Automated Summarizer Model Training for UX Test Insights
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Solution Overview
Problem
User experience (UX) testing often results in sub-optimal product design choices due to poorly crafted tests and inefficient analysis, whether conducted in-house or outsourced, as researchers lack time and expertise to effectively run high-quality tests, and third-party providers struggle to identify relevant insights.
Innovation Solution
A scalable system architecture that automates the selection, curation, normalization, and synthesis of UX test results using artificial intelligence and machine learning to generate actionable insights, reducing the burden on researchers and improving resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If researchers manually conduct and analyze UX tests, then they can identify relevant insights, but it requires significant time and expertise, reducing productivity
Solution Approach 1:
The patent introduces an automated analysis system as an intermediary between UX test execution and insight generation. This system uses machine learning models trained on historical test data to automatically analyze test results, extract insights, and generate recommendations, eliminating the need for manual researcher analysis while maintaining high quality standards
Solution Approach 2:
The patent replaces the mechanical process of manual test analysis with an automated computational system. Machine learning algorithms and natural language processing techniques substitute for human researchers' cognitive processes, enabling rapid automated analysis of test data while preserving the ability to identify relevant insights
2Measurement precision
If third-party service providers conduct UX tests, then they can leverage expertise, but it is expensive and difficult to identify customer-specific relevant insights
Solution Approach 1:
The patent enables organizations to conduct and analyze their own UX tests using automated systems, eliminating dependence on external service providers. The machine learning models are trained on historical data and can be continuously improved with customer-specific data, allowing the system to serve itself and adapt to specific customer needs without external expertise
Solution Approach 2:
The patent dynamically adjusts analysis parameters and model configurations based on customer-specific data and requirements. The system learns from historical test data and adapts its analysis approach to identify customer-relevant insights, transforming the static analysis process into a dynamic, adaptive system
3Measurement precision
If manual analysis tasks are used, then detailed examination is possible, but the process is cumbersome and inefficient
Solution Approach 1:
The patent replaces manual analysis tasks with automated machine learning systems that perform detailed examination of test results. Natural language processing algorithms automatically parse and analyze test data, extracting insights with high detail and precision while eliminating the manual effort and complexity associated with traditional analysis methods
Data Source
AI summary
Techniques are described for producing machine learning models to generate findings associated with user experiences with products and/or services. In some embodiments, a training process receives a set of findings from one or more user experience tests, where a finding includes a summary and a set of one or more references supporting the summary. The training process further identifies a supplemental set of one or more references that were not included in the initial finding to support the summary. The training process trains a machine learning model, such as a neural or generative language model, based on the first set of one or more references and the second set of one or more references to generate summaries from a subset of sampled references based at least in part on the first set of one or more references and the second set of one or more references.