公开(公告)号：US20220147540A1

公开(公告)日：2022-05-12

申请号：US17091941

申请日：2020-11-06

Applicant: ADOBE INC.

Inventor： RYAN Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh ,  Xin Qian

IPC: G06F16/26 ,  G06F11/30 ,  G06F11/34 ,  G06F3/0482

Abstract: 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.

公开(公告)号：US11720590B2

公开(公告)日：2023-08-08

申请号：US17091941

申请日：2020-11-06

Applicant: ADOBE INC.

Inventor： Ryan Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh ,  Xin Qian

IPC: G06F16/9535 ,  G06F16/26 ,  G06F3/0482 ,  G06F11/34 ,  G06F11/30 ,  G06F16/9038

CPC classification number: G06F16/26 ,  G06F3/0482 ,  G06F11/302 ,  G06F11/3438 ,  G06F16/9038

Abstract: 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.

公开(公告)号：US11288541B1

公开(公告)日：2022-03-29

申请号：US17015495

申请日：2020-09-09

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh

IPC: G06K9/62 ,  G06F17/18 ,  G06F3/0482

Abstract: 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.

公开(公告)号：US20250124023A1

公开(公告)日：2025-04-17

申请号：US18486603

申请日：2023-10-13

Applicant: ADOBE INC.

Inventor： Yeuk-Yin Chan ,  Victor S. Bursztyn ,  Eunyee Koh ,  Nathan Ross ,  Vasanthi Holtcamp

IPC: G06F16/242

Abstract: Systems and methods for generating hierarchical queries from text queries are described. Embodiments are configured to encode a text query to obtain a text embedding. Then, embodiments select a field of a data schema by comparing the text embedding to a field embedding corresponding to the field. Subsequently, embodiments generate a hierarchical query including a value corresponding to the selected field. Some embodiments further include one or more formatting models configured to format values included in the text query.

公开(公告)号：US20220076048A1

公开(公告)日：2022-03-10

申请号：US17015495

申请日：2020-09-09

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh

IPC: G06K9/62 ,  G06F3/0482 ,  G06F17/18

Abstract: 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.