Knowledge Graph Rule Translation for Compliance Automation
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Solution Overview
Problem
Developing software programs to determine compliance with textual regulations is complex and time-consuming, requiring skilled technicians to hard-code rules and data inputs, leading to high development costs and rigidity in rule application.
Innovation Solution
A host platform translates text-based regulations into machine-readable knowledge graphs, using semantic technologies and an inference engine to automate data collection and rule application, allowing for flexible rule modeling and execution without hardcoding, thereby reducing development time and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules are hard-coded into software programs, then rule application accuracy is improved, but device complexity and development time increase
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates text-based rules into executable logic, serving as a mediator between human-readable regulations and software execution. This eliminates the need for developers to manually hard-code rules while maintaining accuracy, as the NLP system automatically interprets and applies the rules.
Solution Approach 2:
The patent replaces the mechanical process of manual rule hard-coding with an automated natural language processing system. Instead of requiring skilled technicians to write programming code, the system uses NLP to automatically parse, interpret, and execute rules from text, substituting human labor with an intelligent automated process.
2Manufacturing precision
If skilled technicians hard-code rules and data inputs, then rule application precision is improved, but loss of time and development costs increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and structuring rules into a machine-readable format before execution. The NLP system prepares the rule base in advance, organizing text-based regulations into a structured knowledge representation that can be quickly queried and applied, eliminating the need for time-consuming manual rule configuration during deployment.
Solution Approach 2:
The patent replaces the mechanical process of manual rule hard-coding with an automated natural language processing system. Instead of requiring skilled technicians to write programming code, the system uses NLP to automatically parse, interpret, and execute rules from text, substituting human labor with an intelligent automated process that significantly reduces development time.
3Reliability
If all rules are hard-coded into software, then rule enforcement reliability is improved, but adaptability to rule changes deteriorates
Solution Approach 1:
The patent introduces dynamics by enabling rules to be updated and modified in natural language format without requiring software reconfiguration. The NLP system continuously interprets updated text-based rules and adapts the execution logic accordingly, allowing the system to dynamically respond to rule changes while maintaining reliable enforcement through consistent automated interpretation.
Solution Approach 2:
The patent introduces a natural language processing intermediary that translates text-based rules into executable logic, serving as a mediator between human-readable regulations and software execution. This eliminates the need for developers to manually hard-code rules while maintaining accuracy, as the NLP system automatically interprets and applies the rules.
4Measurement precision
If lengthy software programs are used to determine compliance, then decision accuracy is improved, but productivity decreases
Solution Approach 1:
The patent applies segmentation by dividing the compliance determination process into distinct modular components: data collection, rule interpretation, logic execution, and decision generation. Each component handles a specific aspect of the process, allowing for efficient parallel processing and reducing the computational burden of lengthy software programs while maintaining comprehensive compliance analysis.
Data Source
AI summary
The example embodiments are directed to a host system that can convert human-readable rules (e.g., statutes, regulations, laws, etc.) into a semantic model. The host system can then apply the semantic model to a set of circumstances to determine whether and how the rule applies to the circumstances. In one example, the method may include storing a knowledge graph with a semantic model of a rule embodied therein with nodes representing entities within the rule, edges between the nodes representing relationships between the entities, and identifiers of an input data set used by the rule, receiving input data corresponding to the rule, generating a determination from the rule via execution of the semantic model embodied within the knowledge graph on the received input data, and displaying a notification of the determination via a user interface.