Automated Content Graph Generation via Hybrid Sequencing

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Solution Overview

Problem

Current systems for generating and managing content in computer networks lack efficient automated methods for sequencing and delivering content based on user interactions and preferences, leading to suboptimal content delivery and user engagement.

Innovation Solution

A system and method for automated content delivery using a hybrid knowledge graph database that aligns and modifies content graphs through natural language processing and machine learning algorithms, identifying explicit and implicit sequencing to generate a hybrid content graph for personalized content delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated content sequencing 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 content into discrete content components with associated metadata, allowing independent processing and sequencing of individual components. This segmentation enables automated sequencing without requiring complex holistic analysis of entire content collections, thereby improving efficiency while managing system complexity through modular processing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediate content graphs as a mediating structure between raw content metadata and final content delivery sequences. These graphs serve as an intermediary representation that simplifies the sequencing process by providing a structured framework for applying machine learning algorithms, thus improving content delivery efficiency while avoiding direct complex manipulation of raw content data.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If natural language processing analysis is applied to identify content components, then content sequencing accuracy is improved, but processing time increases

Engineering Contradiction:

Improvecontent sequencing accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary extraction of metadata and content components during content ingestion, before sequencing is required. This preliminary action prepares structured data representations that can be quickly processed by machine learning algorithms during sequencing operations, thereby maintaining high sequencing accuracy while reducing real-time processing time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies natural language processing selectively to portions of content metadata that are most critical for sequencing decisions, rather than analyzing all content components uniformly. This partial action approach focuses computational resources on key sequencing determinants, improving accuracy for critical sequencing decisions while reducing overall processing time through selective analysis.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If hybrid content graphs combining explicit and implicit sequencing are generated, then content relevance to user preferences is improved, but computational resources required increase

Engineering Contradiction:

Improvecontent relevanceVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system merges explicit sequencing information (from metadata) and implicit sequencing information (from machine learning analysis of user interactions) into a unified hybrid content graph. This merging allows the system to leverage both structured content relationships and learned user preference patterns, improving content relevance while sharing computational resources between the two sequencing approaches rather than maintaining separate systems.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The hybrid content graph structure serves multiple functions: it stores explicit content sequencing relationships, captures implicit user preference patterns, and enables personalized content delivery. This multi-functionality allows a single data structure to handle diverse sequencing requirements, reducing the need for separate computational systems and thereby lowering overall computational resource requirements while maintaining high content relevance.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10860940B2System and method for automated sequencing database generation

Publication Date: 2020.12.08 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.