Abstract: 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.

Abstract: 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.

Abstract: Techniques are described herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience (UX) tests. In some embodiments, a system identifies a set of expectation elements associated with one or more UX tests. An expectation element may specify, using unstructured data that does not conform to a schema, an expectation for a user experience and a respective outcome for the user experience. A themer model may generate predictions that map the respective expectation elements to a theme from a theme schema, which may include a plurality of themes. A selector model may generate selection scores for the expectation elements. The predicted themes and selection scores may be used to render user interfaces and/or trigger other actions directed to optimizing a product's design.

Abstract: Provided herein, in some embodiments, are nucleic acid vectors, such as recombinant viral genomes, comprising an inhibitory oligonucleotide that reduces inflammation for use, for example, in gene therapy.