AI Theme Builder for UX Test Analysis
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Solution Overview
Problem
User experience (UX) testing often results in inefficient analysis due to the variability and unstructured nature of user expectation data, leading to sub-optimal product design choices and resource inefficiencies, whether conducted in-house or by third-party service providers.
Innovation Solution
A system leveraging machine learning to normalize, synthesize, and prioritize UX test results by predicting themes and selecting representative quotations, allowing for automated analysis and actionable insights, thereby optimizing product design feedback and development processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of UX test results is performed by researchers, then analysis depth and contextual understanding are improved, but time consumption and resource efficiency deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising NLP models and theme building components that act as a bridge between raw UX test data and human analysts. The system automatically processes unstructured user feedback, extracts themes, and generates structured insights, thereby reducing the time burden on researchers while maintaining analysis depth through multiple processing stages including sentiment analysis and theme validation.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system using natural language processing and machine learning models. The system automatically performs text normalization, theme extraction, sentiment analysis, and result synthesis, substituting human manual labor with algorithmic processing that maintains scalability and consistency while significantly reducing time consumption.
2Reliability
If third-party service providers perform UX testing, then expertise in conducting tests is improved, but relevance to specific customer and extraction efficiency deteriorate
Solution Approach 1:
The patent creates a universal theme building system that can process UX test results from multiple sources including in-house research and third-party providers. The system uses standardized NLP models and theme extraction algorithms that work across different data formats and sources, enabling consistent processing regardless of the original provider, thereby maintaining expertise benefits while improving extraction efficiency through automation.
Solution Approach 2:
The patent enables the UX analysis system to automatically process and extract insights from test results without requiring manual intervention for each data set. The theme building system self-adjusts by learning from processed data, automatically normalizing different input formats, and generating ready-to-use insights, thereby significantly improving productivity while maintaining high-quality analysis.
3Quantity of substance
If comprehensive UX test data is collected from multiple sources, then data quantity and insight potential are improved, but data variability and processing complexity deteriorate
Solution Approach 1:
The patent applies local quality processing by treating different data sources and formats with specialized processing rules tailored to their specific characteristics. The system identifies the source and format of each data set and applies appropriate normalization and processing techniques, thereby managing complexity through localized processing strategies rather than attempting uniform processing of all data.
Solution Approach 2:
The patent transforms unstructured and variable UX test data into standardized structured formats by changing key parameters including text normalization, sentiment scoring, theme categorization, and confidence level assignment. This parameter transformation process converts diverse data sources into a unified format that reduces processing complexity while preserving the full quantity and insight potential of the original data.
Data Source
AI summary
Techniques are described herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience (UX) tests. In some embodiments, a system receives input defining or modifying a theme schema for classifying results of user experience tests. Responsive to receiving the input, the system trains a themer model based at least in part on example classifications in a training dataset, where the classifications map results to themes within the theme schema. When a new set of results for a user experience test is received, the trained machine learning model may generate a set of predicted themes to classify the test results. The output of the model may be used to render user interfaces and/or trigger other actions directed to optimizing a product's design.