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 .
Response to Arguments
Applicant’s response to the last Office Action, filed 11/19/2025, has been entered and made of record.
Applicant has amended claims 15-07. Claims 1-20 are currently pending.
Applicants arguments filed 2/19/2026 have been fully considered but they are not persuasive.
Applicant argues that Xue does not describe determining values of a plurality of parameters of the statistical
tooth model. In response, Examiner refer to Xue et al that teaches “The tooth shape features may comprise, for example, length and/or width of the target tooth, such as mesial-distal width, buccal-lingual width, and crown height, and may additionally include the crown center of the target tooth or the number of cusps of the target tooth. The tooth comparison engine 178 may implement one or more automated agents configured to compare the tooth shape features to one or more reference teeth, paragraph [0098-0099]). Therefore, the values are the “The tooth shape features may comprise, for example, length and/or width of the target tooth, such as mesial-distal width, buccal-lingual width, and crown height, and may additionally include the crown center of the target tooth or the number of cusps of the target tooth”. Additionally, Applicant argues that Xue does not describe determining positions of a plurality of parameters of a statistical tooth model .In response, Cu teaches the eruption status in paragraph [0035-0038]) where is the Tooth eruption is the process by which a developing tooth moves from its initial nonfunctional position within the alveolar bone to its final functional location(paragraph[0125-0128]).
Applicant argues that Van Lierde does not describe associating the plurality of feature points for the patient's
tooth with the 3D model of the patient's tooth. In response, Van Lierde et al. teaches the digital crown information is combined with the 2D radiographs by means of an expert system in order to construct a 3D model of the tooth including the pulp chamber and the root canals. The expert system preferably includes a statistical shape model in order to calculate the 3D model of the tooth as accurately as reasonably possible based on the 2D radiograph data combined with the 3D crown data ( paragraph [0022]).Additionally, Van Lierde clearly teaches The combined 2d and 3D data of each tooth within the set can be analyzed statistically in order to obtain a parameterized shape model including a 3D shape model and an associated parameterized 2D grey value image(s), in which certain components/parameters of the shape model are directly linked to parameters of the 2D grey value image(s). As such the covariances between the statistical shape model and its corresponding parameterized 2D grey value image(s) are known. Fitting this parameterized statistical shape model onto the 2D grey value images of the patient's tooth can be done by iteratively modifying parameters of the parameterized 2D grey value image(s), which will result in a corresponding modification of the 3D shape model, until a good match is obtained. This can further be combined with matching the parameterized statistical shape model onto the 3D crown information of the patient's tooth. As such a 3D model of the patient's tooth is obtained that includes external geometry, shape and location of the pulp chamber and root canals ( paragraph [0053]).. Additionally the applicant’s argument that the combination of all the features recited in claims 1-14 makes the applicant’s invention patentable different is not found persuasive and thus Kondo still reads on the applicant’s claimed invention. All remaining arguments are reliant on the aforementioned and addressed arguments and thus are considered to be wholly addressed herein.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et al (US 2018/0360567) in view of Van Lierde et al (US 2014/0229145)
As to claim 1, Xue et al teaches the computer-implemented method of determining positions of a plurality of feature points of a patient's tooth based on a statistical tooth model, the method comprising: using at least one processor: determining values of a plurality of parameters of the statistical tooth model by comparing a three-dimensional (3D) model of a reference tooth, which has a shape parametrized by the plurality of parameters, to a 3D model of a patient's tooth (paragraph[0098-0099] determining positions of a plurality of feature points for the patient's tooth based at least in part on a plurality of feature points associated with the statistical tooth model(receive a three-dimensional (3D) model of the patient's teeth including the target tooth, determine tooth shape features of the target tooth from the 3D model of the patient's teeth, determine tooth shape features of one or more reference teeth from the 3D model of the patient's teeth, normalize at least some of the tooth shape features of the target tooth using the tooth shape features of the one or more reference teeth, apply the normalized tooth shape features to a classifier of the computing device; paragraph [0035-0038],[0125]);the arch scanning engine 168 may implement one or more automated agents configured to scan a 3D dental mesh model for individual tooth segmentation data. “Individual tooth segmentation data,” as used herein, may include positions, geometrical properties (contours, etc.), and/or other data that can form the basis of segmenting individual teeth from 3D dental mesh models of a patient's dental arch , paragraph [0092]) , and at least in part on the determined values of the plurality of parameters of the statistical tooth model(paragraph[0035-0038][0125-0127],figure 1B).While Xue meets a number of the limitations of the claimed invention, as pointed out more fully above, Xue fails to specifically teach “associating the plurality of feature points for the patient's tooth with the 3D model of the patient's tooth according to the determined positions of the plurality of feature points. “
Specifically, Van Lierde et al. teaches the digital crown information is combined with the 2D radiographs by means of an expert system in order to construct a 3D model of the tooth including the pulp chamber and the root canals. The expert system preferably includes a statistical shape model in order to calculate the 3D model of the tooth as accurately as reasonably possible based on the 2D radiograph data combined with the 3D crown data ( paragraph [0022]).Additionally, Van Lierde clearly teaches The combined 2d and 3D data of each tooth within the set can be analyzed statistically in order to obtain a parameterized shape model including a 3D shape model and an associated parameterized 2D grey value image(s), in which certain components/parameters of the shape model are directly linked to parameters of the 2D grey value image(s). As such the covariances between the statistical shape model and its corresponding parameterized 2D grey value image(s) are known. Fitting this parameterized statistical shape model onto the 2D grey value images of the patient's tooth can be done by iteratively modifying parameters of the parameterized 2D grey value image(s), which will result in a corresponding modification of the 3D shape model, until a good match is obtained. This can further be combined with matching the parameterized statistical shape model onto the 3D crown information of the patient's tooth. As such a 3D model of the patient's tooth is obtained that includes external geometry, shape and location of the pulp chamber and root canals ( paragraph [0053]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to associate the points with the 3D model in order to the use of simulation and provide a qualitative and/or quantitative assessment of treatment related risks. Therefore, the claimed invention would have been obvious to one of ordinary skill in the art at the time of the invention by applicant.
As to claim 2, Van Lierde teaches the method of claim 1, wherein determining the values of the plurality of parameters of the statistical tooth model comprises identifying, for each point of a plurality of points on the 3D model of the reference tooth, a point on the 3D model of the patient's tooth that is closest to the point on the 3D model of the reference tooth (modify the 2D radiographs associated with the statistical shape model by varying the parameter values dictating its variances, to match the 2D radiographs obtained in the mouth of the patient, in order to directly obtain the desired corresponding 3D model of the tooth (i.e. external geometry, shape and location of the pulp chamber and tooth roots) ; the three-dimensional description of the tooth can be a surface model, a set of anatomical landmarks/points characterizing the tooth shape, a volumetric model or yet any other 3D representation detailing the internal and external shape of the tooth, a 2D grey value image is taken of the patient's tooth; 3D surface information of the intra-orally visible part of the patient's tooth is digitized; and a statistical, parameterized shape model of the respective tooth (e.g. incisor, canine, premolar, or molar) is made available, paragraph[0022-0023],[0026] [0047-0053]).
As to claim 3, Van Lierde teaches the method of claim 2, wherein determining the values of the plurality of parameters of the statistical tooth model further comprises measuring a difference between the 3D model of the patient's tooth and the 3D model of the reference tooth by comparing the plurality of points on the 3D model of the reference tooth to the plurality of identified points on the 3D model of the patient's tooth (The 3D statistical shape model is next modified (using previously mentioned parameter values) and repositioned relative to the coordinate system defined by the 2D images, all the while reassessing the outlines, until the outlines of the 3D model match (according to a predefined criterion) the edges calculated on the 2D images. The resulting modified 3D statistical shape model is then used as 3D model for the specific case, paragraph [0022-0023]).
As to claim 4, Van Lierde teaches the method of claim 3, wherein determining the values of the plurality of parameters of the statistical tooth model further comprises optimizing the plurality of parameters of the statistical tooth model based on the measured differences between the 3D model of the patient's tooth and the 3D model of the reference tooth (As such the post selection and positioning, and the core build-up can be optimized prior to the root canal treatment in order to reduce clinical risks or failure within the patient. According to another preferential implementation, the remaining information of the tooth/tooth root is compared against a parameterized 3D statistical model of the relevant tooth type. The statistical model of the tooth is aligned with and modified according to a best fit with said remaining part of the tooth/tooth root. The missing information required to perform the prosthetic restoration (e.g. information of the crown) is given as the difference between the remaining tooth and the modified statistical model., paragraph [0036-0037]).
As to claim 5, Xue et al teaches the method of claim 1, wherein the plurality of parameters of the statistical tooth model include a plurality of principal components of a principle component analysis (PCA) model (a principal component analysis (PCA) of the dental features of the target tooth to obtain PCA features; paragraph [0013]).
As to claim 6, Xue et al teaches the method of claim 1, wherein determining the positions of the plurality of feature points for the patient's tooth comprises transforming the plurality of feature points associated with the statistical tooth model according to the determined values of the plurality of parameters (Determining the tooth shape features of the target tooth may comprise determining one or more of: mesial-distal width, buccal-lingual width, crown height, crown center, and number of cusps. Determining the number of cusps can comprise determining the number of cusps in one or more of the arch direction surfaces including: buccal-mesial, buccal-distal, lingual-mesial, and lingual-distal, paragraph [0031][0033][0041][0042]).
As to claim 7, Xue et al teaches the method of claim 1, wherein determining the positions of the plurality of feature points for the patient's tooth comprises identifying positions of the plurality of feature points associated with the statistical tooth model relative to the 3D model of the reference tooth, whereby the 3D model of the reference tooth has a shape according to the determined values of the plurality of parameters(Determining the tooth shape features of the target tooth may comprise determining one or more of: mesial-distal width, buccal-lingual width, crown height, crown center, and number of cusps. Determining the number of cusps can comprise determining the number of cusps in one or more of the arch direction surfaces including: buccal-mesial, buccal-distal, lingual-mesial, and lingual-distal, paragraph [0031][0033][0041][0042]).
As to claim 8, Xue et al teaches the method of claim 1, wherein the plurality of feature points for the patient's tooth comprise one or more premolar cusps, molar cusps, facial axis points, marginal ridge points, fossa points and/or centroid points ( normalize the at least some of the tooth shape features of the target tooth by determining a total number of cusps in each of the arch direction surfaces including: buccal-mesial, buccal-distal, lingual-mesial, and lingual-distal, paragraph [0047]).
As to claim 9, Xue et al teaches the method of claim 1, further comprising generating the statistical tooth model by: obtaining a plurality of reference 3D tooth models; determining the plurality of parameters based on the plurality of reference 3D tooth models ( The machine learning detector can apply machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, AdaBOOST, K-Nearest Neighbor (KNN), Quadratic Discriminant Analysis, Neural Network, etc., to determine a tooth type (e.g., incisor, canine, pre-molar, molar, etc.), eruption status (permanent, permanent erupting, primary), and/or tooth number of the target tooth by comparing the dental or tooth features and optional patient data to reference dental or tooth features (while accounting for optional patient data), paragraph [00114]).
As to claim 10, Xue et al teaches the method of claim 9, wherein determining the plurality of parameters comprises performing a principal component analysis (PCA) of the plurality of reference 3D tooth models (a principal component analysis (PCA) of the dental features of the target tooth to obtain PCA features; determining an eruption status and primary or permanent tooth type of the target tooth based on the PCA features; and outputting the eruption status and primary or permanent tooth type of the target tooth, paragraph [0013-0014]).
As to claim 11, Xue et al teaches the method of claim 1, wherein the statistical tooth model is defined for, and wherein the reference tooth and patient's tooth are: an incisor, canine, premolar or molar tooth (Examples of machine learning systems implemented by the detector engine(s) 164 may include Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, AdaBOOST, K-Nearest Neighbor (KNN), Quadratic Discriminant Analysis, Neural Network, etc., to determine a tooth type (e.g., incisor, canine, pre-molar, molar, etc.), eruption status (e.g., permanent, permanent erupting, primary), and/or tooth number of the target tooth. The detector engine(s) 164 can incorporate predicted tooth type and/or eruption status into a final segmentation result, paragraph[0089]).
As to claim 12, Van Lierde teaches the method of claim 1, further comprising generating a root model for the 3D model of the patient's tooth based on the statistical tooth model and the determined values of the plurality of parameters of the statistical tooth model (the remaining information of the tooth/tooth root is compared against a parameterized 3D statistical model of the relevant tooth type. The statistical model of the tooth is aligned with and modified according to a best fit with said remaining part of the tooth/tooth root. The missing information required to perform the prosthetic restoration (e.g. information of the crown) is given as the difference between the remaining tooth and the modified statistical model, paragraph [0036-0038][0053]).
As to claim 13, Van Lierde teaches the method of claim 12, wherein generating the root model comprises transforming a 3D model of the reference tooth that includes a root portion according to the plurality of parameters of the statistical tooth model (the remaining information of the tooth/tooth root is compared against a parameterized 3D statistical model of the relevant tooth type. The statistical model of the tooth is aligned with and modified according to a best fit with said remaining part of the tooth/tooth root. The missing information required to perform the prosthetic restoration (e.g. information of the crown) is given as the difference between the remaining tooth and the modified statistical model, paragraph [0036-0038][0053]).
As to claim 14, Xue et al teaches the method of claim 1, further comprising determining one or more orthodontic treatments based on the 3D model of the patient's tooth and associated plurality of feature points (orthodontic treatment plan, see Xue et al paragraph[005]).
The limitation of claims 15-20 has been addressed above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached Mon-Friday from 8:00 am to 5:00 p.m..
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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