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 12 is objected to because of the following informalities: “wherein the step of predicting a size of the mossing tooth is a step of predicting a size of the missing tooth …” should read “wherein the step of predicting a size of the missing tooth is a step of predicting a size of the missing tooth …”. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 and 11-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Azernikov et al. [US 2018/0028294 A1].
Regarding claim 1, Azernikov teaches “A method for predicting a missing tooth in an oral image, comprising the steps of:” as “Training module 123 can also train one or more deep neural networks to predict the shape and size of a missing tooth” [¶0128]
“generating a standard tooth model by statistically learning a plurality of training images;” as “based on the unsupervised learning of hundreds or thousands of sample dentition data sets.” [¶0128]
“whole-area matching an oral image to the standard tooth model; and” as “a plurality of scans can be conducted to obtain a suitable image of the patient's oral or dental anatomy and the plurality of scans can be assembled into a 3D digital image” [¶0085]
“predicting a missing tooth by comparing first and second neighboring teeth located on both sides of the missing tooth, respectively, with first and second standard teeth of the standard tooth model corresponding to the first and second neighboring teeth, respectively.” as “By learning the attributes of various dental features in thousands of training data sets, the neural network can predict the shape and size of various dental restorations such as crowns or dental implants. ” [¶0128]
Regarding claim 2, Azernikov teaches “locally matching the oral image and the standard tooth model based on the first and second standard teeth.” as “training module 123 may train one or more deep neural networks to perform a qualitative evaluation of one or more aspects (e.g., margin line fit, contact surfaces with adjacent teeth” [¶0140]
Regarding claim 3, Azernikov teaches “wherein the step of generating a standard tooth model is a step of generating the standard tooth model by learning a matching image obtained by matching an individual tooth model to an oral scan image.” as “In one embodiment, to train a deep neural network to recognize and learn various attributes (e.g., shape, size) of a dental feature such as tooth #3, the training data set may include thousands of real images” [¶0153]
Regarding claim 11, Azernikov teaches “wherein the step of predicting a missing tooth comprises the step of: predicting a size of the missing tooth based on a first size deviation that is a difference in size between the first neighboring tooth and the first standard tooth and a second size deviation that is a difference in size between the second neighboring tooth and the second standard tooth.” as “to train the deep neural network to recognize and learn various attributes (e.g., shape, size) of a crown, many training data sets from real dental patients with one or more crowns are selected to form a group of training data sets specifically for a crown. ” [¶0066]
Regarding claim 12, Azernikov teaches “wherein the step of predicting a size of the mossing tooth is a step of predicting a size of the missing tooth by applying the first and second size deviations to the size of a third standard tooth of the standard tooth model corresponding to the missing tooth and located between the first and second standard teeth.” as “In one embodiment, training module 123 can train deep neural networks to learn various dental features' attributes such as: the gap defined by a missing tooth; shape and size of the gap; the shape, size, and location of a prepared tooth; the shape, size, and location of a bone graft area; the shape, size, and location of one or more neighboring teeth, the margin line, the spatial relationship of cusps on each tooth and of cusps between neighboring teeth on the same jaw and the opposing jaw; and other surface tooth anatomy, etc.” [¶0128]
Regarding claim 13, Azernikov teaches “wherein the step of predicting a size of the missing tooth is a step of predicting a size of the missing tooth by applying an intermediate value of the first and second size deviations to the size of the third standard tooth.” as “At 1070, training module 123 can train the deep neural network to identify and characterize various dental features present and/or missing from the training data sets by mapping each of those features to a highest probability value of a probability vector. ” [¶0128]
Claim 14 is anticipated by Azernikov under the same rationale of anticipation of claim 1.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. [US 2018/0028294 A1] in view of Ezhov et al. [US 2020/0305808 A1].
Claim 4 is rejected by Azernikov and Ezhov.
Azernikov does not explicitly teach wherein the step of whole-area matching is a step of matching all individual teeth included in the standard tooth model as a group.
However, Ezhov teaches “wherein the step of whole-area matching is a step of matching all individual teeth included in the standard tooth model as a group.” as “The bounding rectangle extends equally in all directions to capture the tooth and surrounding context. In one embodiment, the bounding rectangle may extend 8-15 mm in all directions to capture the tooth and surrounding context.” [¶0047]
Azernikov and Ezhov are analogous arts because they teach tooth implant via machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Azernikov and Ezhov before him/her, to modify the teachings of Azernikov to include the teachings of Ezhov with the motivation of to accumulate positive cases faster, a weak model could be trained and ran for all of the unlabeled data. [Ezhov, ¶0054]
Claim 5 is rejected by Azernikov and Ezhov.
Azernikov does not explicitly teach wherein the step of predicting a missing tooth comprises the step of: predicting a central position of the missing tooth based on a first position deviation that is a central position difference between the first neighboring tooth and the first standard tooth and a second position difference that is a central position difference between the second neighboring tooth and the second standard tooth.
However, Ezhov teaches “wherein the step of predicting a missing tooth comprises the step of: predicting a central position of the missing tooth based on a first position deviation that is a central position difference between the first neighboring tooth and the first standard tooth and a second position difference that is a central position difference between the second neighboring tooth and the second standard tooth.” as “Then individual instances of every class (teeth) could be split, e.g. by separately predicting a boundary between them. In some embodiments, the anatomical structure being localized, includes, but not limited to, teeth, upper and lower jaw bone, sinuses, lower jaw canal and joint.” [¶0049]
Azernikov and Ezhov are analogous arts because they teach tooth implant via machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Azernikov and Ezhov before him/her, to modify the teachings of Azernikov to include the teachings of Ezhov with the motivation of to accumulate positive cases faster, a weak model could be trained and ran for all of the unlabeled data. [Ezhov, ¶0054]
Claim 6 is rejected by Azernikov and Ezhov.
Azernikov does not explicitly teach wherein the step of predicting a central position of the missing tooth is a step of predicting a central position of the missing tooth by applying the first and second position deviations to the central position of a third standard tooth of the standard tooth model corresponding to the missing tooth and located between the first and second standard teeth.
However, Ezhov teaches “wherein the step of predicting a central position of the missing tooth is a step of predicting a central position of the missing tooth by applying the first and second position deviations to the central position of a third standard tooth of the standard tooth model corresponding to the missing tooth and located between the first and second standard teeth.” as “The localization intersection over union (IoU) between the tooth's ground truth volumetric bounding box and the model-predicted bounding box is also defined. In the case where a tooth is missing from ground truth and the model predicted any positive p/v (i.e. the ground truth bounding box is not defined), localization IoU is set to 0. In the case where a tooth is missing from ground truth and the model did not predict any positive p/v for it, localization IoU is set to 1. ” [¶0069]
Claim 7 is rejected by Azernikov and Ezhov.
Azernikov does not explicitly teach wherein the step of predicting a central position of the missing tooth is a step of predicting a central position of the missing tooth by applying an intermediate value of the first and second positional deviations to the central position of the third standard tooth.
However, Ezhov teaches “wherein the step of predicting a central position of the missing tooth is a step of predicting a central position of the missing tooth by applying an intermediate value of the first and second positional deviations to the central position of the third standard tooth.” as “For a human-interpretable metric, tooth localization accuracy which is a percent of teeth is used that have a localization IoU greater than 0:3 by definition. The relatively low threshold value of 0:3 was decided from the manual observation that even low localization IoU values are enough to approximately localize teeth for the downstream processing. ” [¶0069]
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. [US 2018/0028294 A1] in view of Sogo et al. [US 2006/0127848 A1].
Claim 8 is rejected by Azernikov and Sogo.
Azernikov does not explicitly teach wherein the step of predicting a missing tooth comprises the step of: predicting a central axis of the missing tooth based on a first angular deviation that is an angular difference between the central axes between the first neighboring tooth and the first standard tooth and a second angular deviation that is an angular difference between the central axes between the second neighboring tooth and the second standard tooth.
However, Sogo teaches “wherein the step of predicting a missing tooth comprises the step of: predicting a central axis of the missing tooth based on a first angular deviation that is an angular difference between the central axes between the first neighboring tooth and the first standard tooth and a second angular deviation that is an angular difference between the central axes between the second neighboring tooth and the second standard tooth.” as “The guide holes 32 in the guide member 3 are bored so as to communicate with the artificial tooth root cavity that is to be bored, and it is necessary to engage the drill part 52 so that the central axis of the drill part 52 coincides with the central axes of the guide holes 32.” [¶0194]
Azernikov and Sogo are analogous arts because they teach tooth implant via machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Azernikov and Sogo before him/her, to modify the teachings of Azernikov to include the teachings of Sogo with the motivation of by manufacturing a dental crown that repairs the lost 1o portion of the dentition using dental crown data and occlusion data calculated with good precision by the computer 1 as described above, it is possible to achieve effective utilization of the various types of data obtained in the calculation of an appropriate artificial tooth root implantation position [Sogo, ¶0218]
Claim 9 is rejected by Azernikov and Sogo.
Azernikov does not explicitly teach wherein the step of predicting a central axis of the missing tooth is a step of predicting a central axis of the missing tooth by applying the first and second angular deviations to the central axis of a third standard tooth of the standard tooth model corresponding to the missing tooth and located between the first and second standard teeth.
However, Sogo teaches “wherein the step of predicting a central axis of the missing tooth is a step of predicting a central axis of the missing tooth by applying the first and second angular deviations to the central axis of a third standard tooth of the standard tooth model corresponding to the missing tooth and located between the first and second standard teeth.” as “the implantation angle and implantation distance (depth) of the artificial tooth root cavity used to implant the artificial tooth root that supports the dental crown indicated by the dental crown data are acquired.” [¶0149]
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. [US 2018/0028294 A1] in view of Sogo et al. [US 2006/0127848 A1] and in further view of Ezhov et al. [US 2020/0305808 A1].
Claim 10 is rejected by Azernikov, Sogo and Ezhov
The combination of Azernikov and Sogo does not explicitly teach wherein the step of predicting a central axis of the missing tooth is a step of predicting a central axis of the missing tooth by applying an intermediate value of the first and second angular deviations to the central axis of the third standard tooth.
However, Ezhov teaches “wherein the step of predicting a central axis of the missing tooth is a step of predicting a central axis of the missing tooth by applying an intermediate value of the first and second angular deviations to the central axis of the third standard tooth.” as “The relatively low threshold value of 0:3 was decided from the manual observation that even low localization IoU values are enough to approximately localize teeth for the downstream processing.” [¶0069]
Azernikov, Sogo and Ezhov are analogous arts because they teach tooth implant via machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Azernikov, Sogo and Ezhov before him/her, to modify the teachings of combination of Azernikov and Sogo to include the teachings of Ezhov with the motivation of to accumulate positive cases faster, a weak model could be trained and ran for all of the unlabeled data. [Ezhov, ¶0054]
Conclusion
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/MASUD K KHAN/ Primary Examiner, Art Unit 2132