Multi-Region Building Information Query System
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Solution Overview
Problem
Existing search engines struggle with understanding natural language requests for building information in multiple proximate geographical regions and accurately providing specific information, often failing to limit queries to approved topics and failing to handle partial descriptions of geographical regions and point-of-interest locations effectively.
Innovation Solution
An Automated Multi-Region Building Information Determination (AMRBID) system that segments queries into geographical region and POI location designations, determines candidate regions and locations, applies inter-position distance filtering, and ranks combinations using non-distance-based criteria to provide accurate and efficient building information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engines are used to process natural language requests for building information, then information retrieval is performed, but the accuracy of understanding natural language requests and providing specific information deteriorates
Solution Approach 1:
The system segments the natural language query into distinct components: geographical region designations, POI location designations, and building information criteria. Each segment is processed separately through dedicated modules (geographical region determination module, POI location determination module, building information retrieval module), which improves the accuracy of understanding each part of the request while maintaining reliable information provision.
2Loss of information
If comprehensive building information is provided for multiple proximate geographical regions, then information completeness is improved, but the complexity of processing and limiting queries to approved topics increases
Solution Approach 1:
The system performs preliminary actions by pre-determining candidate geographical regions and POI locations before retrieving building information. The geographical region determination module and POI location determination module prepare candidate lists in advance, which are then filtered and used to guide the building information retrieval process. This preliminary processing reduces the complexity of handling comprehensive information by structuring it in advance.
3Measurement precision
If detailed processing is performed to determine intended geographical regions and POI locations from partial descriptions, then measurement precision is improved, but the time required for information retrieval increases
Solution Approach 1:
The system applies partial action by determining only the necessary candidate geographical regions and POI locations based on the partial descriptions provided in the query, rather than processing all possible regions and locations. The system retrieves building information for a focused set of candidates that are most likely to match the user's intent, achieving sufficient precision without excessive processing time.
Data Source
AI summary
Techniques are described for performing automated operations related to determining and providing information about buildings in multiple geographical regions in response to a query with partial descriptions of the regions and optionally partial descriptions of one or more additional locations in the regions, such as to automatically respond to a free-form natural language request having partial descriptions of names of multiple proximate geographical regions and of one or more point-of-interest (POI) locations by determining the intended geographical regions and POI locations and using them in generating corresponding response information. The described techniques may do inter-position distance filtering for at least some combinations of geographical region candidates and POI location candidates, and using a top-ranked combination that has a candidate for each geographical region and POI location designation in the query to determine responsive information for the received query.