Telecommunications KPI Clustering for Granular Local Network Predictions

The patent segments the network tracking system into multiple hierarchical levels: market-level aggregation for broad coverage and zip code-level granularity for precise local measurements. This segmentation allows the system to simultaneously achieve wide area monitoring while maintaining high measurement precision for specific geographic areas through automated ML-based analysis of KPI data at appropriate granularities.

The patent implements self-service through automated machine learning models that independently collect, process, and analyze network KPI data without requiring manual user input. The system automatically parses KPI data, applies ML algorithms to identify patterns and predict network performance, and generates insights autonomously, thereby eliminating repetitive manual operations while significantly improving analysis productivity.

The patent introduces an intermediary automated ML processing layer that sits between raw network KPI data and human users. This intermediary automatically performs data collection, parsing, clustering, and predictive analysis, transforming complex raw data into simplified actionable insights. The ML models act as intermediaries that handle computational complexity internally while presenting simplified results to users, thereby improving productivity without requiring users to directly manage the complexity.