Mobile Device Power Optimization via Request Buffering
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Solution Overview
Problem
Optimizing battery life in mobile devices is challenging due to varying data services, and existing methods often require hardware modifications, which are not feasible for all types of data services.
Innovation Solution
A method and system that schedules and buffers application requests based on battery charge levels and user interaction dwell times, delaying processing until the battery reaches a power preservation threshold and the user interaction meets a dwell time threshold, thereby reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If hardware modifications are made to optimize battery life, then battery life is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent replaces hardware-level power optimization with a software-based request management system. The request manager component buffers and schedules application requests based on battery charge levels and user interaction patterns, eliminating the need for hardware modifications while achieving power optimization through intelligent software control of data service processing.
Solution Approach 2:
The system dynamically changes operational parameters by adjusting request processing timing based on battery charge levels and predicted user dwell time. When battery charge is low and user engagement is predicted to be brief, the system buffers requests to delay processing, thereby reducing power consumption without requiring hardware changes.
2Speed
If requests are processed immediately, then user responsiveness is improved, but power consumption increases
Solution Approach 1:
The request management system dynamically adjusts request processing timing based on real-time conditions including battery charge level and predicted user dwell time. Instead of static immediate processing or fixed delays, the system adaptively buffers requests when conditions indicate power conservation is needed, creating a dynamic balance between responsiveness and power consumption.
Solution Approach 2:
The system performs preliminary assessment of battery charge level and user interaction patterns before processing requests. By predicting future user dwell time and current power state, the system decides in advance whether to buffer requests, allowing proactive power management rather than reactive responses to power depletion.
3Use of energy by moving object
If requests are buffered for power optimization, then power consumption is reduced, but user interaction responsiveness may deteriorate
Solution Approach 1:
The system uses feedback from user interaction monitoring to adjust request processing timing. By tracking actual user dwell time and engagement patterns, the request manager learns user behavior and refines its buffering decisions, ensuring that requests are processed at optimal times that balance power conservation with maintaining user interaction responsiveness.
Data Source
AI summary
Systems and methods for optimizing the power of a battery in a mobile device are provided. The systems and methods include receiving a request from at least one of a plurality of applications running on the mobile device. The systems and methods further include determining user characteristics from interacting with at least one of the applications and determining a user dwell time threshold based upon the user's interactions with an application. The systems and methods further include buffering requests if the user dwell time is less than the user dwell threshold level.


