AI Knowledge Graphs for Real-Time Idea Progression Tracking

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Solution Overview

Problem

Existing knowledge management and intellectual property (IP) analysis systems are fragmented and lack real-time monitoring and predictive capabilities, leading to inefficiencies in idea discovery, tracking, and portfolio management.

Innovation Solution

A processor-driven system utilizing natural language processing, graph neural networks, and large language models to construct a dynamic knowledge graph, perform contextual chunking, and track idea progression, enabling intelligent assignment and feedback mechanisms for enhanced IP asset management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional knowledge management systems are used, then basic idea tracking is possible, but real-time monitoring and predictive capabilities are lacking

Engineering Contradiction:

Improvereal-time monitoring capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical knowledge management systems with an AI-driven system using large language models and graph neural networks. The system processes communications and documents through automated NLP pipelines, transforming manual idea tracking into real-time automated analysis with predictive capabilities for idea evolution and infringement detection.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary AI processing layer between raw communications/documents and the knowledge management system. This layer includes embedding generation, chunking modules, and graph neural networks that mediate the transformation of unstructured data into structured knowledge representations, enabling real-time monitoring without direct system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If comprehensive idea tracking is implemented, then idea discovery improves, but information loss and tracking accuracy deteriorate

Engineering Contradiction:

Improveidea discovery efficiencyVSAvoididea context loss

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent segments communications and documents into contextual chunks using a chunking module that divides large texts into manageable segments while preserving semantic relationships. This segmentation enables comprehensive idea tracking across multiple sources without overwhelming the system or losing contextual information through aggregation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a nested structure where embeddings are contained within knowledge graph nodes, which are themselves nested within the broader enterprise knowledge system. This nested architecture allows ideas to be tracked at multiple levels of abstraction simultaneously, preserving detailed context while enabling high-level pattern recognition and idea discovery.

Inventive Principle:

Principle #7Nested doll (Nesting)

3Productivity

If manual idea management is used, then system simplicity is maintained, but expertise allocation efficiency and redundancy reduction are insufficient

Engineering Contradiction:

Improveexpertise allocation efficiencyVSAvoididea management automation level

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The patent implements self-service capabilities where the AI system automatically performs expertise matching, redundancy detection, and idea routing without manual intervention. The system autonomously analyzes incoming ideas, compares them against the knowledge graph, identifies relevant experts based on skill matching, and distributes ideas appropriately, eliminating the need for manual knowledge management operations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent incorporates feedback loops where the system continuously monitors idea outcomes, tracks which ideas lead to successful innovations, and uses this information to refine expertise matching and redundancy detection algorithms. This feedback mechanism improves expertise allocation efficiency over time while maintaining high levels of automation through learned optimization patterns.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250292184A1System for automated knowledge asset discovery, graph-based representation, and temporal idea progression modeling

Publication Date: 2025.09.18 IP COPILOT INC

AI summary

Various embodiments are generally directed to embodiments discussed herein. An example system includes a processor, and a memory having instructions stored therein. The instructions include a discovery module, a mapping module, an assignment module, a tracking module, a chunking module and an evolution module. When the instructions are executed by the processor, the instructions cause the processor to: monitor, by using the discovery module, documents within document repositories and communications in one or more communication media; identify, by using the discovery module, ideas in the monitored documents and communications; map, using the mapping module, knowledge within an enterprise based on the monitored documents and communications; provide, using the tracking module, tracking and management of the identified ideas; chunk, using the chunking module, the monitored documents and communications to provide context of the identified ideas; and track, using the evolution module, progression of the identified ideas over time.