US 12,339,631 B2
Dynamic monitoring and securing of factory processes, equipment and automated systems
Matthew C. Putman, Brooklyn, NY (US); John B. Putman, Celebration, FL (US); Joanna 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 Apr. 8, 2024, as Appl. No. 18/629,532.
Application 18/629,532 is a continuation of application No. 18/329,295, filed on Jun. 5, 2023, granted, now 11,953,863.
Application 18/329,295 is a continuation of application No. 17/812,879, filed on Jul. 15, 2022, granted, now 11,669,058, issued on Jun. 6, 2023.
Prior Publication US 2024/0329609 A1, Oct. 3, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G05B 13/02 (2006.01)
CPC G05B 13/027 (2013.01) 20 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;
receiving, by the deep learning processor from a third signal splitter disposed between the first process station and the first controller, a control value measured by a sensor at the first process station;
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 within a range of expected values based on the first input operating instruction;
further correlating, by the deep learning processor, the first input operating instruction, the first output control signal, and the control value;
based on the further correlating, determining, by the deep learning processor, that the control value is not within a range of expected control values; and
responsive to determining that the control value is not within the range of expected control values, providing an indication of an unexpected activity.