AI Rule Similarity Detection for Overlapping Code Clustering
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Solution Overview
Problem
In machine learning and artificial intelligence systems, duplicate or similar rules are often created by different users, leading to inefficient use of computing resources, increased storage needs, and longer processing times.
Innovation Solution
Implementing a rule similarity detection and merging system using rule clustering and machine learning operations, which compares rule syntax and generates similarity scores to identify and merge or delete duplicate rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple rules are created by different rule writers to handle various computing operations, then the system can provide more comprehensive decision-making capabilities, but duplicate or similar rules are generated that consume unnecessary computing resources and storage
Solution Approach 1:
The system merges similar rules by identifying overlapping rule code through AI-based similarity detection. When duplicate or redundant rules are detected, they are consolidated into a single rule, reducing the total number of rules while preserving the comprehensive decision-making capabilities provided by multiple specialized rules.
Solution Approach 2:
The system discards redundant rule code by automatically identifying and removing duplicate rules through similarity comparison. The functionality of discarded rules is recovered by merging it into existing non-duplicate rules, ensuring that decision-making capabilities are maintained while eliminating unnecessary computing resource consumption.
2Adaptability or versatility
If multiple similar rules are maintained in the system, then different processing requirements and times can be accommodated, but the system complexity and storage needs increase
Solution Approach 1:
The system combines rules with similar processing requirements into unified rule structures. By merging overlapping rule code, the system reduces the number of individual rules while maintaining the ability to handle diverse processing requirements through the consolidated rule set.
Solution Approach 2:
The system creates universal rules that can handle multiple processing requirements through a single rule structure. By designing rules with multi-functional capabilities and consolidating overlapping functionality, the system reduces complexity while maintaining adaptability to different processing needs.
3Loss of energy
If rule writers manually identify and merge duplicate rules, then resource usage can be optimized, but time and effort are consumed in the manual process
Solution Approach 1:
The system performs self-service by automatically identifying, evaluating, and merging duplicate rules without requiring manual intervention from rule writers. The AI-based similarity detection and automated merging process eliminates the need for time-consuming manual rule review while optimizing resource usage through intelligent rule consolidation.
Solution Approach 2:
The system replaces the mechanical manual process of rule comparison and merging with an automated AI-based system. Machine learning models and natural language processing algorithms substitute for human rule writers in identifying and consolidating duplicate rules, significantly reducing the time and effort required while maintaining or improving resource optimization.
4Productivity
If an automated rule similarity detection system is implemented, then efficiency and productivity are improved, but additional processing steps and system complexity are introduced
Solution Approach 1:
The system implements automated rule similarity detection using AI and machine learning technologies to replace manual rule review processes. This substitution introduces additional processing steps but dramatically improves productivity by enabling rapid, automated identification and merging of duplicate rules, outweighing the added system complexity.
Solution Approach 2:
The system introduces an intermediary AI-based rule analysis layer between rule creation and rule execution. This intermediary automatically evaluates rule similarity, identifies duplicates, and suggests merges, improving efficiency while managing system complexity through a dedicated modular component that can be integrated into existing rule management workflows.
Data Source
AI summary
There are provided systems and methods of an artificial intelligence system for identifying, evaluating, and controlling overlapping rule code. A service provider, such as an electronic transaction processor for digital transactions, may utilize computing services that implement coded rules and rule-based engines for decision-making of data including real-time data processing in production computing environments. Multiple rules may overlap, and the service provider may utilize an AI system to identify the overlap by converting rule code and logic to syntax in a language. The syntax may then be analyzed for overlap and a similarity score calculated for pairwise similarity between each rule in the rule-based system. Thereafter, based on similarity scores and pairwise similarity, clusters of rule pairs may be identified in order to reduce rules through merging or deleting the same, similar, or overlapping rules based on their syntaxes.