The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 03, 2024

Filed:

Dec. 10, 2021
Applicant:

Paypal, Inc., San Jose, CA (US);

Inventors:

Yanfei Dong, Singapore, SG;

Yuan Deng, Singapore, SG;

Hewen Wang, Singapore, SG;

Xiaodong Yu, Singapore, SG;

Assignee:

PayPal, Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 30/414 (2022.01); G06F 40/106 (2020.01); G06F 40/114 (2020.01); G06V 30/148 (2022.01);
U.S. Cl.
CPC ...
G06V 30/414 (2022.01); G06F 40/106 (2020.01); G06F 40/114 (2020.01); G06V 30/153 (2022.01);
Abstract

A system can comprise a processor that can facilitate performance of operations, comprising accessing a document comprising a plurality of text bounding boxes, wherein each respective text bounding box of the plurality of text bounding boxes comprises respective text, for each respective text bounding box, determining respective text bounding box coordinates and respective text bounding box input embeddings, based on the respective text bounding box coordinates, determining respective text bounding box positional encodings for each respective text bounding box, based on a transformer-based deep learning model applied to the respective text bounding box input embeddings, respective text bounding box coordinates, respective text bounding box positional encodings, and bias information representative of a modification to an attention weight of the transformer-based deep learning model, determining respective output embeddings for each respective text bounding box, and based on the respective output embeddings, generating respective bounding box labels for each respective bounding box.


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