Semantic Vector Search for Unified Text, Image, and Video Retrieval
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Solution Overview
Problem
Existing data search methods struggle to effectively integrate and retrieve diverse types of data such as texts, pictures, and videos in a unified manner, leading to suboptimal search performance and user experience.
Innovation Solution
A search method that converts user search requests and various data types into semantic vectors, enabling unified retrieval and similarity matching within a semantic vector space, incorporating contextual and fine-grained analysis to enhance accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional keyword-based search methods are used to search for diverse data types (texts, pictures, videos), then the search system can handle multiple data types, but the search accuracy and relevance deteriorate due to inability to perform unified semantic matching
Solution Approach 1:
The patent transforms diverse data types (texts, pictures, videos) into a unified parameter representation - semantic vectors. By converting all data into the same vector space with consistent dimensions and mathematical properties, the system enables uniform similarity calculation across different modalities, resolving the contradiction between handling diverse data types and maintaining search accuracy
Solution Approach 2:
The patent introduces semantic vectors as an intermediary representation layer between raw diverse data and the search matching process. This intermediary transforms heterogeneous data into a common language that enables accurate similarity computation, allowing the system to maintain both versatility in data type handling and precision in matching
2Reliability
If separate search systems are maintained for different data types (text search, image search, video search), then each system can be optimized for its specific data type, but the overall system complexity increases and unified retrieval becomes difficult
Solution Approach 1:
The patent merges multiple separate search systems into a unified search framework by combining different data types into a single semantic vector space. Instead of maintaining separate text search, image search, and video search systems, the invention consolidates them into one system that processes all data types through unified semantic similarity computation, reducing system complexity while preserving data-type-specific optimization
Solution Approach 2:
The patent creates a universal search system that handles multiple data types through a single interface and unified matching mechanism. The semantic vector representation enables the system to perform text search, image search, video search, and cross-modal search using the same underlying technology, eliminating the need for multiple specialized systems
Data Source
AI summary
A method is provided. The method includes converting a search request of a user into a first request semantic vector. The method further includes searching a search resource database for at least one first data semantic vector matched with the first request semantic vector, wherein the search resource database is constructed as a semantic vector space in which different types of data are converted into corresponding data semantic vectors, and the different types of data include at least texts, pictures and videos. The method further includes generating, based on the at least one first data semantic vector, a search result.