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公开(公告)号：US20210158712A1
公开(公告)日：2021-05-27
申请号：US17095035
申请日：2020-11-11
申请人： Cerego LLC.
发明人： Andrew SMITH LEWIS , Paul MUMMA , Alex VOLKOVITSKY , Iain HARLOW , Kyle STEWART
摘要： 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.
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公开(公告)号：US10861344B2
公开(公告)日：2020-12-08
申请号：US15836631
申请日：2017-12-08
申请人： CEREGO LLC.
发明人： Andrew Smith Lewis , Paul Mumma , Alex Volkovitsky , Iain Harlow , Kyle Stewart
IPC分类号： G09B7/04 , G09B5/06 , G09B7/07 , G09B7/08 , G09B5/12 , G06N20/00 , G06N5/04 , G09B19/00 , G06N7/00
摘要： 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.
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公开(公告)号：US11081018B2
公开(公告)日：2021-08-03
申请号：US17095035
申请日：2020-11-11
申请人： Cerego LLC.
发明人： Andrew Smith Lewis , Paul Mumma , Alex Volkovitsky , Iain Harlow , Kyle Stewart
IPC分类号： G09B7/04 , G09B5/06 , G09B7/07 , G09B7/08 , G09B5/12 , G06N20/00 , G06N5/04 , G09B19/00 , G06N7/00
摘要： 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.
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4.发明申请System, apparatus and method for maximizing effectiveness and efficiency of learning, retaining and retrieving knowledge and skills 审中-公开最大限度地提高学习效能和效率，保留和检索知识和技能的系统，设备和方法公开(公告)号：US20030129574A1
公开(公告)日：2003-07-10
申请号：US10012521
申请日：2001-12-12
申请人： Cerego LLC,
IPC分类号： G09B007/00
摘要： A system, method and apparatus for maximizing the effectiveness and efficiency of learning, retaining and retrieving knowledge and skills includes a learning engine that includes a novel model of human learning that adaptively determines a memory indicator for each item to be learned for each user during all phases of learning, including a short active phase of learning in which items are actively recalled and a long passive phase of learning in which items are passively forgotten. The memory indicator is determined based on a user's actual memory performance during the short-term active phase of learning and is accurately predicted based on mathematical modeling during the long-term passive phase of learning. The learning model makes use of a target level and an alert level of memory performance for each item of information for each user and the learning engine schedules presentation of items for review or study based on the user's performance with respect to the target and alert levels. The learning engine operates to present to the user items to be learned by the user when a memory indicator value for an item is equal to or below the alert level and stops presenting items to the user when the memory indicator for that item is equal to or greater than the target level for that item.
摘要翻译： 用于最大化学习的有效性和效率，保留和检索知识和技能的系统，方法和装置包括学习引擎，其包括人类学习的新颖模型，其自动地确定针对每个用户在每个用户期间学习的每个项目的存储器指示符 学习的阶段，包括学习活动的一个短暂的积极阶段，其中积极地回顾了项目，以及被动地遗忘项目的长期被动学习阶段。 存储器指示器是基于用户在学习的短期有效阶段的实际存储器性能来确定的，并且基于在学习的长期被动阶段期间的数学建模而被准确预测。 学习模型利用每个用户的每个信息项的目标级别和记忆性能的警报级别，并且学习引擎基于用户对目标和警报级别的性能来安排用于审查或研究的项目的呈现。 当项目的存储器指示符值等于或低于警报级别时，学习引擎操作以向用户呈现要被用户学习的项目，并且当该项目的存储器指示符等于或等于或者不存在时，停止向用户呈现项目 大于该项目的目标水平。
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公开(公告)号：US20180373791A1
公开(公告)日：2018-12-27
申请号：US15977952
申请日：2018-05-11
申请人： CEREGO, LLC.
摘要： 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.
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公开(公告)号：US20180357915A1
公开(公告)日：2018-12-13
申请号：US15977950
申请日：2018-05-11
申请人： CEREGO, LLC.
摘要： 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.
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7.发明申请PERSONALIZED LEARNING SYSTEM AND METHOD WITH ENGINES FOR ADAPTING TO LEARNER ABILITIES AND OPTIMIZING LEARNING PROCESSES 审中-公开个性化学习系统和方法与发动机适应学习能力和优化学习过程公开(公告)号：US20170075881A1
公开(公告)日：2017-03-16
申请号：US15264438
申请日：2016-09-13
申请人： Cerego, LLC
发明人： Andrew Smith Lewis , Paul Mumma , Alex Volkovitsky , Kit Richert
CPC分类号： G06F17/2809 , G06F17/24 , G06F17/274 , G09B5/02 , G09B5/125 , G09B7/00
摘要： Various techniques are disclosed for providing a learning system. In one example, such a learning system includes a content editor processor configured or programmed to receive content data packets from a number of learner devices. The learning system is configured to identify a number of items from digital materials based on the content data packets. The learning system may include an adaptive engine configured to transmit interactions to the learner devices based on the identified items. The adaptive engine is also configured to receive respective responses from the learner devices based on the interactions. The learning system is also configured generate an electronic copy of the digital materials with highlighted items based on the received responses. Other examples of learning systems and related methods are also provided.
摘要翻译： 公开了用于提供学习系统的各种技术。 在一个示例中，这样的学习系统包括内容编辑器处理器，其配置或编程为从多个学习者设备接收内容数据分组。 学习系统被配置为基于内容数据分组来识别来自数字材料的多个项目。 学习系统可以包括被配置为基于所识别的项目向学习者设备发送交互的自适应引擎。 自适应引擎还被配置为基于交互来接收来自学习者设备的相应响应。 学习系统还被配置为基于接收的响应生成具有突出显示项目的数字材料的电子副本。 还提供了学习系统和相关方法的其他示例。
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8.发明申请公开(公告)号：US20180218627A1
公开(公告)日：2018-08-02
申请号：US15836631
申请日：2017-12-08
申请人： Cerego LLC.
发明人： Andrew SMITH LEWIS , Paul MUMMA , Alex VOLKOVITSKY , Iain HARLOW , Kyle STEWART
CPC分类号： G09B7/04 , G06N5/04 , G06N7/005 , G06N20/00 , G09B5/065 , G09B5/125 , G09B7/07 , G09B7/08 , G09B19/00
摘要： 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.
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公开(公告)号：US11086920B2
公开(公告)日：2021-08-10
申请号：US15977952
申请日：2018-05-11
申请人： CEREGO, LLC.
摘要： 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.
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