Network Performance Degradation Detection System

The patent replaces manual mechanical investigation processes with an automated machine learning system that collects, processes, and analyzes network performance data automatically. The system substitutes human analysts' manual troubleshooting with algorithmic detection that identifies handset-caused performance degradation in real-time, reducing investigation time from days/weeks to minutes/hours while maintaining detection accuracy through structured data analysis.

The patent implements preliminary detection and warning mechanisms that identify potential performance degradation issues before they significantly impact users. The machine learning system continuously monitors network performance data and generates early warnings when handset software is detected to cause degradation, enabling proactive intervention before widespread user impact occurs. This allows carriers to prevent or mitigate performance issues before they affect millions of users.

The patent segments the complex analysis task by dividing it into distinct processing stages: data collection from multiple sources, baseline computation, deviation calculation, and machine learning classification. The system processes KPI data from hundreds of handsets by breaking down the comprehensive analysis into manageable components, where each stage handles specific aspects of the data. This segmentation maintains high detection accuracy through thorough analysis while managing system complexity through structured modular processing.