System and method for automated sequencing database generation

Patent metadata

Field	Value
Publication number	US-10860940-B2
Application number	US-201715691528-A
Country	US
Kind code	B2
Filing date	Aug 30, 2017
Priority date	Aug 30, 2017
Publication date	Dec 8, 2020
Grant date	Dec 8, 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 sequencing database generation, 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 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.

2. The system of claim 1 , wherein identifying the content components comprises:
identifying metadata associated with the content aggregation;
parsing the identified metadata; and
identifying topics via terminology extraction performed on the parsed metadata.

3. The system of claim 2 , wherein each topic is uniquely associated with a content component.

4. The system of claim 1 , wherein identifying explicit sequencing of the content components comprises extracting explicit sequencing data contained within metadata associated with the content aggregation.

5. The system of claim 4 , wherein the metadata associated with the content aggregation comprises front matter of the content aggregation.

6. The system of claim 5 , wherein the intermediate content graph comprises a plurality of nodes associated with the content components and edges, wherein each of the edges links a pair of nodes from the plurality of nodes in a sequential relationship.

7. The system of claim 6 , wherein each of the content components is associated with a unique one of the plurality of nodes.

8. The system of claim 7 , wherein the at least one server is further configured to identify an implicit sequencing of the content aggregation, and wherein generating the final content graph comprises modifying at least one of the edges of the intermediate content graph according to the implicit sequencing.

9. The system of claim 8 , wherein modifying at least one of the edges of the intermediate content graph comprises at least one of: deleting a node; adding a node; changing directionality of an edge; adding an edge; and removing an edge.

10. The system of claim 8 , wherein identifying the implicit sequencing of the content aggregation comprises:
inferring skills associated with the content components;
extracting implicit sequencing evidence from the content aggregation;
inputting the implicit sequencing evidence into the at least one statistical model; and
generating a sequence of the skills associated with the content components based on an output of the statistical model.

11. The system of claim 10 , wherein the implicit sequencing evidence is extracted via natural language processing from second metadata associated with the content aggregation.

12. The system of claim 11 , wherein the second metadata comprises back matter.

13. The system of claim 8 , wherein the implicit sequencing of the content aggregation is identified via application of Relational Machine Learning to implicit sequencing evidence extracted from the content aggregation.

14. A method for automated sequencing database generation, the method comprising:
receiving a content aggregation from a content library database at at least one server;
identifying content components of the content aggregation from a natural language processing analysis of at least a portion of the content aggregation;
identifying an explicit sequencing of the content components;
generating an intermediate content graph based on the explicit sequencing of the content components;
generating a final content graph from the intermediate content graph according to an implicit sequencing of the content components; and
storing the final content graph within a graph database.

15. The method of claim 14 , wherein identifying the content components comprises:
identifying metadata associated with the content aggregation;
parsing the identified metadata; and
identifying topics via terminology extraction performed on the parsed metadata, wherein each topic is uniquely associated with a content component.

16. The method of claim 14 , wherein identifying explicit sequencing of the content components comprises extracting explicit sequencing data contained within metadata associated with the content aggregation, wherein the metadata associated with the content aggregation comprises front matter of the content aggregation.

17. The method of claim 16 , wherein the intermediate content graph comprises a plurality of nodes associated with the content components and edges, wherein each of the edges links a pair of nodes from the plurality of nodes in a sequential relationship, and wherein each of the content components is associated with a unique one of the plurality of nodes.

18. The method of claim 17 , wherein generating the final content graph comprises:
identifying the implicit sequencing of the content aggregation; and
modifying at least one of the edges of the intermediate content graph according to the implicit sequencing, wherein modifying at least one of the edges of the intermediate content graph comprises at least one of: deleting a node; adding a node; changing directionality of an edge; adding an edge; and removing an edge.

19. The method of claim 18 , wherein identifying the implicit sequencing of the content aggregation comprises:
inferring skills associated with the content components;
extracting implicit sequencing evidence from the content aggregation;
inputting the implicit sequencing evidence into at least one statistical model; and
generating a sequence of the skills associated with the content components based on an output of the at least one statistical model.

20. The method of claim 14 , wherein the implicit sequencing of the content aggregation is identified via application of Relational Machine Learning to implicit sequencing evidence extracted from the content aggregation.

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

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F7/08

  Sorting, i.e. grouping record carriers in numerical or other ordered sequence according to the classification of at least some of the information they carry (by merging two or more sets of carriers in ordered sequence G06F7/16) · CPC title