Search Engine Query Training via Sentence Suggestion
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Solution Overview
Problem
Conventional search engines rely on keyword queries, which do not effectively capture user intent and provide inaccurate, broad results, as they fail to interpret human language patterns.
Innovation Solution
A method and system that trains users to enter complete sentences as search queries by suggesting and presenting lists of popular sentences from search history and online sources, allowing for more accurate and detailed results by interpreting user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use keyword queries, then the search system is simple and easy to operate, but the search results are broad and inaccurate, failing to capture user intent
Solution Approach 1:
The system pre-processes and stores complete sentences from search histories and online sources before they are needed. When a user enters a keyword, the system has already prepared relevant complete sentences that capture user intent, allowing it to quickly suggest accurate search queries without complex real-time analysis
Solution Approach 2:
The system introduces an intermediary layer between the simple keyword input and the search results. This intermediary component retrieves and suggests complete sentences that bridge the gap between basic keywords and accurate search intent, improving precision without requiring the entire system to become complex
2Adaptability or versatility
If search engines accept only keyword queries, then the interface is simple, but it fails to interpret human language patterns and user intent
Solution Approach 1:
The system automatically retrieves and suggests complete sentences based on the user's keyword input without requiring the user to manually construct full sentences. The search engine serves itself by generating appropriate query suggestions, allowing users to benefit from advanced language interpretation while maintaining simple interaction
Solution Approach 2:
Complete sentences capturing various language patterns and user intents are pre-collected and stored in databases before being needed. When users input keywords, the system can quickly retrieve pre-prepared sentence suggestions that demonstrate proper human language usage, making advanced interpretation accessible through simple interfaces
3Measurement precision
If the system presents complete sentence suggestions from search history and online sources, then search accuracy improves, but the system complexity increases due to data collection and processing requirements
Solution Approach 1:
The system performs data collection and processing in advance by gathering complete sentences from search histories and online sources before they are needed. This pre-processing allows the system to store structured data that can be quickly retrieved and matched against user keywords, improving accuracy without requiring complex real-time processing
Solution Approach 2:
The system divides the complex task of improving search accuracy into separate, manageable components: one component collects and stores complete sentences from various sources, another component retrieves relevant sentences based on keywords, and a third component presents suggestions to users. This segmentation reduces overall system complexity by making each component's function specific and manageable
Data Source
AI summary
The present invention provides a method and system for training a user to use a complete sentence as a search query in a site level engine search. The user enters a keyword or partial keyword as a search query, then a list of sentences is found from a search history or online sources. The found sentences containing the keyword or partial keyword can be ordered by popularity of search and presented to the user along with a recommendation to use a sentence similar to a spoken sentence as a search query in order to obtain more accurate and detailed results.