Cerego Japan Kabushiki Kaisha

Cerego, LLC.

Andrew Smith Lewis

Iain M Harlow

Paul T Mumma

Kyle Thomas Stewart

Alex Volkovitsky

Michael A Yen

Andrew Van Schaack

A method for predictively updating one or more user parameters associated with a user of a learning system includes predicting, based on the one or more user parameters, a predicted activity of the user, receiving an actual activity of the user, comparing the predicted activity to the actual activity, and updating the one or more user parameters in response to determining that the predicted activity does not match the actual activity. The method may further include scheduling one or more learning interactions based on the one or more updated learning parameters, where the scheduling includes selecting at least one of a timing of the one or more learning interactions or a type of the one or more learning interactions.

System and method for customizing learning interactions based on a user model

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.

Personalized learning system and method for the automated generation of structured learning assets based on user data

A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.

System and method for automatically generating concepts related to a target concept

A system, method and apparatus for maximizing the effectiveness and efficiency of learning, retaining and retrieving knowledge and skills includes a main engine having a Learn Module, a Review Module and a Test Module. Each of the Learn, Review and Test Modules are capable of operating independently but are preferably arranged to operate interactively such that operation of each of the Learn, Review and Test Modules are changed based on a user's past performance within one or more of the three modules. In addition, the main engine may also include a Schedule Module for flexibly scheduling learning, reviewing and retrieving knowledge and skills based on various factors and input information. The main engine also may include a Progress Module which monitors a user's performance on any of the Learn, Review and Test Modules and changes the future operation of each module based on the monitored performance. The main engine also may include a Help Module to provide assistance with any of the other modules.

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San Diego, CA, United States of America

Andrew Smith Lewis