Derivative works — US Patent 11049248

Defensive Disclosure: Advanced Dental Arch Image Analysis and Orthodontic Aligner Assessment Systems

Publication Date: 2026-07-08

Title: Advanced Methodologies and Systems for Dental Arch Image Analysis and Orthodontic Aligner Assessment leveraging Deep Learning

Abstract:
This defensive disclosure details various advanced methodologies and systems for analyzing dental arch images and assessing orthodontic aligner fit, building upon the foundational concepts of using deep learning and robust image processing techniques. The disclosed variations cover alternative material and component integrations, expanded operational parameters for diverse scales and environmental conditions, cross-domain applications in unrelated industries, sophisticated integrations with emerging technologies such as AI-driven optimization, IoT, and blockchain, and specialized modes for low-power operation or monitoring of failure/training states. The aim is to preemptively disclose incremental improvements and broader applications, rendering them obvious or non-novel for future patent claims by competitors in the evolving field of digital dentistry and beyond.

Core Claim 1 Derivative Variations: Method for Analyzing an Image of a Dental Arch

Core Concept: Analyzing an image of a dental arch using a deep learning device trained on historical images with tooth attribute values.

1.1. Derivative: Multi-Spectral Imaging with Edge-AI Inference

Enabling Description: This derivative employs a specialized multi-spectral or hyperspectral imaging sensor array for dental arch image acquisition, rather than conventional RGB cameras. This sensor operates across defined spectral bands, including visible light (e.g., 450-700nm) and near-infrared (e.g., 750-1000nm), with a spectral resolution of 5-10nm per band. The multi-spectral data provides enhanced tissue differentiation, allowing for improved detection of early carious lesions, demineralization, gingival inflammation, and sub-surface dental anomalies not visible in standard RGB images. The deep learning inference engine for determining tooth attributes is implemented on a custom Field-Programmable Gate Array (FPGA) or Application-Specific Integrated Circuit (ASIC) integrated directly into the handheld imaging device. This embedded hardware accelerator features dedicated convolutional neural network (CNN) processing units and on-chip memory (e.g., 8-16MB SRAM) for storing quantized model weights (e.g., 8-bit integer quantization), enabling ultra-low latency inference (e.g., <50ms per image frame) at the edge. The system processes the multi-spectral image cubes to extract spectral signatures, which are then fed into the embedded CNN for real-time identification of tooth zones and determination of attributes such as caries severity, enamel density, and specific bacterial biofilm composition.

Mermaid Diagram:

graph TD
    A[Multi-Spectral Imaging Sensor] --> B{Spectral Data Acquisition (Image Cube)};
    B --> C[Edge-AI Processing Unit (FPGA/ASIC)];
    C -- CNN Accelerator Block --> D[Quantized Model Weights (On-Chip)];
    C --> E[Tooth Attribute Determination (Real-time)];
    E --> F[Output: Enhanced Dental Analysis Result];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#ccf,stroke:#333,stroke-width:2px

1.2. Derivative: Extreme Scale Dental Arch Analysis

Enabling Description (Micro-scale): The dental arch image analysis method is adapted for microscopic-level evaluation of dental structures. "Analysis images" are acquired using advanced imaging modalities such as Optical Coherence Tomography (OCT), confocal microscopy, or scanning electron microscopy (SEM), yielding volumetric or ultra-high-resolution 2D image stacks. The "learning base" comprises 3D histological reconstructions of tooth tissues (enamel, dentin, cementum, pulp) and corresponding micro-level attribute annotations (e.g., "enamel rod disorientation," "dentinal tubule density," "micro-crack presence/dimension," "bacterial colony morphology and depth," "collagen fiber integrity"). A specialized 3D Convolutional Neural Network (3D CNN) or U-Net architecture is trained on this volumetric learning base. The system determines granular tooth attributes such as specific demineralization depth (in micrometers), bacterial penetration extent, and quantitative assessment of enamel microfractures or structural defects at a cellular or sub-cellular resolution.

Mermaid Diagram (Micro-scale):

graph TD
    A[OCT/Confocal/SEM Microscope] --> B{3D/Ultra-Res Image Stack Acquisition};
    B --> C[Volumetric Image Preprocessing];
    C --> D[Deep Learning Device (3D CNN/U-Net)];
    D -- Trained on Histological Annotations --> E[Micro-level Tooth Attribute Determination];
    E --> F[Output: Quantitative Micro-Pathology Report];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style D fill:#ccf,stroke:#333,stroke-width:2px

Enabling Description (Population-scale): The method is scaled for industrial-level processing of millions of dental arch images for population-wide epidemiological analysis. Images are acquired from a diverse array of sources (e.g., robotic intraoral scanners, public health screening programs, large-scale photographic databases) at high throughput (e.g., >1000 images/minute). The "learning base" encompasses vast, geographically distributed datasets annotated with demographic, genetic, and environmental factors alongside standard tooth attributes. The deep learning system operates on a distributed GPU cluster (e.g., using Kubernetes and TensorFlow Extended) employing federated learning techniques to aggregate model updates from multiple data silos without centralizing raw patient data. The system identifies macroscopic tooth attribute patterns (e.g., prevalence of specific malocclusions in different climate zones, correlation of dietary habits with decay patterns, population-level tooth wear indices) for public health policy formulation and large-scale dental research.

Mermaid Diagram (Population-scale):

graph TD
    A[Diverse Image Sources (Robotic Scanners, Public Health)] --> B{High-Volume Data Ingestion Pipeline};
    B --> C[Distributed GPU Cluster (Federated Learning)];
    C -- Global Learning Base (Annotated Big Data) --> D[Population-level Feature Extraction];
    D --> E[Epidemiological Tooth Attribute Determination];
    E --> F[Output: Public Health/Research Insights];
    style C fill:#ccf,stroke:#333,stroke-width:2px
    style A fill:#f9f,stroke:#333,stroke-width:2px

1.3. Derivative: Cross-Domain Application: Industrial Component Inspection

Enabling Description: The deep learning-based image analysis method is adapted for automated optical inspection (AOI) in precision manufacturing, specifically for quality control of intricate, repeating micro-components like electronic connectors, watch gears, or micro-fluidic channels. The "analysis image" is a high-magnification digital image (e.g., 50x to 1000x optical zoom) of a component captured by an automated vision system. The "learning base" comprises thousands of historical images of components, each with "historical feature zones" (e.g., individual pin connectors, gear teeth profiles, channel walls) precisely annotated with "feature attribute values" such as "defect type: burr, scratch, deformation," "dimensional deviation: +/−5µm," "surface roughness: Ra value," or "material integrity: crack, inclusion." The deep learning device (e.g., a YOLO-based object detection network for feature localization combined with a ResNet for classification) identifies and quantifies anomalies in component features, determining probabilities of specific defect types and their associated severity, thereby providing immediate pass/fail criteria for manufacturing quality control.

Mermaid Diagram:

graph TD
    A[Automated Micro-Optical Vision System] --> B{Component Image Acquisition (High-Res)};
    B --> C[Deep Learning Device (YOLO + ResNet)];
    C -- Trained on Annotated Defect Database --> D[Component Feature/Defect Localization & Classification];
    D --> E[Output: Automated Quality Control Report (Pass/Fail, Defect Metrics)];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#ccf,stroke:#333,stroke-width:2px

1.4. Derivative: Integration with AI-Driven Optimization and IoT for Proactive Monitoring

Enabling Description: This derivative integrates the deep learning dental arch analysis with an Internet of Things (IoT) network of smart oral health sensors and an AI-driven optimization engine. IoT sensors embedded in a patient's toothbrush or smart retainer (e.g., miniaturized pH sensors, biofilm thickness sensors, temperature sensors) continuously stream real-time physiological and behavioral data. The "analysis images" are captured by a smart intraoral camera, and the deep learning device performs tooth attribute determination. An overarching AI optimization engine continuously monitors both the deep learning output and the IoT sensor data. This AI dynamically adjusts the deep learning model's training parameters (e.g., re-prioritizing certain historical image subsets for transfer learning, adjusting learning rates) or triggers targeted data acquisition (e.g., prompting the patient for specific image angles) to maintain optimal diagnostic accuracy based on the patient's evolving oral micro-environment. Furthermore, the analysis results (e.g., early signs of decay, gingivitis) are correlated with IoT data to trigger proactive, personalized interventions or alerts via an IoT-connected patient application or clinician dashboard (e.g., "increase fluoride rinse frequency," "schedule a dental hygienist appointment").

Mermaid Diagram:

graph TD
    A[Smart Intraoral Camera] --> B{Analysis Image};
    C[IoT Oral Health Sensors] --> D{Real-time Physiological/Behavioral Data};
    B --> E[Deep Learning Analysis (Tooth Attributes)];
    D --> F[AI Optimization Engine];
    E -- Analysis Results --> F;
    F -- Feedback Loop (Dynamic Retraining/Data Acquisition) --> E;
    F -- Proactive Alerts/Interventions --> G[IoT Patient App/Clinician Dashboard];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#f9f,stroke:#333,stroke-width:2px
    style E fill:#ccf,stroke:#333,stroke-width:2px
    style F fill:#cfc,stroke:#333,stroke-width:2px

1.5. Derivative: Low-Power Diagnostic Mode with Confidence-Aware Reporting

Enabling Description: This derivative introduces a "low-power diagnostic mode" (LPDM) for the deep learning device, activated when the image acquisition apparatus's battery level is critical (e.g., <15%) or when network bandwidth is severely limited. In LPDM, the system dynamically switches to a pre-trained, highly quantized (e.g., 4-bit or 2-bit integer) and pruned deep learning model (e.g., a MobileNetV3-Small architecture), which operates on lower-resolution "analysis images" (e.g., 256x256 pixels downsampled from 1024x1024). This reduced model executes with significantly lower computational and memory overhead, extending device battery life (e.g., 5x longer inference time for 10x less power). The LPDM prioritizes binary or categorical attribute determinations (e.g., "decay present/absent," "inflammation mild/moderate/severe") over fine-grained measurements. Crucially, the system provides a "confidence score" (e.g., 0-100%) alongside each attribute determination, explicitly indicating the reduced accuracy expected in LPDM. If the confidence score falls below a predefined threshold (e.g., 60%), the system automatically flags the result as potentially unreliable and recommends re-acquiring the image under optimal conditions or forwarding it for human review.

Mermaid Diagram:

stateDiagram-v2
    [*] --> High_Power_Mode: Normal Operation
    High_Power_Mode --> Low_Power_Diagnostic_Mode: Battery Low / Network Limited
    Low_Power_Diagnostic_Mode --> High_Power_Mode: Battery Charged / Network Restored
    Low_Power_Diagnostic_Mode --> Output_LPDM_Result: Perform Reduced Inference
    Output_LPDM_Result --> Confidence_Check: Generate Confidence Score
    Confidence_Check --> Human_Review_Recommended: Score < Threshold
    Confidence_Check --> Final_Report_LPDM: Score >= Threshold
    Human_Review_Recommended --> [*]: Notify User
    Final_Report_LPDM --> [*]: Display Result with Confidence
    style High_Power_Mode fill:#ccf,stroke:#333,stroke-width:2px
    style Low_Power_Diagnostic_Mode fill:#cfc,stroke:#333,stroke-width:2px

Core Claim 14 Derivative Variations: Method for Assessing the Shape of an Orthodontic Aligner

Core Concept: Assessing orthodontic aligner shape (separation between tooth and aligner) using a deep learning device.

2.1. Derivative: Multi-Modal Bio-Mechanical Aligner Fit Assessment

Enabling Description: This derivative enhances aligner fit assessment by integrating visual analysis with real-time bio-mechanical data. The orthodontic aligner is manufactured with embedded, wirelessly transmitting micro-electromechanical systems (MEMS) sensors, including miniature strain gauges (e.g., thin-film piezoresistive sensors) and pressure sensors strategically placed at key tooth-aligner contact points (e.g., mesial, distal, buccal, lingual surfaces). These sensors continuously stream quantitative data on localized pressure distribution and micro-deformations of the aligner. The deep learning device utilizes a multi-modal neural network architecture (e.g., a fusion of a CNN for image features and a fully connected network for sensor data) that concurrently processes the "analysis image" (visual appearance of separation) and the numerical sensor data. The learning base includes historical images paired with corresponding sensor readings, annotated with "tooth attribute values" such as "quantified separation gap (µm)," "localized contact pressure (kPa)," "shear stress (MPa)," and "force vector direction." This allows for a precise, objective assessment of aligner-tooth interface mechanics, not solely relying on visual cues.

Mermaid Diagram:

graph TD
    A[Image Acquisition Apparatus] --> B{Analysis Image (Visual Separation)};
    C[Aligner with Embedded MEMS Sensors] --> D{Real-time Bio-mechanical Data (Strain, Pressure)};
    B --> E[Multi-Modal Data Fusion Module];
    D --> E;
    E --> F[Deep Learning Device (Multi-Modal CNN)];
    F -- Trained on Fused Data + Annotations --> G[Determine Aligner Fit Attributes (Quantified Separation, Stress)];
    G --> H[Output: Bio-Mechanical Fit Report];
    style A fill:#f9ff99,stroke:#333,stroke-width:2px
    style C fill:#99ff99,stroke:#333,stroke-width:2px
    style F fill:#ccf,stroke:#333,stroke-width:2px

2.2. Derivative: Longitudinal Adaptive Aligner Monitoring in Extreme Oral Environments

Enabling Description: This derivative focuses on assessing aligner performance under dynamic, real-world oral conditions and over extended periods. "Analysis images" are acquired as video streams or high-frequency image bursts throughout the day (e.g., during meals, speech, sleep) using a miniaturized, patient-worn intraoral camera. The aligners themselves are fabricated from advanced polymers with integrated micro-sensors (e.g., pH, temperature, salivary flow, occlusal force) that provide continuous environmental data. The deep learning device employs a recurrent neural network (RNN) or a transformer-based architecture capable of processing sequential multi-modal data (image frames + sensor time-series). The learning base includes longitudinal datasets of aligner wear, capturing degradation, fit changes, and patient compliance under varying physiological and environmental stresses. The system determines dynamic "tooth attribute values" such as "average daily separation profile," "peak force-induced separation," "material fatigue index under acidic exposure," and "wear time adherence." This provides a comprehensive understanding of aligner efficacy and durability in a patient's unique oral ecosystem, allowing for adaptive treatment modifications based on real-time performance.

Mermaid Diagram:

sequenceDiagram
    participant P as Patient
    participant IA as Intraoral Camera (Wearable)
    participant AS as Aligner Sensors (pH, Temp, Force)
    participant DLS as Deep Learning Server (RNN/Transformer)
    participant TPO as Treatment Planning/Optimization

    loop Continuous Monitoring (e.g., 24/7)
        P->>IA: Acquire Video Stream
        P->>AS: Generate Environmental Data Stream
        IA->>DLS: Send Video Data
        AS->>DLS: Send Sensor Data
        DLS->>DLS: Process Sequential Multi-Modal Data
        DLS->>DLS: Determine Dynamic Aligner Attributes
        DLS->>TPO: Report Longitudinal Performance Metrics
        TPO->>P: Adaptive Treatment Recommendations (e.g., wear longer, new aligner)
    end
    style IA fill:#f9f,stroke:#333,stroke-width:2px
    style AS fill:#f9f,stroke:#333,stroke-width:2px
    style DLS fill:#ccf,stroke:#333,stroke-width:2px

2.3. Derivative: Cross-Domain Application: Industrial Pipeline Seal Integrity Assessment

Enabling Description: The deep learning method for assessing aligner shape (separation) is adapted for automated, in-situ inspection of critical industrial pipeline seals and gaskets in environments like oil and gas, chemical processing, or aerospace. The "analysis image" is acquired by robotic inspection vehicles using high-resolution optical cameras, thermal cameras, or ultrasonic imaging systems during pipeline operation. The "learning base" consists of historical images and sensor data of various seal types (e.g., O-rings, flange gaskets) with "historical defect zones" (e.g., seal interfaces, material surfaces). These zones are annotated with "integrity attribute values" such as "compression set (mm)," "abrasion damage (µm)," "corrosion depth (mm)," "thermal leakage gradient (°C/mm)," or "fluid ingress detection (binary)." The deep learning device (e.g., a combination of a CNN for visual/thermal data and a U-Net for segmentation of defect regions) analyzes the collected data to detect microscopic gaps, material degradation, and potential leakage pathways in real-time, predicting imminent failure or maintenance needs.

Mermaid Diagram:

graph TD
    A[Robotic Inspection Vehicle (Optical/Thermal/Ultrasonic)] --> B{Pipeline Seal Image Acquisition};
    B --> C[Deep Learning Device (CNN + U-Net)];
    C -- Trained on Annotated Seal Defect Database --> D[Defect Zone Detection & Integrity Attribute Determination];
    D --> E[Output: Predictive Maintenance Alert / Integrity Report];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#ccf,stroke:#333,stroke-width:2px

2.4. Derivative: Blockchain-Verified Aligner Fit Assessment with Smart Contracts

Enabling Description: This derivative integrates the aligner assessment method with a permissioned blockchain network and smart contracts to ensure data integrity, transparency, and automated treatment progression. Each "analysis image" acquired by the patient is first processed by the deep learning device to determine "tooth attribute values" relating to aligner separation. These analysis results, including image metadata, determined attribute values, confidence scores, and a cryptographic hash of the raw image, are then recorded as an immutable transaction on a blockchain (e.g., using a proof-of-authority consensus model for efficiency). A smart contract is deployed on the blockchain, which automatically evaluates the determined "suitability" of the aligner based on predefined, clinician-set thresholds (e.g., "separation gap < 0.2mm"). If the aligner is deemed "unsuitable" by the smart contract, it automatically triggers subsequent actions: notifying the orthodontist and patient, initiating an order for a new aligner series with a certified manufacturer, and updating the patient's digital treatment plan, all recorded transparently on the blockchain. This system provides a verifiable audit trail for regulatory compliance and fosters trust between all stakeholders.

Mermaid Diagram:

sequenceDiagram
    participant P as Patient
    participant AA as Acquisition App
    participant DLS as Deep Learning Server
    participant BC as Blockchain Network
    participant SC as Smart Contract
    participant O as Orthodontist/Manufacturer

    P->>AA: Acquire Analysis Image
    AA->>DLS: Submit Image for Analysis
    DLS->>DLS: Perform DL Analysis (Determine Separation Attribute)
    DLS->>BC: Record Assessment Result (Image Hash, Attributes, Timestamp)
    BC->>SC: Activate Smart Contract (on new assessment record)
    SC->>SC: Evaluate Aligner Suitability vs. Thresholds
    alt Aligner Unsuitable
        SC->>O: Notify Orthodontist & Order New Aligner
        SC->>P: Notify Patient of New Aligner/Action
        SC->>BC: Record Treatment Adjustment Event
    else Aligner Suitable
        SC->>P: Notify Patient to Continue Treatment
        SC->>BC: Record Treatment Continued Event
    end
    style DLS fill:#ccf,stroke:#333,stroke-width:2px
    style BC fill:#cfc,stroke:#333,stroke-width:2px

2.5. Derivative: Aligner Degradation Monitoring and Intentional Misfit Training Modes

Enabling Description (Degradation Monitoring Mode): Instead of solely focusing on aligner-tooth separation for treatment progression, the deep learning system operates in a "Degradation Monitoring Mode" to assess the physical integrity and wear of the aligner material itself. The "analysis images" are captured by a high-resolution camera, potentially with UV or polarized light, to highlight material flaws. The deep learning device is specifically trained on a learning base of historical aligner images exhibiting various forms of degradation (e.g., micro-cracks, surface abrasion, discoloration, loss of transparency, material fatigue) with corresponding "image attribute values" such as "material integrity index (0-100%)," "crack propagation rate (mm/week)," "discoloration severity (scale 1-5)," or "wear pattern classification." This mode detects and quantifies aligner material breakdown, enabling proactive replacement before structural integrity is compromised and treatment efficacy diminishes. This operates as a "limited-functionality" mode focused solely on the aligner's material lifespan.

Mermaid Diagram (Degradation Monitoring):

graph TD
    A[Image Acquisition (High-Res, UV/Polarized)] --> B{Analysis Image};
    B --> C[Deep Learning Device (Degradation Classifier)];
    C -- Trained on Degraded Aligner Database --> D[Determine Aligner Material Attributes];
    D --> E[Output: Aligner Material Integrity Report (Proactive Replacement)];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#ccf,stroke:#333,stroke-width:2px

Enabling Description (Intentional Misfit Training Mode): In certain advanced orthodontic or myofunctional therapy scenarios, a controlled and intentional separation or misfit between the aligner and specific teeth/jaw structures is prescribed to induce targeted muscle activity or facilitate mandibular repositioning (e.g., for Class II correction). In this "Intentional Misfit Training Mode," the deep learning device is specifically trained to recognize and quantify these desired separations as "acceptable therapeutic conditions" rather than flagging them as issues. The learning base includes images of patients undergoing such training, annotated with "target misfit parameters" (e.g., "mandibular advancement gap: 1.2mm at incisors," "tongue posture space: 0.8mm at palatal vault"). The system determines "tooth attribute values" that include measurements of these intentional gaps and provides a "training efficacy score" indicating how well the patient is maintaining the prescribed therapeutic misfit, guiding exercise compliance.

Mermaid Diagram (Intentional Misfit Training):

graph TD
    A[Image Acquisition Apparatus] --> B{Analysis Image};
    B --> C[Deep Learning Device (Training Assessor)];
    C -- Trained for Desired Therapeutic Misfit --> D[Determine Intentional Separation Attributes];
    D --> E[Output: Myofunctional/Mandibular Training Efficacy Score];
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#ccf,stroke:#333,stroke-width:2px

Combination Prior Art Scenarios

Here are three combination prior art scenarios where the core concepts of US11049248 (using deep learning for dental image analysis, especially aligner assessment and model generation) are combined with existing open-source standards. These combinations highlight obvious extensions for a person skilled in the art.

US Patent 11049248 (Concepts) + Digital Imaging and Communications in Medicine (DICOM) Standard (ISO 12052):
- Explanation: The DICOM standard is ubiquitous in medical imaging for handling, storing, printing, and transmitting information. The concepts of US11049248, such as acquiring "analysis images," creating "historical images" with "tooth attribute values," and generating "assembled models" or "updated reference models," can be directly integrated with DICOM.
  - Images: All "analysis images" and "historical images" (2D photographs/videos) would be formatted as DICOM image objects (e.g., Secondary Capture Image objects).
  - Attributes & Descriptions: The "description" of an image (tooth zones, tooth attribute values, image attribute values) determined by the deep learning device would be stored as DICOM Structured Report (SR) objects. This allows for standardized, machine-readable capture of diagnostic findings.
  - 3D Models: The "updated reference models" and "assembled models" (digital 3D models) could be represented using DICOM Segmentation objects or Surface Segmentation objects, linked to the patient's study.
- Implication: This combination makes the dental image analysis and attribute determination interoperable with existing medical imaging workflows, enabling standardized archival, secure transmission, and integration into hospital information systems (HIS) or picture archiving and communication systems (PACS), an obvious benefit for dental practitioners in a medical context.
```
graph TD
    A[Image Acquisition & DL Analysis (US11049248)] --> B{Output: Analysis Image + Attribute Data + 3D Model};
    B -- 2D Image --> C1[DICOM Secondary Capture Object];
    B -- Attribute Data --> C2[DICOM Structured Report (SR) Object];
    B -- 3D Model --> C3[DICOM Segmentation Object];
    C1 & C2 & C3 --> D[DICOM Storage/PACS];
    D --> E[Interoperable Healthcare System];
    linkStyle 0 stroke:#000,stroke-width:2px,fill:none;
    linkStyle 1 stroke:#000,stroke-width:2px,fill:none;
    linkStyle 2 stroke:#000,stroke-width:2px,fill:none;
```
US Patent 11049248 (Concepts) + Fast Healthcare Interoperability Resources (FHIR) Standard (ISO 21720):
- Explanation: FHIR provides a robust framework for exchanging healthcare information. The results of the deep learning analysis from US11049248, particularly the "tooth attribute values" and "image attribute values" (e.g., separation, decay presence, aligner suitability), are directly mappable to FHIR resources.
  - Observations: Individual tooth attribute values (e.g., "tooth 14 separation: 0.15mm," "mouth opening: open") would be represented as FHIR Observation resources, linked to the patient and the specific image event.
  - Diagnostic Reports: The aggregated outcome of an aligner assessment or dental arch analysis would be encapsulated in a FHIR DiagnosticReport resource, containing references to the raw image (possibly stored externally but referenced by a URL) and the specific Observation resources.
  - Patient Context: All data would be linked to a Patient resource, facilitating longitudinal tracking within an Electronic Health Record (EHR) system.
- Implication: This combination enables the deep learning-derived dental insights to be seamlessly integrated into modern, interoperable EHR systems, allowing clinicians to access, track, and utilize this information within a broader patient care context, an obvious step for any contemporary medical data system.
```
sequenceDiagram
    participant PA as Patient/Operator
    participant AQ as Acquisition App
    participant DL as Deep Learning System
    participant EHR as FHIR-compliant EHR
    participant CL as Clinician

    PA->>AQ: Capture Dental Arch Image
    AQ->>DL: Submit Image for Analysis
    DL->>DL: Perform DL Analysis (US11049248 concepts)
    DL->>DL: Determine Tooth/Image Attributes
    DL->>EHR: Create FHIR Observation/DiagnosticReport Resources
    EHR->>CL: Notify/Display New Dental Data
    CL->>EHR: Access Patient's Longitudinal Dental Record
    EHR->>CL: Present FHIR Resources for Review
```
US Patent 11049248 (Concepts) + Open Dental CAD/CAM Data Standards (e.g., STL, PLY):
- Explanation: The patent extensively discusses the creation and manipulation of "digital three-dimensional models," including "initial reference models," "updated reference models," "tooth models," and "assembled models." Standard open-source file formats like STL (Standard Tessellation Language) and PLY (Polygon File Format) are the de facto standards for representing 3D geometries in dental CAD/CAM systems.
  - 3D Model Export: Any 3D models generated during the learning base enrichment (e.g., step A) or analysis for modeling the dental arch (FIGS. 6 & 18) would be routinely exported in STL or PLY format.
  - Metadata Integration: "Tooth attribute values" (e.g., tooth number, type, shape parameters) could be embedded within the PLY file header or as separate XML/JSON metadata files linked to the STL/PLY models.
  - Application: The assessed "optimal tooth models" or "assembled models" from the aligner assessment method (Claim 14) could be immediately fed into commercial or open-source CAD software (e.g., Blender with dental plugins, FreeCAD) for aligner redesign or manufacturing.
- Implication: This combination is an obvious requirement for integrating the patent's 3D modeling and analysis capabilities with the existing ecosystem of dental design, manufacturing, and 3D printing technologies, allowing for practical application in orthodontic laboratories and clinics.
```
graph TD
    A[Image Acquisition & DL Analysis (US11049248)] --> B{Generate 3D Dental/Tooth Models};
    B -- Export --> C1[STL File Format];
    B -- Export --> C2[PLY File Format];
    B -- Associated Attributes --> D[Metadata File (XML/JSON)];
    C1 & C2 & D --> E[Dental CAD/CAM Software];
    E --> F[Aligner/Appliance Design & Manufacturing];
    style B fill:#ccf,stroke:#333,stroke-width:2px
    style E fill:#cfc,stroke:#333,stroke-width:2px
```