Patents by Inventor Jill Budden
Jill Budden 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).
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Patent number: 11875706Abstract: 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.Type: GrantFiled: February 20, 2019Date of Patent: January 16, 2024Assignee: PEARSON EDUCATION, INC.Inventors: Alok Baikadi, Scott Hellman, Jill Budden, Stephen Hopkins, Kyle Habermehl, Peter Foltz, Lee Becker, Mark Rosenstein
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Patent number: 11817014Abstract: 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.Type: GrantFiled: February 20, 2019Date of Patent: November 14, 2023Assignee: PEARSON EDUCATION, INC.Inventors: Lee Becker, William Murray, Peter Foltz, Mark Rosenstein, Alok Baikadi, Scott Hellman, Kyle Habermehl, Jill Budden, Stephen Hopkins, Andrew Gorman
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Patent number: 11741849Abstract: 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.Type: GrantFiled: February 20, 2019Date of Patent: August 29, 2023Assignee: PEARSON EDUCATION, INC.Inventors: Scott Hellman, William Murray, Kyle Habermehl, Alok Baikadi, Jill Budden, Andrew Gorman, Mark Rosenstein, Lee Becker, Stephen Hopkins, Peter Foltz
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Patent number: 11475245Abstract: 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.Type: GrantFiled: February 20, 2019Date of Patent: October 18, 2022Assignee: PEARSON EDUCATION, INC.Inventors: Peter Foltz, Mark Rosenstein, Alok Baikadi, Lee Becker, Stephen Hopkins, Jill Budden, Luis M. Oros, Kyle Habermehl, Scott Hellman, William Murray, Andrew Gorman
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Patent number: 11449762Abstract: 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.Type: GrantFiled: August 19, 2019Date of Patent: September 20, 2022Assignee: PEARSON EDUCATION, INC.Inventors: Mark Rosenstein, Kyle Habermehl, Scott Hellman, Alok Baikadi, Peter Foltz, Lee Becker, Luis M. Oros, Jill Budden, Marcia Derr
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Publication number: 20200005157Abstract: 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.Type: ApplicationFiled: August 19, 2019Publication date: January 2, 2020Inventors: Mark Rosenstein, Kyle Habermehl, Scott Hellman, Alok Baikadi, Peter Foltz, Lee Becker, Luis M. Oros, Jill Budden, Marcia Derr
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Publication number: 20190258903Abstract: 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.Type: ApplicationFiled: February 20, 2019Publication date: August 22, 2019Inventors: Peter Foltz, Mark Rosenstein, Alok Baikadi, Lee Becker, Stephen Hopkins, Jill Budden, Luis M. Oros, Kyle Habermehl, Scott Hellman, William Murray, Andrew Gorman
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Publication number: 20190259293Abstract: 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.Type: ApplicationFiled: February 20, 2019Publication date: August 22, 2019Inventors: Scott Hellman, William Murray, Kyle Habermehl, Alok Baikadi, Jill Budden, Andrew Gorman, Mark Rosenstein, Lee Becker, Stephen Hopkins, Peter Foltz
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Publication number: 20190258900Abstract: 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.Type: ApplicationFiled: February 20, 2019Publication date: August 22, 2019Inventors: Alok Baikadi, Scott Hellman, Jill Budden, Stephen Hopkins, Kyle Habermehl, Peter Foltz, Lee Becker, Mark Rosenstein
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Publication number: 20190258716Abstract: 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.Type: ApplicationFiled: February 20, 2019Publication date: August 22, 2019Inventors: Lee Becker, William Murray, Peter Foltz, Mark Rosenstein, Alok Baikadi, Scott Hellman, Kyle Habermehl, Jill Budden, Stephen Hopkins, Andrew Gorman