Patents by Inventor Janne Hellsten

Janne Hellsten 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: 11921997
    Abstract: User interfaces and methods are disclosed. In some embodiments, a plurality of source artifacts is displayed. A selector is operable to indicate a selected set of the source artifacts. An output artifact is displayed having an output attribute that represents a combination of source attributes from the source artifacts in the selected set. An amount of contribution to the first output attribute by respective ones of the source artifacts in the first selected set is based on a coordinate of the selector relative to coordinates of the source attributes in the first selected set.
    Type: Grant
    Filed: July 22, 2022
    Date of Patent: March 5, 2024
    Assignee: NVIDIA Corporation
    Inventors: Janne Hellsten, Tero Tapani Karras, Samuli Matias Laine
  • Publication number: 20220398005
    Abstract: User interfaces and methods are disclosed. In some embodiments, a plurality of source artifacts is displayed. A selector is operable to indicate a selected set of the source artifacts. An output artifact is displayed having an output attribute that represents a combination of source attributes from the source artifacts in the selected set. An amount of contribution to the first output attribute by respective ones of the source artifacts in the first selected set is based on a coordinate of the selector relative to coordinates of the source attributes in the first selected set.
    Type: Application
    Filed: July 22, 2022
    Publication date: December 15, 2022
    Inventors: Janne Hellsten, Tero Tapani Karras, Samuli Matias Laine
  • Publication number: 20220398004
    Abstract: User interfaces, methods and structures are described for intuitively and fluidly creating new artifacts from existing artifacts and for exploring latent spaces in a visual manner. In example embodiments, source artifacts are displayed along with a selector. The selector is operable to indicate a selected set of the source artifacts by establishing a selection region that includes portions of one or more of the source artifacts displayed. Source vectors are associated with the source artifacts in the selected set. One or more resultant vectors are determined based on the source vectors, and an output artifact is generated based on the one or more resultant vectors.
    Type: Application
    Filed: June 10, 2021
    Publication date: December 15, 2022
    Inventors: Janne Hellsten, Tero Tapani Karras, Samuli Matias Laine
  • Patent number: 11435885
    Abstract: User interfaces and methods are disclosed. In some embodiments, a plurality of source artifacts is displayed. A selector is operable to indicate a selected set of the source artifacts. The selected set corresponds to those of the source artifacts that intersect at least partially with a selection region. An output artifact is displayed having an output attribute that represents a combination of source attributes from the source artifacts in the selected set.
    Type: Grant
    Filed: June 10, 2021
    Date of Patent: September 6, 2022
    Assignee: NVIDIA Corporation
    Inventors: Janne Hellsten, Tero Tapani Karras, Samuli Matias Laine
  • Patent number: 11263525
    Abstract: A neural network learns a particular task by being shown many examples. In one scenario, a neural network may be trained to label an image, such as cat, dog, bicycle, chair, etc. In other scenario, a neural network may be trained to remove noise from videos or identify specific objects within images, such as human faces, bicycles, etc. Rather than training a complex neural network having a predetermined topology of features and interconnections between the features to learn the task, the topology of the neural network is modified as the neural network is trained for the task, eventually evolving to match the predetermined topology of the complex neural network. In the beginning the neural network learns large-scale details for the task (bicycles have two wheels) and later, as the neural network becomes more complex, learns smaller details (the wheels have spokes).
    Type: Grant
    Filed: January 18, 2019
    Date of Patent: March 1, 2022
    Assignee: NVIDIA Corporation
    Inventors: Tero Tapani Karras, Timo Oskari Aila, Samuli Matias Laine, Jaakko T. Lehtinen, Janne Hellsten
  • Publication number: 20190171936
    Abstract: A neural network learns a particular task by being shown many examples. In one scenario, a neural network may be trained to label an image, such as cat, dog, bicycle, chair, etc. In other scenario, a neural network may be trained to remove noise from videos or identify specific objects within images, such as human faces, bicycles, etc. Rather than training a complex neural network having a predetermined topology of features and interconnections between the features to learn the task, the topology of the neural network is modified as the neural network is trained for the task, eventually evolving to match the predetermined topology of the complex neural network. In the beginning the neural network learns large-scale details for the task (bicycles have two wheels) and later, as the neural network becomes more complex, learns smaller details (the wheels have spokes).
    Type: Application
    Filed: January 18, 2019
    Publication date: June 6, 2019
    Inventors: Tero Tapani Karras, Timo Oskari Aila, Samuli Matias Laine, Jaakko T. Lehtinen, Janne Hellsten