POWER SAVING METHOD AND SYSTEM FOR A MOBILE DEVICE

• US 20080071713A1
• Filed: 09/14/2007
• Published: 03/20/2008
• Est. Priority Date: 09/14/2006
• Status: Abandoned Application

• Current Assignee: BenQ Corporation (Qisda Corporation), Qisda Corporation
• Original Assignee: BenQ Corporation (Qisda Corporation), Qisda Corporation
• Inventors: Tung, Yu Teng
• Application Number:
  US11/855,942
• Publication Number:
  US 20080071713A1
• Time in Patent Office: Days
• US Class Current: 706/21
• CPC Class Codes: H04B 1/1615 Switching on; Switching off, e.g. remotely battery saving circuits associated with selective call operation H04W52/00; details of power consumption reduction in a PLL, H03L7/0802, H03L7/14, H03L2207/08, H03L2207/18; muting amplifiers by gain control see H03G3/34
• CPC Class Codes: H04W 52/0251 using monitoring of local events, e.g. events related to user activity
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

A power saving method for a mobile device is disclosed. Multiple user samples are generated. One behavior vector for each of the user samples is calculated. A neural network system is trained using the user samples and the corresponding behavior vectors. Multiple user events are collected. The user events are transformed to multiple behavior samples using a weighting transformation function. The behavior samples are classified into behavior sample groups. The behavior sample group comprising the most behavior samples is obtained. The behavior vector for the behavior sample group comprising the most behavior samples is calculated. The neural network system is trained using the behavior sample group comprising the most behavior samples and the corresponding behavior vector.

Claims

1. A power saving method for a mobile device, comprising:

generating multiple user samples;

calculating one behavior vector for each of the user samples;

training a neural network system using the user samples and the corresponding behavior vectors;

collecting multiple user events;

transforming the user events to multiple behavior samples using a weighting transformation function;

classifying the behavior samples into behavior sample groups;

obtaining the behavior sample group comprising the most behavior samples;

calculating the behavior vector for the behavior sample group comprising the most behavior samples; and

training the neural network system using the behavior sample group comprising the most behavior samples and the corresponding behavior vector.

2. The power saving method as claimed in claim 1, further comprising:

determining whether a result of training the neural network system is convergent;

if the result of training the neural network system is convergent, inputting a median of the behavior samples of the behavior sample group in the neural network system and determining an output of training the neural network system as a current user behavior vector; and

if the result of training the neural network system is not convergent, collecting more behavior samples to train the neural network system.

3. The power saving method as claimed in claim 2, wherein the current user behavior vector comprises multiple elements, the elements represent weighting factors in different time segments.

4. The power saving method as claimed in claim 1, further comprising:

collecting the user events in a time interval;

transforming the user events to the multiple behavior samples using the weighting transformation function; and

inputting the user events to the neural network system for predicting user behaviors.

5. The power saving method as claimed in claim 4, further comprising:

if the neural network system detects the difference between results for predicting user behaviors and a current user behavior vector determined by an output of training the neural network system being greater than a first predetermined range, slightly adjusting the current user behavior vector; and

if the neural network system detects the difference between the results for predicting user behaviors and the current user behavior vector being greater than a second predetermined range, re-collecting behavior samples to train the neural network system.

6. The power saving method as claimed in claim 1, wherein each behavior vector comprises a plurality of weighting factors that correspond to user behaviors.

7. A power saving system for a mobile device, comprising:

a prediction module;

a sample generation module, randomly generating multiple user samples;

an estimation module, coupled to the sample generation module, calculating one behavior vector for each of the user samples;

a training module, coupled to the estimation module, training a neural network system using the user samples and the corresponding behavior vectors;

an event collection module, collecting multiple user events; and

a weighting transformation module, coupled to the event collection module and the training module, transforming the user events to multiple behavior samples using a weighting transformation function, classifying the behavior samples into behavior sample groups, and obtaining the behavior sample group comprising the most behavior samples;

wherein the estimation module calculates the behavior vector for the behavior sample group comprising the most behavior samples, and the training module trains the neural network system using the behavior sample group comprising the most behavior samples and the corresponding behavior vector.

8. The power saving system as claimed in claim 7, wherein the training module determines whether a result of training the neural network system is convergent, if the result of training the neural network system is convergent, inputs a median of the behavior samples of the behavior sample group in the neural network system and determines an output of the neural network system as a current user behavior vector;

and, if the result of training the neural network system is not convergent, collects more behavior samples to train the neural network system.

9. The power saving system as claimed in claim 8, wherein the current user behavior vector comprises multiple elements, the elements representing weighting factors in different time segments.

10. The power saving system as claimed in claim 7, wherein the sample generation module collects the user events in a time interval, the weighting transformation module transforms the user events to the multiple behavior samples using the weighting transformation function, the training module retrieves the behavior samples to train the neural network system, and the prediction module predicts user behaviors according to the training result.

11. The power saving system as claimed in claim 10, wherein the prediction module slightly adjusts the current user behavior vector if the neural network system detects the difference between results for predicting user behaviors and a current user behavior vector determined by an output of training the neural network system being greater than a first predetermined range and re-collects behavior samples to train the neural network system if the neural network system detects the difference between the results for predicting user behaviors and the current user behavior vector being greater than a second predetermined range.

12. The power saving system as claimed in claim 7, wherein each behavior vector comprises a plurality of weighting factors that correspond to user behaviors.

13. A computer-readable storage medium storing a computer program providing a power saving method for a mobile device, comprising using a computer to perform the steps of:

generating multiple user samples;

calculating one behavior vector for each of the user samples;

training a neural network system using the user samples and the corresponding behavior vectors;

collecting multiple user events;

transforming the user events to multiple behavior samples using a weighting transformation function;

classifying the behavior samples into behavior sample groups;

obtaining the behavior sample group comprising the most behavior samples;

calculating the behavior vector for the behavior sample group comprising the most behavior samples; and

training the neural network system using the behavior sample group comprising the most behavior samples and the corresponding behavior vector.

14. The computer-readable storage medium as claimed in claim 13, further comprising:

determining whether a result of training the neural network system is convergent;

if the result of training the neural network system is convergent, inputting a median of the behavior samples of the behavior sample group in the neural network system and determining an output of training the neural network system as a current user behavior vector; and

if the result of training the neural network system is not convergent, collecting more behavior samples to train the neural network system.

15. The computer-readable storage medium as claimed in claim 14, wherein the current user behavior vector comprises multiple elements, the elements representing weighting factors in different time segments.

16. The computer-readable storage medium as claimed in claim 13, further comprising:

collecting the user events in a time interval;

transforming the user events to the multiple behavior samples using the weighting transformation function; and

inputting the user events to the neural network system to predict user behaviors.

17. The computer-readable storage medium as claimed in claim 16, further comprising:

if the neural network system detects the difference between results for predicting user behaviors and a current user behavior vector determined by an output of training the neural network system being greater than a first predetermined range, slightly adjusting the current user behavior vector; and

if the neural network system detects the difference between the results for predicting user behaviors and the current user behavior vector being greater than a second predetermined range, re-collecting behavior samples to train the neural network system.

18. The computer-readable storage medium as claimed in claim 13, wherein each behavior vector comprises a plurality of weighting factors that correspond to user behaviors.