US 11,953,863 B2
Dynamic monitoring and securing of factory processes, equipment and automated systems
Matthew C. Putman, Brooklyn, NY (US); John B. Putman, Celebration, FL (US); Jonathan Lee, Brooklyn, NY (US); and Damas Limoge, Brooklyn, NY (US)
Assigned to Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed by Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed on Jun. 5, 2023, as Appl. No. 18/329,295.
Application 18/329,295 is a continuation of application No. 17/812,879, filed on Jul. 15, 2022, granted, now 11,669,058.
Prior Publication US 2024/0019817 A1, Jan. 18, 2024
Int. Cl. G05B 13/02 (2006.01)
CPC G05B 13/027 (2013.01) 17 Claims
OG exemplary drawing
 
1. A method for detecting unexpected activity in a manufacturing environment, comprising:
receiving, by a deep learning processor deployed in a manufacturing environment from a first signal splitter disposed between a data processing server and a first controller in the manufacturing environment, a first duplicated input signal instance of a first input operating instruction generated by the data processing server, wherein the first signal splitter generates the first duplicated input signal instance of the first input operating instruction and a second duplicated input signal instance of the first input operating instruction;
receiving, by the deep learning processor from a second signal splitter disposed between the first controller and a first process station in the manufacturing environment, a first output control signal generated by the first controller,
correlating, by the deep learning processor, the first input operating instruction and the first output control signal;
based on the correlating, determining, by the deep learning processor, that the first output control signal is not within a range of expected values based on the first input operating instruction; and
responsive to the determining, providing an indication of an unexpected activity as a result of detection of the unexpected activity in the manufacturing environment.