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: 11335443
    Abstract: The application of deep machine learning controllers to derive models of phenotypic patient data from primary economic data is disclosed herein. The use of systems and methods of employing the model are disclosed and useful in predicting treatment outcomes and compound efficacy, suggesting treatment plans and compounds, and clinical studies and phenotypical correlation studies in conjunction with medical records, economic data sets or combinations thereof.
    Type: Grant
    Filed: September 7, 2020
    Date of Patent: May 17, 2022
    Assignee: OpenNano Pte. Ltd.
    Inventors: Vadim Pinskiy, Ashwin Gopinath, Kim E. Drexler, TJ Brunette, George Markou, Serge Faguet, Petr Boiko, Yuliya Seregina
  • Publication number: 20220121169
    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: December 27, 2021
    Publication date: April 21, 2022
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • Patent number: 11294162
    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: Grant
    Filed: January 24, 2020
    Date of Patent: April 5, 2022
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Denis Sharoukhov
  • Publication number: 20220043420
    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: October 25, 2021
    Publication date: February 10, 2022
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • 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: 20220024986
    Abstract: The present invention provides bi-stable functionalized organic molecule and methods of utilizing these bi-stable functionalized organic molecule.
    Type: Application
    Filed: June 9, 2021
    Publication date: January 27, 2022
    Inventors: Kim E. Drexler, Serge Faguet, Ashwin Gopinath, Vadim Pinskiy, Tj Brunette, George Markou
  • Patent number: 11209795
    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: April 20, 2020
    Date of Patent: December 28, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • 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: 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: 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: 20210365549
    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: August 6, 2021
    Publication date: November 25, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Andrew Sundstrom, James Williams, III
  • 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
  • Patent number: 11156982
    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: September 30, 2019
    Date of Patent: October 26, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, Eun-Sol Kim, Andrew Sundstrom
  • 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: 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
  • 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
  • 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