公开(公告)号：US20240056309A1

公开(公告)日：2024-02-15

申请号：US17819540

申请日：2022-08-12

Applicant: Adobe Inc.

Inventor： Songlin He ,  Tong Sun ,  Nedim Lipka ,  Curtis Wigington ,  Rajiv Jain ,  Anindo Roy

IPC: H04L9/32 ,  G06F21/31 ,  G06F40/174

CPC classification number: H04L9/3247 ,  G06F21/31 ,  G06F40/174

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that fill in digital documents using user identity models of client devices. For instance, in one or more embodiments, the disclosed systems receive a digital document comprising a digital fillable field. The disclosed systems further retrieve, for a client device associated with the digital document, a decentralized identity credential comprising a user attribute established under a decentralized identity framework. Using the user attribute of the decentralized identity credential, the disclosed systems modify the digital document by filling in the digital fillable field.

公开(公告)号：US20220148326A1

公开(公告)日：2022-05-12

申请号：US17648718

申请日：2022-01-24

Applicant: Adobe Inc.

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

IPC: G06V30/32 ,  G06F3/04883 ,  G06N3/04 ,  G06N3/08 ,  G06V30/228 ,  G06V30/226

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：US20240419921A1

公开(公告)日：2024-12-19

申请号：US18336380

申请日：2023-06-16

Applicant: Adobe Inc.

Inventor： Joseph Barrow ,  Jennifer Healey ,  Franck Dernoncourt ,  Ani Nenkova ,  Vlad Morariu ,  Rajiv Jain ,  Nedim Lipka

IPC: G06F40/40 ,  G06F40/205 ,  G06V30/19

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract viewpoints from content for syntopical reading using an efficient claim-relation graph construction approach. For example, the disclosed systems utilize sentence transformers with claims from content to embed the claims within a metric space (as claim nodes). Furthermore, in some embodiments, the disclosed systems generate a claim relation graph for the claims by utilizing approximate nearest neighbor searches to determine relational edges between a claim node and the claim node's approximate nearest neighbors. Moreover, in some implementations, the disclosed systems utilize the claim relation graph with an edge weighted graph neural network to determine stance labels during extraction of viewpoints (e.g., stance, aspect, and topic) for the claims. Additionally, in one or more instances, the disclosed systems utilize the extracted viewpoints in content retrieval applications (e.g., viewpoint ranked search results and/or socially contextualized claims).

公开(公告)号：US20240386621A1

公开(公告)日：2024-11-21

申请号：US18318921

申请日：2023-05-17

Applicant: Adobe Inc.

Inventor： Ruiyi Zhang ,  Yufan Zhou ,  Tong Yu ,  Tong Sun ,  Rajiv Jain ,  Jiuxiang Gu ,  Christopher Alan Tensmeyer

IPC: G06T11/00 ,  G06F40/40 ,  G06V10/74 ,  G06V10/774 ,  G06V10/82

Abstract: Techniques and systems for training and/or implementing a text-to-image generation model are provided. A pre-trained multimodal model is leveraged for avoiding slower and more labor-intensive methodologies for training a text-to-image generation model. Accordingly, images without associated text (i.e., bare images) are provided to the pre-trained multimodal model so that it can produce generated text-image pairs. The generated text-image pairs are provided to the text-to-image generation model for training and/or implementing the text-to-image generation model.

公开(公告)号：US20230059367A1

公开(公告)日：2023-02-23

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 ,  G06F40/295 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a natural language model that provides user-entity differential privacy. For example, in one or more embodiments, the disclosed systems sample sensitive data points from a natural language dataset. Using the sampled sensitive data points, the disclosed systems determine gradient values corresponding to the natural language model. Further, the disclosed systems generate noise for the natural language model. The disclosed systems generate 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 disclosed systems generate the natural language model through an iterative process (e.g., by iteratively modifying the parameters).

公开(公告)号：US20220067449A1

公开(公告)日：2022-03-03

申请号：US17003149

申请日：2020-08-26

Applicant: Adobe Inc.

Inventor： Pramuditha Perera ,  Vlad Morariu ,  Rajiv Jain ,  Varun Manjunatha ,  Curtis Wigington

IPC: G06K9/62 ,  G06F11/32 ,  G06N3/04

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for classifying an input image utilizing a classification model conditioned by a generative model and/or self-supervision. For example, the disclosed systems can utilize a generative model to generate a reconstructed image from an input image to be classified. In turn, the disclosed systems can combine the reconstructed image with the input image itself. Using the combination of the input image and the reconstructed image, the disclosed systems utilize a classification model to determine a classification for the input image. Furthermore, the disclosed systems can employ self-supervised learning to cause the classification model to learn discriminative features for better classifying images of both known classes and open-set categories.

公开(公告)号：US11250252B2

公开(公告)日：2022-02-15

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 ,  G06K9/22 ,  G06N3/08

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：US11899927B2

公开(公告)日：2024-02-13

申请号：US17648718

申请日：2022-01-24

Applicant: Adobe Inc.

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

IPC: G06K9/00 ,  G06F3/04883 ,  G06N3/08 ,  G06V30/32 ,  G06V30/228 ,  G06V30/226 ,  G06N3/045 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06F3/04883 ,  G06N3/045 ,  G06N3/08 ,  G06V10/454 ,  G06V10/82 ,  G06V30/228 ,  G06V30/2264 ,  G06V30/2276 ,  G06V30/347

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：US11893345B2

公开(公告)日：2024-02-06

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 ,  G06N3/08 ,  G06F40/126 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06F40/284 ,  G06F40/126 ,  G06F40/211 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  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：US11709915B2

公开(公告)日：2023-07-25

申请号：US17003149

申请日：2020-08-26

Applicant: Adobe Inc.

Inventor： Pramuditha Perera ,  Vlad Morariu ,  Rajiv Jain ,  Varun Manjunatha ,  Curtis Wigington

IPC: G06F18/21 ,  G06F18/214 ,  G06F18/241 ,  G06F11/32 ,  G06N3/045

CPC classification number: G06F18/2185 ,  G06F11/327 ,  G06F18/214 ,  G06F18/241 ,  G06N3/045

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for classifying an input image utilizing a classification model conditioned by a generative model and/or self-supervision. For example, the disclosed systems can utilize a generative model to generate a reconstructed image from an input image to be classified. In turn, the disclosed systems can combine the reconstructed image with the input image itself. Using the combination of the input image and the reconstructed image, the disclosed systems utilize a classification model to determine a classification for the input image. Furthermore, the disclosed systems can employ self-supervised learning to cause the classification model to learn discriminative features for better classifying images of both known classes and open-set categories.