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:
Apr. 06, 2021

Filed:

Apr. 08, 2016
Applicant:

Equifax, Inc., Atlanta, GA (US);

Inventors:

Vickey Chang, Suwanee, GA (US);

Jeffrey Ouyang, Atlanta, GA (US);

Wei Zhang, Alpharetta, GA (US);

Assignee:

EQUIFAX INC., Atlanta, GA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 30/20 (2020.01); G06F 17/18 (2006.01); G06Q 40/02 (2012.01); G06Q 10/06 (2012.01);
U.S. Cl.
CPC ...
G06F 30/20 (2020.01); G06F 17/18 (2013.01); G06Q 40/025 (2013.01); G06Q 10/067 (2013.01);
Abstract

An automated model development tool can be used for automatically developing a model (e.g., an analytical model). The automated model development tool can perform various automated operations for automatically developing the model including, for example, performing automated operations on variables in a data set that can be used to develop the model. The automated operations can include automatically analyzing the predictor variables. The automated operations can also include automatically binning (e.g., combining) data associated with the predictor variables to provide monotonicity between the predictor variables and one or more output variables. The automated operations can further include automatically reducing the number of predictor variables in the data set and using the reduced number of predictor variables to develop the analytical model. The model developed using the automated model development tool can be used to identify relationships between predictor variables and one or more output variables in various machine learning applications.


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