Patents by Inventor Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny

Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny 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: 11514244
    Abstract: Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.
    Type: Grant
    Filed: December 22, 2015
    Date of Patent: November 29, 2022
    Assignee: Adobe Inc.
    Inventors: Scott D. Cohen, Walter Wei-Tuh Chang, Brian L. Price, Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny
  • Patent number: 10460033
    Abstract: Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.
    Type: Grant
    Filed: December 22, 2015
    Date of Patent: October 29, 2019
    Assignee: Adobe Inc.
    Inventors: Scott D. Cohen, Walter Wei-Tuh Chang, Brian L. Price, Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny
  • Publication number: 20170132526
    Abstract: Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.
    Type: Application
    Filed: December 22, 2015
    Publication date: May 11, 2017
    Inventors: Scott D. Cohen, Walter Wei-Tuh Chang, Brian L. Price, Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny
  • Publication number: 20170132498
    Abstract: Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.
    Type: Application
    Filed: December 22, 2015
    Publication date: May 11, 2017
    Inventors: Scott D. Cohen, Walter Wei-Tuh Chang, Brian L. Price, Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny