公开(公告)号：US20250013831A1

公开(公告)日：2025-01-09

申请号：US18493465

申请日：2023-10-24

Applicant: Adobe Inc. ,  University of Maryland

Inventor： Puneet Mathur ,  Vlad Morariu ,  Verena Kaynig-Fittkau ,  Jiuxiang Gu ,  Franck Dernoncourt ,  Quan Tran ,  Ani Nenkova ,  Dinesh Manocha ,  Rajiv Jain

IPC: G06F40/30

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a temporal dependency graph. For example, the disclosed systems generate from a text document, a structural vector, a syntactic vector, and a semantic vector. In some embodiments, the disclosed systems generate a multi-dimensional vector by combining the various vectors. In these or other embodiments, the disclosed systems generate an initial dependency graph structure and an adjacency matrix utilizing an iterative deep graph learning model. Further, in some embodiments, the disclosed systems generate an entity-level relation matrix utilizing a convolutional graph neural network. Moreover, in some embodiments, the disclosed systems generate a temporal dependency graph from the entity-level relation matrix and the adjacency matrix.

公开(公告)号：US11886815B2

公开(公告)日：2024-01-30

申请号：US17333892

申请日：2021-05-28

Applicant: Adobe Inc.

Inventor： Jiuxiang Gu ,  Vlad Morariu ,  Varun Manjunatha ,  Tong Sun ,  Rajiv Jain ,  Peizhao Li ,  Jason Kuen ,  Handong Zhao

IPC: G06F40/279 ,  G06F40/205 ,  G06F16/93 ,  G06F40/30 ,  G06N3/088 ,  G06N3/045

CPC classification number: G06F40/279 ,  G06F16/93 ,  G06F40/205 ,  G06F40/30 ,  G06N3/045 ,  G06N3/088

Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.

公开(公告)号：US11816243B2

公开(公告)日：2023-11-14

申请号：US17397407

申请日：2021-08-09

Applicant: Adobe Inc.

Inventor： Thi Kim Phung Lai ,  Tong Sun ,  Rajiv Jain ,  Nikolaos Barmpalios ,  Jiuxiang Gu ,  Franck Dernoncourt

IPC: G06F21/62 ,  G06N20/00 ,  G06F40/295

CPC classification number: G06F21/6245 ,  G06F40/295 ,  G06N20/00

Abstract: Systems, methods, and non-transitory computer-readable media can generate a natural language model that provides user-entity differential privacy. For example, in one or more embodiments, a system samples sensitive data points from a natural language dataset. Using the sampled sensitive data points, the system determines gradient values corresponding to the natural language model. Further, the system generates noise for the natural language model. The system generates parameters for the natural language model using the gradient values and the noise, facilitating simultaneous protection of the users and sensitive entities associated with the natural language dataset. In some implementations, the system generates the natural language model through an iterative process (e.g., by iteratively modifying the parameters).

公开(公告)号：US11783008B2

公开(公告)日：2023-10-10

申请号：US17091403

申请日：2020-11-06

Applicant: Adobe Inc.

Inventor： Rajiv Jain ,  Varun Manjunatha ,  Joseph Barrow ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Sasha Spala ,  Nicholas Miller

IPC: G06F18/214 ,  G06F40/30 ,  G06F40/117 ,  G06V30/413 ,  G06F18/21 ,  G06F18/2415 ,  G06F16/33

CPC classification number: G06F18/2148 ,  G06F18/217 ,  G06F18/2415 ,  G06F40/117 ,  G06F40/30 ,  G06V30/413 ,  G06F16/33 ,  G06V2201/10

Abstract: Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.

公开(公告)号：US20220318505A1

公开(公告)日：2022-10-06

申请号：US17223166

申请日：2021-04-06

Applicant: ADOBE INC.

Inventor： Amir Pouran Ben Veyseh ,  Franck Dernoncourt ,  Quan Tran ,  Varun Manjunatha ,  Lidan Wang ,  Rajiv Jain ,  Doo Soon Kim ,  Walter Chang

IPC: G06F40/284 ,  G06F40/211 ,  G06F40/30 ,  G06F40/126 ,  G06N3/04 ,  G06N3/08

Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a document comprising a plurality of words organized into a plurality of sentences, the words comprising an event trigger word and an argument candidate word, generate word representation vectors for the words, generate a plurality of document structures including a semantic structure for the document based on the word representation vectors, a syntax structure representing dependency relationships between the words, and a discourse structure representing discourse information of the document based on the plurality of sentences, generate a relationship representation vector based on the document structures, and predict a relationship between the event trigger word and the argument candidate word based on the relationship representation vector.

公开(公告)号：US11416672B2

公开(公告)日：2022-08-16

申请号：US17102675

申请日：2020-11-24

Applicant: Adobe Inc.

Inventor： Vlad Morariu ,  Rajiv Jain ,  Nishant Sankaran

IPC: G06F40/117 ,  G06N5/04 ,  G06K9/62 ,  G06V30/416

Abstract: Certain embodiments involve transforming an electronic document into a tagged electronic document. For instance, an electronic document processing application generates a tagged electronic document from an input electronic document. The electronic document processing application accesses one or more feature maps that identify, via a set of object-recognition rules, identified objects in the electronic document. The electronic document processing application also obtains a heat map of the electronic document that represents attributes in a pixel-wise manner. The electronic document processing application computes a tag by applying a fusion deep learning model to the one or more feature maps and the heat map. The electronic document processing application generates the tagged electronic document by applying the tag to the electronic document.

公开(公告)号：US20210166013A1

公开(公告)日：2021-06-03

申请号：US16701586

申请日：2019-12-03

Applicant: ADOBE INC.

Inventor： Christopher Alan Tensmeyer ,  Rajiv Jain ,  Curtis Michael Wigington ,  Brian Lynn Price ,  Brian Lafayette Davis

IPC: G06K9/00 ,  G06F3/0488 ,  G06N3/04 ,  G06N3/08 ,  G06K9/22

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

公开(公告)号：US20210103695A1

公开(公告)日：2021-04-08

申请号：US17102675

申请日：2020-11-24

Applicant: Adobe Inc.

Inventor： Vlad Morariu ,  Rajiv Jain ,  Nishant Sankaran

IPC: G06F40/117 ,  G06N5/04 ,  G06K9/62 ,  G06K9/00

Abstract: Certain embodiments involve transforming an electronic document into a tagged electronic document. For instance, an electronic document processing application generates a tagged electronic document from an input electronic document. The electronic document processing application accesses one or more feature maps that identify, via a set of object-recognition rules, identified objects in the electronic document. The electronic document processing application also obtains a heat map of the electronic document that represents attributes in a pixel-wise manner. The electronic document processing application computes a tag by applying a fusion deep learning model to the one or more feature maps and the heat map. The electronic document processing application generates the tagged electronic document by applying the tag to the electronic document.