AI Taxonomy System for Regulatory Compliance Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for regulatory compliance are inefficient, prone to errors, and unable to dynamically respond to the rapidly changing regulatory landscape, leading to significant research and transaction costs for companies.
Innovation Solution
A system that uses an attribute-based classification system and intelligent taxonomy to query users about their business operations, automatically classify relevant regulations, and generate compliance information, featuring a graphical user interface for task management and real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current regulatory compliance systems are used, then companies can identify applicable regulations, but the process is inefficient and prone to errors due to lack of intelligent classification
Solution Approach 1:
The patent segments the complex regulatory classification process into distinct modular components: attribute extraction module, taxonomy classification module, and compliance rule matching module. Each module handles a specific aspect of the classification task, improving both efficiency and reliability through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary taxonomy structure that acts as a bridge between raw regulatory text and compliance decisions. This intermediary layer standardizes the classification process, reducing errors and improving consistency across different regulatory domains.
2Reliability
If comprehensive regulatory analysis is performed, then compliance accuracy improves, but research and transaction costs increase significantly
Solution Approach 1:
The patent performs preliminary classification of regulations into taxonomic categories before detailed compliance analysis. This preliminary action filters and organizes regulatory content, enabling more efficient subsequent processing and reducing the overall research and transaction costs while maintaining accuracy.
Solution Approach 2:
The patent transforms unstructured regulatory text into structured classified data through parameter extraction and taxonomy assignment. This parameter transformation enables more efficient storage, retrieval, and analysis, reducing processing costs while improving compliance accuracy.
3Adaptability or versatility
If manual regulatory classification is used, then flexibility in handling complex regulations is maintained, but the process becomes inefficient and cannot respond dynamically to changing regulations
Solution Approach 1:
The patent implements a dynamic classification system where the taxonomy and attribute extraction rules can be updated to reflect changing regulations. The system adapts to new regulatory frameworks by incorporating updated classification criteria, maintaining versatility while improving processing speed through automated mechanisms.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated intelligent systems that use attribute extraction and taxonomy-based classification. This substitution dramatically improves processing speed and productivity while maintaining adaptability through programmable classification rules.
4Measurement precision
If detailed attribute-based classification is implemented, then compliance information accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex classification system into manageable modular components, each handling specific attribute extraction or classification tasks. This modularization reduces overall system complexity while maintaining high classification precision through specialized processing in each module.
Solution Approach 2:
The patent implements a universal taxonomy structure that can classify multiple types of regulations across different domains using a common framework. This universal approach reduces system complexity by avoiding the need for separate classification systems for each regulatory type, while maintaining precision through domain-specific attribute extraction.
Data Source
AI summary
An artificially intelligent system for generating compliance-related content, the system includes monitoring circuitry that receives regulatory compliance data from published regulatory texts; a taxonomy engine that generates taxonomy-based classifications of the regulatory compliance data, the taxonomy-based classifications having a plurality of modules and compliance requirements within each module. The system includes databases storing the taxonomy-based classifications of the regulatory compliance data; a processor that receives at least two of the plurality of modules from the taxonomy-based classifications and processes the compliance requirements within each received module using one or more artificially intelligent processes by mapping semantic relationship pairs between received modules; and scoring circuitry that: (1) produces a respective similarity score for one or more mapped semantic relationship pairs between received modules; and (2) uses the one or more respective similarity scores to generate a set of compliance steps covering compliance requirements from each of the received modules.