ML Environment Layer Arrangement Based on Training Noise
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Solution Overview
Problem
Current frameworks, such as HEART and USER, are inadequate for measuring overall user experience within cloud computing environments and tracking a user's journey from initial engagement to becoming a customer of a cloud service provider.
Innovation Solution
A computer-implemented method and system that generates a unified user experience (UX) score using sentiment analysis and theme classification, training multiple layers of a machine learning environment to perform these analyses, and arranging layers based on noise from training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional frameworks (HEART, USER) are used to measure user experience, then measurement simplicity is maintained, but measurement precision and comprehensiveness deteriorate
Solution Approach 1:
The patent segments the user experience measurement into multiple layers: sentiment analysis layer, theme classification layer, and unified score aggregation layer. Each layer processes specific aspects of user feedback independently, allowing comprehensive measurement while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including sentiment analysis models, theme classification models, and noise estimation mechanisms that mediate between raw user feedback data and the final unified UX score. These intermediaries transform unstructured feedback into structured, measurable metrics.
2Measurement precision
If multiple layers of machine learning models are trained for comprehensive analysis, then measurement precision improves, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training sentiment analysis and theme classification models on large datasets before deployment. Noise characteristics are estimated during the training phase, allowing the models to be optimized in advance. This enables fast, accurate inference during actual user experience measurement without requiring extensive training time at measurement time.
Solution Approach 2:
The patent applies partial action by focusing training efforts on the most critical aspects of user feedback analysis. The multi-layer architecture allows selective training of specific layers based on data availability and computational resources, rather than requiring all layers to be trained exhaustively.
3Reliability
If layers are arranged based on noise from training data, then measurement reliability improves, but data processing complexity increases
Solution Approach 1:
The patent changes the arrangement parameter of model layers from fixed architectural ordering to noise-based dynamic ordering. By estimating noise characteristics from training data for each layer and arranging them in ascending order of noise, the system optimizes the flow of information through the network, placing cleaner, more reliable layers earlier in the processing pipeline.
4Loss of information
If automated monitoring of multiple data metrics is implemented, then user experience insights improve, but system complexity and resource consumption increase
Solution Approach 1:
The patent merges multiple data metrics and feedback sources into a unified user experience score. By combining sentiment analysis results, theme classification outcomes, and noise-adjusted measurements into a single comprehensive metric, the system reduces information loss while presenting a simplified output that avoids the complexity of monitoring numerous separate indicators.
Data Source
AI summary
Techniques for generating a unified user experience (UX) score using sentiment analysis and theme classification, training multiple layers of a machine learning environment to perform sentiment analysis and theme classification, and arranging layers of a machine learning environment based on noise from training data are provided. A unified UX score is generated from categories that are indicative of a user's journey in association with the cloud service provider. Machine learning environments are trained and used to perform sentiment analysis and theme classification on user feedback data. The layers of a machine learning environment can also be arranged based on noise generated from training data used to train the models of the machine learning environments.