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: Data mapping alternatives for creating a visual representation of a set of data are presented. A set of data is identified for analysis. The identified set of data and properties associated with the identified set of data are analyzed. Based on the analysis, data mapping alternatives for the identified set of data are determined. The determined data mapping alternatives for the identified set of data are ranked. The determined data mapping alternatives are presented in an order according to the ranking of the determined data mapping alternatives.

Abstract: Upon receiving an input comprising an area of a user interface, a user interface element associated with the area of the user interface may be identified and a polygon-based representation of the at least one user interface element may be created. If the input is determined to comprise a selection of the user interface element according to the polygon-based representation, an operation associated with the user interface element may be performed.

Abstract: Data mapping alternatives for creating a visual representation of a set of data are presented. A set of data is identified for analysis. The identified set of data and properties associated with the identified set of data are analyzed. Based on the analysis, data mapping alternatives for the identified set of data are determined The determined data mapping alternatives for the identified set of data are ranked. The determined data mapping alternatives are presented in an order according to the ranking of the determined data mapping alternatives.

Abstract: Data mapping alternatives for creating a visual representation of a set of data are presented. A set of data is identified for analysis. The identified set of data and properties associated with the identified set of data are analyzed. Based on the analysis, data mapping alternatives for the identified set of data are determined. The determined data mapping alternatives for the identified set of data are ranked. The determined data mapping alternatives are presented in an order according to the ranking of the determined data mapping alternatives.

Abstract: Upon receiving an input comprising an area of a user interface, a user interface element associated with the area of the user interface may be identified and a polygon-based representation of the at least one user interface element may be created. If the input is determined to comprise a selection of the user interface element according to the polygon-based representation, an operation associated with the user interface element may be performed.

Abstract: Data mapping alternatives for creating a visual representation of a set of data are presented. A set of data is identified for analysis. The identified set of data and properties associated with the identified set of data are analyzed. Based on the analysis, data mapping alternatives for the identified set of data are determined. The determined data mapping alternatives for the identified set of data are ranked. The determined data mapping alternatives are presented in an order according to the ranking of the determined data mapping alternatives.

Abstract: Chart recommendations may be provided. First, a summary of a dataset may be determined and each column and row in the dataset, based on the summary, may be classified into classifications. Next, based upon the classifications of each column and row in the dataset, the dataset may be mapped to a plurality of chart types. Each of the plurality of chart types may then be ranked.