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:
Jun. 27, 2023

Filed:

Jul. 20, 2021
Applicant:

Intuit Inc., Mountain View, CA (US);

Inventors:

Conrad De Peuter, Chevy Chase, MD (US);

Karpaga Ganesh Patchirajan, Plano, TX (US);

Saikat Mukherjee, Fremont, CA (US);

Assignee:

Intuit Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/295 (2020.01); G10L 15/22 (2006.01); G06F 16/35 (2019.01); G06N 20/00 (2019.01); G06F 16/33 (2019.01);
U.S. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 16/3344 (2019.01); G06F 16/3347 (2019.01); G06F 16/35 (2019.01); G06N 20/00 (2019.01);
Abstract

Systems and methods for recognizing domain specific named entities are disclosed. An example method may be performed by one or more processors of a text incorporation system and include extracting a number of terms from a text under consideration, identifying, among the number of terms, a set of unmatched terms that do not match any of a plurality of known terms, passing each respective unmatched term to a vectorization module, embedding a vectorized version of each respective unmatched term in a vector space, comparing each vectorized version to known term vectors, passing, to a machine learning model, candidate terms corresponding to known term vectors closest to the vectorized versions, identifying, using the machine learning model, a best candidate term for each respective unmatched term, mapping the best candidate terms to unmatched terms in the text under consideration, and incorporating the text under consideration into the system based on the mappings.


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