DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 11 is objected to because of the following informalities: the claim limitation “the gum is generalized redness” should recite “the gum has generalized redness” for proper grammar. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Specifically, it appears that Applicant is reciting an independent claim in the statutory category of process (a method of processing intraoral images…) that is analogous to the apparatus independent claim 1; however, claim 7 is written a dependent claim from claim 1; therefore, the claim is indefinite; for Examination, claim 7 will be interpreted as an independent process claim not dependent upon system claim 1; further corrections of all terms with incorrect antecedent basis should also must be addressed for the claim to be properly independent; Proper Corrections are Requested.
Claim 9 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Specifically, the claim recites “wherein the step of analyzing and annotating the regions and the segmented pixel blocks comprises analyzing parameters comprising the colors of the gum, the smoothness of the gingival margin, the curvature of the gingival margin, texture features of the gum near the gingival margin such as stippling, swelling appearances” and the term “such as” is an example of Exemplary Claim Language (“for example,” “such as”) (See MPEP 2173.05(d): Examples of claim language which have been held to be indefinite because the intended scope of the claim was unclear are: … (B) “material such as rock wool or asbestos” Ex parteHall, 83 USPQ 38 (Bd. App. 1949)); Since claim depends from claim 9 and recites “wherein the texture features of the gum near the gingival margin comprise stippling and swelling appearance”, Examiner will interpret claim 9 to recite “wherein the step of analyzing and annotating the regions and the segmented pixel blocks comprises analyzing parameters comprising the colors of the gum, the smoothness of the gingival margin, the curvature of the gingival margin, and texture features of the gum near the gingival margin” to fix the coordinating conjunction grammar and to remove the “such as” clause that is delineated in claim 10. Proper Corrections are Requested.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over International Patent Application Publication No.: WO 2024256347 A1 (Alemao et al.) (hereinafter Alemao), in view of well-known art (Official Notice).
Regarding claim 1, Alemao teaches
a machine learning (ML) based image processing system for image processing of intraoral images comprising: (Alemao, abstract; FIG. 6: “According to an embodiment a computer-implemented method for detecting dental plaque on a digital 3D model (101) of a dental situation is disclosed. The method comprises receiving, by a processor, the digital 3D model (101) of the dental situation, providing at least a part of the digital 3D model (101) as an input (601) to a trained neural network (600) … the method comprises obtaining an output (605) from the trained neural network (600) based on the provided input (610), wherein the output (605) comprises a dental plaque parameter associated with the at least one tooth surface, assigning the dental plaque parameter to the at least part of the digital 3D model (101) and displaying the digital 3D model (101) with the dental plaque parameter assigned to the at least part of the digital 3D model (101).”;
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an image receiver for processing one or more frontal view intraoral images of intraoral structures (Alemao, FIG. 1, 102 input image; see FIG. 1 above; page 6, lines 12-17; page 43, lines 9-10: “In another embodiment, the input may be in 2D format such as a 2D image or a plurality of 2D images of the at least one tooth surface of the digital 3D model. Alternatively, the input may be in 2D format such as the 2D image or the plurality of 2D images of the at least one tooth surface of the actual dental situation, captured by the intraoral scanner.”; “Figure 1 illustrates a user interface 100 with a displayed digital 3D model 101 of a patient's dental situation.”;
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an object detector that identifies regions for further image processing in the frontal view intraoral images, and segment the regions into pixel blocks (Alemao, page 44, lines 6-15; page 50, lines 5-25: “To identify and separate individual teeth 102 and gingiva 103 within the digital 3D model 101, the digital 3D model 101 may be segmented. This means that facets, for example facets of the 3D mesh representation of the digital 3D model, belonging to individual teeth 102 as per Universal Numbering System/Notation (UNN), may be determined. In this way individual teeth 102, or parts of the individual teeth 102, may be analyzed for identifying plaque”; “The segmentation process may be performed in different ways, for example based on identification of individual facets or group of facets belonging to a tooth representation. This may allow for identifying objects such as individual teeth and/or surrounding gingiva in the digital 3D models 101, 503 representing the patient's dentition. Therefore, the output of the segmentation process may be individual tooth representations which are usually displayed in form of solid tooth objects, tooth meshes or tooth point clouds.”; “In yet another example, segmenting the digital 3D model 101 and/or further digital 3D model 503 may comprise use of a segmentation machine learning model. In particular, the digital 3D model may be converted into a series of 2D digital images taken from different perspectives. The segmentation machine learning model may be applied to the series of 2D digital images. For each 2D digital image, classification can be performed to distinguish between different teeth classes and gingiva. After classification of each 2D digital image, back-projection onto the digital 3D model may be performed.”; each segmentation of a region (tooth) includes a number of pixels);
multiple autoencoders with multiple deep neural networks for image analysis and image annotation of the regions and the segmented pixel blocks for generating oral structure condition markings; and an ensemble integrator that integrates the oral structure condition markings to a complete marking (Alemao, pages 18, lines 16-30; page 19, lines 1-27: “- providing the at least part of the digital 3D model to a first autoencoder to obtain an output of the first autoencoder, wherein the first autoencoder is trained to detect plaque thickness over a first threshold, - comparing the output of the first autoencoder to the at least part of the digital 3D model to get a first difference, - providing the at least part of the digital 3D model to a second autoencoder to obtain an output of the second autoencoder, wherein the second autoencoder is trained to detect plaque thickness over a second threshold and up to the first threshold, - comparing the output of the second autoencoder to the at least part of the digital 3D model to get a second difference, - providing the at least part of the digital 3D model to a third autoencoder to obtain an output of the third autoencoder, wherein the third autoencoder is trained to detect plaque thickness up to the second threshold, - comparing the output of the third autoencoder to the at least part of the digital 3D model to get a third difference; - identifying a lowest value of the first difference, the second difference and the third difference, - assigning the plaque thickness value to the at least one tooth surface based on the identified lowest value. According to the mentioned embodiment, the first autoencoder may be trained solely on data comprised of cases with thick plaque, the second autoencoder may be trained solely on data comprised of cases with medium plaque and the third autoencoder may be trained solely on data comprised of cases with thin plaque. This in turn ensures that each autoencoder only creates output with the plaque thickness it was trained with. The one autoencoder producing output with the lowest difference to the input may thus determine most accurately the plaque thickness level present in the input.”; the three outputs of the three respective autoencoders are each compared to the original segmented region pixel block that was input into each of them and those respective differences are then “ensemble integrated” together by identifying the lowest of the three differences to be designated as the official marking of the segmented region of the intraoral image; the three differences are high plaque (diseased), medium plaque (questionable), and low plaque (healthy); Examiner uses broadest reasonable interpretation of the term “ensemble integrator” to be means for evaluating/comparing the outputs of the ensemble autoencoders;
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Alemao fails to expressly teach
an object detector that filters frontal view intraoral images.
The Examiner takes Official Notice, that it was well known in the art before the effective filing date of the claimed invention to filter frontal view intraoral images (non-patent literature "Jaw and teeth segmentation on the panoramic X-ray images for dental human identification" Journal of digital imaging 33.6 (2020): 1410-1427 (Bozkurt et al.) (hereinafter Bozkurt), page 1414, Section “Preprocessing”: “In dental image segmentation applications, preprocessing is applied to clarify the differences in the image and distinguish teeth, soft tissue, jaw, and gaps from one another by enhancing the contrast in the same region. In this study, the homomorphic and Butterworth filters were used in preprocessing … After these filters are applied, the preprocessing step was completed by increasing the contrast in the image”; pre-processing filtering before object detection and segmentation in image analysis is well known and typically done before the detection and segmentation are performed).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the object detector, as taught by Alemao, to filter frontal view intraoral images.
The suggestion/motivation for doing so would have been to “clarify the differences in the image and distinguish teeth, soft tissue, jaw, and gaps from one another by enhancing the contrast in the same region” (Bozkurt, page 1414, Section “Preprocessing”).
Therefore, it would have been obvious to combine Alemao, with well-known art, to obtain the invention as specified in claim 1.
Regarding claim 5, Alemao, in view of well-known art, teaches the system of claim 1, further comprising a user interface and a display for displaying the complete marking (Alemao, Fig. 5; see rejection of claim 1 above; page 47, lines 13-30; FIG. 4: “Figure 4 illustrates the user interface 100 for detecting and visualizing plaque on the digital 3D model 101 of the dental situation … The dental plaque parameter may comprise the plaque thickness value. Depending on the detected plaque thickness value, regions of the digital 3D model 101 may be colored in a different shade of the color used. This has effect of visualizing the thin, medium or thick plaque. A gradient bar 401 may be displayed and may serve as a legend to interpret how thick a specific point of plaque is. The plaque thickness value may also be displayed if the user brings the pointer, for example a mouse pointer, over a point on the digital 3D model 101 that comprises plaque”;
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Regarding claim 6, Alemao, in view of well-known art, teaches the system of claim 1, further comprising a memory unit for storing a database of annotated intraoral images, wherein the multiple autoencoders are trained on this database to improve the accuracy and efficiency of the image analysis (Alemao, pages 18, lines 16-30; page 19, lines 1-27: “According to the mentioned embodiment, the first autoencoder may be trained solely on data comprised of cases with thick plaque, the second autoencoder may be trained solely on data comprised of cases with medium plaque and the third autoencoder may be trained solely on data comprised of cases with thin plaque. This in turn ensures that each autoencoder only creates output with the plaque thickness it was trained with. The one autoencoder producing output with the lowest difference to the input may thus determine most accurately the plaque thickness level present in the input. The first threshold may be set in range 150-250 micrometers and the second threshold may be set in range 50-150 micrometers. The first threshold may be higher than the second threshold. Each of the autoencoders may be trained with a sufficient number of samples with corresponding plaque thickness levels, to ensure that each of the autoencoders can adequately represent the plaque thickness levels. In an example, the number of samples may be at least 1000 cases, preferably at least 10000 cases.”).
With regards to claim 7, it recites the functions of the apparatus of claim 1, as a process. Thus, the analysis in rejecting claim 1 is equally applicable to claim 1.
Regarding claim 8, Alemao, in view of well-known art, teaches the method of claim 7, wherein the complete marking comprises annotations of a healthy gingival margin, a questionable gingival margin and a diseased gingival margin marked on the frontal view intraoral images (Alemao, pages 18, lines 16-30; page 19, lines 1-27; see rejection of claim 7 above; the three outputs of the three respective autoencoders are each compared to the original segmented region pixel block that was input into each of them and those respective differences are then “ensemble integrated” together by identifying the lowest of the three differences to be designated as the official marking of the segmented region of the intraoral image; the three differences are high plaque (diseased), medium plaque (questionable), and low plaque (healthy); Alemao, Fig. 5; see rejection of claim 1 above; page 47, lines 13-30; FIG. 4: “Figure 4 illustrates the user interface 100 for detecting and visualizing plaque on the digital 3D model 101 of the dental situation … The dental plaque parameter may comprise the plaque thickness value. Depending on the detected plaque thickness value, regions of the digital 3D model 101 may be colored in a different shade of the color used. This has effect of visualizing the thin, medium or thick plaque. A gradient bar 401 may be displayed and may serve as a legend to interpret how thick a specific point of plaque is. The plaque thickness value may also be displayed if the user brings the pointer, for example a mouse pointer, over a point on the digital 3D model 101 that comprises plaque”; the scale has three different color gradients correlated to thin plaque (healthy), medium plaque (questionable), and high plaque (diseased);
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dental plaque, specifically when accumulated at the gingival margin (gum line), is the primary indicator and cause of plaque-induced gingivitis; this bacterial biofilm buildup triggers an inflammatory response, leading to redness, swelling, and bleeding at the margin; it is often assessed using specific plaque indices).
Regarding claim 12, Alemao, in view of well-known art, teaches the method of claim 7, wherein the multiple autoencoders with multiple deep neural networks are trained with standard frontal view intraoral photographs assessed by at least one qualified dentist, wherein the qualified dentist marks the gingival margins displayed in the standard frontal view intraoral photographs and denotes them as healthy, questionable and diseased (Alemao, page 29, lines 24-28; page 30, lines 1-24: “the training process may comprise the use of a training dataset that has been labeled manually. The training dataset may be in 2D format, i.e. it may comprise a plurality of 2D training images of teeth or parts of teeth. Additionally, the training dataset may comprise associated labels where the labels correspond to the dental plaque parameter. The labeling may be performed for example by a qualified dental practitioner. The training dataset may be labeled according to the indication of plaque presence, for example with "Yes" or "No" labels indicating plaque presence or absence, respectively. Additionally, or alternatively, the training dataset may be labeled with plaque type labels and/or plaque thickness labels. In an example, the training dataset may also be in 3D format in which the labels may be placed directly. The labels may be regarded as a target or a ground truth for a given training data set. Additionally, or alternatively, the ground truth may be information on plaque presence, plaque thickness and/or plaque type, obtained through use of the disclosing agent on a plurality of various dental situations. An advantage of such ground truth data lies in the clinical accuracy and acceptance, due to information originating from use of the disclosing agent.”).
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Alemao, in view of well-known art, and in view of U.S. Patent Application Publication No.: 2024/0144480 (Seeber et al.) (hereinafter Seeber).
Alemao, in view of well-known art, teaches the system of claim 1.
Alemao, in view of well-known art, fails to teach
wherein the multiple autoencoders using deep neural networks corrects intraoral images by image analysis that corrects the frontal view intraoral images containing blurriness caused by body movement or scaling and performs image color balance.
Seeber teaches
wherein the multiple autoencoders using deep neural networks corrects intraoral images by image analysis that corrects the frontal view intraoral images containing blurriness caused by body movement or scaling and performs image color balance (Seeber, para. [0083]; para. [0127]; para. [0092]: “In some embodiments, the sequence of operations performed by historical smile processing module 155 includes one or more of the above-described operations as well as one or more further operations such as distortion correction operations, blur correction operations, and so on. In one embodiment, to perform distortion correction, landmarks are detected from a generated 3D model of a patient's dental arch. The detected 3D landmarks can then be used to correct distortion that is induced by a camera that generated one or more of the historical images”; “Returning to FIG. 2, the captured image(s) 135 (e.g., those that satisfy the image quality assessment) and/or replacement images output by image replacement module 273 may be input into a color transfer module 267, which may include a trained neural network. The trained neural network of color transfer module 267 may be trained to adjust colors, lighting, white balance, etc. of input images. For instances in which there are multiple captured images 135 of a patient's teeth over time (e.g., at different stages of treatment), the color transfer module 267 causes each of these images to have colors, shading, white balance, etc. that is uniform across the images. The machine learning model of color transfer module 267 may output updated or modified versions of each of the input images, or instructions on how to modify the input images, which may be applied by another logic of color transfer module 267 to generate modified images that are color balanced.”; “Image generation/interpolation—this can include generating (e.g., interpolating) simulated images that show teeth, gums, etc. as they might look between those teeth, gums, etc. in images at hand. Such images may be photo-realistic images. In some embodiments, a generative model such as a … variational autoencoder (VAE) … is used to generate intermediate simulated images.”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the multiple autoencoders using deep neural networks, as taught by Alemao, in view of well-known art, to correct intraoral images by image analysis that corrects the frontal view intraoral images containing blurriness caused by body movement or scaling and performs image color balance, as taught by Seeber.
The suggestion/motivation for doing so would have been that color balance and blurriness correction in intraoral images are critical for modern dentistry, as they directly improve diagnostic accuracy, treatment planning, and patient communication; these corrections transform raw, often inconsistent camera data into reliable, high-detail, and true-to-life images, essential for assessing tooth structure and soft tissue conditions.
Therefore, it would have been obvious to combine Alemao and well-known art, with Seeber, to obtain the invention as specified in claim 2.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Alemao, in view of well-known art, and in view of U.S. Patent Application Publication No.: 2025/0005755 (Gao et al.) (hereinafter Gao).
Regarding claim 3, Alemao, in view of well-known art, teaches the system of claim 1.
Alemao, in view of well-known art, fails to teach
wherein the segmentation of the regions of interest is able to be performed multiple times to generate different sets of the pixel blocks in different sizes, so as to increase the accuracy of the marking.
Gao teaches
wherein the segmentation of the regions of interest is able to be performed multiple times to generate different sets of the pixel blocks in different sizes, so as to increase the accuracy of the marking (Gao, para. [0017]-[0020]: “In general, the 2D image of the subject's dentition may be segmented to identify individual teeth corresponding to the one or more regions within the 2D image. For example, any of these methods may include segmenting the 2D image to identify a plurality of individual teeth from the 2D image. The images may be segmented before registering the 2D image with the 3D representation … Any of these methods and apparatuses for performing them may include determining a length of a region of the 2D image using the pixel size scaling. For example, determining the length of the region of the 2D image using the pixel size scaling may include calculating and outputting the results of the calculation. Regions that may be calculated may include, but are not limited to one or more of: an overbite distance, an underbite distance, a posterior open bite distance, an interproximal spacing, and a distance between a tooth and an aligner. In general, the size of the one or more pixels may be in units of length per pixel (e.g., micrometers per pixel, mm per pixel, etc.). In any of these methods generating the pixel size scaling for the 2D image of the subject's dentition may comprise determining the scaling factor for each visible tooth in the 2D image. In some cases, at least two of the visible teeth in the 2D image may have different pixel sizes.”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the segmentation of the regions of interest, as taught by Alemao, in view of well-known art, to be able to be performed multiple times to generate different sets of the pixel blocks in different sizes, so as to increase the accuracy of the marking, as taught by Gao.
The suggestion/motivation for doing so would have been that using different pixel sizes (multi-scale resolution) in intraoral image segmentation enhances accuracy by capturing both fine details and global context, improving the detection of varied lesion sizes and anatomical structures. It optimizes the balance between high-resolution detail for precise margins and lower-resolution speed for efficient computational processing.
Therefore, it would have been obvious to combine Alemao and well-known art, with Gao, to obtain the invention as specified in claim 3.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Alemao, in view of well-known art, and in further view of additional well-known art (Official Notice).
Regarding claim 4, Alemao, in view of well-known art, teaches the system of claim 1.
Alemao, in view of well-known art, fails to expressly teach
wherein the frontal view intraoral images show at least 3 mm gingival tissue from maxillary and mandibular gingival margins and a clear gingival margin between the gum and teeth.
The Examiner takes Official Notice, that it was well known in the art before the effective filing date of the claimed invention to take frontal intraoral images of people’s teeth such that images show at least 3 mm gingival tissue from maxillary and mandibular gingival margins and a clear gingival margin between the gum and teeth (non-patent literature: "Excessive gingival display"; StatPearls; StatPearls Publishing, 2023; Brizuela et al. (hereinafter Brizula); Page 1, Introduction: “Exposing the gingiva when smiling up to some extent provides a youthful look and is cosmetically appealing … A gingival display of 1 to 2 mm when smiling is considered normal … Excessive gingival display, also known as "gummy smile," is the overexposure of the maxillary gingiva while smiling (see Images. Excessive Gingival Display, Gummy Smile, Excessive Gingival Display, Overexposure of the Maxillary Gingiva). In some severe cases, the overexposure of the gingival tissue is evident even in the resting position of the lips.”; page 7, Section: History and Physical, para. 2: “Some authors define a gummy smile as more than 3 to 4 mm of exposed gingival tissue in a smile, whereas others consider more than 2 mm of gingival exposure as excessive … In general, a gum-to-lip distance of 4 mm or more during a smile is deemed "unattractive" by dentists.”); it is well-known for dentists to ask patients to open their mouth and smile bigger than normally so the gums are exposed (greater than 3 mm gingival tissue exposed) to give a clear view of the gingival margin between gum and teeth for intraoral imaging.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the frontal view intraoral images, as taught by Alemao, in view of well-known art, to show at least 3 mm gingival tissue from maxillary and mandibular gingival margins and a clear gingival margin between the gum and teeth, as taught by additional well-known art.
The suggestion/motivation for doing so would have been that a crisp image of the scalloped edge where the gum meets the tooth is essential to diagnose periodontal pockets or gingival recession.
Therefore, it would have been obvious to combine Alemao and well-known art, with additional well-known art, to obtain the invention as specified in claim 4.
With regards to claim 13, it recites the functions of the apparatus of claim 4, as a process. Thus, the analysis in rejecting claim 4 is equally applicable to claim 13.
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Alemao, in view of well-known art, and in view of International Patent Application Publication No.: WO 2023192652 A1 (Farkash et al.) (hereinafter Farkash).
Regarding claim 9, Alemao, in view of well-known art, teaches the method of claim 7.
Alemao, in view of well-known art, fails to teach
wherein the step of analyzing and annotating the regions and the segmented pixel blocks comprises analyzing parameters comprising the colors of the gum, the smoothness of the gingival margin, the curvature of the gingival margin, and texture features of the gum near the gingival margin.
Farkash teaches
wherein the step of analyzing and annotating the regions and the segmented pixel blocks comprises analyzing parameters comprising the colors of the gum (Farkash, par. [00257]: “In one embodiment, color info is used as an additional 3 layers (e.g., RGB), thus, getting 4 layers input for the network. Two types of color info may be used, which may include viewfinder images and scan textures. Viewfinder images are of better quality but need alignment with respect to heightmaps. Scan textures are aligned with height maps, but may have color artifacts.”),
the smoothness of the gingival margin (Farkash, para. [00141]; para. [00257]: “Returning to FIG. 2A, at block 215, the first intraoral scan data (e.g., the intraoral scans used to generate the 3D surface) and/or data from the 3D surface are processed to identify hard tissue and soft tissue. The data from the 3D surface may include 3D data (e.g., 3D point clouds) or 2D projections of the 3D surface. The first intraoral scan data may additionally be processed to identify points associated with a margin line and points not associated with a margin line, to identify points associated with moving tissue and/or with a dental tool, to identify points associated with excess material, and so on. Such processing may include inputting the intraoral scan data or data from the 3D surface into one or more trained machine learning models, which may output segmentation information indicating, for each point, probabilities of that point having one or more classification (e.g., a hard tissue classification, a soft tissue classification, a margin line classification, a dental tool classification, a moving tissue classification, an excess material classification, and so on)”),
the curvature of the gingival margin (Farkash, para. [00186]: “In one embodiment, processing the second intraoral scan data using the one or more algorithms configured to determine a three-dimensional surface of a non-static dental site further comprises determining a conflicting surface for a pair of intraoral scans from the second intraoral scan data. This may be performed as part of a merging algorithm. Processing logic may determine a first mean curvature or Gaussian curvature for the conflicting surface from a first intraoral scan from the pair. Processing logic may additionally determine a second mean curvature or Gaussian curvature for the conflicting surface from a second intraoral scan from the pair. Processing logic may then determine which of the mean curvatures is greater (e.g., processing logic may determine that the second mean curvature is less than the first mean curvature). Processing logic may additionally determine a difference between the two mean or Gaussian curvatures, and determine whether the difference between the first mean or Gaussian curvature and the second mean or Gaussian curvature is greater than a difference threshold. Responsive to determining that the difference is greater than the difference threshold, processing logic discards a representation of the conflicting surface from the intraoral scan with the smaller mean or Gaussian curvature (e.g., from the first intraoral scan in the above example). The second 3D surface of the non-static dental site (e.g., of the preparation tooth) may then be determined by combining data from the first intraoral scan and the second intraoral scan, wherein the discarded representation of the conflicting surface from the first intraoral scan is not used to determine the surface. If the difference is less than the difference threshold, then the data for the conflicting surface from the two intraoral scans may be averaged together.”), and
texture features of the gum near the gingival margin (Farkash, para. [00122]: “. The machine learning model may have been trained using a training dataset that includes scans of gingiva over margin lines and scans of exposed margin lines not covered by gingiva. Such a machine learning model may be trained to remove gingiva and leave exposed margin lines in an embodiment. In one embodiment, a specific excess gingiva removal algorithm (e.g., trained machine learning model) is used rather than a generic excess material removal algorithm. In one embodiment, two excess material removal algorithms are used, where one is for removing excess gingiva and the other is for removing other excess material. In one embodiment, inputs for the trained machine learning model that has been trained to remove excess gingiva are sets of scans. The trained machine learning model may determine for the sets of scans which scan data represents excess gingiva and should be removed.”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the step of analyzing and annotating the regions and the segmented pixel blocks, as taught by Alemao, in view of well-known art, to include analyzing parameters comprising the colors of the gum, the smoothness of the gingival margin, the curvature of the gingival margin, and texture features of the gum near the gingival margin, as taught by Farkash.
The suggestion/motivation for doing so would have been that “Accuracy of segmentation can be improved by means of additional classes, inputs and multiple views support; multiple sources of information can be incorporated into model inputs and used jointly for prediction; multiple dental classes can be predicted concurrently from a single model or using multiple models; multiple problems can be solved simultaneously: tissue classification, moving tissue detection/classification, margin line detection/classification, excess material classification/detection, etc.; accuracy is higher than traditional image and signal processing approaches” (Farkash, para. [00255]).
Therefore, it would have been obvious to combine Alemao and well-known art, with Farkash, to obtain the invention as specified in claim 9.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Alemao, in view of well-known art, in view of Farkash, and in view of U.S. Patent Application Publication No.: 2024/0335162 (Bundsgaard et al.) (hereinafter Bundsgaard).
Regarding claim 10, Alemao, in view of well-known art, and in view of Farkash, teaches the method of claim 9.
Alemao, in view of well-known art, and in view of Farkash, fails to teach
wherein the texture features of the gum near the gingival margin comprise stippling and swelling appearances.
Bundsgaard teaches
wherein the texture features of the gum near the gingival margin comprise stippling and swelling appearances (Bundsgaard, para. [0203]; para. [0039]-[0044]: “FIG. 7 shows a flow diagram of determining oral care means based on an assigned oral cavity profile in accordance with another example of the present disclosure. In this example determining the oral care means 6 is based on analyzing the oral cavity profile 5 using a machine learning algorithm 25 trained to identify oral care means 6 likely to be effective based on associated information with the oral cavity profile 5, the associated information comprising dental features 3 and optionally respective feature scores 4, non-image data 2, and optionally follow-up user input.” “In a possible embodiment the identified dental features may comprise and may trigger assignment of oral health findings of: swelling gums”, “bleeding gums”, “redness of gums”, “dental plaque” may trigger assignment of oral health finding of “gingivitis” “exposure of root”, “exposure of dentin”, “abrasion of tooth”, “staining of tooth”, “recession of gums” may trigger assignment of oral health finding of “periodontitis”; “cavities catalyzing bacteria”, “exposure of decayed dentin”, “density of enamel”, “white spot lesion”, “brown/black discoloration of tooth”, “enamel breakdown”, “shade on enamel” may trigger assignment of oral health finding of “dental caries”; “inflamed papules and vesicles”, “fluid-filled blister”, “local redness of lip”, “open ulcer” may trigger assignment of oral health finding of “herpes labialis”; and “light coral pink gums”, “stippled surface gums”, “tightly fitting gumline on tooth” may trigger assignment of oral health finding of “healthy gums””;).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the texture features of the gum near the gingival margin, as taught by Alemao, in view of well-known art, and in view of Farkash, to include stippling and swelling appearances, as taught by Bundsgaard).
The suggestion/motivation for doing so would have been that recognizing stippling (orange-peel texture) and swelling near the gingival margin is crucial for the early detection of gingivitis and periodontal disease; stippling typically indicates healthy, attached gingiva, while its loss or the appearance of swelling/inflammation (spongy texture) serves as a key indicator of periodontal destruction.
Therefore, it would have been obvious to combine Alemao, well-known art, and Farkash, with Bundsgaard, to obtain the invention as specified in claim 10.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Alemao, in view of well-known art, and in view of U.S. Patent Application Publication No.: 2024/0172943 (Sorenson et al.) (hereinafter Sorenson).
Regarding claim 11, Alemao, in view of well-known art, teaches the method of claim 8.
Alemao, in view of well-known art, fails to teach
wherein the healthy gingival margin indicates that the gum is pink, the gingival margin is smooth and there is no bleeding spot on the gum; the questionable gingival margin represents that the gum turns red, the gingival margin is rough or the gum is swollen; and the diseased gingival margin indicates that there are white/red patches on the gum, the gum is generalized redness, there is ulcer on the gum, the gum is swollen or there is bleeding spot on the gum.
Sorenson teaches
wherein the healthy gingival margin indicates that the gum is pink, the gingival margin is smooth and there is no bleeding spot on the gum; the questionable gingival margin represents that the gum turns red, the gingival margin is rough or the gum is swollen; and the diseased gingival margin indicates that there are white/red patches on the gum, the gum is generalized redness, there is ulcer on the gum, the gum is swollen or there is bleeding spot on the gum (Sorenson, para. [0064]; para. [0118-[0119]; Fig. 2: “FIG. 2 illustrates relatively general examples of three different conditions of a tooth/gum pairing. A tooth 204 may be rooted in a generally healthy gum 206. A tooth 208 may be rooted in a gum 210 that may demonstrate at least some degree of gingivitis. A tooth 212 may be rooted in a gum 214 that may or might not demonstrate at least some degree of gingivitis. The gum 214 may include periodontal pockets 216 and/or 218 that may indicate at least some degree of periodontitis.”; “At 1212, one or more techniques may comprise determining, via one or more machine-learning algorithms, a first modified gingival index (MGI) value (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) for the first assessment location based, at least in part, on the first image and the one or more first data channels. t 1214, one or more techniques may comprise providing an indication of the first MGI value (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) in a visually interpretable format via the digital representation of at least a part of the oral cavity on the display device. At 1216, the process may stop or restart.”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the 1) healthy gingival margin, 2) the questionable gingival margin, and 3) the diseased gingival margin, as taught by Alemao, in view of well-known art, to respectively, 1) indicate that the gum is pink, the gingival margin is smooth and there is no bleeding spot on the gum, 2) to represent that the gum turns red, the gingival margin is rough or the gum is swollen, and 3) indicate that there are white/red patches on the gum, the gum is generalized redness, there is ulcer on the gum, the gum is swollen or there is bleeding spot on the gum, respectively, as taught by Sorenson.
The suggestion/motivation for doing so would have been that “the results from the intra-oral scan (IOS) are expected to be much more reproducible which is an advantage for comparing over time (e.g., monitoring) (Sorenson, para. [0078]).
Therefore, it would have been obvious to combine Alemao and well-known art, with Sorenson, to obtain the invention as specified in claim 11.
Conclusion
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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