TECHNIQUES FOR DETERMINING RELATIONSHIP INFORMATION

• US 20150193888A1
• Filed: 01/06/2014
• Published: 07/09/2015
• Est. Priority Date: 01/06/2014
• Status: Abandoned Application

• Current Assignee: LinkedIn Corporation (Microsoft Corporation)
• Original Assignee: Linkedln Corporation
• Inventors: Sayed, Sameer
• Application Number:
  US14/147,858
• Publication Number:
  US 20150193888A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

A method of proposing actions for users to take with respect to a social networking site is disclosed. Activity-location profiles for users of a social networking site are generated. The activity-location profiles are based on an analysis of locations and activities of each of the users over a time period. A recommendation is generated for at least one of the users. The recommendation is based on an analysis of the activity-location profiles of the at least one of the users and activity-location profiles of other users. The recommendation proposes an action for the at least one of the users to take with respect to the social networking site.

Claims

1. A method comprising:

generating activity-location profiles for users of a social networking site based on an analysis of locations and activities of each of the users over a time period, the generating of the activity-location profiles including storing activity and location data for the users in data structures associated with the users, the data structures including associations between the locations and activities performed by the users at the locations;

determining probabilities that each of the users will perform particular activities at particular locations at particular times based on the activity and location data stored in the data structures; and

generating a recommendation for at least one of the users based on an analysis of the probabilities, the recommendation proposing an action for the at least one of the users to take with respect to the social networking site, the generating of the recommendation further based on a combination of a weighting assigned to an activity by the at least one user and a weighting assigned to the activity by the at least one of the other users, wherein one or more modules are incorporated into the social networking site to configure one or more processors of the social networking site to perform the generating of the activity-location profiles and the generating of the recommendation.

2. The method of claim 1, wherein the recommendation comes in the form of a notification and the action includes declaring a relationship between the at least one of the users and at least one of the other users.

3. The method of claim 1, further comprising determining the activities of each of the plurality of users based on patterns of data collected from sensors on devices carried by each of the plurality of users.

4. The method of claim 1, wherein the generating of the recommendations is based on an application of an algorithm for determining people that the at least one of the users may know or be interested in.

5. The method of claim 1, wherein the analysis of the activity-location profiles of the at least one of the user to the activity-location profiles of the other users includes mapping of activities to locations.

6. The method of claim 1, further comprising generating a visual depiction of the activity-location profiles of the at least one of the users in comparison to the activity-location profiles of the other users.

7. The method of claim 6, wherein the visual depiction includes a user interface element representing an activity of the at least one of the users overlaid over a depiction of a physical location on a geographic map at which the at least one of the users performed the activity.

8. A system comprising:

one or more modules incorporated into a social networking site to configured one or more processors of the social networking site to, at least;

generate activity-location profiles for users of the social networking site based on an analysis of locations and activities of each of the users over a time period, the generating of the activity-location profiles including storing activity and location data for the users in data structures associated with the users, the data structures including associations between the locations and activities performed by the users at the locations;

determining probabilities that each of the users will perform particular activities at particular locations at particular times based on the activity and location data stored in the data structures; and

generate a recommendation for at least one of the users based on an analysis of the probabilities, the recommendation proposing an action for the at least one of the users to take with respect to the social networking site, the generating of the recommendation further based on a combination of a weighting assigned to an activity by the at least one user and a weighting assigned to the activity by the at least one of the other users.

9. The system of claim 8, wherein the action includes declaring a relationship between the at least one of the users and at least one of the other users.

10. The system of claim 8, further comprising determining the activities of each of the plurality of users based on patterns of data collected from sensors on devices carried by each of the plurality of users.

11. The system of claim 8, wherein the generating of the recommendations is based on an application of an algorithm for determining people that the at least one of the users may know or be interested in.

12. The system of claim 8, wherein the analysis of the activity-location profiles of the at least one of the user to the activity-location profiles of the other users includes mapping of activities to locations.

13. The system of claim 8, further comprising generating a visual depiction of the activity-location profiles of the at least one of the users in comparison to the activity-location profiles of the other users.

14. The system of claim 13, wherein the visual depiction includes a user interface element representing an activity of the at least one of the users overlaid over a depiction of a physical location on a geographic map at which the at least one of the users performed the activity.

15. A non-transitory machine readable medium embodying a set of instructions that, when incorporated into one or more modules of a social networking site, cause one or more processors of the social networking site to perform operations, the operations comprising:

generating activity-location profiles for users of the social networking site based on an analysis of locations and activities of each of the users over a time period, the generating of the activity-location profiles including storing activity and location data for the users in data structures associated with the users, the data structures including associations between the locations and activities performed by the users at the locations;

determining probabilities that each of the users will perform particular activities at particular locations at particular times based on the activity and location data stored in the data structures; and

generating a recommendation for at least one of the users based on an analysis of the probabilities, the recommendation proposing an action for the at least one of the users to take with respect to the social networking site, the generating of the recommendation further based on a combination of a weighting assigned to an activity by the at least one user and a weighing assigned to the activity by the at least one of the other users.

16. The non-transitory machine readable medium of claim 15, wherein the action includes declaring a relationship between the at least one of the users and at least one of the other users.

17. The non-transitory machine readable medium of claim 15, further comprising determining the activities of each of the plurality of users based on patterns of data collected from sensors on devices carried by each of the plurality of users.

18. The non-transitory machine readable medium of claim 15, wherein the generating of the recommendations is based on an application of an algorithm for determining people that the at least one of the users may know or be interested in.

19. The non-transitory machine readable medium of claim 15, wherein the analysis of the activity-location profiles of the at least one of the user to the activity-location profiles of the other users includes mapping of activities to locations.

20. The non-transitory machine readable medium of claim 15, further comprising generating a visual depiction of the activity-location profiles of the at least one of the users in comparison to the activity-location profiles of the other users.