Multimodal Search Indexing for Natural Language File Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computing systems struggle to accommodate searches for files that include content of different types (e.g., text, images, charts, audio, video) based on complex natural language queries, often returning irrelevant results and consuming additional computing resources.
Innovation Solution
A computer-implemented platform that supports searching over multiple modalities using multiple indices for each modality, including reverse and embedding indices, to identify files that match complex natural language queries by performing parallel searches across text, images, and other content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional reverse index searching is used for file searches, then simple keyword matching is achieved, but complex natural language queries cannot be adequately processed and irrelevant results are returned
Solution Approach 1:
The patent combines multiple search approaches (reverse index searching and embedding-based semantic searching) into a unified system. The forward search module uses reverse indices for exact keyword matching, while the semantic search module uses embedding models for meaning-based matching. Both search results are then merged and ranked together, allowing the system to handle both simple keyword queries and complex natural language queries effectively.
Solution Approach 2:
The patent introduces embedding models as an intermediary between the query and the search results. The embedding model converts natural language queries into semantic vectors that can be compared with embedded document representations. This intermediary layer enables the system to understand the semantic meaning of queries beyond simple keyword matching, improving relevance for complex natural language queries.
2Measurement precision
If multiple refined queries and manual filtering are required, then search accuracy may improve, but additional computing resources and time are consumed
Solution Approach 1:
The patent performs preliminary embedding of all documents into the search index in advance, creating embedding-based indices that enable direct semantic searching. This preliminary action eliminates the need for iterative refinement queries, as the system can directly compute semantic similarity between the original query and documents in a single pass, improving both accuracy and efficiency.
Solution Approach 2:
The patent replaces the mechanical iterative process of manual filtering and refined queries with an automated semantic similarity computation system. The embedding-based search automatically ranks results by semantic relevance, substituting the manual mechanical process of sifting through irrelevant results with an automated intelligent ranking system.
3Adaptability or versatility
If conventional single-modality search is used, then simple text-based searches are efficient, but multi-modality content (images, charts, video, audio) cannot be effectively searched
Solution Approach 1:
The patent creates a universal search system that handles multiple content modalities (text, images, charts, video, audio) through a unified architecture. The embedding model generates semantic representations for different modalities, and the search module uniformly processes queries across all modalities using the same embedding-based comparison mechanism, enabling multi-modality search without requiring separate specialized systems for each content type.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided is 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.