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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/31/26 has been entered.
Claims1-20 have been presented for examination based on the response filed on 3/31/26.
Claims 1-6, 11-15 and 20 are amended.
Claims 1-20 remain rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB No. US 20210357555 A1 by LIU; Wing Kam et al.
This action is made Non-Final.
Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.9:
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(Response 1) Contrary to applicant’s allegation Liu teaches artifacts as voids (spaces between the laminated material) and artifact model (a model that shows models such voids). Specifically artifacts1 can be voids, which are captured from actual of high fidelity simulation/prediction [0065] "... Defects, voids in this case, can be from measurements or predictions, here two experimental methods (x-ray tomography and FIB-SEM serial sectioning) are given as examples. These input data are paired (spatially), and used for the MVE, resulting in response predictions based directly on experimental images...."; The clustering in context of machine learning as mentioned in [0321] is done with method like SCA, VCA and FCA which can include voids "... [0462] We have outlined, related, and compared three different clustering-discretization methods (SCA, VCA, and FCA) that rely on unsupervised learning for order reduction and the solution of mechanistic governing equations for prediction. [0463] One of these methods, SCA, was used to develop an example material behavior database suitable for training neural networks. This approach to database development substantially reduces the effort required to acquire the information upon which neural networks may be trained...." SCA enables predictions of voids as in [0484] & Fig.37 "... [0484] In this study, the mechanistic equations that SCA relies on to make predictions are reformulated for finite strain elastoplastic materials. Numerical convergence of this new method is verified. This new formulation of SCA enables the prediction of the nucleation of voids in ductile materials by debonding and fragmentation of inclusions at the scale of their microstructure, which is shown in FIG. 37, where ductile materials' microstructures are discretized using voxel meshes..."; Also see [0391]-[0418] used of SCA and FFNN+CNN).
(Argument 2) Applicant has argued in Remarks Pg.9-10:
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(Response 2)As shown in Response 1 above Liu teaches SCA enables predictions of voids as in [0484] & Fig.37 and as per [0462] clustering-discretization methods (SCA, VCA, and FCA) … rely on unsupervised learning for order reduction and the solution of mechanistic governing equations for prediction. The SCA with machine learning is mapped to artifact model.
Contrary to applicant’s assertion SCA (with material and void data) is shown stored in database. See [0396] "... The fast, predictive models (SCA, VCA, FCA) outlined above are thus desirable for quickly populating relatively large materials databases....", and further in section starting [0404] Database Generation for Machine Learning Using SCA.
(Argument 3) Applicant has argued in Remarks Pg.10:
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(Response 3) Liu teaches variation in properties at least in [0746] " …In addition, SCA, a reduced order modeling scheme, is combined with the inverse modeling procedure to compute interphase properties for the first time.... The present method is general enough and can incorporate other details of the microstructure, such as variation of interphase thickness...."; [872] "... This study approaches this challenge with a reduced-order method SCA…. It is shown that the reduced-order method enables fast prediction of microstructure-property relationships with quantified variation...."). Examiner respectfully maintains the rejection as mapped below.
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Claim Rejections - 35 USC § 102
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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB No. US 20210357555 A1 by LIU; Wing Kam et al.
Regarding Claim 1, 11 & 20 (Updated 5/26/2026)
Liu teaches (Claim 1). A computer-implemented method for generating a three-dimensional (3D) multi-scale model of a 3D system (Liu: Figs. 1, 7 and 10; [0010] [0043]-[0044]) , the computer-implemented method/
(Claim 11) A computer-based system for generating a three-dimensional (3D) multi-scale model of a 3D system, the computer-based system comprising :at least one memory (Liu: [0261]-[0262]) ; and at least one processor coupled to the at least one memory (Liu: [0261]-[0262]) , the at least one processor configured to/
(Claim 20) A non-transitory computer-readable medium for generating a three-dimensional (3D) multi-scale model of a 3D system, the non-transitory computer-readable medium having encoded thereon a sequence of instructions which, when loaded and executed by at least one processor, causes the at least one processor (Liu: [0261]-[0262]) to/comprising:
generating (Liu: [0010] "... the method includes generating a representation of the material system at a number of scales...") , at a given scale (Liu: [0010] also see Fig.7 & [0064]) , an artifact model that indicates properties, characteristics, and artifacts of a 3D system (Liu: [0010] See Fig.1 material properties, characteristics; [0313]-[0319]), wherein (i) the generating of the artifact model includes identifying the properties, characteristics (Liu : [0319]-[0324] showing unsupervised learning as machine learning being trained on high fidelity MVE data (real/actual/high fidelity simulation data) in [0320] to produce properties/ characteristics e.g. interaction tensors in [0321]; These tensors are produced at macroscale and microscale geometry as shown in [0328]-[0329]; the cluster based properties, characteristics, and artifacts are stored in databased in [0045]-[0049]), and artifacts (Liu: artifacts2 can be like voids, which are captured from actual of high fidelity simulation/prediction [0065] "... Defects, voids in this case, can be from measurements or predictions, here two experimental methods (x-ray tomography and FIB-SEM serial sectioning) are given as examples. These input data are paired (spatially), and used for the MVE, resulting in response predictions based directly on experimental images...."; The clustering in context of machine learning as mentioned in [0321] is done with method like SCA, VCA and FCA which can include voids "... [0462] We have outlined, related, and compared three different clustering-discretization methods (SCA, VCA, and FCA) that rely on unsupervised learning for order reduction and the solution of mechanistic governing equations for prediction. [0463] One of these methods, SCA, was used to develop an example material behavior database suitable for training neural networks. This approach to database development substantially reduces the effort required to acquire the information upon which neural networks may be trained...." SCA enables predictions of voids as in [0484] & Fig.37 "... [0484] In this study, the mechanistic equations that SCA relies on to make predictions are reformulated for finite strain elastoplastic materials. Numerical convergence of this new method is verified. This new formulation of SCA enables the prediction of the nucleation of voids in ductile materials by debonding and fragmentation of inclusions at the scale of their microstructure, which is shown in FIG. 37, where ductile materials' microstructures are discretized using voxel meshes..."; Also see [0391]-[0418] used of SCA and FFNN+CNN), automatically, via machine learning (Liu: [0092][0484][0319]-[0418] Fig.34-38) and (ii) an artifact of the artifacts of the 3D system includes a variation in a) a property of the properties (Liu : Fig.39 & [0096] shows SCA (machined learning based clustering) compared to FFT (actual measurements ) or b) a characteristic of the characteristics, due to fabrication or testing of the 3D system (Liu: the original microstructure volume elements (MVE) of building blocks of the material system at said scale MVE can be built using measurement of fabricated 3D system - "... [0065] FIG. 8 shows MVEs generated from a number of sources, according to embodiments of the invention. This example for metallic materials shows grains measured using x-ray diffraction, reconstructed from a statistical description, and predicted from a processing model. Defects, voids in this case, can be from measurements or predictions, here two experimental methods (x-ray tomography and FIB-SEM serial sectioning) are given as examples. These input data are paired (spatially), and used for the MVE, resulting in response predictions based directly on experimental images...."); [0066] showing original UD MVE is based on measurement data "... [0066] FIG. 9 shows a multiscale cluster-based process according to embodiments of the invention. Left: the original DNS description of a UD MVE...."; [0067] "... [0067] FIG. 10 shows a physics guide NN may be “layered” on top of the MVE ROM according to embodiments of the invention: the NN is trained on a large database of rapidly-computed behavior. Once trained, this NN is thought to contain microstructural information similar to the ROM, but is much faster to evaluate. This alternative makes it practical to conduct microstructure-based structural optimization and design....");
modifying a series of representational models of the 3D system based on the artifact model generated (Liu: [0010]"... manipulating the governing partial differential equation (PDE) using Green's function to form a generalized Lippmann-Schwinger integral equation;...") , the modifying including mapping the properties, characteristics, and artifacts (Liu : [0010] [0023]-[0026] as modifying the boundaries) to a representational model in the series of representational models at a higher scale or lower scale relative to the given scale (Liu: [0011]"... passing the resulted response prediction to a next coarser scale as an overall response of that building block, and iterating the process until a final scale is reached...."; See Fig.1 & 7 flow for multi scale modeling progression) , the mapping bridging a given representational model of the series of representational models at the given scale and the representational model at the higher scale or lower scale (Liu: Fig.7 and [0871] "... FEM×SCA woven laminate modeling framework which captures macroscale (FEM mesh) and mesoscale mechanical behavior simultaneously during the analysis. The mesoscale field evolution can be tracked as the load increases and the bridge between microstructure and macro-response is built....") the mapping including mapping the variation in a) the property of the properties (Liu : thickness as property in [0746] " …In addition, SCA, a reduced order modeling scheme, is combined with the inverse modeling procedure to compute interphase properties for the first time.... The present method is general enough and can incorporate other details of the microstructure, such as variation of interphase thickness...."; [872] "... This study approaches this challenge with a reduced-order method SCA…. It is shown that the reduced-order method enables fast prediction of microstructure-property relationships with quantified variation....") or b) the characteristic of the characteristics with at least one production characteristic (Liu: properties like loading and tensile properties are performed for both DNS (original model based on measurements [0065]-[0066]) and Reduced Order Model (with SCA) based on Neural Network trained on DNS ([0067], [0576]-[0681] ) with characteristics like stress vs strain are compared) ; and automatically storing, in a database, the artifact model in association with the series of representational models modified (Liu: [0043]-[0044], [0262], [0299], [0313] showing storage of models and instructions on disk or memory; also see section under "... [0404] Database Generation for Machine Learning Using SCA...") , thereby generating a 3D multi-scale model of the 3D system (Liu: [0010]) .
Regarding Claims 2 & 12
Liu teaches The computer-implemented method of Claim 1, further comprising automatically storing model information in the database (Liu: [0043]-[0044], [0050]-[0051] database; [0262], [0299], [0313] showing storage of models and instructions on disk or memory) and wherein: the model information represents provenance information, training data set information, learning method information, ancillary data, measured or predicted data to which the series of representational models correspond, or a combination thereof; the model information is associated in the database with the series of representational models, the artifact model, or a combination thereof (Liu: [0045]-[0049] [0266][0303]) ; and the identifying is performed at the given scale (Liu: Fig.1 & 7).
Regarding Claims 3 & 13
Liu teaches The computer-implemented method of Claim 2, wherein the machine learning includes employing deep learning, adversarial learning, a genetic or evolutionary method, other modeling or segmentation-classification approach to modeling, or a combination thereof (Liu: [0356]-[0359]) .
Regarding Claims 4 & 14
Liu teaches The computer-implemented method of Claim 2, further comprising performing the machine learning against a set of systematic test results of samples (Liu: [0049][0052][0313] Fig.6 & [0063] as training process) .
Regarding Claims 5 & 15
Liu teaches the computer-implemented method of Claim 2, further comprising: controlling the machine learning with a closed loop or subject to at least one optimality criterion (Liu: [0266]-[0267]; [0313] "... A predefined set (usually carefully selected and simplified) of material properties and boundary conditions are supplied to a direct numerical solver to compute nominal response fields...." ) ; performing the machine learning, iteratively, based on a performance criterion, convergence threshold, quality metric, limit value or group of limit values, or a combination thereof (Liu: [0266] as minimizing a distance in iterative manner; [0417]"... [0417] Usually, the MSE gradually decreases with each training step. To ensure the trained neural network is general enough for all possible input states, some data points called verification data are used to monitor trends in the error. The minimization iterations terminates before the error of the verification data starts to increase. This ensures the neural network is able to provide certain extrapolating capability for data points that are not within the training set....") ; and determining, via the closed loop or subject to the at least one optimality criterion, whether the performance criterion, convergence threshold, quality metric, limit value or group of limit values, or the combination thereof has been satisfied (Liu: [0266]-[0267]) .
Regarding Claims 6 & 16
Liu teaches the computer-implemented method of Claim 1 further comprising: generating the series of representational models by: (i) generating at least one representational model, of the series of representational models, based on a manufacturing process (Liu: [0045]-[0049], [0357] e.g. [0607]-[0610] machine learning based on the big data from additive manufacturing) and (ii) employing characteristics of a plurality of test coupons, the plurality of test coupons manufactured via the manufacturing process (Liu: [0562] "... [0562] The cured UD CFRP lamina plaque is manufactured by Dow Chemical and the cross-section of the UD CFRP under microscope is shown in FIG. 51. Fibers are shown in lighter color and epoxy is shown in dark color. ..." ; [0558]; [0591], [0349]-[0350]) .
Regarding Claims 7 & 17
Liu teaches the computer-implemented method of Claim 1, wherein each representational model of the series of representational models is built at a different scale of a plurality of scales, wherein the plurality of scales includes the given scale, and wherein the computer- implemented method further comprises generating a respective artifact model at each scale of the plurality of scales (Liu teaches the: Fig. 7 & [0338] ) .
Regarding Claims 8 & 18
Liu teaches the computer-implemented method of Claim 1, further comprising: in a training phase, training the series of representational models based on at least one respective training data set (Liu: [0049], [0303] training and validation on multiscale modeling) ; in an execution phase, running the 3D multi-scale model, the running producing a prediction of an onset of failure in the 3D system (Liu: [0440]"... These show that the CNN can effectively map the stress contour to the applied external strain. Such a map may play an important role in linking microstructure information with macroscale information, e.g., connecting microstructure failure strength to a macroscale strain state....") ; and in a validation phase, improving accuracy of the prediction, produced in the execution phase, by relearning the series of representational models and artifact model based on measured data or predicted data input for the series of representational models (Liu: [0049], [0303] training and validation on multiscale modeling; improving in [0440]) .
Regarding Claims 9 &19
Liu teaches the computer-implemented method of Claim 1, wherein the 3D system is an architectural system, component, material, or structure, the structure including i) a plurality of raw materials or intermediate materials or ii) a mixture or formulation of the plurality of raw or intermediate materials (Liu: [0268], [0818]) .
Regarding Claim 10
Liu teaches the computer-implemented method of Claim 1, wherein the 3D system is a real-world system and wherein each representational model in the series of representational models is built at a different scale, wherein the different scales include a chemical-substance scale, materials-substance scale, engineering-design scale, engineering-production- process scale, system lifetime scale, or combination thereof (Liu: [0314]-[0316]; [0352] "... [0352] In addition, multiscale structure-property materials and structures design are illustrated by two examples (composites and alloys) in FIGS. 4 and 7. As shown in FIG. 7, the method can be used for a 3-scale material system, and can be extended to N-scale. ..."; [0525] "... An appropriate micro-scale material law-crystal plasticity (CP)—is solved cluster-wise to obtain the cyclic change in plastic shear strain (Δγ.sub.p) and stress normal (σ.sub.n) to that strain in the matrix material...." – as material substance scale; [1145] "... generate each of these realizations, we introduce the Top-down sampling approach where realizations are assigned to the spatially varying parameters from the coarsest scale to the finest scale in the material system....") .
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Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKASH SAXENA whose telephone number is (571)272-8351. The examiner can normally be reached Mon-Fri, 7AM-3:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RYAN PITARO can be reached on (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Tuesday, May 26, 2026
1 Specification [0041] "... Variability in the manufacturing process of the wind turbine blade, such as fiber diameter, volume fraction of fiber, and minimum space between fibers, can lead to artifacts, such as gases being trapped during the curing process of the resin, creating porous material...." – here the artifacts/gases trapped are considered as voids and void is used to map the limitations pertaining to artifacts
2 Specification [0041] "... Variability in the manufacturing process of the wind turbine blade, such as fiber diameter, volume fraction of fiber, and minimum space between fibers, can lead to artifacts, such as gases being trapped during the curing process of the resin, creating porous material...." – here the artifacts/gases trapped are considered as voids and void is used to map the limitations pertaining to artifacts
Prosecution Insights