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:
Apr. 14, 2025

Filed:

Aug. 29, 2021
Applicant:

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

Inventors:

Charles Howard Cella, Pembroke, MA (US);

Richard Spitz, Fort Lauderdale, FL (US);

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

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2018.12); B25J 9/16 (2005.12); G05B 13/02 (2005.12); G05B 19/4155 (2005.12); G06F 16/23 (2018.12); G06F 16/27 (2018.12); G06F 16/28 (2018.12); G06F 18/21 (2022.12); G06F 18/214 (2022.12); G06F 30/27 (2019.12); G06N 3/04 (2022.12); G06N 3/08 (2022.12); G06Q 10/0631 (2022.12); G06Q 10/0633 (2022.12); G06Q 10/0635 (2022.12); G06Q 10/0637 (2022.12); G06Q 10/067 (2022.12); G06Q 10/08 (2022.12); G06Q 10/083 (2023.12); G06Q 10/0833 (2022.12); G06Q 10/0835 (2022.12); G06Q 10/087 (2022.12); G06Q 10/10 (2022.12); G06Q 30/018 (2022.12); G06Q 30/0201 (2022.12); G06Q 30/0202 (2022.12); G06Q 30/0601 (2022.12); G06Q 50/26 (2023.12); H04L 67/1097 (2021.12); H04L 67/12 (2021.12); B64U 101/64 (2022.12); G16Y 10/40 (2019.12); G16Y 10/45 (2019.12); G16Y 10/75 (2019.12); G16Y 40/10 (2019.12); H04W 84/04 (2008.12);
U.S. Cl.
CPC ...
G06Q 10/0833 (2012.12); B25J 9/163 (2012.12); G05B 13/0265 (2012.12); G05B 19/4155 (2012.12); G06F 16/2315 (2018.12); G06F 16/27 (2018.12); G06F 16/284 (2018.12); G06F 18/214 (2022.12); G06F 18/2178 (2022.12); G06F 30/27 (2019.12); G06N 3/04 (2012.12); G06N 3/08 (2012.12); G06N 20/00 (2018.12); G06Q 10/06313 (2012.12); G06Q 10/06315 (2012.12); G06Q 10/0633 (2012.12); G06Q 10/0635 (2012.12); G06Q 10/06375 (2012.12); G06Q 10/067 (2012.12); G06Q 10/08 (2012.12); G06Q 10/083 (2012.12); G06Q 10/0835 (2012.12); G06Q 10/0838 (2012.12); G06Q 10/087 (2012.12); G06Q 10/103 (2012.12); G06Q 30/018 (2012.12); G06Q 30/0201 (2012.12); G06Q 30/0202 (2012.12); G06Q 30/0206 (2012.12); G06Q 30/0631 (2012.12); G06Q 50/26 (2012.12); H04L 67/1097 (2012.12); H04L 67/12 (2012.12); B64U 2101/64 (2022.12); G05B 2219/40408 (2012.12); G06Q 2220/00 (2012.12); G06Q 2220/10 (2012.12); G16Y 10/40 (2019.12); G16Y 10/45 (2019.12); G16Y 10/75 (2019.12); G16Y 40/10 (2019.12); H04W 84/042 (2012.12);
Abstract

A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.


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