US 11,915,142 B2
Creating equipment control sequences from constraint data
Troy Aaron Harvey, Brighton, UT (US); and Jeremy David Fillingim, Salt Lake City, UT (US)
Assigned to PassiveLogic, Inc., Salt Lake City, UT (US)
Filed by PassiveLogic, Inc., Salt Lake City, UT (US)
Filed on Apr. 12, 2021, as Appl. No. 17/228,119.
Claims priority of provisional application 62/704,976, filed on Jun. 5, 2020.
Prior Publication US 2021/0383042 A1, Dec. 9, 2021
Int. Cl. G06F 30/18 (2020.01); G06N 3/084 (2023.01); G06N 3/063 (2023.01); G06F 9/30 (2018.01); G06N 3/04 (2023.01); G05B 13/02 (2006.01); G06F 17/16 (2006.01); G06N 3/08 (2023.01); G06F 30/27 (2020.01); F24F 11/64 (2018.01); G05B 19/042 (2006.01); F24F 11/65 (2018.01); G06Q 10/067 (2023.01); G05B 13/04 (2006.01); G06N 3/047 (2023.01); G06N 3/048 (2023.01); G06F 119/08 (2020.01); F24F 120/10 (2018.01); F24F 120/20 (2018.01); G06F 119/06 (2020.01); F24F 140/50 (2018.01)
CPC G06N 3/084 (2013.01) [F24F 11/64 (2018.01); F24F 11/65 (2018.01); G05B 13/027 (2013.01); G05B 13/04 (2013.01); G05B 19/042 (2013.01); G06F 9/30036 (2013.01); G06F 17/16 (2013.01); G06F 30/18 (2020.01); G06F 30/27 (2020.01); G06N 3/04 (2013.01); G06N 3/047 (2023.01); G06N 3/048 (2023.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06Q 10/067 (2013.01); G06Q 50/163 (2013.01); F24F 2120/10 (2018.01); F24F 2120/20 (2018.01); F24F 2140/50 (2018.01); G05B 2219/2614 (2013.01); G06F 2119/06 (2020.01); G06F 2119/08 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method for creating equipment control sequences from constraint data comprising:
accessing a first constraint state curve;
accessing a structure model that thermodynamically represents a controlled space;
accessing an equipment model associated with the controlled space that thermodynamically represents equipment associated with the controlled space;
running the structure model using a machine learning engine that accepts a first state injection time series as input and outputs a second constraint state curve that is compared to the first constraint state curve to determine a first goal state; and
running the equipment model using a machine learning engine that accepts a first control sequence as input and produces a second state injection time series as output that is compared to the first state injection time series to determine a second goal state.