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 16-17 and 19-29 are presented for examination based on the amended claims in the application filed on March 5, 2026. Claims 1-15 and 18 have been cancelled by the applicant.
Claims 16-17 and 20-29 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by US 2024/0185028 A1 Liu, Wing et al. [herein “Liu”].
Claim 19 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu as applied to claim 16, and in view of US 2018/0052445 A1 Shapiro, Vadim et al. [herein “Shapiro”].
This action is made Final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed March 5, 2026 has been entered. Claims 16-17 and 19-29 remain pending in the application. Applicant’s amendments to the Specification and Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed December 3, 2025.
Claim Rejections - 35 U.S.C. § 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(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 16-17 and 20-29 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by US 2024/0185028 A1 Liu, Wing et al. [herein “Liu”].
As per claim 16, Liu teaches “A system comprising: a computing system comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to cause the system to perform a method”. (Claim 50, “A non-transitory tangible computer-readable medium storing instructions which, when executed by one or more processors, cause a system to perform a method for design optimization and/or performance prediction of a material system, wherein the method is in accordance with claim 26”. The examiner as interpreted that having a non-transitory tangible computer-readable medium storing instructions which, when executed by one or more processors, cause a system to perform a method as a system comprising: a computing system comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to cause the system to perform a method.)
Liu teaches “describing governing equations of an additive manufacturing process”. (Para. 0096, “The current invention introduces the Hierarchical Deep Learning Neural Networks-Artificial Intelligence (HiDeNN-AI), which is a mechanistic artificial intelligence framework for the development of new scientific principles, knowledge creation processes and material systems and simulation technology innovation, aimed at tackling the aforementioned types of problems”. Para. 0099, “HiDeNN-AI and related data science techniques have shown a wide array of applications including data-driven modeling of elastic and elastic-plastic material laws and heterogenous material laws through component expansions, prediction of adolescent idiopathic scoliosis, data-driven characterization of thermal models, data-driven microstructure and microhardness design in additive manufacturing using self-organizing map, among others” [A method comprising: equations of an additive manufacturing process]. Para. 0135, “To discover unknown governing equations from data, HiDeNN has operation layers” [describing governing equations of an additive manufacturing process]. Further see Para. 0096-0099 and 0135. The examiner has interpreted that using a Hierarchical Deep Learning Neural Networks-Artificial Intelligence to model elastic material laws through data-driven microstructure and microhardness design in additive manufacturing using governing equations as a method comprising: describing governing equations of an additive manufacturing process.)
Liu teaches “refactoring the governing equations into (1) constitutive laws with unknown coefficients and (2) conservation laws” (Para. 0124, “Type 2 or mechanistically insufficient problems with limited data: The term mechanistic refers to the theories which explain a phenomenon in purely physical or deterministic terms. Type 2 problems are characterized by physical equations that require complementary data to provide a complete solution” [refactoring the governing equations into constitutive laws with unknown coefficients]. Para. 0180, “Type 2 problems are problems for which the available physical information is incomplete. For example, the governing equations may be known, but all the parameters in the governing equations are not explicitly identified. To illustrate, we present here how fatigue life of an AM part can be predicted from statistical information about microstructures with porosity. In this case, we know the governing physics of the problem on the continuum scale but there is limited data relating microstructural porosity and process parameters, and the spread in fatigue life is quite large making empirical fatigue predictions inaccurate” [refactoring the governing equations into (1) constitutive laws with unknown coefficients and (2) conservation laws]. Further see Para. 0124 and 0180. The examiner has interpreted that having known governing equations containing governing physics on the continuum scale and not explicitly identified parameters that is mechanistically insufficient to provide a complete solution as refactoring the governing equations into (1) constitutive laws with unknown coefficients and (2) conservation laws.)
Liu teaches “discretizing the governing equations”. (Para. 0190, “In HiDeNN-FEM, enrichment functions through the multiplication of neurons is equivalent to the enrichment in standard finite element methods, that is, generalized, extended, and partition of unity finite element methods” [e.g., discretizing the governing equations]. Further see Para. 0190, 0204, and 0234. The examiner has interpreted that enriching the functions such as through the use of finite element methods as discretizing the governing equations.)
Liu teaches “training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training regardless of a granularity of the constitutive laws, thereby yielding a reduced-order set of governing equations”. (Para. 0135, “To discover unknown governing equations from data, HiDeNN has operation layers. In this layer, the neurons are connected through weights and biases in a way that mimics the behavior of different spatiotemporal operators. Through proper training (i.e. minimization of the loss function in the HiDeNN), the operation layer can be trained to discover hidden physics from data” [training the unknown coefficients of the constitutive laws]. Para. 0189, “Furthermore, we apply HiDENN to discover governing dimensionless parameters from experimental mechanistic data. The successful application of HiDeNN to such problems implies that similar framework can be applied to the field where the explicit physics is scarce, such as additive manufacturing” [training the unknown coefficients of the constitutive laws with experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training]. Para. 0183, “Another approach to solve type 2 problems is using transfer learning to combine the experimental and simulation data. Transfer learning refers to taking a pre-trained machine learning model and extending it to new circumstances combining experimental and computational data. These pre-trained models can be trained by experimental data and improved by combining simulation data or vice versa. It is an effective and efficient solution because experimental data come from a more realistic source but harder to get and simulation data can be easily generated but suffer from simplified assumptions in physics. By fusing the models with transfer learning, the HiDENN can leverage small amount of experimental data to compensate for the lack of knowledge in physics coming from computational data” [training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process]. Para. 0097, “HiDENN-AI platform mimics the way human civilization has discovered solution to difficult and unsolvable problems from time immemorial. Instead of heuristics, the HiDeNN-AI uses machine learning methods such as active deep learning and hierarchical neural network(s) to process input data, extract mechanistic features from it, reduce dimensions, learn hidden relationships through regression and classification, and provide a knowledge database. The resulting reduced order form can be utilized for design and optimization of new scientific and engineering systems” [thereby yielding a reduced-order set of governing equations]. Para. 0192, “As a trade-off between accuracy and efficiency, we propose a highly efficient solution strategy called HiDeNN-PGD. Although the solution is less accurate than HiDENN-TD, HiDENN-PGD still provides a higher accuracy than PGD/TD and FEM with only a small amount of additional cost to PGD” [producing trained models that have the tradeoff between high accurate or high efficiency, e.g. regardless of a granularity of the constitutive laws]. Further see Para. 0097, 0135, 0183, 0189, and 0192. The examiner has interpreted that discovering governing equations through training to discover the hidden physics using experimental mechanistic data and scarce explicit physics in addition to combine experiment and simulation data in the training to compensate for the lack of knowledge in the physics in a trade-off between different models of accuracy and efficiency to find a solution that results in a reduced order as training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training regardless of a granularity of the constitutive laws, thereby yielding a reduced-order set of governing equations.)
Liu teaches “an additive manufacturing apparatus coupled to the computing system; wherein the additive manufacturing apparatus produces a part using parameters derived from simulating of the additive manufacturing process for the part, the computing system simulating the additive manufacturing process and determining the parameters.” (Para. 0236, “Module (200) is used wherein mechanistic features such as strain concentration, von-Mises stress distribution, etc., can be extracted and further dimension can be reduced by applying a K-means clustering algorithm in module (300). A mechanistic reduce order model (500) can be established by utilizing the offline clustering database and solving Lipmann-Schwinger equations online. This reduce order model can predict the mechanical response in a very fast and efficient manner and can be extended to multiple scales” [e.g., parameters derived during the simulating of the additive manufacturing process for the part and the computing system simulating the additive manufacturing process and determining the parameters]. Para. 0208, “In one embodiment of the present invention, a data-driven concurrent n-scale modeling theory (FExSCA.sup.n−1) is proposed utilizing a mechanistic reduced order model (ROM) called self-consistent clustering analysis (SCA). The present invention demonstrated this theory with a FExSCA.sup.2 approach to study the 3-scale woven carbon fiber reinforced polymer (CFRP) laminate structure. FExSCA.sup.2 significantly reduced expensive 3D nested composite representative volume clements (RVEs) computation for woven and unidirectional (UD) composite structures by developing a material database. The modeling procedure is established by integrating the material database into a woven CFRP structural numerical model, formulating a concurrent 3-scale modeling framework. This framework provides an accurate prediction for the structural performance (e.g., nonlinear structural behavior under tensile load), as well as the woven and UD physics field evolution. The concurrent modeling results are validated against physical tests that link structural performance to the basic material microstructures” [an additive manufacturing apparatus coupled to the computing system, wherein the additive manufacturing apparatus produces a part using parameters]. Further see Para. 0208 and 0236. The examiner has interpreted that validating a model that produces mechanistic features to establish a reduced order model using physical tests as an additive manufacturing apparatus coupled to the computing system; wherein the additive manufacturing apparatus produces a part using parameters derived from simulating of the additive manufacturing process for the part, the computing system simulating the additive manufacturing process and determining the parameters.)
As per claim 17, Liu teaches “wherein the additive manufacturing process for the part is simulated using the reduced-order set of governing equations and employing neural networks”. (Para. 0196, “the present invention is directed to adaptive hyper reduction for additive manufacturing thermal fluid analysis. In particular, thermal fluid coupled analysis is essential to enable an accurate temperature prediction in additive manufacturing. However, numerical simulations of this type are time-consuming, due to the high non-linearity, the underlying large mesh size and the small time step constraints. The present invention discloses a novel adaptive hyper reduction method for speeding up these simulations. The difficulties associated with non-linear terms for model reduction are tackled by designing an adaptive reduced integration domain. The proposed online basis adaptation strategy is based on a combination of a basis mapping, enrichment by local residuals and a gappy basis reconstruction technique. The efficiency of the proposed method is demonstrated by representative 3D examples of additive manufacturing models, including single-track and multi-track cases” [wherein the additive manufacturing process for the part is simulated using the reduced-order set of governing equations]. Further see Para. 0236, “Where finite element (FE) software can be integrated into the SCA methodology—wherein FE software can interface at the macro (or top level) of the simulation, with n sub-levels of a multiscale simulation being handled through SCA, for composite design. FE-SCA^n is integrated into the HiDeNN-AI platform. Module (100) can be used by defining composite constituents, microstructure, volume fraction and temperature as inputs to FE-SCA^n for generation of stress-strain data. Module (200) is used wherein mechanistic features such as strain concentration, von-Mises stress distribution, etc., can be extracted and further dimension can be reduced by applying a K-means clustering algorithm in module (300). A mechanistic reduce order model (500) can be established by utilizing the offline clustering database and solving Lipmann-Schwinger equations online. This reduce order model can predict the mechanical response in a very fast and efficient manner and can be extended to multiple scales” [e.g., wherein the additive manufacturing process for the part is simulated using the reduced-order set of governing equations]. Para. 0190, “The current invention introduces the Hierarchical Deep Learning Neural Networks-Artificial Intelligence (HiDeNN-AI), which is a mechanistic artificial intelligence framework for the development of new scientific principles, knowledge creation processes and material systems and simulation technology innovation, aimed at tackling the aforementioned types of problems” [employing neural networks]. Para. 0097, “HiDENN-AI platform mimics the way human civilization has discovered solution to difficult and unsolvable problems from time immemorial. Instead of heuristics, the HiDeNN-AI uses machine learning methods such as active deep learning and hierarchical neural network(s) to process input data, extract mechanistic features from it, reduce dimensions, learn hidden relationships through regression and classification, and provide a knowledge database. The resulting reduced order form can be utilized for design and optimization of new scientific and engineering systems” [wherein the additive manufacturing process for the part is simulated using the reduced-order set of governing equations and employing neural networks]. Further see Para. 0097-0099, 0196, 0208, and 0236. The examiner has interpreted that enabling accurate temperature prediction in additive manufacturing using a novel adaptive hyper reduction method for speeding up these simulations using FE software in predicting the mechanical repose using the reduced order model and through the use of Hierarchical Deep Learning Neural Networks-Artificial Intelligence as wherein the additive manufacturing process for the part is simulated using the reduced-order set of governing equations and employing neural networks.)
As per claim 20, Liu teaches “wherein the additive manufacturing process is one of material extrusion, powder bed fusion, material jetting, binder jetting, or directed energy deposition.” (Para. 0184, “One example of such a problem is the prediction of the melt pool dimensions in metal additive manufacturing. The melt pool dimension can be predicted from computational models. However, these models fail to capture the uncertainties coming from process parameters, spatial distribution of powder particles and corresponding instantaneous change in the melt pool dimension. FIG. 39 presents a schematic of the problem. A single track sample (printed using an EOS M280 Laser Powder Bed Fusion (L-PBF) system) of commercially available Inconel 625 gas atomized powder is shown in the FIG. 39” [wherein the additive manufacturing process is powder bed fusion]. Further see Para. 0184. The examiner has interpreted that predicting melt pool dimensions in metal additive manufacturing such as using an EOS M280 Laser Powder Bed Fusion system as wherein the additive manufacturing process is powder bed fusion.)
As per claim 21, Liu teaches “wherein the neural networks are recurrent neural network for temporal integration.” (Para. 0132, “The Hierarchical DNNs can be any type of neural network, including convolutional neural network (CNN), recurrent neural network (RNN)” [wherein the neural network is a recurrent neural network]. Para. 0135, “To discover unknown governing equations from data, HiDeNN has operation layers. In this layer, the neurons are connected through weights and biases in a way that mimics the behavior of different spatiotemporal operators” [e.g., temporal operators]. Para. 0179, “P(x, t, T, UTSexp) is a function of operators and expressions such as addition, multiplication, differentiation, or integration” [for temporal integration]. Further see Para. 0127-0135, 0179, and 0234. The examiner has interpreted that having the Hierarchical DNNs be a recurrent neural network that mimics the behavior of different spatiotemporal operators such as integration as wherein the neural networks are recurrent neural network for temporal integration.)
As per claim 22, Liu teaches “wherein training of the unknown coefficients uses tensor-based computations for spatial operators and/or temporal operators.” (Para. 0192, “the present invention is directed to a tensor decomposition (TD) based reduced-order model of the hierarchical deep-learning neural networks (HiDeNN)” [uses tensor-based computations]. Para. 0135, “To discover unknown governing equations from data, HiDeNN has operation layers. In this layer, the neurons are connected through weights and biases in a way that mimics the behavior of different spatiotemporal operators. Through proper training (i.e. minimization of the loss function in the HiDeNN), the operation layer can be trained to discover hidden physics from data” [wherein training of the unknown coefficients uses tensor-based computations for spatial operators and/or temporal operators]. Further see Para. 0135 and 0192. The examiner has interpreted that discovering hidden physics in governing equations through spatiotemporal operators in training operation layers and tensor decomposition as wherein training of the unknown coefficients uses tensor-based computations for spatial operators and/or temporal operators.)
As per claim 23, Liu teaches “wherein the governing equations are one or more of ordinary or partial differential equations, differential-algebraic equations, integral equations, or integro-differential equations.” (Para. 0205, “The key idea of this method is to solve a set of fully coupled governing partial differential equations using the clusters generated from unsupervised machine learning at multiple length scales” [wherein the governing equations are partial differential equations]. Further see Para. 0205. The examiner has interpreted that solving a set of governing partial differential equations as wherein the governing equations are partial differential equations.)
As per claim 24, Liu teaches “wherein discretizing of the governing equations comprises using one or more of a finite difference scheme, a finite volume scheme, a finite element scheme, a spectral scheme, or a mimetic scheme.” (Para. 0190, “In HiDeNN-FEM, enrichment functions through the multiplication of neurons is equivalent to the enrichment in standard finite element methods, that is, generalized, extended, and partition of unity finite element methods” [e.g., wherein the discretizing of the governing equations comprises using a finite element scheme]. Further see Para. 0190, 0204, and 0234. The examiner has interpreted that enriching the functions such as through the use of finite element methods as wherein the discretizing of the governing equations comprises using a finite element scheme.)
As per claim 25, Liu teaches “wherein training of the constitutive laws comprises assuming an algebraic form for the constitutive laws with the unknown coefficients and fitting the unknown coefficients to the simulated data and/or the experimental data.” (Para. 0124, “Type 2 or mechanistically insufficient problems with limited data: The term mechanistic refers to the theories which explain a phenomenon in purely physical or deterministic terms. Type 2 problems are characterized by physical equations that require complementary data to provide a complete solution” [wherein training of the constitutive laws comprises assuming an algebraic form for the constitutive laws with the unknown coefficients]. Para. 0183, “Another approach to solve type 2 problems is using transfer learning to combine the experimental and simulation data. Transfer learning refers to taking a pre-trained machine learning model and extending it to new circumstances combining experimental and computational data. These pre-trained models can be trained by experimental data and improved by combining simulation data or vice versa. It is an effective and efficient solution because experimental data come from a more realistic source but harder to get and simulation data can be easily generated but suffer from simplified assumptions in physics. By fusing the models with transfer learning, the HiDENN can leverage small amount of experimental data to compensate for the lack of knowledge in physics coming from computational data” [experimental data]. Para. 0097, “HiDENN-AI platform mimics the way human civilization has discovered solution to difficult and unsolvable problems from time immemorial. Instead of heuristics, the HiDeNN-AI uses machine learning methods such as active deep learning and hierarchical neural network(s) to process input data, extract mechanistic features from it, reduce dimensions, learn hidden relationships through regression and classification, and provide a knowledge database” [fitting the unknown coefficients to the experimental data]. Further see Para. 0097, 0124, and 0183. The examiner has interpreted that characterized by physical equations of mechanistically insufficient problems with complementary data to provide a complete solution such as experimental data to extract mechanistic features through regression as wherein training of the constitutive laws comprises assuming an algebraic form for the constitutive laws with the unknown coefficients and fitting the unknown coefficients to the simulated data and/or the experimental data.)
As per claim 26, Liu teaches “wherein the unknown coefficients parameterize material properties including one or more of elasticity, viscosity, conductivity, heat capacity, surface tension, solidification parameters, or any combination thereof.” (Para. 0124, “Type 2 or mechanistically insufficient problems with limited data: The term mechanistic refers to the theories which explain a phenomenon in purely physical or deterministic terms. Type 2 problems are characterized by physical equations that require complementary data to provide a complete solution” [unknown coefficients]. Para. 0099, “HiDeNN-AI and related data science techniques have shown a wide array of applications including data-driven modeling of elastic and elastic-plastic material laws and heterogenous material laws through component expansions, prediction of adolescent idiopathic scoliosis, data-driven characterization of thermal models, data-driven microstructure and microhardness design in additive manufacturing using self-organizing map, among others” [wherein the unknown coefficients parameterize material properties including elasticity]. Further see Para. 0099 and 0124. The examiner has interpreted that characterizing a problem to explain a purely physical phenomenon including elastic material laws as wherein the unknown coefficients parameterize material properties including elasticity.)
As per claim 27, Liu teaches “wherein the constitutive laws comprise one or more of: a single-physics constitutive relation, a multi-physics coupling interaction, or any combination thereof.” (Para. 0124, “Type 2 or mechanistically insufficient problems with limited data: The term mechanistic refers to the theories which explain a phenomenon in purely physical or deterministic terms. Type 2 problems are characterized by physical equations that require complementary data to provide a complete solution” [the constitutive laws]. Para. 0099, “HiDeNN-AI and related data science techniques have shown a wide array of applications including data-driven modeling of elastic and elastic-plastic material laws and heterogenous material laws through component expansions, prediction of adolescent idiopathic scoliosis, data-driven characterization of thermal models, data-driven microstructure and microhardness design in additive manufacturing using self-organizing map, among others” [wherein the unknown coefficients parameterize material properties including elasticity]. Para. 0138, “the HiDENN is used to solve a solid mechanics problem and capture stress concentration by training the position of the nodes used for the discretization to minimize the potential energy of the system” [multi-physics coupling interaction]. Further see Para. 0099, 00122-0125, and 0138-0139. The examiner has interpreted that characterizing a problem to explain a purely physical phenomenon including elastic material laws in addition to solving mechanics problems of stress to minimize potential energy as wherein the constitutive laws comprise one or more of a single-physics constitutive relation and a multi-physics coupling interaction.)
As per claim 28, Liu teaches “wherein the constitutive laws comprise a relationship between one or more physical quantities related to the additive manufacturing process measured at one or more of at points, along curve segments, over surface areas, or within volumes of finite length scale.” (Para. 0103, “Experimental data may come in the form of measurement” [additive manufacturing process measured]. Para. 0178, “this AI approach can capture very complex relationships between temperature history and ultimate tensile strength in AM” [wherein the constitutive laws comprise a relationship between one or more physical quantities related to the additive manufacturing process]. Para. 0189, “Furthermore, we apply HiDENN to discover governing dimensionless parameters from experimental mechanistic data. The successful application of HiDeNN to such problems implies that similar framework can be applied to the field where the explicit physics is scarce, such as additive manufacturing” [wherein the constitutive laws comprise a relationship between one or more physical quantities related to the additive manufacturing process]. Para. 0189, “Unique features of HiDENN can offer automatic enrichment at the locations of strain concentration thus capturing effect of variable microstructure at part-scale” [at points]. Further see Para. 0103, 0178, and 0189. The examiner has interpreted that capturing a relationship between the temperature history and the ultimate tensile strength in additive manufacturing using measured experimental mechanistic data to capture features at strain concentration locations as wherein the constitutive laws comprise a relationship between one or more physical quantities related to the additive manufacturing process measured at points.)
As per claim 29, Liu teaches “wherein the one or more physical quantities comprise flow velocity and pressure, temperature, stress, strain, strain rate, heat flux, heat content, force, displacement, phase, or any combination thereof.” (Para. 0154-0155, “The present invention postulates that the resistance factor λ depends on four parameters: the steady-state velocity of fluid U, kinematic viscosity ν, pipe diameter d, and surface roughness of the pipe Ra: λ=f (U, ν, d, Ra)” [wherein the one or more physical quantities comprise flow velocity]. Para. 0189, “Unique features of HiDENN can offer automatic enrichment at the locations of strain concentration thus capturing effect of variable microstructure at part-scale” [stress]. Para. 0178, “this AI approach can capture very complex relationships between temperature history and ultimate tensile strength in AM” [strain]. Further see Para, 0154-0155, 0178, and 0189. The examiner has interpreted that capturing relationships between temperature and ultimate tensile strength in additive manufacturing in addition to steady-state velocity of fluid at locations of strain concentration as wherein the one or more physical quantities comprise flow velocity and temperature, stress, or strain.)
Claim Rejections - 35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claim 19 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu as applied to claim 16, and in view of US 2018/0052445 A1 Shapiro, Vadim et al. [herein “Shapiro”].
As per claim 19, Liu teaches “wherein the parameters include one or more of temperature, pressure, laser power, scan rate, [and material deposition rate].” Para. 0178, “this AI approach can capture very complex relationships between temperature history and ultimate tensile strength in AM” [wherein the parameters include temperature]. Para. 0179, “We can use the HiDeNN framework to solve this problem and obtain insight on the governing physics as shown in FIG. 36. To solve this example, the HiDeNN will consist of input layer (location, time, temperature, and manufacturing process parameter (such as scan speed) as inputs), pre-processing functions, EXP-NN layer, solution layer, and operation layers” [wherein the parameters include temperature, scan rate]. Para. 0185, “FIG. 40 shows the HiDENN structure for this problem. The inputs are the coordinates and time in spatial and temporal space. For the parametric space, P refers to the laser power, v refers to the scan speed, and C, and σ(ε) refer to the material properties. The pre-processing functions are used to extract features from experimental data such as images of the melt pool” [wherein the parameters include laser power, scan rate, pressure]. Para. 0244, ““FIG. 20 shows a process of system and design of a non-orthogonal woven composite in multiscale. During the process, ignoring yarn plasticity leads to inaccurate prediction; the 3-scale model predicts the loading force history with good accuracy; and two different Vf form the lower and upper bounds for the loading force history. Lower plastic strain concentrates in low stress regions. Matrix phase in the yarn carries considerable loads under shear deformation” [e.g., wherein the parameters include pressure]. Further see Para. 0178—0179, 0185, and 0244.. The examiner has interpreted that capturing relationships between temperature and ultimate tensile strength, temperature, laser power, scan speed, force history, location, and stress in additive manufacturing as wherein the parameters include one or more of temperature, pressure, laser power, and scan rate.)
Liu does not specifically teach “wherein the parameters include one or more of material deposition rate”.
However, in the same field of endeavor namely estimating characteristics in additive manufacturing, Shapiro teaches “wherein the parameters include one or more of material deposition rate”. (Para. 0018, “The AM tool 105 constructs the article 300 by forming a series of roads 400 over multiple layers to approximate the shape defined by the design model 145” [e.g., constructing a part using parameters] Para. 0019, “FIG. 5 illustrates the construct of the printed device resulting from the execution of the tool path model 150 by the tool. The tool path is decomposed into a plurality of layers and linear road segments having dimensions consistent with the volume of material being deposited and the speed and direction of the print head. At any given instant, the print head deposits some minimum manufacturing volume (MMV), whose shape may be approximated in terms of simple quadratic and/or superelliptic primitives with dimensions determined by the road width and layer height” [e.g., constructing a part using parameters including material deposition rate] Para. 0067, “A simulation model may be used to predict how the mesoscale geometry-material model 165 behaves under externally applied boundary conditions” [e.g., from a simulation]. Further see Para. 0018-0019 and 0067. The examiner has interpreted that constructing an article defined by the design model that resulting from the execution of the tool path model having the volume of material being deposited and the speed and direction of the print head verified in a simulation model as wherein the parameters include one or more of material deposition rate.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the parameters include one or more of material deposition rate” as conceptually seen from the teaching of Shapiro, into that of Liu because this modification of producing a part based on the modeling of the material deposition rate for the advantageous purpose of accurately model and assess impacting and printing imperfections on the performance of the printed structure (Shapiro Para. 0022). Further motivation to combine be that Liu and Shapiro are analogous art to the current claim are directed to estimating characteristics in additive manufacturing.
Response to Arguments
Applicant’s arguments, see Pg. 8-14, filed March 5, 2026, with respect to the rejection(s) of the independent claim 16 under 35 U.S.C. § 101 have been fully considered and are persuasive with regards to the amended independent claim that integrates the claimed invention into a practical application. Therefore, the rejection has been withdrawn.
Applicant's arguments filed on March 5, 2026 have been fully considered but they are not persuasive with respect to the rejection of claim 16 under 35 U.S.C. § 102(a)(2).
Applicant argues that the reference does not teach each and every limitation in the amend claim 16 because cited reference fails to teach “an additive manufacturing apparatus coupled to the computing system; wherein the additive manufacturing apparatus produces a part using parameters derived from simulating of the additive manufacturing process for the part, the computing system simulating the additive manufacturing process and determining the parameters” (See Applicant’s response, Pg. 15-16).
Applicant rightly asserts the MPEP § 2143.03 “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
As mapped in the rejection above, Liu discloses “an additive manufacturing apparatus coupled to the computing system; wherein the additive manufacturing apparatus produces a part using parameters derived from simulating of the additive manufacturing process for the part, the computing system simulating the additive manufacturing process and determining the parameters” as validating a model that produces mechanistic features to establish a reduced order model using physical tests. After the simulating and finding a solution that results in a reduced order model that discovers the hidden physics using a trained neural network, physical tests are conduct to validate the findings of the model. In terms of additive manufacturing, this is creating a physical part using the model, and thus the claimed limitation is taught. Additional emphasis has been added to this mapping in the rejection above to the amended claim.
Therefore, all of the limitations of the amended claims 16 are disclosed in Liu. Therefore, applicant’s arguments are not persuasive and the rejection of claim 16 as anticipate by Liu is maintained.
Applicant argues that the reference does not teach each and every limitation in the amend claim 16 because cited reference fails to teach “training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training regardless of a granularity of the constitutive laws, thereby yielding a reduced-order set of governing equations” (See Applicant’s response, Pg. 16-17).
Applicant rightly asserts the MPEP § 2143.03 “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
As mapped in the rejection above, Liu discloses “training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training regardless of a granularity of the constitutive laws, thereby yielding a reduced-order set of governing equations” as discovering governing equations through training to discover the hidden physics using experimental mechanistic data and scarce explicit physics in addition to combine experiment and simulation data in the training to compensate for the lack of knowledge in the physics in a trade-off between different models of accuracy and efficiency to find a solution that results in a reduced order. HiDENN can leverage small amount of experimental data to compensate for the lack of knowledge in physics coming from computational data and the models are trained by the experimental data to discover governing dimensionless parameters from experimental mechanistic data resulting in a reduced order form, e.g., distinctly training the unknown coefficients of the constitutive laws regardless of a granularity of the constitutive laws yielding a reduced-order set of governing equations. Thus, the claimed limitation is taught.
Therefore, all of the limitations of the amended claims 16 are disclosed in Liu. Therefore, applicant’s arguments are not persuasive and the rejection of claim 16 as anticipate by Liu is maintained.
Applicant’s amendment to the claims filed March 5, 2026, with respect to the rejection(s) of claim 19 under 35 U.S.C. 102(a)(2) has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the amended claims and previously cited art, as necessitated by the amendment, in the rejection above
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2019/0337232 A1 US 2022/0129602 A1 Prabha Narra, Sneha et al. teaches training a machine learning engine for modeling of a physical system includes using a set of generated training data including the non-dimensionalized parameter and outputting a prediction of a value of a physical effect of the physical system for values of the variables that are not included in the process data. The method includes controlling an additive manufacturing process for the material by setting the at least one physical property to the value of the at least one process variable during fabrication of a part.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
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/SIMEON P DRAPEAU/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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