Patents by Inventor Asaf Rendel

Asaf Rendel 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).

  • Hybrid predictive model for enhancing prosodic expressiveness
    Patent number: 9972302
    Abstract: Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.
    Type: Grant
    Filed: September 29, 2016
    Date of Patent: May 15, 2018
    Assignee: International Business Machines Corporation
    Inventors: Raul Fernandez, Asaf Rendel
  • HYBRID PREDICTIVE MODEL FOR ENHANCING PROSODIC EXPRESSIVENESS
    Publication number: 20170018267
    Abstract: Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.
    Type: Application
    Filed: September 29, 2016
    Publication date: January 19, 2017
    Inventors: Raul Fernandez, Asaf Rendel
  • Hybrid predictive model for enhancing prosodic expressiveness
    Patent number: 9484015
    Abstract: Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.
    Type: Grant
    Filed: August 28, 2013
    Date of Patent: November 1, 2016
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Raul Fernandez, Asaf Rendel
  • Hybrid predictive model for enhancing prosodic expressiveness
    Patent number: 9484016
    Abstract: Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.
    Type: Grant
    Filed: September 17, 2013
    Date of Patent: November 1, 2016
    Assignee: International Business Machines Corporation
    Inventors: Raul Fernandez, Asaf Rendel
  • HYBRID PREDICTIVE MODEL FOR ENHANCING PROSODIC EXPRESSIVENESS
    Publication number: 20140358547
    Abstract: Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.
    Type: Application
    Filed: September 17, 2013
    Publication date: December 4, 2014
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Raul Fernandez, Asaf Rendel
  • HYBRID PREDICTIVE MODEL FOR ENHANCING PROSODIC EXPRESSIVENESS
    Publication number: 20140358546
    Abstract: Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.
    Type: Application
    Filed: August 28, 2013
    Publication date: December 4, 2014
    Applicant: International Business Machines Corporation
    Inventors: Raul Fernandez, Asaf Rendel