Automated Listing Creation via Image Recognition
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Solution Overview
Problem
The process of creating listings for items on websites is time-consuming and tedious, requiring manual effort and lacking efficiency in identifying and listing items using image data.
Innovation Solution
A system that utilizes image data recognition to identify items by comparing submitted images with a catalog, pre-populating a template with attribute information, and allowing modifications through text or voice recognition to generate listings automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to create listings, then accuracy of item information can be maintained through human review, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system enables automatic self-service listing creation by capturing images, automatically identifying items through pattern recognition, extracting attributes, and generating complete listings without human intervention. The computer vision system processes images autonomously to create sellable listings, eliminating the need for manual data entry and review while maintaining high accuracy through automated attribute extraction.
Solution Approach 2:
The patent replaces manual mechanical processes (human image inspection, attribute identification, and listing creation) with automated computer vision and pattern recognition systems. The mechanical system of manual listing creation is substituted with an automated digital system that uses image processing algorithms to identify items and extract attributes, dramatically increasing productivity while reducing time loss.
2Productivity
If automated image recognition is implemented, then productivity increases and time is reduced, but system complexity increases
Solution Approach 1:
The system employs a universal pattern recognition framework that handles multiple item types, categories, and attribute variations through a single integrated computer vision platform. This multi-functional system can identify diverse items (electronics, clothing, furniture, etc.) and extract various attributes (color, size, material, brand) using the same core technology, managing complexity through generalization rather than requiring separate systems for each item type.
Solution Approach 2:
The patent introduces an intermediary attribute extraction layer between image capture and listing generation. The computer vision system first identifies the item and extracts key attributes, then uses these structured attributes to automatically populate listing templates. This intermediary processing step simplifies the overall system architecture by breaking down the complex task of listing creation into manageable stages: image processing, attribute extraction, and template population.
3Measurement precision
If detailed attribute extraction is performed, then listing accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-defining attribute templates and expected attribute structures for different item categories before processing images. The computer vision system knows in advance what attributes to look for (color, size, material, brand) and uses these pre-established frameworks to guide the image analysis process. This preliminary preparation enables rapid extraction of detailed attributes without requiring exhaustive analysis of every image, as the system is already configured to focus on relevant features.
Solution Approach 2:
The patent applies local quality by focusing image analysis resources on specific regions and features of interest within each image rather than uniformly processing the entire image. The computer vision system identifies key areas containing important attributes (e.g., brand logos, size tags, material textures) and concentrates processing power on these localized regions. This selective analysis maintains high measurement precision for critical attributes while reducing overall processing time by ignoring irrelevant image areas.
Data Source
AI summary
In various example embodiments, a system and method to provide services associated with an image is disclosed. The method includes receiving image data of an item of interest from a client device. The image data is used to identify a similar item from an image catalog based on the image data of the item. Attribute information associated with the similar item is retrieved and used to pre-populate a template. The pre-populated template is sent to the client device, and modified data from the client device is received in response, with the modified data resulting in a final template. A listing based on the final template is generated.