Patents by Inventor Alok Baikadi
Alok Baikadi 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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Patent number: 11443140Abstract: 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: September 13, 2022Assignee: PEARSON EDUCATION, INC.Inventors: Scott Hellman, Lee Becker, Samuel Downs, Alok Baikadi, William Murray, Kyle Habermehl, Peter Foltz, Mark Rosenstein
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Patent number: 11416551Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: GrantFiled: September 21, 2020Date of Patent: August 16, 2022Assignee: PEARSON EDUCATION, INC.Inventors: William Murray, Alok Baikadi
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Publication number: 20210073292Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: ApplicationFiled: September 21, 2020Publication date: March 11, 2021Inventors: William Murray, Alok Baikadi
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Patent number: 10860940Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: GrantFiled: August 30, 2017Date of Patent: December 8, 2020Assignee: PEARSON EDUCATION, INC.Inventors: William Murray, Alok Baikadi
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Patent number: 10783185Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: GrantFiled: August 30, 2017Date of Patent: September 22, 2020Assignee: PEARSON EDUCATION, INC.Inventors: William Murray, Alok Baikadi
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Patent number: 10754899Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: GrantFiled: August 30, 2017Date of Patent: August 25, 2020Assignee: PEARSON EDUCATION, INC.Inventors: William Murray, Alok Baikadi
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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: 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: 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: 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: 20190258715Abstract: 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, Lee Becker, Samuel Downs, Alok Baikadi, William Murray, Kyle Habermehl, Peter Foltz, 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
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Publication number: 20190065620Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: ApplicationFiled: August 30, 2017Publication date: February 28, 2019Inventors: William Murray, Alok Baikadi
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Publication number: 20190065976Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: ApplicationFiled: August 30, 2017Publication date: February 28, 2019Inventors: William Murray, Alok Baikadi
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Publication number: 20190065597Abstract: Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.Type: ApplicationFiled: August 30, 2017Publication date: February 28, 2019Inventors: William Murray, Alok Baikadi