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 .
The IDS filed 04/30/2024 has been received and considered.
Claims 1 – 20, all of the claims pending in this application, have been rejected.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the following claim limitation “depending on an output of the first level binary classifier”. It is unclear as to what the output of the first level binary classifier is supposed to be or what it is supposed to represent, or what active step is being required by this language. Therefore, without knowing all of the potential output(s) of the first level binary classifier and then knowing exactly which from that list was actually output by the classifier, it is unclear as to which respective output “outputting the eruption status and primary or permanent tooth type of the target tooth, or applying a second level binary classifier to the normalized tooth shape features and then outputting the eruption status and primary or permanent tooth type of the target tooth” should be output next. Therefore claim 1 is rejected.
In Claim 3 the instructions lack antecedent basis and are unclear for that reason.
Claims 2 and 3 are also rejected by virtue of their dependency on claim 1.
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 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2017/0169562 A1 to Somasundaram et al. (hereinafter Somasundaram) in view of US Publication No. 2016/0135924 A1 to Choi et al. (hereinafter Choi).
Claim 1
Regarding Claim 1, an independent method claim, Somasundaram teaches a method of automatically determining an eruption status and primary or permanent tooth type of a target tooth, the method comprising: receiving, in a computing device, a three-dimensional (3D) model of the patient’s teeth including the target tooth (Figure 4; "Method 22 includes receiving a segmented digital 3D model of a patient's teeth (step 24) and identifying the tooth type by classification of the aggregated features of the tooth (step 26), which can also include receiving contextual information (step 27) for use in identifying the type of tooth.", Paragraph [0028]);
determining, in the computing device, tooth shape features of the target tooth from the 3D model of the patient’s teeth (Figure 4; "Method 22 includes receiving a segmented digital 3D model of a patient's teeth (step 24) and identifying the tooth type by classification of the aggregated features of the tooth (step 26), which can also include receiving contextual information (step 27) for use in identifying the type of tooth.", Paragraph [0028]);
determining, in the computing device, tooth shape features of one or more reference teeth from the 3D model of the patient’s teeth (Rejected as applied directly above), wherein the contextual information would be reference teeth information;
normalizing, in the computing device, at least some of the tooth shape features of the target tooth using the tooth shape features of the one or more reference teeth (Figures 12 and 13; "The tooth widths of the teeth in the sample arch (as given by the dental status) can be concatenated and their sum normalized by the arch form length. FIG. 13 shows an unwrapped arch form 45 a linearized version of the arch curve with the interstices determined by the average tooth widths. The length of this arch line has been normalized to one. FIG. 13 also shows a similar line 47 derived from the interstices of the sample arch, as illustrated in FIG. 10.", Paragraph [0055]);
Somasundaram partially teaches the following limitation: applying the normalized tooth shape features to a classifier of the computing device, wherein applying the normalized tooth shape features to the classifier comprises applying a first level binary classifier and, depending on an output of the first level binary classifier, either outputting the eruption status and primary or permanent tooth type of the target tooth, or applying a second level binary classifier to the normalized tooth shape features and then outputting the eruption status and primary or permanent tooth type of the target tooth.
Somasundaram teaches applying the normalized tooth shape features to a classifier of the computing device, wherein applying the normalized tooth shape features to the classifier comprises applying a first level binary classifier and outputting primary or permanent tooth type of the target tooth ("An aggregation of the plurality of distinct features of the tooth is computed to generate a single feature describing the digital 3D model of the tooth. A type of the tooth is identified based upon the aggregation, which can include comparing the aggregation with features corresponding with known tooth types. The methods also include identifying a type of tooth, without segmenting it from an arch, based upon tooth widths and a location of the tooth within the arch.", Abstract; "There are two possible ways to perform tooth classification, with each tooth sample being represented by the 29,646-dimensional feature vector. The method can learn an N-class, such as a 32-class, discriminative (or generative) classifier such as linear or kernel SVMs directly in the ambient high-dimensional, space. The method can also, or alternatively, project these high-dimensional feature vectors corresponding to each tooth class to a lower-dimensional subspace, and learn a multi-class classifier in this subspace. This projection can be performed using Fisher Linear Discriminant Analysis, Principal Component Analysis, and other supervised or unsupervised dimensionality reduction techniques. Once the tooth type is identified or predicted, it can be stored in an electronic library of tooth shapes corresponding with the tooth type, as recited in step 28.", Paragraph [0038]).
Somasundaram does not teach outputting the eruption status.
However, Choi teaches determining the status of eruption ("In some embodiments, the executable instructions can, for example, be executed to perform a method including to: receive, via a computing device, data representing a plurality of teeth, identify data indicating which of the plurality of teeth are unerupted or erupting, evaluate the data for tooth size information, predict size and orientation of the unerupted or erupting teeth after they have fully erupted using the tooth size information, generate new data representing the unerupted or erupting teeth in multiple states of eruption based upon the predicted size and orientation of the fully erupted teeth, and generate a series of incremental tooth arrangements with the new data to define a proposed orthodontic treatment based on the new data representing the unerupted or erupting teeth in multiple states of eruption.", Paragraph [0090]; Paragraphs [0091 - 0095]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Somasundaram to incorporate including an eruption status of the teeth being identified, as disclosed by Choi. The suggestion/motivation for doing so would have been to properly classify each tooth in a patients mouth and identifying the eruption status of each tooth so as to propose the best orthodontic treatment plan for correction, as disclosed by Choi in Paragraph [0090].
Claim 2
Regarding Claim 2, dependent on claim 1, Somasundaram, in view of Choi, teaches the invention as claimed in claim 1.
Somasundaram does not teach wherein the eruption status comprises whether the target tooth has fully erupted, partially erupted, or not erupted.
However, Choi further teaches wherein the eruption status comprises whether the target tooth has fully erupted, partially erupted, or not erupted (Rejected as applied to claim; Choi, Paragraph [0090]).
Claim 3
Regarding Claim 3, dependent on claim 1, Somasundaram, in view of Choi, teaches the invention as claimed in claim 1.
Somasundaram further teach wherein the instructions are further configured to determine as part of the tooth shape features one or more of: mesial-distal width, buccal-lingual width, crown height, crown center, and number of cusps ("Any subset of these features, as well as optional additional features can also be used for tooth classification. Additional features can include tooth cross-sectional area, perimeter of a cross-section, tooth length, width, and height, surface area, volume, profiles as viewed along any dental plane (occlusal, facial, etc.), Radon transform, features, bag-of-words descriptors, or other features.", Paragraph [0033]; "The typical values for the individual tooth covariance {C.sub.1, . . . , C.sub.32} can be learned from a training set of covariance features from individual segmented teeth corresponding to each tooth type. These values can be put together to form a dictionary model of teeth covariance features. The dictionary model can have more than one covariance feature for each tooth type to account for large variations—for example, lower 1.sup.st molars can be either 4-cusp or 5-cusp, which might yield different covariances—resulting in a dictionary of length much larger than the number of tooth types present.", Paragraph [0048]).
Examiner notes that Choi also teaches this limitation ("As discussed above, in some embodiments, the expected dimensions of a partially-erupted or unerupted tooth can be extrapolated from the known characteristics of one or more neighboring teeth (adjacent, opposing, and/or counterpart tooth). For instance, one or more of the tooth eruption prediction factors, such as, Buccolingual (BL) widths and/or Mesiodistal (MD) widths of partially erupted or fully erupted neighboring teeth can be used as regressors in a multivariate regression model, as discussed in more detail below. Such an analysis can, for example be used in a tooth size prediction.", Paragraph [0047]).
Claim 8
Regarding Claim 8, dependent on claim 6, Somasundaram, in view of Choi, teaches the claim as claimed in claim 6.
Somasundaram further teaches wherein the instructions are further configured to receive the 3D model from a three-dimensional scanner (" FIG. 1 is a diagram of a system 10 for predicting or identifying tooth types using a digital 3D model based upon intra-oral 3D scans. System 10 includes a processor 20 receiving digital 3D models of teeth (12) from intra-oral 3D scans or scans of impressions of teeth.", Paragraph [0026]).
Claim 9
Regarding Claim 9, dependent on claim 6, Somasundaram, in view of Choi, teaches the claim as claimed in claim 6.
Somasundaram further teaches wherein the instructions are configured to include the patient information with the normalized tooth shape features applied to the classifier ("Given an input 3D scan of a patient's dental arch, the point classification of step 26 as described above rises 3D mesh features along with learned models of 3D tooth shapes to predict the tooth types of the individual teeth. In particular, each segmented tooth is passed to a tooth type classifier, which computes the covariance descriptor of 3D mesh features over the entire tooth shape, and classifies this feature to one of thirty-two tooth types based on the learned classification model. In the aforementioned approach, the individual teeth are being classified independently of each other. There is not necessarily any influence on a tooth's structure, location, and predicted tooth type on the predicted tooth types for the neighboring teeth, or any other teeth in that particular patient's mouth.", Paragraph [0042]).
Although Somasundaram teaches the inclusion of patient information, Somasundaram does not explicitly teach wherein the patient information includes one or more of: patient age, eruption sequence, measured space available for eruption, and patient gender.
However, Choi further teaches wherein the patient information includes one or more of: patient age, eruption sequence, measured space available for eruption, and patient gender ("In such situations, various embodiments of the present disclosure can be utilized to use available space size in the arch and the predicted dimension of erupting tooth in a single- or multivariate regression model (e.g., a buccal displacement canine (BDC) prediction model) to determine a potential displacement magnitude of the erupting tooth for buccally displaced canines in upper and/or lower jaws. The potential displacement magnitude can be determined, for example, by determining the difference in available space size and predicted tooth Mesioddistal (MD) width as a first regressor in the BDC prediction model. The larger the Buccolingual (BL) width of erupting tooth is, the more pronounced the buccal-displacement may be. Therefore, the predicted BL width of the erupting tooth can be used as an additional regressor in the BDC prediction model.", Paragraph [0070]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Somasundaram, in view of Choi, to incorporate including specific patient details, as disclosed by Choi. The suggestion/motivation for doing so would have been to evaluate the present dental issues, allowing for a specific treatment tailored on a criterion based on a patient-by-patient case.
Claim 12
Regarding Claim 12, dependent on claim 6, Somasundaram, in view of Choi, teaches the claim as claimed in claim 6.
Somasundaram further teaches wherein the instructions are configured to determine the tooth shape features of the one or more reference teeth from one reference tooth (Paragraph [0042]).
Examiner notes that Choi goes into further details pertaining to actual measurements used to determine neighboring tooth features ("As discussed above, in some embodiments, the expected dimensions of a partially-erupted or unerupted tooth can be extrapolated from the known characteristics of one or more neighboring teeth (adjacent, opposing, and/or counterpart tooth). For instance, one or more of the tooth eruption prediction factors, such as, Buccolingual (BL) widths and/or Mesiodistal (MD) widths of partially erupted or fully erupted neighboring teeth can be used as regressors in a multivariate regression model, as discussed in more detail below. Such an analysis can, for example be used in a tooth size prediction.", [0047]).
Claim 13
Regarding Claim 13, dependent on claim 12, Somasundaram, in view of Choi, teaches the claim as claimed in claim 12.
Somasundaram further teaches wherein the one reference tooth comprises a molar ("The thirty-two different tooth types comprise the following for each of the four quadrants upper left, upper right, lower left, and lower right: central incisor; lateral incisor; canine; first premolar; second premolar; first molar: second molar; and third molar. The present method can also be used for the twenty primary teeth. The tooth recognition and identification can involve predicting the type of tooth or identifying the type of tooth with a particular degree of accuracy of the actual type for the tooth where the degree of accuracy is high enough for the identification of tooth type to be useful. For example, the identifying can include identifying a type of tooth with 90%, or 95%, or 99% accuracy.", Paragraph [0025]).
Claim 14
Regarding Claim 14, dependent on claim 6, Somasundaram, in view of Choi, teaches the claim as claimed in claim 6.
Somasundaram further teaches wherein the instructions are configured to determine the tooth shape features of the one or more reference teeth from two reference teeth (Rejected as applied to claim 12), where the characteristics of neighboring teeth can be used to determine the respective information of other teeth.
Claim 16
Regarding Claim 16, dependent on claim 6, Somasundaram, in view of Choi, teaches the claim as claimed in claim 6.
Somasundaram further teaches wherein the instructions are further configured to normalize the at least some of the tooth shape features of the target tooth using the tooth shape features of the one or more reference teeth by normalizing one or more of a mesial-distal width, a buccal-lingual width, a crown height, and a crown center to the one or more reference teeth ("These features are consolidated into a 243-dimensional feature descriptor per vertex, including but not limited to these features. Any subset of these features, as well as optional additional features can also be used for tooth classification. Additional features can include tooth cross-sectional area, perimeter of a cross-section, tooth length, width, and height, surface area, volume, profiles as viewed along any dental plane (occlusal, facial, etc.), Radon transform, features, bag-of-words descriptors, or other features.", Paragraph [0033]; "(Figures 12 and 13; "The tooth widths of the teeth in the sample arch (as given by the dental status) can be concatenated and their sum normalized by the arch form length. FIG. 13 shows an unwrapped arch form 45 a linearized version of the arch curve with the interstices determined by the average tooth widths. The length of this arch line has been normalized to one. FIG. 13 also shows a similar line 47 derived from the interstices of the sample arch, as illustrated in FIG. 10.", Paragraph [0055]), therefore given the various features extracted for tooth identification, they can then be normalized.
Claim 4, an independent system claim, is rejected for the same reasons as applied to claim 1.
Claim 6, an independent non-transitory computing device readable medium claim, is rejected for the same reasons as applied to claim 1.
Claims 5, 7, 10 – 11, 15, and 17 – 19 are rejected for the same reasons as applied to the above rejected claims.
Claims 20 is rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2017/0169562 A1 to Somasundaram et al. (hereinafter Somasundaram) in view of US Publication No. 2016/0135924 A1 to Choi et al. (hereinafter Choi) in further view of Non-Patent Literature "Tooth eruption sequence and dental crowding: a case-control study" to Moshkelgosha et al. (hereinafter Moshkelgosha).
Claim 20
Regarding Claim 20, dependent on claim 6, Somasundaram, in view of Choi, teaches the claim as claimed in claim 6.
Neither Somasundaram, or Choi, or the combination teach wherein the instructions are further configured to output an indication of a percentage of eruption.
However, Moshkelgosha teaches wherein the instructions are further configured to output an indication of a percentage of eruption ("The eruption percent of canines was then calculated by dividing the first measurement by sum of both (a/a+b). A similar procedure was done to calculate the eruption percentage of permanent first premolar.", Materials and Methods).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Somasundaram, in view of Choi, to incorporate an indication of the percent of eruption, as disclosed by Moshkelgosha. The suggestion/motivation for doing so would have been to get a more precise tooth analysis/evaluation so as to suggest the best orthodontic treatment plan to the respective patient being examined.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ronde Miller whose telephone number is (703) 756-5686 The examiner can normally be reached Monday-Friday 8:00-4:00.
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/RONDE LEE MILLER/Examiner, Art Unit 2663
/GREGORY A MORSE/ Supervisory Patent Examiner, Art Unit 2698
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