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
35 USC § 101
With regards to the 35 USC § 101 abstract idea rejection of claim(s) 1-5 and 8, applicant argues the claims should not be rejected under 35 USC § 101 because the claims as amended amount to significantly more than an abstract idea. Applicant’s remarks and amendments have been fully considered and are found convincing. The rejection under 35 USC § 101 with regards to claim(s) 1-5 and 8 is withdrawn.
35 USC § 103
With regards to the 35 USC § 103 rejection of claim(s) 1-5 and 8, applicant argues the claims as amended are not taught by the immediate prior art and the claim(s) should not be rejected under 35 USC § 103. Applicant’s remarks and amendments have been fully considered and are found convincing, however, upon further search and consideration, the newly discovered prior art document(s), referenced in the updated rejection below, teaches the limitations as claimed. Please see below for full rejection. Accordingly, applicant’s amendments have necessitated the new grounds of rejection set forth and this action is made final.
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.
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) 1-5 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 [Implementation of Computer‑Assisted Design, Analysis, and Additive Manufactured Customized Mandibular Implants] in view of D2 [Design of patient specific dental implant using FE analysis and computational intelligence techniques] further in view of D3 [US 2022/0304782 A1].
Claim 1. Computer-implemented method for the automatic generation of component-describing data for use in a preparation of additive/subtractive manufacturing jobs for a dental component comprising:
setting, for each at least one component type, surface and/or volume attributes of the dental component, using a specialized pre-trained neural network, [D1, Section 2 and [Figure 2]] D1 teaches the acquisition of the image data. CT scan and the image processing/3D modeling.
wherein the surface and/or volume attributes describe the accuracy and quality requirements of construction elements of the dental components with regard to the intended use, [D1, Section 2/4 and [Figure 2]] D1 teaches the analysis of the customized implant in which the rehearsal fitting evaluation is completed. There are multiple revisions including a virtual assembly and framework done of the implant. There is an implant verification completed.
wherein the accuracy and/or quality requirements comprise at least one of the following: geometric dimensional accuracy, mechanical strength, surface texture color, and the avoidance of the attachment of support elements, [D1, Section 4 and [Figure 10]] D1 teaches the rehearsal for evaluation and fitting which is at least of one of the accuracy of the dimensions.
wherein the neural network has been pre-trained by means of other dental components for which the surface and/or volume attribution has already been carried out, and selectively making an adjustment in the preparation of the additive/subtractive manufacturing jobs for a region associated with a set surface and/or volume attribute, the adjustment comprising at least one of variation of layer thickness, exposure dose, mask type or tool type, and forcing presence or absence of support elements in the region in accordance with the set attribute..
D1 teaches determining data for producing an accurate dental component. Although D1 does not expressly disclose using a neural network to perform that determination or the adjustments being made, D2 teaches automation of previously manual tasks through use of an appropriate neural network. As set forth in the Abstract of D2, data generated by finite element analysis is converted into an artificial neural network model, and that model is used in an optimization framework with a desirability function and genetic algorithm, with the results further validated by finite element analysis.
D1 in view of D2 does not teach the determination or the adjustments being made as described in claim 1, however, D3 teaches, see D3 [0020 and 0021] The illustrative embodiments used to describe the invention generally address and solve the above-described problems and other related problems by employing a machine learning engine to configure dental workflows in a patient and/or user specific manner through intelligent recommendations. [0021] Such a configuration includes restoration parameters such as minimum wall thickness, edge thickness, edge design (chamfer or rounded, chamfer length, edge radius), spacer and the like. The configuration also includes manufacturing parameters such as type of machining device (for example, a particular milling unit or a 3D printer), a type of machining (for example, grinding, milling), a type of material (for example, feldspar, lithium disilicate, polymethacrylate, titanium, zirconium, etc.), a type or size of machining tool (for example, step bur, cylinder bur, cylinder bur EF (extra fine)), a mode of machining (for example, optimization of triangle quality, machining time/cost (e.g. fast, normal, fine). The restoration parameters and manufacturing parameters are collectively referred to herein as dental parameters. Of note, the configuration process may also include the designing of a restoration geometry in preparation for manufacturing.
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 computer-assisted dental implant design and additive manufacturing workflow of D1 to use the artificial neural-network optimization approach of D2 and the machine-learning-based dental workflow configuration of D3. D1 provides the base dental component workflow, including computer-assisted image acquisition, three-dimensional modeling, implant design, verification, and additive manufacturing. D2 teaches using artificial neural networks trained from finite-element-analysis data to automate and optimize patient-specific dental implant design determinations. D3 teaches using a machine-learning engine to configure dental restoration workflows, including restoration parameters and manufacturing parameters such as tool type, machining device, machining type, material type, and machining mode. The motivation to combine arises from the recognized benefits of automating and optimizing dental component design and manufacturing preparation, including improving efficiency, reducing manual design burden, improving repeatability, and tailoring dental component manufacturing parameters to patient-specific design requirements. Accordingly, claim 1 would have been obvious over D1 in view of D2 and further in view of D3.
Claim 2. Computer-implemented method according to claim 1, wherein the construction elements have characteristic properties within the variations of a component type, selected from the list consisting of morphology, position within the dental component, and environmental morphology, on the basis of which they can be classified with the aid of the neural network and provided with corresponding attributes. [D2, [Figure 3]] D2 teaches after placing the implant in molar position of mandible bone, they are meshed with solid 187 elements in ANSYS workbench (Fig. 2c) and the element and node file are transferred into ANSYS Mechanical APDL. The interface stress and strain are observed and compared with the set of data in case of natural molar tooth. Those analysis results were used for ANN GA modeling.
Claim 3. Computer-implemented method according to claim 1, wherein, the construction element to be attributed is at least one of the following: drill spoon support on a drill template, base/socket in models, tooth pocket in denture bases. [D2, Figure 2] D2 discloses the type of base/socket in models.
Claim 4. Computer-implemented method according to claim 1, wherein test and customer cases from a CAD/CAM software serve as training data, in which the surface and/or volume attributes are at least partially set manually and/or at least partially set with the CAD/CAM software on the basis of distinguishable construction elements. [D2, [1 and 4]] D2 teaches the training and testing being done in order to avoid overfitting. The predictions are used as the training data to develop ANN models for predicting the micro strain and the implant stress. 3D models of mandible are created from the DICOM data based on 120 set of computerized tomography (CT) scan data where, individual scan is processed in Mimics 11.0 and the final 3D solid model of mandible is created. Then, the scanned 3D models are imported in ANSYS Workbench. 3D models of molar tooth (Fig. 2a) are hatched primarily using computer tomography (CT) images, digital edge detection technique and computer aided design (CAD) methods (Fig. 2b). These predictions are used as training data.
Claim 5. Computer-implemented method according to claim 1, wherein a component type classification is performed using a neural network based on triangulation nodes and/or triangles of the dental components. [D2, [2.2 and 4.2]] D2 teaches the data classification or prediction through the learning process. The ANN model uses the highest predictability with the nodes optimized by trial and error.
Claim 8. Claim 8 is rejected for similar reasons as to those described in claim 1.
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.
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/AMANDEEP SAINI/
Supervisory Patent Examiner, Art Unit 2662
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