Search Query Processing With Reinforcement Learning Resource Control

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Solution Overview

Problem

The increasing complexity and volume of search queries in distributed environments require efficient processing to maintain low response times and reduce computational requirements, particularly in time-critical applications, while minimizing energy consumption and resource usage.

Innovation Solution

A reinforcement learning architecture is employed to dynamically optimize search responses by determining actions that impact resource utilization, using a policy network to generate search responses and update neural networks asynchronously across multiple devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional search query processing methods are used to handle increasing query complexity and volume, then comprehensive search results can be generated, but computational requirements and energy consumption increase significantly

Engineering Contradiction:

Improvesearch query handling capabilityVSAvoidcomputational energy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adapts its processing strategy by using reinforcement learning to learn optimal search behaviors from past queries. The policy network continuously updates based on feedback, allowing the system to adapt to varying query complexities and data source characteristics, thereby reducing unnecessary computational energy consumption while maintaining comprehensive search capabilities

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The reinforcement learning approach changes the parameters of search processing by learning optimal configurations for query expansion, data source selection, and result generation. By adjusting these parameters based on learned patterns rather than using fixed traditional methods, the system reduces computational overhead while maintaining search effectiveness

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If more data sources are made available to increase search comprehensiveness, then better search results are achieved, but response time increases

Engineering Contradiction:

Improvedata source coverageVSAvoidsearch response time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-learning from historical search queries and outcomes. The reinforcement learning agent accumulates knowledge about which data sources are most valuable for different types of queries, allowing it to make rapid decisions about data source selection without exhaustively querying all available sources, thus reducing response time while maintaining comprehensiveness

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where search outcomes are used to update the policy network. By continuously learning from feedback about which data sources provide the most valuable information for different query types, the system optimizes its data source selection strategy to achieve comprehensive results faster

Inventive Principle:

Principle #23Feedback

3Speed

If computational resources are increased to process search queries faster, then response time decreases, but energy consumption and resource requirements increase

Engineering Contradiction:

Improvesearch processing speedVSAvoidcomputational resource energy

Core Design Contradiction:

SpeedVSUse of energy by stationary object

Solution Approach 1:

The reinforcement learning system serves itself by automatically learning and optimizing its own processing strategies. Instead of requiring continuously increased computational resources to handle growing query volumes, the system self-adapts by learning from past performance, thereby maintaining fast processing speeds while using computational resources more efficiently

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12517962B2Processing a search query

Publication Date: 2026.01.06 AMADEUS SAS

AI summary

A computerized method of processing a search query using reinforcement learning is presented. The method comprises receiving a search query, determining a state vector representing a current state of processing the search query based on at least one query parameter included in the search query, determining a search response to the search query according to at least one action determined by a policy network based on the state vector, the at least one action impacting an amount of resources to be utilized for determining the search response, determining a score based on the search response, the score defining a reward given for the search response, and updating the policy network according to the score.