US 10755409

US Patent 10755409, titled "Method for analyzing an image of a dental arch," was granted to Dental Monitoring SAS. The inventors listed are Philippe Salah, Thomas Pellissard, Guillaume Ghyselinck, and Laurent Debraux. The patent was filed on July 10, 2018, and issued on August 25, 2020.

Abstract:
The patent describes a method for acquiring and analyzing an image of a patient's dental arch. It involves activating an image acquisition apparatus to capture an "analysis image" of the arch. This image is then analyzed by a deep learning device, which has been trained using a learning base. Based on this analysis, a value for an "image attribute" is determined for the analysis image. This attribute value is then compared against a setpoint. Depending on this comparison, an information message is sent, which can relate to the quality of the image, the position or settings of the acquisition apparatus, the patient's mouth opening, or the presence of a dental appliance.

Plain-Language Overview of Independent Claims (based on the "Summary of the Invention" and "Detailed Description" sections):

While the complete numbered claims are not provided in the prompt's text, the patent describes several distinct "methods according to the invention" which would typically form the basis of independent claims. Here's an overview of the primary independent methods described:

1. General Method for Analyzing an Image of a Dental Arch: This overarching method involves submitting an analysis image of a patient's dental arch to a deep learning device (preferably a neural network). The purpose is to determine at least one value for a "tooth attribute" (relating to a specific tooth in the image) and/or at least one value for an "image attribute" (relating to the overall image).
2. Method for Detailed Analysis of an Image: This method comprises four main steps:
   • Step 1: Creating a learning base containing over 1000 "historical images" of dental arches. Each historical image has "historical tooth zones" (representing individual teeth), with assigned "tooth attribute values" for at least one attribute.
   • Step 2: Training a deep learning device (preferably a neural network) using this learning base.
   • Step 3: Submitting the analysis image to the trained deep learning device. The device then determines probabilities for attribute values of teeth represented in "analysis tooth zones" on the image.
   • Step 4: Using these probabilities to determine the presence of a tooth at a given analysis tooth zone and its attribute value.
3. Method for Enriching a Learning Base (First Main Embodiment): This method involves:
   • Step A: Producing an "updated reference model" of a patient's dental arch at an "updated instant," segmenting it into "tooth models," and assigning tooth attribute values to each.
   • Step B: Acquiring "updated images" (e.g., at least one, preferably many) of the arch in real acquisition conditions, preferably around the updated instant.
   • Step C: For each updated image, searching for virtual acquisition conditions that, when used to acquire a "reference image" of the updated reference model, result in a maximal match with the updated image.
   • Step D: Identifying "reference tooth zones" in the reference image and transferring them to the updated image to define "updated tooth zones."
   • Step E: Assigning the tooth attribute values of the corresponding tooth model to these updated tooth zones.
   • Step F: Adding this updated image, enriched with its description (updated tooth zones and their attribute values), as a "historical image" to the learning base.
     (A second embodiment, A'-C', describes enriching the learning base by deforming an initial reference model based on updated images, allowing for a longer time interval between initial model creation and updated image acquisition without new scans.)
4. Method for Global Analysis of an Analysis Image: This method focuses on image-level attributes:
   • Step 1': Creating a learning base of "historical images," where each historical image includes a value for at least one "image attribute" (an "image attribute value") relating to the whole image, not just individual teeth.
   • Step 2': Training a deep learning device (preferably a neural network) with this learning base.
   • Step 3': Submitting an analysis image to the deep learning device. The device determines a probability for the image attribute value of the analysis image, and then determines a value for that image attribute. This allows for a global assessment (e.g., "pathological" or "not pathological" situation, mouth open/closed, image quality, orientation) without individual tooth examination.
5. Method for Acquiring an Image of a Dental Arch: This method helps guide the image acquisition process:
   • Step a': An image acquisition apparatus is activated to acquire an analysis image.
   • Step b': The analysis image is analyzed by a deep learning device to determine a value for an image attribute (e.g., image quality, acquisition apparatus position, mouth opening, dental appliance presence). This can be done via a detailed analysis (identifying tooth zones and attributes) or global analysis.
   • Step c': The determined image attribute value is compared with an instruction (setpoint).
   • Step d': If the instruction is not respected, an information message (e.g., on image quality, position, or setting) is sent to the operator (patient).
   • Step e': If the instruction is not respected, the operator is guided to acquire a new, satisfactory analysis image.
6. Method for Assessing the Shape of an Orthodontic Aligner: This method evaluates the fit of an aligner:
   • Step a'': An analysis image is acquired, showing the aligner worn by the patient's teeth.
   • Step b'': The analysis image is analyzed by a deep learning device (trained on historical images with separation attribute values). This analysis determines, for each analysis tooth zone, the existence and extent of a separation between the tooth and the aligner, or a global image attribute related to separation.
   • Step c'': Based on the results, the suitability of the aligner (e.g., compatibility with orthodontic treatment) is assessed, possibly leading to a decision to replace the aligner.
   • Step d'': Information relating to this assessment is sent to the patient and/or orthodontist.

CAFC 2026 Dockets for US10755409:

The provided patent information indicates that US10755409B2 is involved in litigation at the Court of Appeals for the Federal Circuit (CAFC). Specifically, two cases are mentioned:

• Case number 25-1752 was filed in the Court of Appeals for the Federal Circuit.
• Case number 24-2270 was also filed in the Court of Appeals for the Federal Circuit.

Given the current date of April 26, 2026, both cases (filed in 2025 and 2024, respectively) would likely be ongoing or have had activity in 2026. These links confirm active litigation related to this patent in the CAFC.