Automated Business Rule Classification via Bayesian Intent Analysis
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Solution Overview
Problem
Current technologies lack an efficient method for classifying business rules from text documents, which is essential for automating business processes and reusing rules across different systems and documents.
Innovation Solution
A method and system for automated classification of business rules from text documents, involving the creation of a rule repository with identified rule intents and patterns, using Bayesian classification and clustering algorithms to categorize rules into types, and associating them with knowledge elements and stakeholders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classification methods are implemented, then productivity and efficiency improve, but device complexity increases due to requiring Bayesian classification and clustering algorithms
Solution Approach 1:
The patent introduces an intermediary classification system that bridges raw business rules and their categorical classifications. The system uses Bayesian classification algorithms as intermediaries to process rule intents and map them to appropriate rule types, reducing the complexity burden on end users while maintaining high productivity through automated intelligent categorization.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational systems. Instead of manually categorizing business rules, the system employs Bayesian classification and clustering algorithms to automatically analyze rule intents and assign classifications, significantly improving productivity while the computational complexity is managed through algorithmic efficiency.
2Device complexity
If manual classification methods are used, then device complexity is reduced, but loss of time increases due to manual analysis requirements
Solution Approach 1:
The patent implements a self-service classification system where the business rules automatically classify themselves through the Bayesian classification algorithm. The system extracts rule intents from the text and performs self-categorization without requiring manual intervention, eliminating time loss while keeping the underlying computational complexity managed through efficient algorithm design.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the business rules to extract rule intents before classification. The system prepares the data by identifying and structuring rule intents in advance, which accelerates the subsequent classification process and reduces overall classification time while maintaining manageable system complexity through structured data preparation.
3Measurement precision
If comprehensive rule analysis is performed, then measurement precision improves for rule categorization, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent segments the rule analysis process into distinct modular steps: rule intent extraction, Bayesian classification, and clustering-based categorization. Each segment handles a specific aspect of the classification task, improving measurement precision through focused analysis while managing device complexity through modular architecture that allows independent optimization of each processing step.
Solution Approach 2:
The patent adds another dimension to the classification process by introducing rule intent extraction as a separate analytical layer. Instead of directly classifying rules, the system first extracts and analyzes rule intents, creating an intermediate representation that enhances classification accuracy. This dimensional addition improves measurement precision while the systematic approach manages processing complexity.
Data Source
AI summary
The present subject matter relates to an automated classification of business rules. In one embodiment, a method for automated classification of the business rules comprises identifying a business rule from a text document, wherein the business rule comprises one or more rule intents. Further, the method comprises comparing the one or more rule intents in the business rule with rule intents associated with a plurality of rule types in a rule repository. Furthermore, the method comprises classifying the business rule under at least one of the rule types based on the comparison.