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:
Feb. 22, 2022

Filed:

Feb. 28, 2020
Applicant:

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

Inventors:

Shlomi Medalion, Lod, IL;

Alexander Zhicharevich, Hod Hasharon, IL;

Yair Horesh, Kfar Sava, IL;

Oren Sar Shalom, Nes Ziona, IL;

Elik Sror, Hod Hasharon, IL;

Adi Shalev, Herzliya, IL;

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G10L 15/18 (2013.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G10L 15/16 (2006.01); G10L 15/06 (2013.01); G10L 15/197 (2013.01); G10L 15/22 (2006.01);
U.S. Cl.
CPC ...
G10L 15/1815 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G10L 15/063 (2013.01); G10L 15/16 (2013.01); G10L 15/197 (2013.01); G10L 15/22 (2013.01); G10L 2015/223 (2013.01);
Abstract

A method of training machine learning models (MLMs). An issue vector is generated using an issue MLM to generate a first output including first embedded natural language issue statements. An action vector is generated using an action MLM to generate a second output including related embedded natural language action statements. The issue and action MLMs are of a same type. An inner product of the first and second output is calculated, forming a third output. The third output is processed according to a sigmoid gate process to predict whether a given issue statement and corresponding action statement relate to a same call, resulting in a fourth output. A loss function is calculated from the fourth output by comparing the fourth output to a known result. The issue MLM and the action MLM are modified using the loss function to obtain a trained issue MLM and a trained action MLM.


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