Search Request Processing with Context Data Retrieval

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Solution Overview

Problem

Complex search systems face challenges in achieving deterministic and short response times, especially with 'open queries' that have limited input information, leading to numerous generic results, and hardware performance enhancements are limited in addressing this issue.

Innovation Solution

A method is introduced where a search platform receives a search request from a client, retrieves context data from third-party platforms based on technical data like user names, email addresses, or session IDs, generates an adapted search request, and returns optimized search results, utilizing rule-based and machine-learning algorithms to refine the search process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If open queries with limited input information are processed, then the system can handle diverse user needs, but the number of generic results increases and response time deteriorates

Engineering Contradiction:

Improvehandling diverse user needsVSAvoidresponse time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by retrieving context data from third-party platforms before processing the search request. This includes obtaining user profile data, browsing history, and interaction data in advance, which allows the search algorithm to work with pre-prepared information rather than starting from scratch, thus reducing response time while maintaining adaptability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The search processing is segmented into multiple independent components: context data retrieval from third-party platforms, technical data extraction from the search request, adaptive parameter generation, and result ranking. This segmentation allows parallel processing of different data sources and reduces the overall processing time while maintaining comprehensive adaptability

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If more search parameters are requested from clients, then search precision improves, but the ease of operation deteriorates

Engineering Contradiction:

Improvesearch precisionVSAvoidease of operation

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system implements self-service by automatically generating adaptive search parameters based on context data retrieved from third-party platforms. Instead of requiring users to manually specify all search parameters, the system autonomously enriches the search request with additional parameters derived from user profiles, browsing history, and interaction data, thereby maintaining high search precision while preserving ease of operation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Context data from third-party platforms serves as an intermediary between the user's simple search request and the complex search processing requirements. This intermediary data layer provides the necessary information to generate adaptive parameters without requiring direct user input, thus bridging the gap between ease of operation and search precision

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If hardware performance is increased to speed up data processing, then processing speed improves, but the cost and complexity increase

Engineering Contradiction:

Improveprocessing speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system changes operational parameters by dynamically adjusting search parameters based on context data from third-party platforms. This includes adapting query terms, filtering criteria, and ranking weights according to user-specific context, which allows efficient processing of personalized searches without requiring increased hardware capacity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent substitutes mechanical hardware improvements with an information-based solution by retrieving and utilizing context data from third-party platforms. Instead of increasing processing power through better hardware, the system leverages external data sources to pre-enrich search requests, thereby achieving faster processing speeds through smarter data utilization rather than brute-force computational power

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20240403375A1Processing search requests

Publication Date: 2024.12.05 AMADEUS SAS

AI summary

Processing queries from an unknown client in a distributed computing environment comprises: receiving, from the client, a search request comprising one or more search parameters and technical data related to the search request; retrieving, from a number of third-party platforms having communicated with the requesting client before receiving the search request, context data for the requesting client based on the technical data; generating an adapted search request based on the one or more search parameters and the retrieved context data. The method further comprises: preparing a number of search results corresponding to the adapted search request; and returning the number of search results to the client.