[7] System and Method for Improving Assembly Line Processes
U.S. Patent No. 11,675,330, with Matthew C. Putman, Vadim Pinskiy, and Eun-Sol Kim
Issued 13 Jun 2023 [USPTO] [JUSTIA]
Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.
[6] Assembly Error Correction for Assembly Lines
U.S. Patent No. 11,209,795, with Matthew C. Putman, Vadim Pinskiy, and Eun-Sol Kim
Issued 28 Dec 2021 [USPTO] [JUSTIA]
Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.
[5] Dynamic Monitoring and Securing of Factory Processes, Equipment and Automated Systems
U.S. Patent No. 11,100,221, with Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, and James Williams III
Issued 24 Aug 2021 [USPTO] [JUSTIA]
A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.
[4] Method, Systems and Apparatus for Intelligently Emulating Factory Control Systems and Simulating Response Data
U.S. Patent No. 11,086,988, with Matthew C. Putman, John B. Putman, Vadim Pinskiy, and James Williams III
Issued 10 Aug 2021 [USPTO] [JUSTIA]
A controller emulator, coupled to an interface that exposes the controller emulator to inputs from external sources, provides one or more control signals to a process simulator and a deep learning process. In response, the process simulator simulates response data that is provided to the deep learning processor. The deep learning processor generates expected response data and expected behavioral pattern data for the one or more control signals, as well as actual behavioral pattern data for the simulated response data. A comparison of at least one of the simulated response data to the expected response data and the actual behavioral pattern data to the expected behavioral pattern data is performed to determine whether anomalous activity is detected. As a result of detecting anomalous activity, one or more operations are performed to address the anomalous activity.
[3] Dynamic Monitoring and Securing of Factory Processes, Equipment and Automated Systems
U.S. Patent No. 11,063,965, with Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, and James Williams III
Issued 13 Jul 2021 [USPTO] [JUSTIA]
A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.
[2] Dynamic Training for Assembly Lines
U.S. Patent No. 10,481,579, with Matthew C. Putman, Vadim Pinskiy, and Eun-Sol Kim
Issued 19 Nov 2019 [USPTO] [JUSTIA]
Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.
[1] Methods and Systems for Measuring a Property of a Macromolecule
U.S. Patent No. 9,995,766, with Jason Reed and Bud Mishra
Issued 12 Jun 2018 [USPTO] [JUSTIA]
The present disclosure provides methods of measuring a property of a macromolecule. The methods generally involve applying an empirically learned correction term to a test metric to generate a high-accuracy measurement. The present disclosure further provides a computer program product and a computer system for carrying out a subject method.