Search Request Processing With Cached ML Completion

Solution Overview

Problem

Existing search systems face challenges in efficiently handling search requests by optimizing the use of multiple data sources with varying response times and resource requirements.

Innovation Solution

A method for processing search requests that utilizes a cache with incomplete search results and a machine learning model to generate missing data fields, allowing for efficient assembly and return of complete search results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If complete search results are generated from original data sources for every search request, then data validity and freshness are improved, but response time and processing resource usage increase

Engineering Contradiction:

Improvedata validityVSAvoidresponse time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-computing and storing incomplete search results in a cache before they are fully needed. When a search request arrives, the system retrieves these pre-computed incomplete results and completes them using machine learning models, avoiding the need to generate complete results from scratch for every request. This preliminary caching of intermediate results resolves the contradiction by preparing data in advance while maintaining the ability to deliver complete results quickly.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary machine learning model that acts as a mediator between the cached incomplete search results and the final complete search results. Instead of directly generating complete results from original data sources for every request, the ML model fills in missing data fields based on patterns learned from training data. This intermediary approach maintains data validity while significantly reducing response time and processing resources compared to generating complete results from scratch.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If complete search results are generated from original data sources for every search request, then data freshness is improved, but processing resource usage increases

Engineering Contradiction:

Improvedata freshnessVSAvoidprocessing resource usage

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by pre-computing and storing incomplete search results in a cache before they are fully needed. When a search request arrives, the system retrieves these pre-computed incomplete results and completes them using machine learning models, avoiding the need to generate complete results from scratch for every request. This preliminary caching of intermediate results resolves the contradiction by preparing data in advance while maintaining the ability to deliver complete results quickly.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary machine learning model that acts as a mediator between the cached incomplete search results and the final complete search results. Instead of directly generating complete results from original data sources for every request, the ML model fills in missing data fields based on patterns learned from training data. This intermediary approach maintains data freshness while significantly reducing response time and processing resources compared to generating complete results from scratch.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If cached incomplete search results are used and completed with machine learning models, then response time and processing efficiency are improved, but data completeness and accuracy may be compromised

Engineering Contradiction:

Improvesearch processing efficiencyVSAvoiddata completeness

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system introduces an intermediary machine learning model that acts as a mediator between the cached incomplete search results and the final complete search results. Instead of directly generating complete results from original data sources for every request, the ML model fills in missing data fields based on patterns learned from training data. This intermediary approach maintains data freshness while significantly reducing response time and processing resources compared to generating complete results from scratch.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback mechanisms where the machine learning model's predictions are validated and refined based on performance metrics and data quality assessments. The model learns from feedback about prediction accuracy and adjusts its parameters to improve data completeness and reliability over time. This feedback loop ensures that while using cached incomplete results improves efficiency, the system maintains and continuously improves data completeness and accuracy.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250291799A1Search request processing

Publication Date: 2025.09.18 AMADEUS SAS

AI summary

Method, systems and computer programs for handling search requests at a search platform are provided. The search platform determines, using a cache with a number of incomplete search results, one or more of the incomplete search results with first data fields that correspond to the least one search parameter. For each determined incomplete search result, the search platform generates at least one second data field using a machine learning model. The at least one second data field corresponds to at least one search parameter and the at least one first data field of each determined incomplete search result. The search platform assembles a number of completed search results on the basis of the determined incomplete search results and the generated at least one second data field and returns at least one of the completed search results.