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:

Oct. 25, 2018
Applicant:

The Boeing Company, Chicago, IL (US);

Inventors:

Seema Chopra, Bengaluru, IN;

Akshata Kishore Moharir, Bengaluru, IN;

Arvind Sundararaman, Bengaluru, IN;

Kaustubh Kaluskar, Bangalore, IN;

Assignee:

THE BOEING COMPANY, Chicago, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 20/00 (2019.01); G01C 23/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6253 (2013.01); G01C 23/005 (2013.01); G06K 9/6262 (2013.01); G06N 20/00 (2019.01);
Abstract

A method is provided that includes generating a visual environment for interactive development of a machine learning (ML) model. The method includes accessing observations of data each of which includes values of independent variables and a dependent variable, and performing an interactive exploratory data analysis (EDA) of the values of a set of the independent variables. The method includes performing an interactive feature construction and selection based on the interactive EDA, and in which select independent variables are selected as or transformed into a set of features for use in building a ML model to predict the dependent variable. The method includes building the ML model using a ML algorithm, the set of features, and a training set produced from the set of features and observations of the data. And the method includes outputting the ML model for deployment to predict the dependent variable for additional observations of the data.


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