 700 via the radio interface layer 772 or via a distributed computing network. Similarly, such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data/information sharing systems.
As stated above, a number of program modules and data files may be stored in the system memory 804. While executing on the processing unit 802, the program modules 806 (e.g., event scheduling application 820) may perform processes including, but not limited to, the aspects, as described herein. According to examples, the event signal extraction engine 811 may perform one or more operations associated with identifying one or more signals associated with events and/or event invitations. The event analysis engine 813 may perform one or more operations associated with determining whether one or more events are associated with one another based on analysis of one or more shared signals. The event analysis engine 813 may make the determination based on application of one or more machine learning models and/or values associated with one or more signals. Event metrics engine 815 may perform one or more operations associated with generating displayable metrics associated with one or more linked sets of events. New event generation engine 817 may perform one or more operations associated with automatically scheduling a next event in a linked set of events (e.g., automatically sending out an event invitation and related content for a next event in a chain of events).
Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. For example, embodiments of the disclosure may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated in
The computing device 800 may also have one or more input device(s) 812 such as a keyboard, a mouse, a pen, a sound or voice input device, a touch or swipe input device, etc. The output device(s) 814 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used. The computing device 800 may include one or more communication connections 816 allowing communications with other computing devices 850. Examples of suitable communication connections 816 include, but are not limited to, radio frequency (RF) transmitter, receiver, and/or transceiver circuitry; universal serial bus (USB), parallel, and/or serial ports.
The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules. The system memory 804, the removable storage device 809, and the non-removable storage device 810 are all computer storage media examples (e.g., memory storage). Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing device 800. Any such computer storage media may be part of the computing device 800. Computer storage media does not include a carrier wave or other propagated or modulated data signal.
Communication media may be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
Aspects of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to aspects of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
The systems, methods, and devices described herein provide technical advantages for scheduling calendar events. By automatically linking calendar events by utilizing shared properties and signals, an event scheduling service can automatically: identify a next calendar event in a chain of events that should be scheduled; identify what information should be included in event invitations associated with that next calendar event; and identify which individuals to send the corresponding calendar event invitations to, thereby significantly decreasing computer processing costs associated with users having to execute contact list and calendar queries identify that information and/or sift through documents and other content that the users would like to include with the calendar event invitations. From a management standpoint, time and resources may also be saved via the mechanisms described herein. For example, utilizing metrics comprised of related event chains that are associated with similar tasks, managers may make determinations regarding efficient work flow (e.g., which individuals are key to making certain decisions, how much meeting time is generally necessary to accomplish certain tasks). Utilizing the systems, methods, and devices described herein, processing and storage costs may also be reduced through digital assistant leveraging of linked calendar event relationships and properties. Digital assistants may identify information that should be included with event invitations (e.g., attachments, information in the body of an invite, information in the subject line of an invite), and identify appropriate times, locations, and communication types for events to take place, thereby significantly reducing the amount of computing resources that would otherwise be required in the back-and-forth amongst event attendees in identifying and agreeing on that information.
The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the disclosure as claimed in any way. The aspects, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use the best mode of claimed disclosure. The claimed disclosure should not be construed as being limited to any aspect, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present disclosure, one skilled in the art may envision variations, modifications, and alternate aspects falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed disclosure.
The various embodiments described above are provided by way of illustration only and should not be construed to limit the claims attached hereto. Those skilled in the art will readily recognize various modifications and changes that may be made without following the example embodiments and applications illustrated and described herein, and without departing from the true spirit and scope of the following claims.
