Activity Recognition Systems and Methods

• US 20140128105A1
• Filed: 11/05/2013
• Published: 05/08/2014
• Est. Priority Date: 11/06/2012
• Status: Active Grant

• Current Assignee: Intertrust Technologies Corporation (Fidelio Acquisition Co LLC)
• Original Assignee: Intertrust Technologies Corporation (Fidelio Acquisition Co LLC)
• Inventors: SU, Jiawen, MA, Yiming
• Granted Patent: US 9,736,652 B2
• Time in Patent Office: Days
• US Class Current: 455/456.3
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Systems and methods for recognizing and/or predicting activities of a user of a mobile device are disclosed. In certain embodiments, the systems and methods may predict a future activity and/or location of a mobile device user based on current and/or historical device data and/or other personal information relating to the user. In some embodiments, probabilistic determinations and/or other statistical models may be used to predict future activities and locations of a mobile device user. The disclosed systems and methods may further utilize location and/or activity recognition and/or prediction methods to deliver personalized services to a user of a mobile device at a particular time and/or location.

Claims

1. A method of providing a targeted service to a mobile device associated with a user, the method performed by a system comprising a processor and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the system to perform the method, the method comprising:

receiving, at an interface of the system, device data from the mobile device;

identifying one or more first activities based on the device data, the first set of activities reflecting at least one current activity of the user;

identifying one or more second activities based on the one or more first activities, the one or more second activities reflecting at least one predicted future activity of the user; and

transmitting, from the interface, information associated with a personalized service to the user based on the at least one predicted future activity.

2. The method of claim 1, wherein at least one activity of the one or more first activities is associated with a current location of the user.

3. The method of claim 1, wherein at least one activity of the one or more second activities is associated with a predicted future location of the user.

4. The method of claim 1, wherein the personalized service comprises a targeted advertising service and the transmitted information comprises a targeted advertisement.

5. The method of claim 4, wherein the targeted advertisement is associated with an entity proximate to a location associated with the one or more second activities.

6. The method of claim 1, wherein the device data comprises at least one of data obtained by one or more sensors of the mobile device, location information of the mobile device, data generated by the user'"'"'s interactions with the mobile device, spatial historical travel patterns of the user of the mobile device, and temporal historical travel patterns of the user of the device.

7. The method of claim 1, wherein identifying the one or more second activities is further based on information associated with one or more other users.

8. The method of claim 1, wherein identifying the one or more second activities further comprises modeling a probability of one or more candidate activities occurring after the one or more first activities and selecting the one or more second activities from the one or more candidate activities based on the one or more second activities having an associated probability exceeding a threshold.

9. The method of claim 8, wherein the probability of the one or more candidate activities occurring after the one or more first activities is modeled using a hidden Markov model.

10. The method of claim 8, wherein modeling the probability of the one or more candidate activities occurring after the one or more first activities is based on historical device data collected by the mobile device.