Adaptive Mobile Phone Backlight Power Management
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Solution Overview
Problem
Mobile phone power-saving features are not user-specific and fail to adjust to individual behavior, leading to inefficient battery usage.
Innovation Solution
A system and method that uses algorithms to modify mobile phone settings, such as backlight settings, based on learned user behavior, including data from selections, spatial location, and user interactions, to optimize power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If fixed power-saving settings are used, then device complexity is reduced and ease of operation is improved, but power consumption cannot be optimized for individual user behavior
Solution Approach 1:
The power-saving system automatically learns and adapts to user behavior patterns without requiring manual configuration. The device monitors user interactions (such as backlight usage after calls) and autonomously adjusts power-saving settings based on learned patterns, eliminating the need for users to manually program power-saving parameters while achieving personalized optimization
Solution Approach 2:
The system performs preliminary learning during an initial period to establish user behavior patterns before actively applying optimized power-saving settings. By collecting data on user interactions in advance and analyzing behavioral patterns, the system prepares personalized power-saving parameters that are then automatically applied, resolving the contradiction between simplicity and customization
2Adaptability or versatility
If preprogrammed power-saving features are used, then ease of operation is improved, but adaptability to individual user behavior deteriorates
Solution Approach 1:
The system automatically learns and adapts to user behavior patterns without requiring manual configuration. The device monitors user interactions (such as backlight usage after calls) and autonomously adjusts power-saving settings based on learned patterns, eliminating the need for users to manually program power-saving parameters while achieving personalized optimization
Solution Approach 2:
The system continuously monitors user interactions and uses this feedback to refine and update power-saving settings. By tracking whether users actually utilize the backlight after calls or other actions, the system adjusts its learned models and modifies settings accordingly, enabling continuous adaptation to individual behavior while maintaining automatic operation
Data Source
AI summary
A method for controlling the operation of a mobile phone backlight. The method may include receiving an incoming call. The method may also include receiving a selection from a user in response to receiving the incoming call. The method may include storing information relating to the selection received from the user. The method may further include turning off the mobile phone backlight after the passing of a predetermined time span of user inaction. The passing of the predetermined time span may follow the selection received from the user. The length of the predetermined time span may be determined at least in part using an algorithm. The method may also include modifying the algorithm based at least in part on the stored information.