Patents by Inventor Federico Zarfati

Federico Zarfati 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: 20220239707
    Abstract: A system described herein may provide a technique for Embodiments described herein provide for the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.
    Type: Application
    Filed: April 18, 2022
    Publication date: July 28, 2022
    Applicant: Verizon Patent and Licensing Inc.
    Inventors: Daniel Chen, Federico Zarfati, Stuart R. Burkhart, Chiang Ming Yang, Aude Claire Marzuoli
  • Patent number: 11330023
    Abstract: A system described herein may provide a technique for Embodiments described herein provide for the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.
    Type: Grant
    Filed: June 12, 2020
    Date of Patent: May 10, 2022
    Assignee: Verizon Patent and Licensing Inc.
    Inventors: Daniel Chen, Federico Zarfati, Stuart R. Burkhart, Chiang Ming Yang, Aude Claire Marzuoli
  • Publication number: 20210392173
    Abstract: A system described herein may provide a technique for Embodiments described herein provide for the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.
    Type: Application
    Filed: June 12, 2020
    Publication date: December 16, 2021
    Applicant: Verizon Patent and Licensing Inc.
    Inventors: Daniel Chen, Federico Zarfati, Stuart R. Burkhart, Chiang Ming Yang, Aude Claire Marzuoli