Multi-Focus Gemstone Imaging with Neural Networks for Objective Grading
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Solution Overview
Problem
Existing gemstone evaluation methods are subjective, costly, and ineffective in distinguishing between natural and lab-created diamonds, and in identifying gemstone features from blurry or poorly lit images, leading to fraud and inaccuracies in grading and identification.
Innovation Solution
A gemstone imaging and evaluation system using a camera with multiple focal settings and neural networks to analyze gemstone images, capable of identifying features from blurry images and providing objective grading, including distinguishing between natural and lab-created diamonds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual evaluation methods are used by human evaluators, then the process is simple and low-cost, but the evaluation is subjective and prone to bias
Solution Approach 1:
The patent replaces the mechanical visual evaluation system (human eyes and brain processing) with an optical-mechanical imaging system coupled with neural network algorithms. The camera captures gemstone images and the neural network objectively analyzes features like cut, color, clarity, and carat, eliminating human subjectivity while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent introduces an intermediary neural network system between the gemstone and the evaluator. This intermediary processes images through trained algorithms that detect subtle features invisible to the human eye, providing objective measurements of gemstone characteristics without requiring complex manual inspection procedures.
2Measurement precision
If expensive high-end machines are used to differentiate natural from lab-created diamonds, then identification accuracy improves, but the cost increases significantly
Solution Approach 1:
The patent creates a digital copy (image) of the gemstone and analyzes it through neural networks trained on extensive datasets of natural and lab-created diamonds. This virtual analysis method captures subtle optical differences without requiring physical interaction with expensive specialized instruments, making authentication accessible and cost-effective.
Solution Approach 2:
The patent substitutes complex mechanical spectroscopic or microscopic analysis systems with an optical imaging system and neural network algorithm. The neural network learns to identify patterns specific to natural versus lab-created diamonds from training data, achieving high accuracy through software intelligence rather than expensive hardware.
3Measurement precision
If multiple focal settings are used to capture clear gemstone images, then image quality improves, but the time required for evaluation increases
Solution Approach 1:
The patent performs preliminary action by training the neural network on extensive datasets beforehand. During actual evaluation, the pre-trained model quickly processes images without requiring multiple focal adjustments or repeated captures. The system achieves high accuracy through intelligent algorithm design rather than exhaustive image acquisition.
Solution Approach 2:
The patent replaces the mechanical process of adjusting focal settings and capturing multiple images with an intelligent neural network that can extract features from a single or few images. The algorithm compensates for varying image qualities through learned robustness, eliminating time-consuming manual focusing and multiple captures.
4Reliability
If subjective grading criteria are used, then flexibility in evaluation is maintained, but consistency and reliability of grades decrease
Solution Approach 1:
The patent transforms subjective grading parameters into objective measurable quantities. The neural network converts qualitative assessments of cut, color, clarity, and carat into quantifiable metrics based on image analysis. This parameter transformation enables consistent, repeatable grading while maintaining the flexibility to evaluate various gemstone types through configurable algorithms.
Data Source
AI summary
A method, system, and device evaluate a gemstone using a gemstone imaging and evaluation device. The method includes capturing a plurality of training images of a plurality of gemstones using an image capturing device having a plurality of different focal settings, training a machine learning module using the plurality of training images, capturing a query image of a gemstone, analyzing the query image using the trained machine learning module, identifying a selected feature of the gemstone within the query image, and outputting a notification of the identified selected feature. The system and the gemstone imaging and evaluation device implement the method.