Patents by Inventor Cassius D'Helon

Cassius D'Helon 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 decision making for selecting scaffolds after a partially correct answer in conversational intelligent tutor systems (ITS)
    Patent number: 11501654
    Abstract: At least one hardware processor implementing a conversational intelligent tutor system obtains, from a student, a partially correct natural language answer to a question posed by the conversational intelligent tutor system. The at least one hardware processor implementing the conversational intelligent tutor system accesses contextual knowledge related to the student. Based on the partially correct answer and the contextual knowledge, the at least one hardware processor applies at least one of a rule set and a predictive model to select at least one scaffolding option for the student. the at least one scaffolding option is caused to be displayed to the student.
    Type: Grant
    Filed: June 23, 2019
    Date of Patent: November 15, 2022
    Assignee: International Business Machines Corporation
    Inventors: Cassius D'Helon, Vinay Kumar Reddy Kasireddy, Fabrizio Jose Pisciotta Sanabria, Srijith Prabhu
  • Feedback system and method for improving performance of dialogue-based tutors
    Patent number: 11037459
    Abstract: Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.
    Type: Grant
    Filed: May 24, 2018
    Date of Patent: June 15, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Cassius D'Helon, Jae-Wook Ahn, Nirmal K. Mukhi, Vinay Kumar Reddy Kasireddy
  • AUTOMATED DECISION MAKING FOR SELECTING SCAFFOLDS AFTER A PARTIALLY CORRECT ANSWER IN CONVERSATIONAL INTELLIGENT TUTOR SYSTEMS (ITS)
    Publication number: 20200402414
    Abstract: At least one hardware processor implementing a conversational intelligent tutor system obtains, from a student, a partially correct natural language answer to a question posed by the conversational intelligent tutor system. The at least one hardware processor implementing the conversational intelligent tutor system accesses contextual knowledge related to the student. Based on the partially correct answer and the contextual knowledge, the at least one hardware processor applies at least one of a rule set and a predictive model to select at least one scaffolding option for the student. the at least one scaffolding option is caused to be displayed to the student.
    Type: Application
    Filed: June 23, 2019
    Publication date: December 24, 2020
    Inventors: Cassius D'Helon, Vinay Kumar Reddy Kasireddy, Fabrizio Jose Pisciotta Sanabria, Srijith Prabhu
  • FEEDBACK SYSTEM AND METHOD FOR IMPROVING PERFORMANCE OF DIALOGUE-BASED TUTORS
    Publication number: 20190362643
    Abstract: Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.
    Type: Application
    Filed: May 24, 2018
    Publication date: November 28, 2019
    Applicant: International Business Machines Corporation
    Inventors: Cassius D'Helon, Jae-Wook Ahn, Nirmal K. Mukhi, Vinay Kumar Reddy Kasireddy