Multi-Item Listing Generation Using Image-Description Matching
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Solution Overview
Problem
The process of manually uploading images and descriptions for multiple items on e-commerce platforms is user-interaction intensive, time-consuming, and increases demand for data storage and diminishes data processing speeds.
Innovation Solution
A listing generating system that automatically identifies and matches images and descriptions using machine learning models to generate item listings, reducing the need for manual input and optimizing data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual uploading and description providing is performed for each item, then listing accuracy is maintained, but user interaction intensity and time consumption increase significantly
Solution Approach 1:
The system performs automatic image-to-description matching using machine learning models, allowing the system to serve itself in generating listings without requiring manual user input for each item. The ML model autonomously processes images and generates corresponding descriptions, eliminating the need for sellers to manually create listings one-by-one.
Solution Approach 2:
The patent replaces the manual mechanical process of uploading and describing items with an automated machine learning-based system. The ML model processes images and generates descriptions algorithmically, substituting the manual human-operated mechanical listing creation process with an intelligent automated system.
2Productivity
If multiple items are listed one-by-one with manual input, then data accuracy is maintained, but data processing speed diminishes
Solution Approach 1:
The patent replaces the manual mechanical process of listing items one-by-one with an automated machine learning-based system. The ML model processes images and generates descriptions algorithmically, substituting the manual human-operated mechanical listing creation process with an intelligent automated system that maintains accuracy while improving speed.
Solution Approach 2:
The system enables continuous automated processing of multiple item listings simultaneously through the ML model, rather than processing items sequentially one-by-one. The model can handle multiple images and generate multiple descriptions in parallel, maintaining continuous productive action without interruption or manual intervention between items.
3Ease of operation
If manual listing creation process is used, then system complexity remains low, but user interaction intensity increases
Solution Approach 1:
The patent replaces the manual mechanical process of listing items with an automated machine learning-based system. The ML model processes images and generates descriptions algorithmically, substituting the manual human-operated mechanical listing creation process with an intelligent automated system.
Data Source
AI summary
A machine is configured to automatically generate listings for multiple items. For example, the machine receives, from a client device, two or more images and two or more descriptions. The two or more images depict two or more items. The two or more descriptions pertain to the two or more items. The machine matches one or more images of the two or more images to a description of the two or more descriptions. The one or more images depict an item of the two or more items. The description pertains to the item. The machine, based on the matching, generates a listing of the item. The listing includes the one or more images depicting the item, and the description pertaining to the item. The machine causes display of the listing of the item in a user interface.