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:
May. 14, 2024

Filed:

Mar. 31, 2023
Applicant:

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

Inventors:

Akshay Ravindran, Mountain View, CA (US);

Avinash Thekkumpat, Mountain View, CA (US);

Raja Sabra, San Jose, CA (US);

Shylaja R. Deshpande, Fremont, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/0282 (2023.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06Q 30/0282 (2013.01); G06F 40/295 (2020.01); G06N 7/01 (2023.01); G06F 40/30 (2020.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01);
Abstract

A method including preprocessing natural language text by cleaning and vectorizing the natural language text. A first machine learning model (MLM) extracts negative reviews. A first input to the first MLM is the natural language text and a first output of the first MLM is first probabilities that the negative reviews have negative sentiments. The method also includes categorizing the negative reviews by executing a second MLM. A second input to the second MLM is the negative reviews. A second output of the second MLM is second probabilities that the negative reviews are assigned to categories. The method also includes identifying, using a name recognition controller and based on categorizing, a name of a software application in the negative reviews and sorting the negative reviews into a subset of negative reviews relating to the name. The software application is adjusted based on the subset of negative reviews.


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