Mobile App Control via AI Habit Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobile terminals face challenges in efficiently reducing power consumption, as users must manually close applications one by one, which is time-consuming and often forgotten, leading to continued high power usage.
Innovation Solution
An AI module is integrated into the mobile terminal to analyze user habits and automatically close applications based on acquired data, including closing records and power consumption monitoring, to optimize battery-saving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If user manually closes applications one by one, then power consumption is reduced, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically monitors application power consumption and closes applications without user intervention. The mobile terminal itself performs the task of selecting and closing applications based on power consumption data, eliminating the need for manual user operation while achieving power savings.
Solution Approach 2:
The manual mechanical action of closing applications by user is replaced by an automated electronic system. The system uses power consumption monitoring mechanisms and algorithms to automatically determine which applications to close, substituting human operation with automated computational processes.
2Loss of energy
If user manually closes applications one by one, then power consumption is reduced, but time consumption increases
Solution Approach 1:
The system performs preliminary monitoring and analysis of application power consumption in the background before user needs to take action. By continuously tracking power consumption data and pre-determining which applications should be closed, the system prepares the optimization action in advance, eliminating time delays when the user actually needs to close applications.
Solution Approach 2:
The system autonomously performs the time-consuming task of monitoring and closing applications without requiring user time investment. The automated system handles the entire process from monitoring to execution, freeing the user from time-consuming manual operations while achieving the same power consumption reduction goal.
3Ease of operation
If applications remain open, then user convenience is maintained, but power consumption increases
Solution Approach 1:
The system continuously monitors application power consumption and provides feedback to automatically adjust application states. By establishing a feedback loop that tracks power usage and triggers automatic closing actions when thresholds are exceeded, the system dynamically balances user convenience with power conservation without requiring manual user intervention.
Solution Approach 2:
The system changes the operational state parameter of applications from open to closed based on power consumption thresholds. By monitoring power consumption parameters and automatically transitioning application states when certain conditions are met, the system achieves power savings while maintaining user convenience through automated decision-making.
4Ease of operation
If AI module automatically closes applications, then power consumption is reduced and operation simplicity is improved, but system complexity increases
Solution Approach 1:
The AI module serves as an intermediary between the user and the application management system. It automatically processes power consumption data and makes closing decisions, acting as a mediator that handles the complexity of monitoring and decision-making while presenting a simple interface to the user. This intermediary approach resolves the contradiction by hiding system complexity from the user.
Data Source
AI summary
An application control method includes acquiring M running applications on a mobile terminal, wherein M is a positive integer; acquiring user habit data related to closing at least one application within the M running applications; and closing the at least one application within the M running applications according to the user habit data. With embodiments of the present disclosure, an intelligent closing of the applications can be achieved, and power consumption of a mobile terminal can be reduced.