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.

公开(公告)号：US11475295B2

公开(公告)日：2022-10-18

申请号：US16394227

申请日：2019-04-25

Applicant: Adobe Inc.

Inventor： Fan Du ,  Eunyee Koh ,  Sungchul Kim ,  Shunan Guo ,  Sana Malik Lee

IPC: G06N3/04 ,  G06N3/08 ,  G06N5/02 ,  G06N7/00 ,  G06N20/10

Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

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：US11157680B1

公开(公告)日：2021-10-26

申请号：US17183055

申请日：2021-02-23

Applicant: Adobe Inc.

Inventor： Tak Yeon Lee ,  Sana Malik Lee ,  Ryan A. Rossi ,  Qisheng Li ,  Fan Du ,  Eunyee Koh

IPC: G06F40/14 ,  G06F40/197 ,  G06F40/186 ,  G06F3/0482

Abstract: In implementations of systems for suggesting content components, a computing device implements a design system to receive input data describing a feature of a content component to be included in a hypertext markup language (HTML) document. The design system represents that feature of the content component as a document object model (DOM) element and determines a hash value for the DOM element using locality-sensitive hashing. Manhattan distances are computed between the has value and has values described by a segment of content component data. The hash values were determined using the locality-sensitive hashing for DOM elements extracted from a corpus of HTML documents. The design system generates indications, for display in a user interface, of candidate content components for inclusion in the HTML document based on the Manhattan distances.

公开(公告)号：US20210319333A1

公开(公告)日：2021-10-14

申请号：US16844006

申请日：2020-04-09

Applicant: Adobe Inc.

Inventor： Sana Lee ,  Po Ming Law ,  Moumita Sinha ,  Fan Du

IPC: G06N5/04 ,  G06N20/00

Abstract: This disclosure involves detecting biases in predictive models and the root cause of those biases. For example, a processing device receives test data and training data from a client device. The processing device identifies feature groups from the training data and the test data generates performance metrics and baseline metrics for a feature group. The processing device detects biases through a comparison of the performance metrics and the baseline metrics the feature group. The processing device then isolates a portion of the training data that corresponds to the detected bias. The processing device generates a model correction usable to remove the bias from the predictive model.

公开(公告)号：US20200342305A1

公开(公告)日：2020-10-29

申请号：US16394227

申请日：2019-04-25

Applicant: Adobe Inc.

Inventor： Fan Du ,  Eunyee Koh ,  Sungchul Kim ,  Shunan Guo ,  Sana Malik Lee

IPC: G06N3/08 ,  G06N5/02 ,  G06N20/10 ,  G06N7/00

Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

公开(公告)号：US12182493B2

公开(公告)日：2024-12-31

申请号：US18484674

申请日：2023-10-11

Applicant: Adobe Inc.

Inventor： Md Main Uddin Rony ,  Fan Du ,  Iftikhar Ahamath Burhanuddin ,  Ryan Rossi ,  Niyati Himanshu Chhaya ,  Eunyee Koh

IPC: G06F17/00 ,  G06F40/106 ,  G06F40/40

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

公开(公告)号：US12093322B2

公开(公告)日：2024-09-17

申请号：US17654933

申请日：2022-03-15

Applicant: Adobe Inc.

Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

IPC: G06F16/904 ,  G06N3/02

CPC classification number: G06F16/904 ,  G06N3/02

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

公开(公告)号：US20240160890A1

公开(公告)日：2024-05-16

申请号：US18052463

申请日：2022-11-03

Applicant: ADOBE INC.

Inventor： Namyong Park ,  Ryan A. Rossi ,  Eunyee Koh ,  Iftikhar Ahamath Burhanuddin ,  Sungchul Kim ,  Fan Du

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: Systems and methods for contrastive graphing are provided. One aspect of the systems and methods includes receiving a graph including a node; generating a node embedding for the node based on the graph using a graph neural network (GNN); computing a contrastive learning loss based on the node embedding; and updating parameters of the GNN based on the contrastive learning loss.