公开(公告)号：US20240362941A1

公开(公告)日：2024-10-31

申请号：US18140143

申请日：2023-04-27

Applicant: Adobe Inc.

Inventor： Silky Singh ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain ,  Abhinav Java

IPC: G06V30/262 ,  G06V30/14 ,  G06V30/19 ,  G06V30/414

CPC classification number: G06V30/274 ,  G06V30/1444 ,  G06V30/19147 ,  G06V30/414

Abstract: A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.

公开(公告)号：US20240355020A1

公开(公告)日：2024-10-24

申请号：US18304534

申请日：2023-04-21

Applicant: Adobe Inc.

Inventor： Yaman Kumar ,  Somesh Singh ,  Seoyoung Park ,  Pranjal Prasoon ,  Nithyakala Sainath ,  Nisarg Shailesh Joshi ,  Nikitha Srikanth ,  Nikaash Puri ,  Milan Aggarwal ,  Jayakumar Subramanian ,  Ganesh Palwe ,  Balaji Krishnamurthy ,  Matthew William Rozen ,  Mihir Naware ,  Hyman Chung

IPC: G06T11/60 ,  G06N20/20

CPC classification number: G06T11/60 ,  G06N20/20

Abstract: In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

公开(公告)号：US20240330351A1

公开(公告)日：2024-10-03

申请号：US18190686

申请日：2023-03-27

Applicant: Adobe Inc.

Inventor： Abhinav Java ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain

IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

公开(公告)号：US12223002B2

公开(公告)日：2025-02-11

申请号：US17454445

申请日：2021-11-10

Applicant: ADOBE INC.

Inventor： Pinkesh Badjatiya ,  Tanay Anand ,  Simra Shahid ,  Nikaash Puri ,  Milan Aggarwal ,  S Sejal Naidu ,  Sharat Chandra Racha

IPC: G06F16/9536 ,  G06F16/9538 ,  G06F40/20

Abstract: A method of finding online relevant conversing posts, comprises receiving, by a web server serving an online forum, a query post from an inquirer using the online forum, computing a contextual similarity score between each conversing post of a set of conversing posts with a query post, wherein the contextual similarity score is computed between the body of each of conversing posts and of the query post, wherein N1 conversing posts with a highest contextual similarity score are selected; computing a fine grained similarity score between the subject of the query post and of each of the N1 conversing posts, wherein N2 conversing posts with a highest fine grained similarity score are selected; and boosting the fine grained similarity score of the N2 conversing posts based on relevance metrics, wherein N3 highest ranked conversing posts are selected as a list of conversing posts most relevant to the query post.

公开(公告)号：US12124497B1

公开(公告)日：2024-10-22

申请号：US18190686

申请日：2023-03-27

Applicant: Adobe Inc.

Inventor： Abhinav Java ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain

IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

公开(公告)号：US12086728B2

公开(公告)日：2024-09-10

申请号：US18135948

申请日：2023-04-18

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06N5/04 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06V10/82

CPC classification number: G06N5/04 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06V10/82

Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.

公开(公告)号：US11997056B2

公开(公告)日：2024-05-28

申请号：US17897419

申请日：2022-08-29

Applicant: ADOBE INC.

Inventor： Sumit Bhatia ,  Jivat Neet Kaur ,  Rachit Bansal ,  Milan Aggarwal ,  Balaji Krishnamurthy

IPC: H04L51/02 ,  G06F40/295 ,  G06N5/022

CPC classification number: H04L51/02 ,  G06F40/295 ,  G06N5/022

Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

公开(公告)号：US20230169271A1

公开(公告)日：2023-06-01

申请号：US17644856

申请日：2021-12-17

Applicant: ADOBE INC.

Inventor： Shashank Shailabh ,  Madhur Panwar ,  Milan Aggarwal ,  Pinkesh Badjatiya ,  Simra Shahid ,  Nikaash Puri ,  S Sejal Naidu ,  Sharat Chandra Racha ,  Balaji Krishnamurthy ,  Ganesh Karbhari Palwe

IPC: G06F40/289 ,  G06F40/40 ,  G06F40/30

CPC classification number: G06F40/289 ,  G06F40/40 ,  G06F40/30

Abstract: Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.

公开(公告)号：US20230154186A1

公开(公告)日：2023-05-18

申请号：US17455126

申请日：2021-11-16

Applicant: ADOBE INC.

Inventor： Sumegh Roychowdhury ,  Sumedh A. Sontakke ,  Mausoom Sarkar ,  Nikaash Puri ,  Pinkesh Badjatiya ,  Milan Aggarwal

IPC: G06K9/00 ,  G06N3/08

CPC classification number: G06K9/00718 ,  G06K9/00751 ,  G06N3/088 ,  G06K2009/00738

Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

公开(公告)号：US20230153534A1

公开(公告)日：2023-05-18

申请号：US17526824

申请日：2021-11-15

Applicant: ADOBE INC.

Inventor： Rachit Bansal ,  Milan Aggarwal ,  Sumit Bhatia ,  Jivat Neet Kaur ,  Balaji Krishnamurthy

IPC: G06F40/295 ,  G06F16/332 ,  G06N20/00

CPC classification number: G06F40/295 ,  G06F16/3329 ,  G06N20/00

Abstract: Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.