公开(公告)号：US20240161529A1

公开(公告)日：2024-05-16

申请号：US18055752

申请日：2022-11-15

Applicant: Adobe Inc.

Inventor： Vlad Morariu ,  Puneet Mathur ,  Rajiv Jain ,  Ashutosh Mehra ,  Jiuxiang Gu ,  Franck Dernoncourt ,  Anandhavelu N ,  Quan Tran ,  Verena Kaynig-Fittkau ,  Nedim Lipka ,  Ani Nenkova

IPC: G06V30/413 ,  G06V10/82

CPC classification number: G06V30/413 ,  G06V10/82

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.

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：US11995394B1

公开(公告)日：2024-05-28

申请号：US18165579

申请日：2023-02-07

Applicant: ADOBE INC.

Inventor： Vlad Ion Morariu ,  Puneet Mathur ,  Rajiv Bhawanji Jain ,  Jiuxiang Gu ,  Franck Dernoncourt

IPC: G06F40/166 ,  G06F3/16 ,  G06F40/284 ,  G06N20/00 ,  G10L15/22 ,  G10L15/26

CPC classification number: G06F40/166 ,  G06F3/167 ,  G06F40/284 ,  G06N20/00 ,  G10L15/22 ,  G10L15/26

Abstract: Systems and methods for document editing are provided. One aspect of the systems and methods includes obtaining a document and a natural language edit request. Another aspect of the systems and methods includes generating a structured edit command using a machine learning model based on the document and the natural language edit request. Yet another aspect of the systems and methods includes generating a modified document based on the document and the structured edit command, where the modified document includes a revision of the document that incorporates the natural language edit request.