US 12,217,153 B2
Training server and method for generating a predictive model of a neural network through distributed reinforcement learning
Steve Lupien, Boucherville (CA); and Francois Gervais, Lachine (CA)
Assigned to DISTECH CONTROLS INC., Brossard (CA)
Filed by Distech Controls Inc., Brossard (CA)
Filed on Nov. 16, 2023, as Appl. No. 18/511,309.
Application 18/511,309 is a continuation of application No. 16/697,684, filed on Nov. 27, 2019, granted, now 11,861,482.
Claims priority of provisional application 62/891,585, filed on Aug. 26, 2019.
Prior Publication US 2024/0086686 A1, Mar. 14, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/047 (2023.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01)
CPC G06N 3/047 (2023.01) [G06N 3/063 (2013.01); G06N 3/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An environment controller comprising:
at least one communication interface;
memory for storing a predictive model comprising weights of a neural network; and
a processing unit comprising one or more processor configured to:
(a) determine at least one environmental characteristic value in an area;
(b) receive at least one set point via one of the at least one communication interface or a user interface of the environment controller;
(c) execute a neural network inference engine using the predictive model for generating one or more output based on inputs, the one or more output comprising one or more command for controlling a controlled appliance, the inputs comprising the at least one environmental characteristic value in the area and the at least one set point;
(d) modify the one or more command;
(e) transmit the one or more modified command to the controlled appliance via the at least one communication interface;
(f) generate at least one metric representative of an execution of the one or more modified command by the controlled appliance;
(g) transmit the inputs, the one or more output and the at least one metric to a training server via the at least one communication interface, the one or more output comprising the one or more modified command; and
(h) receive an update of the predictive model comprising updated weights from the training server via the at least one communication interface.