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:
Nov. 15, 2022

Filed:

Feb. 27, 2019
Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Swati Tata, Bangalore, IN;

Abhishek Gunjan, Gaya, IN;

Pratip Samanta, Bangalore, IN;

Madhura Shivaram, Bangalore, IN;

Ankit Chouksey, Bhopal, IN;

Arnest Tony Lewis, Bangalore, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06N 5/04 (2006.01); G06F 16/36 (2019.01); G06F 16/35 (2019.01); G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/253 (2020.01); G06F 40/284 (2020.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06F 16/35 (2019.01); G06F 16/367 (2019.01); G06F 40/205 (2020.01); G06F 40/253 (2020.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
Abstract

An Artificial Intelligence (AI)-based data processing system employs a trained AI model for extracting features of products from various product classes and building a product ontology from the features. The product ontology is used to respond to user queries with product recommendations and customizations. Training data for the generation of the AI model for feature extraction is initially accessed and verified to determine of the training data meets a data density requirement. If the training data does not meet the data density requirement, data from one of a historic source or external sources is added to the training data. One of the plurality of AI models is selected for training based on the degree of overlap and the inter-class distance between the datasets of the various product classes within the training data.


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