Predictive Search Request Enrichment for Lower Computational Load

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Solution Overview

Problem

Computing systems face significant computational burdens and inefficiencies in handling large volumes of complex search requests, particularly in generating and processing search results that are often computationally intensive and result in substantial resource expenditure with limited commercial return.

Innovation Solution

An aggregator system enriches search requests with historical data and previous search results, using auxiliary parameters to guide suppliers in generating relevant results, thereby reducing the computational load and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If the system processes large volumes of complex search requests by generating comprehensive search results, then the completeness and coverage of search results is improved, but the computational load and resource expenditure increase significantly

Engineering Contradiction:

Improvevolume of search resultsVSAvoidcomputational load

Core Design Contradiction:

Quantity of substanceVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by storing previous search results and generating predictions about which results will be relevant before actually processing the current search request. The prediction module uses historical data to pre-identify relevant search results, reducing the computational burden during real-time request processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of previous search results and uses them as predictions for current search requests. Instead of generating entirely new search results for each request, the system copies and adapts results from historical data that are likely to be relevant, significantly reducing computational requirements.

Inventive Principle:

Principle #26Copying

2Quantity of substance

If the system generates comprehensive search results for all search requests, then the coverage and completeness is improved, but the commercial return decreases due to resource expenditure

Engineering Contradiction:

Improvesearch results generatedVSAvoidresource expenditure vs commercial return

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The system implements feedback mechanisms by analyzing handling indicators from previous search results (such as click-through rates, user interactions, and relevance feedback). This feedback is used to train and improve the prediction model, enabling the system to progressively identify more relevant results with lower computational cost.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes parameters by using auxiliary search parameters derived from previous search results to guide the generation of current search results. This parameter transformation allows the system to focus computational resources on generating only the most relevant results rather than exhaustive searches.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If the system processes all search requests with full computational resources, then the accuracy and relevance of results is improved, but the processing time and efficiency decrease

Engineering Contradiction:

Improverelevance of search resultsVSAvoidrequest processing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system segments the search result generation process into multiple components: prediction of relevant results using historical data, verification of predicted results, and supplementary generation of additional results. This segmentation allows parallel processing and reduces the time required for each individual search request.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The prediction module acts as an intermediary between the search request and the full search result generation process. It filters and pre-processes potential results using historical patterns, reducing the volume of data that requires detailed processing and improving overall system efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12579140B2Computational load mitigation with predictive search request enrichment

Publication Date: 2026.03.17 AMADEUS SAS

AI summary

A method at an aggregator includes: storing previous search results resulting from previous client search requests, and for each previous search result, a previous handling indicator, indicating a relevance of the previous search result to the client; receiving, from the client, a search request containing search parameters; in response to the search request, selecting a subset of previous search results based on correspondence between attributes of the previous search results and the search parameters, and on the previous handling indicators; providing, to a supplier, the search request and auxiliary search parameters corresponding to the selected previous search results and indicating characteristics of the selected previous search results, for generation of current search results at the supplier employing the auxiliary search parameters as inputs; receiving, from the supplier, the current search results generated at the supplier; and returning at least one of the current search results to the client.