Patents by Inventor Damas Limoge
Damas Limoge has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20250147499Abstract: 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.Type: ApplicationFiled: November 25, 2024Publication date: May 8, 2025Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Andrew Sundstrom, Damas Limoge, Vadim Pinskiy, Aswin Raghav Nirmaleswaran, Eun-Sol Kim
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Publication number: 20250130538Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.Type: ApplicationFiled: November 25, 2024Publication date: April 24, 2025Applicant: Nanotronics Imaging, Inc.Inventors: John B. Putman, Sarah Constantin, Valerie Bordelanne, Damas Limoge, Joanna Lee
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Publication number: 20250094577Abstract: 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.Type: ApplicationFiled: November 25, 2024Publication date: March 20, 2025Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom
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Publication number: 20250093853Abstract: 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.Type: ApplicationFiled: November 25, 2024Publication date: March 20, 2025Applicant: Nanotronics Imaging, Inc.Inventors: Andrew Sundstrom, Damas Limoge, Eun-Sol Kim, Vadim Pinskiy, Matthew C. Putman
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Patent number: 12153387Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.Type: GrantFiled: August 31, 2023Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: John B. Putman, Sarah Constantin, Valerie Bordelanne, Damas Limoge, Jonathan Lee
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Patent number: 12153412Abstract: Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.Type: GrantFiled: July 24, 2023Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
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Patent number: 12153414Abstract: 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.Type: GrantFiled: February 25, 2022Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Andrew Sundstrom, Damas Limoge, Vadim Pinskiy, Aswin Raghav Nirmaleswaran, Eun-Sol Kim
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Patent number: 12153668Abstract: 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.Type: GrantFiled: November 20, 2020Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom
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Patent number: 12155673Abstract: 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.Type: GrantFiled: June 23, 2021Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
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Patent number: 12153401Abstract: 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.Type: GrantFiled: November 6, 2020Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Andrew Sundstrom, Damas Limoge, Eun-Sol Kim, Vadim Pinskiy, Matthew C. Putman
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Patent number: 12153411Abstract: Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.Type: GrantFiled: June 5, 2023Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
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Patent number: 12153408Abstract: 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.Type: GrantFiled: June 18, 2021Date of Patent: November 26, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Andrew Sundstrom, Eun-Sol Kim, Damas Limoge, Vadim Pinskiy, Matthew C. Putman
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Patent number: 12120131Abstract: 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.Type: GrantFiled: June 23, 2021Date of Patent: October 15, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
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Patent number: 12117799Abstract: 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.Type: GrantFiled: November 6, 2020Date of Patent: October 15, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Andrew Sundstrom, Damas Limoge, Eun-Sol Kim, Vadim Pinskiy, Matthew C. Putman
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Patent number: 12118081Abstract: 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.Type: GrantFiled: November 20, 2020Date of Patent: October 15, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom
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Patent number: 12117812Abstract: 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.Type: GrantFiled: June 18, 2021Date of Patent: October 15, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Andrew Sundstrom, Eun-Sol Kim, Damas Limoge, Vadim Pinskiy, Matthew C. Putman
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Patent number: 12117814Abstract: Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.Type: GrantFiled: July 24, 2023Date of Patent: October 15, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
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Patent number: 12111923Abstract: 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.Type: GrantFiled: June 28, 2023Date of Patent: October 8, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
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Patent number: 12111645Abstract: Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.Type: GrantFiled: June 5, 2023Date of Patent: October 8, 2024Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
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Publication number: 20240329609Abstract: A training set that includes at least two data types corresponding to operations and control of a manufacturing process is obtained. A deep learning processor is trained to predict expected characteristics of output control signals that correspond with one or more corresponding input operating instructions. A first input operating instruction is received from a first signal splitter. A first output control signal is received from a second signal splitter. The deep learning processor correlates the first input operating instruction and the first output control signal. Based on the correlating, the deep learning processor determines that the first output control signal is not within a range of expected values based on the first input operating instruction. Responsive to the determining, an indication of an anomalous activity is provided as a result of detection of the anomalous activity in the manufacturing process.Type: ApplicationFiled: April 8, 2024Publication date: October 3, 2024Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, John B. Putman, Joanna Lee, Damas Limoge