DETAILED ACTION
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, 3-10 and 12-16 are presented for examination based on amendment filed on 05/28/2026.
Claims 4 , 7, 13 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ).
This action is Final rejection.
Priority
Acknowledgment is made of applicant's claim for domestic priority for provisional application 63/286,566 filed on 12/07/2021.
Information Disclosure Statement
The IDS filed on 02/22/2023 is reviewed and see the attached file for consideration.
Response to Arguments
Following Applicants amendments to the claims, 35 USC 112(b) rejection is Withdrawn.
Applicants Argument: Applicant’s arguments directed the 103 rejection are based on newly amended subject matter.
Examiner’s Response: All arguments are addressed in the 103 rejection of the claims below.
Applicant’s argument: Heng 1 and Garcia do not disclose configuring or reconfiguring the fully connected DNN, or any other machine learning model disclosed by Heng 1and Garcia, to provide the aforementioned adding function of the second feature for the purpose of promoting the accuracy in computing the corresponding local portion of the fine-scale mechanical field. The combined model of Heng 1, Garcia, Heng 2, Qian, Lee, Yu, Huang and Qu, would not have anticipated the second feature that at each deconvolutional layer, a density field with a same scale down sampled from the fine-scale density field is added.
Examiner’s Response: The examiner disagrees, that the combined model does not disclose configuring or reconfiguring the fully connected DNN, or any other machine learning model, since teaches configuring of connected DNN on section 4.2, “The fully-connected DNN Heng 1 consists of one input layer, multiple hidden layers, and one output layer. Each hidden layer has a set of neurons, each of which takes an input value and performs a non-linear activation to generate its output value. The number of hidden layers is a hyper-parameter and can be tuned according to the trade-off between the computational complexity and model accuracy”. As it cited Heng 1 teaches tune the number of hidden layers as a hyperparameter, so it is adjustable to meet the accuracy or performance, so Heng 1 teaches configuring of the neural network but it does not explicitly teach adding of the second feature of the amended claim 1.
The examiner agrees Lee and Yu do not disclose adding to each deconvolution layer a density field having a same scale down sampled from a fine-scale density field that is inputted to the neural network model but on the previous office action, Lee and Yu was not used to teach it but Heng 2 was used to teach the above claim limitation. Since a new limitation of “wherein the ANN includes first and second features for promoting an accuracy in computing the corresponding local portion of the fine-scale mechanical field” is recited on the amended claim 1, it overcomes the previous office action and, a new ground of rejection is made. Based on the newly modified model Zhang teaches the second feature that at each deconvolutional layer, a density field with a same scale down sampled from the fine-scale density field is added. See 103 claim rejection below (Zhang teaches, the encoding- down sample is inputted into each decoding layers ( see fig 2, by Zhang on claim 1)).
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.
Claims 4 , 7, 13 and 16 are 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.
Claims recites the limitation
“a fine-scale density field” in claim 4,
“the coarse- scale field” in claim 7.
There is insufficient antecedent basis for this limitation in the claim.
Dependent claims (13 and 16) are rejected under the same rational since they depend on claim 4 and 6 respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ).
As of claim 1 Heng 1 teaches A computer-implemented method for computing a
mechanical field of a structure ( section 4.1, Topology optimization is an iterative
process which often involves hundreds of steps. Every time we come up with a new
design, we need to solve for the structural response of the current design to compute the sensitivity information. For large-scale topology optimization, this procedure is
computationally intensive. A large amount of history data (e.g., design variables, their
corresponding sensitivities, and displacement solutions) are generated during topology
optimization, of which we typically do not make full use).
modelling the structure by a fine-scale structure, wherein the fine-scale structure
is obtained by dividing the structure into a plurality of fine-scale elements (section
3, In this section, the topology optimization formulation for the classical compliance minimization problem is briefly reviewed. Throughout, we assume that the design
domain is discretized by a finite element mesh and adopt the standard density-based
approach [4,33], where the material distribution is characterized by an element-wise
constant function. For a given finite element mesh with N nodes and M elements, we
denote f E RdNx1 as the applied global force vector).
applying a finite element method (FEM) to the coarse scale structure to calculate
a coarse-scale mechanical field of the structure (section 4.1, algorithm 1, step 4-7,
Filter design variables: z(k) = Pz(k); Assemble the global stiffness matrix KC on the
coarse-scale mesh based on ( 14 ); Solve the state equation on coarse-scale mesh: uC = (KC)-1fC; Evaluate the strain vector EC,(k) on the coarse-scale mesh based on uC).
fragmenting the coarse-scale mechanical field into a plurality of fragments,
whereby an individual fragment has a fragment boundary on the coarse-scale
mechanical field such that the individual fragment is a local portion of the coarse scale mechanical field within the fragment boundary( section 2, we devise a tailored
two-scale topology optimization formulation, which allows for the training of machine
learning models based on local features of the topology optimization.
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For a given coarse-scale finite element with a total of nG integration points, as
illustrated in Fig. 2, we divide it into a total of nG sub-regions and each sub-region is
associated with one of its integration point).
combining the respective local portions of the fine scale mechanical field to
generate the fine-scale mechanical field;( section2 and algorithm 1 step 21-23, The
proposed framework is capable of handling 3D large-scale design of a wide range of
problem sizes while achieving significant speedup. For example, we demonstrate that
the proposed framework can achieve close to an order of magnitude speedup in a 3D
design problem with more than 1 million design variables. 21. Use the machine learning model to predict -G (k) based on the input z(k) and EC; 22,Compute the predicted sensitivity as -G(k) = PT-G(k). 23, Update z(k+1) using -G(k)
based on (8) ).
Heng 1 also teaches wherein a first number of respective coarse-scale
elements in the plurality of coarse-scale elements is less than a second number
of respective fine-scale elements in the plurality of fine-scale elements; (section
4.3.1, Assuming that the number of elements of the coarse-scale mesh is much smaller
than that of the fine-scale mesh, the time spent in solving the state equation on the coarse-scale mesh will be negligible. The setup of the coarse-scale and fine-scale
meshes is illustrated in Fig. 2. We note that although the illustration is in 2D, the
numerical examples in this work also consider 3D problems.)
Heng 1 does not explicitly teach coarsening the fine-scale structure to yield a coarse-scale structure such that the coarse-scale structure is composed of a plurality of coarse-scale elements, computing a fine-scale mechanical field of the structure from the coarse-scale mechanical field, using an artificial neural network (ANN) to map the local portion of the coarse-scale mechanical field to a corresponding local portion of the fine-scale mechanical field, whereby respective local portions of the fine-scale mechanical field for the plurality of fragments are computed, according to a plurality of inputs of the ANN wherein the ANN comprises plural convolutional and layers and plural deconvolutional layers wherein the ANN includes first and second features for promoting an accuracy in computing the corresponding local portion of the fine-scale mechanical field, wherein the first feature is that the plurality of inputs comprises the local portion of the coarse-scale mechanical field, and a corresponding local portion of a fine-scale density field within the fragment boundary, the fine-scale structure defining the fine-scale density field, wherein the second feature is that at each deconvolutional layer, a density field with a same scale down sampled from the fine-scale density field is added, and whereby respective local portions of the fine-scale mechanical field for the plurality of fragments are computed . setting the generated fine-scale mechanical field as the mechanical field of the structure, thereby allowing the fine-scale mechanical field with a higher accuracy than the coarse-scale mechanical field to be used as the mechanical field without a need to use the FEM to directly compute the entire fine-scale mechanical field from the fine-scale structure for computation cost saving.
While Garcia teaches coarsening the fine-scale structure to yield a coarse scale structure such that the coarse-scale structure is composed of a plurality of coarse-scale elements (
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as shown on fig 1 high resolution model is upscaled to low resolution and one element or sample of coarse scale is used to compute coarse properties.
computing a fine-scale mechanical field of the structure from the coarse-scale
mechanical field ((section 3.1 “Rationale”, If this is the case, then we argue that there can be a transformation f that within an acceptable error margin would map the solutions of the partial deferential equations in-between scales:
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The term f(σ*; xi) in 4 represents such a generic transformation (or function). The terms σ ij and σ*ij are respectively the solutions in the fine-scale and the coarse scale… section 3.2 The next step is to obtain a solution σ * to the pertinent equations (Figure 1C). A key step in the workflow would then be how to approximate the fine-scale solution σ from the coarse solution σ*).
using an artificial neural network (ANN) to map the local portion of the coarse-scale mechanical field to a corresponding local portion of the fine-scale mechanical field according to a plurality of inputs of the ANN, whereby respective local portions of the fine-scale mechanical field for the plurality of fragments are computed( (abstract, Fig 8, This paper, proposes a methodology to estimate stress in the subsurface by a hybrid method combining finite element modeling and neural networks ... section 3.1, “Rational” As shown in Figure 1B, the mechanical properties, pressure, boundary conditions and other relevant features xn are upscaled (mapped) from the high resolution into a new set of features x*1; x*2… x*n at the coarse scale
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The term f(σ*; xi) in 4 represents such a generic transformation (or function). The terms σ ij and σ*ij are respectively the solutions in the fine-scale and the coarse scale We
propose to use techniques of machine learning to obtain f and the workflow to
implement this idea). The mechanical field as stress and it map stress of the coarse scale to the fine scale as shown on equation 4 and Fig 8.
wherein the ANN comprises plural convolutional and layers (section 5.4. “Network architecture” Figure 8 sketches the neural network architecture used in this work. The network included a convolutional first stage, followed by two densely connected hidden layers and an output layer)
wherein the ANN includes first and second features for promoting an accuracy in computing the corresponding local portion of the fine-scale mechanical field, (section 3.1 “Rational” Figure 1. Schematic of the method proposed. A), a high-resolution model is built including the relevant x features. B: All the features are scaled into a model of coarse geometry. C: Finite element solutions (FEM) are obtained in the coarse model. D: FEM solutions are obtained in a sub volume of the fine scale model. E: Free parameters and solutions at both scales are used to train a neural network to find the relationship between coarse and fine-scale solutions and section 6 “results”, The results shown in Fig. 11 also suggest that the percentwise accuracy of the stress predictions improves with depth.) as it listed and shown on figure 1, a coarse properties and fine properties are used in the neural network and the accuracy of computing stress is improved.
setting the generated fine-scale mechanical field as the mechanical field of the
structure, thereby allowing the fine-scale mechanical field with a higher accuracy
than the coarse-scale mechanical field to be used as the mechanical field without
a need to use the FEM to directly compute the entire fine-scale mechanical field
from the fine-scale structure for computation cost saving (Introduction, Given the
relatively large volumes of interest, the computational cost in this kind of problems can
render these workflows impractical. One alternative to cope with the computational over
head is to make use of upscaling techniques. These aim at solving the relevant
equations in a coarse and manage able resolution. Previous to simulation, the
properties of the physical system are represented as effective properties at the coarse scale while attempting to capture as much as possible the characteristic behavior of the
fine scale).
Garcia is considered to be analogous with Heng 1 and the claim invention, since
they focus on multi scale analysis of large structure for optimal solution. Therefore it
would be obvious to try for a person of ordinary skill in the art to use artificial neural
network in Garcia teaching of using neural network and coarsening(upscaling) fine scale
into coarse scale on Heng 1 model to apply fragmentation on the low resolution to
compute mechanical field.
The motivation would have been to minimize the computation by computing
stress on small size elements since as the size of the model increases, or as more
resolution is needed, the number of cells in the model increases and the computational
cost increases linearly ( Garcia, section 2.2).
The modified model does not explicitly teach plural deconvolutional layers, wherein the first feature is that the plurality of inputs comprises the local portion of the coarse-scale mechanical field, and a corresponding local portion of a fine-scale density field within the fragment boundary, the fine-scale structure defining the fine-scale density field, wherein the second feature is that at each deconvolutional layer, a density field with a same scale down sampled from the fine-scale density field is added.
While Zhang teaches and plural deconvolutional layers, (abstract, The architecture of the neural network is made up of encoding and decoding parts, which provide down- and up-sampling operations. Figure 2, The architecture of the neural network, decoding – transpose).
wherein the second feature is that at each deconvolutional layer, a density field with a same scale down sampled from the fine-scale density field is added, and (section 4.2 Our network could be divided into two parts: 1) Encoding part: down-sampling the given array and return a dimension reduced array; 2) Decoding part: up-sampling the given array and return a dimension ascended array. Fig. 2 shows the architecture of the neural network…. There are two kinds of inputs for each decoding block: one is from the former convolution layer and the other is from the corresponding encoding block. The inputs are first concatenated in the channel dimension. Then in the transpose convolution layer, the dimension reduced array is sampled to restore the shape. And a convolution layer 2 i is followed to generate the shape restored feature map
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As shown on the figure the encoding- down sample is inputted into each decoding layers.
Zhang is considered to be analogous to the claimed invention since it focus on topology optimization using a deep convolutional neural network. Therefore it would be obvious to try for a perform of ordinary skill in the art before the effective filing date to add down sampled fine scale density field into each deconvolutional layer based on Zhang’s teaching as it shown on figure 2 by using encoding, encoding and it adds encoded input (down sampled) in to each layer of decoding using the modified model density field.
The motivation would have been to output the layout of the optimal structure and to significantly reduce the computation cost by improving the performance of the proposed neural network by adapting U-Net and by using large dataset which is generated by a conventional topology optimization approach (Zhang, abstract).
The modified model of Heng 1 – Garcia – Zhang does not explicitly teach wherein the first feature is that the plurality of inputs comprises the local portion of the coarse-scale mechanical field, and a corresponding local portion of a fine-scale density field within the fragment boundary, the fine-scale structure defining the fine-scale density field,
While Heng 2 teaches wherein the first feature is that the plurality of inputs comprises the local portion of the coarse-scale mechanical field, and a corresponding local portion of a fine-scale density field within the fragment boundary, the fine-scale structure defining the fine-scale density field, (Para 23- 24, Strain information on the coarse-scale mesh, together with the filtered design variables on the fine-scale mesh, are used as inputs to the machine learning-based model. In embodiments of this disclosure, machine learning module 115 employs fully- connected Deep Neural Networks (DNNs) as the universal function approximator that takes the input from the two-scale topology optimization module 111 and predicts the sensitivities of the compliance function).
Heng 2 is considered to be analogous to the modified model and the claim
invention, since they focus on multi scale analysis for large structure. Therefore it would
be obvious to try for a person of ordinary skill in the art, before the effective filing date to use the local portion of coarse scale and fine scale density as input in to the Neural network of the modified model to compute the mechanical field of the structure.
The motivation would have been A machine learning-based topology optimization
framework provides a general approach which greatly accelerates the design process of
large-scale problems in 3D ( Heng 2 para 13).
As of claim 4, the modified model teaches all the limitations of claim 1 and Heng 2 also teaches wherein in mapping the local portion of the coarse-scale mechanical field to the corresponding local portion of the fine-scale mechanical
field(Para 25 Coarse-scale mapping module 112 generates the coarse-scale mesh 302
based on fine-scale mesh 301, which is generated by fine-scale mapping module 114. For example, fine-scale elements 301 are mapped to coarse-scale mesh element 302
divided into sectors 302a, 302b, 302c, 302d according to shading of corresponding
quadrant clusters of the fine-scale mesh elements 301, where the shading represents
state variable values (e.g., strain) computed by the topology optimization module 111
for the current optimization step).The ANN predicts the corresponding local portion
of the fine scale mechanical field according to a fine-scale density field and the
local portion of the coarse-scale mechanical field, wherein the fine-scale
structure defines the fine-scale density field (para 18, A large amount of historical
data (e.g., design variables, their corresponding sensitivities, and displacement
solutions) is generated during topology optimization, but typically, not all of the historical
data is fully explored and used. In view of this, a universal machine learning approach is
proposed herein to learn the mapping between the current design and their
corresponding sensitivities from historical data. Once the machine learning model is
trained, it can be employed in the later optimization steps to directly predict the
sensitivities based on the current design without solving the state equations).
As of claim 7, the modified model teaches all the limitations of claim 1, and Heng 1 also teaches wherein in fragmenting the coarse-scale mechanical field into the plurality of fragments, fragment overlapping among respective fragments in the plurality of fragments is absent (section 4.3.1 and FIG 3, every element in the coarse-scale mesh contains the same number of
elements in the fine-scale mesh. Thus, we introduce a parameter called block size
NB to quantify how many fine-scale elements are contained on each side of the
coarse-scale element. For example, the illustration in Fig. 2 has a block size of NB = 5, meaning every element in the coarse-scale mesh constrains 5 x 5 = 25 fine-scale Elements
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Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ), further in the view of Yu, Yonggyun, et al. "Deep learning for determining a near-optimal topological design without any iteration." Structural and Multidisciplinary Optimization 59.3 (2019).
As of claim 3, the modified model teaches all the limitations of claim 1, and Zhang also teaches an activation function of RELU (Figure 2, the architecture of the neural network, include Conv 2*2, ReLu).
The modified model does not explicitly teach wherein the ANN further comprises residual block, wherein each of the convolutional and deconvolutional layers has a filter size of 3 x3, a stride of 2x2 except for a last layer of the ANN, and wherein the last layer of the ANN has a Ix I stride
While Yu teaches wherein the ANN further comprises residual block, wherein each of the convolutional and deconvolutional layers has a filter size of 3 x3, a stride of 2x2 except for a last layer of the ANN, and wherein the last layer of the ANN has a Ix I stride (section 3.2, fig. 7 and 9,
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Mass fraction, which is a scalar value and does not represent spatial information, was
directly inputted to the layer of latent variables. The encoder and decoder network
except the output layer used a ReLU activation (Nair and Hinton 2010),).
Yu is considered to be analogous to the modified model and the claim invention
since they focus on multi scale analysis for large structure. Therefor it will be obvious to
try for a person of ordinary skill in the art, before the effective filing date to combine different convolutional, deconvolutional (up sampling) and residual size and stride value on the modified model to map the local portion of the coarse scale mechanical field to the corresponding local portion of the fine scale mechanical field using the modified model which have a convolutional and deconvolutional layer.
The motivation would have been a convolutional neural network (CNN)-based
encoder and decoder network is trained using the training dataset generated at low
resolution and this proposed method can determine a near-optimal structure in terms of
pixel values and compliance with negligible computational time (Yu, abstract).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ), further in the view of Lee, Seunghye, et al. "CNN-based image recognition for topology optimization." Knowledge-Based Systems 198 (2020):
As of claim 5, the modified model teach all the limitations of claim 1, but it does not explicitly teach the limitations of claim 5. While Lee teaches wherein in coarsening the fine-scale structure to yield the coarse-scale structure, the coarse-scale structure is obtained by scaling down the fine scale density field to give a coarse-scale density field (section 3.1 and Fig 2, All the material density information of each element obtained in the previous iteration, are fed into the proposed method. However, because the topology information has been discretized by a fine mesh, the resizing process is needed to convert the fine mesh domain into a coarse mesh domain. These resizing processes are due to the trained CNN model by using coarse mesh domains
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Wherein the coarse-scale density field defines the coarse-scale structure (section 3.2, As shown in Fig. 3, the topology image is the input of the CNN model; the material density information of each discretized cell can be an input node. The number of links between the input nodes and the first CNN hidden layers can easily go to the order of millions. Moreover, finely discretized meshes require significant computational time for the whole domain. Therefore, in the proposed method, instead of training the CNN model by using the target mesh information, dataset of lower resolution was used to train the CNN structure. Finally, to predict the compliance information of the original resolution).
Lee is considered to be analogous to the modified model and the claim invention
since they focus on multi scale analysis of large structure. Therefore it would be obvious
for a person of ordinary skill in the art, before the effective filing date to compute a mechanical field of original resolution of the whole structure from a coarse structure and the coarse-scale structure is obtained by scaling down the fine scale density field using Lee’s teaching.
The motivation would have been to can eliminate the step of finite element
analysis and accelerate topology optimization processes by the training CNN structure
using coarse elements, after the training process, compliance information of domains
composed with finer elements can be predicted ( Lee Conclusion).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ), further in the view of Huang, Junbin, and Klaus-Jurgen Bathe. "Overlapping finite element meshes in AMORE." Advances in Engineering Software 144 (2020).
As of claim 6, the modified model teaches all the limitations of claim 1, but it does not explicitly teach the presence of overlapping among respective fragments during fragmentation.
While Huang teaches wherein in fragmenting the coarse-scale mechanical field
into the plurality of fragments, fragment overlapping among respective fragments
in the plurality of fragments is present (section 2 and fig 1, the analysis domain is
first divided into several subdomains, each of which is then meshed independently. If
the domain decomposition is reasonable, each subdomain is of regular shape and can
be given a regular conforming mesh. Any interpolation technique may be chosen for a
mesh as long as the interpolation is compatible. Good candidates include the
isoperimetric interpolations [1 ], the finite elements enriched by interpolation covers [21 ], and the overlapping finite elements [14-20].
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Huang is considered to be analogous to the modified model and the claim invention, since they focus on multi scale analysis of large-scale structure. Therefore it would be obvious for a person of ordinary skill in the art, before the effective filing date apply Huang's teaching of overlapping mesh on the modified model in order to compute a mechanical field for the structure.
The motivation would have been to create global interpolation with desired
compatibility and accuracy by using the method of overlapping finite element meshes to
couple these (local) fields (interpolated independently over each subdomain)(Huang,
section 2).
Claims 8, 9, 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Heng 1, et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2021) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020), in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ) further in the view of Qian, Chao, and Wenjing Ye. "Accelerating gradient-based topology optimization design with dual-model neural networks." arXiv e-prints (2020).
As of claim 8, the modified model of Heng 1- Garcia -Zhang-Heng 2 also teaches (b) computing a mechanical field of the candidate structure according to the method of claim 1 ( the modified model teach computing of mechanical field, refer back to claim 1 ), but the modified model do not explicitly teach the rest limitations of claim 8.
While Qian teaches A computer-implemented method for performing topology
optimization (TO) of a structure according to a design requirement, the design
requirement being specified as minimizing or maximizing an objective function
subjected to one or more constraints, the method comprising the steps of
(Abstract, Topology optimization (TO) is a common technique used in free-form
designs. However, conventional TO-based design approaches suffer from high
computational cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as Finite
Element Analysis (FEA). In this work, neural networks are used as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization).
A, selecting a candidate structure for testing whether the candidate structure satisfies the design requirement ( Fig 8, Initial design
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( c) using the computed mechanical field to evaluate the objective function, the
one or more constraints, or both of the objective function and the one or more
constraints (Fig 8, step 2, objective function evaluation and sensitivity analysis using
the dual model neural network, and section 2, A general topology optimization
formulation for density-based structure design is listed as follows: minimize pe:f(u(pe),pe) Subject to: K(pe)U=F other constraints (2) 0:s;pe:s;1, e=1, ... ,N
where f(u,pe) is the objective function, u is a state field that satisfies a linear or nonlinear state equation, pe, is the density distribution, which is also the design
variables, KU=F is the equilibrium equation that needs to be satisfied and N is the total number of element).
(d) determining whether the candidate structure satisfies the design
requirement;( Fig 8, step 3 , meeting stopping criterion)
( e) if the candidate structure does not satisfy the design requirement, updating
the candidate structure and repeating the steps (b)-(e), otherwise setting the
candidate structure that satisfies the design requirement as the structure
obtained by TO (Fig 8. When the meeting criterion is not meet, it updates the
information with gradient information and it goes back to step 2).
Qian is considered to be analogous to the claimed invention since it focus on multiscale analysis of large structure. Therefore it would be obvious for a person of
ordinary skill in the art, before the effective filing date to integrate the mechanical filed computed by the combined model in to the Qian flow chart of topology optimization to output optimize design based on the requirement by performing updates.
The motivation would have been by using neural networks as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization (Qian ,abstract)
As of claim 9, the modified model teach all the limitations of claim 8 and Zhang also teaches wherein the objective function and the one or more constraints are related to a structural compliance minimization design problem, and wherein the mechanical field is a strain energy field(section 3, “The typical topology optimization problem”, In this study, the proposed method is described along- side with the classical compliance minimization problem solved by SIMP, while other optimization problems are also fit to the framework in principle. The compliance minimization problem is formulated as follows:
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where c is the compliance, K is the global stiffness matrix, U and F are the displacement and force vectors, respectively, ue is the element displacement vector, k0 is the element stiffness matrix for an element with fully distributed solid material, x is the vector of design variables (i.e. the element relative densities), x min is the lower bound, which aims to avoid singularity, N is the number of elements used to discretize the design do- main, V (x) and V0 are the material volume and design domain volume, respectively, and f is the prescribed volume fraction).
As of claim 13, the modified model of Heng 1-Garcia-Zhang-Heng 2 teaches (b) computing a mechanical field of the candidate structure according to the method of claim 4 ( the combined model of Heng 1-Garcia-Zhang-Heng 2 teaches all the limitations of claim 4, refer back to claim 4), but the modified model do no explicitly teach the rest of claim 13.
While Qian teaches A computer-implemented method for performing topology
optimization (TO) of a structure according to a design requirement, the design
requirement being specified as minimizing or maximizing an objective function
subjected to one or more constraints, the method comprising the steps of
(Abstract, Topology optimization (TO) is a common technique used in free-form
designs. However, conventional TO-based design approaches suffer from high
computational cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as Finite
Element Analysis (FEA). In this work, neural networks are used as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization).
A, selecting a candidate structure for testing whether the candidate structure
satisfies the design requirement ( Fig 8, Initial design
PNG
media_image9.png
492
576
media_image9.png
Greyscale
).
( c) using the computed mechanical field to evaluate the objective function, the
one or more constraints, or both of the objective function and the one or more
constraints (Fig 8, step 2, objective function evaluation and sensitivity analysis using
the dual model neural network, and section 2, A general topology optimization
formulation for density-based structure design is listed as follows: minimize pe: f(u(pe),pe) Subject to: K(pe)U=F other constraints (2) Q:s;pe:s;1, e=1, ... ,N where f(u,pe) is the objective function, u is a state field that satisfies a linear or
nonlinear state equation, pe, is the density distribution, which is also the design
variables, KU=F is the equilibrium equation that needs to be satisfied and N is the total number of element).
(d) determining whether the candidate structure satisfies the design
requirement;( Fig 8, step 3 , meeting stopping criterion)
( e) if the candidate structure does not satisfy the design requirement, updating
the candidate structure and repeating the steps (b)-(e), otherwise setting the
candidate structure that satisfies the design requirement as the structure
obtained by TO (Fig 8. When the meeting criterion is not meet, it updates the
information with gradient information and it goes back to step 2).
Qian is considered to be analogous to the claimed invention since it focus on
multiscale analysis of large structure. Therefore it would be obvious for a person of
ordinary skill in the art , before the effective filing date to integrate the mechanical filed computed by the combined model in to the Qian flow chart of topology optimization to output optimize design based on the requirement by performing update as well.
The motivation would have been by using neural networks as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization (Qian ,abstract)
As of claim 16, the modified model of Heng 1- Garcia-Zhang-Heng 2 also teach (b) computing a mechanical field of the candidate structure according to the
method of claim 7( the combined model of Heng1 , Garcia and Lee teach
computing of mechanical field by the method of claim 7, refer back to claim 7), but
the modified model do not explicitly teach the rest limitations of claim 16.
While Qian teaches A computer-implemented method for performing topology
optimization (TO) of a structure according to a design requirement, the design
requirement being specified as minimizing or maximizing an objective function
subjected to one or more constraints, the method comprising the steps of
(Abstract, Topology optimization (TO) is a common technique used in free-form
designs. However, conventional TO-based design approaches suffer from high
computational cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as Finite
Element Analysis (FEA). In this work, neural networks are used as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization).
A, selecting a candidate structure for testing whether the candidate structure
satisfies the design requirement ( Fig 8, Initial design
PNG
media_image9.png
492
576
media_image9.png
Greyscale
).
( c) using the computed mechanical field to evaluate the objective function, the
one or more constraints, or both of the objective function and the one or more
constraints (Fig 8, step 2, objective function evaluation and sensitivity analysis using
the dual model neural network, and section 2, A general topology optimization
formulation for density-based structure design is listed as follows: minimize pe: f(u(pe),pe) Subject to: K(pe)U=F other constraints (2) Q:s;pe:s;1, e=1, ... ,N where f(u,pe) is the objective function, u is a state field that satisfies a linear or
nonlinear state equation, pe, is the density distribution, which is also the design
variables, KU=F is the equilibrium equation that needs to be satisfied and N is the total number of element).
(d) determining whether the candidate structure satisfies the design
requirement;( Fig 8, step 3 , meeting stopping criterion)
( e) if the candidate structure does not satisfy the design requirement, updating
the candidate structure and repeating the steps (b)-(e), otherwise setting the
candidate structure that satisfies the design requirement as the structure
obtained by TO (Fig 8. When the meeting criterion is not meet, it updates the
information with gradient information and it goes back to step 2).
Qian is considered to be analogous to the claimed invention since it focus on
multiscale analysis of large structure. Therefore it would be obvious for a person of
ordinary skill in the art , before the effective filing date to integrate the mechanical filed computed by the combined model in to the Qian flow chart of topology optimization to output optimize design based on the requirement by performing update as well.
The motivation would have been by using neural networks as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization (Qian ,abstract).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Heng 1, et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2021) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020), in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ) further in the view of Qian, Chao, and Wenjing Ye. "Accelerating gradient-based topology optimization design with dual-model neural networks." arXiv e-prints (2020) further in the view of Qu, Xueyong, et al. "Thermal topology optimization in optistruct software." 17th AIA AIISSMO Multidisciplinary analysis and optimization conference. 2016.
As of claim 10, the modified model of Heng 1-Garcia- Zhang-Heng 2-Qian teaches all the limitations of claim 8, but it does not explicitly teach wherein the objective function and the one or more constraints are related to a thermal compliance minimization design problem, and wherein the mechanical field is a temperature field.
While Qu teaches wherein the objective function and the one or more
constraints are related to a thermal compliance minimization design problem, and
wherein the mechanical field is a temperature field ( Section 5, Thermal compliance
can be used as objective function and/or constraint. It can be used together with all
existing responses such as volume, displacement, etc. Thermal compliance[5] is
defined as= 1/ 2 'T P = 1/ 2 'T [K + H]T c TCOMP TT (6) where [Kc] is the conductivity matrix, [H] is the convection matrix, {T} is the unknown temperature. When thermal
compliance is minimized, temperature at grids where power is applied is minimized,
which typically is highest in the structure).
Qu is considered to be analogous to the claim invention since it focuses on
topology optimization. Therefore it would be obvious for a person of ordinary skill in the
art , before the effective filing date to compute temperature field as a mechanical field on the modified model.
The motivation would have been thermal compliance optimization is shown to be
significantly faster than previously implemented formulation of minimizing the maximum
temperature of the entire structure (Qu, conclusion).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ), further in the view of Yu, Yonggyun, et al. "Deep learning for determining a near-optimal topological design without any iteration." Structural and Multidisciplinary Optimization 59.3 (2019) further in the view of Qian, Chao, and Wenjing Ye. "Accelerating gradient-based topology optimization design with dual-model neural networks." arXiv e-prints (2020),
As of claim 12, the modified model of Heng 1-Garcia-Zhang-Heng 2-Yu teaches (b) computing a mechanical field of the candidate structure according to the method of claim 3 ( the combined model of Heng 1-Garcia-Zhang-Heng 2 teaches all the limitations of claim 3, refer back to claim 3), but the modified model do no explicitly teach the rest of claim 12.
While Qian teaches A computer-implemented method for performing topology
optimization (TO) of a structure according to a design requirement, the design
requirement being specified as minimizing or maximizing an objective function
subjected to one or more constraints, the method comprising the steps of
(Abstract, Topology optimization (TO) is a common technique used in free-form
designs. However, conventional TO-based design approaches suffer from high
computational cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as Finite
Element Analysis (FEA). In this work, neural networks are used as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization).
A, selecting a candidate structure for testing whether the candidate structure
satisfies the design requirement ( Fig 8, Initial design
PNG
media_image9.png
492
576
media_image9.png
Greyscale
).
( c) using the computed mechanical field to evaluate the objective function, the
one or more constraints, or both of the objective function and the one or more
constraints (Fig 8, step 2, objective function evaluation and sensitivity analysis using
the dual model neural network, and section 2, A general topology optimization
formulation for density-based structure design is listed as follows: minimize pe: f(u(pe),pe) Subject to: K(pe)U=F other constraints (2) Q:s;pe:s;1, e=1, ... ,N where f(u,pe) is the objective function, u is a state field that satisfies a linear or
nonlinear state equation, pe, is the density distribution, which is also the design
variables, KU=F is the equilibrium equation that needs to be satisfied and N is the total number of element).
(d) determining whether the candidate structure satisfies the design
requirement;( Fig 8, step 3 , meeting stopping criterion)
( e) if the candidate structure does not satisfy the design requirement, updating
the candidate structure and repeating the steps (b)-(e), otherwise setting the
candidate structure that satisfies the design requirement as the structure
obtained by TO (Fig 8. When the meeting criterion is not meet, it updates the
information with gradient information and it goes back to step 2).
Qian is considered to be analogous to the claimed invention since it focus on
multiscale analysis of large structure. Therefore it would be obvious for a person of
ordinary skill in the art , before the effective filing date to integrate the mechanical filed computed by the combined model in to the Qian flow chart of topology optimization to output optimize design based on the requirement by performing update as well.
The motivation would have been by using neural networks as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization (Qian ,abstract).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ), further in the view of Lee, Seunghye, et al. "CNN-based image recognition for topology optimization." Knowledge-Based Systems 198 (2020), further in the view of Qian, Chao, and Wenjing Ye. "Accelerating gradient-based topology optimization design with dual-model neural networks." arXiv e-prints (2020).
As of claim 14 the modified model of Heng-Garcia-Zhang-Heng 2-Lee teaches all the limitations of claim 1 and 5 so, (b) computing a mechanical field of the candidate structure according to the method of claim 5( the modified model of Heng-Garcia-Zhang-Lee teach computing of mechanical field by method 5, refer back to claim 5), but the modified model do not teach the rest limitations of claim 14.
While Qian teaches A computer-implemented method for performing topology
optimization (TO) of a structure according to a design requirement, the design
requirement being specified as minimizing or maximizing an objective function
subjected to one or more constraints, the method comprising the steps of
(Abstract, Topology optimization (TO) is a common technique used in free-form
designs. However, conventional TO-based design approaches suffer from high
computational cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as Finite
Element Analysis (FEA). In this work, neural networks are used as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization).
A, selecting a candidate structure for testing whether the candidate structure
satisfies the design requirement ( Fig 8, Initial design
PNG
media_image9.png
492
576
media_image9.png
Greyscale
).
( c) using the computed mechanical field to evaluate the objective function, the
one or more constraints, or both of the objective function and the one or more
constraints (Fig 8, step 2, objective function evaluation and sensitivity analysis using
the dual model neural network, and section 2, A general topology optimization
formulation for density-based structure design is listed as follows: minimize pe: f(u(pe),pe) Subject to: K(pe)U=F other constraints (2) Q:s;pe:s;1, e=1, ... ,N where f(u,pe) is the objective function, u is a state field that satisfies a linear or
nonlinear state equation, pe, is the density distribution, which is also the design
variables, KU=F is the equilibrium equation that needs to be satisfied and N is the total number of element).
(d) determining whether the candidate structure satisfies the design
requirement;( Fig 8, step 3 , meeting stopping criterion)
( e) if the candidate structure does not satisfy the design requirement, updating
the candidate structure and repeating the steps (b)-(e), otherwise setting the
candidate structure that satisfies the design requirement as the structure
obtained by TO (Fig 8. When the meeting criterion is not meet, it updates the
information with gradient information and it goes back to step 2).
Qian is considered to be analogous to the claimed invention since it focus on
multiscale analysis of large structure. Therefore it would be obvious for a person of
ordinary skill in the art, before the effective filing date to integrate the mechanical filed computed by the combined model in to the Qian flow chart of topology optimization to output optimize design based on the requirement by performing update as well.
The motivation would have been by using neural networks as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization (Qian ,abstract).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over, Heng 1 et al. "Universal machine learning for topology optimization." Computer Methods in Applied Mechanics and Engineering 375 (2020) in the view of Garcia, Xavier, and Adrian Rodriguez-Herrera. "Machine learning applied in the multi-scale 3D stress modelling." arXiv preprint arXiv:2008.11244 (2020) further in the view of Zhang, Yiquan, et al. "A deep convolutional neural network for topology optimization with strong generalization ability." arXiv preprint arXiv:1901.07761 (2019) further in the view of Heng 2 chi (WO2020160099A 1 ), further in the view of Huang, Junbin, and Klaus-Jurgen Bathe. "Overlapping finite element meshes in AMORE." Advances in Engineering Software 144 (2020) further in the view of Qian, Chao, and Wenjing Ye. "Accelerating gradient-based topology optimization design with dual-model neural networks." arXiv e-prints (2020),
As of claim 15, The modified model of Heng 1- Garcia- Zhang- Heng 2- Huang also teach (b) computing a mechanical field of the candidate structure according to the
method of claim 6 ( the combined model of Heng 1- Garcia- Zhang- Heng 2- Huang teach computing of mechanical field by method of claim 6, refer back to claim 6), but the
modified model do not explicitly teach the rest limitations of claim 15.
While Qian teaches A computer-implemented method for performing topology
optimization (TO) of a structure according to a design requirement, the design
requirement being specified as minimizing or maximizing an objective function
subjected to one or more constraints, the method comprising the steps of
(Abstract, Topology optimization (TO) is a common technique used in free-form
designs. However, conventional TO-based design approaches suffer from high
computational cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as Finite
Element Analysis (FEA). In this work, neural networks are used as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization).
A, selecting a candidate structure for testing whether the candidate structure
satisfies the design requirement ( Fig 8, Initial design
PNG
media_image9.png
492
576
media_image9.png
Greyscale
).
( c) using the computed mechanical field to evaluate the objective function, the
one or more constraints, or both of the objective function and the one or more
constraints (Fig 8, step 2, objective function evaluation and sensitivity analysis using
the dual model neural network, and section 2, A general topology optimization
formulation for density-based structure design is listed as follows: minimize pe: f(u(pe),pe) Subject to: K(pe)U=F other constraints (2) Q:s;pe:s;1, e=1, ... ,N where f(u,pe) is the objective function, u is a state field that satisfies a linear or
nonlinear state equation, pe, is the density distribution, which is also the design
variables, KU=F is the equilibrium equation that needs to be satisfied and N is the total number of element).
(d) determining whether the candidate structure satisfies the design
requirement;( Fig 8, step 3 , meeting stopping criterion)
( e) if the candidate structure does not satisfy the design requirement, updating
the candidate structure and repeating the steps (b)-(e), otherwise setting the
candidate structure that satisfies the design requirement as the structure
obtained by TO (Fig 8. When the meeting criterion is not meet, it updates the
information with gradient information and it goes back to step 2).
Qian is considered to be analogous to the claimed invention since it focus on
multiscale analysis of large structure. Therefore it would be obvious for a person of
ordinary skill in the art , before the effective filing date to integrate the mechanical filed computed by the combined model in to the Qian flow chart of topology optimization to output optimize design based on the requirement by performing update as well.
The motivation would have been by using neural networks as efficient surrogate
models for forward and sensitivity calculations in order to greatly accelerate the design
process of topology optimization (Qian ,abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Goel; Tushar (US-20100262406-A 1, Date Published, 2010-10-14) this invention also teaches similar concept with the claim invention since it teaches a way of improvement for topology optimization for engineering product.
Lunati; Ivan Fabrizio (US-8594986-B2, Date Published, 2013-11-26) is also similar with the claim invention since this application also teaches multi scale finite volume method to compute pressure in the primary coarse-scale cells using the computed pressure in the dual coarse-scale cells.
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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/ABRHAM ALEHEGN TAMIRU/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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