CURRENT USAGE ESTIMATION FOR ELECTRONIC DEVICES

• US 20140244191A1
• Filed: 02/28/2013
• Published: 08/28/2014
• Est. Priority Date: 02/28/2013
• Status: Abandoned Application

• Current Assignee: Malikie Innovations Limited (Key Patent Innovations Limited)
• Original Assignee: Research In Motion Limited (Blackberry Limited)
• Inventors: OKA, Anand Ravindra, SIMMONS, Sean Bartholomew, SNOW, Christopher Harris
• Application Number:
  US13/781,047
• Publication Number:
  US 20140244191A1
• Time in Patent Office: Days
• US Class Current: 702/61
• CPC Class Codes: G01R 21/133 by using digital technique
• CPC Class Codes: G06F 1/3203 Power management, i.e. event-based initiation of a power-saving mode
• CPC Class Codes: G06F 1/3206 Monitoring of events, devices or parameters that trigger a change in power modality

Abstract

Various embodiments are described herein for a system and method for estimating current consumption for an electronic device by obtaining log data comprising a record of at least some activities of the electronic device during a selected time period, parsing the log data into a plurality of component digests, estimating current consumption values for the component digests based on component signatures of the component digests, a current consumption model and a machine learning technique; and processing the estimated current consumption values to estimate current consumption data for the electronic device.

Claims

1. A method of estimating current consumption of an electronic device, the method comprising:

obtaining log data comprising a record of at least some activities of the electronic device during a selected time period;

parsing the log data into a plurality of component digests;

estimating current consumption values for the component digests based on component signatures of the component digests, a current consumption model and a machine learning technique; and

processing the estimated current consumption values to estimate current consumption data for the electronic device.

2. The method of claim 1, wherein estimating the current consumption value for a given component digest comprises:

determining the component signature of the given component digest;

locating a training signature from training set data that corresponds to the component signature; and

setting the estimated current consumption value to a current label associated with the located training signature.

3. The method of claim 2, wherein determining the component signature of the given component digest comprises mapping the component digest to an N-dimensional vector using a transformation that is at least approximate distance preserving.

4. The method of claim 3, wherein the mapping comprises mapping the component digest to a bag-of-word array by counting occurrences of text in the component digest and applying different weights to occurrences of different text in the component digest and then mapping the bag-of-word array to the component signature using the at least approximate distance preserving transformation.

5. The method of claim 1, wherein the method comprises estimating the current consumption model by:

obtaining training log data when executing a plurality of activities on the electronic device, the training log data comprising kernel event log data and current log data;

synchronizing the current log data with the kernel event log data when the current log data and the kernel event log data are not synchronized;

merging the kernel event log data with the current log data to form kernel-event-current log data;

parsing the kernel-event-current-log data into a plurality of raw digests;

determining training signatures for the raw digests after removing the current log values from the raw digests;

determining labels for the training signatures from the current usage values of the raw digests that correspond to the training signatures; and

applying the machine learning algorithm to the training signatures and corresponding labels to determine a relationship between the activities and the current usage used to generate the current consumption model.

6. The method of claim 5, wherein determining a label associated with a given raw digest further comprises:

extracting the current consumption values from the given raw digest;

computing an average current consumption value from averaging the current consumption values over the length of the given raw digest; and

assigning the average current consumption value as the label associated with the training signature corresponding to the given raw digest.

7. The method of claim 1, wherein the machine learning algorithm is one of a VP-tree based nearest-neighbor algorithm, a support vector machine algorithm, a decision-tree algorithm, a linear or logistic regression algorithm, a boosted decision tree and a neural network.

8. An electronic device comprising:

a plurality of subsystems for providing various functions;

an operating system for enabling execution of software applications and operation of the subsystems as well as logging activity of the electronic device in log data;

a processor that controls the operation of the device, the processor being configured to estimate current consumption of the electronic device by obtaining log data comprising a record of at least some activities of the electronic device during a selected time period;

parsing the log data into a plurality of component digests;

estimating current consumption values for the component digests based on component signatures of the component digests, a current consumption model and a machine learning technique; and

processing the estimated current consumption values to estimate current consumption data for the electronic device.

9. The device of claim 8, wherein estimating the current consumption value for a given component digest comprises:

determining the component signature of the given component digest;

locating a training signature from training set data that corresponds to the component signature; and

setting the estimated current consumption value to a current label associated with the located training signature.

10. The device of claim 9, wherein determining the component signature of the given component digest comprises mapping the component digest to an N-dimensional vector using a transformation that is at least approximate distance preserving.

11. The device of claim 10, wherein the mapping comprises mapping the component digest to a bag-of-word array by counting occurrences of text in the component digest and applying different weights to occurrences of different text in the component digest and then mapping the bag-of-word array to the component signature using the at least approximate distance preserving transformation.

12. The device of claim 8, wherein the method comprises estimating the current consumption model by:

obtaining training log data when executing a plurality of activities on the electronic device, the training log data comprising kernel event log data and current log data;

synchronizing the current log data with the kernel event log data when the current log data and the kernel event log data are not synchronized;

merging the kernel event log data with the current log data to form kernel-event-current log data;

parsing the kernel-event-current-log data into a plurality of raw digests;

determining training signatures for the raw digests after removing the current log values from the raw digests;

determining labels for the training signatures from the current usage values of the raw digests that correspond to the training signatures; and

applying the machine learning algorithm to the training signatures and corresponding labels to determine a relationship between the activities and the current usage used to generate the current consumption model.

13. The device of claim 12, wherein determining a label associated with a given raw digest further comprises:

extracting the current consumption values from the given raw digest;

computing an average current consumption value from averaging the current consumption values over the length of the given raw digest; and

assigning the average current consumption value as the label associated with the training signature corresponding to the given raw digest.

14. The device of claim 8, wherein the machine learning algorithm is one of a VP-tree based nearest-neighbor algorithm, a support vector machine algorithm, a decision-tree algorithm, a linear or logistic regression algorithm, a boosted decision tree and a neural network.

15. A computer readable medium comprising a plurality of instructions executable on a microprocessor of an electronic device for adapting the electronic device to implement a method of estimating current consumption for the electronic device, wherein the method comprises:

obtaining log data comprising a record of at least some activities of the electronic device during a selected time period;

parsing the log data into a plurality of component digests;

estimating current consumption values for the component digests based on component signatures of the component digests, a current consumption model and a machine learning technique; and

processing the estimated current consumption values to estimate current consumption data for the electronic device.

16. The computer readable medium of claim 15, wherein estimating the current consumption value for a given component digest comprises:

determining the component signature of the given component digest;

locating a training signature from training set data that corresponds to the component signature; and

setting the estimated current consumption value to a current label associated with the located training signature.

17. The computer readable medium of claim 16, wherein determining the component signature of the given component digest comprises mapping the component digest to an N-dimensional vector using a transformation that is at least approximate distance preserving.

18. The computer readable medium of claim 17, wherein the mapping comprises mapping the component digest to a bag-of-word array by counting occurrences of text in the component digest and applying different weights to occurrences of different text in the component digest and then mapping the bag-of-word array to the component signature using the at least approximate distance preserving transformation.

19. The computer readable medium of claim 15, wherein the method comprises estimating the current consumption model by:

obtaining training log data when executing a plurality of activities on the electronic device, the training log data comprising kernel event log data and current log data;

synchronizing the current log data with the kernel event log data when the current log data and the kernel log data are not synchronized;

merging the kernel event log data with the current log data to form kernel-event-current log data;

parsing the kernel-event-current-log data into a plurality of raw digests;

determining training signatures for the raw digests after removing the current log values from the raw digests;

determining labels for the training signatures from the current usage values of the raw digests that correspond to the training signatures; and

applying the machine learning algorithm to the training signatures and corresponding labels to determine a relationship between the activities and the current usage used to generate the current consumption model.

20. The computer readable medium of claim 19, wherein determining a label associated with a given raw digest further comprises:

extracting the current consumption values from the given raw digest;

computing an average current consumption value from averaging the current consumption values over the length of the given raw digest; and

assigning the average current consumption value to be the label associated with the training signature corresponding to the given raw digest.

21. The computer readable medium of claim 15, wherein the machine learning algorithm is one of a VP-tree based nearest-neighbor algorithm, a support vector machine algorithm, a decision-tree algorithm, a linear or logistic regression algorithm, a boosted decision tree and a neural network.