Background App Cleanup Using Decision Tree Prediction

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Solution Overview

Problem

Electronic devices experience performance degradation and increased power consumption when background applications are not cleaned up, leading to slow operation and lag due to high CPU occupancy and reduced memory availability.

Innovation Solution

A method involving the collection of multi-dimensional feature information from applications, construction of sample sets, extraction of feature information to form training sets, generation of decision trees, prediction of cleanup feasibility, and automatic cleanup of applications determined to be cleanable.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple applications run simultaneously in the background, then application functionality and user convenience are improved, but memory availability and CPU performance deteriorate

Engineering Contradiction:

Improveapplication functionalityVSAvoidmemory availability

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system automatically monitors and manages background applications using machine learning models that analyze app behavior patterns. The cleanup mechanism operates autonomously based on predicted app revival probability, eliminating the need for manual user intervention while maintaining system performance.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes the state of background applications by transitioning them from active to cleaned-up state based on multiple parameters including app behavior history, resource consumption patterns, and predicted revival probability. This parameter-based decision-making enables intelligent memory management.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If multiple applications run simultaneously in the background, then application functionality is improved, but CPU occupancy and device performance deteriorate

Engineering Contradiction:

Improveapplication functionalityVSAvoiddevice performance

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The machine learning-based cleanup system operates autonomously to manage CPU resources. It continuously learns from app behavior patterns and automatically makes decisions about which background applications to terminate, optimizing CPU occupancy without requiring user input.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements a feedback loop where the machine learning model continuously monitors app behavior, predicts revival probability, and adjusts cleanup decisions accordingly. This feedback mechanism ensures that the system adapts to changing usage patterns while maintaining optimal device performance.

Inventive Principle:

Principle #23Feedback

3Quantity of substance

If background applications are cleaned up aggressively, then memory availability and device performance are improved, but application revival capability and user convenience deteriorate

Engineering Contradiction:

Improvememory availabilityVSAvoidapplication revival capability

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system uses multiple parameters including app behavior history, resource consumption patterns, and predicted revival probability to dynamically determine cleanup decisions. This multi-parameter approach enables nuanced decision-making that balances memory management with app revival capability.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The machine learning model autonomously analyzes app characteristics and makes intelligent decisions about which applications to clean up. This self-service approach ensures that only applications with low revival probability are terminated, preserving user convenience while managing memory effectively.

Inventive Principle:

Principle #25Self-service

4Use of energy by stationary object

If background applications are cleaned up aggressively, then CPU occupancy and power consumption are improved, but application revival capability and user convenience deteriorate

Engineering Contradiction:

Improvepower consumptionVSAvoidapplication revival capability

Core Design Contradiction:

Use of energy by stationary objectVSReliability

Solution Approach 1:

The system dynamically adjusts cleanup decisions based on power consumption patterns and app behavior parameters. By analyzing historical data and predicting revival probability, the system optimizes power usage while maintaining the ability to revive frequently used applications.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11544633B2Method for cleaning up background application, storage medium, and electronic device

Publication Date: 2023.01.03 GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP LTD

AI summary

A method for cleaning up a background application, a storage medium, and an electronic device are provided. The method includes the following. Collect multi-dimensional feature information associated with an application as samples to construct a sample set associated with the application. Extract feature information from the sample set to construct multiple training sets. Train each training set to generate a corresponding decision tree. Predict, with multiple decision trees generated, current feature information associated with the application and output multiple predicted results when the application is switched to the background, where the predicted results include predicted results indicative of that the application is able to be cleaned up and predicted results indicative of that the application is unable to be cleaned up. Determine whether the application is able to be cleaned up according to the multiple predicted results. Clean up the application when the application can be cleaned up.