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:
Feb. 06, 2024

Filed:

Jun. 24, 2020
Applicant:

Predictiveiq Llc, Boston, MA (US);

Inventors:

Daniel Augusto Betts, Parkland, FL (US);

Matthew Tilghman, Fort Lauderdale, FL (US);

Juan Fernando Betts, Parkland, FL (US);

Assignee:

PREDICTIVEIQ LLC, Boston, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B64F 5/10 (2017.01); G06F 30/28 (2020.01); G16C 20/10 (2019.01); G16C 20/70 (2019.01); G06F 30/27 (2020.01); G06F 111/10 (2020.01);
U.S. Cl.
CPC ...
G06F 30/28 (2020.01); G16C 20/10 (2019.02); G16C 20/70 (2019.02); G06F 30/27 (2020.01); G06F 2111/10 (2020.01);
Abstract

This application relates to apparatus and methods for generating, and executing, surrogate models. In some examples, a computing device generates and evaluates correlations between input and output variables for a system to identify highly correlated input and output parameters. In addition, weights for one or more of the parameters may be determined. The computing device identifies a mathematical relationship between input and output variables to generate a physics model. The computing device may also identify other features not captured by the physical relationship that are highly correlated to each other, and generates a feature model that is based on the highly correlated features. The computing device may optimize the feature model based on a culling process that reduces the computational resources required to execute the feature model. The physics model is then combined with the feature model to generate a system output model that can simulate the system.


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