Search Engine Suggestion System Using Pre-stored Media Data
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Solution Overview
Problem
Current search engines often fail to provide users with relevant results, especially when searching for information outside their cultural or linguistic familiarity, leading to difficulties in obtaining desired information.
Innovation Solution
A computer-implemented method within a server system generates suggestions by comparing search queries with stored digital media items, terms, and objects in a database, allowing for the transmission of tailored suggestions and search results to user devices, incorporating algorithms for ranking and scoring based on user traits, demographics, and social interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines return results based only on keywords and terms, then the search process is simple and fast, but the relevance and cultural appropriateness of results deteriorate for users from different localities
Solution Approach 1:
The system pre-processes and stores queries, terms, and objects in a database before actual search operations. By organizing data in advance with associated metadata and relationships, the system can quickly retrieve and process relevant information during search without performing complex analysis in real-time, thus improving result relevance while maintaining processing speed.
Solution Approach 2:
The patent introduces an intermediary processing layer between the user query and the search results. This layer includes suggestion generation mechanisms that act as mediators, analyzing user intent and providing refined search suggestions before final results are returned. This intermediary process enhances result relevance without requiring the entire search system to become exponentially more complex.
2Adaptability or versatility
If search engines provide generic results to all users, then the system is easy to operate, but the ability to provide culturally and linguistically appropriate results deteriorates
Solution Approach 1:
The system applies local quality by tailoring search results and suggestions to individual users based on their cultural background, language preferences, and location. The database stores queries and terms with associated cultural and linguistic metadata, allowing the system to provide locally-appropriate results while maintaining a unified global system architecture, thus achieving adaptability without compromising ease of operation.
Solution Approach 2:
The search system is segmented into modular components: a core search engine that handles basic keyword matching, and separate suggestion generation modules that handle cultural and linguistic adaptation. This segmentation allows the core system to remain simple and easy to operate, while specialized modules provide adaptive capabilities when needed.
3Measurement precision
If search engines analyze user traits, demographics, and social interactions to generate suggestions, then the personalization and relevance of results improve, but the computational complexity and processing time increase
Solution Approach 1:
User traits, demographics, and social interaction data are pre-analyzed and stored in the database before actual search operations. The system maintains pre-computed user profiles and preference data that can be quickly retrieved during search, eliminating the need for real-time analysis of all user attributes and significantly reducing processing time while maintaining precise user intent matching.
Solution Approach 2:
The system performs partial analysis of user data by focusing only on the most relevant traits and demographics for each specific search query, rather than analyzing all available user information. This selective approach achieves sufficient precision for matching user intent while avoiding the computational overhead of comprehensive analysis, thus reducing processing time.
Data Source
AI summary
As disclosed herein, a computer-implemented method within a server system generates suggestions by a search engine in response to search queries. The method uses a database associated with the search engine, the database storing digital media items each selectively associated with queries, terms and/or objects. The search engine receives a search query from a user device. The method compares one or more parts of the search query with stored queries, terms and/or objects within the database to find matching digital media items and generates at least one suggestion based on search query, the suggestion comprising one of the matching digital media items and at least one search result based on search query. The search engine transmits an output to the user device comprising the at least one suggestion and/or the at least one search result.