Hybrid Content Graph Generation for Automated Sequencing

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Solution Overview

Problem

Current systems for automated content sequencing and graph alignment in computer networks lack efficiency in identifying and linking content components, explicit and implicit sequencing, and aligning hybrid knowledge graphs, leading to suboptimal content delivery and user experience.

Innovation Solution

A system and method that utilize a memory with a content library database, graph database, and statistical model to perform natural language processing, Relational Machine Learning, and machine learning algorithms to identify and align content components, generate intermediate and final content graphs, and create hybrid content graphs for efficient content delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated content sequencing and graph alignment systems are implemented, then content delivery efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvecontent delivery efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the complex content sequencing task into distinct modules: natural language processing for identifying content components, explicit sequencing extraction, implicit sequencing inference, and graph generation. Each module handles a specific aspect of the sequencing process, making the overall system more manageable and maintainable while achieving high content delivery efficiency

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediate content graphs as a mediator between raw content data and final sequenced content. These intermediate graphs serve as a bridge that gradually transforms unstructured content into structured sequences through multiple processing stages, reducing the complexity of direct transformation while improving processing efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If natural language processing and machine learning algorithms are used to identify content components and sequencing, then content component identification accuracy is improved, but processing time increases

Engineering Contradiction:

Improvecontent component identification accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary extraction of explicit sequencing information from content metadata and structure before applying complex machine learning algorithms for implicit sequencing inference. This preliminary processing reduces the complexity and time required for subsequent machine learning operations while maintaining high identification accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies machine learning algorithms selectively to portions of content where implicit sequencing is most critical, rather than processing all content uniformly. This partial application of complex algorithms reduces overall processing time while maintaining accuracy for the most important content components

Inventive Principle:

Principle #16Partial or excessive action

3Manufacturing precision

If intermediate and final content graphs are generated through multiple processing stages, then graph alignment quality is improved, but computational resources required increase

Engineering Contradiction:

Improvegraph alignment qualityVSAvoidcomputational resources

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The graph generation process is segmented into intermediate graph creation and final graph refinement stages. Each stage processes specific aspects of content relationships with appropriate computational intensity, reducing peak resource requirements while maintaining high alignment quality through progressive refinement

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system creates intermediate copies of content graphs at different processing stages, allowing parallel processing and optimization of computational resources. These graph copies enable iterative refinement without repeatedly processing the original data, reducing overall computational resource consumption

Inventive Principle:

Principle #26Copying

Data Source

PatentUS11416551B2System and method for automated hybrid sequencing database generation

Publication Date: 2022.08.16 PEARSON EDUCATION INC

AI summary

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.