Adaptive Knowledge Graph Updates With Threshold-Based AI Validation
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Solution Overview
Problem
Conventional workflows for updating rule engines and artificial intelligence models are time-consuming, resource-intensive, and prone to inefficiencies due to manual validation and integration steps, and existing generative AI systems face challenges in dynamic environments where ontologies and node maps are non-static, leading to increased computational resource usage and development time.
Innovation Solution
A graph reconfiguration platform that dynamically updates ontologies and knowledge graphs using adaptive mechanisms, including perturbation evaluation and validation, to generate code snippets efficiently and accurately, while ensuring compliance with policies and regulatory requirements, without the need for specific training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional workflows are used to update rule engines and AI models, then manual validation and integration steps can be performed, but the process becomes resource-intensive and inefficient
Solution Approach 1:
The system enables self-service by allowing the AI model to autonomously generate code and perform updates without requiring extensive manual validation and integration steps. The generative AI component automatically handles tasks that previously required human intervention, thereby improving productivity while maintaining reliability through automated quality checks.
Solution Approach 2:
The patent replaces manual mechanical validation processes with automated AI-based verification. Instead of human reviewers manually checking code and integration steps, the system uses AI models to automatically validate updates, generating and verifying code changes without human intervention, thus reducing resource consumption while maintaining accuracy.
2Productivity
If existing generative AI systems are used for code generation, then development speed can increase, but the systems struggle with dynamic environments and cause resource wastage
Solution Approach 1:
The system incorporates dynamic adaptability by enabling the AI model to adjust its code generation based on real-time feedback from the execution environment. The perturbation mechanism allows the system to dynamically modify generated code in response to changing conditions, ensuring adaptability to dynamic environments while maintaining high development speed through automated iterative improvements.
Solution Approach 2:
The patent implements feedback mechanisms where the generated code is executed and the results are fed back into the AI model to refine subsequent generations. This feedback loop enables the system to learn from previous attempts and adapt to dynamic environments, reducing resource wastage by avoiding generation of obviously incorrect code and focusing computational resources on productive iterations.
3Manufacturing precision
If manual code generation and validation are performed, then quality control can be maintained, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system replaces manual mechanical validation with automated AI-based quality control. The generative AI model produces code that is immediately subjected to automated testing and verification, eliminating the need for time-consuming manual review while maintaining or improving code quality through consistent automated checking and immediate feedback.
Solution Approach 2:
The patent applies preliminary action by performing automated validation and testing immediately upon code generation, before the code is deployed or used in production. This preliminary verification step catches errors early in the process, reducing the need for later corrective actions and minimizing overall time loss while ensuring high code quality through proactive rather than reactive quality control.
Data Source
AI summary
Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.