Patents by Inventor Noah Jonathan Gamboa

Noah Jonathan Gamboa 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).

  • Method and device for improved motion planning
    Patent number: 11710276
    Abstract: In one implementation, a method for improved motion planning. The method includes: obtaining a macro task for a virtual agent within a virtual environment; generating a search-tree based on at least one of the macro task, a state of the virtual environment, and a state of the virtual agent, wherein the search-tree includes a plurality of task nodes corresponding to potential tasks for performance by the virtual agent in furtherance of the macro task; and determining physical motion plans (PMPs) for at least some of the plurality of task nodes within the search-tree in order to generate a lookahead planning gradient for the first time, wherein a granularity of a PMP for a respective task node in the first search-tree is a function of the temporal distance of the respective task node from the first time.
    Type: Grant
    Filed: June 25, 2021
    Date of Patent: July 25, 2023
    Assignee: Apple Inc.
    Inventors: Daniel Laszlo Kovacs, Siva Chandra Mouli Sivapurapu, Payal Jotwani, Noah Jonathan Gamboa
  • Representing a neural network utilizing paths within the network to improve a performance of the neural network
    Patent number: 11507846
    Abstract: Artificial neural networks (ANNs) are computing systems that imitate a human brain by learning to perform tasks by considering examples. By representing an artificial neural network utilizing individual paths each connecting an input of the ANN to an output of the ANN, a complexity of the ANN may be reduced, and the ANN may be trained and implemented in a much faster manner when compared to an implementation using fully connected ANN graphs.
    Type: Grant
    Filed: March 13, 2019
    Date of Patent: November 22, 2022
    Assignee: NVIDIA CORPORATION
    Inventors: Alexander Keller, Gonçalo Felipe Torcato Mordido, Noah Jonathan Gamboa, Matthijs Jules Van Keirsbilck
  • REPRESENTING A NEURAL NETWORK UTILIZING PATHS WITHIN THE NETWORK TO IMPROVE A PERFORMANCE OF THE NEURAL NETWORK
    Publication number: 20220172072
    Abstract: Artificial neural networks (ANNs) are computing systems that imitate a human brain by learning to perform tasks by considering examples. By representing an artificial neural network utilizing individual paths each connecting an input of the ANN to an output of the ANN, a complexity of the ANN may be reduced, and the ANN may be trained and implemented in a much faster manner when compared to an implementation using fully connected ANN graphs.
    Type: Application
    Filed: February 15, 2022
    Publication date: June 2, 2022
    Inventors: Alexander Keller, Gonçalo Filipe Torcato Mordido, Noah Jonathan Gamboa, Matthijs Jules Van Keirsbilck
  • REPRESENTING A NEURAL NETWORK UTILIZING PATHS WITHIN THE NETWORK TO IMPROVE A PERFORMANCE OF THE NEURAL NETWORK
    Publication number: 20190294972
    Abstract: Artificial neural networks (ANNs) are computing systems that imitate a human brain by learning to perform tasks by considering examples. By representing an artificial neural network utilizing individual paths each connecting an input of the ANN to an output of the ANN, a complexity of the ANN may be reduced, and the ANN may be trained and implemented in a much faster manner when compared to an implementation using fully connected ANN graphs.
    Type: Application
    Filed: March 13, 2019
    Publication date: September 26, 2019
    Inventors: Alexander Keller, Gonçalo Felipe Torcato Mordido, Noah Jonathan Gamboa, Matthijs Jules Van Keirsbilck