Multimodal Product Data Labeling for Logistics Automation
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Solution Overview
Problem
Existing methods require significant time and cost for selecting and labeling data to train AI models for logistics processing, hindering efficient data utilization in modern logistics and supply chain management.
Innovation Solution
An automated method to extract and label data for training AI models by assigning unique codes to products, registering corresponding data, and using these to determine product similarity, issue shipment commands, and manage storage locations based on order sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data extraction and labeling is implemented, then productivity and efficiency are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The system segments the logistics processing workflow into distinct modules: data extraction module, data labeling module, product matching module, and shipment management module. Each module handles specific tasks independently, allowing automated processing while maintaining manageable system complexity through functional decomposition.
Solution Approach 2:
A centralized server acts as an intermediary between various logistics components, coordinating data flow between extraction systems, labeling algorithms, and shipment management. This intermediary architecture enables automated processing without requiring complex direct integration between all system components.
2Measurement precision
If manual data selection and labeling is performed, then data quality for AI training is improved, but time consumption and costs increase
Solution Approach 1:
The system implements self-service data labeling through automated algorithms that extract product information from images and text, generate labels autonomously, and train AI models without human intervention. This eliminates manual data preparation while maintaining labeling accuracy through machine learning-based extraction and validation processes.
Solution Approach 2:
Manual mechanical data labeling processes are replaced with automated computer vision and natural language processing systems. These systems automatically extract product features from images and descriptions, generate training labels, and feed them to AI models, substituting human labor with automated computational processes.
3Measurement precision
If comprehensive product data is collected and stored, then matching accuracy is improved, but data storage requirements and processing overhead increase
Solution Approach 1:
The system extracts only the most critical product features and attributes needed for matching, such as key visual characteristics, product category, and essential descriptors. By selecting and storing only these essential features rather than complete product datasets, the system achieves accurate matching while minimizing data storage requirements.
Solution Approach 2:
Different levels of data detail are stored based on their utility for matching. The system stores high-detail data only for products requiring precise matching, while using simplified feature representations for routine matches. This localized quality approach optimizes storage by applying detailed data only where necessary.
Data Source
AI summary
The present invention relates to a method for building a multimodal dataset, a learning method using the same, and an artificial intelligence-based logistics processing method using the same. The invention includes, in a computer-implemented method: extracting data from a target site; filtering the extracted data to display the desired information to the customer; extracting and storing the order product name, which is the name of the product displayed, and the order image, which is the image of the product displayed, when the customer places an order; scanning and storing the unique code displayed on the incoming product; and determining whether the unique code corresponds to learned data, and if it does not, downloading the verification product name and verification image corresponding to the unique code.