DETAILED ACTION
Claims 1-10 are presented for examination. Claims 7-10 are withdrawn from further consideration.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings received on 25 August 2023 are accepted.
Election/Restriction
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-6, drawn to machine learning optimization of geometric parameters and material of the assembly, classified in G06F30/17.
II. Claims 7-10, drawn to machine learning optimization of subassembly parameters, classified in G06F30/27.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as combination and subcombination. Inventions in this relationship are distinct if it can be shown that (1) the combination as claimed does not require the particulars of the subcombination as claimed for patentability, and (2) that the subcombination has utility by itself or in other combinations (MPEP § 806.05(c)). In the instant case, the combination as claimed does not require the particulars of the subcombination as claimed because claim group I does not require optimization of any subassembly parameters. Claim group II does not require optimization of any geometry or material of the assembly. The subcombination of claim group II has separate utility such as the claimed optimization of subassembly parameters.
The examiner has required restriction between combination and subcombination inventions. Where applicant elects a subcombination, and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
A different field of search is required. Group I requires searching assembly optimization while group II requires searching subassembly optimization and each group optimizes a mutually exclusive set of parameters (geometry & material versus subassembly parameters). Machine learning models are defined almost entirely by the respective input and output parameters which define how a respective model is trained and what inferences the resulting trained model is capable of. Here, because the different optimization objectives of inventions I and II are different, they each involve fundamentally different training and result in materially different trained machine learning models for different optimizations.
Furthermore, machine learning models including encoder/decoder models have different status in the art based on what those models are encoding and decoding respectively.
During a telephone conversation with Keith Baxter [Reg. no. 31,233] on 1 September 2026 a provisional election was made without traverse to prosecute the invention of group I, claims 1-6. Affirmation of this election must be made by applicant in replying to this Office action. Claims 7-10 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
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 1-7 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 pre-AIA the applicant regards as the invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are:
A lack of relationship between the first machine learning decoder and any other part of the claim. The first machine learning decoder is “operating to receive the differentiable representation to decode the subassembly parameters. However, nowhere are any subassembly parameter, decoding step, or other use of the first machine learning decoder recited within the claim.
Dependent claims 2-6 are rejected for depending from a rejected claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 6
Claims 1-4 and 6 are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Chandrasekhar, A., et al. “Integrating Material Selection with Design Optimization Via Neural Networks” arXiv:2112.12566v1 (Dec. 2021) (cited in IDS dated 28 September 2023) [herein “Chandrasekhar”].
Claim 1 recites “1. An optimizer for physical structures having an assembly of subassemblies constructed of materials.” Chandrasekhar title discloses “Integrating Material Selection with Design Optimization Via Neural Networks” Design optimization is an optimizing.
Chandrasekhar abstract discloses:
The engineering design process often entails optimizing the underlying geometry while simultaneously selecting a suitable material. …. In this paper, we propose the use of variational autoencoders (VAE) for simultaneous optimization. …. The proposed framework is demonstrated using trusses, where an optimal material needs to be chosen from a database, while simultaneously optimizing the cross-sectional areas of the truss members
Trusses are an assembly physical structure of subassemblies.
Claim 1 further recites “and comprising: a parametric model of the assembly having geometric parameters to be optimized.” Chandrasekhar section 1 discloses “the truss design problem illustrated in fig. 1.” Chandrasekhar section 1 discloses “The design variables are the cross-sectional areas of the members […], with limits Amin and Amax and the material choice m.” The design problem with respective design variables correspond with a parametric model of the truss assembly having geometric parameters to be optimized.
Chandrasekhar section 3 first sentence discloses “one can now pose the optimization problem discussed in section 1, now using […] as continuous design variables.” Chandrasekhar page 8 section 3 further discloses “Lk is its length.” Length is a geometric parameter.
Claim 1 further recites “a first machine learning decoder having weights trained with a training set having a first dimension of multiple subassembly parameters of multiple different mechanical subassemblies received by an encoder to encode the multiple subassembly parameters as a first differentiable representation having a second dimension smaller than the first dimension, the first machine learning decoder operating to receive the differentiable representation to decode the subassembly parameters.” Chandrasekhar page 8 figure 8 discloses “Topology Network NNT.” Chandrasekhar page 8 second paragraph discloses “The truss network NNT is a simple feed-forward NN with two hidden layers with a width of 20 neurons, each containing an ReLU NNT activation function. The output layer of consists of neurons where is the number of truss members.” The feed-forward neural network for the topology network corresponds with a machine learning model having weights trained with multiple subassembly parameters.
Chandrasekhar page 3 figure 2 shows NNT outputs a truss area defining an “optimal geometry.” The optimal geometry corresponds with decoded subassembly parameters.
Claim 1 further recites “a second machine learning decoder having weights trained with a training set having a first dimension of multiple material parameters of multiple different materials received by an encoder to encode the multiple material parameters as a second differentiable representation having a second dimension smaller than the first dimension, the second machine learning decoder operating to receive the differentiable representation to decode the material parameters.” Chandrasekhar section 1.2 figure 2 shows a decoder which outputs “latent material space” and is used for “optimal material” of the proposed method.
Chandrasekhar page 4 section 2.1 discloses:
In particular, the proposed VAE architecture for capturing material properties is illustrated in fig. 3, and consists of the following components:
1. A four-dimensional input module corresponding to the four properties in table 1, namely the Young’s modulus (E), cost (C), mass density (ρ) and yield strength (Y). The input set is denoted by ζ.
2. An encoder F consisting of a fully-connected network of 250 neurons, where each neuron is associated with an ReLU activation function and weights [24].
3. A two-dimensional latent space, denoted by z0, z1 that lies at the heart of the VAE.
4. A decoder D, which is similar to the encoder, consists of a fully-connected network of 250 neurons.
5. A four-dimensional output corresponding to the same four properties; the output set is denoted by
ζ
^
.
The four input properties correspond with multiple material parameters. The encoder F corresponds with an encoder of the multiple material parameters as a first differentiable representation. The decoder D corresponds with the first machine learning model decoder having weights trained and operative to receive the differentiable representation from the encoder and to decode the multiple material parameters.
Claim 1 further recites “and an optimizer employing the parametric model, an objective function, and one or more constraints to vary the geometric parameters and decoded material parameters applied to the parametric model to optimize the geometric parameters and material of the assembly.” Chandrasekhar section 3.1 “loss function” discloses a loss function which corresponds with and objective function.
Chandrasekhar section 3.1 discloses “we convert the constrained minimization problem in eq. (3) into an unconstrained minimization by employing a log-barrier scheme.” The converted constraints correspond with one or more constraints to vary the geometry parameters.
Chandrasekhar abstract discloses:
The engineering design process often entails optimizing the underlying geometry while simultaneously selecting a suitable material. …. In this paper, we propose the use of variational autoencoders (VAE) for simultaneous optimization. …. The proposed framework is demonstrated using trusses, where an optimal material needs to be chosen from a database, while simultaneously optimizing the cross-sectional areas of the truss members
Simultaneous optimization of underlying geometry while simultaneously selecting a suitable material corresponds with optimizing the geometric parameters and material of the assembly. See further Chandrasekhar page 3 figure 1 disclosing “optimal geometry” and “optimal material” combined as output.
Claim 2 further recites “2. The optimizer of claim 1 further including a first catalog of subassemblies linked to multiple subassembly parameters.”
Claim 2 further recites “and a second catalog of materials linked to multiple material parameters.” Chandrasekhar page 3 section 1.2 discloses “VAEs are a special form of neural networks that can convert discrete data, such as a material database, into a continuous and differentiable representation, making the design problem amenable to gradient-based optimization.” The material database corresponds with a catalog of materials.
Claim 2 further recites “and wherein the optimizer employs a first step of optimizing the subassembly parameters of the given structure to a first coordinate in the first differentiable representation and optimizing the material parameters of the given structure to a second coordinate in the second differentiable representation, and a second step of identifying a closest subassembly to the first coordinate and a closest material to the second coordinate from the first and second catalogs of materials respectively.” Chandrasekhar page 10 section 3.4 disclose:
Once the optimization process is complete, we obtain an optimal set of latent coordinates z*. However, there might not exist a material in the database corresponding precisely to z*. We therefore define a confidence metric for each material m by using the distance from z* to the material in the latent space:
[equation (9)]
The metric serves to rank the materials based on their distance from z*. After finding the closest material, we repeat the geometry optimization to compute the optimal areas A*.
The first optimal set of latent coordinates corresponds with the optimizing to a first coordinate in the combined differentiable representation. Finding the closest material corresponds with identifying a closest material. Repeating the geometry optimization to compute new optimal areas corresponds with identifying a closest subassembly closest to a second coordinate.
Claim 3 further recites “3. The optimizer of claim 2 wherein the optimizer performs a third step of using the parametric model and objective function and one or more constraints to optimize physical dimensions of the given structure using the material parameters of the closest material and the subassembly parameters of the closest subassembly.” Chandrasekhar page 10 section 3.4 disclose:
Once the optimization process is complete, we obtain an optimal set of latent coordinates z*. However, there might not exist a material in the database corresponding precisely to z*. We therefore define a confidence metric for each material m by using the distance from z* to the material in the latent space:
[equation (9)]
The metric serves to rank the materials based on their distance from z*. After finding the closest material, we repeat the geometry optimization to compute the optimal areas A*.
Repeating the geometry optimization to compute new optimal areas corresponds with identifying a closest subassembly closest to a second coordinate. The repeated geometry optimization corresponds with a third step to optimize physical dimensions of the structure using the closest material coordinate.
Chandrasekhar page 8 section 3 further discloses “Lk is its length.” Length is a geometric parameter of a physical dimension of the structure.
Claim 4 further recites “4. The optimizer of claim 2 further including outputting a display representing the differentiable representation with materials of the first catalog superimposed on that representation at corresponding locations in the differentiable representation.” Chandrasekhar page 3 figure 2 shows a visual representation being output with a superimposed “Steel 4340” on that representation. This visual representation corresponds with a display of the differentiable representation with superimposed material representation at locations on the representation.
Claim 6 further recites “6. The optimizer of claim 1 wherein the first catalog of subassemblies provides subassembly parameters selected from the group of bearings, springs, and fasteners.” From the above list of alternatives Examiner is selecting “fasteners.”
Chandrasekhar page 15 first sentence discloses “Finally, the method can be extended to the selection of discrete components such as springs, bolts, etc., and to the selection of discrete microstructures.” Bolts are a fastener. Extending the method to selection of bolts corresponds to subassemblies providing as selection of fasteners.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 5
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Chandrasekhar as applied to claim 1 above, and further in view of Ambrozkiewicz, O. & Kriegesmann, B. “Simultaneous topology and fastener layout optimization of assemblies considering joint failure” Int’l J. Numerical Methods Eng., vol. 122, pp. 294-319 (2021) [herein “Ambrozkiewicz”].
Claim 5 further recites “5. The optimizer of claim 2 further including outputting a display representing the differentiable representation with subassemblies of the second catalog superimposed on that representation at corresponding locations in the differentiable representation.” Chandrasekhar page 15 first sentence discloses “Finally, the method can be extended to the selection of discrete components such as springs, bolts, etc., and to the selection of discrete microstructures.” Springs and bolts correspond with subassemblies of the truss.
Chandrasekhar does not explicitly disclose displaying representation with the subassemblies; however, in analogous art of topology optimization, Ambrozkiewicz abstract teaches:
This article provides a method for the simultaneous topology optimization of parts and their corresponding joint locations in an assembly. …. The presented method models the force transfer at a joint location not only by using single spring elements but accounts for the size and type of the joints. When considering riveted or bolted joints, the local part geometry at the joint location consists of holes that are surrounded by material.
Ambrozkiewicz page 296 rigure 2 shows “(B) Physical model with fasteners.” The model being shown with the fasteners corresponds with outputting a display with representation of subassemblies. Here, the bolt fasteners correspond with respective subassemblies.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Chandrasekhar and Ambrozkiewicz. One having ordinary skill in the art would have found motivation to use fastener layout optimization into the system of integrated material selection with design optimization because:
Making the locations of bolts part of the design variables in the optimization might not only provide a better bolt pattern but also influence the result of the topology optimization of the part itself. Therefore, for a full exploitation of topology optimization, it is important to optimize the parts and their connections simultaneously, that is, to optimize an assembly as a whole.
See Ambrozkiewicz page 294 section 1 ¶1.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220405448 A1 MEZGHANNI; Mariem et al.
teaches
Deep Parameterization for 3D Shape Optimization
US 20240378330 A1 Hansen; Scott R. et al.
Technology background on the types of structural optimization;
VAEs; multi-objective optimization.
Chandrasekhar, A. & Suresh, K. “TOuNN: Topology Optimization using Neural Networks” Structural & Multidisciplinary Optimization, vol. 63, pp. 1135-1149 (2021)
Topology optimization (TO) using neural networks (NN).
Chandrasekhar, A. & Suresh, K. “Multi-Material Topology Optimization Using Neural Networks” Computer-Aided Design, vol. 136, no. 103017 (2021)
A neural network (NN) based MMTO method where the density fields are represented in a mesh-independent manner, using the NN’s activation functions, with the weights and biases associated with the NN serving as the design variables
Chan, Y.C., et al. “Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending” arXiv:2112.00648v2 (April 2022)
A new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay B Hann whose telephone number is (571)272-3330. The examiner can normally be reached M-F 10am-7pm EDT.
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/Jay Hann/Primary Examiner, Art Unit 2186 2 September 2026
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