Patents by Inventor Benjamin Listyg

Benjamin Listyg 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).

  • Automated generation of related subject matter footer links and previously answered questions
    Patent number: 11651141
    Abstract: A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.
    Type: Grant
    Filed: June 19, 2019
    Date of Patent: May 16, 2023
    Assignee: Wyzant, Inc.
    Inventors: Benjamin Listyg, Edward Corcoran, Michael Angellotti, Brent Zajaczkowski, Jonathan Gostelow, Jorel Casal, David Jaress
  • AUTOMATED GENERATION OF RELATED SUBJECT MATTER FOOTER LINKS AND PREVIOUSLY ANSWERED QUESTIONS
    Publication number: 20200401655
    Abstract: A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.
    Type: Application
    Filed: June 19, 2019
    Publication date: December 24, 2020
    Applicant: Wyzant, Inc.
    Inventors: Benjamin Listyg, Edward Corcoran, Michael Angellotti, Brent Zajaczkowski, Jonathan Gostelow, Jorel Casal, David Jaress