DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in PEOPLE'S REPUBLIC OF CHINA on 11/08/2023. It is noted, however, that applicant has not filed a certified copy of the CN202311486295.3 application as required by 37 CFR 1.55.
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.
Claim 1 recites the limitation " the characteristics to be restored". There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation " the disconnected neural conduit" in claim 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the instance neural network" in claim 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the 3D oral image of a target object" in claim 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the interest region" in claim 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the 3D oral model" in claim 2. There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1-2 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ezhov et al (US 20220361992) in view of Scharlack et al (US 20070154868).
Regarding claim 1, Ezhov discloses a method for generating a restoration dental model (¶1 automated system for the planning of crown and implant selection and placement to improve dental treatment outcomes), comprising:
obtaining a restoration type of a tooth to be restored (Fig. 21-22 & ¶180 method flow diagrams of the automated crown and implant feature prediction pipeline), wherein the restoration type comprises at least one of the following: a dental implant type (¶169 the automated and AI-aided prediction of crown and implant features for improved crown and implant planning; ¶180 predicting at least one of an implant feature based on a predicted phantom/missing tooth in place of the segmented missing tooth, wherein the predicted implant feature is based on the position and angulation of roots of the segmented missing tooth, wherein the implant is selected from a library of polygonal prototypes by finding a prototype with the closest position and geometry by selecting a set of points on the surface of the segmented image and prototype, and running a pointset-matching algorithm for selecting the closest matched prototype 2106), a crown restoration type, and an inlay restoration type;
determining the restoration dental model based on the restoration type and the key points of the tooth to be restored (¶169 predict at least one of a tooth crown or implant feature based on a predicted “phantom crown” in place of the segmented missing tooth by the prediction module 1805, wherein the predicted crown and/or implant feature is selected from a library of polygonal prototypes by finding a prototype with the closest position and geometry by selecting a set of points on the surface of either one of the predicted “phantom crown” and prototype, and running a pointset-matching algorithm for selecting the closest matched prototype by either the prediction module 1805 itself, or optionally, by a dedicated matching module 1806).
Ezhov fails to specifically teach where Scharlack teaches determining key points of the tooth to be restored based on the restoration type (¶75 in act 130 (sometimes referred to as Dentition Feature Detection (DFD), dental features are measured, such as the locations of contact points of adjacent teeth, the location of the surface of the opposing tooth (i.e., in the opposite jaw bone), or the locations of opposing tooth cusps and fossa; the locations of the facial and lingual dimensions of adjacent teeth and/or contra-lateral tooth; and the shape of the dental arch near the missing tooth or teeth. These features are used in act 140 to constrain the crown design, taking into account the abutment design), wherein the key points of the tooth to be restored are configured to represent the characteristics of the tooth to be restored (¶75 These features are used in act 140 to constrain the crown design, taking into account the abutment design. Based on those constraints and the measured positions and angles of the implants and soft tissue measurements, in act 150, the abutment design is constrained. Thus, a compatible crown and abutment design result, satisfying all constraints. Consequently, using the constraints, the range limits for acceptable crown size, location and orientation are first determined and it is then assured that the abutment design will be compatible with the crown design and that both will be within the limits of the design constraints.).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of determining key points of the tooth to be restored based on the restoration type, wherein the key points of the tooth to be restored are configured to represent the characteristics of the tooth to be restored from Scharlack into the method as disclosed by Ezhov. The motivation for doing this is to improve methods of designing a dental restoration component.
Regarding claim 2, the combination of Ezhov and Scharlack teach the method of claim 1, wherein determining the restoration dental model based on the restoration type and the key points, comprises:
obtaining a 3D oral image in response to the restoration type being the dental implant type (Ezhov ¶180 a method for implant selection may comprise the steps of: receiving at least one of a volumetric or surface scan image, wherein the volumetric image is a three-dimensional voxel array of a maxillofacial anatomy of a patient and the surface scan image is a polygonal mesh of a maxillofacial anatomy of a patient 2102);
performing a tooth instance segmentation on the 3D oral image to obtain a tooth position region of a missing tooth (Ezhov ¶180 segmenting at least one of the volumetric image or surface scan image into a set of distinct anatomical structures, wherein the distinct anatomical structures include at least one of-a tooth, jaw, mandibular canal, maxillary sinus, fossae, and a missing tooth 2104);
determining the key points of the dental implant based on the key points of the tooth to be restored (¶180 predicting at least one of an implant feature based on a predicted phantom/missing tooth in place of the segmented missing tooth); and
predicting the key points of the dental implant based on the tooth position region to obtain the restoration dental model, wherein the key points of the dental implant are configured to determine the implantation posture and size information of the dental implant (Ezhov ¶180 wherein the predicted implant feature is based on the position and angulation of roots of the segmented missing tooth, wherein the implant is selected from a library of polygonal prototypes by finding a prototype with the closest position and geometry by selecting a set of points on the surface of the segmented image and prototype, and running a pointset-matching algorithm for selecting the closest matched prototype 2106).
Regarding claim 14, the combination of Ezhov and Scharlack teach the method of claim 2, wherein predicting the key points of the dental implant based on the tooth position region to obtain the restoration dental model, comprises: predicting the key points of the dental implant based on the tooth position region to obtain a prediction result (Ezhov ¶180 predicting at least one of an implant feature based on a predicted phantom/missing tooth in place of the segmented missing tooth, wherein the predicted implant feature is based on the position and angulation of roots of the segmented missing tooth); and generating the restoration dental model in the 3D oral model based on the prediction result (Ezhov ¶180 wherein the implant is selected from a library of polygonal prototypes by finding a prototype with the closest position and geometry by selecting a set of points on the surface of the segmented image and prototype, and running a pointset-matching algorithm for selecting the closest matched prototype 2106).
Claim(s) 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Ezhov and Scharlack as applied to claim 2 above, and further in view of Shen et al (US 20230368438).
Regarding claim 11, the combination of Ezhov and Scharlack teach the method of claim 2, wherein obtaining the 3D oral image of a target object, comprises: obtaining multiple 2D oral images of the target object (Ezhov ¶53 the input data is 2D image data; ¶184 the performance of the localization model allows to build a high-quality 2D panoramic reconstruction), but fails to teach where Shen teaches and performing 3D transformation on the multiple 2D oral images to obtain the 3D oral image (¶47 Using geometry priors 206, a geometric back-projection operator 202, 203 (see also FIG. 4) transforms the 2D projections 200 and 210 to 3D images 204 and 212, respectively, referred as geometric preserving images (GPIs)).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of performing 3D transformation on the multiple 2D oral images to obtain the 3D oral image from Shen into the method as disclosed by the combination of Ezhov and Scharlack. The motivation for doing this is to improve imaging tools for various clinical imaging and image-guided interventions such as image reconstruction.
Regarding claim 12, the combination of Ezhov, Scharlack, and Shen teach the method of claim 11, wherein performing 3D transformation on the multiple 2D oral images to obtain the 3D oral image, comprises: performing 3D transformation on the multiple 2D oral images to obtain an initial 3D oral image (Shen ¶47 Using geometry priors 206, a geometric back-projection operator 202, 203 (see also FIG. 4) transforms the 2D projections 200 and 210 to 3D images 204 and 212, respectively, referred as geometric preserving images (GPIs); ¶104 utilizes both known k-space sampling patterns and fixed transformations between k-space and image space to unfold 2D representations to 3D volumetric space); and resampling multiple voxels in the initial 3D oral image to the same voxel spacing to obtain the 3D oral image (Shen ¶74 The dataset contains 1018 patient cases, each including a volumetric image from a clinical thoracic CT scan; ¶75 we resample all the CT images to the same 1 mm resolution in z-axis direction and resize cross-sectional images on the xy-plane to the size of 128×128). The motivation to combine the references is discussed above in the rejection for claim 11.
Regarding claim 13, the combination of Ezhov, Scharlack, and Shen teach the method of claim 12, wherein resampling the multiple voxels in the initial 3D oral image to the same voxel spacing to obtain the 3D oral image, comprises: resampling the multiple voxels in the initial 3D oral image to the same voxel spacing to obtain a target 3D oral image (Shen ¶74 The dataset contains 1018 patient cases, each including a volumetric image from a clinical thoracic CT scan; ¶75 we resample all the CT images to the same 1 mm resolution in z-axis direction and resize cross-sectional images on the xy-plane to the size of 128×128); and cropping the interest region of the target 3D oral image to obtain a 3D oral image, wherein the interest region is configured to represent the area where the tooth of the target object are located (Ezhov ¶56-57 The processor 103 is further configured to select all voxels belonging to the localized anatomical structure by finding a minimal bounding rectangle around the voxels and the surrounding region for cropping as a defined anatomical structure by the localization layer; the processor 103 is configured to detect or classify the conditions for each defined anatomical structure within the cropped image by a detection module or classification layer 106). The motivation to combine the references is discussed above in the rejection for claim 11.
Allowable Subject Matter
Claims 3-5, 7, and 9-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, the prior art of record, alone or in combination, fails to teach at least “wherein performing the tooth instance segmentation on the 3D oral image to obtain the tooth position region of the missing tooth in an oral cavity, comprises: constructing a 3D oral model based on the 3D oral image by using a model construction neural network model; and performing the dental instance segmentation on the 3D oral model by using an instance segmentation neural network model to obtain the tooth position region of the missing tooth in the oral cavity.”. Claims 4-5, 7 and 9 depend off of claims 3 and would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 10, the prior art of record, alone or in combination, fails to teach at least wherein predicting the key points of the dental implant based on the tooth position region to obtain a prediction result, comprises: predicting the key points of the dental implant based on the tooth position region by using a key points prediction neural network model to obtain a key points heatmap, wherein the key points heatmap is configured to represent the key points of the dental implant by Gaussian distribution, and wherein the key points of the dental implant at least comprises the implant insertion point and the implant root tip point; determining the implantation posture and the size information of the dental implant based on the key points heatmap; and generating the restoration dental model based on the implantation posture and the size information.
At best, Ezhov teaches in ¶174 The prediction module 1805 (neural network) is trained to predict the phantom crown by: inputting one of a manually or a machine-produced segmentation of a radiological image, wherein the segmentation comprises of tooth segmentation, or tooth and anatomy segmentation; removing a random subset of segmented teeth from the input segmentation and replacing the teeth with background; and instructing the prediction module/neural network to predict one or more sites of missing teeth, and for the missing tooth sites, predict a tooth segmentation, wherein the training target is the removed segmented tooth.
Claim 6 and 8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
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/KEVIN KY/ Primary Examiner, Art Unit 2671
