Invention Grant
- Patent Title: Systems and methods for interface-based machine learning model output customization
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Application No.: US16281048Application Date: 2019-02-20
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Publication No.: US11741849B2Publication Date: 2023-08-29
- Inventor: Scott Hellman , William Murray , Kyle Habermehl , Alok Baikadi , Jill Budden , Andrew Gorman , Mark Rosenstein , Lee Becker , Stephen Hopkins , Peter Foltz
- Applicant: Pearson Education, Inc.
- Applicant Address: US NJ Hoboken
- Assignee: PEARSON EDUCATION, INC.
- Current Assignee: PEARSON EDUCATION, INC.
- Current Assignee Address: US MN Bloomington
- Agency: QUARLES & BRADY LLP
- Main IPC: G09B7/02
- IPC: G09B7/02 ; G06F16/904 ; G06N20/00 ; G06F9/451 ; G06F16/40 ; G06F3/0481 ; G09B7/06 ; G06F40/30 ; G06F40/205 ; G06F18/214 ; G06F18/21

Abstract:
Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.
Public/Granted literature
- US20190259293A1 SYSTEMS AND METHODS FOR INTERFACE-BASED MACHINE LEARNING MODEL OUTPUT CUSTOMIZATION Public/Granted day:2019-08-22
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