US 11,790,287 B2
Systems and methods for machine forward energy and energy storage transactions
Charles Howard Cella, Pembroke, MA (US)
Assigned to Strong Force TX Portfolio 2018, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force TX Portfolio 2018, LLC, Santa Monica, CA (US)
Filed on Nov. 27, 2019, as Appl. No. 16/698,355.
Application 16/698,355 is a continuation of application No. 16/457,890, filed on Jun. 29, 2019, abandoned.
Application 16/457,890 is a continuation of application No. PCT/US2019/030934, filed on May 6, 2019.
Claims priority of provisional application 62/787,206, filed on Dec. 31, 2018.
Claims priority of provisional application 62/667,550, filed on May 6, 2018.
Claims priority of provisional application 62/751,713, filed on Oct. 29, 2018.
Prior Publication US 2020/0098064 A1, Mar. 26, 2020
Int. Cl. G06Q 10/04 (2023.01); G06Q 30/0201 (2023.01); G06N 20/00 (2019.01); G06F 16/23 (2019.01); G06Q 30/0202 (2023.01); G06F 9/46 (2006.01); G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/067 (2023.01); G06Q 40/04 (2012.01); G06Q 50/06 (2012.01); G06Q 50/18 (2012.01); G06N 5/04 (2023.01); G06Q 20/22 (2012.01); G06Q 20/38 (2012.01); G06Q 30/0241 (2023.01); G06Q 30/0273 (2023.01); H02J 3/00 (2006.01); H02J 3/28 (2006.01); G06F 21/10 (2013.01); G06Q 20/40 (2012.01); G06F 9/48 (2006.01); G06Q 20/36 (2012.01); H04L 47/78 (2022.01); G06N 3/02 (2006.01); G06Q 20/06 (2012.01); G06F 16/951 (2019.01); G06F 9/38 (2018.01); H04L 47/783 (2022.01); G06Q 30/0204 (2023.01); G06Q 40/10 (2023.01); G06F 16/24 (2019.01); G06Q 50/04 (2012.01); H04L 9/00 (2022.01); H04L 12/14 (2006.01); H04L 47/70 (2022.01); G06F 16/18 (2019.01); H02J 3/38 (2006.01); G05B 19/00 (2006.01); G05B 19/418 (2006.01); G06F 9/54 (2006.01); G06Q 30/06 (2023.01); G06F 18/214 (2023.01); H02J 3/14 (2006.01); G06F 16/27 (2019.01); G06F 16/182 (2019.01); G06F 30/27 (2020.01); G06N 3/04 (2023.01); G06Q 50/00 (2012.01); H04L 9/06 (2006.01); G06F 16/2457 (2019.01); G06Q 30/0251 (2023.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); H04L 67/12 (2022.01)
CPC G06Q 10/04 (2013.01) [G05B 19/00 (2013.01); G05B 19/4188 (2013.01); G05B 19/41865 (2013.01); G06F 9/3836 (2013.01); G06F 9/3891 (2013.01); G06F 9/466 (2013.01); G06F 9/4806 (2013.01); G06F 9/4881 (2013.01); G06F 9/50 (2013.01); G06F 9/5005 (2013.01); G06F 9/5016 (2013.01); G06F 9/5027 (2013.01); G06F 9/5072 (2013.01); G06F 9/541 (2013.01); G06F 16/182 (2019.01); G06F 16/1865 (2019.01); G06F 16/23 (2019.01); G06F 16/2365 (2019.01); G06F 16/2379 (2019.01); G06F 16/24 (2019.01); G06F 16/27 (2019.01); G06F 16/951 (2019.01); G06F 18/2148 (2023.01); G06F 18/2155 (2023.01); G06F 21/105 (2013.01); G06F 30/27 (2020.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/067 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/06314 (2013.01); G06Q 10/06315 (2013.01); G06Q 20/06 (2013.01); G06Q 20/065 (2013.01); G06Q 20/0655 (2013.01); G06Q 20/29 (2013.01); G06Q 20/367 (2013.01); G06Q 20/389 (2013.01); G06Q 20/38215 (2013.01); G06Q 20/405 (2013.01); G06Q 20/4016 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0205 (2013.01); G06Q 30/0206 (2013.01); G06Q 30/0247 (2013.01); G06Q 30/0273 (2013.01); G06Q 30/06 (2013.01); G06Q 40/04 (2013.01); G06Q 40/10 (2013.01); G06Q 50/04 (2013.01); G06Q 50/06 (2013.01); G06Q 50/184 (2013.01); H02J 3/008 (2013.01); H02J 3/14 (2013.01); H02J 3/28 (2013.01); H02J 3/388 (2020.01); H04L 9/50 (2022.05); H04L 12/14 (2013.01); H04L 47/783 (2013.01); H04L 47/788 (2013.01); H04L 47/823 (2013.01); G05B 2219/36542 (2013.01); G06F 9/3838 (2013.01); G06F 16/2457 (2019.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); G06N 3/0418 (2013.01); G06Q 20/4015 (2020.05); G06Q 30/0254 (2013.01); G06Q 30/0276 (2013.01); G06Q 50/01 (2013.01); G06Q 2220/00 (2013.01); G06Q 2220/12 (2013.01); G06Q 2220/18 (2013.01); H02J 3/003 (2020.01); H04L 9/0643 (2013.01); H04L 67/12 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A transaction-enabling system, comprising:
a controller, comprising one or more high-speed processing devices, that includes:
a resource requirement system executed by at least one of the one or more high-speed processing devices that automatically aggregates a resource requirement for a fleet of instrumented machines to perform a task, each instrumented machine of the fleet of instrumented machines including a component related to a facility and having an energy storage capacity requirement and at least one instrument that provides energy storage data related to the instrumented machine, by observing at least one set of operations performed by the fleet of instrumented machines while receiving energy storage data from at least one of the instrumented machines, wherein the resource requirement comprises an energy storage capacity requirement;
a forward resource market system executed by at least one of the one or more high-speed processing devices in communication with and capable of executing transactions on a forward market for energy; and
a resource distribution system executed by at least one of the one or more high-speed processing devices and comprising an expert system that includes at least one of a machine learning component, an artificial intelligence component, or a neural network;
wherein the expert system:
inputs at least one of the aggregated resource requirement or energy storage data provided by the resource requirement system into the at least one of a machine learning component, an artificial intelligence component, or a neural network, which determine a set of effective parameters to selectively execute at least one of:
allocating a first amount of energy among tasks performed by the fleet of instrumented machines;
initiating a transaction for a second amount of energy on the forward market for energy; or
storing a third amount of energy within the fleet of instrumented machines for later use; and
monitors an outcome of the determination and selective execution to determine an output value which is used to continuously train the at least one of a machine learning component, an artificial intelligence component, or a neural network to improve allocation of energy to the fleet of instrumented machines by adaptively improving one of an aggregate output value of the fleet of machines or a cost of operation of the fleet of machines.