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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
The claims are directed to a statutory category (process, machine, manufacture, or composition of matter). The claims employ abstract idea of neural-network-based classification/attribution of dental-component regions and do not integrate that exception into a practical application or add significantly more.. The claims lack an inventive concept sufficient to transform the abstract idea into a patent-eligible invention. The claim does not include additional step(s)/element(s) that are sufficient to amount to significantly more than the judicial exception because the recited step(s)/element(s), when considered both individually and as an ordered combination, do not amount to more than the above-identified abstract idea. The additional elements or combination of elements “computer” in the claim taken individually or in combination is not sufficient to amount to significantly more than the judicial exception (abstract idea) itself because the “computer” is recited at a high level of generality as performing generic computer functions routinely used in computer applications. The use of generic computer components does not impose any meaningful limits on the computer implementation of the abstract idea. A claim without significantly additional limitations is not patent eligible.
In the present case, claim 1 and 8 is directed to the abstract idea of data evaluation/classification and predictive model application, as shown by “setting, for each at least one component type, surface and/or volume attributes of the dental component, using a specialized pre-trained neural network”. The additional clause “wherein the surface and/or volume attributes describe the accuracy and quality requirements of construction elements” also reads as informational characterization of data, consistent with a mental-process/mathematical-model type abstraction. Using the 101 subject matter eligibility test, the claims pass Step 1 since they are directed to a statutory category (process, machine, manufacture, or composition of matter). Analyzing under Step 2A, i.e., part 1- Mayo Test, the claims are directed to abstract idea and therefore must be analyzed at Step 2B. Using Step 2B, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Dependent claim(s) 2-5 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea.
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].
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.
D1 teaches determining data for producing an accurate dental component. Although D1 does not expressly disclose using a neural network to perform that determination, 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.
In view of D2’s teaching, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to modify D1 so that its data-determination process is performed using a neural network. Such a modification would have been motivated by the recognized benefit of automating tasks that were previously manual, thereby improving efficiency and reducing potential variability associated with manual processing. The combination involves the predictable use of known neural-network automation techniques from D2 in the dental-component workflow of D1, and each element would have performed no more than its established function. Accordingly, the Examiner determines that combination of the prior art in view of claim 1 is obvious for at least the reasons set forth above.
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
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/AMANDEEP SAINI/ Supervisory Patent Examiner, Art Unit 2662
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