Gemstone Identification via Machine Learning Spectral Analysis
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Solution Overview
Problem
Existing methods for gemstone tracking and tracing are limited in application, costly, and prone to alteration, failing to exploit inherent characteristics that distinguish one gemstone from another based on provenance.
Innovation Solution
A method using machine learning models to process spectral information from gemstones, leveraging defects and inclusions caused by magma droplets, to generate unique identifiers or origin information without relying on inscriptions, utilizing spectral data sets from various regions and comparing them with known data clusters to determine identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If laser encryption or artificial embedding of nano-sized particles is used to mark gemstones, then traceability information can be stored, but the process adds additional industrial steps, increases cost, and has limited application
Solution Approach 1:
The invention extracts and eliminates the need for external marking or tagging processes. Instead of adding artificial markers to gemstones, the system uses inherent spectral characteristics of the gemstones themselves to encode identification information, thereby removing the complex industrial steps of laser encryption or nano-particle embedding while maintaining traceability
Solution Approach 2:
The invention creates a spectral fingerprint copy of each gemstone's inherent optical properties. By capturing and analyzing the unique spectral signature of each gemstone, the system generates identification data without physical contact or modification, replacing the need for physical markers with a digital spectral representation
2Reliability
If laser encryption or artificial embedding is used to mark gemstones, then identification information can be stored, but the marking can be altered and detection of alterations is complex
Solution Approach 1:
The gemstone itself serves as the identification medium through its inherent spectral properties. The natural defects, inclusions, and optical characteristics that make each gemstone unique are directly utilized for identification without requiring external markers. This self-identifying capability ensures that the identification data cannot be altered or removed, as it is intrinsic to the gemstone's physical structure
Solution Approach 2:
The invention transforms the identification approach from physical marking to spectral parameter analysis. By measuring optical properties such as absorption, reflection, and transmission spectra, the system converts physical characteristics into identification data. These spectral parameters are inherent to the gemstone and cannot be changed without altering the gemstone itself, providing tamper-evident identification
3Loss of information
If existing marking techniques are used, then gemstones can be identified, but the techniques do not exploit fundamental elements that distinguish one gemstone from another based on provenance
Solution Approach 1:
The invention replaces mechanical marking systems (laser engraving, physical tagging) with optical spectroscopy-based identification. By substituting mechanical intervention with non-contact optical measurement, the system preserves provenance information embedded in the gemstone's natural spectral characteristics while eliminating the complexity of physical marking technologies
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables non-invasive, accurate, and cost-effective tracking and tracing of gemstones from mine to market, independent of physical state, with high identification accuracy and transparency in gemstone provenance.
Implementation Method 1
obtain spectral information for the gemstone, wherein obtaining the spectral information comprises receiving data over a network
Data Source
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AI summary
A method comprising obtaining spectral information for a gemstone, wherein obtaining the spectral information comprises receiving data that has been transmitted over a network, and determining, at one or more data processing devices, identification information for the gemstone using a machine learning model. Determining the identification information comprises processing the spectral information using the machine learning model to generate a set of one or more outputs of the machine learning model from which the identification information is derived. The identification information comprises: a unique identifier that has been assigned to the gemstone, or information identifying an origin of the gemstone.