Patents by Inventor Michael Teichner

Michael Teichner 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 determining actions performed by objects within images
    Patent number: 11151412
    Abstract: A system for determining an action performed within an input image includes a memory to store one or more instructions, and a processor communicatively coupled to the memory, and configured to execute the one or more instructions in the memory. The processor employs a convolutional neural network (CNN) that includes a predefined number of initial stages for extracting one or more significant features corresponding to the input image, wherein each initial stage includes a first layer, and a residual block, and wherein the first layer is selected from a group consisting of a convolution layer, a max pooling layer, and an average pooling layer. The CNN includes a final stage for classifying the extracted significant features into one or more predefined classes, wherein the final stage is formed of a global average pooling layer, and a dense layer.
    Type: Grant
    Filed: July 1, 2019
    Date of Patent: October 19, 2021
    Assignee: Everseen Limited
    Inventors: Michael Teichner, Bogdan Ciubotaru
  • SYSTEMS AND METHODS FOR DETERMINING ACTIONS PERFORMED BY OBJECTS WITHIN IMAGES
    Publication number: 20210004641
    Abstract: A system for determining an action performed within an input image includes a memory to store one or more instructions, and a processor communicatively coupled to the memory, and configured to execute the one or more instructions in the memory. The processor employs a convolutional neural network (CNN) that includes a predefined number of initial stages for extracting one or more significant features corresponding to the input image, wherein each initial stage includes a first layer, and a residual block, and wherein the first layer is selected from a group consisting of a convolution layer, a max pooling layer, and an average pooling layer. The CNN includes a final stage for classifying the extracted significant features into one or more predefined classes, wherein the final stage is formed of a global average pooling layer, and a dense layer.
    Type: Application
    Filed: July 1, 2019
    Publication date: January 7, 2021
    Inventors: Michael Teichner, Bogdan Ciubotaru