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:
Aug. 23, 2022

Filed:

Jun. 12, 2020
Applicant:

Capital One Services, Llc, McLean, VA (US);

Inventors:

Austin Walters, Savoy, IL (US);

Vincent Pham, Champaign, IL (US);

Ernest Kwak, Urbana, IL (US);

Galen Rafferty, Mahomet, IL (US);

Reza Farivar, Champaign, IL (US);

Jeremy Goodsitt, Champaign, IL (US);

Anh Truong, Champaign, IL (US);

Assignee:

Capital One Services, LLC, McLean, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/284 (2020.01); G06F 40/35 (2020.01); G06F 16/903 (2019.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 40/216 (2020.01); G06F 40/44 (2020.01); G06F 40/45 (2020.01);
U.S. Cl.
CPC ...
G06F 40/35 (2020.01); G06F 16/90344 (2019.01); G06F 40/216 (2020.01); G06F 40/284 (2020.01); G06N 3/049 (2013.01); G06N 3/084 (2013.01); G06F 40/44 (2020.01); G06F 40/45 (2020.01); G06N 3/0454 (2013.01);
Abstract

A method for identifying phrases in a text document having a similar discourse to a candidate phrase includes separating text in a document file into a plurality of phrases and generating a plurality of embedding vectors in a textual embedding space by inputting the plurality of phrases into an embedding engine. A mapping of each embedding vector in the textual embedding space is generated with each corresponding phrase and a document location of each corresponding phrase in the document file. A candidate phrase is received by a user and a candidate embedding vector is generated using the embedding engine. Similarity scores are computed based on the plurality of embedding space distances between the candidate phrase embedding vector location and each respective location of each embedding vector in the textual embedding space. A listing of phrases with the highest similarity scores are outputted with respective document locations in the text.


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