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).

  • Publication number: 20220269254
    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.
    Type: Application
    Filed: February 25, 2022
    Publication date: August 25, 2022
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Andrew Sundstrom, Damas Limoge, Vadim Pinskiy, Aswin Raghav Nirmaleswaran, Eun-Sol Kim
  • Patent number: 11411293
    Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.
    Type: Grant
    Filed: December 28, 2021
    Date of Patent: August 9, 2022
    Assignee: Nanotronics Imaging, Inc.
    Inventors: John B. Putman, Matthew C. Putman, Damas Limoge, Michael Moskie, Jonathan Lee
  • Publication number: 20220024140
    Abstract: Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low-resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.
    Type: Application
    Filed: August 6, 2021
    Publication date: January 27, 2022
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
  • Publication number: 20210394456
    Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a computing system. The computing system receives an image of the product at a step of the multi-step manufacturing process. The computing system determines a current state of the product based on the image of the product. The computing system determines, via a deep learning model, that the product is not within specification based on the current state of the product and the image of the product. Based on the determining, the computing system adjusts a control logic for at least a following station. The adjusting includes generating, by the deep learning model, a corrective action to be performed by the following station.
    Type: Application
    Filed: September 8, 2021
    Publication date: December 23, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Fabian Hough, John B. Putman, Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, Sadegh Nouri Gooshki
  • Publication number: 20210387421
    Abstract: Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.
    Type: Application
    Filed: August 23, 2021
    Publication date: December 16, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
  • Publication number: 20210378190
    Abstract: A controlled growth system is provided herein. The controlled growth system includes a controlled growth environment, a controller, a sensor, and a computing system. The controlled growth environment is configured to grow a biologic. The controller is in communication with the controlled growth environment. The controller is configured to manage process parameters of the controlled growth environment. The sensor is configured to monitor the biologic during a growth process. The computing system is in communication with the sensor and the controller. The computing system is programmed to perform operations for achieving a desired final quality metric for the biologic.
    Type: Application
    Filed: June 3, 2021
    Publication date: December 9, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Damas Limoge, Vadim Pinskiy, Parker Musselman
  • Publication number: 20210382990
    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.
    Type: Application
    Filed: August 23, 2021
    Publication date: December 9, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Publication number: 20210342979
    Abstract: An inspection apparatus includes a specimen stage configured to retain a specimen, at least three imaging devices arranged in a triangular array positioned above the specimen stage, each of the at least three imaging devices configured to capture an image of the specimen, one or more sets of lights positioned between the specimen stage and the at least three imaging devices, and a control system in communication with the at least three imaging devices.
    Type: Application
    Filed: July 14, 2021
    Publication date: November 4, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Jonathan Lee, Damas Limoge, Matthew C. Putman, John B. Putman, Michael Moskie
  • Patent number: 11156991
    Abstract: 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: Grant
    Filed: July 23, 2019
    Date of Patent: October 26, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Patent number: 11156992
    Abstract: 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: Grant
    Filed: October 24, 2019
    Date of Patent: October 26, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20210320931
    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.
    Type: Application
    Filed: June 23, 2021
    Publication date: October 14, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Publication number: 20210318674
    Abstract: 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: Application
    Filed: June 23, 2021
    Publication date: October 14, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20210311465
    Abstract: 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: Application
    Filed: June 23, 2021
    Publication date: October 7, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20210311440
    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.
    Type: Application
    Filed: June 18, 2021
    Publication date: October 7, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Andrew Sundstrom, Eun-Sol Kim, Damas Limoge, Vadim Pinskiy, Matthew C. Putman
  • Patent number: 11117328
    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 product. The monitoring platform is configured to monitor progression of the product 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 product.
    Type: Grant
    Filed: September 9, 2020
    Date of Patent: September 14, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Fabian Hough, John B. Putman, Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, Sadegh Nouri Gooshki
  • Publication number: 20210263495
    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.
    Type: Application
    Filed: February 19, 2021
    Publication date: August 26, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Sadegh Nouri Gooshki, Aswin Raghav Nirmaleswaran, Fabian Hough
  • Patent number: 11097490
    Abstract: Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.
    Type: Grant
    Filed: December 20, 2019
    Date of Patent: August 24, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
  • Patent number: 11100221
    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.
    Type: Grant
    Filed: June 18, 2020
    Date of Patent: August 24, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Publication number: 20210256116
    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.
    Type: Application
    Filed: November 20, 2020
    Publication date: August 19, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom
  • Patent number: 11084225
    Abstract: Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low-resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.
    Type: Grant
    Filed: April 20, 2020
    Date of Patent: August 10, 2021
    Assignee: NANOTRONICS IMAGING, INC.
    Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran