AI Query Routing Rules Engine for Policy-Compliant Endpoint Selection
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Solution Overview
Problem
Current systems lack the capability to dynamically and intelligently route user requests to the appropriate AI endpoint based on the content and context of the request, leading to inefficiencies and potential security breaches when personal and enterprise AI systems are used interchangeably.
Innovation Solution
A rules engine that integrates management rules, user preferences, and contextual analysis to evaluate requests and determine the most suitable AI endpoint, using a scoring system and specialized routing AI Models to ensure compliance with enterprise policies and user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users can access both personal and managed AI model endpoints, then user flexibility and access to multiple AI systems is improved, but resource misallocation and security risks increase
Solution Approach 1:
The patent introduces an intermediary system that sits between the user and multiple AI endpoints. This intermediary analyzes the user's request, determines the appropriate AI endpoint based on predefined rules and policies, and routes the request accordingly. This mediator prevents direct unauthorized access while maintaining user flexibility, resolving the contradiction between access versatility and security/reliability.
2Productivity
If intelligent routing is implemented to direct requests to appropriate AI endpoints, then resource utilization and security are improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-defining rules, policies, and routing criteria before requests are made. The system establishes predetermined conditions for routing decisions (such as user roles, request types, security requirements) in advance, allowing the routing engine to make quick decisions based on pre-established logic rather than complex real-time analysis, thus improving efficiency while managing complexity.
Solution Approach 2:
The routing system is segmented into distinct functional components: rule definition modules, policy management modules, request analysis modules, and endpoint selection modules. This segmentation allows each component to handle specific aspects of the routing decision independently, making the overall complex system more manageable and maintainable while achieving intelligent routing.
Data Source
AI summary
The invention provides a rules engine that manages user requests within an interface integrating multiple AI platforms and AI Models. Upon receiving a query, the engine assigns scores based on factors like management rules, user preferences, and contextual information. Determinative scores, such as those enforcing strict enterprise policies, can override others, leading the engine to block or reroute the query. If no score is determinative, the engine forwards the query and associated prompts to a specialized routing AI Model for contextual analysis. Based on this analysis, the rules engine directs the query to the most appropriate AI Model or defaults to the user-designated AI Model. This system balances user intent with rule enforcement, optimizing query processing across various AI platforms while ensuring compliance with enterprise guidelines.