Adobe, Inc.

Nathan Carl Ross

Eunyee Koh

Ryan A Rossi

Fan Du

Vasanthi Holtcamp

Sana Malik Lee

Tak Yeon Lee

John Anderson

Chen Chen

Sungchul Kim

Shunan Guo

Jane Elizabeth Hoffswell

Xin Qian

Victor Soares Bursztyn

Yeuk Yin Chan

Yeuk-yin Chan

The present disclosure relates to generating hierarchical queries from text queries. A query generation system is configured to encode a text query to obtain a text embedding. Then, the system may select a field of a data schema by comparing the text embedding to a field embedding corresponding to the field. Subsequently, the system may generate a hierarchical query including a value corresponding to the selected field. Some implementations of the system may further include one or more formatting models configured to format values included in the text query.

Generating hierarchical queries from natural language

Systems and methods for data analysis are described. Embodiments of the present disclosure data analysis include displaying, via a data analysis interface, a data visualization in a first region of the data analysis interface; and displaying, via the data analysis interface, an analysis thread visualization in a second region of the data analysis interface. The analysis thread visualization depicts an analysis thread graph including a first node corresponding to the data visualization and an edge corresponding to an analysis path between the first node and a second node.

Generating data insights

Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

Personalized visualization recommendation system

This disclosure involves generating, from a user data set, a ranked list of recommended secondary variables in a user interface field similar to primary variable selected in another user interface field. A system receives a data set having variables and corresponding sets of values. The data visualization system determines a feature vector for each variable based on statistics of a corresponding values set. The system generates a variable similarity graph having nodes representing variables and links representing degrees of similarity between feature vectors of variables. The system receives a selection of a first variable via a first field of the user interface, detects a selection of a second field, and identifies a relationship between the first field and the second field. The system generates a contextual menu of recommended secondary variables for use with the selected first variable based on similarity value of the links in the variable similarity graph.

Graph-based configuration of user interface for selection of features in visualization applications

In some embodiments, a processor accesses electronic content that includes multiple selectable objects that are renderable in a graphical interface. The processor generates multiple selection areas respectively associated with the selectable objects. An input to the graphical interface received within each selection area selects an associated selectable object. Generating the selection areas includes generating a boundary around at least one of the selectable objects. Any point within the boundary is closer to the associated selectable object than any other selectable object. Generating the selection areas also includes clipping the boundary to define the selection area for the selectable object. The processor adds the selection areas to a document object model associated with the electronic content. The document object model is usable for rendering the graphical interface with the selectable objects and identifying the selection areas.

Providing selection areas for selectable objects in graphical interface

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Highland, UT, United States of America

Nathan Carl Ross