Patents by Inventor Bailey Miller

Bailey Miller 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: 20260141628
    Abstract: Certain aspects and features of this disclosure relate to rendering images by training a neural material and applying the material map to a coarse geometry to provide high-fidelity asset encoding. For example, training can involve sampling for a set of lighting and camera configurations arranged to render an image of a target asset. A value for a loss function comparing the target asset with the neural material can be optimized to train the neural material to encode a high-fidelity model of the target asset. This technique restricts the application of the neural material to a specific predetermined geometry, resulting in a reproducible asset that can be used efficiently. Such an asset can be deployed, as examples, to mobile devices or to the web, where the computational budget is limited, and nevertheless produce highly detailed images.
    Type: Application
    Filed: January 12, 2026
    Publication date: May 21, 2026
    Inventors: Krishna Bhargava Mullia Lakshminarayana, Valentin Deschaintre, Nathan Carr, Milos Hasan, Bailey Miller
  • Patent number: 12548241
    Abstract: Certain aspects and features of this disclosure relate to rendering images by training a neural material and applying the material map to a coarse geometry to provide high-fidelity asset encoding. For example, training can involve sampling for a set of lighting and camera configurations arranged to render an image of a target asset. A value for a loss function comparing the target asset with the neural material can be optimized to train the neural material to encode a high-fidelity model of the target asset. This technique restricts the application of the neural material to a specific predetermined geometry, resulting in a reproducible asset that can be used efficiently. Such an asset can be deployed, as examples, to mobile devices or to the web, where the computational budget is limited, and nevertheless produce highly detailed images.
    Type: Grant
    Filed: April 10, 2023
    Date of Patent: February 10, 2026
    Assignee: Adobe Inc.
    Inventors: Krishna Bhargava Mullia Lakshminarayana, Valentin Deschaintre, Nathan Carr, Milos Hasan, Bailey Miller
  • Publication number: 20250074945
    Abstract: In one aspect, the invention relates to cyclopeptides and methods of using the disclosed cyclopeptides to treat bacterial infections due to, for example, Gram-negative pathogens. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.
    Type: Application
    Filed: August 13, 2024
    Publication date: March 6, 2025
    Inventors: Eric W. Schmidt, Margo Haygood, Bailey Miller
  • Patent number: 12215129
    Abstract: In one aspect, the invention relates to cyclopeptides and methods of using the disclosed cyclopeptides to treat bacterial infections due to, for example, Gram-negative pathogens. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.
    Type: Grant
    Filed: October 29, 2021
    Date of Patent: February 4, 2025
    Assignee: University of Utah Research Foundation
    Inventors: Eric W. Schmidt, Margo Haygood, Bailey Miller
  • Publication number: 20240338888
    Abstract: Certain aspects and features of this disclosure relate to rendering images by training a neural material and applying the material map to a coarse geometry to provide high-fidelity asset encoding. For example, training can involve sampling for a set of lighting and camera configurations arranged to render an image of a target asset. A value for a loss function comparing the target asset with the neural material can be optimized to train the neural material to encode a high-fidelity model of the target asset. This technique restricts the application of the neural material to a specific predetermined geometry, resulting in a reproducible asset that can be used efficiently. Such an asset can be deployed, as examples, to mobile devices or to the web, where the computational budget is limited, and nevertheless produce highly detailed images.
    Type: Application
    Filed: April 10, 2023
    Publication date: October 10, 2024
    Inventors: Krishna Bhargava Mullia Lakshminarayana, Valentin Deschaintre, Nathan Carr, Milos Hasan, Bailey Miller
  • Publication number: 20230391827
    Abstract: In one aspect, the invention relates to cyclopeptides and methods of using the disclosed cyclopeptides to treat bacterial infections due to, for example, Gram-negative pathogens. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.
    Type: Application
    Filed: October 29, 2021
    Publication date: December 7, 2023
    Inventors: Eric W. Schmidt, Margo Haygood, Bailey Miller