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 .
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9, 11-14, 17-25, and 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al (US 2023/0113425) in view of SONG et al (US 2023/0310126).
As per claim 1, Wang teaches the claimed “dental crown model generation method,” comprising: “providing a three-dimensional dental image model including a plurality of geometric features” (Wang, [0041] - The method may model and/or design a dental crown and/or a to-be-produced tooth using the oral digital impression instrument, and may be part of another method to make the dental crown and/or the produced tooth, and place the dental crown and/or the produced tooth in a patient's oral cavity. The oral digital impression instrument comprises oral digital acquisition equipment, such as an intraoral scanner, configured to scan an oral cavity of a patient and obtain three-dimensional (3D) data of teeth in the oral cavity; [0049] - 1) Data acquisition: acquiring 3D model data of an oral cavity model after tooth preparation with oral digital acquisition equipment, the data acquired by the oral digital acquisition equipment including an upper jaw and a lower jaw, as well as confirming occluding relations between the upper jaw and the lower jaw); “retrieving data of a preset teeth model based on a target position of a tooth to be treated” (Wang, [0053] - Data preprocessing: first labeling the 3D model of the oral cavity obtained in step 2), and labeling an abutment tooth position, single crown/crown bridge and an abutment edge line; [0058]-[0064] - automatically designing the produced teeth according to the 3D model data… This step mainly comprises the following steps: calculating the distance between adjacent teeth…; calculating the buccal and lingual dental arch convexity curves…; obtaining the buccal apex of incisor contact surface of the jaw where the abutment is, the point of bilateral canines with the maximum buccal curvature, and the point of bilateral second permanent molars with the maximum buccal curvature, with the five points…; the buccal dental arch convexity curve is determined…; calculating the lingual dental arch convexity curve in the same manner); “step A: adjusting the preset teeth model by using a plurality of dental model parameters to generate a test tooth model, thereby obtaining a plurality of preset geometric features” (Wang, [0084]-[0089] -
dental pattern adjustment: 3.6.1) integral adjustment, 3.6.2) occlusal surface adjustment, 3.6.3) adjusting the lower edge of the dental crown); “step B: generating a plurality of conditional operations based on the plurality of geometric features” (Wang, [0068]-[0071] - calculating the cusp pit and fissure ridge shape features of opposite jaws: calculating the occlusal surface features of the opposite jaw teeth according to the 3D model data of the upper jaw and lower jaw… interactively selecting the occlusal surface of the opposite jaw tooth; calculating the selected dental surface feature vector: first, obtaining the effective neighborhood around the cusp pit and fissure ridge in the occlusal surface according to the selection results; then establishing a local spherical coordinate system for the surface; next, calculating the elevation and azimuth of the normal at each vertex of the surface through the 2D histogram statistical method, and determining the position index in turn; finally, generating the maxillofacial feature vector (f1, f2, . . . , fn) of the opposite jaw tooth according to the 2D histogram); “step C: scoring a matching degree between the plurality of preset geometric features and the plurality of conditional operations to obtain a plurality of scores” (Wang, [0087] - The alternative dental crown is scaled by virtue of the scaling matrix S, so that the size of the alternative dental crown is best fit…; occlusal surface adjustment: adjusting the normal vector of the occlusal surface of the alternative dental crown according to the maxillofacial feature vector of the opposite jaw tooth of the to-be-produced tooth to best fit…; so that the distance from each point on the lower edge of the dental crown to the highest point on the occlusal surface matches the distance from each point on the abutment edge line to the occlusal plane) (Noted: the best fit is a statistical concept that estimates a score degree for matching through a set of data points, minimizing the distance between the preset geometric features and the plurality of conditional operations); and “using the test tooth model corresponding to the updated plurality of dental model parameters as a final dental crown model” (Wang, [0042] - through computer aided design, several matching alternative dental crowns can be quickly retrieved from the database, and the most suitable alternative dental crowns can be determined by the similarity of the overall size feature vectors of the to-be-produced tooth and the alternative dental crown). It is noted that Wang does not teach “step D: performing weighted calculations on the plurality of scores using a plurality of weights to obtain a denture score; if the denture score being below a threshold score, inputting the denture score into a parameter correction artificial intelligence unit to update the plurality of dental model parameters and repeating step A to step D until the denture score exceeds the threshold score.” However, Wang’s determined best match of the overall size feature vectors of the to-be-produced tooth and the alternative dental crown (e.g., [0091] - The dentists can carry out secondary manual design according to the actual inspection, and then conduct quick matching in the database to accelerate the denture (e.g., dental crown or dental bridge) design) suggests a method for measuring the similarity between two tooth models in which the matching score of parameters is a weighted combination of individual parameter scores for the compared models in which each weight indicates the contribution of its associated parameter to the dental crown model; furthermore, the use of a parameter correction artificial intelligence unit to adjust the parameters for matching of designed crown model is well-known in the art (Song, [0198] - In addition, the alignment operation in operation S830 may be performed through a neural network operation based on an artificial intelligence (AI) technology. Here, the neural network may optimize weight values in the neural network by receiving two mutually couplable objects and training learning data … such that the two input objects are aligned. In addition, a desired result may be output by self-learning the input data through the neural network having an optimized weight value). Thus, it would have been obvious, in view of Song, to configure Wang’s method as claimed by using a neural network to implement the matching score of the compared models by a combination of weighted matching scores of their parameters. The motivation is to set up a standard for matching two teeth models, and to improve the comparison by using a neural network to calculate a combination of weighted matching scores of their parameters.
Claim 2 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: inputting the three-dimensional dental image model; locating, by an image recognition artificial intelligence unit, the target position of the tooth to be treated from the three-dimensional dental image model” (Song, [0176]-[0177] - Hereinafter, a case, in which the ‘artifact’ in operation S810 is a crown, is described as an example. Then, the electronic device 400 may acquire a design model representing an artifact to be attached to a tooth based on the acquired first scan model (S810). Specifically, in operation S810, a design model three-dimensionally representing an artifact to be attached or coupled to a tooth may be generated through a design based on a computer operation based on the first scan model; [0213] - In addition, operation S840 may be performed by using an operation through a neural network based on the Al technology described in operation S830); and determining, by the image recognition artificial intelligence unit, a plurality of feature coordinates based on at least one adjacent residual tooth adjacent to the target position” (Song, [0198] - In addition, the alignment operation in operation S830 may be performed through a neural network operation based on an artificial intelligence (AI) technology. Here, the neural network may optimize weight values in the neural network by receiving two mutually couplable objects and training learning data … such that the two input objects are aligned. In addition, a desired result may be output by self-learning the input data through the neural network having an optimized weight value). Thus, it would have been obvious, in view of Song, to configure Wang’s method as claimed by using a neural network to determine the feature coordinates of the dental crown model. The motivation is to improve the dental crown model’s recognition by using a neural network to determine the feature coordinates.
Claim 3 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of protruding portions as a plurality of outer edge feature coordinates in the three-dimensional dental image model” (Wang, [0061] - calculating the buccal and lingual dental arch convexity curves); wherein generating the plurality of conditional operations further comprises: “performing a regression analysis operation for the plurality of outer edge feature coordinates to obtain a parabolic regression equation for an outer edge of a top-down view” which would have been obvious in view of Wang’s fitting a dental curve (Wang, [0028] - determining the β function according to the points (which may be 5 points or more) obtained in step 3.2.1) to fit a dental arch convexity curve (e.g., the buccal dental arch convexity curve)) because, based on the scanned samples (i.e., points) on the top-down view, a regression model can be used for statistical estimate an approximated line (e.g., a parabolic curve) representing the scanned sample points.
Claim 4 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of protruding portions as a plurality of inner edge feature coordinates in the three-dimensional dental image model” (Wang, [0061] - calculating the buccal and lingual dental arch convexity curves); “wherein generating the plurality of conditional operations further comprises: performing a regression analysis operation for the plurality of inner edge feature coordinates to obtain a straight-line regression equation for an inner edge of a top-down view” which would have been obvious in view of Wang’s fitting a dental curve (Wang, [0028] - determining the β function according to the points (which may be 5 points or more) obtained in step 3.2.1) to fit a dental arch convexity curve (e.g., the buccal dental arch convexity curve)) because, based on the scanned samples (i.e., points) on the top-down view, a regression model can be used for statistical estimate an approximated line (e.g., a straight-line) representing the scanned sample points.
Claim 5 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of groove portions as a plurality of inner portion feature coordinates in the three-dimensional dental image model” (Wang, [0061] - calculating the buccal and lingual dental arch convexity curves) (Noted: The lingual aspect (the surface facing the tongue or palate) of an incisor is characterized by a scoop-like concavity); wherein generating the plurality of conditional operations further comprises: “performing a regression analysis operation for the plurality of inner portion feature coordinates to obtain a straight-line regression equation for an inner portion of a top-down view” which would have been obvious in view of Wang’s fitting a dental curve (Wang, [0028] - determining the β function according to the points (which may be 5 points or more) obtained in step 3.2.1) to fit a dental arch convexity curve (e.g., the buccal dental arch convexity curve)) because, based on the scanned samples (i.e., points) on an inner portion of the top-down view, a regression model can be used for statistical estimate an approximated line (e.g., a straight-line) representing the scanned sample points.
Claim 6 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of protruding portions as a plurality of upper edge feature coordinates in the three-dimensional dental image model” (Wang, [0061] - calculating the buccal and lingual dental arch convexity curves); wherein generating the plurality of conditional operations further comprises: “performing a regression analysis operation for the plurality of upper edge feature coordinates to obtain a parabolic regression equation for an upper edge of an outer side-view” which would have been obvious in view of Wang’s fitting a dental curve (Wang, [0028] - determining the β function according to the points (which may be 5 points or more) obtained in step 3.2.1) to fit a dental arch convexity curve (e.g., the buccal dental arch convexity curve)) because, based on the scanned samples (i.e., points) on an upper edge of an outer side-view, a regression model can be used for statistical estimate an approximated line (e.g., a parabolic curve) representing the scanned sample points.
Claim 7 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: “setting a plurality of protruding portions as a plurality of upper edge feature coordinates in the three-dimensional dental image model” (Wang, [0061] - calculating the buccal and lingual dental arch convexity curves); wherein generating the plurality of conditional operations further comprises: “performing a regression analysis operation for the plurality of upper edge feature coordinates to obtain a straight-line regression equation for an upper edge of an inner side-view which would have been obvious in view of Wang’s fitting a dental curve (Wang, [0028] - determining the β function according to the points (which may be 5 points or more) obtained in step 3.2.1) to fit a dental arch convexity curve (e.g., the buccal dental arch convexity curve)) because, based on the scanned samples (i.e., points) on an upper edge of an inner side-view, a regression model can be used for statistical estimate an approximated line (e.g., a straight-line) representing the scanned sample points.
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Claim 8 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of groove portions as a plurality of inner portion feature coordinates in the three-dimensional dental image model” (Wang, [0061] - calculating the buccal and lingual dental arch convexity curves) (Noted: The lingual aspect (the surface facing the tongue or palate) of an incisor is characterized by a scoop-like concavity); wherein generating the plurality of conditional operations further comprises: “performing a regression analysis operation for the plurality of inner portion feature coordinates to obtain a straight-line regression equation for an inner portion of an inner side-view” which would have been obvious in view of Wang’s fitting a dental curve (Wang, [0028] - determining the β function according to the points (which may be 5 points or more) obtained in step 3.2.1) to fit a dental arch convexity curve (e.g., the buccal dental arch convexity curve)) because, based on the scanned samples (i.e., points) on an inner portion of an inner side-view, a regression model can be used for statistical estimate an approximated line (e.g., a straight-line) representing the scanned sample points.
Claim 9 adds into claim 1 “wherein using the test teeth model corresponding to the updated plurality of dental model parameters as the final dental crown model further comprising: performing three-dimensional printing to produce a temporary dental crown based on the final dental crown model” (Wang, [0090] - the designed tooth model being exported as a data format which can be imported by 3shape and EXOcad software) (Noted: a temporary dental crown can be 3D printed based on the final dental crown model).
Claim 11 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of feature coordinates at a plurality of positions of an abutment tooth in the three-dimensional dental image model” (Wang, [0008] - labeling an abutment tooth position, single crown/crown bridge and an abutment edge line; [0081] - tooth number and tooth type matching: finding alternative dental crown data in the database according to the tooth number of the selected abutment and the selected dental crown type (single crown or crown bridge)); wherein generating the plurality of conditional operations further comprises: “performing a plurality of dot product operations respectively between normal vectors of the plurality of feature coordinates and vectors of closest points corresponding to the test tooth model” (Wang, [0082] - The cosine of the included angle between the two vectors is calculated to measure the similarity between the feature vector of the to-be-produced tooth and the feature vectors of the alternative dental crown, and the cosine range of the included angle is [0,1]; the larger the cosine of the included angle, the smaller the included angle of the two vectors. When the directions of the two vectors coincide, the maximum cosine of the included angle is 1. The specific calculation formula of the cosine of the included angle is shown as formula (6)) (Noted: the dot product of two unit vectors in formula (6) equals to the cosine of the angle formed by the two unit vectors).
Claim 12 adds into claim 11 “wherein scoring the matching degree further comprises: summing results of the plurality of dot product operations to obtain a score for abutment coverage and manufacturing compliance” (Wang, [0082] - The cosine of the included angle between the two vectors is calculated to measure the similarity between the feature vector of the to-be-produced tooth and the feature vectors of the alternative dental crown, and the cosine range of the included angle is [0,1]; the larger the cosine of the included angle, the smaller the included angle of the two vectors. When the directions of the two vectors coincide, the maximum cosine of the included angle is 1. The specific calculation formula of the cosine of the included angle is shown as formula (6)).
Claim 13 adds into claim 1 “wherein providing the three- dimensional dental image model further comprising: setting a plurality of feature coordinates at edge positions of teeth adjacent to an abutment tooth in the three-dimensional dental image model; wherein generating the plurality of conditional operations further comprises: performing a plurality of dot product operations between normal vectors of the plurality of feature coordinates and vectors of closest points corresponding to the test tooth model” (Wang, [0082] - The cosine of the included angle between the two vectors is calculated to measure the similarity between the feature vector of the to-be-produced tooth and the feature vectors of the alternative dental crown, and the cosine range of the included angle is [0,1]; the larger the cosine of the included angle, the smaller the included angle of the two vectors. When the directions of the two vectors coincide, the maximum cosine of the included angle is 1. The specific calculation formula of the cosine of the included angle is shown as formula (6)).
Claim 14 adds into claim 13 “wherein scoring the matching degree further comprises: summing results of the plurality of dot product operations to obtain a score for proximity relationship and occlusal thickness adjustment” (Wang, [0082] - The cosine of the included angle between the two vectors is calculated to measure the similarity between the feature vector of the to-be-produced tooth and the feature vectors of the alternative dental crown, and the cosine range of the included angle is [0,1]; the larger the cosine of the included angle, the smaller the included angle of the two vectors. When the directions of the two vectors coincide, the maximum cosine of the included angle is 1. The specific calculation formula of the cosine of the included angle is shown as formula (6)).
Claims 17-25, and 27-30 claim a dental crown model generation system based on the method of claims 1-9, and 11-14; therefore, they are rejected under a similar rationale.
Claims 15-16, and 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al (US 2023/0113425) in view of SONG et al (US 2023/0310126), and further in view of Orozco-Varo et al (Biometric analysis of the clinical crown and the width/length ratio in the maxillary anterior region).
Claim 15 adds into claim 1 “wherein generating the plurality of conditional operations further comprises: performing a calculation on a width-to-length ratio of the test tooth model” which would have been obvious for designing a crown according to tooth morphology which is the study of the physical features, shape, and structure of teeth. Noted: In aesthetic dentistry and morphology, the ideal width-to-length ratio for a healthy, unworn maxillary (upper) central incisor is widely taught to be between 75% and 85% (Orozco-Varo, page 568, Table2 - Mean(mm), range (min and max), and standard deviations (SD) of width, length, and width/length ratio for each tooth individually and within its type of tooth). Thus, it would have been obvious, in view of Song and Orozco-Varo, to configure Wang’s method as claimed by using the width-to-length ratio of the dental crown model. The motivation is to improve the aesthetic dentistry and morphology based on the width-to-length ratio of the test tooth model.
Claim 16 adds into claim 15 “wherein scoring the matching degree further comprises: subtracting a proportional constant from the width-to-length ratio of the test tooth model to obtain a score for morphological symmetry and shape constraints” which would have been obvious in a standard of aesthetic dentistry and morphology, which defines “the ideal width-to-length ratio for a healthy, unworn maxillary (upper) central incisor” being between 75% and 85% (Orozco-Varo, page 568, Table2 - Mean(mm), range (min and max), and standard deviations (SD) of width, length, and width/length ratio for each tooth individually and within its type of tooth) (Noted: since an ideal width-to-length ratio of a tooth is larger than 0.75, for aesthetic dentistry and morphology, a score for aesthetic crown can be defined, for example, as (width-to-length ratio – 0.75)). Thus, it would have been obvious, in view of Song and Orozco-Varo, to configure Wang’s method as claimed by using the width-to-length ratio of the dental crown model. The motivation is to improve the aesthetic dentistry and morphology based on the width-to-length ratio of the dental crown model.
Claims 31-32 claim a dental crown model generation system based on the method of claims 15-16; therefore, they are rejected under a similar rationale.
Claims 10 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al (US 2023/0113425) in view of SONG et al (US 2023/0310126), and further in view of SCHNABEL et al (US 2021/0085238) and LIPNIK et al (20230149135).
Claim 10 adds into claim 1 “wherein the plurality of dental model parameters at least include: a rotation parameter for determining an angle of the test tooth model relative to a fixed point; a stretching parameter for determining a stretching length of at least one axis of the test tooth model; and a translation parameter for determining a position of the test tooth model relative to the fixed point” which Wang does not teach, but these parameters are well known in a dental crown model (Schnabel, [0035] - Furthermore, standard data augmentation procedures such as synthetic rotations, scalings etc. may be applied to the training data sets and/or inputs; Lipnik, [0068] - The 3D rigid transformation may comprise a translation (change in position with respect to one or more reference axes) and/or a rotation (change in orientation with respect to one or more reference axes); [0073] - In some cases, the area of the gums surrounding a base of a tooth may be bent or stretched to simulate a physical rigid material and preserve the fine surface details. This optimization process can be performed jointly for all teeth or for each tooth sequentially. In some cases, a joint update may be performed for all teeth using a surface deformation algorithm such as an As-Rigid-As-Possible (ARAP) algorithm. Applying the ARAP algorithm may permit shape to be smoothly deformed (e.g., stretched, bent, or sheared) to satisfy the modeling constraints (e.g., fix set of surface points) while allowing small parts of the shape to change as rigidly as possible). Thus, it would have been obvious, in view of Song, Schnabel and Lipniz, to configure Wang’s method as claimed by building the dental crown model using rotation parameter, stretch parameter, and translation parameter. The motivation is to enhance the dental crown model through its parameters.
Claim 26 claims a dental crown model generation system based on the method of claim 10; therefore, it is rejected under a similar rationale.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHU K NGUYEN whose telephone number is (571)272-7645. The examiner can normally be reached M-F 8-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel F. Hajnik can be reached at (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHU K NGUYEN/Primary Examiner, Art Unit 2616
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