Smart Service Analyzer KPI Normalization for Capacity Planning
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Solution Overview
Problem
Network planners face challenges in accurately predicting user experience and network performance in mobile networks due to exponential traffic growth, dynamic performance changes, and long cycles in capacity addition, necessitating improved capacity planning and estimation methods.
Innovation Solution
A Smart Service Analyzer using Machine Learning-based architecture normalizes User Level Qualitative and Quantitative Key Performance Indicators (KPIs) through Multi Scale and Trend Deviation Based KPI Normalizers, applying KPI Performance Thresholds and trend updates to generate normalized KPIs, enabling automated estimation of Customer Experience Index (CEI) and Service Quality Index (SQI) without logic updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual capacity planning methods are used, then network performance can be monitored, but the planning process is slow and cannot keep up with exponential traffic growth
Solution Approach 1:
The patent replaces manual mechanical planning processes with an automated machine learning-based system that processes network data and generates capacity planning recommendations automatically, eliminating the slow iterative nature of manual planning
Solution Approach 2:
The system enables self-service capacity planning by automatically monitoring network performance, detecting trends, and generating planning recommendations without requiring continuous manual intervention from network planners
2Measurement precision
If comprehensive performance measurements are collected across the network, then accurate performance estimation is achieved, but processing and transforming the large volume of data into actionable intelligence becomes difficult
Solution Approach 1:
The patent extracts only the most relevant features and metrics from the large volume of collected performance data, focusing computational resources on key indicators that drive capacity planning decisions rather than processing all raw data
Solution Approach 2:
The machine learning model acts as an intermediary layer between raw performance measurements and actionable intelligence, automatically transforming complex multi-dimensional data into simplified planning recommendations
3Adaptability or versatility
If the system adapts to changing network conditions and traffic patterns, then prediction accuracy is maintained, but the system requires continuous updates and logic changes
Solution Approach 1:
The patent implements dynamic adaptation through machine learning models that continuously learn from incoming network data, automatically adjusting to changing traffic patterns and network conditions without requiring manual reconfiguration or logic updates
Data Source
AI summary
A Key Performance Indicator (KPI) Normalizer for a Smart Service Analyzer. User Level Qualitative Key Performance Indicators (KPIs) and User Level Quantitative KPIs are receive by a KPI Normalizer. The User Level Qualitative KPIs are provided to a Multi Scale Normalizer. The User Level Qualitative KPIs are normalized using the Multi Scale Normalizer based on KPI Performance Thresholds associated with the User Level Qualitative KPIs to produce Normalized Qualitative KPIs. User Level Quantitative KPIs are provided to a Trend Deviation Based KPI Normalizer. The User Level Quantitative KPIs are normalized using the Trend Deviation Based KPI Normalizer to produce Normalized Quantitative KPIs based on a Trend Update.