Multi-Modal Component Search with Semantic Query Refinement
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Solution Overview
Problem
Existing e-commerce procurement platforms face challenges with limited search modalities, reliance on exact keyword matching, inefficient handling of complex queries, and lack of support for multi-modal inputs such as natural language descriptions or Bill of Materials (BOM) files, leading to difficulties for users lacking domain-specific expertise.
Innovation Solution
An intelligent, multi-modal component search and procurement system that supports diverse user inputs, including part numbers, natural language descriptions, and BOM files, with dynamic interface updates and conversational interfaces for iterative query refinement, leveraging retrieval-augmented generation (RAG) and vector embeddings for accurate category identification and schema validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact keyword matching is used for search, then search precision is improved, but ease of operation deteriorates for users lacking domain expertise
Solution Approach 1:
The patent introduces an intermediary processing layer between user input and product database that performs semantic understanding, query expansion, and intent recognition. This mediator translates natural language queries into structured search parameters, enabling both high precision and ease of use by bridging the gap between user intent and database requirements
Solution Approach 2:
The system dynamically changes search parameters based on query analysis, transforming unstructured natural language into structured search criteria. It adjusts weighting, filtering thresholds, and matching algorithms according to the detected query type and user context, maintaining precision while accommodating varied user input formats
2Device complexity
If limited search modalities are used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent implements a universal search interface that handles multiple input modalities including natural language, structured queries, image uploads, and voice commands through a single unified system architecture. This multi-functional design enables the system to adapt to diverse user needs without proportionally increasing system complexity
Solution Approach 2:
The search system is segmented into modular components: input processing module, semantic analysis module, query transformation module, and result generation module. Each segment handles specific tasks independently, allowing the system to support multiple search modalities while maintaining manageable complexity through modular architecture
3Speed
If conventional search methods are used, then processing speed is improved, but productivity deteriorates for complex queries
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing product data with multiple attribute hierarchies, pre-computing semantic relationships, and preparing query templates before actual user searches. This preliminary preparation enables rapid processing of complex procurement queries without sacrificing productivity
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching search mechanisms with intelligent systems including natural language processing, semantic analysis algorithms, and automated query refinement. This substitution enables the system to efficiently handle complex procurement scenarios that would require multiple manual search iterations
Data Source
AI summary
An intelligent multi-modal component search and procurement system is provided. The system enables users to search for industrial components through multiple input modalities, including keyword queries, BOM (Bill of Materials) file uploads, and natural language interactions. The system dynamically refines search results using a combination of structured filtering, semantic similarity analysis, and machine learning. Key features include a chat interface for natural language processing (NLP), a selection panel for real-time filtering, and a product listing that adapts to user inputs. The invention improves efficiency in industrial procurement by integrating contextual understanding, schema validation, and embedding vector conversions.