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:
May. 10, 2022

Filed:

Nov. 11, 2021
Applicant:

Sas Institute Inc., Cary, NC (US);

Inventors:

Guohui Wu, Cary, NC (US);

Jan Chvosta, Raleigh, NC (US);

Wan Xu, Cary, NC (US);

Gunce Eryuruk Walton, Raleigh, NC (US);

Xilong Chen, Chapel Hill, NC (US);

Assignee:

SAS Institute Inc., Cary, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 17/16 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 17/16 (2013.01);
Abstract

A computing device selects a trained spatial regression model. A spatial weights matrix defined for observation vectors is selected, where each element of the spatial weights matrix indicates an amount of influence between respective pairs of observation vectors. Each observation vector is spatially referenced. A spatial regression model is selected from spatial regression models, initialized, and trained using the observation vectors and the spatial weights matrix to fit a response variable using regressor variables. Each observation vector includes a response value for the response variable and a regressor value for each regressor variable of the regressor variables. A fit criterion value is computed for the spatial regression model and the spatial regression model selection, initialization, and training are repeated until each spatial regression model is selected. A best spatial regression model is selected and output as the spatial regression model having an extremum value of the fit criterion value.


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