Patents by Inventor Mohammad Reza Tayebnejad

Mohammad Reza Tayebnejad 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: 20150046351
    Abstract: An exemplary system that includes a computing device receiving data that identifies a set of providers including a target provider. The computing device performs a link-based attribute pre-process on the data to identify, based on the set of providers, a node network structured around the target provider and to generate a set of link-based attributes for the target provider. The computing device classifies the target provider based on the set of link-based attributes.
    Type: Application
    Filed: August 6, 2013
    Publication date: February 12, 2015
    Applicant: Verizon Patent and Licensing Inc.
    Inventor: Mohammad Reza Tayebnejad
  • Patent number: 8806591
    Abstract: A computer is configured to receive an authentication request that identifies one or more authentication form factors, and for each form factor identified, further identifies at least one parameter. The computer is further configured to generate a risk score for the authentication request using the parameter, the risk score being based at least in part on a complexity associated with each of the one or more authentication form factors. The computer is further configured to provide the risk score to a requester.
    Type: Grant
    Filed: January 7, 2011
    Date of Patent: August 12, 2014
    Assignee: Verizon Patent and Licensing Inc.
    Inventors: Charles Dallas, Mohammad Reza Tayebnejad, Ken Mckeever, Vidhyaprakash Ramachandran, Paul Andrew Donfried
  • Publication number: 20120180124
    Abstract: A computer is configured to receive an authentication request that identifies one or more authentication form factors, and for each form factor identified, further identifies at least one parameter. The computer is further configured to generate a risk score for the authentication request using the parameter, the risk score being based at least in part on a complexity associated with each of the one or more authentication form factors. The computer is further configured to provide the risk score to a requester.
    Type: Application
    Filed: January 7, 2011
    Publication date: July 12, 2012
    Applicant: Verizon Patent and Licensing Inc.
    Inventors: Charles Dallas, Mohammad Reza Tayebnejad, Ken McKeever, Vidhyaprakash Ramachandran, Paul Andrew Donfried
  • Patent number: 7403931
    Abstract: A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.
    Type: Grant
    Filed: September 18, 2006
    Date of Patent: July 22, 2008
    Assignee: Verizon Business Global LLC
    Inventors: Mohammad Reza Tayebnejad, Karl Aric Van Camp, Charles Alan Dallas, John Hans Van Arkel
  • Patent number: 7113932
    Abstract: A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.
    Type: Grant
    Filed: January 10, 2002
    Date of Patent: September 26, 2006
    Assignee: MCI, LLC
    Inventors: Mohammad Reza Tayebnejad, Karl Aric Van Camp, Charles Alan Dallas, John Hans Van Arkel
  • Publication number: 20020161731
    Abstract: A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.
    Type: Application
    Filed: January 10, 2002
    Publication date: October 31, 2002
    Inventors: Mohammad Reza Tayebnejad, Karl Aric Van Camp, Charles Alan Dallas, John Hans Van Arkel