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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.

Patent No.:

US 12361399 B1

PDFFull Text
Date of Patent:
Jul. 15, 2025

Filed:

Sep. 09, 2022

Distributed-ledger-based manufacturing for value chain networks

Applicant:

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

Inventors:

Charles Howard Cella, Pembroke, MA (US);

Brent Bliven, Austin, TX (US);

Kunal Sharma, Mumbai, IN;

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

Assignee:

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

Attorney:

Miller Johnson

Primary Examiner:

Hafiz A Kassim

Assistant Examiner:

Zahra undefined Elkassabgi

Int. Cl.
CPC ...
G06Q 10/063 (2023.01); B25J 9/16 (2006.01); B29C 64/386 (2017.01); B29C 64/393 (2017.01); B33Y 10/00 (2015.01); B33Y 50/00 (2015.01); B33Y 50/02 (2015.01); G02B 3/14 (2006.01); G02B 26/00 (2006.01); G05B 13/02 (2006.01); G05B 13/04 (2006.01); G05B 17/02 (2006.01); G05B 19/402 (2006.01); G05B 19/4099 (2006.01); G05D 1/00 (2006.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0633 (2023.01); G06Q 20/14 (2012.01); G06T 7/70 (2017.01); H04L 9/00 (2022.01); H04L 9/32 (2006.01); H04L 9/40 (2022.01); G06F 113/10 (2020.01); G06Q 10/06 (2023.01); G06Q 10/0831 (2023.01); G06Q 10/0833 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0201 (2023.01);
U.S. Cl.
CPC ...
G06Q 20/14 (2013.01); B25J 9/163 (2013.01); B25J 9/1653 (2013.01); B25J 9/1661 (2013.01); B25J 9/1671 (2013.01); B25J 9/1682 (2013.01); B25J 9/1697 (2013.01); B29C 64/386 (2017.08); B29C 64/393 (2017.08); B33Y 10/00 (2014.12); B33Y 50/00 (2014.12); B33Y 50/02 (2014.12); G02B 3/14 (2013.01); G02B 26/00 (2013.01); G05B 13/0265 (2013.01); G05B 13/042 (2013.01); G05B 17/02 (2013.01); G05B 19/402 (2013.01); G05B 19/4099 (2013.01); G05D 1/0027 (2013.01); G05D 1/0297 (2013.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/06311 (2013.01); G06Q 10/0633 (2013.01); G06T 7/70 (2017.01); H04L 9/3239 (2013.01); H04L 9/50 (2022.05); H04L 63/1441 (2013.01); G05B 2219/32015 (2013.01); G05B 2219/40113 (2013.01); G05B 2219/49023 (2013.01); G06F 2113/10 (2020.01); G06Q 10/06 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/063114 (2013.01); G06Q 10/06313 (2013.01); G06Q 10/06316 (2013.01); G06Q 10/0831 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0201 (2013.01); G06Q 2220/00 (2013.01); G06T 2207/20081 (2013.01);
Abstract

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

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