Dental Image Deep Learning for National Security Identification
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Solution Overview
Problem
Current technologies lack an effective method for utilizing dental images for national security purposes, particularly in e-commerce transactions, where secure and efficient processing and analysis of dental images are necessary for identification and authentication of individuals, especially for law enforcement and security agencies.
Innovation Solution
A system utilizing deep learning and machine learning mechanisms to process dental images, including microprocessors configured to execute instructions for processing, matching, and generating dental image landmark probability maps, which are correlated with e-commerce datasets for national security applications, enabling secure transactions and identification over communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dental images are processed using traditional methods, then processing time is lengthy and accuracy is insufficient, but implementing deep learning mechanisms increases computational complexity and resource requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing dental images to extract key features and landmarks before main analysis. The deep learning model is trained in advance on large datasets of dental images, so when a new image is submitted, the heavy computational work has already been done during training, reducing real-time processing complexity.
Solution Approach 2:
The patent introduces an intermediary layer between raw dental images and final analysis results. This intermediary consists of feature extraction modules that convert complex dental images into simplified representations (landmark coordinates, probability maps), making the subsequent security check matching computationally manageable while maintaining high accuracy.
2Measurement precision
If deep learning mechanisms are implemented for dental image processing, then identification accuracy improves, but processing speed and transaction efficiency may be reduced
Solution Approach 1:
The dental image processing is segmented into distinct independent modules: image pre-processing, feature extraction, landmark identification, probability map generation, and security check matching. Each module can be optimized independently and processed in parallel, improving overall transaction speed while maintaining the accuracy benefits of deep learning.
Solution Approach 2:
The system creates simplified copies or representations of dental images in the form of landmark probability maps and feature vectors. These compact representations can be quickly compared against security databases without requiring repeated processing of the full-resolution original images, significantly speeding up transaction verification while preserving identification accuracy.
3Reliability
If dental images are integrated with e-commerce platforms for security checks, then national security monitoring is enhanced, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent designs the dental image processing system with universal applicability across multiple functions: it can process various types of dental images (X-rays, intraoral photos), serve different purposes (identification, authentication, security screening), and integrate with multiple platforms (e-commerce, law enforcement databases, national security systems). This multi-functionality reduces the need for separate specialized systems, actually simplifying overall implementation.
4Reliability
If comprehensive dental image analysis is performed including landmark probability maps, then identification reliability improves, but computational resources and processing time increase
Solution Approach 1:
The system implements partial action by selectively analyzing only the most relevant features and landmarks in each dental image based on the specific security check requirements. Rather than processing every possible feature uniformly, the deep learning model focuses computational resources on key identification landmarks, achieving high reliability with reduced energy consumption.
Data Source
AI summary
Deep learning of dental images for national security is described. A computer may receive dental images of a patient from a dental provider. The computer is configured to match dental images to law enforcement databases such as a terrorist database. The process begins with matching a dental image to an anatomy probability dataset and a pathology probability dataset, then correlating it with a patient database to identify a person of interest. With different resolutions each neural network deep learns to probability map and detect different dental object probabilities to authenticate an individual. Further, the dental images will be associated with GPS coordinates to track movements of an individual. The datasets will be provided to e-commerce providers, e-commerce consumers, e-commerce administrators, machine learning entities, government entities and law enforcement entities which may exchange or transfer the data over a communication network such as the internet to locate a person of interest.