Query Complexity Routing for Resource-Efficient AI Responses
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Solution Overview
Problem
Existing generative AI models consume excessive resources due to varying query complexities, leading to inefficient resource utilization and potential overload on higher-complexity models.
Innovation Solution
A system that classifies input queries based on complexity and selects appropriate AI models with varying parameter counts to optimize resource consumption, incorporating load balancing and dynamic threshold adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a higher-complexity AI model is used to handle all queries, then response quality for complex queries is improved, but resource consumption and computational load increase excessively
Solution Approach 1:
The patent segments the query processing workload by dividing queries into different complexity levels (simple, moderate, complex) and routing them to appropriately matched AI models. This segmentation allows simple queries to be handled by smaller, more efficient models while reserving larger models only for queries that truly require their capabilities, thereby resolving the contradiction between maintaining high response quality and reducing overall resource consumption.
Solution Approach 2:
The patent dynamically changes the parameter selection (model complexity) based on query characteristics. By analyzing query complexity and adjusting which AI model parameters are activated, the system optimizes resource allocation to match actual computational needs, preventing unnecessary consumption of resources on simple queries while ensuring adequate resources are allocated to complex queries requiring higher response quality.
2Reliability
If a higher-complexity AI model is used, then response quality is improved, but computational time and latency increase
Solution Approach 1:
The patent segments queries by complexity level and assigns them to appropriately sized models. Simple queries are routed to smaller models that can process them faster, while only complex queries are sent to larger models. This segmentation reduces overall computational latency by avoiding the use of computationally intensive models for queries that do not require their full processing power, while still maintaining high response quality for complex queries.
Solution Approach 2:
The patent applies partial action by using only the necessary model complexity required for each query. Instead of always deploying the full-capability model, the system uses a partial version (smaller model) for simple queries, reserving the excessive computational power of larger models only when truly needed for complex queries, thereby reducing average latency while preserving response quality where necessary.
3Productivity
If multiple AI models with varying complexities are deployed, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary query analysis component that assesses query complexity and determines the appropriate AI model selection. This intermediary layer manages the complexity of having multiple models by providing a systematic decision-making process, automatically routing queries to the most suitable model based on analyzed characteristics, thereby improving resource utilization efficiency while keeping system complexity manageable through automated management.
Solution Approach 2:
The patent changes system parameters dynamically based on query characteristics. By adjusting model selection parameters according to query complexity, the system optimizes resource utilization efficiency without requiring manual intervention. The parameter-based routing mechanism manages the complexity of multiple models through automated, rule-based decisions, improving productivity while maintaining acceptable system complexity levels.
4Reliability
If load balancing is implemented across multiple AI models, then system reliability is improved, but decision-making complexity and processing overhead increase
Solution Approach 1:
The patent implements load balancing by changing routing parameters based on query complexity analysis. The system uses parameter-based decisions (simple vs. complex query classification) to distribute load across multiple AI models, improving system reliability through diversified model usage while managing decision-making complexity through automated parameter-based routing rules rather than complex manual load-balancing algorithms.
Data Source
AI summary
Systems and methods for resource conservation based on query complexity are disclosed. An input query is received (e.g., via a chat interface) and provided to a response classifier, which is a machine-learning classifier that is trained to classify input queries with complexity scores that indicate how difficult it is likely to be for an artificial intelligence (AI) model to generate a response to the input query. If the complexity score exceeds a threshold score, the input query is provided to a first AI model (e.g., a relatively high-complexity AI model having a large number of parameters, relatively long response latencies, and/or other performance characteristics). If the complexity score does not exceed the threshold score, the input query is provided to a second AI model (e.g., a lower-complexity AI model having fewer parameters, shorter response latencies, and/or other differences in performance characteristics relative to the first AI model).