QoE Scoring for Subscriber Network Issue Detection
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Solution Overview
Problem
Service provider networks face overwhelming operational data from numerous subscriber devices, requiring personnel to sift through vast amounts of information to identify performance issues, which is inefficient and time-consuming.
Innovation Solution
Implementing a system to evaluate operational information from subscriber devices and premises to generate Quality of Experience (QoE) scores, providing quantitative measures of subscriber satisfaction and facilitating quick identification of performance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operational information is collected from all subscriber devices, then measurement precision is improved, but device complexity increases due to the need to process and store vast amounts of data
Solution Approach 1:
The patent segments the operational information into distinct categories (service quality data, device performance data, error logs) and processes each segment separately using specialized algorithms. This segmentation allows the system to handle vast amounts of data without overwhelming complexity, as each category can be analyzed independently with appropriate metrics and thresholds.
Solution Approach 2:
The patent introduces intermediary processing layers including edge computing devices and intermediate servers that aggregate and pre-process operational information before it reaches the central analysis system. These intermediaries filter, summarize, and normalize data from multiple subscriber devices, reducing the complexity burden on the central system while maintaining measurement precision.
2Measurement precision
If operational information is aggregated over 24 hours, then measurement precision is improved, but loss of time increases due to delayed detection of performance issues
Solution Approach 1:
The patent implements periodic action by analyzing operational information at multiple time intervals - both continuous real-time monitoring and periodic 24-hour aggregations. This multi-timescale approach allows the system to detect performance issues immediately when they occur while also maintaining precise measurements through longer-term aggregation, thus resolving the contradiction between speed and accuracy.
Solution Approach 2:
The patent applies preliminary action by establishing baseline performance metrics and thresholds in advance, and by pre-configuring alert rules and analysis algorithms. When operational information is collected, the system can immediately compare data against pre-established criteria and generate alerts without requiring complex real-time analysis, thereby reducing detection time while maintaining measurement precision through pre-planned evaluation frameworks.
3Ease of operation
If personnel manually analyze operational information, then ease of operation is maintained, but productivity decreases due to the time required to process large data volumes
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze operational information, generate performance assessments, and identify issues without requiring manual personnel intervention. The automated analysis engine processes vast amounts of data, applies evaluation algorithms, and produces actionable insights independently, dramatically improving productivity while maintaining ease of operation through user-friendly interfaces that present results in accessible formats.
Solution Approach 2:
The patent replaces the mechanical system of manual data analysis with automated computational systems including machine learning algorithms, statistical analysis engines, and computerized reporting tools. This substitution transforms the labor-intensive manual review process into an automated electronic analysis system that can process vast datasets rapidly while presenting results in an easily interpretable format, thereby improving both productivity and ease of operation.
Data Source
AI summary
Systems, methods, and apparatuses disclosed herein can evaluate collections of operational information received from one or more subscriber devices to determine one or more Quality of Experience (QoE) scores for the one or more subscriber devices and/or one or more subscriber premises that are associated with the one or more subscriber devices. Generally, the one or more QoE scores can represent one or more numerically quantitative measures relating to the facilitation of a service by a service provider system. In some embodiments, the one or more QoE scores can represent one or more numerically quantitative measures relating to the experience of one or more subscribers that are associated with the one or more subscriber devices. In these embodiments, the one or more QoE scores can represent numerically quantitative measures of the subjective experience, for example, satisfaction or unsatisfaction, of the one or more subscribers with the service.