Natural-Language Dwelling Search With Heterogeneous Strategies
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Solution Overview
Problem
Existing search engines struggle with accurately and efficiently providing information about dwellings in response to natural language queries due to difficulties in understanding complex search requests, limiting information to specific topics, and ensuring relevance.
Innovation Solution
A system that segments natural language queries into multiple criteria, employs a combination of heterogeneous search strategies, including keyword-based, phrase-based, and vector embedding-based searches, to identify candidate dwellings that match the query criteria, using machine learning models to encode semantic information into vector embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single search strategy is used to process natural language queries, then the system is simpler to implement, but the accuracy and relevance of search results deteriorate
Solution Approach 1:
The search query is segmented into multiple segments based on its structural characteristics (e.g., identifying dwelling type, location, price range, bedroom count). Each segment is then processed by appropriate search strategies tailored to its specific requirements, allowing the system to handle complex queries comprehensively while maintaining clarity in processing each individual segment.
Solution Approach 2:
The system implements a multi-functional search architecture where a single query processing system can handle multiple search strategies (keyword-based, phrase-based, vector embedding-based) simultaneously. This universal approach allows the system to adapt to different query types and complexities without requiring separate specialized systems for each search method.
2Reliability
If multiple search strategies are employed to improve search accuracy, then the relevance of results improves, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial action by selectively employing different search strategies based on the specific segments identified in the query. Not all segments require the same level of search intensity - for example, location-based segments might use efficient geographic indexing while descriptive segments use vector embeddings. This partial application of search strategies optimizes resource usage by focusing computational effort where it matters most.
Solution Approach 2:
The system changes the search parameters and strategies dynamically based on the query segments. Different segments trigger different search mechanisms (keyword matching, phrase matching, vector similarity), and the system adjusts its behavior based on the identified segment types, thereby optimizing computational efficiency while maintaining high relevance.
3Ease of operation
If the system processes complex natural language queries, then the ease of operation improves for users, but the difficulty of understanding and processing the queries increases
Solution Approach 1:
The query understanding process is segmented into identifiable components (dwelling type, location, price, bedrooms, etc.). By breaking down the complex natural language query into these discrete segments, the system can process each component systematically using appropriate strategies, making the overall complex query manageable and interpretable.
Solution Approach 2:
The system uses an intermediary query segmentation and analysis module that translates complex natural language queries into a standardized internal representation. This intermediary layer simplifies the processing by converting diverse user inputs into a uniform format that can be efficiently processed by the various search strategies.
Data Source
AI summary
Techniques are described for performing automated operations related to determining and providing information about dwellings within geographical regions specific to indicated locations, such as within an indeterminate distance from an indicated point-of-interest (POI) location by determining and using individualized geographical search regions specific to each POI location. In some situations, for each of a plurality of POI locations, a geographical region specific to that POI location is predetermined in an individualized manner for that POI location using attribute(s) of that POI location, to represent a geographical region for that POI location considered to be nearby that POI location, and then using such predefined POI-specific nearby geographical regions when responding to a later received search query that specifies multiple search criteria using a sequence of multiple free-form natural language terms that indicate such a POI location, such as in combination with other search criteria.