Patents by Inventor Verena Sabine Kaynig-Fittkau

Verena Sabine Kaynig-Fittkau 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).

  • Multiple channels of rasterized content for page decomposition using machine learning
    Patent number: 11386685
    Abstract: Techniques are provided for identifying structural elements of a document. One Methodology includes generating a first channel of rasterized content by rasterizing a full page of the document and generating one or more additional channels of rasterized content from the page of the document by rasterizing one or more corresponding content types from the page of the document. Each of the one or more additional channels includes a specific type of content that is different from each of the other one or more additional channels. The methodology further includes inputting the first channel of rasterized content and the one or more additional channels of rasterized content into a machine learning (ML) model. The methodology continues with determining location and classification for each of a plurality of structural elements on the page of the document using the ML model.
    Type: Grant
    Filed: October 17, 2019
    Date of Patent: July 12, 2022
    Assignee: Adobe Inc.
    Inventors: Verena Sabine Kaynig-Fittkau, Smitha Bangalore Naresh, Shawn Alan Gaither, Richard Cohn, Paul John Asente, Eylon Stroh, Emily Seminerio
  • MULTIPLE CHANNELS OF RASTERIZED CONTENT FOR PAGE DECOMPOSITION USING MACHINE LEARNING
    Publication number: 20210117666
    Abstract: Techniques are provided for identifying structural elements of a document. One Methodology includes generating a first channel of rasterized content by rasterizing a full page of the document and generating one or more additional channels of rasterized content from the page of the document by rasterizing one or more corresponding content types from the page of the document. Each of the one or more additional channels includes a specific type of content that is different from each of the other one or more additional channels. The methodology further includes inputting the first channel of rasterized content and the one or more additional channels of rasterized content into a machine learning (ML) model. The methodology continues with determining location and classification for each of a plurality of structural elements on the page of the document using the ML model.
    Type: Application
    Filed: October 17, 2019
    Publication date: April 22, 2021
    Applicant: Adobe Inc.
    Inventors: Verena Sabine Kaynig-Fittkau, Smitha Bangalore Naresh, Shawn Alan Gaither, Richard Cohn, Paul John Asente, Eylon Stroh, Emily Seminerio