Predicting the future state of a mobile device user

• US 9,219,668 B2
• Filed: 10/19/2012
• Issued: 12/22/2015
• Est. Priority Date: 10/19/2012
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Johnson, Michael Dudley, Williams, Joshua
• Primary Examiner(s): Meky, Moustafa M
• Application Number:
  US13/656,531
• Publication Number:
  US 20140115146A1
• Time in Patent Office: 1,159 Days
• Field of Search: 709200-205, 709217-227, 709/228, 709/229
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01S 5/0027 of actual mobile position, i.e. position determined on mobile
• CPC Class Codes: G08G 1/096888 where input information is obtained using learning systems, e.g. history databases
• CPC Class Codes: G08G 1/20 Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• 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
• CPC Class Codes: H04W 88/02 Terminal devices

Abstract

In one embodiment, a method includes accessing data associated with the mobile-computing-device usage of a user; accessing data associated with past user-states of the user, where the past user-states represent previous activities or actions associated with the user'"'"'s mobile-computing-device usage; predicting a future user-state of the user based, at least in part, on the accessed data; and adapting the operation of the mobile computing device of the user at the future time based on the predicted future user-state.

Claims

1. A method comprising:

by one or more computing devices, accessing first data associated with mobile-computing-device usage by a user of a social-networking system;

by one or more computing devices, accessing second data associated with past user states of the user, a user state of the user comprising a temporal, spatial, modal, or social accessibility of the user;

by one or more computing devices, predicting, by a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state, based at least in part on the first and second data;

a future user state of the user at a future time, the future user state comprising the

user traveling within a geographical area; and

a future mobile-computing-device-usage by the user at the future time;

by one or more computing devices, adapting the operation of a mobile computing device of the user at the future time to the future user state; and

by one or more computing devices, adapting network interaction with a mobile computing device of the user at the future time to the future mobile-computing-device usage, the adaptation comprising requesting interaction by the mobile computing device with the social-networking system by automatically selecting social-networking information to push to the mobile computing device of the user relevant to the user traveling within the geographical area.

2. The method of claim 1, wherein:

the future user state of the user further comprises a location and an activity of the user; and

requesting interaction by the mobile computing device with the social-networking system further comprises modifying one or more logical operations of a social-networking application on the mobile computing device relevant to the location and activity of the user for access by the user at the future time.

3. The method of claim 1, wherein adapting the operation of the mobile computing device comprises modifying the interaction of a telecommunications network with the mobile computing device to provide wireless connectivity to the mobile computing device.

4. The method of claim 1, wherein:

the future user state of the user comprises the user commuting; and

adapting the operation of the mobile computing device comprises requesting content with lower bandwidth requirements for transmission to the mobile computing device while the user is commuting.

5. The method of claim 1, wherein:

the future user state of the user comprises a location of the user; and

adapting the operation of the mobile computing device comprises reducing polling of the mobile computing device for its location.

6. The method of claim 1, wherein the future time is proximate to a current time.

7. The method of claim 1, wherein the first data comprises:

an identifier of the mobile computing device;

an identifier of an application on the mobile computing device;

an Internet Protocol (IP) address of the mobile computing device;

a location of the mobile computing device

a vector of movement of the mobile computing device;

or

a local time reported by the mobile computing device.

8. The method of claim 1, wherein:

the user is a user of a social-networking system, the social-networking system comprising a graph that comprises a plurality of nodes and edges connecting the nodes, at least one node in the graph corresponding to the user; and

the second data comprises social-networking information from the graph associated with the user.

9. The method of claim 1, wherein predicting the future user state of the user comprises:

regression analysis of the first or second data;

decision-tree analysis of the first or second data;

neural-network analysis of the first or second data;

or

expert-system analysis of the first or second data.

10. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

access first data associated with mobile-computing-device usage by a user of a social-networking system;

access second data associated with past user states of the user, a user state of the user comprising a temporal, spatial, modal, or social accessibility of the user;

predict, by a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state, based at least in part on the first and second data;

a future user state of the user at a future time, the future user state comprising the

user traveling within a geographical area; and

a future mobile-computing-device usage by the user at the future time;

adapt the operation of a mobile computing device of the user at the future time to the future user state; and

adapt network interaction with a mobile computing device of the user at the future time to the future mobile-computing-device usage, the adaptation comprising requesting interaction by the mobile computing device with the social-networking system by automatically selecting social-networking information to push to the mobile computing device of the user relevant to the user traveling within the geographical area.

11. The media of claim 10, wherein adapting the operation of the mobile computing device comprises modifying the interaction of a telecommunications network with the mobile computing device to provide wireless connectivity to the mobile computing device.

12. The media of claim 10, wherein:

the future user state of the user comprises the user commuting; and

adapting the operation of the mobile computing device comprises requesting content with lower bandwidth requirements for transmission to the mobile computing device while the user is commuting.

13. The media of claim 10, wherein:

the future user state of the user comprises a location of the user; and

adapting the operation of the mobile computing device comprises reducing polling of the mobile computing device for its location.

14. The media of claim 10, wherein the future user state of the user comprises a location and an activity of the user, and the software is further operable when executed to:

further request interaction by the mobile computing device with the social-networking system by modifying one or more logical operations of a social-networking application on the mobile computing device relevant to the location and activity of the user for access by the user at the future time.

15. A system comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

access first data associated with mobile-computing-device usage by a user of a social-networking system;

access second data associated with past user states of the user, a user state of the user comprising a temporal, spatial, modal, or social accessibility of the user;

predict, by a weighted set of predictor functions, wherein each predictor function comprises a machine-learned algorithm trained for a particular user state, based at least in part on the first and second data;

a future user state of the user at a future time, the future user state comprising the

user traveling within a geographical area; and

a future mobile-computing-device usage by the user at the future time;

adapt the operation of a mobile computing device of the user at the future time to the future user state; and

adapt network interaction with a mobile computing device of the user at the future time to the future mobile-computing-device usage, the adaptation comprising requesting interaction by the mobile computing device with the social-networking system by automatically selecting social-networking information to push to the mobile computing device of the user relevant to the user traveling within the geographical area.

16. The system of claim 15, wherein the future user state of the user comprises a location and an activity of the user, and the processors are further operable when executing the instructions to:

further request interaction by the mobile computing device with the social-networking system by modifying one or more logical operations of a social-networking application on the mobile computing device relevant to the location and activity of the user for access by the user at the future time.

17. The system of claim 16, wherein adapting the operation of the mobile computing device comprises modifying the interaction of a telecommunications network with the mobile computing device to provide wireless connectivity to the mobile computing device.