Dynamic Resource Consumption Profiles for Mobile Apps
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Solution Overview
Problem
Current permission models for mobile devices are inadequate in controlling resource consumption dynamically and fail to protect against malicious exploitation, particularly in managing internet bandwidth and storage usage, as they are based on static settings rather than real-time device and user context.
Innovation Solution
A method and system that dynamically generate and manage resource consumption profiles based on device context and application usage, allowing for real-time adjustment of resource access to prevent exploitation by malicious actors, by identifying user preferences and application behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static permission settings are used for resource management, then device complexity is reduced and ease of operation is improved, but resource consumption cannot be dynamically controlled and malicious exploitation cannot be prevented
Solution Approach 1:
The patent implements dynamic resource consumption profiles that automatically adjust resource allocation based on real-time device context and application behavior. Instead of static permissions, the system continuously monitors and adapts resource limits, enabling dynamic control without requiring complex manual configuration by users.
Solution Approach 2:
The system employs machine learning models that automatically analyze application behavior patterns and generate appropriate resource consumption profiles without user intervention. The resource management system self-adjusts based on observed usage patterns, eliminating the need for users to manually configure complex permission settings while maintaining adaptability.
2Productivity
If manual adjustment of resource permissions at individual application level is required, then resource consumption can be controlled, but user time and operational effort increase significantly
Solution Approach 1:
The system automatically monitors application behavior and dynamically adjusts resource permissions based on observed patterns, eliminating the need for users to manually configure each application's permissions. The resource management system serves itself by automatically generating and updating consumption profiles based on real-time data.
Solution Approach 2:
The system continuously monitors resource consumption patterns and provides feedback to the machine learning model, which then automatically adjusts permission settings. This closed-loop feedback mechanism enables the system to learn from actual usage and optimize resource allocation without requiring user input or manual adjustments.
3Reliability
If resource permissions are selected by users for individual applications, then some level of control is achieved, but comprehensive protection against malicious exploitation is insufficient
Solution Approach 1:
The patent introduces an intermediary resource management layer between applications and device resources. This intermediary system uses machine learning models to analyze application behavior and enforce resource consumption limits, providing comprehensive protection without requiring direct user configuration of each permission. The intermediary automatically mediates resource access based on learned patterns.
Solution Approach 2:
The system proactively establishes resource consumption profiles and limits before malicious exploitation can occur. By continuously monitoring application behavior and pre-configuring appropriate resource limits based on observed patterns, the system prevents exploitation attempts rather than merely responding to them after detection.
Data Source
AI summary
A method, system, and computer program product for preventing resource exploitation in mobile devices are provided. The method receives a resource request from a mobile application stored on a mobile computing device. A device context is determined for the mobile computing device. A resource consumption context is determined for the mobile computing device based on the device context and resource consumption of one or more mobile applications operating on the mobile computing device. In response to the resource request, the method generates a set of resource consumption profiles based on the device context and the resource consumption context. A resource consumption profile is selected for the mobile application from the set of resource consumption profiles. The method manages consumption of resources, of the mobile computing device, by the mobile application based on the selected resource consumption profile.