Patents by Inventor Sean Marcus Sanders

Sean Marcus Sanders 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).

  • Patent number: 10530671
    Abstract: Methods, systems, and computer readable media for generating and using a web page classification model are disclosed. The method may include identifying a plurality of web pages for generating a web page classification model, assigning a label to each of the plurality of web pages, accessing Transmission Control Protocol/Internet Protocol (TCP/IP) traffic traces associated with downloading content from each of the plurality of web pages, processing TCP/IP headers from the TCP/IP traffic traces to identify and extract features that discriminate between the labels, that are uncorrelated and whose discriminatory accuracy remains stable across time and/or browser platform. The method may further include generating a web page classification model by training a trainer to learn a combination of the features that accurately discriminates between the labels. The model is usable to classify unlabeled web pages by applying the model to TCP/IP traffic traces used to access the unlabeled web pages.
    Type: Grant
    Filed: January 14, 2016
    Date of Patent: January 7, 2020
    Assignee: The University of North Carolina at Chapel Hill
    Inventors: Sean Marcus Sanders, Jasleen Kaur
  • Publication number: 20180013639
    Abstract: Methods, systems, and computer readable media for generating and using a web page classification model are disclosed. The method may include identifying a plurality of web pages for generating a web page classification model, assigning a label to each of the plurality of web pages, accessing Transmission Control Protocol/Internet Protocol (TCP/IP) traffic traces associated with downloading content from each of the plurality of web pages, processing TCP/IP headers from the TCP/IP traffic traces to identify and extract features that discriminate between the labels, that are uncorrelated and whose discriminatory accuracy remains stable across time and/or browser platform. The method may further include generating a web page classification model by training a trainer to learn a combination of the features that accurately discriminates between the labels. The model is usable to classify unlabeled web pages by applying the model to TCP/IP traffic traces used to access the unlabeled web pages.
    Type: Application
    Filed: January 14, 2016
    Publication date: January 11, 2018
    Applicant: The University of North Carolina at Chapel Hill
    Inventors: Sean Marcus Sanders, Jasleen Kaur