Compliance Knowledge Graph for Regulatory Navigation
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Solution Overview
Problem
The complexity of compliance with statutes, federal regulations, and governmental requirements in manufacturing processes is exacerbated by constant changes, leading to high costs and risks of non-compliance, necessitating an efficient method to integrate and navigate these regulations.
Innovation Solution
A dynamic compliance knowledge graph system is generated using natural language processing and machine learning to map a user's manufacturing status to relevant regulations, calculating metrics and providing pathways to achieve compliance, thereby simplifying compliance navigation and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to track and navigate compliance regulations, then comprehensive coverage of regulations can be maintained, but the complexity of navigating and understanding these regulations increases significantly
Solution Approach 1:
The patent introduces an AI-powered knowledge graph as an intermediary layer between compliance regulations and users. This knowledge graph automatically structures, connects, and visualizes regulatory relationships, serving as a mediator that simplifies navigation while maintaining comprehensive coverage. The system translates complex regulatory text into an intuitive graphical representation that shows hierarchies, connections, and compliance paths without requiring users to manually parse dense regulatory documents.
Solution Approach 2:
The patent replaces manual compliance tracking methods with an automated AI-based system. Instead of requiring users to manually search, interpret, and track regulatory requirements through traditional document review processes, the system uses natural language processing, entity recognition, and automated graph construction to mechanically process and present compliance information, significantly reducing the cognitive and temporal burden on users.
2Reliability
If comprehensive regulatory data is integrated into the knowledge graph, then compliance coverage is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and structuring regulatory data during ingestion. The system performs entity recognition, relationship extraction, and graph construction in advance, creating a ready-to-query knowledge graph structure. This preliminary processing allows the system to quickly respond to compliance queries without re-processing the entire regulatory corpus each time, significantly reducing response time while maintaining comprehensive coverage.
Solution Approach 2:
The patent segments the compliance knowledge graph into modular components representing different regulatory domains, hierarchies, and relationship types. This segmentation allows the system to process and query specific portions of the regulatory framework independently, reducing the computational burden compared to processing the entire regulatory corpus as a single monolithic structure. Users can focus on relevant segments rather than navigating all compliance data.
3Ease of operation
If the system provides detailed compliance pathways, then usability is improved, but the amount of information to process increases
Solution Approach 1:
The patent applies local quality by providing customized, context-specific compliance pathways based on user needs, current status, and target compliance states. Rather than presenting all possible compliance information uniformly to all users, the system adapts the level and type of detail provided to match the specific compliance scenario, user expertise level, and immediate needs. This allows users to receive comprehensive guidance when needed while avoiding information overload in simpler cases.
Data Source
AI summary
A computer system for implementing a knowledge graph includes one or more processors and one or more computer-readable media having executable instructions stored thereon. The executable instruction, when executed by the one or more processors, configure the computer system to receive a digital file including a compliance file, which includes one or more levels of compliance processes in a hierarchical order, generate a knowledge graph from text within the digital file, receive a query regarding compliance to the digital file, calculate a metric between each node to a query vector of the query, and provide one more compliance paths to one or more target nodes based on metrics. The knowledge graph is generated by creating nodes based on one or more text entities within the digital file, creating edges based on relationships between the nodes within the digital file, and generating the knowledge graph based on the nodes and the edges.