logo

For the Inventor, By the Inventor

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.

Patent No.:

US 11640563 B1

PDFFull Text
Date of Patent:
May. 02, 2023

Filed:

Mar. 23, 2020

Automated data processing and machine learning model generation

Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Luke Higgins, West Pymble, AU;

Liang Han, Wantirna, AU;

Koushik M Vijayaraghavan, Chennai, IN;

Rajendra T. Prasad, Basking Ridge, NJ (US);

Aditi Kulkarni, Bengaluru, IN;

Gayathri Pallail, Bangalore, IN;

Charles Grenet, Antony, FR;

Jean-Francois Depoitre, Magnée, BE;

Xiwen Sun, McKinnon, AU;

Jérémy Aeck, Chambourcy, FR;

Yuqing Xi, Paris, FR;

Srikanth Prasad, Bangalore, IN;

Pankaj Jetley, Basking Ridge, NJ (US);

Jayashri Sridevi, Chennai, IN;

Easwer Chinnadurai, Lutz, FL (US);

Niju Prabha, Bangalore, IN;

Assignee:

Accenture Global Solutions Limited, Dublin, IE;

Attorney:

Harrity & Harrity, LLP

Primary Examiner:

Mohammed Rachedine

Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06N 20/00 (2019.01); G06F 9/54 (2006.01); G06F 18/23 (2023.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 18/241 (2023.01); G06F 18/20 (2023.01);
U.S. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 9/542 (2013.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/23 (2023.01); G06F 18/241 (2023.01); G06F 18/285 (2023.01); G06N 20/00 (2019.01);
Abstract

A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Find Patent Forward Citations

Loading…