US 12,456,092 B2
Control tower and enterprise management platform with a machine learning/artificial intelligence managing sensor and the camera feeds into digital twin
Charles Howard Cella, Pembroke, MA (US); Richard Spitz, Franklin, TN (US); Teymour S. El-Tahry, Detroit, MI (US); Joshua Dobrowitsky, Birmingham, MI (US); Jenna Parenti, Denver, CO (US); Brent Bliven, Austin, TX (US); and Andrew Cardno, San Diego, CA (US)
Assigned to STRONG FORCE VCN PORTFOLIO 2019, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force VCN Portfolio 2019, LLC, Fort Lauderdale, FL (US)
Filed on Dec. 4, 2020, as Appl. No. 17/112,503.
Application 17/112,503 is a continuation of application No. PCT/US2020/059224, filed on Nov. 5, 2020.
Application PCT/US2020/059224 is a continuation of application No. PCT/US2020/059227, filed on Nov. 5, 2020.
Claims priority of provisional application 63/087,292, filed on Oct. 4, 2020.
Claims priority of provisional application 63/069,533, filed on Aug. 24, 2020.
Claims priority of provisional application 63/054,606, filed on Jul. 21, 2020.
Claims priority of provisional application 63/016,976, filed on Apr. 28, 2020.
Claims priority of provisional application 62/969,153, filed on Feb. 3, 2020.
Claims priority of provisional application 62/931,193, filed on Nov. 5, 2019.
Prior Publication US 2021/0133670 A1, May 6, 2021
Int. Cl. G06Q 10/067 (2023.01); G05B 19/042 (2006.01); G06F 30/20 (2020.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0635 (2023.01); G06Q 10/0637 (2023.01); G06Q 10/0834 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0601 (2023.01); H04L 9/00 (2022.01); H04L 9/32 (2006.01)
CPC G06Q 10/087 (2013.01) [G05B 19/042 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/0635 (2013.01); G06Q 10/0637 (2013.01); G06Q 10/0834 (2013.01); G06Q 30/0635 (2013.01); G05B 2219/31229 (2013.01); H04L 9/3236 (2013.01); H04L 9/50 (2022.05)] 12 Claims
OG exemplary drawing
 
1. A value chain system that provides recommendations for designing a logistics system comprising:
a machine learning system that trains a first machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the first machine-learned model based, at least in part, on a first training data set that includes features of logistics systems and corresponding outcomes;
an artificial intelligence system that receives a first request for a first logistics system design and determines a first logistics system design recommendation based on the first machine-learned model and a set of features included in the first request; and
a digital twin system configured to:
generate a logistics environment digital twin of a logistics environment that incorporates the first logistics system design recommendation and one or more physical asset digital twins of physical assets;
simulate a first logistics operation performance based on the logistics environment digital twin and the one or more physical asset digital twins to generate a simulation result that includes at least one simulated outcome corresponding to the first logistics system design recommendation;
provide the simulation result of the first logistics operations performance simulation to the machine learning system to retrain the first machine-learned model, wherein the retraining results in a second machine-learned model;
issue a logistics system design request to the artificial intelligence system for a second logistics system design recommendation based on a result of the first logistics operations performance simulation and the second machine-learned model; and
update the logistics environment digital twin based on the second logistics system design recommendation,
wherein the retraining includes training the second machine-learned model based on the first training data set and the result of the first logistics operations performance simulation.