GUI Image Ordering Using Attribute Matching for Catalog Consistency
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Solution Overview
Problem
Inconsistent image ordering and display in e-commerce platforms lead to consumer confusion and frustration, particularly in large catalogues or those with frequently changing inventory, due to manual reordering being tedious, error-prone, and time-consuming.
Innovation Solution
A system that automates the ordering and display of images by analyzing and tagging them with attributes, comparing them to existing ordered images, and assigning order indices based on matching attributes, using machine learning models for image analysis and color dominance to group and order images consistently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual reordering of images is performed, then image ordering can be customized, but it becomes tedious, error-prone, and time-consuming
Solution Approach 1:
The system automatically analyzes uploaded images using machine learning models to determine optimal ordering without requiring manual intervention. The image analysis module extracts features and the ordering module automatically sequences images based on analyzed attributes, enabling the system to serve itself rather than requiring manual reordering operations.
Solution Approach 2:
The patent replaces manual mechanical ordering operations with automated machine learning-based image analysis. Machine learning models process image data to determine ordering, substituting human manual operations with automated computational processes that are faster and more consistent.
2Ease of operation
If manual reordering of images is performed, then image ordering can be customized, but it is error-prone
Solution Approach 1:
Manual ordering operations are replaced with automated machine learning-based analysis that consistently applies defined criteria for image ordering. This eliminates human errors in manual reordering while maintaining customization through learnable patterns and rules.
Solution Approach 2:
The system incorporates feedback mechanisms where image analysis results are used to automatically adjust ordering. The machine learning models continuously refine their ordering decisions based on analyzed image attributes, ensuring consistent and reliable ordering accuracy.
3Productivity
If images are analyzed and ordered automatically, then speed and consistency improve, but system complexity increases
Solution Approach 1:
The patent divides the image ordering system into separate functional modules: image analysis module, feature extraction module, ordering module, and display module. This segmentation allows each component to perform its specific function independently, managing overall system complexity while improving productivity through specialized processing.
Solution Approach 2:
The machine learning models are designed to handle multiple image attributes and ordering criteria within a unified framework. The system can adapt to different product types and ordering requirements using the same core infrastructure, reducing the need for separate specialized systems for different tasks.
Data Source
AI summary
Methods and systems for automatically ordering a set of images. The methods may include receiving a set of further images related to a first record, the first record referencing an ordered set of existing images, each of the existing images being assigned one or more respective image attributes. It may include assigning, using image analysis, one or more respective image attributes to each image in the set of further images and comparing image attributes assigned to the further images with image attributes assigned to the existing images to determine, for each of the further images, a corresponding one of the existing images. The further images are then ordered based on the determined corresponding ones of the existing images and the ordering of those existing images in the ordered set of existing images, and displayed in order in a user interface.