US 11,989,288 B2
Dynamic monitoring and securing of factory processes, equipment and automated systems
Matthew C. Putman, Brooklyn, NY (US); John B. Putman, Celebration, FL (US); Vadim Pinskiy, Wayne, NJ (US); Damas Limoge, Brooklyn, NY (US); Andrew Sundstrom, Brooklyn, NY (US); and James Williams, III, New York, NY (US)
Assigned to Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed by Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed on Jun. 28, 2023, as Appl. No. 18/343,421.
Application 18/343,421 is a continuation of application No. 17/445,657, filed on Aug. 23, 2021, granted, now 11,693,956.
Application 17/445,657 is a continuation of application No. 16/904,984, filed on Jun. 18, 2020, granted, now 11,100,221, issued on Aug. 24, 2021.
Application 16/904,984 is a continuation in part of application No. 16/781,193, filed on Feb. 4, 2020, granted, now 11,063,965, issued on Jul. 13, 2021.
Claims priority of provisional application 62/983,487, filed on Feb. 28, 2020.
Claims priority of provisional application 62/950,588, filed on Dec. 19, 2019.
Claims priority of provisional application 62/938,158, filed on Nov. 20, 2019.
Claims priority of provisional application 62/932,063, filed on Nov. 7, 2019.
Claims priority of provisional application 62/931,453, filed on Nov. 6, 2019.
Claims priority of provisional application 62/912,291, filed on Oct. 8, 2019.
Prior Publication US 2023/0359730 A1, Nov. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G05B 19/4155 (2006.01); G05B 23/02 (2006.01); G06F 21/55 (2013.01); G06F 21/56 (2013.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01)
CPC G06F 21/552 (2013.01) [G05B 19/4155 (2013.01); G05B 23/0275 (2013.01); G06F 21/554 (2013.01); G06F 21/56 (2013.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G05B 2219/31368 (2013.01); G06F 2221/034 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A manufacturing system, comprising:
a process station configured to perform a step of a manufacturing process;
a station controller programmed to control an operation of the process station;
a deep learning controller in communication with the process station and the station controller, wherein the deep learning controller is trained to identify anomalous activity in the manufacturing process;
a first signal splitter positioned between the station controller and the process station, the first signal splitter having an input, a first output, and a second output, the first signal splitter configured to receive a control signal transmitted from the station controller to the process station, duplicate the control signal, and provide a first portion of the duplicated control signal to the deep learning controller via the first output and a second portion of the duplicated control signal to the process station via the second output; and
a second signal splitter positioned downstream of the process station, the second signal splitter having a second input, a third output, and a fourth output, the second signal splitter configured to receive control values output by the process station, duplicate the control values, and provide a first portion of the duplicated control values to the deep learning controller via the third output and a second portion of the duplicated control values is provided to the station controller via the fourth output.