AI Rule Engine Update Detection via Code Generation
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Solution Overview
Problem
Updating rule engines using artificial intelligence models is hindered by the need for specific training data, complexity in interpreting rule engine intricacies, and the challenge of ensuring seamless integration with existing systems.
Innovation Solution
The system detects when a rule engine should be updated and generates code to effectively use current model components without requiring specific training data. This is achieved by using artificial intelligence models to determine modified inputs and generate outputs, comparing actual and modified outputs to determine necessary updates, and using generative models to modify rule engine scripts while mimicking existing formats and structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If artificial intelligence models are used to directly create or update rule engines, then automation and intelligence are improved, but training data requirements and complexity increase
Solution Approach 1:
The patent uses code generation models to create modified rule engine scripts that copy and adapt existing code patterns. Instead of training complex AI models on specific rule engine data, the system generates new code by copying and transforming existing code structures, thereby reducing training data requirements while maintaining automation.
Solution Approach 2:
The patent introduces an intermediary code generation model that acts as a mediator between the existing rule engine and the updated version. This model generates intermediate code modifications that can be reviewed and approved, reducing the need for extensive training data while maintaining controlled automation.
2Adaptability or versatility
If rule engines are updated with new rules and data structures, then functionality and adaptability are improved, but integration compatibility and data migration complexity worsen
Solution Approach 1:
The patent performs preliminary code generation and modification before actual rule engine updates. By generating modified scripts in advance and reviewing them before deployment, the system prepares for integration challenges beforehand, reducing the complexity of actual updates and data migration.
Solution Approach 2:
The patent implements a feedback mechanism where generated code modifications are reviewed and validated before being applied to the rule engine. This feedback loop allows for checking integration compatibility and making adjustments before deployment, reducing the risk of integration issues.
3Measurement precision
If training data is gathered and labeled for AI model training, then model accuracy is improved, but time consumption and resource intensity increase
Solution Approach 1:
The patent uses code generation models that copy existing code patterns and structures to generate new rule engine scripts. This approach eliminates the need for extensive labeled training data, as the models learn from existing code rather than requiring manually labeled datasets, thereby reducing time consumption and resource intensity.
4Productivity
If rule engine scripts are modified to improve performance, then productivity is improved, but explainability and reviewability may worsen
Solution Approach 1:
The patent implements a review mechanism where generated code modifications are subjected to validation and approval processes before deployment. This feedback loop ensures that productivity improvements maintain explainability, as human reviewers can assess and understand the generated code changes before they are applied to the production rule engine.
Data Source
AI summary
The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and/or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).