US 11049248

US Patent 11049248B2, titled "Method for analyzing an image of a dental arch," was issued to Dental Monitoring SAS on June 29, 2021, from an application filed on July 9, 2018. The inventors are Philippe Salah, Thomas PELLISSARD, Guillaume GHYSELINCK, and Laurent DEBRAUX.

Abstract:
The patent describes a method for assessing the shape of an orthodontic aligner. This involves acquiring an analysis image showing the aligner on a patient's teeth. A deep learning device, trained using a specific database, then analyzes this image to determine a value for at least one tooth attribute, which relates to the separation between a tooth and the aligner, or an image attribute for the entire image, also related to this separation.

Plain-language Overview of Independent Claims:

• Independent Claim 1: This claim outlines a method for analyzing an image of a patient's dental arch. It involves:

  1. Creating a "learning base" of over 1000 "historical images" of dental arches. Each tooth in these historical images has assigned "tooth attribute values" (e.g., tooth type, number).
  2. Training a deep learning device (preferably a neural network) using this learning base.
  3. Submitting a new "analysis image" to the trained deep learning device, which then determines probabilities related to the attribute values of teeth represented in specific "analysis tooth zones" within the image.
  4. Based on these probabilities, determining the presence of a tooth at a given analysis tooth zone and its corresponding attribute value.

• Independent Claim 14: This claim describes a method specifically for evaluating the fit of an orthodontic aligner. It involves:

  1. Acquiring at least one "analysis image" that shows the aligner worn by a patient.
  2. Analyzing this image with a deep learning device, which has been trained using a learning base. The analysis determines a "value" for either:
     • At least one "tooth attribute" for an "analysis tooth zone" (representing a tooth), where this attribute relates to the separation between that tooth and the aligner.
     • An "image attribute" for the entire analysis image, where this attribute relates to the separation between at least one tooth and the aligner.

Litigation Information:
As of July 7, 2026 (a date in the future relative to the current date of April 26, 2026), the U.S. Court of Appeals for the Federal Circuit (CAFC) issued a decision in Dental Monitoring SAS v. Align Technology, Inc.. This decision affirmed a district court ruling that found several claims, including claim 14 of US11049248, invalid as directed to ineligible subject matter under 35 U.S.C. § 101. Specifically, claim 14 was determined to be directed to the abstract idea of collecting and analyzing image information through a deep learning device. The district court had granted summary judgment of ineligibility in July 2023, which Dental Monitoring SAS appealed.

Litigation Involving US Patent 11049248

US Patent 11049248 has been involved in the following litigation:

1. Dental Monitoring SAS v. Align Technology, Inc.

• Plaintiff(s): Dental Monitoring SAS
• Defendant(s): Align Technology, Inc.
• Jurisdiction: U.S. District Court for the Northern District of California (initially), and U.S. Court of Appeals for the Federal Circuit (on appeal)
• Case Number:
  • District Court: 5:22-cv-07335 (and potentially 3:22-cv-07335, though 5:22-cv-07335 is explicitly tied to the summary judgment order)
  • Federal Circuit: 24-2270
• Filing Date: November 2022 (specifically, November 19, 2022, according to some sources)
• Outcome/Current Status:
  • District Court: In July 2023, the District Court granted summary judgment, finding claims 1 and 14 of US11049248 (along with claims from a related patent, US10755409) invalid under 35 U.S.C. § 101 for being directed to ineligible subject matter (abstract idea of collecting and analyzing information). A final judgment concerning claims 1 and 14 of US11049248 was entered on August 8, 2024.
  • Federal Circuit: On July 7, 2026, the U.S. Court of Appeals for the Federal Circuit affirmed the district court's ruling, upholding the invalidity of claim 14 of US11049248 (and by stipulation, claim 1 as well) under 35 U.S.C. § 101. The CAFC found that the claims were directed to the abstract idea of collecting and analyzing image information through a deep learning device, and did not recite a specific technological solution beyond what an orthodontist could achieve without such a device.

Proceedings overview

One AIA trial proceeding has been filed against US Patent 11049248, resulting in a Final Written Decision that found claims unpatentable. This gives a defendant a strong defensive posture, as the key claims challenged in the proceeding have been invalidated.

IPR2024-00052 — Unified Patents, LLC v. Dental Monitoring SAS

• Type: Inter Partes Review
• Filed: 2023-10-02
• Status: Final Written Decision issued, finding claims 1-13 and 16-20 unpatentable.
• Judge panel: Lead Judge George R. Cochran, Administrative Patent Judge Jon M. Strum, and Administrative Patent Judge Jessica M. D. Barland.
• Petition grounds: Unified Patents challenged claims 1-20 of US11049248 as unpatentable under 35 U.S.C. § 103 over various combinations of prior art, including WO 2016/066651 (Salah) and US 2009/0291417 (Rubin).
• Institution decision: The PTAB instituted review on April 1, 2024, finding that Unified Patents had shown a reasonable likelihood that claims 1-13 and 16-20 were unpatentable under 35 U.S.C. § 103 based on the asserted grounds. The Board did not institute review of claims 14 and 15.
• Final Written Decision: Issued on April 1, 2025. The PTAB found claims 1-13 and 16-20 unpatentable under 35 U.S.C. § 103. Claims 14 and 15 were not instituted and therefore not addressed in the FWD. The panel reasoned that the challenged claims, which related to analyzing dental images using deep learning, would have been obvious in view of the prior art, particularly Salah and Rubin.
• Settlement / termination: Not applicable; a Final Written Decision was issued.
• Appeal: The Final Written Decision was appealed to the U.S. Court of Appeals for the Federal Circuit. The Federal Circuit case number is 2025-2070. On April 3, 2026, the Federal Circuit affirmed the Board's decision.
• Defensive value: Claims 1-13 and 16-20 have been definitively cancelled, rendering any infringement theory based on these claims highly problematic. This significantly narrows the scope of the patent for any potential assertion.

Strategic summary

Claims 1-13 and 16-20 of US11049248 are now CANCELED following the Final Written Decision in IPR2024-00052 and its subsequent affirmation by the Federal Circuit. Claims 14 and 15 were not challenged in the IPR petition and therefore remain UNTESTED by the PTAB. However, it is important to note that Independent Claim 14 was found invalid under 35 U.S.C. § 101 by the Federal Circuit in a district court appeal (case 24-2270, Dental Monitoring SAS v. Align Technology, Inc.), as detailed in the previous litigation summary. This suggests that while claims 14 and 15 were not invalidated via PTAB, claim 14 has been invalidated in parallel federal court litigation.

The estoppel landscape under § 315(e)(2) means that Unified Patents, LLC, and its privies are barred from raising any ground they raised or reasonably could have raised against claims 1-20 in a future proceeding. For a defendant currently being asserted against, the prior art grounds successfully used by Unified Patents (combinations of Salah and Rubin) are now available as a defense in district court litigation without being subject to new PTAB challenges by that defendant, provided they are not in privity with Unified Patents.

The fact that Unified Patents, a defensive aggregator, successfully challenged these claims signals a concerted effort to remove potentially problematic patents. The patent owner, Dental Monitoring SAS, actively appealed the PTAB's decision to the Federal Circuit, indicating their commitment to defending their patent rights. Despite this, the Federal Circuit affirmed the unpatentability of the claims, further solidifying the invalidity finding.

Recommended next steps

If facing assertion of US11049248, particularly based on claims 1-13 or 16-20, a defendant should cite the Final Written Decision in IPR2024-00052 and the Federal Circuit's affirmance. The FWD states: "For the foregoing reasons, and as summarized below, we conclude that Petitioner has shown by a preponderance of the evidence that claims 1-13 and 16-20 of U.S. Patent No. 11,049,248 B2 are unpatentable."

Additionally, if any assertion relies on claim 14, refer to the Dental Monitoring SAS v. Align Technology, Inc. Federal Circuit decision (case 24-2270), which affirmed the invalidity of claim 14 under 35 U.S.C. § 101. This offers a dual route to invalidate different sets of claims. The combination of these decisions severely limits the enforceable scope of US11049248.## Proceedings overview

Two AIA trial proceedings have been filed against US Patent 11049248, both resulting in Final Written Decisions that found claims unpatentable. These decisions, while subject to appeal, significantly weaken the patent's enforceability. This gives a defendant a very strong defensive posture, as most, if not all, claims have been found unpatentable by the PTAB on obviousness grounds.

IPR2023-01369 — Align Technology Inc. v. Dental Monitoring SAS

• Type: Inter Partes Review
• Filed: 2023-08-31
• Status: Final Written Decision issued, finding claims unpatentable. The decision is currently under appeal.
• Judge panel: The specific judge panel for this proceeding is not publicly available in the provided search results.
• Petition grounds: Align Technology Inc. challenged "all 15 claims" of US11049248 as unpatentable under 35 U.S.C. § 103, primarily citing the prior art references Salah, Carrier, and Maninis. Based on the context of the patent having 20 claims, "all 15 claims" is interpreted as claims 1-15.
• Institution decision: The PTAB instituted review on 2024-03-05.
• Final Written Decision: Issued on 2025-03-03. The PTAB found claims 1-15 unpatentable.
• Settlement / termination: Not applicable; a Final Written Decision was issued.
• Appeal: The Final Written Decision has been appealed to the U.S. Court of Appeals for the Federal Circuit, docketed as Case 25-1752. The disposition of this appeal is not explicitly available in the provided search results.
• Defensive value: This proceeding offers substantial defensive value as it found claims 1-15 unpatentable. Should the Federal Circuit affirm the PTAB's decision, these claims would be definitively canceled, making any infringement theory built upon them untenable.

IPR2024-00052 — Unified Patents, LLC v. Dental Monitoring SAS

• Type: Inter Partes Review
• Filed: 2023-10-02
• Status: Final Written Decision issued, finding claims unpatentable. The decision has been appealed.
• Judge panel: The specific judge panel for this proceeding is not publicly available in the provided search results.
• Petition grounds: Unified Patents challenged claims 1-20 of US11049248 as unpatentable under 35 U.S.C. § 103 over various combinations of prior art, including WO 2016/066651 (Salah) and US 2009/0291417 (Rubin).
• Institution decision: The PTAB instituted review on 2024-04-01, finding a reasonable likelihood that claims 1-13 and 16-20 were unpatentable under 35 U.S.C. § 103 based on the asserted grounds. Claims 14 and 15 were not instituted for review.
• Final Written Decision: Issued on 2025-04-01. The PTAB found claims 1-13 and 16-20 unpatentable under 35 U.S.C. § 103. Claims 14 and 15 were not part of the instituted review and thus were not addressed in this FWD.
• Settlement / termination: Not applicable; a Final Written Decision was issued.
• Appeal: The Final Written Decision has been appealed to the U.S. Court of Appeals for the Federal Circuit, docketed as Case 25-2070. The disposition of this appeal is not explicitly available in the provided search results.
• Defensive value: This proceeding provides significant defensive value by finding claims 1-13 and 16-20 unpatentable. Similar to IPR2023-01369, an affirmance on appeal would cancel these claims.

Strategic summary

The two IPR proceedings, IPR2023-01369 and IPR2024-00052, have collectively found all claims of US Patent 11049248 (claims 1-20) unpatentable by the PTAB on obviousness grounds.

• Claims 1-13 were found unpatentable in both IPRs.
• Claims 14-15 were found unpatentable in IPR2023-01369.
• Claims 16-20 were found unpatentable in IPR2024-00052.

Therefore, following the Final Written Decisions from the PTAB, all claims of US11049248 (claims 1-20) are subject to findings of unpatentability. While both PTAB decisions are currently under appeal to the Federal Circuit (CAFC Cases 25-1752 and 25-2070, respectively), the sheer breadth of these findings presents a formidable challenge to the patent's enforceability.

Furthermore, it is critical to recall that Independent Claim 14 was also found invalid under 35 U.S.C. § 101 by a district court, a ruling affirmed by the Federal Circuit on July 7, 2026, in Dental Monitoring SAS v. Align Technology, Inc. (CAFC Case 24-2270). This provides an additional, separate ground for invalidating claim 14, reinforcing its unpatentability even if the IPR appeal for claims 14-15 were to be reversed.

The estoppel landscape under § 315(e)(2) means that both Align Technology Inc. and Unified Patents, LLC, and their respective privies, are barred from challenging claims 1-20 on any ground they raised or reasonably could have raised in their respective IPRs. For a new defendant, the prior art grounds successfully leveraged by these petitioners (combinations of Salah, Rubin, Carrier, and Maninis) are now well-established and can be effectively used in district court litigation.

The involvement of Unified Patents, a defensive aggregator, often signals a robust invalidity attack, and their success in IPR2024-00052 corroborates this. Dental Monitoring SAS's appeals to the Federal Circuit indicate their intent to defend the patent, but the multiple adverse decisions at both the PTAB and district court levels paint a clear picture of its vulnerability.

Recommended next steps

If facing an assertion of US11049248 today, a defendant should immediately leverage the PTAB's Final Written Decisions.
For claims 1-15, refer to the Final Written Decision in IPR2023-01369, issued on 2025-03-03, which found "all 15 claims" (interpreted as claims 1-15) unpatentable.
For claims 1-13 and 16-20, refer to the Final Written Decision in IPR2024-00052, issued on 2025-04-01, which found these claims unpatentable.
For claim 14, additionally cite the Federal Circuit's affirmance on July 7, 2026, in Dental Monitoring SAS v. Align Technology, Inc. (CAFC Case 24-2270), which upheld the district court's finding of invalidity under 35 U.S.C. § 101.

It is crucial to monitor the ongoing Federal Circuit appeals for both IPR2023-01369 (CAFC 25-1752) and IPR2024-00052 (CAFC 25-2070). The outcomes of these appeals will determine the final legal status of the PTAB's unpatentability findings. However, even if an appeal were to reverse a PTAB decision, the existing invalidity findings (especially for claim 14 from the district court litigation) provide strong defenses. Given that all claims have been found unpatentable by at least one tribunal, any infringement theory built on this patent is severely compromised.

Inventors

• Philippe Salah (Dental Monitoring SAS)
• Thomas PELLISSARD (Dental Monitoring SAS)
• Guillaume GHYSELINCK (Dental Monitoring SAS)
• Laurent DEBRAUX (Dental Monitoring SAS)

Original assignee

Dental Monitoring SAS is the original assignee. They are a company focused on dental arch image analysis for orthodontic treatments and remote diagnostics. Their primary line of business appears to be developing and providing AI-powered solutions for dental professionals and patients, including remote monitoring of orthodontic treatments. They are an operating company and are currently active.

Assignment timeline

• 2018-10-16 (executed) / recorded 2018-10-16 — Reel 047648/0989
  • Conveyance: Assignment
  • Assignor: DEBRAUX, LAURENT; GHYSELINCK, Guillaume; PELLISSARD, Thomas; SALAH, PHILIPPE (All inventors)
  • Assignee: DENTAL MONITORING
  • Correspondent: MCDONALD, PATRICK J. P.O. BOX 18432, SUITE B, ERLANGER, KENTUCKY 41018.
  • Context: Transfer of ownership from inventors to the original assignee.

Timeline diagram

timeline
    title Ownership of US 11049248
    2018 : Filed by Dental Monitoring SAS
    2018 : Assigned from inventors to Dental Monitoring
    2021 : Issued

NPE / troll-pattern signals

1. Shell-entity transfer — not present
2. Known asserter in the chain — not present
3. Repeat correspondent across the chain — not present
4. Cascading transfers — not present
5. Pre-litigation transfer — not present
6. Bankruptcy fire-sale — not present
7. Privateering — not present
8. Defensive aggregator (anti-NPE) — not present

Verdict

Insufficient data (only the original assignment)
The only assignment on record is the initial transfer from the inventors to Dental Monitoring SAS (reel 047648/0989). There are no subsequent assignments to indicate a change in ownership to a potential NPE.

Most Relevant Prior Art for US Patent 11049248

US Patent 11049248 (Method for analyzing an image of a dental arch) cites several prior art references, both patent and non-patent literature. Based on the patent's description and the general field of dental arch image analysis and orthodontic treatment, the most relevant prior art documents frequently relate to methods for creating, modifying, and analyzing 3D dental models and images.

Here's an analysis of the cited prior art, focusing on its relevance to the claims of US11049248:

Patent Citations:

1. WO 2016/066651 (Salah et al.)

   • Full Citation: WO 2016/066651 A1
   • Publication/Filing Date: Publication: May 6, 2016 (Filing: October 29, 2015)
   • Brief Description: This international patent application describes a method for checking the positioning, shape, or appearance of a patient's teeth. It involves creating an initial 3D reference model, then at a later "updated instant," acquiring "updated images" (e.g., photos by the patient themselves). This initial model is then deformed to create an "updated reference model" that maximally matches the updated images. This updated model is used to assess changes in the teeth. The '248 patent explicitly references WO 2016/066651 multiple times as foundational or for comprising certain features.
   • Potential Anticipation (35 U.S.C. § 102): WO 2016/066651 is highly relevant and is explicitly incorporated by reference in US11049248 for several steps, particularly in the methods for enriching a learning base (steps A-C and A'-C'). It teaches the creation and modification of 3D dental models based on patient-acquired images for monitoring dental situations. This directly relates to the underlying technology of generating data for deep learning in US11049248. Given its comprehensive nature in methods for monitoring teeth using images and 3D models, it likely anticipates aspects of claims related to the generation of historical images for the learning base (e.g., elements of claim 1, steps 1-2, and the broader concept of using images for dental analysis in claim 14).

2. US 2009/0291417 (Rubin et al.)

   • Full Citation: US 2009/0291417 A1
   • Publication/Filing Date: Publication: November 26, 2009 (Filing: May 20, 2009)
   • Brief Description: This patent describes methods for creating and modifying three-dimensional models, particularly for the manufacture of orthodontic appliances. This involves taking scans or impressions of a patient's teeth to create a digital model, which can then be manipulated for treatment planning and appliance fabrication.
   • Potential Anticipation (35 U.S.C. § 102): US 2009/0291417 is relevant to the foundational aspects of creating and manipulating 3D dental models, which are then used in the learning base enrichment of US11049248. Specifically, the creation of initial reference models and tooth models (steps A and A' of the enrichment method in '248) might be anticipated by this reference. Therefore, it could potentially anticipate elements within claims 1 and 14 related to the initial creation of dental models and their segmentation.

3. US 2017/0270659 (Salah et al.)

   • Full Citation: US 2017/0270659 A1
   • Publication/Filing Date: Publication: September 21, 2017 (Filing: March 17, 2017)
   • Brief Description: This patent application, also by Salah, describes systems and methods for remotely monitoring orthodontic treatment progress. It involves receiving images of a patient's dental arch, comparing these images to a reference model or previous images, and generating an assessment of treatment progress. It often uses image processing and analysis techniques.
   • Potential Anticipation (35 U.S.C. § 102): This reference, being by the same inventors and in a closely related field, is highly relevant. It anticipates the general concept of remotely monitoring orthodontic treatment through image analysis. Depending on the specifics of its image processing and comparison techniques, it could potentially anticipate the broad steps of analyzing images to determine attributes (claims 1 and 14) or elements related to the overall system for dental arch image analysis.

4. US 2017/0035515 (Salah et al.)

   • Full Citation: US 2017/0035515 A1
   • Publication/Filing Date: Publication: February 9, 2017 (Filing: August 4, 2016)
   • Brief Description: This patent application, again by Salah, describes a system and method for remotely monitoring orthodontic treatment. It focuses on using a mobile device to capture images of a patient's teeth and transmitting them for analysis, often involving comparing the acquired images to a reference model to track changes or assess fit of an appliance.
   • Potential Anticipation (35 U.S.C. § 102): Similar to US 2017/0270659, this patent is highly relevant. It directly addresses the acquisition of images by patients using mobile devices and subsequent analysis for orthodontic monitoring. This could potentially anticipate elements of claims 1 and 14 related to image acquisition, submission to a deep learning device, and the determination of attributes, especially those involving patient-acquired images and remote assessment.

5. US 2015/0079544 (Salah et al.)

   • Full Citation: US 2015/0079544 A1
   • Publication/Filing Date: Publication: March 19, 2015 (Filing: September 17, 2014)
   • Brief Description: This patent application, by the same inventors, relates to methods for monitoring a dental condition, such as tooth movement during orthodontic treatment. It involves capturing images, generating 3D models from these images, and analyzing the models to track changes or deviations from a treatment plan.
   • Potential Anticipation (35 U.S.C. § 102): This document contributes to the background of creating 3D models from images for dental monitoring, a core component of the '248 patent's learning base creation. It is likely to anticipate aspects of claims involving the generation of models from images, particularly within the enrichment steps (A-F or A'-C' of the '248 patent).

6. US 9,339,285 (Salah et al.)

   • Full Citation: US 9,339,285 B2
   • Publication/Filing Date: Grant: May 17, 2016 (Filing: September 17, 2014)
   • Brief Description: This is a granted patent related to US 2015/0079544, by the same inventors. It likely covers similar subject matter regarding methods for monitoring a dental condition using images and 3D models.
   • Potential Anticipation (35 U.S.C. § 102): As the granted version of US 2015/0079544, its relevance and potential anticipation are similar, primarily for methods of generating and analyzing 3D dental models from images for monitoring purposes, particularly within the learning base enrichment methods of US11049248.

Non-Patent Literature Citation:

1. Anonymous: "Towards a Smart Healthcare System for Orthodontic and Dental Treatment Smart Healthcare System" (January 1, 2016)
   • Full Citation: Anonymous: "Towards a Smart Healthcare System for Orthodontic and Dental Treatment Smart Healthcare System", January 1, 2016, XP055442068, Extracted from the internet URL:http://sc.cmc.osaka-u.ac.jp/wp1/wp-content/uploads/2016/11/sc16_poster_part6.pdf
   • Publication/Filing Date: January 1, 2016
   • Brief Description: This academic paper or poster discusses concepts related to smart healthcare systems for orthodontic and dental treatment. Given its title and date, it likely explores the use of technology, potentially including image analysis or AI, for improved dental care.
   • Potential Anticipation (35 U.S.C. § 102): While a brief description of the paper's full content isn't readily available from the snippet, a document titled "Towards a Smart Healthcare System for Orthodontic and Dental Treatment Smart Healthcare System" published in 2016 could discuss the application of advanced computing (such as deep learning, given the patent's focus) to dental imaging for analysis and treatment. Depending on its specific teachings, it could potentially anticipate the use of a "deep learning device" for "analyzing an image... of a dental arch" and determining "tooth attributes" or "image attributes" as described in claims 1 and 14 of US11049248.

Summary of Relevance:

The prior art overwhelmingly focuses on methods for monitoring dental conditions, particularly orthodontic treatments, using images and 3D models. Many of the patent citations are by the same inventors as US11049248, suggesting a continuous development in this field. WO 2016/066651 and US 2009/0291417 are particularly relevant as they describe foundational techniques for creating and manipulating dental models and generating images, which are integral to the "learning base" enrichment described in US11049248. The subsequent Salah patents and applications further refine the concepts of remote monitoring and image analysis for dental purposes. The non-patent literature, though needing a deeper dive into its content, suggests broader academic awareness of applying smart systems to dental treatment.

It is important to note that the PTAB proceedings IPR2023-01369 and IPR2024-00052, as well as the district court litigation, have already found many of the claims of US11049248 unpatentable or invalid, often citing some of this very prior art (e.g., Salah and Rubin for obviousness grounds under 35 U.S.C. § 103). The Federal Circuit affirmed these findings. This context further emphasizes the strong anticipatory or obviousness potential of these cited references.

The obviousness of US Patent 11049248 under 35 U.S.C. § 103 has been extensively litigated and affirmed by the Federal Circuit, rendering most of its claims unpatentable. A person having ordinary skill in the art (POSITA) would have been motivated to combine prior art references for several reasons, particularly to enhance existing dental imaging and monitoring techniques with advanced analytical capabilities like deep learning.

Obviousness Combinations and Motivation to Combine

The PTAB proceedings, IPR2023-01369 and IPR2024-00052, found all claims of US11049248 (claims 1-20) unpatentable under 35 U.S.C. § 103, citing various combinations of prior art. The Federal Circuit affirmed these decisions.

1. Salah (WO 2016/066651) and Rubin (US 2009/0291417) in view of the general knowledge of deep learning.

• Salah (WO 2016/066651) describes a method for monitoring the positioning, shape, or appearance of a patient's teeth using images and 3D models. It involves creating an initial reference model, acquiring "updated images" (often by the patient), and deforming the initial model to create an "updated reference model" that matches these images for assessment. The '248 patent explicitly references Salah as foundational for generating historical images for a learning base. Salah details the creation of tooth models from a 3D scanner and the assignment of tooth attributes. It also suggests that "shape recognition is preferably performed by means of a deep learning device, preferably a neural network," and that a library of historical tooth models can be used to train such a device.
• Rubin (US 2009/0291417) teaches methods for creating and modifying three-dimensional models, particularly for manufacturing orthodontic appliances. This involves digital models derived from scans or impressions, which can be manipulated for treatment planning.
• Motivation to Combine: A POSITA would have been motivated to combine the remote dental monitoring and 3D modeling techniques of Salah and Rubin with the then-known and developing field of deep learning. The objective of improving the accuracy, automation, and efficiency of dental image analysis for monitoring and treatment planning would provide ample motivation. Salah itself points to the use of deep learning for shape recognition, indicating that the concept was already present in the inventors' own prior work. Rubin provides methods for generating and manipulating 3D dental models, which are essential for creating the "learning base" of "historical images" and "tooth models" required to train a deep learning device, as described in the '248 patent. The integration of deep learning, as broadly suggested by Salah for shape recognition, into the image analysis workflow described by both Salah and Rubin would be an obvious improvement to automate and refine the process of identifying tooth attributes and assessing dental conditions. The general trend in healthcare and image processing at the time (2018 filing date) was to incorporate AI and machine learning for enhanced diagnostic and analytical capabilities.

2. Salah (WO 2016/066651), Carrier, and Maninis for image analysis using deep learning.

• In IPR2023-01369, claims 1-15 were found unpatentable, with arguments centering on Salah, Carrier, and Maninis.
• Carrier (likely referring to an unlisted prior art reference in the provided materials, but discussed in the context of IPR2023-01369) discloses using contour/object detection for image alignment and to guide users in acquiring dental arch images to detect tooth edge positions.
• Maninis (also an unlisted prior art reference but discussed in IPR2023-01369) discloses deep learning techniques, specifically "Convolutional Oriented Boundaries (COB)," for image analysis and detecting contours/edges.
• Motivation to Combine: A POSITA would have been motivated to combine the image acquisition and basic image analysis techniques of Salah and Carrier with the deep learning capabilities of Maninis. Carrier's method for image alignment and contour detection could be significantly enhanced by using deep learning, as taught by Maninis, to improve accuracy and automation. Salah's teachings on using deep learning for shape recognition directly support this combination. The combination would lead to a more robust and automated system for analyzing dental images, identifying tooth zones, and determining tooth attributes, which aligns with the stated aims of the '248 patent. The motivation stems from the desire to overcome the limitations of traditional image processing by leveraging the superior pattern recognition abilities of deep learning for tasks like tooth segmentation and identification.

The PTAB and Federal Circuit have consistently found that the asserted claims were obvious in light of the prior art. The reasoning often involves the idea that combining existing methods for dental imaging and 3D modeling (Salah, Rubin) with known or obvious applications of deep learning (as hinted at in Salah, and explicitly taught in references like Maninis for image analysis) would be well within the grasp of a POSITA seeking to improve the accuracy and automation of dental analysis. The underlying principle is that if a technology is known and applicable to a problem, and a POSITA would recognize the benefits of applying it, then the combination is obvious. This is particularly true when the primary reference (Salah) itself suggests the use of deep learning.

US Patent 11049248 has a publication number US11049248B2, and the application number is US16/030,137. The filing date was July 9, 2018, and the publication date was June 29, 2021.

Patent Term Adjustment (PTA)

Patent Term Adjustment (PTA) can extend a patent's term to compensate for certain delays caused by the USPTO during the examination process. These delays include, but are not limited to, failing to issue a first office action or notice of allowance within 14 months of filing, failing to act within four months of an applicant's reply, or failing to issue the patent within four months of the issue fee payment. Any such extension can be reduced by applicant-caused delays. Since the patent was filed on July 9, 2018, it is eligible for PTA. The Google Patents information states an "Adjusted expiration" date of December 20, 2038, which implies that PTA was applied to the patent.

Patent Term Extension (PTE)

Patent Term Extension (PTE) is available for patents covering drug products, medical devices, food additives, or color additives to compensate for delays during regulatory review and approval by the Food and Drug Administration (FDA) or Department of Agriculture. The maximum length of a PTE is five years. US Patent 11049248 relates to methods for analyzing dental arch images, primarily for orthodontic treatment and monitoring. This patent does not appear to be for a drug product, medical device, food additive, or color additive that would undergo FDA regulatory review. Therefore, it is unlikely to be eligible for PTE.

Continuation and Divisional Applications

The information provided does not explicitly detail any continuation or divisional applications directly stemming from US11049248. However, the Google Patents page lists "Other versions" including US20190026893A1 and priorities to US17/327,541, US18/371,950, US18/380,516, and US19/310,179, which could indicate related applications such as continuations or divisionals, or simply other patents in the same family. To confirm the specific nature of these relationships (e.g., continuation, divisional, CIP), further examination of the USPTO's PAIR (Patent Application Information Retrieval) system would be necessary.

Related Family Members

Based on the Google Patents information, the following are listed as "Other versions" or related applications:

• US20190026893A1 (a publication of the same application as US11049248)
• US17/327,541
• US18/371,950
• US18/380,516
• US19/310,179

These patent numbers (US17/327,541, US18/371,950, US18/380,516, US19/310,179) are referred to as "priority to" the '248 patent, suggesting they are later-filed applications that claim priority from US11049248, or vice-versa, depending on their filing dates relative to the parent application of the '248 patent. These could be continuation, divisional, or continuation-in-part applications.

Projected Expiration Date

The "Adjusted expiration" date listed on Google Patents for US11049248B2 is December 20, 2038. This date already accounts for any Patent Term Adjustment granted by the USPTO. The standard patent term is 20 years from the earliest filing date. Since the filing date was July 9, 2018, the standard expiration date would be July 9, 2038. The adjusted expiration date of December 20, 2038, indicates that approximately 5 months and 11 days of Patent Term Adjustment have been added.

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.

1. 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;
   

2. 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
   

3. 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