Digital Service Evaluation Using Session Scoring and Metric Weights
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Solution Overview
Problem
Developers lack adequate tools to determine the overall impact of changes made to digital services and identify which changes cause significant changes in user experience, as they do not have comprehensive scoring systems to evaluate performance metrics across multiple client sessions.
Innovation Solution
A digital service evaluation system that collects performance data from client sessions to calculate an overall score and identifies which metrics contribute most to the score, using machine learning and artificial intelligence to analyze user feedback and session data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers make changes to digital services, then service functionality is improved, but the overall impact on user experience cannot be determined
Solution Approach 1:
The system implements automated feedback loops that collect user interaction data, calculate experience scores, and provide developers with actionable insights about how service changes affect user experience. This enables continuous measurement and adjustment of service improvements.
Solution Approach 2:
The patent replaces manual impact assessment with automated machine learning models that analyze user behavior data and calculate experience scores. This substitution enables precise, scalable measurement of service change impacts without manual intervention.
2Measurement precision
If comprehensive performance metrics are collected from multiple client sessions, then evaluation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments the complex evaluation process into distinct components: data collection from client sessions, feature extraction, score calculation, and impact analysis. This segmentation allows each component to be optimized independently while maintaining overall evaluation accuracy.
Solution Approach 2:
The patent introduces intermediary processing layers including feature extraction modules and scoring algorithms that transform raw performance metrics into meaningful experience scores. These intermediaries simplify the relationship between complex input data and evaluation results.
3Productivity
If automated evaluation systems are implemented, then evaluation efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service capabilities where the automated evaluation framework independently collects data, processes metrics, and generates insights without requiring complex manual configuration. This reduces operational complexity while maintaining high evaluation efficiency.
Solution Approach 2:
The patent creates a universal evaluation platform that can assess multiple types of digital services across different client sessions using the same core methodology. This multi-functionality reduces system complexity by avoiding the need for separate evaluation systems for each service type.
Data Source
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AI summary
A digital service evaluation system evaluates services and user sessions provided by a service, to provide an overall score of the service. The digital service evaluation system detects client sessions associated with one or more devices. The digital service evaluation system obtains a first plurality of scores associated with performance metrics of the client session, and calculates an overall score for the client session. The digital service evaluation system obtains a second plurality of scores and calculates a second overall score. The digital service evaluation system determines a weight for each performance metric based on the first and second plurality of scores and the overall scores. The digital service evaluation system uses the weights to determine which performance metric caused a change in the overall scores. The digital service evaluation system takes an action based on the determination that a performance metric caused a change in the overall scores.