公开(公告)号：US11868714B2

公开(公告)日：2024-01-09

申请号：US17682911

申请日：2022-02-28

Applicant: ADOBE INC.

Inventor： Natwar Modani ,  Muskan Agarwal ,  Vishesh Kaushik ,  Aparna Garimella ,  Akhash N A ,  Garvit Bhardwaj ,  Manoj Kilaru ,  Priyanshu Agarwal

IPC: G06F17/00 ,  G06F40/186 ,  G06F40/284

CPC classification number: G06F40/186 ,  G06F40/284

Abstract: Methods and systems are provided for facilitating generation of fillable document templates. In embodiments, a document having a plurality of tokens is obtained. Using a machine learned model, a token state is identified for each token of the plurality of tokens. Each token state indicates whether a corresponding token is a static token that is to be included in a fillable document template or a dynamic token that is to be excluded in the fillable document template. Thereafter, a fillable document template corresponding with the document is generated, wherein for each dynamic token of the document, the fillable document template includes a fillable field corresponding to the respective dynamic token.

公开(公告)号：US20230274084A1

公开(公告)日：2023-08-31

申请号：US17682911

申请日：2022-02-28

Applicant: ADOBE INC.

Inventor： Natwar Modani ,  Muskan Agarwal ,  Vishesh Kaushik ,  Aparna Garimella ,  Akhash N A ,  Garvit Bhardwaj ,  Manoj Kilaru ,  Priyanshu Agarwal

IPC: G06F40/186 ,  G06F40/284

CPC classification number: G06F40/186 ,  G06F40/284

Abstract: Methods and systems are provided for facilitating generation of fillable document templates. In embodiments, a document having a plurality of tokens is obtained. Using a machine learned model, a token state is identified for each token of the plurality of tokens. Each token state indicates whether a corresponding token is a static token that is to be included in a fillable document template or a dynamic token that is to be excluded in the fillable document template. Thereafter, a fillable document template corresponding with the document is generated, wherein for each dynamic token of the document, the fillable document template includes a fillable field corresponding to the respective dynamic token.

公开(公告)号：US20250028911A1

公开(公告)日：2025-01-23

申请号：US18355573

申请日：2023-07-20

Applicant: ADOBE INC.

Inventor： Akshay Ganesh Iyer ,  Nikunj Goyal ,  Kanad Shrikar Pardeshi ,  Pranamya Prashant Kulkarni ,  Abhilasha Sancheti ,  Praneetha Vaddamanu ,  Aparna Garimella ,  Apoorv Umang Saxena ,  Vishwa Vinay

IPC: G06F40/40 ,  G06V10/22 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

Abstract: One or more aspects of the method, apparatus, and non-transitory computer readable medium include obtaining an image and a detail level, wherein the detail level comprises a value indicating a level of detail for a description of the image. One or more aspects of the method, apparatus, and non-transitory computer readable medium further include identifying a set of regions for the image based on the detail level using a machine learning model, and generating a description for the image based on the set of regions, wherein an amount of detail in the description is based on the detail level.

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：US20220147713A1

公开(公告)日：2022-05-12

申请号：US17092230

申请日：2020-11-07

Applicant: Adobe Inc.

Inventor： Aparna Garimella ,  Kiran Kumar Rathlavath ,  Balaji Vasan Srinivasan ,  Anandhavelu Natarajan ,  Akhash Nakkonda Amarnath ,  Akash Pramod Yalla

IPC: G06F40/284 ,  G06F40/56 ,  G06K9/62

Abstract: A system for generating text using a trained language model comprises an encoder that includes a debiased language model that penalizes generated text based on an equalization loss that quantifies first and second probabilities of respective first and second tokens occurring at a first point in the generated text. The first and second tokens define respective first and second groups of people. The system further comprises a decoder configured to generate text using the debiased language model. The decoder is further configured to penalize the generated text based on a bias penalization loss that quantifies respective probabilities of the first and second tokens co-occurring with a generated word. The encoder and decoder are trained to produce the generated text using a task-specific training corpus.