The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 14, 2022

Filed:

Jan. 23, 2018
Applicant:

Pearson Education, Inc., New York, NY (US);

Inventors:

Jose Gonzalez-Brenes, San Francisco, CA (US);

John Behrens, Mishawaka, IN (US);

Johann Larusson, Phoenix, AZ (US);

Yetian Chen, Seattle, WA (US);

Assignee:

PEARSON EDUCATION, INC., New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 7/00 (2006.01); G09B 7/04 (2006.01); G09B 5/06 (2006.01); G09B 7/07 (2006.01); G06F 16/28 (2019.01); G06F 16/901 (2019.01); G09B 7/02 (2006.01); G06K 9/62 (2022.01); G06N 5/04 (2006.01); G06F 21/62 (2013.01); H04L 9/40 (2022.01); G06F 17/00 (2019.01);
U.S. Cl.
CPC ...
G06N 7/005 (2013.01); G06F 16/285 (2019.01); G06F 16/288 (2019.01); G06F 16/9024 (2019.01); G06K 9/6224 (2013.01); G06K 9/6296 (2013.01); G06N 5/04 (2013.01); G09B 5/06 (2013.01); G09B 7/02 (2013.01); G09B 7/04 (2013.01); G09B 7/07 (2013.01); G06F 21/6254 (2013.01); H04L 63/0428 (2013.01); H04L 63/10 (2013.01);
Abstract

Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.


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