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 .
Summary of the Claims
Claim 1 is amended.
Claims 2-8 and 10-16 have been previously presented.
Claim 9 is cancelled.
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 occlusion design…”. There is insufficient antecedent basis for this limitation in the claim. Therefore claims 1-8 and 10-16 are rejected under 35 U.S.C. 112(b).
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.
Claims 1-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al.(hereinafter “Azernikov”, US Patent 11,007,040) in view of Xu et al.(hereinafter “Xu”, “3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks”).
Regarding claim 1, Azernikov teaches a computer-implemented geometric processing method for generating the design of a model of a dental prosthesis (col. 3 lines 32-41), comprising the steps of:
obtaining a blended prosthesis dataset for dental prostheses including natural tooth data (col. 10 lines 2-10), prosthesis tooth data designed by a technician (col. 2 lines 13-21), and prosthesis tooth data designed by a technician with clinical adjustment of the occlusion (col. 5 lines 47-67) so as to integrate natural tooth, technical design tooth and clinically adjusted tooth data (col. 5 lines 47-67);
preprocessing the blended prosthesis dataset by generating a depth map (col. 11 lines 9-21) so that the data is more suitable for deep learning (DL) (col. 10 lines 2-10 and col. 3 lines 36-41);
providing an artificial intelligence neural network generation model (col. 1 lines 57-67, in which neural network models are provided using deep neural processing, which serves as artificial intelligence);
training the artificial intelligence neural network generation model on the preprocessed blended prosthesis dataset (col. 2 lines 33-44); and
using the artificial intelligence neural network generation model to form a model of the prosthesis (col. 3 lines 18-21). However, Azernikov fails to teach deep learning (DL) in order to ensure generation of a smooth surface in a DL approach. Xu teaches deep learning (DL) in order to ensure generation of a smooth surface in a DL approach (abst. lines 3-11 and sec. 4.6 1st para. lines 1-10). Therefore it would have been obvious to one of ordinary skill in the art at the time of invention to modify the prosthesis model of Azernikov with the smooth surface operation of Xu because this combination would improve analysis of medical prosthesis models through rendering the surface of the models as smooth and anatomically accurate as possible to reduce visual ambiguities and improve the medical analysis.
Regarding claim 2, Azernikov fails to teach wherein preprocessing the data of the dataset is adapted to ensure generation of a smooth surface in a DL approach, the preprocessing step is a digital image/geometric pre-processing method using a filter with edge-preservation and a noise-reducing smoothing function. Xu teaches wherein preprocessing the data of the dataset is adapted to ensure generation of a smooth surface in a DL approach, the preprocessing step is a digital image/geometric pre-processing method using a filter with edge-preservation and a noise-reducing smoothing function (abst. lines 3-11 and sec. 4.6 1st para. lines 1-10). Therefore it would have been obvious to one of ordinary skill in the art at the time of invention to modify the prosthesis model of Azernikov with the smooth surface operation of Xu because this combination would improve analysis of medical prosthesis models through rendering the surface of the models as smooth and anatomically accurate as possible to reduce visual ambiguities and improve the medical analysis.
Regarding claim 3, Azernikov fails to teach wherein the digital image/geometric pre-processing method is one of a Bilateral filter for the image, a Bilateral Mesh Denoising function and/or a Bilateral Normal Filter for the mesh. Xu teaches wherein the digital image/geometric pre-processing method is one of a Bilateral filter for the image, a Bilateral Mesh Denoising function and/or a Bilateral Normal Filter for the mesh (abst. lines 3-11 and sec. 4.6 1st para. lines 1-10, in which a smoothing process is implemented on the dental model. Therefore any process of removing or avoiding noise commonly known in the art, such as a mesh denoising function, could be implemented on the dental model, as commonly known in the art.). Therefore it would have been obvious to one of ordinary skill in the art at the time of invention to modify the prosthesis model of Azernikov with the smooth surface operation of Xu because this combination would improve analysis of medical prosthesis models through rendering the surface of the models as smooth and anatomically accurate as possible to reduce visual ambiguities and improve the medical analysis.
Regarding claim 4, Azernikov teaches wherein the artificial intelligence neural network generation model is a combination of Residual Network (ResNet) and Generative Adversarial Network with Gradient Penalty (GAN-GP) (col. 7 lines 54-59, in which the dental restoration server is disclosed to contain several deep neural networks, therefore a plurality of artificial intelligence networks, such as the ResNet & GAN-GP commonly known in the art, would be utilized in the dental restoration server).
Regarding claim 5, Azernikov teaches further comprising the step of applying a loss function to the artificial intelligence neural network generation model (col. 1 lines 41-53, in which deep neural networks commonly known in the art are utilized to design dental models. Therefore the application of a loss function would clearly be implemented by one of ordinary skill in the art because loss functions are commonly used functions in conjunction with neural networks to ensure the networks are performing as intended).
Regarding claim 8, Azernikov teaches identifying the dental information associated with the dental model of dentition (col. 3 lines 32-41);
generating a 3D dental prosthesis surface with a post-processing method to meet the requirement of the dental prosthesis (col. 10 lines 2-10); and
completing the rest of the dental prosthesis to meet full function (col. 5 lines 22-46).
Allowable Subject Matter
Claims 6, 7 and 10-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome the 35 U.S.C. 112(b) rejection of claims 1-8 and 10-16, and if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 11/10/25 have been fully considered but they are not persuasive.
The 35 U.S.C. 112(b) rejection of claims 1-16 for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention has been withdrawn. However, a new 35 U.S.C. 112(b) rejection for claims 1-8 and 10-16 has been provided due the lack of antecedence basis for the phrase “the occlusion design”. Appropriate correction to claim 1 is required.
In regards to claim 1, the applicant argues that Azernikov does not refer to a depth map as recited in claims 1. However, Azernikov clearly teaches utilizing depth maps to help to generate models of the user’s teeth during prosthesis analysis (col. 11 lines 9-21). Therefore the rejection of claim 1 has been maintained.
In regards to claim 1, the applicant argues that Azernikov does not provide any information regarding clinical adjustment of the occlusion design. However, Azernikov clearly teaches refinement of the dental model design by the technician/user (col. 5 lines 47-67), in which the design takes the occlusion design in to account with the refinement (col. 5 lines 47-67). Therefore, the applicant’s arguments in regards to claim 1 are unpersuasive in view of the teachings of Azernikov.
In regards to claim 4, the applicant argues that Azernikov does not teach ResNet. However, Azernikov teaches utilizing deep neural networks commonly known in the art (col. 7 lines 54-59). Therefore, one skilled in the art at the time of invention would have clearly recognized that any deep neural network could be applied to the invention of claim 4, including ResNet or any other generic deep neural network. Therefore, the applicant’s arguments in regards to claim 4 are unpersuasive in view of the teachings of Azernikov.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/Said Broome/Supervisory Patent Examiner, Art Unit 2612
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