Customer Experience Index Estimator for Network Trend Alerts
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Solution Overview
Problem
Existing mobile networks face challenges in accurately predicting network performance and user experience due to exponential traffic growth, dynamic changes, and increasing user demands for throughput and latency, necessitating improved capacity planning and network management.
Innovation Solution
A Smart Service Analyzer employs machine learning (ML) to normalize key performance indicators (KPIs) into customer experience indexes (CEIs) and service quality indexes (SQIs), detecting trend shifts and automatically generating alerts for network engineers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network performance monitoring methods are used, then network operators can track basic KPIs, but they cannot accurately predict network performance and detect trend shifts due to exponential traffic growth and dynamic changes
Solution Approach 1:
The patent introduces a Smart Service Analyzer as an intermediary system between traditional network monitoring tools and network performance prediction. This analyzer normalizes multi-source KPI data, applies machine learning models to detect trend shifts, and generates predictive insights without requiring direct modification of existing network infrastructure, thus improving prediction accuracy while maintaining manageable system complexity
Solution Approach 2:
The patent replaces traditional mechanical/statistical network monitoring methods with machine learning-based analysis. The Smart Service Analyzer uses ML algorithms to automatically detect trend shifts and predict network performance, substituting manual threshold-based monitoring with intelligent, adaptive prediction systems that handle exponential traffic growth more effectively
2Productivity
If manual capacity planning is performed, then network operators can make informed decisions, but the process is time-consuming and cannot keep pace with dynamic network changes
Solution Approach 1:
The Smart Service Analyzer enables self-service capacity planning by automatically collecting, normalizing, and analyzing network KPI data. The system autonomously detects trend shifts using machine learning, generates performance predictions, and provides actionable insights without requiring continuous manual intervention, thus significantly improving capacity planning efficiency while reducing analysis time
Solution Approach 2:
The system performs preliminary analysis of network performance trends continuously in the background using machine learning models. By pre-processing KPI data and maintaining updated trend predictions, the system is ready to provide immediate capacity planning insights when needed, eliminating the time required for ad-hoc analysis and enabling faster decision-making
3Adaptability or versatility
If traditional KPI monitoring systems are used, then basic performance metrics can be tracked, but the systems require logic updates whenever new KPIs are added or removed
Solution Approach 1:
The Smart Service Analyzer employs a universal normalization framework that can handle multiple types of KPIs (quantitative and qualitative) from various network sources through a single unified process. The machine learning-based trend detection mechanism is agnostic to specific KPI types, allowing the system to adapt to new KPIs without requiring specialized logic updates, thus improving flexibility while reducing maintenance effort
4Reliability
If network operators increase capacity investment to meet growing user demands, then network performance can be maintained, but the cost increases and may not be optimally allocated
Solution Approach 1:
The Smart Service Analyzer implements continuous feedback loops that monitor network performance trends and predict future capacity requirements. By providing real-time insights into actual network utilization patterns and performance degradation risks, the system enables network operators to make data-driven decisions about capacity investment timing and allocation, ensuring network service quality is maintained while optimizing resource utilization and avoiding premature or excessive capacity additions
Data Source
AI summary
A method includes normalizing one or more key performance indicator (KPI), where the one or more KPI include user-level quantitative KPI or user-level qualitative KPI; determining whether weights are available for each normalized KPI; and in response to the weights being available for each normalized KPI, converting, based on the weights for each normalized KPI, each normalized KPI to a customer experience index (CEI) for each user and for each network service. The method further includes determining whether a trend shift has occurred based on the CEI; and automatically generating an alert in response to a determination that the trend shift occurred.