公开(公告)号：US20240403313A1

公开(公告)日：2024-12-05

申请号：US18328980

申请日：2023-06-05

Applicant: ADOBE INC.

Inventor： Chen Chen ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Fan Du ,  Nathan Carl Ross ,  Ryan A. Rossi ,  Yeuk Yin Chan ,  Eunyee Koh

IPC: G06F16/26 ,  G06F16/248 ,  G06T11/20

Abstract: 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.

公开(公告)号：US20240163238A1

公开(公告)日：2024-05-16

申请号：US18055238

申请日：2022-11-14

Applicant: Adobe Inc.

Inventor： Yeuk-yin Chan ,  Andrew Thomson ,  Caroline Kim ,  Cole Connelly ,  Eunyee Koh ,  Michelle Lee ,  Shunan Guo

IPC: H04L51/07 ,  G06F3/04842

CPC classification number: H04L51/07 ,  G06F3/04842

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates editable email components by utilizing an Answer Set Programming (ASP) model with hard and soft constraints. For instance, in one or more embodiments, the disclosed systems generate editable email components from email fragments of an email file utilizing an Answer Set Programming (ASP) model. In particular, the disclosed systems extract facts for the ASP model from the email file. In addition, the disclosed systems determine rows or columns defining cells of the email file utilizing ASP hard constraints defined by a first set of ASP atoms corresponding to the facts. Moreover, the disclosed systems determine editable email component classes for the email fragments utilizing ASP soft constraints defined by ASP classification weights and a second set of ASP atoms corresponding to the facts.

公开(公告)号：US11860675B2

公开(公告)日：2024-01-02

申请号：US17373281

申请日：2021-07-12

Applicant: ADOBE INC.

Inventor： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC: G06F16/24 ,  G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

CPC classification number: G06F16/2465 ,  G06F16/215 ,  G06F16/26 ,  G06F16/288 ,  G06F16/9024 ,  G06F2216/03

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US11836172B2

公开(公告)日：2023-12-05

申请号：US17354954

申请日：2021-06-22

Applicant: ADOBE INC.

Inventor： Fan Du ,  Zening Qu ,  Vasanthi Swaminathan Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Saurabh Mahapatra ,  Sana Malik Lee ,  Ryan A. Rossi ,  Nikhil Belsare ,  Eunyee Koh ,  Andrew Thomson ,  Sumit Shekhar

IPC: G06F16/33 ,  G06N5/046 ,  G06F16/338

CPC classification number: G06F16/3344 ,  G06F16/338 ,  G06F16/3346 ,  G06N5/046

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

公开(公告)号：US20230386143A1

公开(公告)日：2023-11-30

申请号：US17664972

申请日：2022-05-25

Applicant: ADOBE INC.

Inventor： Chang Xiao ,  Ryan A. Rossi ,  Eunyee Koh

IPC: G06T19/00 ,  G06T7/00 ,  G06T7/73

CPC classification number: G06T19/006 ,  G06T7/97 ,  G06T7/73 ,  G06T2207/30204 ,  G06T2207/20224

Abstract: A system and methods for providing human-invisible AR markers is described. One aspect of the system and methods includes identifying AR metadata associated with an object in an image; generating AR marker image data based on the AR metadata; generating a first variant of the image by adding the AR marker image data to the image; generating a second variant of the image by subtracting the AR marker image data from the image; and displaying the first variant and the second variant of the image alternately at a display frequency to produce a display of the image, wherein the AR marker image data is invisible to a human vision system in the display of the image.

公开(公告)号：US20230306194A1

公开(公告)日：2023-09-28

申请号：US17656254

申请日：2022-03-24

Applicant: ADOBE INC.

Inventor： Fan Du ,  Cameron Elise Womack ,  Dylan Robert Kario ,  Molly Josette Bloom ,  Elizabeth Waters ,  Matthew Samuel Deutsch ,  Ryan Wilkes ,  Yeuk-Yin Chan ,  Eunyee Koh ,  Andrew Douglas Thomson ,  Cole Edward Connelly ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06F40/186 ,  G06F40/143 ,  G06F40/177 ,  G06K9/62 ,  G06N5/02 ,  G06N5/00

CPC classification number: G06F40/186 ,  G06F40/143 ,  G06F40/177 ,  G06K9/6218 ,  G06N5/025 ,  G06N5/003

Abstract: Systems and methods for data processing are described. Example embodiments include identifying chart data corresponding to a visual element of a user interface; selecting an insight type based on a chart category of the chart data; generating insight data for the insight type based on the chart data using a statistical measure corresponding to the insight type; generating an insight caption for the insight type by combining the insight data with a sentence template corresponding to the insight type; and communicating the insight caption to a user of the user interface.

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：US11621892B2

公开(公告)日：2023-04-04

申请号：US17095070

申请日：2020-11-11

Applicant: Adobe Inc.

Inventor： Sungchul Kim ,  Di Jin ,  Ryan A. Rossi ,  Eunyee Koh

IPC: H04L41/12 ,  H04L43/067 ,  G06F16/901 ,  H04L41/14 ,  H04L43/045

Abstract: Deriving network embeddings that represent attributes of, and relationships between, different nodes in a network while preserving network data temporal and structural properties is described. A network representation system generates a plurality of graph time-series representations of network data that each includes a subset of nodes and edges included in a time segment of the network data, constrained either by time or a number of edges included in the representation. A temporal graph of the network data is generated by implementing a temporal model that incorporates temporal dependencies into the graph time-series representations. From the temporal graph, network embeddings for the network data are derived, where the network embeddings capture temporal dependencies between nodes, as indicated by connecting edges, as well as temporal structural properties of the network data. Network embeddings represent network data in a low-dimensional latent space, which is useable to generate a prediction regarding the network data.

公开(公告)号：US20230021797A1

公开(公告)日：2023-01-26

申请号：US17383051

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Binit Kumar Sinha ,  Eunyee Koh ,  Fan Du ,  Gaurav Makkar ,  Silky Kedawat ,  Subrahmanya Kumar Giliyaru ,  Vasanthi Holtcamp ,  Nikhil Belsare

IPC: G06F16/33 ,  G06F40/40 ,  G06F16/338

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

公开(公告)号：US11562019B2

公开(公告)日：2023-01-24

申请号：US17161406

申请日：2021-01-28

Applicant: Adobe Inc.

Inventor： Shenyu Xu ,  Eunyee Koh ,  Fan Du ,  Tak Yeon Lee ,  Sana Malik Lee ,  Ryan Rossi

IPC: G06F16/30 ,  G06F16/738 ,  G06F16/901 ,  G06F16/783 ,  G06F16/34 ,  G06F16/9032 ,  G06F16/44

Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories. Based on the visual-data-story graph, the disclosed systems can select a relevant visual data story to display on a graphical user interface.