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:
Mar. 24, 2026

Filed:

Nov. 30, 2023
Applicant:

Strong Force Vcn Portfolio 2019, Llc, Fort Lauderdale, FL (US);

Inventors:

Charles H. Cella, Pembroke, MA (US);

Andrew Cardno, San Diego, CA (US);

Jenna Parenti, Denver, CO (US);

Andrew S. Locke, Farmington, MI (US);

Brad Kell, Seattle, WA (US);

Teymour S. El-Tahry, Detroit, MI (US);

Leon Fortin, Jr., Providence, RI (US);

Andrew Bunin, Lakewood Ranch, FL (US);

Kunal Sharma, Mumbai, IN;

Taylor Charon, Troy, MI (US);

Hristo Malchev, Alta Loma, CA (US);

Eric P. Vetter, Cary, NC (US);

David Stein, Fairfax, VA (US);

Benjamin D. Goodman, Los Angeles, CA (US);

Assignee:

STRONG FORCE VCN PORTFOLIO 2019, LLC, Fort Lauderdale, FL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/0833 (2023.01); G05B 19/4155 (2006.01); G05B 19/418 (2006.01); G05D 1/00 (2024.01); G05D 1/223 (2024.01); G05D 1/698 (2024.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06Q 10/06 (2023.01); G06Q 10/0635 (2023.01); G06Q 10/0637 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/087 (2023.01); G06Q 10/0875 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0202 (2023.01); G06Q 50/04 (2012.01); G06Q 50/40 (2024.01); G05D 101/15 (2024.01); G05D 107/70 (2024.01); G05D 109/10 (2024.01); G06N 3/006 (2023.01); G06N 3/044 (2023.01); G06N 3/0455 (2023.01); G06N 3/049 (2023.01); G06N 3/084 (2023.01); G06N 3/088 (2023.01); G06N 5/01 (2023.01); G06N 7/01 (2023.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G05D 1/0297 (2013.01); G05B 19/4155 (2013.01); G05B 19/41885 (2013.01); G05D 1/223 (2024.01); G05D 1/6987 (2024.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 10/06 (2013.01); G06Q 10/0635 (2013.01); G06Q 10/06375 (2013.01); G06Q 10/06395 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/087 (2013.01); G06Q 10/0875 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0202 (2013.01); G06Q 50/04 (2013.01); G06Q 50/40 (2024.01); G05B 2219/50391 (2013.01); G05D 2101/15 (2024.01); G05D 2107/70 (2024.01); G05D 2109/10 (2024.01); G06N 3/006 (2013.01); G06N 3/044 (2023.01); G06N 3/0455 (2023.01); G06N 3/049 (2013.01); G06N 3/084 (2013.01); G06N 3/088 (2013.01); G06N 5/01 (2023.01); G06N 7/01 (2023.01); G06N 20/20 (2019.01); G06Q 2220/00 (2013.01);
Abstract

A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.


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