Patents by Inventor Vadim Pinskiy

Vadim Pinskiy 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).

  • Patent number: 11953893
    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: June 5, 2023
    Date of Patent: April 9, 2024
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Patent number: 11948270
    Abstract: Systems, methods, and computer-readable media for feedback on and improving the accuracy of super-resolution imaging. In some embodiments, a low resolution image of a specimen can be obtained using a low resolution objective of a microscopy inspection system. A super-resolution image of at least a portion of the specimen can be generated from the low resolution image of the specimen using a super-resolution image simulation. Subsequently, an accuracy assessment of the super-resolution image can be identified based on one or more degrees of equivalence between the super-resolution image and one or more actually scanned high resolution images of at least a portion of one or more related specimens identified using a simulated image classifier. Based on the accuracy assessment of the super-resolution image, it can be determined whether to further process the super-resolution image. The super-resolution image can be further processed if it is determined to further process the super-resolution image.
    Type: Grant
    Filed: August 31, 2023
    Date of Patent: April 2, 2024
    Assignee: Nanotronics Imaging , Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Joseph R. Succar
  • Patent number: 11889797
    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: Grant
    Filed: June 3, 2021
    Date of Patent: February 6, 2024
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Damas Limoge, Vadim Pinskiy, Parker Musselman
  • Publication number: 20230419444
    Abstract: Systems, methods, and computer-readable media for feedback on and improving the accuracy of super-resolution imaging. In some embodiments, a low resolution image of a specimen can be obtained using a low resolution objective of a microscopy inspection system. A super-resolution image of at least a portion of the specimen can be generated from the low resolution image of the specimen using a super-resolution image simulation. Subsequently, an accuracy assessment of the super-resolution image can be identified based on one or more degrees of equivalence between the super-resolution image and one or more actually scanned high resolution images of at least a portion of one or more related specimens identified using a simulated image classifier. Based on the accuracy assessment of the super-resolution image, it can be determined whether to further process the super-resolution image. The super-resolution image can be further processed if it is determined to further process the super-resolution image.
    Type: Application
    Filed: August 31, 2023
    Publication date: December 28, 2023
    Applicant: Nanotronics Imaging , Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim PINSKIY, Josephn R. Succar
  • Publication number: 20230391016
    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 21, 2023
    Publication date: December 7, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
  • Publication number: 20230367301
    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: July 24, 2023
    Publication date: November 16, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20230359163
    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.
    Type: Application
    Filed: July 17, 2023
    Publication date: November 9, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • Publication number: 20230359730
    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: June 28, 2023
    Publication date: November 9, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Patent number: 11796785
    Abstract: An automatic focus system for an optical microscope that facilitates faster focusing by using at least two offset focusing cameras. Each offset focusing camera can be positioned on a different side of an image forming conjugate plane so that their sharpness curves intersect at the image forming conjugate plane. Focus of a specimen can be adjusted by using sharpness values determined from images taken by the offset focusing cameras.
    Type: Grant
    Filed: December 5, 2022
    Date of Patent: October 24, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: John B. Putman, Matthew C. Putman, Vadim Pinskiy, Denis Y. Sharoukhov
  • Publication number: 20230324874
    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.
    Type: Application
    Filed: June 12, 2023
    Publication date: October 12, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • Publication number: 20230315071
    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 5, 2023
    Publication date: October 5, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20230296872
    Abstract: A fluorescence microscopy inspection system includes light sources able to emit light that causes a specimen to fluoresce and light that does not cause a specimen to fluoresce. The emitted light is directed through one or more filters and objective channels towards a specimen. A ring of lights projects light at the specimen at an oblique angle through a darkfield channel. One of the filters may modify the light to match a predetermined bandgap energy associated with the specimen and another filter may filter wavelengths of light reflected from the specimen and to a camera. The camera may produce an image from the received light and specimen classification and feature analysis may be performed on the image.
    Type: Application
    Filed: May 26, 2023
    Publication date: September 21, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Denis Sharoukhov
  • Publication number: 20230297668
    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.
    Type: Application
    Filed: May 26, 2023
    Publication date: September 21, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Andrew Sundstrom, James Williams, III
  • Patent number: 11748846
    Abstract: Systems, methods, and computer-readable media for feedback on and improving the accuracy of super-resolution imaging. In some embodiments, a low resolution image of a specimen can be obtained using a low resolution objective of a microscopy inspection system. A super-resolution image of at least a portion of the specimen can be generated from the low resolution image of the specimen using a super-resolution image simulation. Subsequently, an accuracy assessment of the super-resolution image can be identified based on one or more degrees of equivalence between the super-resolution image and one or more actually scanned high resolution images of at least a portion of one or more related specimens identified using a simulated image classifier. Based on the accuracy assessment of the super-resolution image, it can be determined whether to further process the super-resolution image. The super-resolution image can be further processed if it is determined to further process the super-resolution image.
    Type: Grant
    Filed: April 5, 2021
    Date of Patent: September 5, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Joseph Succar
  • Patent number: 11731368
    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: August 6, 2021
    Date of Patent: August 22, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
  • Patent number: 11709483
    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: June 23, 2021
    Date of Patent: July 25, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Patent number: 11703824
    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.
    Type: Grant
    Filed: December 27, 2021
    Date of Patent: July 18, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • Patent number: 11693956
    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: August 23, 2021
    Date of Patent: July 4, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Publication number: 20230186502
    Abstract: An imaging system is disclosed herein. The imaging system includes an imaging apparatus and a computing system. The imaging apparatus includes a plurality of light sources positioned at a plurality of positions and a plurality of angles relative to a stage configured to support a specimen. The imaging apparatus is configured to capture a plurality of images of a surface of the specimen. The computing system in communication with the imaging apparatus. The computing system configured to generate a 3D-reconstruction of the surface of the specimen by receiving, from the imaging apparatus, the plurality of images of the surface of the specimen, generating, by the imaging apparatus via a deep learning model, a height map of the surface of the specimen based on the plurality of images, and outputting a 3D-reconstruction of the surface of the specimen based on the height map generated by the deep learning model.
    Type: Application
    Filed: February 6, 2023
    Publication date: June 15, 2023
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Tanaporn Na Narong, Denis Sharoukhov, Tonislav Ivanov
  • Patent number: 11675330
    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.
    Type: Grant
    Filed: October 25, 2021
    Date of Patent: June 13, 2023
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom