Abstract: Techniques are described for producing machine-generating findings associated with user experiences with products and/or services. In some embodiments, a finding generator receives a set of user experience test results, generates a set of permutations of user attribute values, and, for each permutation, determines distributions of quantitative values that measure one or more facets of the user experience with a product or service for users that have the user attribute values and users that do not. Based on a comparison of the distributions, the finding generator identifies a subset of permutations to retain, generates segments of user experience test results based on the permutations, and generates findings summaries based on the results included in each segment. The findings may be presented to an analyst and or consumed by downstream application to perform actions directed at improving the design of the product or service.

Abstract: 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.

Abstract: 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.