Regulatory Compliance Classification System Using Knowledge Bases
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Solution Overview
Problem
Real estate owners face challenges in identifying and classifying maintenance compliance regulations due to their complexity and the need for continuous review as new laws are added, leading to laborious and error-prone processes.
Innovation Solution
A computer-implemented method using a Positive Knowledge Base with directive words and designated verbs, and a Negative Knowledge Base with designated phrases, to isolate and rank maintenance compliances by grouping regulations into relatable tasks, leveraging deep learning techniques for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and classification of regulations is performed, then compliance identification can be achieved, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer-implemented system that uses machine learning models and natural language processing to identify and classify maintenance compliances in regulations, thereby reducing labor and time while maintaining accuracy
Solution Approach 2:
The system creates structured representations (copies) of regulation content by extracting key phrases, concepts, and compliance requirements into a standardized format that can be efficiently processed and classified without requiring repeated manual reading of the original voluminous documents
2Reliability
If comprehensive regulation review is conducted to ensure accuracy, then compliance identification improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex regulation review process into distinct modular components including phrase extraction, concept identification, compliance classification, and task grouping. Each module performs a specific function with defined inputs and outputs, making the overall system more manageable and maintainable while ensuring comprehensive coverage
Solution Approach 2:
The system introduces intermediary elements such as trained machine learning models, knowledge graphs, and standardized classification schemas that act as mediators between the raw regulation text and the final compliance identification, thereby managing complexity while enhancing reliability through multiple layers of validation
3Adaptability or versatility
If continuous review of new laws is performed, then compliance currency is maintained, but the loss of time and resources increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on large corpora of legal and regulatory language, and by establishing classification frameworks in advance. This preliminary preparation enables rapid processing and adaptation to new regulations without requiring extensive retraining or reconfiguration when new laws are introduced
Solution Approach 2:
The patent implements dynamic capabilities that allow the system to adapt to new regulations through continuous learning mechanisms. The machine learning models can be retrained with new data, and the system can incorporate feedback from ongoing compliance monitoring, enabling it to evolve with changing regulations while maintaining efficient processing speeds
Data Source
AI summary
A computer implemented method includes building a Positive Knowledge Base with directive words, designated verbs and designated objects. A Negative Knowledge Base with designated phrases and designated legal terms is built. Tasks and phrases from the Positive Knowledge Base and the Negative Knowledge Base are built. Regulations are received. Phrases from the regulations are weighted against the Positive Knowledge Base and the Negative Knowledge Base to isolate positive Maintenance Compliances. The positive Maintenance Compliances are matched to tasks to derive ranked Maintenance Compliances. The ranked Maintenance Compliances are supplied.