公开(公告)号：US12147499B2

公开(公告)日：2024-11-19

申请号：US18242075

申请日：2023-09-05

Applicant: Adobe Inc.

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

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

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：US20230409672A1

公开(公告)日：2023-12-21

申请号：US18242075

申请日：2023-09-05

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

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

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：US20210357644A1

公开(公告)日：2021-11-18

申请号：US16876540

申请日：2020-05-18

Applicant: Adobe Inc.

Inventor： Rajiv Bhawanji Jain ,  Vlad Ion Morariu ,  Vitali Petsiuk ,  Varun Manjunatha ,  Ashutosh Mehra ,  Vicente Ignacio Ordonez Roman

IPC: G06K9/00 ,  G06K9/62 ,  G06T7/70 ,  G06T11/00 ,  G06K9/46

Abstract: Introduced here are computer programs and associated computer-implemented techniques for creating visualizations to explain the outputs produced by models designed for object detection. To accomplish this, a graphics editing platform can obtain a reference output that identifies a region of pixels in a digital image that allegedly contains an object. Then, the graphics editing platform can compute the similarity between the reference output and a series of outputs generated by a model upon being applied to masked versions of the digital image. A visualization component can be produced based on the similarity.

公开(公告)号：US11978272B2

公开(公告)日：2024-05-07

申请号：US17883811

申请日：2022-08-09

Applicant: Adobe Inc.

Inventor： Kai Li ,  Christopher Alan Tensmeyer ,  Curtis Michael Wigington ,  Handong Zhao ,  Nikolaos Barmpalios ,  Tong Sun ,  Varun Manjunatha ,  Vlad Ion Morariu

IPC: G06V30/413 ,  G06F17/18 ,  G06F18/213 ,  G06F18/2415 ,  G06N3/047 ,  G06N3/084 ,  G06N20/00 ,  G06N20/10 ,  G06V10/25 ,  G06V10/82 ,  G06V20/20 ,  G06V30/19 ,  G06V30/414

CPC classification number: G06V30/413 ,  G06F17/18 ,  G06F18/213 ,  G06F18/2415 ,  G06N3/047 ,  G06N3/084 ,  G06N20/00 ,  G06N20/10 ,  G06V10/25 ,  G06V10/82 ,  G06V20/20 ,  G06V30/19173 ,  G06V30/414

Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.

公开(公告)号：US11922110B2

公开(公告)日：2024-03-05

申请号：US17535067

申请日：2021-11-24

Applicant: Adobe Inc.

Inventor： Vlad Ion Morariu ,  Yuexi Chen ,  Christopher Alan Tensmeyer ,  Zhicheng Liu ,  Lars Niklas Emanuel Elmqvist

IPC: G06F40/00 ,  G06F40/106 ,  G06F40/117 ,  G06F40/166

CPC classification number: G06F40/106 ,  G06F40/117 ,  G06F40/166

Abstract: Systems and techniques for generating responsive documents are described. Digital content is organized into a structure that defines how content is presented when a document is displayed by a computing device. To generate the responsive document, relationships are defined among different digital content objects, such as groups of content objects to be presented together and content objects that are to be presented as alternatives of one another. Responsive patterns are assigned to grouped content objects, where each responsive pattern defines different layout configurations for displaying grouped content objects based on computing device display characteristics. In some implementations, multiple responsive patterns are assigned to a single content group and individual responsive patterns are associated with activation ranges for display characteristics that activate the responsive pattern. For groups of digital content objects that are assigned multiple responsive patterns, responsive patterns are prioritized to create a hierarchy dictating display of the responsive document.

公开(公告)号：US11880648B2

公开(公告)日：2024-01-23

申请号：US17456143

申请日：2021-11-22

Applicant: ADOBE INC.

Inventor： Aparna Garimella ,  Sumit Shekhar ,  Bhanu Prakash Reddy Guda ,  Vinay Aggarwal ,  Vlad Ion Morariu ,  Ashutosh Mehra

IPC: G06F40/174 ,  G06F40/30 ,  G06F40/284 ,  G06N3/045

CPC classification number: G06F40/174 ,  G06F40/284 ,  G06F40/30 ,  G06N3/045

Abstract: Embodiments provide systems, methods, and computer storage media for extracting semantic labels for field widgets of form fields in unfilled forms. In some embodiments, a processing device accesses a representation of a fillable widget of a form field of an unfilled form. The processing device generates an encoded input representing text and layout of a sequence of tokens in a neighborhood of the fillable widget. The processing device uses a machine learning model to extract a semantic label representing a field type of the fillable widget in view of the encoded input. The processing device causes execution of an action using the semantic label.

公开(公告)号：US20230376687A1

公开(公告)日：2023-11-23

申请号：US17746779

申请日：2022-05-17

Applicant: ADOBE INC.

Inventor： Vlad Ion Morariu ,  Tong Sun ,  Nikolaos Barmpalios ,  Zilong Wang ,  Jiuxiang Gu ,  Ani Nenkova Nenkova ,  Christopher Tensmeyer

IPC: G06F40/279 ,  G06N5/02

CPC classification number: G06F40/279 ,  G06N5/022

Abstract: Embodiments are provided for facilitating multimodal extraction across multiple granularities. In one implementation, a set of features of a document for a plurality of granularities of the document is obtained. Via a machine learning model, the set of features of the document are modified to generate a set of modified features using a set of self-attention values to determine relationships within a first type of feature and a set of cross-attention values to determine relationships between the first type of feature and a second type of feature. Thereafter, the set of modified features are provided to a second machine learning model to perform a classification task.

公开(公告)号：US20230033114A1

公开(公告)日：2023-02-02

申请号：US17384136

申请日：2021-07-23

Applicant: ADOBE INC.

Inventor： Joseph Barrow ,  Rajiv Bhawanji Jain ,  Nedim Lipka ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Varun Manjunatha

IPC: G06F16/35 ,  G06F40/169 ,  G06F40/289 ,  G06N3/04

Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.

公开(公告)号：US11443193B2

公开(公告)日：2022-09-13

申请号：US16865605

申请日：2020-05-04

Applicant: Adobe Inc.

Inventor： Kai Li ,  Christopher Alan Tensmeyer ,  Curtis Michael Wigington ,  Handong Zhao ,  Nikolaos Barmpalios ,  Tong Sun ,  Varun Manjunatha ,  Vlad Ion Morariu

IPC: G06K9/00 ,  G06N3/08 ,  G06N20/10 ,  G06K9/62 ,  G06F17/18 ,  G06V10/75 ,  G06V20/20 ,  G06V30/413 ,  G06V30/414

Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.

公开(公告)号：US12038962B2

公开(公告)日：2024-07-16

申请号：US17384136

申请日：2021-07-23

Applicant: ADOBE INC.

Inventor： Joseph Barrow ,  Rajiv Bhawanji Jain ,  Nedim Lipka ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Varun Manjunatha

IPC: G06F16/35 ,  G06F40/169 ,  G06F40/289

CPC classification number: G06F16/358 ,  G06F40/169 ,  G06F40/289

Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.