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:
Oct. 10, 2023

Filed:

May. 06, 2019
Applicant:

John Snow Labs Inc., Lewes, DE (US);

Inventors:

Jose Pablo Andreotti, Cordoba, AR;

Saif Addin Ellafi, Buenos Aires, AR;

David Talby, Mercer Island, WA (US);

Assignee:

John Snow Labs Inc., Lewes, DE (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 16/2457 (2019.01); G06N 20/00 (2019.01); G06F 40/30 (2020.01);
U.S. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 16/24578 (2019.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01);
Abstract

A computer process for entity resolution of natural language records including training a semantic embedding function on a corpus of unlabeled training materials. The semantic embedding function can take a word and represent it as a vector, where the vector represents the word as it relates to the semantic information of the corpus of unlabeled training materials. The process may transform a list of normalized descriptions using the semantic embedding function into a list of vector representations of the descriptions. The process may transform words from a natural language record to a vector representation of the natural language record using the semantic embedding function, and may use a named entity recognizer. The process may find a best match description from the list of normalized descriptions using the list of vector representations of the descriptions and the vector representation of the natural language record, and may include using word mover distance.


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