Optimizing Operability of Mobile Devices based on Learned Usage Models

Patent metadata

Field	Value
Publication number	US-2018262016-A1
Application number	US-201715455290-A
Country	US
Kind code	A1
Filing date	Mar 10, 2017
Priority date	Mar 10, 2017
Publication date	Sep 13, 2018
Grant date	—

Abstract

A computer-implemented method, system and computer program product for managing remaining battery charge capacity in a battery-powered device having a power saving mode are provided. The computer-implemented method, system and computer program product ingest history data, the history data includes user application usage history data, user location history data, and battery charging history data for the device; forecast, based on real-time usage data, location data, battery charge data, and the ingested history data, a risk of running out of battery power; and adjust, in response to the forecasted risk, the device from a normal operating mode to a power saving mode to reduce battery consumption.

First claim

What is claimed is:

1 . A computer-implemented method of managing remaining battery charge capacity of a battery in a battery-powered device having a power saving mode, the method comprising:
ingesting history data, the history data including user application usage history data, user location history data, and battery charging history data for the device; forecasting, based on real-time usage data, location data, battery charge data, and the ingested history data, a risk of running out of battery power; and adjusting, in response to the forecasted risk, the device from a normal operating mode to a power saving mode to reduce battery consumption.

2 . The computer-implemented method of claim 1 , wherein the power saving mode includes at least one application lockout mode based on user usage history.

3 . The computer-implemented method of claim 1 , wherein the power saving mode includes at least one application lockout mode based on stated user preferences.

4 . The computer-implemented method of claim 1 , wherein a recurrent neural network (RNN) is used to forecast the risk based on predicting usage of the device between a current time and a predicted time when the battery may be recharged.

5 . The computer-implemented method of claim 4 , wherein the predicted time is based on a predicted location of the device.

6 . A computing system for managing remaining battery charge capacity of a battery in a battery-powered device having a power saving mode, the computing system comprising:
at least one storage system for storing code data; and at least one processor for processing the stored code data to: ingest history data, the history data including user application usage history data, user location history data, and battery charging history data for the device; forecast, based on real-time usage data, location data, battery charge data, and the ingested history data, a risk of running out of battery power; and adjust, in response to the forecasted risk, the device from a normal operating mode to a power saving mode to reduce battery consumption.

7 . The computing system of claim 6 , wherein the power saving mode includes at least one application lockout mode based on user usage history.

8 . The computing system of claim 6 , wherein the power saving mode includes at least one application lockout mode based on stated user preferences.

9 . The computing system claim 6 , wherein a recurrent neural network (RNN) is used to forecast the risk based on predicting usage of the device between a current time and a predicted time when the battery may be recharged.

10 . The computing system claim 9 , wherein the predicted time is based on a predicted location of the device.

11 . A computer program product for managing remaining battery charge capacity of a battery in a battery-powered device having a power saving mode, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the instructions executable by a processor to cause the processor to:
ingest history data, the history data including user application usage history data, user location history data, and battery charging history data for the device; forecast, based on real-time usage data, location data, battery charge data, and the ingested history data, a risk of running out of battery power; and adjust, in response to the forecasted risk, the device from a normal operating mode to a power saving mode to reduce battery consumption.

12 . The computer program product of claim 11 , wherein the power saving mode includes at least one application lockout mode based on user usage history.

13 . The computer program product of claim 11 , wherein the power saving mode includes at least one application lockout mode based on stated user preferences.

14 . The computer program product claim 11 , wherein a recurrent neural network (RNN) is used to forecast the risk based on predicting usage of the device between a current time and a predicted time when the battery may be recharged.

15 . The computer program product of claim 14 , wherein the predicted time is based on a predicted location of the device.

16 . A computer-implemented method comprising:
ingesting history data, the history data including application usage history data, critical activity history data, location history data, and battery charging history data for a computing device; forecasting, based on real-time usage data, location data, battery charge data, and the ingested history data, a must-succeed moment of the computing device; and modifying, in response to the forecasted must-succeed moment, present activity of the computing device.

17 . The computer-implemented method of claim 16 , wherein modifying the present activity of the computing device includes blocking the device from performing potentially disruptive activities.

18 . The computer-implemented method of claim 16 , wherein modifying the present activity of the computing device includes blocking the device from performing non-critical activities.

19 . The computer-implemented method of claim 16 , wherein the potentially disruptive activities include software updates.

20 . The computer-implemented method of claim 19 , wherein the potentially disruptive activities are blocked to ensure that software bugs are not introduced into the computing device before the forecasted must-succeed moment.

Assignees

• IBM

Inventors

• Baughman Aaron K
• Diamanti Gary F
• Marzorati Mauro

Classifications

• H02J7/933

  the cycle being controlled or terminated in response to electric parameters · CPC title

• H02J7/751

  concerning the insertion or the connection of the batteries · CPC title

• H02J7/63

  against overdischarge · CPC title

• H02J7/90 Primary

  Regulation of charging or discharging current or voltage · CPC title

• H02J9/002 Primary

  in which a reserve is maintained in an energy source by disconnecting non-critical loads, e.g. maintaining a reserve of charge in a vehicle battery for starting an engine · CPC title