Patents by Inventor Jordan Hochenbaum

Jordan Hochenbaum 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: 10095850
    Abstract: On-line course offerings can be made available to users using computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content. Authentication methods and systems include applications of behavioral biometrics.
    Type: Grant
    Filed: May 19, 2015
    Date of Patent: October 9, 2018
    Assignee: Kadenze, Inc.
    Inventors: Perry R. Cook, Ajay Kapur, Owen S. Vallis, Jordan Hochenbaum
  • Publication number: 20180075358
    Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.
    Type: Application
    Filed: August 25, 2017
    Publication date: March 15, 2018
    Inventors: Ajay Kapur, Perry Raymond Cook, Jordan Hochenbaum, Colin Bennett Honigman, Owen Skipper Vallis, Chad A. Wagner, Eric Christopher Heep
  • Patent number: 9792553
    Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.
    Type: Grant
    Filed: August 15, 2014
    Date of Patent: October 17, 2017
    Assignee: Kadenze, Inc.
    Inventors: Ajay Kapur, Perry Raymond Cook, Jordan Hochenbaum, Colin Bennett Honigman, Owen Skipper Vallis, Chad A. Wagner, Eric Christopher Heep
  • Publication number: 20150379253
    Abstract: On-line course offerings can be made available to users using computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content. Authentication methods and systems include applications of behavioral biometrics.
    Type: Application
    Filed: May 19, 2015
    Publication date: December 31, 2015
    Inventors: Perry R. Cook, Ajay Kapur, Owen S. Vallis, Jordan Hochenbaum
  • Publication number: 20150066820
    Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.
    Type: Application
    Filed: August 15, 2014
    Publication date: March 5, 2015
    Inventors: Ajay Kapur, Perry Raymond Cook, Jordan Hochenbaum, Colin Bennett Honigman, Owen Skipper Vallis, Chad A. Wagner, Eric Christopher Heep
  • Publication number: 20150039541
    Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.
    Type: Application
    Filed: July 31, 2014
    Publication date: February 5, 2015
    Inventors: Ajay Kapur, Perry Raymond Cook, Jordan Hochenbaum, Owen Skipper Vallis, Chad A. Wagner, Eric Christopher Heep