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:
Apr. 30, 2024

Filed:

Nov. 30, 2021
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Zhong Fang Yuan, Xi'an, CN;

Tong Liu, Xi'an, CN;

Li Juan Gao, Xi'an, CN;

Si Heng Sun, Xi'an, CN;

Na Liu, Xi'an, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/33 (2019.01); G06F 16/332 (2019.01); G06F 16/338 (2019.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06F 40/56 (2020.01); G06N 3/045 (2023.01); G06T 7/70 (2017.01); G06V 10/82 (2022.01); G06V 30/18 (2022.01); G06V 30/19 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01);
U.S. Cl.
CPC ...
G06F 16/3344 (2019.01); G06F 16/3329 (2019.01); G06F 16/338 (2019.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06F 40/56 (2020.01); G06N 3/045 (2023.01); G06T 7/70 (2017.01); G06V 10/82 (2022.01); G06V 30/18181 (2022.01); G06V 30/191 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01); G06T 2207/20072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A system and method for table conversion including converting a table containing text in tabular form to an image, labeling each text area of the image with a bounding box, determining for each bounding box, a position information, a semantic information, and an image information, reconstructing the image into a graph form having a plurality of nodes, wherein each node represents the bounding box of the text areas of the image, inputting at least two nodes into a trained neural network to determine a relative relationship between the at least two nodes, building a knowledge graph using the relative relationship of the at least two nodes, and translating the knowledge graph into machine readable natural language.


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