Multimodal Search Indexing for Relevant Natural Language Retrieval
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Solution Overview
Problem
Conventional computing systems struggle to search for files that include content of different types, such as text, images, charts, and audio, based on complex natural language queries, often returning irrelevant results and consuming additional computing resources.
Innovation Solution
A computer-implemented platform that supports multi-modality searches by using multiple indices for each modality, including reverse and embedding indices for text, images, and other content types, and performs parallel searches to identify and rank relevant files based on natural language queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional single-modality search systems are used, then the system structure remains simple, but the system cannot adequately handle complex natural language queries involving multiple content types
Solution Approach 1:
The patent segments the search system into multiple independent modality-specific components: text search module, image search module, video search module, audio search module, and chart search module. Each module has its own indexer and search algorithm optimized for that specific content type. This segmentation allows the system to handle diverse query types effectively while maintaining clear module boundaries that manage complexity.
Solution Approach 2:
The patent implements a universal search interface that accepts natural language queries and automatically routes them to appropriate modality-specific search modules. The query parser and result aggregator serve as universal components that work across all modalities, allowing a single system to fulfill multiple search functions simultaneously without requiring separate systems for each content type.
2Measurement precision
If conventional keyword-only search is used, then the search process is fast, but the search results are not relevant to the user's information retrieval intent
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing content from multiple modalities in advance: extracting text from images, transcribing video audio, generating metadata for all content types, and organizing everything in modality-specific indexes. When a query arrives, the system can immediately search pre-computed indexes rather than analyzing raw content in real-time, thus maintaining fast response times while enabling sophisticated relevance matching across multiple content types.
Solution Approach 2:
The patent introduces query parsers and result aggregators as intermediary components that translate natural language queries into modality-specific search parameters and synthesize results from multiple sources. These intermediaries enable precise relevance matching by understanding user intent and mapping it to appropriate content types without requiring the user to know the underlying search mechanics.
3Reliability
If the system returns all potential search results, then comprehensive coverage is achieved, but additional computing resources are consumed and display real estate is wasted
Solution Approach 1:
The patent implements partial action by returning a curated subset of the most relevant results from each modality rather than all possible matches. The result aggregator prioritizes results based on relevance scores and query intent, presenting only the top matches that truly satisfy the user's information need. This approach provides sufficient comprehensiveness for practical use while significantly reducing the number of results that require processing and display.
Solution Approach 2:
The system performs preliminary filtering and ranking of search results from multiple modalities before presentation to the user. By pre-computing relevance scores and prioritizing results based on query intent and content quality, the system eliminates low-value results early in the process, reducing subsequent computing resources needed for further processing and minimizing display real estate requirements.
Data Source
AI summary
A computing system that is configured to identify computer-readable items based upon input queries is described. The computing system receives a query and creates a semantic embedding of the query. The computing system searches over multiple indices based upon a keyword in the query and additionally searches over multiple indices based upon the semantic embedding of the query. Search results are identified based upon the multiple searches.