CTNF 18/366,728 CTNF 92307 Notice of Pre-AIA or AIA Status 07-03-01-aia AIA 07-03-01-r-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-9 are presented in the case. Priority Acknowledgment is made of applicant's claim for foreign priority based on application 2022-201127 filed in Japan on 12/16/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement submitted on 08/08/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-9 are rejected under 35 U.S.C. 101 because they are directed to non-statutory subject matter. Claims 1, 8 and 9 have the following abstract idea analysis. Step 1 : The claim is directed to “a method, apparatus and crm”. The claims are directed to the statutory categories accordingly. Step 2A Prong 1 : claims recite the abstract idea limitation of "performing a convolutional operation on the feature vectors of the plurality of nodes; ". The limitation include mathematical concepts see MPEP § 2106.04(a)(2)) where it cites "the phrase “calculating the force of the object by multiplying its mass by its acceleration” is using a textual replacement for the particular equation". The specification also provides example calculation of scale factor and scaling (See USPGPUB ¶125). See USPTO 2024 example 47 where an ANN was deemed eligible. That claim did not recite a mathematical concept whereas here the claim itself recites a mathematical concept. Thus, these steps are an abstract idea in the “mathematical concept”. Other sections of the claims such as "generating a feature vector", " entering feature vectors of the plurality of nodes to a trained machine learning model" and “estimating an element stiffness matrix" are advanced processes, too generic or high level to be listed as a judicial exception given the available descriptions and MPEP comparisons. Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "a machine learning model", "training", "computers", "memory", "processor" or "recording medium" does not yield eligibility. Claims are still in line with mathematical concepts such as claim 1, 8 and 9 are not specific to a practical application. The additional elements as such are processors and instructions which do not include specialized hardware. See MPEP § 2106.05(f). Stiffness of a mesh appears to be a general field rather than a practical application. Claim 1, 8 and 9 do not include a more specific field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. MPEP § 2106.05(h). Step 2B : The claims do not contain significantly more than their judicial exceptions. Processors, memory and other hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations. Regarding claims 2-7, they merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claim 1, 8 and 9 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-7. With respect to step 2B These claims disclose similar limitations described for the dependent claims above and do not provide anything significantly more than organizing human activity concepts. Claims 2-16 and 19-20 recite the additional elements of "wherein the machine learning model is a graph convolutional neural network. wherein the estimating includes estimating a plurality of element stiffness matrixes corresponding to a plurality of elements in parallel by using a graphics processing unit (GPU). wherein the process further includes generating an adjacency matrix indicating that each of the plurality of nodes in the element is adjacent to all other nodes, and wherein the convolutional operation is performed based on the adjacency matrix. wherein information about the location includes an initial location of the each node and a displacement amount of the each node computed in a previous time step on a simulation. wherein the generating includes generating the feature vector based on a shape differentiation matrix indicating differentiation of an interpolation function between the each node and another node, in addition to the location, the elastic modulus, and the stress. generating another feature vector for each of a plurality of other nodes included in another element, performing a finite element method simulation, generating another element stiffness matrix corresponding to the another element, and training the machine learning model by using training data including the another feature vector and the another element stiffness matrix.". These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-16 and 19-20 also recites abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 1, 5 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over JUNG et al. (US 20220129520 A1 hereinafter Jung) in view of Aksit et al. (US 20220301262 A1 hereinafter Aksit) As to independent claim 1, Jung teaches a non-transitory computer-readable recording medium storing therein a computer program that causes a computer to execute a process comprising: [medium with computer ¶155] generating a feature vector for each node of a plurality of nodes included in an element in mesh data, based on a location of the each node, [input vectors for each node (mesh ¶118), including strain ¶100 "vector information related to each node are input."], [positions of points (location of nodes) used in training (features ¶12 "determine positions of integration points from the discretized analysis domain, compute strain values corresponding to the integration points by reflecting the attribute information applied to the training data"] entering feature vectors of the plurality of nodes to a trained machine learning model; [trained model and input data for it ¶14, ¶100 "input data input to the deep learning network"] estimating an element stiffness matrix indicating stiffness of the element based on a result of the convolutional operation. [generating a stiffness matrix from trained model ¶17, ¶7 "generating a stiffness matrix of finite elements, capable of computing a stiffness matrix of an analysis domain more accurately and quickly than when computing a stiffness matrix of an analysis domain according to a finite element method assuming an internal field as an interpolation function"] Jung does not specifically teach an elastic modulus at the each node, and stress applied to the each node and performing a convolutional operation on the feature vectors of the plurality of nodes. However, Aksit teaches an elastic modulus at the each node, and stress applied to the each node; [elastic modulus for stiffness ¶34, ¶96 " The Young's Modulus (or Elastic Modulus) indicates the stiffness of a material. In other words, it is how easily it is bended or stretched."] performing a convolutional operation on the feature vectors of the plurality of nodes; [CNN with convolution operations on input (features/nodes ¶89), ¶98 "estimating is further based on using a deep neural network machine learning model. A “deep neural network machine learning model” as described herein refers to a neural network (e.g., Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann machines, AutoEncoders, etc.) that has an input layer, an output layer and at least one hidden layer in between the input layer and output layer"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the matrix generation disclosed by Jung by incorporating the elastic modulus at the each node, and stress applied to the each node and performing a convolutional operation on the feature vectors of the plurality of nodes disclosed by Aksit because both techniques address the same field of machine learning and by incorporating Aksit into Jung provide a more accurate motion estimation while saving resources [Aksit ¶2] As to dependent claim 5, the rejection of claim 1 is incorporated, Jung and Aksit further teach wherein information about the location includes an initial location of the each node and a displacement amount of the each node computed in a previous time step on a simulation. [Aksit positions (location), displacement and prior time frame (previous time step) ¶95 "“displacement data” refers to the directional and magnitude of displacement of a vertex from a current position at a current time frame relative to the displacement or position of the vertex at a prior position or prior time frame"] As to independent claim 8, Jung teaches a structural analysis method comprising: [structure of an object ¶118] generating a feature vector for each node of a plurality of nodes included in an element in mesh data, based on a location of the each node, [input vectors for each node (mesh ¶118), including strain ¶100 "vector information related to each node are input."], [positions of points (location of nodes) used in training (features ¶12 "determine positions of integration points from the discretized analysis domain, compute strain values corresponding to the integration points by reflecting the attribute information applied to the training data"] entering feature vectors of the plurality of nodes to a trained machine learning model; [trained model and input data for it ¶14, ¶100 "input data input to the deep learning network"] estimating an element stiffness matrix indicating stiffness of the element based on a result of the convolutional operation. [generating a stiffness matrix from trained model ¶17, ¶7 "generating a stiffness matrix of finite elements, capable of computing a stiffness matrix of an analysis domain more accurately and quickly than when computing a stiffness matrix of an analysis domain according to a finite element method assuming an internal field as an interpolation function"] Jung does not specifically teach an elastic modulus at the each node, and stress applied to the each node and performing a convolutional operation on the feature vectors of the plurality of nodes. However, Aksit teaches an elastic modulus at the each node, and stress applied to the each node; [elastic modulus for stiffness ¶34, ¶96 " The Young's Modulus (or Elastic Modulus) indicates the stiffness of a material. In other words, it is how easily it is bended or stretched."] performing a convolutional operation on the feature vectors of the plurality of nodes; [CNN with convolution operations on input (features/nodes ¶89), ¶98 "estimating is further based on using a deep neural network machine learning model. A “deep neural network machine learning model” as described herein refers to a neural network (e.g., Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann machines, AutoEncoders, etc.) that has an input layer, an output layer and at least one hidden layer in between the input layer and output layer"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the matrix generation disclosed by Jung by incorporating the elastic modulus at the each node, and stress applied to the each node and performing a convolutional operation on the feature vectors of the plurality of nodes disclosed by Aksit because both techniques address the same field of machine learning and by incorporating Aksit into Jung provide a more accurate motion estimation while saving resources [Aksit ¶2] As to independent claim 9, Jung teaches an information processing apparatus comprising: [computer system ¶155] a memory configured to store a trained machine learning model; and [memory and learning system with network (model) ¶62-63] a processor coupled to the memory and the processor configured to: [computer with storage RAM ¶155] generate a feature vector for each node of a plurality of nodes included in an element in mesh data, based on a location of the each node, [input vectors for each node (mesh ¶118), including strain ¶100 "vector information related to each node are input."], [positions of points (location of nodes) used in training (features ¶12 "determine positions of integration points from the discretized analysis domain, compute strain values corresponding to the integration points by reflecting the attribute information applied to the training data"] entering feature vectors of the plurality of nodes to a trained machine learning model; [trained model and input data for it ¶14, ¶100 "input data input to the deep learning network"] estimating an element stiffness matrix indicating stiffness of the element based on a result of the convolutional operation. [generating a stiffness matrix from trained model ¶17, ¶7 "generating a stiffness matrix of finite elements, capable of computing a stiffness matrix of an analysis domain more accurately and quickly than when computing a stiffness matrix of an analysis domain according to a finite element method assuming an internal field as an interpolation function"] Jung does not specifically teach an elastic modulus at the each node, and stress applied to the each node and performing a convolutional operation on the feature vectors of the plurality of nodes. However, Aksit teaches an elastic modulus at the each node, and stress applied to the each node; [elastic modulus for stiffness ¶34, ¶96 " The Young's Modulus (or Elastic Modulus) indicates the stiffness of a material. In other words, it is how easily it is bended or stretched."] performing a convolutional operation on the feature vectors of the plurality of nodes; [CNN with convolution operations on input (features/nodes ¶89), ¶98 "estimating is further based on using a deep neural network machine learning model. A “deep neural network machine learning model” as described herein refers to a neural network (e.g., Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann machines, AutoEncoders, etc.) that has an input layer, an output layer and at least one hidden layer in between the input layer and output layer"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the matrix generation disclosed by Jung by incorporating the elastic modulus at the each node, and stress applied to the each node and performing a convolutional operation on the feature vectors of the plurality of nodes disclosed by Aksit because both techniques address the same field of machine learning and by incorporating Aksit into Jung provide a more accurate motion estimation while saving resources [Aksit ¶2] 07-21-aia AIA Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Jung in view of Aksit, as applied in claim 1 above, and further in view of Ren et al. (US 20210158127 A1 hereinafter Ren) As to dependent claim 2, Jung and Aksit teach the method of claim 1 above that is incorporated, Jung and Aksit do not specifically teach wherein the machine learning model is a graph convolutional neural network. However, Ren teaches wherein the machine learning model is a graph convolutional neural network. [graph neural networks with convolutions (GCN) ¶7 "One popular type of GNN is the graph convolutional network (GCN). “Graph convolutional network” refers to refers to a class of neural network architectures for processing inputs taking the form of graph structures"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Jung and Aksit by incorporating the wherein the machine learning model is a graph convolutional neural network disclosed by Ren because all techniques address the same field of machine learning and by incorporating Ren into Jung and Aksit improves prediction accuracy of models while reducing errors [Ren ¶27] . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Jung in view of Aksit, as applied in claim 1 above, and further in view of Gorchetchnikov et al. (US 20080117220 A1 hereinafter Gorchetchnikov) As to dependent claim 3, Jung and Aksit teach the method of claim 1 above that is incorporated, Jung and Aksit do not specifically teach wherein the estimating includes estimating a plurality of element stiffness matrixes corresponding to a plurality of elements in parallel by using a graphics processing unit (GPU). However, Gorchetchnikov teaches wherein the estimating includes estimating a plurality of element stiffness matrixes corresponding to a plurality of elements in parallel by using a graphics processing unit (GPU). [element analysis ¶4 with GPUs and parallel ¶40 "processed in parallel on a GPU"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Jung and Aksit by incorporating the wherein the estimating includes estimating a plurality of element stiffness matrixes corresponding to a plurality of elements in parallel by using a graphics processing unit (GPU) disclosed by Gorchetchnikov because all techniques address the same field of graphics processing and by incorporating Gorchetchnikov into Jung and Aksit enable a more efficient performance with 3D operations [Gorchetchnikov ¶2] . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Jung in view of Aksit, as applied in claim 1 above, and further in view of Feinberg et al. (US 20190272468 A1 hereinafter Feinberg) As to dependent claim 4, Jung and Aksit teach the method of claim 1 above that is incorporated, Jung and Aksit do not specifically teach wherein the process further includes generating an adjacency matrix indicating that each of the plurality of nodes in the element is adjacent to all other nodes, and wherein the convolutional operation is performed based on the adjacency matrix. However, Feinberg teaches wherein the process further includes generating an adjacency matrix indicating that each of the plurality of nodes in the element is adjacent to all other nodes, and [adjacency matrix for nodes being neighbors (adjacent) ¶45 "adjacency matrix A, which designates whether a pair of nodes belong to each other's neighbor sets N"] wherein the convolutional operation is performed based on the adjacency matrix. [convolution based on the distance (from matrix ¶7) ¶4 "graph convolutions are based on at least a distance between each atom and other atoms"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Jung and Aksit by incorporating the wherein the process further includes generating an adjacency matrix indicating that each of the plurality of nodes in the element is adjacent to all other nodes, and wherein the convolutional operation is performed based on the adjacency matrix disclosed by Feinberg because all techniques address the same field of machine learning and by incorporating Feinberg into Jung and Aksit provides more accurate models with lower data use [Feinberg ¶93] . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Jung in view of Aksit, as applied in claim 1 above, and further in view of Nagetgaal (US 6697770 B1) As to dependent claim 6, Jung and Aksit teach the method of claim 1 above that is incorporated, Jung and Aksit do not specifically teach wherein the generating includes generating the feature vector based on a shape differentiation matrix indicating differentiation of an interpolation function between the each node and another node, in addition to the location, the elastic modulus, and the stress. However, Nagetgaal teaches wherein the generating includes generating the feature vector based on a shape differentiation matrix indicating differentiation of an interpolation function between the each node and another node, in addition to the location, the elastic modulus, and the stress. [shape function with spatial derivatives Col. 7 ln. 45-56, displacement matrix (differentiation matrix) Col. 8 ln. 32-40 "effective strain displacement matrix B.sub.I.sup.eff for node I is then constructed "], [interpolation Col. 10 ln. 30-41] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Jung and Aksit by incorporating the wherein the generating includes generating the feature vector based on a shape differentiation matrix indicating differentiation of an interpolation function between the each node and another node, in addition to the location, the elastic modulus, and the stress disclosed by Nagetgaal because all techniques address the same field of data modeling and by incorporating Nagetgaal into Jung and Aksit attain more sufficient accuracy of force estimation [Nagetgaal Col. 2 ln. 56-60] . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Jung in view of Aksit, as applied in claim 1 above, and further in view of Nagetgaal (US 6697770 B1) As to dependent claim 7, Jung and Aksit teach the method of claim 1 above that is incorporated, Jung and Aksit further teach wherein the process further includes generating another feature vector for each of a plurality of other nodes included in another element, [Jung generates a dataset for training input (features) ¶60 "data sets for the respective training data may be generated based on the computed strain values, and input data of the respectively generated data sets may be input to the deep learning network 130."] training the machine learning model by using training data including the another feature vector and the another element stiffness matrix. [Jung matrix during training ¶63] Jung and Aksit do not specifically teach performing a finite element method simulation, generating another element stiffness matrix corresponding to the another element. However, Nair teaches performing a finite element method simulation, generating another element stiffness matrix corresponding to the another element. [FEA simulation ¶8 “(FEA) simulation for hardware used in the well completion to generate the hardware training data”, stress matrix ¶45 “a stress-strain material matrix for elastic isotropic material”] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Jung and Aksit by incorporating the performing a finite element method simulation, generating another element stiffness matrix corresponding to the another element disclosed by Nair because all techniques address the same field of machine learning and by incorporating Nair into Jung and Aksit reduce the impact of noise in training data and improve models accordingly [Nair ¶53] . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action . Daviet (US 11210833 B1) teaches a graphical model with computation of a stiffness matrix (see Col. 29 ln. 26-39) It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866 217 9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800 786 9199 (IN USA OR CANADA) or 571 272 1000. /BEAU D SPRATT/ Primary Examiner, Art Unit 2143 Application/Control Number: 18/366,728 Page 2 Art Unit: 2143 Application/Control Number: 18/366,728 Page 3 Art Unit: 2143 Application/Control Number: 18/366,728 Page 4 Art Unit: 2143 Application/Control Number: 18/366,728 Page 5 Art Unit: 2143 Application/Control Number: 18/366,728 Page 6 Art Unit: 2143 Application/Control Number: 18/366,728 Page 7 Art Unit: 2143 Application/Control Number: 18/366,728 Page 8 Art Unit: 2143 Application/Control Number: 18/366,728 Page 9 Art Unit: 2143 Application/Control Number: 18/366,728 Page 10 Art Unit: 2143 Application/Control Number: 18/366,728 Page 11 Art Unit: 2143 Application/Control Number: 18/366,728 Page 12 Art Unit: 2143 Application/Control Number: 18/366,728 Page 13 Art Unit: 2143 Application/Control Number: 18/366,728 Page 14 Art Unit: 2143 Application/Control Number: 18/366,728 Page 15 Art Unit: 2143 Application/Control Number: 18/366,728 Page 16 Art Unit: 2143
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