Mobile Station Application Performance Detection System
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Solution Overview
Problem
Business-to-customer (B2C) applications on mobile stations experience poor user interaction due to issues like slow responsiveness, device heating, and battery draining, leading to inefficient network resource usage and revenue loss for telecom operators, with existing analysis methods being time-consuming and operator-centric rather than user-centric.
Innovation Solution
A method and system for determining application performance on mobile stations by configuring devices to collect device and network traffic parameters, calculating Key Performance Indices (KPIs) such as fair usage, information availability, network load, and device resource consumption, and providing absolute and relative ratings to assess and improve application performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing offline probe-based analysis methods are used to evaluate application performance, then comprehensive network data can be collected, but the analysis process becomes time-consuming and non-economical
Solution Approach 1:
The patent configures mobile stations in advance with application performance detection systems, data collectors, and reporting mechanisms before actual performance evaluation. This preliminary setup enables real-time data collection and automatic reporting, eliminating the need for time-consuming offline probe-based analysis while maintaining measurement precision
Solution Approach 2:
The patent implements self-service by enabling mobile stations to automatically collect their own application performance data, generate performance reports, and submit them to the server without external intervention. This automation eliminates manual probe deployment and offline processing, significantly reducing analysis time while maintaining comprehensive measurement capabilities
2Loss of energy
If existing operator-centric analysis methods are used to evaluate application performance, then network resource usage can be monitored, but the analysis does not reflect actual user experience
Solution Approach 1:
The patent implements local quality by deploying detection systems directly on mobile stations (user devices) rather than only on network infrastructure. This enables collection of localized user experience data including device-specific performance metrics, application responsiveness, and user-interaction quality, providing a comprehensive view that combines both network resource usage and actual user experience
Solution Approach 2:
The patent introduces mobile stations as intermediaries between the application and the analysis system. These mobile stations collect performance data from both the application layer (user experience) and network layer (resource usage), bridging the gap between operator-centric network monitoring and user-centric experience evaluation
3Productivity
If B2C applications are deployed heavily on mobile stations, then user interaction and service usage increase, but device heating and battery draining occur
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor device resource consumption (battery level, temperature, CPU usage) alongside application performance metrics. This feedback enables dynamic adjustment of application behavior and resource allocation, allowing high productivity usage while preventing excessive battery draining and device overheating through real-time performance optimization
4Productivity
If B2C applications consume excessive network resources, then application functionality is maintained, but voice and data services for other users are affected
Solution Approach 1:
The patent applies partial action by implementing differentiated resource allocation where applications receive sufficient network resources to maintain functionality, but not excessive resources that would impact other services. The system monitors network resource consumption and dynamically adjusts allocation to ensure application productivity while preserving network reliability for voice and data services
Data Source
AI summary
The present disclosure is related in general to performance assessment and a method and a system for determining performance of an application installed on mobile stations. An application performance detection system configures each of the mobile stations upon receiving a performance assessment request to ensure deactivation of hardware and software modules that cause additional network traffic and resource utilization. Further, the application performance detection system receives application data from the mobile stations thus configured and determine Key Performance. Indices (KPIs) based on the application data. Rating of the application is determined based on the KPIs that can efficiently detect low rated application design that requires re-engineering. Finally, performance of the application is verified that helps in achieving significant reduction in operational data costs, improved device battery life, improved user experience and efficient network resource usage. Further, the present disclosure suggests an optimized application to users based on their geographic location.