AI-Driven UX Test Result Synthesis for Design Optimization
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Solution Overview
Problem
User experience (UX) testing faces inefficiencies due to the burden on researchers to compose and analyze tests, and third-party providers struggle to identify relevant insights, leading to sub-optimal product design and resource waste.
Innovation Solution
A scalable system architecture that automates UX testing by normalizing, enriching, and synthesizing test results using artificial intelligence and machine learning to generate actionable insights, reducing the need for manual analysis and optimizing product design feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If researchers manually compose and analyze UX tests, then test composition and analysis can be customized, but the process is time-consuming and requires significant expertise
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing test results as they are collected, preparing the data in advance for automated analysis. This reduces the time required when analysis is needed, as the data is already structured and ready for insight generation.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between test composition and insight generation. This intermediary automatically processes test results, eliminating the need for researchers to manually analyze each test, thus reducing time loss while maintaining ease of operation.
2Reliability
If third-party service providers perform UX testing, then expertise in conducting tests is leveraged, but it is difficult to identify test results most relevant to the specific customer
Solution Approach 1:
The system enables self-service by allowing each customer's data to automatically identify and extract its own relevant insights. The automated analysis process tailors insights to each specific customer without requiring third-party interpretation, thus maintaining reliability while improving information relevance.
Solution Approach 2:
The patent applies local quality by customizing the analysis and insight generation for each specific customer rather than applying a uniform approach. Each customer's data is processed with attention to their specific context, ensuring that the insights generated are locally optimized for relevance to that customer.
3Measurement precision
If manual analysis of test results is performed, then detailed examination of data is possible, but the process is expensive and inefficient
Solution Approach 1:
The patent replaces the mechanical system of manual analysis with an automated computational system. This substitution maintains measurement precision by systematically examining all test data while dramatically improving productivity through automation, eliminating the expense and inefficiency of manual processes.
Solution Approach 2:
The system changes parameters by transitioning from manual to automated processing, which alters the efficiency and cost parameters while maintaining or improving the precision of data examination. The automated system can process larger volumes of data with the same level of detail that was previously only achievable through expensive manual analysis.
4Productivity
If automated systems are used for UX testing, then efficiency and scalability are improved, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by dividing the automated testing system into distinct functional modules: data collection, normalization, analysis, and insight generation. This modular approach improves productivity through automation while managing complexity by organizing the system into separable, independently maintainable components.
Solution Approach 2:
The system achieves universality by creating a multi-functional automated platform that can handle various types of UX test data through a single normalized processing pipeline. This universal approach improves productivity across different test types while reducing overall system complexity by avoiding the need for separate specialized systems for each test type.
Data Source
AI summary
Techniques are described herein for producing machine-generated findings given a set of user experience test results. In some embodiments, the system generates the findings using an artificial intelligence and machine learning engine. The findings may highlight areas that are predicted to provide the most insight into optimizing a product's design. A finding may be generated based on all or a subset of the test result elements, including qualitative and/or quantitative data contained therein. A finding may summarize a subset of the UX test results that are interrelated. A finding may link a summary to one or more references extracted from the set of test results to show support for the machine-generated insights in the underlying raw test data. Machine-generated findings reports may provide near instantaneous guidance for optimizing product designs while removing extraneous information from a vast quantity of raw test result data.