Multimodal Dwelling Search Using Semantic Vector Embeddings
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Solution Overview
Problem
Existing search engines struggle with accurately identifying dwellings based on natural language requests and multiple data modes, failing to provide specific and relevant information due to difficulties in understanding and processing free-form text and images.
Innovation Solution
Utilizing machine learning models to generate vector-based embeddings for textual and visual data of dwellings, and matching these embeddings with query embeddings to identify matching dwellings based on multiple search criteria, including natural language text and images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engines are used to identify dwellings based on multi-modal search queries, then the search process can be performed, but the accuracy of identifying matching dwellings deteriorates due to difficulties in understanding and matching semantic content across different data modes
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation that bridges different data modes (textual descriptions and images). The embedding model converts both text and image inputs into unified vector representations in the same semantic space, enabling accurate matching between search queries and dwelling listings despite the multi-modal nature of the data. This mediator approach resolves the contradiction by providing a common framework for comparing heterogeneous data types.
Solution Approach 2:
The patent transforms the search problem from operating on raw multi-modal data to operating on vector embedding representations. By changing the parameter space from heterogeneous data formats to unified vector spaces, the system achieves accurate matching while managing complexity through dimensionality reduction and semantic abstraction. The embedding vectors capture essential semantic features in a compressed form that facilitates efficient comparison.
2Productivity
If traditional search methods are used for dwelling queries, then the system structure remains simple, but the efficiency of information retrieval deteriorates due to inability to effectively process and match semantic content across multiple data modes
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing vector embeddings for dwelling listings in advance. When a search query is received, the system generates embeddings for the query and performs efficient similarity searches against the pre-processed embedding database. This pre-processing approach significantly improves retrieval efficiency by avoiding complex real-time analysis of raw images and text, while the added complexity of the embedding model is justified by the substantial performance gain.
3Measurement precision
If multi-modal data processing is implemented to improve search accuracy, then the precision of matching dwellings improves, but the computational resources and system complexity increase
Solution Approach 1:
The patent uses vector embeddings as simplified copies or representations of the original multi-modal data. Instead of processing and comparing actual images and text documents directly, the system works with compressed vector copies that capture the essential semantic information. This copying approach maintains matching precision while dramatically reducing computational resource requirements, as vector comparisons are far less intensive than multi-modal data processing.
Data Source
AI summary
Techniques are described for performing automated operations related to determining and providing information about dwellings for searches with search criteria combining data of multiple modes, such as at least free-form natural language text and one or more images. In some situations, the described techniques include training machine learning (“ML”) model(s) to encode semantic information about dwellings from multiple data modes into corresponding vector-based embeddings, using the trained ML model(s) to generate vector embeddings for dwellings in one or more geographical areas to represent dwelling data of multiple data modes, using the trained ML model(s) to generate vector embeddings for a search query with multiple search criteria including data of multiple modes, and determining one or more matching target dwellings for the query by matching generated vector embeddings of candidate dwellings to the generated vector embedding(s) for the query, with information about matching target dwelling(s) then further used.