Pearson Education, Inc.

Perry M Spagnola

Gregory Dean McFall

Hong-Yu Chiang

Richard Paul Hughes

Mark DeLeon

Techniques described herein relate to requesting and receiving adaptive content resources from content management servers and other content repositories, executing the adaptive content resources on one or more user devices, and performing caching determinations and caching behaviors based on the execution of the adaptive content resources. In some embodiments, content resources may be executed on user devices in a content distribution network. User inputs may be received in connection with the execution of the adaptive content resources, and a user context may be determined based on user interactions and feedback during execution of the adaptive content resources. Additional content resources may be requested and received from content repositories for caching within user devices based on the determined user context.

Adaptive system cache

A method/apparatus/system for generating a request for improvement of a data object in a neural network is described herein. The neural network contains a plurality of data objects each made of an aggregation of content. The data objects of the neural network are interconnected based on one or several skill levels embodied in the content of the data objects via a plurality of connecting vectors. These connecting vectors can be generated and/or modified based on data collected from the iterative transversal of the connecting vectors by one or several users of the neural network.

Neural networking system and methods

A privacy server protects private information by substituting a token or an identifier for the private information. The privacy server recognizes that a communication includes private information and intercepts the communication. The privacy server replaces the private information with a random or pseudo-random token or identifier. The privacy server maintains the private information in a local database and associates the private information for a particular person with the token or identifier for that person.

Privacy server for protecting personally identifiable information

Techniques described herein provide data access and data anonymity management within various implementations of content distribution networks. In some embodiments, a data anonymity management system may include multiple different data stores configured to store identity data, user data, and/or linking data, as well as a data anonymity server configured to support data anonymity features such as active user data anonymity, one-way linking and permanently breakable links between user identity data and associated user data. In some embodiments, storage systems and techniques may be used for storing identity data, user data, and link data in multiple different data stores, with different data storage requirements and different processes and policies for data availability, data backup, online disaster recovery, offline storage archiving, and the like. Additionally, cryptographic hash functions may be used to implement one-way encryption between identity data and user data.

Data access and anonymity management

A method/apparatus/system for generation of a cross-dimensional gaming network is described herein. The gaming network contains a plurality of gaming objects each made of an aggregation of gaming content. The gaming objects of the gaming network are interconnected based on one or several skill levels embodied in the gaming content of the gaming objects. These skill levels can be based on the subject matter of the gaming content and/or can be independent of the subject matter of the gaming content. A new gaming object can be placed within the gaming network based on the skill level of the gaming object.

Vector-based gaming content management

A method/system for learning system self-optimization is disclosed. The learning system can include a plurality of learning objects that are connected to each other by a plurality of learning vectors. The learning vectors can identify a prerequisite relationship between the connected learning objects and include data indicating a likelihood of success of a student in traversing the learning vector and/or an expected speed for traversing the learning vector. Data generated from a student's traversal of one of the learning vectors can be used to strengthen or weaken the traversed learning vector based on the student experience.

Learning system self-optimization

A method/apparatus/system for learning path recommendation based on student and learning vector context is disclosed. The incident learning object and the target learning object are identified. Learning paths between the incident learning object and the target learning object are identified, which learning paths each include a plurality of learning objects and learning vectors. A magnitude is calculated for the learning paths, which magnitude is based on the sum of the magnitudes of the learning vectors in each learning path. The magnitudes of the learning paths are compared, and one of the learning paths is selected based on the comparison of the learning paths and provided to a student.

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Phoenix, AZ, United States of America

Perry M Spagnola