Map Search Intent Parsing with Generative Language Models
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Solution Overview
Problem
Existing map applications struggle to accurately understand user search requirements, often requiring manual adjustments to query keywords for relevant results, leading to inefficient location search and route navigation.
Innovation Solution
A map search method utilizing a generative language model to interpret natural language input, generating search requirement information, which is used to query a preset map database for accurate results, and presenting them on a map interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search is used in map applications, then the system is simple to operate, but the search accuracy and understanding of user requirements deteriorates
Solution Approach 1:
The patent introduces a generative language model as an intermediary between the user's natural language query and the map database search. The model translates casual user input into structured search requirements, eliminating the need for manual keyword adjustment while improving search accuracy through intelligent interpretation of user intent.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with an intelligent language understanding system. Instead of relying on users to manually optimize search keywords, the system uses a generative language model to automatically comprehend and interpret natural language queries, substituting manual adjustment operations with automated semantic analysis.
2Measurement precision
If users manually adjust query keywords to improve search results, then the search accuracy improves, but the time consumption and operation complexity increases
Solution Approach 1:
The patent enables the search system to serve itself by automatically interpreting and optimizing queries without user intervention. The generative language model autonomously analyzes user intent, formulates appropriate search requirements, and retrieves accurate results, eliminating the time users would otherwise spend manually adjusting keywords.
Solution Approach 2:
The patent performs preliminary interpretation and structuring of user queries before the actual search execution. The generative language model pre-processes natural language input into well-defined search requirements, ensuring that the subsequent database query is optimized and accurate from the start, eliminating the need for iterative manual adjustments.
3Adaptability or versatility
If simple keyword matching is used, then the system complexity is low, but the ability to understand user requirements deteriorates
Solution Approach 1:
The patent employs a generative language model that serves multiple functions: it interprets user intent, extracts search requirements, and adapts to various types of natural language queries. This single multi-functional component handles diverse search scenarios (location search, route planning, point of interest查找) without requiring separate specialized systems for each function.
Data Source
AI summary
A map search method is provided, performed by a server, and including: obtaining a map search request transmitted by a terminal, the map search request including map search content, and the map search content being natural language content (401); inputting the map search content into a generative language model in response to the map search request, to obtain search requirement information generated by the generative language model, the search requirement information being configured for representing a search requirement of the map search content (402); querying a preset map database for a search result matching the search requirement information (403); and feeding back the search result to the terminal, so that the terminal presents the search result on a map interface (404).