35 U.S. Patents in 11 Invention Areas [JUSTIA]
[IA 11] Imitation Learning in a Manufacturing Environment
Coinvented with: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, and Eun-Sol Kim
Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.
U.S. Patent No. 12,153,414: issued 26 Nov 2024 [USPTO] [JUSTIA]
[IA 10] Systems, Methods, and Media for Manufacturing Processes
Coinvented with: Matthew C. Putman, Damas Limoge, Vadim Pinskiy, Aswin Raghav Nirmaleswaran, and Eun-Sol Kim
Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.
U.S. Patent No. 12,039,750: issued 16 Jul 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,066,818: issued 20 Aug 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,073,584: issued 27 Aug 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,117,799: issued 15 Oct 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,117,812: issued 15 Oct 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,125,236: issued 22 Oct 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,153,401: issued 26 Nov 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,153,408: issued 26 Nov 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,165,353: issued 10 Dec 2024 [USPTO] [JUSTIA]
[IA 9] Securing Industrial Production from Sophisticated Attacks
Coinvented with: Matthew C. Putman, Vadim Pinskiy, and Damas Limoge
Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to detect a cyberattack to the manufacturing system. The control module is configured to perform operations. The operations include receiving control values for a first station of the one or more stations. The operations further include determining that there is a cyberattack based on the control values for the first station using one or more machine learning algorithms. The operations further include generating an alert to cease processing of the component. In some embodiments, the operations further include correcting errors caused by the cyberattack.
U.S. Patent No. 12,039,040: issued 16 Jul 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,072,973: issued 27 Aug 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,118,081: issued 15 Oct 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,153,668: issued 26 Nov 2024 [USPTO] [JUSTIA]
[IA 8] System and Method for Improving Assembly Line Processes
Coinvented with: Matthew C. Putman, Vadim Pinskiy, and Eun-Sol Kim
Abstract: 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.
U.S. Patent No. 11,675,330: issued 13 Jun 2023 [USPTO] [JUSTIA]
[IA 7] Defect Detection System
Coinvented with: Tonislav Ivanov, Denis Babeshko, Vadim Pinskiy, and Matthew C. Putman
Abstract: A computing system generates a training data set for training the prediction model to detect defects present in a target surface of a target specimen and training the prediction model to detect defects present in the target surface of the target specimen based on the training data set. The computing system generates the training data set by identifying a set of images for training the prediction model, the set of images comprising a first subset of images. A deep learning network generates a second subset of images for subsequent labelling based on the set of images comprising the first subset of images. The deep learning network generates a third subset of images for labelling based on the set of images comprising the first subset of images and the labeled second subset of images. The computing system continues the process until a threshold number of labeled images is generated.
U.S. Patent No. 11,416,711: issued 16 Aug 2022 [USPTO] [JUSTIA]
[IA 6] Assembly Error Correction for Assembly Lines
Coinvented with: Matthew C. Putman, Vadim Pinskiy, and Eun-Sol Kim
Abstract: 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.
U.S. Patent No. 11,209,795: issued 28 Dec 2021 [USPTO] [JUSTIA]
U.S. Patent No. 11,703,824: issued 18 Jul 2023 [USPTO] [JUSTIA]
U.S. Patent No. 12,140,926: issued 12 Nov 2024 [USPTO] [JUSTIA]
[IA 5] Method, Systems and Apparatus for Intelligently Emulating Factory Control Systems and Simulating Response Data
Coinvented with: Matthew C. Putman, John B. Putman, Vadim Pinskiy, and James Williams III
Abstract: 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.
U.S. Patent No. 11,086,988: issued 10 Aug 2021 [USPTO] [JUSTIA]
U.S. Patent No. 11,663,327: issued 30 May 2023 [USPTO] [JUSTIA]
U.S. Patent No. 12,111,922: issued 8 Oct 2024 [USPTO] [JUSTIA]
[IA 4] Dynamic Monitoring and Securing of Factory Processes, Equipment and Automated Systems
Coinvented with: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, and James Williams III
Abstract: 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.
U.S. Patent No. 11,100,221: issued 24 Aug 2021 [USPTO] [JUSTIA]
U.S. Patent No. 11,693,956: issued 4 Jul 2023 [USPTO] [JUSTIA]
U.S. Patent No. 11,989,288: issued 21 May 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,026,251: issued 2 Jul 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,072,972: issued 27 Aug 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,111,923: issued 8 Oct 2024 [USPTO] [JUSTIA]
[IA 3] Dynamic Monitoring and Securing of Factory Processes, Equipment and Automated Systems
Coinvented with: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, and James Williams III
Abstract: 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.
U.S. Patent No. 11,063,965: issued 13 Jul 2021 [USPTO] [JUSTIA]
U.S. Patent No. 12,034,742: issued 9 Jul 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,120,131: issued 15 Oct 2024 [USPTO] [JUSTIA]
U.S. Patent No. 12,155,673: issued 26 Nov 2024 [USPTO] [JUSTIA]
[IA 2] Dynamic Training for Assembly Lines
Coinvented with: Matthew C. Putman, Vadim Pinskiy, and Eun-Sol Kim
Abstract: 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.
U.S. Patent No. 10,481,579: issued 19 Nov 2019 [USPTO] [JUSTIA]
U.S. Patent No. 11,156,982: issued 26 Oct 2021 [USPTO] [JUSTIA]
[IA 1] Methods and Systems for Measuring a Property of a Macromolecule
Coinvented with: Jason Reed and Bud Mishra
Abstract: 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.
U.S. Patent No. 9,995,766: issued 12 Jun 2018 [USPTO] [JUSTIA]