Translator Model for Adaptable UX Testing Benchmarking
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Solution Overview
Problem
Existing user experience (UX) testing models struggle to adapt to evolving user expectations and changing standards, leading to sub-optimal analysis and design choices, as they rely on fixed testing methodologies that become outdated.
Innovation Solution
A bi-directional translator model, such as a neural network, is used to predict UX test results and compute real-time benchmarks by mapping historical test results to new testing methodologies, allowing for continuous evolution of testing methodologies without losing historical context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed testing model is used, then direct comparisons can be quickly performed with historical context, but the model becomes outdated and difficult to generalize as user experience facets evolve
Solution Approach 1:
The patent implements a dynamic testing model where test facets, questions, and scoring criteria can be continuously updated to reflect evolving user experience standards. The system allows benchmark models to adapt to new UX facets while maintaining historical comparison capabilities through standardized mapping relationships between old and new test structures.
Solution Approach 2:
The system changes key parameters of the testing model over time, including updating UX facets, modifying test questions, adjusting scoring criteria, and redefining benchmark thresholds. These parameter changes enable the model to stay current with evolving user expectations while maintaining structural consistency for historical comparisons.
2Reliability
If testing methodologies are updated to reflect current standards, then analysis accuracy improves, but historical context is lost
Solution Approach 1:
The patent introduces mapping relationships as intermediaries between historical test structures and updated test structures. These mappings preserve the connection to historical data while allowing the testing methodology to evolve. The system can translate new test results into historical frameworks and vice versa, maintaining continuity across different testing eras.
Solution Approach 2:
The system creates standardized copies of test structures that maintain historical compatibility. When updating testing methodologies, the system preserves the essential structure and scoring framework that enables historical comparisons, while allowing content and specific criteria to be updated to reflect current standards.
3Productivity
If standardized tests are used across different iterations, then quantitative comparisons are enabled, but the tests fail to capture evolving user expectations
Solution Approach 1:
The patent segments the testing model into modular components including UX facets, test questions, scoring criteria, and benchmark thresholds. This segmentation allows individual components to be updated independently without requiring complete redesign of the testing framework, enabling both efficiency and adaptability.
Solution Approach 2:
The system creates a universal testing framework that can accommodate multiple versions of UX tests and benchmarking methodologies. The standardized structure allows historical and contemporary tests to be performed within the same system, enabling both efficient comparison and tracking of evolution over time.
Data Source
AI summary
Techniques are described herein for providing adaptable testing and benchmarking of user experiences with respect to one or more products. In some embodiments, the techniques include systems and methods for predicting performance of facets of a user experience under new testing and benchmarking methodologies. The systems and methods may generate a prediction for the results of a UX test even though the methodology and mechanics to quantify the user experience may vary significantly from previous methodologies. The techniques allow for methodologies to evolve over time without losing historical context or the ability to meaningfully compare historical test results with tests run using updated testing and benchmark models. Further, the techniques allow for benchmarks to be computed in real-time or near real-time as methodologies change without requiring tests to be run according to the new methodologies.