Pearson Education, Inc.

Phenom People

Ilya Goldin

Johann A Larusson

John Behrens

Thomas McTavish

Jacob Anderson

Gennadiy Kukartsev

Yun Jin Rho

José Pablo González Brenes

Jose Gonzalez-Brenes

Shivaramakrishna Nyshadham

Mahe Bayireddi

Hari Bayireddy

Sivanand Akella

Suresh Babu Devineni

S S S Venkateswara Rao Alapati

A system may receive, identify, and/or extract one or more pieces of information (e.g., categories of knowledge) from an input (e.g., a knowledge source, as described herein). The categories of knowledge may include one or more phrase and/or multi-word phrase, which may be referred to as individual pieces of knowledge (e.g., knowledge entities). The system may identify one or more relationships between the categories of knowledge and/or the individual pieces of knowledge. For example, the relationships may be inter and/or intra-categorical relationships. The relationships may be organized to form a hierarchical relationship driven knowledge engine. The knowledge engine may organize the knowledge entities by entity (e.g., operator, company, job posting) and/or contextualize the knowledge entities by domain (e.g., profession, employer, etc.) by, for example, creating one or more knowledge profiles. The knowledge engine may then use these knowledge profile to dynamically respond to informational requests.

Knowledge engine using machine learning and predictive modeling for optimizing recruitment management systems

Systems and methods for simplifying data structuring in a database by applying a first content structure to interrelate a plurality of data packets and refining the interrelations based on data packet attributes are disclosed herein. The system can include a memory including: a structure database that can include a content structure including a plurality of content categories; and a content library database. The content library database can include a plurality of data packets. The system can include a server that can receive a plurality of data packets, organize the content according to the content structure, and update the organization of at least some of the data packets based on a determined attribute of those data packets.

Content database generation

Systems and methods for increasing data transmission rates through a content distribution network by generating a customized aggregation comprising data packets selected to maximize a data acceptance rate are disclosed herein. The system can include a memory including a content library database having a plurality of data packets and a user profile database. The system can further include a server that can: receive aggregation information identifying a set of delivery data packets and a set of assessment data packets; receive data packet data from the content library database; identify a recipient cohort; determine a recipient cohort lapsed time; generate an estimate of data packet transfer time for the aggregation; generate an updated aggregation based on the recipient cohort lapsed time and the estimate of data packet transfer time; and provide the updated aggregation to the user devices.

System and method for increasing data transmission rates through a content distribution network with customized aggregations

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Pittsburgh, PA, United States of America

Ilya Goldin