Invention Grant
- Patent Title: Personalized learning system and method for the automated generation of structured learning assets based on user data
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Application No.: US17350121Application Date: 2021-06-17
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Publication No.: US11217110B2Publication Date: 2022-01-04
- Inventor: Andrew Smith Lewis , Paul Mumma , Alex Volkovitsky , Iain Harlow , Kyle Stewart
- Applicant: Cerego Japan Kabushiki Kaisha
- Applicant Address: JP Tokyo
- Assignee: Cerego Japan Kabushiki Kaisha
- Current Assignee: Cerego Japan Kabushiki Kaisha
- Current Assignee Address: JP Tokyo
- Agency: Keating and Bennett, LLP
- Main IPC: G09B7/04
- IPC: G09B7/04 ; G09B5/06 ; G09B7/07 ; G09B7/08 ; G09B5/12 ; G06N20/00 ; G06N5/04 ; G09B19/00 ; G06N7/00

Abstract:
Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
Public/Granted literature
Information query