Patents by Inventor Denis Eric Goupil

Denis Eric Goupil 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).

  • SYSTEMS AND METHODS FOR IMAGE BASED CONTENT CAPTURE AND EXTRACTION UTILIZING DEEP LEARNING NEURAL NETWORK AND BOUNDING BOX DETECTION TRAINING TECHNIQUES
    Publication number: 20240135700
    Abstract: Systems, methods, and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.
    Type: Application
    Filed: December 19, 2023
    Publication date: April 25, 2024
    Inventors: Arnaud Gilles Flament, Christopher Dale Lund, Guillaume Bernard Serge Koch, Denis Eric Goupil
  • Systems and methods for image based content capture and extraction utilizing deep learning neural network and bounding box detection training techniques
    Patent number: 11893777
    Abstract: Systems, methods, and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.
    Type: Grant
    Filed: January 12, 2021
    Date of Patent: February 6, 2024
    Assignee: Open Text Corporation
    Inventors: Arnaud Gilles Flament, Christopher Dale Lund, Guillaume Bernard Serge Koch, Denis Eric Goupil
  • SYSTEMS AND METHODS FOR IMAGE BASED CONTENT CAPTURE AND EXTRACTION UTILIZING DEEP LEARNING NEURAL NETWORK AND BOUNDING BOX DETECTION TRAINING TECHNIQUES
    Publication number: 20210133436
    Abstract: Systems, methods, and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.
    Type: Application
    Filed: January 12, 2021
    Publication date: May 6, 2021
    Inventors: Arnaud Gilles Flament, Christopher Dale Lund, Guillaume Bernard Serge Koch, Denis Eric Goupil
  • Systems and methods for image based content capture and extraction utilizing deep learning neural network and bounding box detection training techniques
    Patent number: 10902252
    Abstract: Systems, methods and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.
    Type: Grant
    Filed: July 13, 2018
    Date of Patent: January 26, 2021
    Assignee: OPEN TEXT CORPORATION
    Inventors: Arnaud Gilles Flament, Christopher Dale Lund, Guillaume Bernard Serge Koch, Denis Eric Goupil
  • SYSTEMS AND METHODS FOR IMAGE BASED CONTENT CAPTURE AND EXTRACTION UTILIZING DEEP LEARNING NEURAL NETWORK AND BOUNDING BOX DETECTION TRAINING TECHNIQUES
    Publication number: 20190019020
    Abstract: Systems, methods and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.
    Type: Application
    Filed: July 13, 2018
    Publication date: January 17, 2019
    Inventors: Arnaud Gilles Flament, Christopher Dale Lund, Guillaume Bernard Serge Koch, Denis Eric Goupil