Patents by Inventor Mark Marsh

Mark Marsh 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).

  • Publication number: 20230349033
    Abstract: Provided herein are highly formable aluminum alloys and methods of making the same. The highly formable aluminum alloys described herein can be prepared from recycled materials without significant addition of primary aluminum alloy material. The aluminum alloys are prepared by casting an aluminum alloy that can include such recycled materials and processing the resulting cast aluminum alloy article. Also described herein are methods of using the aluminum alloys and alloy products.
    Type: Application
    Filed: July 3, 2023
    Publication date: November 2, 2023
    Applicant: Novelis Inc.
    Inventors: Sazol Kumar Das, Matthew Heyen, Peter Evans, Martin Beech, ChangOok Son, Mark Marsh, Rajeev G. Kamat, Rainer Kossak, David Fryatt, David Scott Fisher, Guillaume Florey, Cyrille Bezencon, Juergen Timm
  • Publication number: 20210383096
    Abstract: A system and method are provided for training a machine learning system. In an embodiment, the system generates a three-dimensional model of an environment using a video sequence that includes individual frames taken from a variety of perspectives and environmental conditions. An object in the environment is identified and labeled, in some examples, by an operator, and a three-dimensional model of the object is created. Training data for the machine learning system is created by applying the label to the individual video frames of the video sequence, or by applying a rendering of the three-dimensional model to additional images or video sequences.
    Type: Application
    Filed: June 8, 2020
    Publication date: December 9, 2021
    Inventors: Steven James White, Olof Fredrik Ryden, Donald Mark Marsh
  • Publication number: 20200302241
    Abstract: A system and method are provided for training a machine learning system. In an embodiment, the system generates a three-dimensional model of an environment using a video sequence that includes individual frames taken from a variety of perspectives and environmental conditions. An object in the environment is identified and labeled, in some examples, by an operator, and a three-dimensional model of the object is created. Training data for the machine learning system is created by applying the label to the individual video frames of the video sequence, or by applying a rendering of the three-dimensional model to additional images or video sequences.
    Type: Application
    Filed: June 7, 2020
    Publication date: September 24, 2020
    Inventors: Steven James White, Olof Fredrik Ryden, Donald Mark Marsh
  • Publication number: 20200024713
    Abstract: Provided herein are highly formable aluminum alloys and methods of making the same. The highly formable aluminum alloys described herein can be prepared from recycled materials without significant addition of primary aluminum alloy material. The aluminum alloys are prepared by casting an aluminum alloy that can include such recycled materials and processing the resulting cast aluminum alloy article. Also described herein are methods of using the aluminum alloys and alloy products.
    Type: Application
    Filed: July 22, 2019
    Publication date: January 23, 2020
    Applicant: Novelis Inc.
    Inventors: Sazol Kumar Das, Matthew Heyen, Peter Evans, Martin Beech, ChangOok Son, Mark Marsh, Rajeev G. Kamat, Rainer Kossak, David Fryatt, David Scott Fisher, Guillaume Florey, Cyrille Bezencon, Juergen Timm