Hierarchical Scenario Retrieval for Precise Agentic Query Routing
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Solution Overview
Problem
Computing systems with vast and structurally diverse data face challenges in accurately matching user queries due to high semantic similarity and overlap among scenarios, leading to incorrect executions and resource wastage.
Innovation Solution
Optimizing the embedding space for scenarios using refined embeddings and a hierarchical, reasoning-based approach that selectively applies advanced agentic capabilities only when necessary, enhancing query-to-scenario routing precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional retrieval-based approaches are used to match user queries to scenarios, then the system can handle simple queries efficiently, but it fails to accurately distinguish between scenarios with high semantic similarity and overlap, leading to incorrect executions
Solution Approach 1:
The patent segments the scenario matching process into multiple hierarchical levels: first matching queries to scenario groups, then to specific scenarios within groups. This segmentation allows the system to handle high-level routing efficiently while applying more sophisticated matching only where needed, improving both accuracy and reliability without processing all queries with maximum complexity.
Solution Approach 2:
The patent introduces scenario groups as an intermediary layer between user queries and specific scenarios. This intermediary structure helps distinguish between semantically similar scenarios by first categorizing them into broader groups, reducing the ambiguity in matching and improving execution correctness.
2Measurement precision
If advanced agentic capabilities are applied to all queries to improve routing accuracy, then scenario distinction improves, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies advanced agentic capabilities partially - only to queries that require them based on their complexity and the difficulty level of their scenario groups. Simple queries are handled by efficient retrieval-based approaches, while only complex queries triggering on ambiguous scenario groups receive the computationally intensive agentic reasoning, optimizing the balance between routing accuracy and resource consumption.
Solution Approach 2:
The patent applies different processing qualities to different parts of the query-scenario matching process. High-quality agentic reasoning is applied locally only where needed (in difficult scenario groups or for complex queries), while simple queries receive standard retrieval processing. This local quality differentiation maintains routing accuracy for complex cases while conserving computational resources overall.
3Measurement precision
If the system processes all queries with high-level reasoning to ensure accuracy, then routing precision improves, but the system complexity and processing time increase
Solution Approach 1:
The patent segments the reasoning process into hierarchical levels: scenario group matching (simpler) followed by scenario-level matching (more complex). This segmentation reduces overall system complexity by organizing processing into manageable stages, applying high-level reasoning only where necessary while maintaining routing precision through the structured approach.
Data Source
AI summary
Systems and methods select a candidate scenario of a plurality of scenarios in a scenario group associated with a hard level, based on a user query, generate a candidate selection rationale using a first machine learning model and analyze, using a second machine learning model, the user query, the candidate scenario, and the candidate selection rationale to generate a selection decision and selection decision feedback. The systems and methods further, until a selection decision is positive or a maximum number of iterations has been reached, select a new candidate scenario of the plurality of scenarios in the scenario group and generate a new candidate selection rational for based on the user query and the selection decision feedback, using the first machine learning model, and generate a new selection decision and new selection decision feedback based on the user query and new candidate selection rationale, using the second machine learning model.