Neural Network Fabric Supplier Matching Platform
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of international fabric transactions, involving multiple institutions and foreign exchange banks, leads to time-consuming and unsafe procedures, making it difficult for buyers to secure high-quality fabrics from reliable suppliers across countries.
Innovation Solution
A method and apparatus using multiple neural networks to manage fabric supplier and buyer terminals, which involves screening, matching, and facilitating transactions by processing information on suppliers and buyers, including business registration, transaction history, and fabric details, to recommend suitable matches and manage the trading process efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional offline document-based transaction procedures are used for international fabric trade, then compliance with import/export regulations is achieved, but the transaction process becomes time-consuming and complex
Solution Approach 1:
The patent replaces manual offline document processing with an automated online platform that uses neural networks and big data analytics. The system automatically generates required documents (invoices, packing lists, bills of lading) through digital workflows, eliminating the need for physical document handling while maintaining regulatory compliance through automated verification processes.
Solution Approach 2:
The patent introduces an online transaction platform as an intermediary between fabric buyers and suppliers. This platform mediates the entire transaction process, including automated document generation, regulatory compliance verification, and coordination with foreign exchange banks, thereby streamlining the traditionally complex offline process.
2Quantity of substance
If fabric buyers order from suppliers meeting volume requirements, then order volume is secured, but fabric quality decreases
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously learns from transaction data, quality assessments, and supplier performance metrics. This feedback loop enables the system to identify and recommend suppliers who can meet both volume requirements and quality standards, dynamically adjusting recommendations based on accumulated knowledge about supplier capabilities.
Solution Approach 2:
The patent changes the selection parameters from simple volume-based criteria to multi-dimensional parameters including quality metrics, supplier reputation, transaction history, and capacity analysis. The neural network processes these multiple parameters simultaneously to identify suppliers that satisfy both volume and quality requirements.
3Measurement precision
If information about fabric suppliers in each country is collected, then supplier matching accuracy improves, but data collection complexity increases
Solution Approach 1:
The patent creates a universal online platform that handles multiple functions: data collection from various sources, neural network processing, supplier matching, transaction management, and document generation. This multi-functional system consolidates what would otherwise require separate complex systems for each function, making the overall data collection and processing more manageable.
Solution Approach 2:
The patent performs preliminary data collection and processing by pre-establishing supplier profiles with comprehensive information (business registration, production capacity, quality metrics, transaction history) before actual matching occurs. This preliminary preparation reduces the complexity of real-time data collection during transactions.
4Reliability
If multiple institutions issue transaction documents, then regulatory compliance is ensured, but the number of documents and process complexity increase
Solution Approach 1:
The patent merges the document generation and verification functions of multiple institutions into a single integrated online platform. The system automatically generates all required documents (import/export approvals, invoices, packing lists, bills of lading) through coordinated digital workflows, eliminating the need for separate physical document handling by each institution while maintaining compliance through automated verification.
Data Source
AI summary
Disclosed herein are a method and apparatus for managing fabric supplier terminals and fabric buyer terminals using a plurality of neural networks. According to an embodiment, the server may manage the fabric supplier terminals and fabric buyer terminals by using a plurality of neural networks. For example, the server may determine a plurality of supplier groups matched with a fabric buyer terminal through a matching model using a plurality of neural networks based on estimate information, information about fabric suppliers, and information about fabrics. For example, the server may group and match fabric suppliers capable of supplying corresponding fabrics in consideration of a country or region.