TECHNIQUES FOR PREDICTIVE POWER MANAGEMENT OF A MOBILE DEVICE

• US 20160150072A1
• Filed: 11/21/2014
• Published: 05/26/2016
• Est. Priority Date: 11/21/2014
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Rangarajan, Kumar, Murthy, Giridhar Sreenivasa, Kandula, Veera Venkata Naga Rama, Kakkirala, Naga Venkata Surya Maruthi Lakshman
• Granted Patent: US 9,781,542 B2
• Time in Patent Office: Days
• CPC Class Codes: H04M 1/72454 according to context-related or environment-related conditions
• CPC Class Codes: H04W 4/60 Subscription-based services using application servers or record carriers, e.g. SIM application toolkits
• CPC Class Codes: H04W 52/0254 detecting a user operation or a tactile contact or a motion of the device
• CPC Class Codes: H04W 52/0258 controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day
• CPC Class Codes: H04W 52/0277 according to available power supply, e.g. switching off when a low battery condition is detected
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

Techniques for managing discussion sharing on a mobile platform, comprising a power application. The power application may include, among other components, a power monitoring component to monitor at least one component and/or at least one application of a mobile device to determine device profile information, and a power management component to provide the determined device profile information of a mobile device to a server device and receive predicted information representative of forecasted power utilization of the mobile device and/or context sensitive recommendation information representative of one or more context sensitive recommendations for the mobile device.

Claims

1. A computer-implemented method, comprising:

monitoring at least one component and/or at least one application of a mobile device to determine device profile information;

providing the determined device profile information of a mobile device to a server device; and

receiving predicted information representative of forecasted power utilization of the mobile device and/or context sensitive recommendation information representative of one or more context sensitive recommendations for the mobile device, wherein the predicted information and/or the context sensitive recommendation information are determined based at least partially on the device profile information of the mobile device.

2. The computer-implemented method of claim 1, wherein the device profile information further comprises device application information, device component information, device event information, and device location information and the predicted information further comprises predicted power curve information, predicted context information, and predicted power event information.

3. The computer-implemented method of claim 2, wherein the predicted power curve information is generated based at least partially on future power curve information, past power curve information, and/or analytics power curve information and comprises at least a portion of the future power curve information, the past power curve information, and/or the analytics power curve information.

4. The computer-implemented method of claim 3, wherein the future power curve information is generated based at least partially on a corresponding future location context information representing scheduled locations associated with the mobile device and/or corresponding future event context information representative of future event contexts that identify at least one scheduled device event, the at least one scheduled device event including at least one device calendar event representative of a scheduled meeting.

5. The computer-implemented method of claim 4, further comprising:

presenting, visually, context sensitive recommendation information representative of at least one context sensitive recommendation on the display screen of the mobile device, the at least one context sensitive recommendation comprising a first context sensitive recommendation for reducing power utilization of the mobile device based at least partially on a corresponding analytics context and/or future context.

6. The computer-implemented method of claim 4, wherein the at least one context sensitive recommendation comprises at least one context sensitive application recommendation, context sensitive component recommendation, and/or context sensitive power recommendation.

7. The computer-implemented method of claim 1, wherein the device profile information is provided to the server device on a predefined interval.

8. A non-transitory computer-readable storage medium comprising instructions that, when executed, cause a system to:

receive device profile information determined by a mobile device;

determine analytics information based at least partially on the received device profile information;

determine predicted information comprising a first time of a predicted charge event and a second time when a power level of the mobile device will drop below a threshold based at least partially on the determined analytics information, the first time comprising a predicted time at which a power reserve of the mobile device will begin to increase;

determine that the first time occurs after the second time; and

provide the predicted information to the mobile device in response to determining that the first time occurs after the second time.

9. The computer-readable storage medium of claim 8, wherein the predicted information further comprises predicted power curve information representative of a predicted power curve for estimating power utilization of the mobile device during a time period for a reoccurring day of the week.

10. The computer-readable storage medium of claim 9, wherein the predicted power curve information is determined by replacing at least a portion of the analytics power curve information with at least a portion of the past power curve information, future power curve information, and/or translated analytics power curve information.

21. The computer-readable storage medium of claim 8, the instructions further operative, when executed, to cause the system to:

determine context sensitive recommendation information based at least partially on the determined analytics information; and

provide the context sensitive recommendation information to the mobile device.

22. The computer-readable storage medium of claim 21, wherein the context sensitive recommendation information is determined based at least partially on at least one predicted context represented by predicted context information before a forecasted time associated with a predicted threshold limit event, the predicted threshold limit event indicating the forecasted time for a power reserve of the mobile device to fall below a predetermined threshold.

23. The computer-readable storage medium of claim 22, wherein the context information comprises future context information representative of one or more future contexts scheduled to occur, past context information representative of one or more past contexts that already occurred, and/or analytics context information representative of one or more forecasted analytics contexts that is likely to occur.

24. The computer-readable storage medium of claim 21, wherein context sensitive recommendation information comprises at least one context sensitive application recommendation to recommend terminating at least one non-predominant application executed during a predicted context.

11-13. -13. (canceled)

14. An apparatus, comprising:

a processor circuit;

memory operatively coupled to the processor circuit, the memory to store a mobile power application for execution by the processor circuit, the mobile power application comprising;

power monitoring component to monitor at least one component and/or at least one application of a mobile device to determine device profile information;

power management component to provide the determined device profile information of a mobile device to a server device and receive predicted information representative of forecasted power utilization of the mobile device and/or context sensitive recommendation information representative of one or more context sensitive recommendations for the mobile device, wherein the predicted information and the context sensitive recommendation information are determined based at least partially on the device profile information of the mobile device.

15. The apparatus for claim 14, wherein the device profile information further comprises device application information, device component information, device event information, and device location information and the predicted information further comprises predicted power curve information, predicted context information, and predicted power event information.

16. The computer-implemented method of claim 14, wherein the predicted power curve information is generated based at least partially on past power curve information, future power curve information, and/or analytics power curve information and comprises at least a portion of the past power curve information, future power curve information, and/or the analytics power curve information.

17. The apparatus for claim 15, wherein the future power curve information is generated based at least partially on device application event information, the device application event information represents at least one device application event including an application install event for an installation of an application.

18. The apparatus for claim 17, wherein the future power curve information is further generated based at least partially on the device application event information and analytics model information for other mobile devices, and the application has not been previously executed by the mobile device.

19. The apparatus of claim 18, wherein the mobile device of a user is socially connected to the other mobile devices of other users in a social networking system, based at least partially on user profile information associated with the user and the other users.

20. The apparatus of claim 14, further comprising:

presenting, visually, context sensitive recommendation information representative of at least one context sensitive recommendation on the display screen of the mobile device, the at least one context sensitive recommendation comprising a first context sensitive recommendation for reducing power utilization of the mobile device based at least partially on a corresponding analytics context and/or future context.

25. A computer-implemented method comprising:

receiving device profile information determined by a mobile device at a server system;

determining analytics information based at least partially on the received device profile information at the server system;

determining, at the server system, predicted information comprising a first time of a predicted charge event and a second time when a power level of the mobile device will drop below a threshold based at least partially on the determined analytics information, the first time comprising a predicted time at which a power reserve of the mobile device will begin to increase;

determining that the first time occurs after the second time; and

provide the predicted information to the mobile device in response to determining that the first time occurs after the second time.

26. The method of claim 25, wherein determining predicted information comprises determining predicted power curve information representative of a predicted power curve for estimating power utilization of the mobile device during a time period for a reoccurring day of the week.

27. The method of claim 26, comprising determining the predicted power curve information by replacing at least a portion of the analytics power curve information with at least a portion of the past power curve information, future power curve information, and/or translated analytics power curve information.

28. The method of claim 25, comprising:

determining context sensitive recommendation information based at least partially on the determined analytics information; and

providing the context sensitive recommendation information to the mobile device.

29. The method of claim 28, comprising determining the context sensitive recommendation information based at least partially on at least one predicted context represented by predicted context information before a forecasted time associated with a predicted threshold limit event, the predicted threshold limit event indicating the forecasted time for a power reserve of the mobile device to fall below a predetermined threshold.

30. The method of claim 28, wherein the context information comprises future context information representative of one or more future contexts scheduled to occur, past context information representative of one or more past contexts that already occurred, and/or analytics context information representative of one or more forecasted analytics contexts that is likely to occur.

31. The method of claim 28, wherein context sensitive recommendation information comprises at least one context sensitive application recommendation to recommend terminating at least one non-predominant application executed during a predicted context.

32. An apparatus, comprising:

a processor circuit;

memory operatively coupled to the processor circuit, the memory to store a power analytics application for execution by the processor circuit, the power analytics application comprising;

a power analytics management component to receive device profile information determined by a mobile device;

a power analytics modeling component to determine analytics information based at least partially on the received device profile information; and

a power analytics prediction component to determine predicted information comprising a first time of a predicted charge event and a second time when a power level of the mobile device will drop below a threshold based at least partially on the determined analytics information, the first time comprising a predicted time at which a power reserve of the mobile device will begin to increase, determine that the first time occurs after the second time, and provide the predicted information to the mobile device in response to determining that the first time occurs after the second time.

33. The apparatus of claim 32, wherein the predicted information further comprises predicted power curve information representative of a predicted power curve for estimating power utilization of the mobile device during a time period for a reoccurring day of the week.

34. The apparatus of claim 33, wherein the predicted power curve information is determined by replacing at least a portion of the analytics power curve information with at least a portion of the past power curve information, future power curve information, and/or translated analytics power curve information.

35. The apparatus of claim 32, the power analytics prediction component further to determine context sensitive recommendation information based at least partially on the determined analytics information, and provide the context sensitive recommendation information to the mobile device.

36. The apparatus of claim 35, wherein the context sensitive recommendation information is determined based at least partially on at least one predicted context represented by predicted context information before a forecasted time associated with a predicted threshold limit event, the predicted threshold limit event indicating the forecasted time for a power reserve of the mobile device to fall below a predetermined threshold.

37. The apparatus of claim 35, wherein context sensitive recommendation information comprises at least one context sensitive application recommendation to recommend terminating at least one non-predominant application executed during a predicted context.