Patents by Inventor Delia Fernandez

Delia Fernandez 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).

  • Video tagging system and method
    Patent number: 11256741
    Abstract: An automatic video tagging system which learns from videos, their web context and comments shared on social networks is described. Massive multimedia collections are analyzed by Internet crawling and a knowledge base is maintained that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. Practical applications of video recognition require a label scheme that is appealing to the end-user (i.e. obtained from social curation) and a training dataset that can be updated in real-time to be able to recognize new actions, scenes and people. To create this dataset that evolves in real-time and uses labels that are relevant to the users, a weakly-supervised deep learning approach is utilized combining both a machine-learning pre-processing stage together with a set of keywords obtained from the internet.
    Type: Grant
    Filed: October 30, 2017
    Date of Patent: February 22, 2022
    Assignee: Vertex Capital LLC
    Inventors: Elisenda Bou Balust, Juan Carlos Riveiro Insua, Delia Fernandez Cañellas, Joan Espadaler Rodes, Asier Aduriz Berasategi, David Varas Gonzalez
  • Video Tagging System and Method
    Publication number: 20190258671
    Abstract: An automatic video tagging system which learns from videos, their web context and comments shared on social networks is described. Massive multimedia collections are analyzed by Internet crawling and a knowledge base is maintained that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. Practical applications of video recognition require a label scheme that is appealing to the end-user (i.e. obtained from social curation) and a training dataset that can be updated in real-time to be able to recognize new actions, scenes and people. To create this dataset that evolves in real-time and uses labels that are relevant to the users, a weakly-supervised deep learning approach is utilized combining both a machine-learning pre-processing stage together with a set of keywords obtained from the internet.
    Type: Application
    Filed: October 30, 2017
    Publication date: August 22, 2019
    Inventors: Elisenda Bou, Juan Carlos Riveiro, Delia Fernandez, Joan Espadaler, Asier Aduriz, David Varas