DETAILED ACTION
This action is in response to the original filing on December 14, 2021 and the Remarks and Amendments filed on July 24, 2025. Claims 1, 3, 5 and 6 are pending and have been considered below. Claims 1, 5 and 6 are independent claims. Claims 1, 5 and 6 are amended and claim 4 is canceled.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP-2021063522-A, filed on 4/2/2021.
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 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 3, the terms the first mesh data and the second mesh data lack sufficient antecedent basis. It is unclear as to whether the first mesh data and the coarse mesh data are related. Similarly, it is unclear as to whether the second mesh data and the fine mesh data are related. For examination purposes, the terms will be interpreted to mean the [[first]] coarse mesh data and the fine mesh data, respectively. Appropriate correction is required.
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, 3, 5 and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2016 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a non-transitory computer readable recording medium; therefore, it is directed to the statutory category of an article of manufacture.
Step 2A Prong 1: The claim recites, inter alia:
generating, from fine mesh data representing a fillet, coarse mesh data representing a sharp angle which is sharper than the filler and having a smaller number of edges than the fine mesh data…by removing some of the plurality of edges from the mesh data: This limitation encompasses the mental process of removing edges from graph data to create a reduced graph, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
determining features of mesh data, which includes a plurality of nodes and a plurality of edges connecting the plurality of nodes: This limitation encompasses the mental process of determining features from graph data, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
generating…first simulation result data that indicates stress in the industrial component; and: This limitation encompasses the mathematical concept of calculating the stress of an industrial component, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
inferring, from the coarse mesh data and the first simulation result data…, second simulation result data that would be obtained: This limitation encompasses the mathematical concept of estimating a physical measurement of a high-resolution graph from a low-resolution graph and its associated physical measurement. This is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
reduces a number of the plurality of edges in a stepwise fashion, and: This limitation encompasses the mental process of reducing the number of edges in a graph in a step-by-step manner, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
the coarse mesh data is generated by extracting an output of an intermediate pooling layer among the plurality of pooling layers, and: This limitation encompasses the mental process of creating a graph from the intermediate data output by a model, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
infers second stress acting on the fillet from a first parameter corresponding to a shape of the sharp angle, a second parameter corresponding to first stress acting on the sharp angle, and a third parameter corresponding to a shape of the fillet: This limitation encompasses the mathematical concept of estimating a physical measurement of a high-resolution graph from an angle measurement of a corresponding low-resolution graph, a stress measurement of said low-resolution graph, and a shape attribute of the high-resolution graph. This is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: The abstract ideas listed above are not integrated into a practical application. Specifically, the additional elements, by use of a first trained model for and the first trained model is a convolutional neural network including a plurality of pooling layers that, amount to invoking computers or other machinery merely as a tool to perform an existing process. Thus, these additional elements are recited at such a high level of generality that they represent no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, the fine mesh data being generated from computer aided design (CAD) data of an industrial component, amounts to no more than limiting the use of an abstract idea (generating mesh data) to a particular technological environment or field of use (computer aided design (CAD) data of an industrial component) (see MPEP § 2106.05(h)).
The additional element, by running a simulation using the coarse mesh data, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, by use of a second trained model, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, by running the simulation using the fine mesh data, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, the second trained model is a neural network that, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
Nothing in the claim integrates the abstract ideas into a practical application, and the
claim is thus directed to the abstract ideas.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception because when considered separately or in
combination, they do not constitute an inventive concept. The claim recites additional elements such as first and second models, a convolutional neural network and a simulation, which merely apply the judicial exception on a computer. Therefore, the claim is subject-matter ineligible.
Claim 3
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining a class of an object represented by the mesh data, and: This limitation encompasses the mental process of identifying the class of an object represented in graph form, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
the first mesh data is generated during class examination of the second mesh data: This limitation encompasses the mental process of identifying the class of an object in graph form while removing edges from said graph to create a reduced graph, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: The abstract ideas listed above are not integrated into a practical application. Specifically, the additional element, the first trained model is a class determination model for, simply limits the first trained model referenced in claim 1 to a class determination model, which does not affect the analysis presented in the rejection of claim 1. Thus, this additional element is similarly recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
Nothing in the claim integrates the abstract ideas into a practical application, and the
claim is thus directed to the abstract ideas.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception because when considered separately or in
combination, they do not constitute an inventive concept. The claim recites the additional element of a class determination model, which merely applies the judicial exception on a computer. Therefore, the claim is subject-matter ineligible.
Claim 5
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
generating…from fine mesh data representing a fillet, coarse mesh data representing a sharp angle which is sharper than the filler and having a smaller number of edges than the fine mesh data…by removing some of the plurality of edges from the mesh data: This limitation encompasses the mental process of removing edges from graph data to create a reduced graph, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
determining features of mesh data, which includes a plurality of nodes and a plurality of edges connecting the plurality of nodes: This limitation encompasses the mental process of determining features from graph data, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
generating…first simulation result data that indicates stress in the industrial component; and: This limitation encompasses the mathematical concept of calculating the stress of an industrial component, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
inferring…from the coarse mesh data and the first simulation result data…second simulation result data that would be obtained: This limitation encompasses the mathematical concept of estimating a physical measurement of a high-resolution graph from a low-resolution graph and its associated physical measurement. This is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
reduces a number of the plurality of edges in a stepwise fashion, and: This limitation encompasses the mental process of reducing the number of edges in a graph in a step-by-step manner, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
the coarse mesh data is generated by extracting an output of an intermediate pooling layer among the plurality of pooling layers: This limitation encompasses the mental process of creating a graph from the intermediate data output by a model, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
infers second stress acting on the fillet from a first parameter corresponding to a shape of the sharp angle, a second parameter corresponding to first stress acting on the sharp angle, and a third parameter corresponding to a shape of the fillet: This limitation encompasses the mathematical concept of estimating a physical measurement of a high-resolution graph from an angle measurement of a corresponding low-resolution graph, a stress measurement of said low-resolution graph, and a shape attribute of the high-resolution graph. This is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: The abstract ideas listed above are not integrated into a practical application. Specifically, the additional element, by a processor, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional elements, by use of a first trained model for and the first trained model is a convolutional neural network including a plurality of pooling layers that, amount to invoking computers or other machinery merely as a tool to perform an existing process. Thus, these additional elements are recited at such a high level of generality that they represent no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, the fine mesh data being generated from computer aided design (CAD) data of an industrial component, amounts to no more than limiting the use of an abstract idea (generating mesh data) to a particular technological environment or field of use (computer aided design (CAD) data of an industrial component) (see MPEP § 2106.05(h)).
The additional element, by running a simulation using the coarse mesh data, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, by use of a second trained model, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, by running the simulation using the fine mesh data, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, the second trained model is a neural network that, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
Nothing in the claim integrates the abstract ideas into a practical application, and the
claim is thus directed to the abstract ideas.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception because when considered separately or in
combination, they do not constitute an inventive concept. The claim recites additional elements such as first and second models, a convolutional neural network and a simulation, which merely apply the judicial exception on a computer. Therefore, the claim is subject-matter ineligible.
Claim 6
Step 1: The claim recites an apparatus; therefore the claim is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
determining features of mesh data, which includes a plurality of nodes and a plurality of edges connecting the plurality of nodes, by: This limitation encompasses the mental process of determining features from graph data, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
removing some of the plurality of edges from the mesh data; and: This limitation encompasses the mental process of removing edges from a graph, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper
generate, from fine mesh data representing a fillet, coarse mesh data representing a sharp angle which is sharper than the fillet and having a smaller number of edges than the fine mesh data: This limitation encompasses the mental process of removing edges from graph data to create a reduced graph, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper
generate…first simulation result data that indicates a physical quantity of an object represented by the coarse mesh data; and: This limitation encompasses the mathematical concept of calculating an inherent physical quantity of a shape from its graph representation, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
infer, from the coarse mesh data and the first simulation result…second simulation result data that would be obtained: This limitation encompasses the mathematical concept of estimating a physical measurement of a high-resolution graph from a low-resolution graph and its associated physical measurement. This is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
reduces a number of the plurality of edges in a stepwise fashion, and: This limitation encompasses the mental process of reducing the number of edges in a graph in a step-by-step manner, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
the coarse mesh data is generated by extracting an output of an intermediate pooling layer among the plurality of pooling layers: This limitation encompasses the mental process of creating a graph from the intermediate data output by a model, which is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
infers second stress acting on the fillet from a first parameter corresponding to a shape of the sharp angle, a second parameter corresponding to first stress acting on the sharp angle, and a third parameter corresponding to a shape of the fillet: This limitation encompasses the mathematical concept of estimating a physical measurement of a high-resolution graph from an angle measurement of a corresponding low-resolution graph, a stress measurement of said low-resolution graph, and a shape attribute of the high-resolution graph. This is an evaluation capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: The abstract ideas listed above are not integrated into a practical application. Specifically, the additional elements, a memory configured to store a first trained model and a processor configured to, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional elements, by use of a first trained model for and the first trained model is a convolutional neural network including a plurality of pooling layers that, amount to invoking computers or other machinery merely as a tool to perform an existing process. Thus, these additional elements are recited at such a high level of generality that they represent no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, the fine mesh data being generated from computer aided design (CAD) data of an industrial component, amounts to no more than limiting the use of an abstract idea (generating mesh data) to a particular technological environment or field of use (computer aided design (CAD) data of an industrial component) (see MPEP § 2106.05(h)).
The additional element, by running a simulation using the coarse mesh data, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, by use of a second trained model, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, by running the simulation using the fine mesh data, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
The additional element, the second trained model is a neural network that, amounts to invoking computers or other machinery merely as a tool to perform an existing process. Thus, this additional element is recited at such a high level of generality that it represents no more than mere instructions to apply the abstract idea on a computer (see MPEP § 2106.05(f)).
Nothing in the claim integrates the abstract ideas into a practical application, and the
claim is thus directed to the abstract ideas.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception because when considered separately or in
combination, they do not constitute an inventive concept. The claim recites additional elements such as first and second models, a convolutional neural network and a simulation, which merely apply the judicial exception on a computer. Therefore, the claim is subject-matter ineligible.
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.
Claims 1, 3, 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Yamaguchi et al. (“Prediction of stress concentration at fillets using a neural network for efficient finite element analysis,” hereinafter Yamaguchi) in view of Hanocka et al. (“MeshCNN: A Network with an Edge,” hereinafter Hanocka) in further view of Schlegel (US 2021/0027003 A1).
Regarding claim 1, Yamaguchi teaches a non-transitory computer-readable recording medium storing therein a computer program that causes a computer to execute a process comprising (Yamaguchi, 2.2 Datasets for training the NN, pp 5, paragraph 1; “We used a computer with an Intel CoreTM i7-7700HQ CPU and 32 GB of memory,” wherein “32 GB of memory” necessarily encompasses a non-transitory computer-readable recording medium storing therein a computer program that causes a computer to execute a process.)
generating, from fine mesh data representing a fillet, coarse mesh data representing a sharp angle which is sharper than the fillet and having a smaller number of edges than the fine mesh data…, which includes a plurality of nodes and a plurality of edges connecting the plurality of nodes, by removing some of the plurality of edges from the mesh data, (Yamaguchi, 1 Introduction, pp. 2, paragraph 2; “For efficient FEA, we propose a method to predict the stress at a fillet by using the simulation results of a corner model without the fillet,” wherein “using the simulation results of a corner model without the fillet” necessarily requires generating, from fine mesh data representing a fillet, coarse mesh data representing a sharp angle. Yamaguchi, Fig. 5;
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The figure above illustrates (a) coarse mesh data representing a sharp angle which is sharper than the fillet created by removing some of the plurality of edges from the mesh data as well as (b) fine mesh data representing the fillet.) the fine mesh data being generated from computer aided design (CAD) data of an industrial component (Yamaguchi, 1. Introduction, pp. 2, paragraph 1; “Therefore, small fillets that do not affect the overall stiffness are often removed from a CAD model before performing finite element analysis (FEA),” wherein removing fillets “from a CAD model” is equivalent to generating fine mesh data…from computer aided design (CAD) data of an industrial component.)
…
generating, by running a simulation using the coarse mesh data, first simulation result data that indicates stress in the industrial component; and (Yamaguchi, 1 Introduction, pp. 2, paragraph 2; “For efficient FEA, we propose a method to predict the stress at a fillet by using the simulation results of a corner model without the fillet,” wherein “predict[ing] stress at a fillet by using the simulation results of a corner model without the fillet” is equivalent to generating, by running a simulation using the coarse mesh data, first simulation result data that indicates stress in the industrial component.).
Yamaguchi does not explicitly teach by use of a first trained model for determining features of mesh data…the first trained model is a convolutional neural network including a plurality of pooling layers that reduces a number of the plurality of edges in a stepwise fashion, and the coarse mesh data is generated by extracting an output of an intermediate pooling layer among the plurality of pooling layers. However, Hanocka, in the area of using convolutional neural networks (CNNs) to generate dimensionally-reduced mesh models, teaches these limitations (Hanocka, 1. Introduction, pp. 2, col. 1, paragraph 5, col. 2, paragraph 1; “A key feature of MeshCNN is the unique pooling operation, mesh pooling, which operates on irregular structures and spatially adapts to the task. In CNNs, pooling downsamples the number of features in the network, thereby learning to eliminate less informative features,” wherein using a CNN, or a first trained model, “to eliminate less informative features,” is equivalent to determining features of mesh data (i.e., those that are informative). Hanocka, 3 Overview: Applying CNN on Meshes, Pooling; “Mesh pooling is accomplished by an edge collapse process, as illustrated in Figure 2 (b) and (c). In (b), the dashed edge is collapsing to a point, and, subsequently, the four incident edges (blue) merge into the two (blue) edges in (c),” thereby reduc[ing] a number of the plurality of edges in a stepwise fashion. Hanocka, Table 6; This table discloses four standard pooling layers and a final global average pooling layer, thereby satisfying a plurality of pooling layers. Hanocka, Fig. 6; “Intermediate pooled meshes on the SHREC11 classification dataset. The input meshes are all simplified to roughly 750 edges (500 faces), and are sequentially pooled to 600, 450, 300 and 150 edges.” The “intermediate pooled meshes” necessarily requires extracting an output of an intermediate pooling layer.).
Hanocka is analogous to the claimed invention as both are from the same field of endeavor, that is, generating dimensionally-reduced mesh data. Yamaguchi teaches a method of generating simplified corner mesh data from finer fillet mesh data but does not explicitly disclose doing so with a convolutional neural network. Hanocka teaches this limitation. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to implement Yamaguchi’s method of generating coarse corner mesh data on the “MeshCNN” of Hanocka. The motivation to do so would be to exploit a CNN’s inherent structural advantages in handling irregular graph data, such as mesh data (Hanocka, 6. Discussion and Future Work, pp. 10, col. 1, paragraph 1; “The key contribution of our work is the definition and application of convolution and pooling operations tailored to irregular and non-uniform structures.”).
Yamaguchi further teaches inferring, from the coarse mesh data and the first simulation result data and by use of a second trained model, second simulation result data that would be obtained by running the simulation using the fine mesh data (Yamaguchi, 1. Introduction, pp. 2, paragraph 2; “For efficient FEA, we propose a method to predict the stress at a fillet by using the simulation results of a corner model without the fillet. We use a neural network (NN) (Alom et al., 2019), which is a type of machine learning, as a prediction technique,” wherein “the simulation results” denotes the first simulation result data, “a corner model” denotes the coarse mesh data, “a neural network (NN) denotes a second trained model and “predict the stress at a fillet” denotes inferring…second simulation result data that would be obtained by running the simulation using the fine mesh data. Yamaguchi, 1. Introduction, pp. 2, paragraph 1; “A simplified model including corners is used for efficient analysis. Methods for automatically searching and removing fillet parts from a CAD model have also been developed (Danglade et al., 2014; Chow et al., 2015). However, because corners have stress singularities, it is necessary to analyze them using a detailed model with fillets in order to accurately evaluate the stress concentrations.” Here, the “simplified model including corners” corresponds to the coarse mesh data with fewer edges, and the “detailed model with fillets” corresponds to the fine mesh data.).
the second trained model infers second stress acting on the fillet from…a second parameter corresponding to first stress acting on the sharp angle, and a third parameter corresponding to a shape of the fillet. (Yamaguchi, 2.1 NN Configuration, pp. 3, paragraph 2; “We evaluated the effect on the prediction accuracy of the stress distribution used as an input to the NN by comparing three types of NNs: one that inputs only the fillet radius (Type I), one that inputs the fillet radius and the nodal von Mises stress ratios (𝜎1/𝜎c, 𝜎3/𝜎c) (Type II), and one that inputs the fillet radius and the nodal von Mises stress ratios (𝜎3/𝜎c, 𝜎4/𝜎c) (Type III),” wherein “Type II” and “Type III” denote a second trained model with a second parameter corresponding to first stress acting on the sharp angle (“von Mises stress”) and a third parameter corresponding to a shape of the fillet (“fillet radius”). Yamaguchi, Fig. 6)
Yamaguchi does not explicitly teach a first parameter corresponding to a shape of the sharp angle. However, Schlegel, in the area of modelling fatigue strength in weld seams, teaches this limitation (Schlegel, [0074]; “The notches 5a, 5b, 5c of the weld 4a are not rounded but modelled sharp-edged. This allows a relatively coarse meshing and thus saves considerable calculation effort and calculation time,” thereby disclosing a sharp angle from a coarse mesh. Schlegel, [0076]; “A number of parameters of the weld seams 4a, 4b are evaluated and made available to the effective notch stress prognosis algorithm as input data. The parameters can be stresses, strains and/or reaction forces of the weld seam elements 6a, 6b, 6c and nodes. In addition, material and/or geometry parameters such as the dimensions of the weld cross-section, relative position coordinates of individual nodes within the weld cross-section or connection angles of the connected geometry in the individual weld cross-sections and notches can be used,” wherein using “geometry parameters such as…connection angles of the connected geometry” from “weld seams 4a, 4b” corresponding to the “coarse meshing” in a “notch stress prognosis algorithm” is equivalent to infer[ring] second stress acting on the fillet from a first parameter corresponding to a shape of the sharp angle.)
Schlegel is analogous to the claimed invention as it is from the same field of endeavor, that is, predicting stress from mesh data. Yamaguchi teaches using stress calculated from this coarse mesh data along with radii measurements from refined fillet mesh data as parameters for predicting fillet stress. However, Yamaguchi does not explicitly teach using shape data from the coarse mesh as a parameter for calculating this final stress. Schlegel teaches this parameter. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the fillet radius and von Mises corner stress parameters of Yamaguchi with Schlegel’s geometric parameters. The motivation to do so is to capture variations in weld or fillet seams that may affect an industrial component’s strength (Schlegel, [0038-39]; “According to a further embodiment, the effective notch stress prediction algorithm is trained with a plurality of weld seam parameter variants using the defined mesh pattern before step c) is applied. Each weld seam constellation preferably has different geometric dimensions (parameters) of the components and the weld seam as well as different loads (parameters) and represents a design point in the parameter space.”).
Regarding claim 3, the combination of Yamaguchi, Hanocka and Schlegel teaches the non-transitory computer-readable recording medium according to claim 1, wherein: (and thus the rejection of claim 1 is incorporated).
The combination further teaches the first trained model is a class determination
model for determining a class of an object represented by the mesh data, and (Hanocka, 5 Experiments, pp. 6, col. 2, paragraph 2; “In what follows, we demonstrate MeshCNN performance on classification and segmentation tasks. Details on the network architectures used are given in Appendix A,” wherein “classification” is equivalent to class determination. Hanocka, Fig. 8; “Fig. 8. MeshCNN trained to predict the class of icon engraving. Observe how the network learns to preserve important edges and remove redundant edges with regards to the classification task,” wherein “the class of icon” encompasses a class of an object represented by the mesh data.)
the first mesh data is generated by extracting intermediate data from the first trained model during class determination of the second mesh data (Hanocka, Fig. 6; “Intermediate pooled meshes on the SHREC11 classification dataset. The input meshes are all simplified to roughly 750 edges (500 faces), and are sequentially pooled to 600, 450, 300 and 150 edges.” The “intermediate pooled meshes” denote extract[ed] intermediate data that is created during the “classification” of the original “input meshes” corresponding to the second mesh data.).
Hanocka is analogous to the claimed invention as both are from the same field of endeavor, that is, generating dimensionally-reduced mesh data. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Yamaguchi’s method of generating coarse corner mesh data by extracting reduced mesh data generated by the pooling layers of a convolutional neural network, as taught by Hanocka. The motivation to do so is to utilize the inherent advantages of convolutional neural networks in processing image data to isolate important features and, thus, improve computational efficiency (Hanocka, Abstract; “Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones.”).
Claim 5 is a method claim corresponding to the steps of claim 1, and are rejected for the same reasons as claim 1. Note that Yamaguchi further teaches the additional hardware element, a processor (Yamaguchi, 2.2 Datasets for training the NN, pp 5, paragraph 1; “We used a computer with an Intel CoreTM i7-7700HQ CPU and 32 GB of memory,” wherein a “CPU” denotes a processor.).
Claim 6 is an apparatus claim corresponding to the steps of claim 1, and are rejected for the same reasons as claim 1. Note that Yamaguchi further teaches the additional hardware element, a memory (Yamaguchi, 2.2 Datasets for training the NN, pp 5, paragraph 1; “We used a computer with an Intel CoreTM i7-7700HQ CPU and 32 GB of memory.”).
Response to Arguments
Applicant’s arguments and amendments, filed July 24, 2025, regarding the rejections
from the previous office action made under 35 U.S.C. 101 have been fully considered but are not
persuasive.
On page 6 of the remarks, Applicant argues both that “an implementation of a neural network having specific input and output for structural analysis” constitutes “an improvement to a structural analysis technique” and thus “amounts to significantly more than an abstract idea.” Examiner respectfully disagrees as Applicant has failed to identify which claim element captures the alleged improvement (MPEP § 2016.05(a), “During examination, the examiner should analyze the ‘improvements’ consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement” (emphasis added)). Examiner adds that merely implementing a neural network to perform an existing method amounts to invoking computers or other machinery merely as a tool to perform an existing process as outlined in the rejections above (see MPEP § 2106.05(f)).
As such, independent claims 1, 5 and 6 are subject-matter ineligible and, thus, the rejections under 35 U.S.C. 101 stand.
Applicant’s arguments and amendments, filed July 24, 2025, regarding the rejections
from the previous office action made under 35 U.S.C. 103 have been fully considered but are
moot as they do not apply to the reference Schlegel being used in the current rejections of claims
1, 5 and 6 and their associated dependent claims to teach the amended claim limitations direct to a first parameter corresponding to a shape of the sharp angle.
Specifically, Schlegel teaches a “notch stress prognosis algorithm” using “geometry parameters such as…connection angles of the connected geometry in the individual weld cross-sections” from a “sharp-edged…coarse meshing” (Schlegel, [0074] and [0076]). The “geometry” of a “coarse meshing” necessarily encompasses the shape of the sharp angle, thereby teaching this limitation.
With the addition of the reference Schlegel, which teaches the subject matter introduced in the amendments, the rejections under 35 U.S.C. 103 stand.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAXWELL AUGUST LUDWIKOWSKI whose telephone number is (571)272-2185. The examiner can normally be reached Monday - Friday, 7:00 am - 4:00 pm ET.
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/MAXWELL AUGUST LUDWIKOWSKI/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145