System and method for sequencing database-based content recommendation

Patent metadata

Field	Value
Publication number	US-10754899-B2
Application number	US-201715691591-A
Country	US
Kind code	B2
Filing date	Aug 30, 2017
Priority date	Aug 30, 2017
Publication date	Aug 25, 2020
Grant date	Aug 25, 2020

Abstract

Systems and methods for automated sequencing database generation are disclosed herein. The system can include memory that can include a content library database; a graph database; and a model database. The system can include a user device and at least one server. The at least one server can: receive a content aggregation from the content library database; identify content components of the content aggregation based on a natural language processing analysis of at least a portion of the content aggregation; identify explicit sequencing of the content components; generate an intermediate content graph based on the explicit sequencing of the content components; generate a final content graph from the intermediate content graph based on implicit sequencing of the content components; and store the final content graph within the graph database.

First claim

What is claimed is:

1. A system for automated content delivery via a hybrid content graph, the system comprising:
memory comprising:
a content library database comprising at least one content aggregation for presentation to a user;
a graph database containing at least one intermediate content graph and at least one final content graph, wherein each of the intermediate and final content graphs identify and link portions of the content aggregation; and
a model database comprising at least one statistical model;

a user device; and
at least one server, wherein the at least one server is configured to:
receive a first content aggregation and a second content aggregation;
generate a first content graph identifying skills and sequential relationships between the skills from the first content aggregation via application of a machine learning algorithm to features generated from portions of the first content aggregation, wherein generating the first content graph comprises extracting features from the first content aggregation to identify content components of the first content aggregation and to determine a sequencing of the content components of the first content aggregation, wherein determining the sequencing of the content components of the first content aggregation comprises identifying an explicit sequencing of the first content aggregation and an implicit sequencing of the first content aggregation, wherein the implicit sequencing of the first content aggregation is identified via application of Relational Machine Learning to implicit sequencing evidence extracted from the first content aggregation;
generate a second content graph identifying skills and sequential relationships between the skills from the second content aggregation via application of a machine learning algorithm to features generated from portions of the second content aggregation;
generate a hybrid content graph via aligning of the first and second content graphs, wherein aligning the first and second content graphs comprises:
identifying first nodes within the first content graph and second nodes within the second content graph;
identifying matching nodes between the first and second content graphs; and
identifying nodes contained in one of the first and second content graphs and missing in the other of the first and second content graphs;

determine a node corresponding to a user position in the hybrid content graph;
retrieve edge information identifying nodes linked to the node corresponding to a user position in the hybrid content graph;
select one of the identified nodes linked to the node corresponding to the user position in the hybrid content graph; and
automatically deliver content associated with the selected one of the identified nodes to the user.

2. The system of claim 1 , wherein determining the sequencing of the content components of the first content aggregation further comprises generating edges linking the skills in the first content graph in sequential relationships according to a combination of the explicit sequencing and the implicit sequencing.

3. The system of claim 2 , wherein the first content graph and the second content graph are aligned according to a machine learning algorithm.

4. The system of claim 1 , wherein aligning the first content graph and the second content graph further comprises: extracting features from content associated with each of the nodes of the first and second content graphs; inputting the extracted features into a machine learning model; and receiving an output from the machine learning model identifying matching nodes between the first and the second content graphs.

5. The system of claim 1 , wherein aligning the first content graph and the second content graph further comprises: identifying sequential relationships between the identified matching nodes, wherein the sequential relationships between the identified matching nodes are identified via a Relational Machine Learning analysis.

6. The system of claim 1 , wherein selecting one of the identified nodes linked to the node corresponding to the user position in the hybrid content graph further comprises retrieving edge information identifying child nodes linked to the node corresponding to the user position in the hybrid content graph.

7. A method for automated content delivery via a hybrid content graph, the method comprising:
receiving a first content aggregation and a second content aggregation;
generating a first content graph identifying skills and sequential relationships between the skills from the first content aggregation via application of a machine learning algorithm to features generated from portions of the first content aggregation, wherein generating the first content graph comprises extracting features from the first content aggregation to identify content components of the first content aggregation and to determine a sequencing of the content components of the first content aggregation, wherein determining the sequencing of the content components of the first content aggregation comprises identifying an explicit sequencing of the first content aggregation and an implicit sequencing of the first content aggregation, and wherein the implicit sequencing of the first content aggregation is identified via application of Relational Machine Learning to implicit sequencing evidence extracted from the first content aggregation;
generating a second content graph identifying skills and sequential relationships between the skills from the second content aggregation via application of a machine learning algorithm to features generated from portions of the second content aggregation;
generating a hybrid content graph via aligning of the first and second content graphs, wherein aligning the first and second content graphs comprises:
identifying first nodes within the first content graph and second nodes within the second content graph;
identifying matching nodes between the first and second content graphs; and
identifying nodes contained in one of the first and second content graphs and missing in the other of the first and second content graphs;

determining a node corresponding to a user position in the hybrid content graph;
retrieving edge information identifying nodes linked to the node corresponding to a user position in the hybrid content graph;
selecting one of the identified nodes linked to the node corresponding to the user position in the hybrid content graph; and
automatically delivering content associated with the selected one of the identified nodes to the user.

8. The method of claim 7 , wherein determining the sequencing of the content components of the first content aggregation further comprises generating edges linking the skills in the first content graph in sequential relationships according to a combination of the explicit sequencing and the implicit sequencing.

9. The method of claim 8 , wherein the first content graph and the second content graph are aligned according to a machine learning algorithm.

10. The method of claim 7 , wherein aligning the first content graph and the second content graph further comprises: extracting features from content associated with each of the nodes of the first and second content graphs; inputting the extracted features into a machine learning model; and receiving an output from the machine learning model identifying matching nodes between the first and the second content graphs.

11. The method of claim 7 , wherein aligning the first content graph and the second content graph further comprises: identifying sequential relationships between the identified matching nodes, wherein the sequential relationships between the identified matching nodes are

Assignees

• Pearson Education Inc

Inventors

• Murray William
• Baikadi Alok

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F16/9024 Primary

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• G06F16/9536

  Search customisation based on social or collaborative filtering · CPC title