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:
Dec. 28, 2021

Filed:

Jul. 30, 2019
Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Janardan Misra, Bangalore, IN;

Sanjay Podder, Thane, IN;

Narendranath Sukhavasi, Nizamabad, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06N 5/04 (2006.01); G06F 40/205 (2020.01); G06F 40/268 (2020.01); G06F 40/284 (2020.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/268 (2020.01); G06F 40/284 (2020.01); G06N 5/047 (2013.01); G06N 20/00 (2019.01);
Abstract

In some examples, machine learning based quantification of performance impact of data irregularities may include generating an irregularity feature vector for each text analytics application of a plurality of text analytics applications. Normalized data associated with a corresponding text analytics application may be generated for each text analytics application and based on minimization of irregularities present in un-normalized data associated with the corresponding text analytics application. An un-normalized data machine learning model may be generated for each text analytics application and based on the un-normalized data associated with the corresponding text analytics application. A normalized data machine learning model may be generated for each text analytics application and based on the normalized data associated with the corresponding text analytics application. A difference in performances may be determined with respect to the un-normalized data machine learning model and the normalized data machine learning model.


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