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:
Jul. 18, 2023

Filed:

Mar. 16, 2021
Applicant:

Arva Intelligence Corp, Park City, UT (US);

Inventors:

John A. McEntire, Park City, UT (US);

Thomas A. Dye, Austin, TX (US);

Assignee:

ARVA INTELLIGENCE CORP., Salt Lake City, UT (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 11/30 (2006.01); G06N 5/04 (2023.01); G06Q 50/02 (2012.01); G06N 7/00 (2023.01); G06F 16/2457 (2019.01); G06F 16/29 (2019.01); G01N 33/24 (2006.01); G01N 27/04 (2006.01); G06N 20/20 (2019.01); G06Q 10/0637 (2023.01); G06Q 30/0601 (2023.01); G06N 5/01 (2023.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G01N 27/041 (2013.01); G01N 33/24 (2013.01); G06F 16/24578 (2019.01); G06F 16/29 (2019.01); G06N 7/00 (2013.01); G06N 20/20 (2019.01); G06Q 50/02 (2013.01); G01N 2033/245 (2013.01); G06N 5/01 (2023.01); G06Q 10/06375 (2013.01); G06Q 30/0631 (2013.01);
Abstract

A system and method for visualizing one or more crop response surfaces. The system includes a geospatial database associated with a crop prediction engine. The geospatial database receives soil composition information for plots of land. The crop prediction engine identifies covariates from the soil composition information, which has a feature matrix. The crop prediction engine generates a multi-dimensional covariate training data set from the covariates. The crop prediction engine then applies the multi-dimensional covariate training data set to a machine learning training model to generate at least one predictive crop-yield predictive model. The crop prediction engine ranks covariates having feature set interactions. Subsequently, the crop prediction engine determines a dominant crop-yield feature set interaction from the ranked covariates having feature set interactions. The crop prediction engine generates a crop response surface from the dominant crop-yield feature set interaction. The crop prediction engine then visualizes the crop response surface.


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