Composite Ring Analysis Using Staged Machine Learning Models
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Solution Overview
Problem
Existing image recognition techniques struggle to reliably identify characteristics of composite jewelry items, such as rings, particularly when they are part of broader compositions or captured at sub-optimal angles, due to challenges in distinguishing and identifying multiple components and complex overlapping regions.
Innovation Solution
A system utilizing staged machine learning models, including stone-locating, setting-locating, stone-classifying, and setting-classifying models, to accurately determine characteristics of rings within images, enabling precise identification and classification of stone and setting features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image recognition techniques are used to identify jewelry characteristics, then the system is simple and fast, but the identification reliability is poor for composite objects
Solution Approach 1:
The patent segments the composite jewelry object into multiple distinct components (e.g., stone, setting, band) and applies separate machine learning models to identify each component's characteristics. This segmentation approach improves identification reliability by treating each component independently rather than attempting to recognize the entire composite object as a single unit.
Solution Approach 2:
The patent employs a unified machine learning framework that performs multiple functions: detecting object boundaries, classifying component types, and extracting characteristics across different jewelry items. This multi-functional system handles diverse jewelry compositions (rings, necklaces, earrings) and various component types (diamonds, gemstones, metal settings) within a single integrated platform.
2Measurement precision
If multiple machine learning models are deployed to analyze composite objects, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by first detecting and localizing each component of the composite object before applying detailed classification models. The system identifies the presence and location of stones, settings, and bands in an initial processing stage, then uses these localized regions to guide subsequent characteristic extraction, avoiding unnecessary processing of entire images.
Solution Approach 2:
By segmenting the analysis process into distinct stages (detection, localization, classification, characteristic extraction) and applying specialized models to specific components, the system achieves high measurement precision for each jewelry characteristic while optimizing overall processing efficiency through targeted analysis.
3Ease of operation
If traditional search methods are used for online jewelry shopping, then the interface is simple, but the user experience deteriorates when users cannot determine search attributes
Solution Approach 1:
The patent implements a self-service mechanism where the machine learning system automatically extracts and provides jewelry characteristics (stone type, setting style, metal color) without requiring users to manually specify search attributes. Users simply upload or select an image, and the system autonomously identifies and presents relevant product attributes, enabling informed shopping decisions.
Solution Approach 2:
The system provides feedback by presenting identified jewelry characteristics to users and allowing them to refine or confirm the detected attributes. This feedback loop ensures that the information provided accurately reflects user intent and enables precise product matching, improving both ease of operation and information completeness.
Data Source
AI summary
Users may provide images to the ring selection system via a ring selection interface of a client application. The ring selection system may analyze the images using one or more machine learning models to determine ring characteristics of rings depicted in the images. The ring selection system may determine a set of ring listings having the determined ring characteristics and display the set of ring listings in the ring selection interface.