DETAILED ACTION
Claims 1-13, 15, 35, and 36 are presented for examination. Claims 1-13, 15, 35, and 36 stand currently amended.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 17 March 2026 has been entered.
Response to Arguments
Applicant's remarks filed 17 March 2026 have been fully considered and Examiner’s response is as follows:
Regarding §101:
Applicant remarks page 8 argues:
Claim 1 recites steps such as "applying ... machine learning based clustering," "computing ... an interaction tensor," and "solving ... the generalized Lippmann-Schwinger integral equation." Applicant acknowledges that these steps, viewed in isolation, may involve mathematical concepts and algorithms. If the claim were merely directed to performing these calculations on generic data, it might be considered directed to a judicial exception.
However, the claim must be viewed as a whole. The claim is not directed solely to the mathematics. It is directed to a computer-implemented method for design optimization and performance prediction of a physical material system comprising additively manufactured (AM) metal. The mathematical steps are components of a larger process tied to physical manufacturing.
This argument is unpersuasive.
The body of the claims are not closely confined to any specific physical manufacturing steps. Claim 1 last clause does recite a generic “apply it” step of: “automatically control, optimizing, or validating fabrication parameters or operational limits of the physical material system during live material fabrication or operation with the response prediction.” This amounts to mere instructions to “apply it” with the response prediction. See MPEP §2106.05(f).
Applicant remarks page 9 further argues:
1. Improvement to a Technology or Technical Field (MPEP § 2106.0S(a)): The claim effects an improvement to the technology of additive manufacturing (AM).
This argument is unpersuasive. As discussed immediately above, the claims are not closely confined to any specific manufacturing steps.
Applicant remarks page 9 further argues:
This specific combination allows for real-time control of a physical manufacturing process that was previously impossible due to computational latency. This is analogous to Enfish, where the claims were directed to an improvement in computer functionality (self-referential table), and McRO, where the claims improved the technology of lip synchronization. Here, the claim improves the technology of AM process control by enabling microstructure-based feedback loops that were previously computationally infeasible.
This argument is unpersuasive.
Unlike Enfish, the claims do not involve a particular data structure within memory. The claimed MVEs are recited at the level of a mathematical construction and is not tied to any particular representation within computer memory or computer data types. Examiner notes the MVEs are interpreted to encompass such generic elements as “finite elements.”
Unlike McRO, the claims herein recite a non-specific “controlling, optimizing, or validating” of a non-specific “fabrication parameter or operational limits” somehow or someway “with the response prediction.” The instant claims involve a high level of generality regarding the implementation steps of the last clause while the claims in McRO involve significantly more specificity on how the lip synchronization is actually accomplished.
Applicant remarks page 9 further argues:
The input is not generic data; it is "sensor-generated experimental data obtained from physical characterization of the physical material system during fabrication or qualification." This ties the data to physical sensors monitoring a physical process.
This argument is unpersuasive. Applicant appears to be confusing generic data gathering and generic data. Examiner has asserted the acquiring and collection steps are generic data gathering because the data gathering is not tied to any particular method or machine for acquiring said data. See MPEP §2106.05(g).
Applicant remarks page 9-10 further argues:
This goes beyond "mere instructions to apply an exception on a computer." The calculation is inextricably linked to the operation of the AM machine. This mirrors Diehr, where the Arrhenius equation was used to control the curing process of rubber. Here, the Lippmann-Schwinger equation is used to control the fabrication of AM metal.
This argument is unpersuasive. Diehr involved a claim limitation of opening the rubber press. The press is a particular machine and the instruction to open it is a particular operation of a particular machine. The instant claims are not as specific and involve neither particular apparatus nor particular steps or operations.
Applicant remarks page 10 further argues:
3. Transformation of Article (MPEP § 2106.05(c)): The method effects the transformation of the physical material system. By "automatically controlling ... fabrication parameters ... during live material fabrication," the method directly influences the physical structure and properties of the AM metal being produced. The ''physical response prediction comprising a fatigue loading response" is used to alter the physical manufacturing parameters, thereby transforming the raw material into a finished part with specific, optimized properties.
This argument is unpersuasive. MPEP §2106.05(c) lists five factors to consider when a transformation is recited in a claim including the particularity or generality of the transformation. Claim 1 last clause merely requires the transformation is “with the response prediction [of fatigue loading]” but does not confine the claim to an optimization of fatigue load. Claim 1 encompasses transformation of other unspecified characteristics by the automatic controlling, optimizing, or validating.
Furthermore, with the alternative of validating when the validation determines the current parameters or operational limits are acceptable there is no physical change at all to the resulting fabricated article because performing a validation does not physically change the characteristics of that which is validated.
Applicant remarks page 10 further argues:
Claim 1 imposes significant meaningful limits:
• Specific data source: The data is not abstract; it is "sensor-generated. .. comprising at least one of x-ray diffraction (XRD) data, transmission electron microscope (TEM) image data, and in-situ process sensor data." This limits the claim to data derived from specific physical characterization tools, not just any data.
This argument is unpersuasive. The particularity of the data merely establishes the respective technological environment. See MPEP §2106.05(h) which states:
For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data)
Applicant remarks page 10 further argues:
Claim 1 imposes significant meaningful limits:
• Specific technical structure: The digital representation is not generic; it comprises "microstructure volume elements (MVEs)" associated with "corresponding physical portion[s]" of the material. This ties the digital model to the physical geometry of the part.
This argument is unpersuasive. MVEs are not a particular technical structure. Specification page 2 last paragraph states “Microstructure Volume Element” and “When done with finite elements.” This shows finite elements are an example of MVE. MVEs are thus more generic than “finite elements” because MVEs encompass finite elements but also encompass other volume element structures.
Applicant remarks page 10 further argues:
Claim 1 imposes significant meaningful limits:
• Specific timing and context: The solving step occurs "in real time during fabrication or operational use." This is not a post-solution calculation performed after the fact; it is an integral part of the live manufacturing loop.
This argument is unpersuasive.
The “context” is the respective field of use. Generally linking to a field of use fails to integrate an abstract idea into a practical application. See MPEP §2106.05(h).
The timing occurring in “real-time” is not an advantage derived from any improvement to computer technology. Here, the alleged improvement is to the calculating and the computer elements are recited generically. Merely adding generic computer components to perform an abstract method is insufficient. Any alleged speed improvement appears to be a result of the improvement to the mathematical calculations. Improved mathematical concepts remains ineligible subject matter under §101.
Applicant remarks page 10 further argues:
Claim 1 imposes significant meaningful limits:
• Specific action: The output is used for "automatically controlling ... fabrication parameters." This is a specific technical action on a physical machine, not a generic "output result."
This argument is unpersuasive. The output is insignificant extra solution activity. Outputting a response prediction of calculated fatigue loading response is insignificant extra solution activity in the form of an insignificant outputting of the result of the abstract idea. See MPEP §2106.05(g).
Applicant remarks page 11 further argues:
While clustering and finite element analysis are known individually (see Bessa.docx regarding Self-Consistent Clustering Analysis; Ma.html regarding hybrid computational fabrication), the application of reduced-order modeling (clustering+ interaction tensors) to solve the Lippmann-Schwinger equation specifically for real-time control of Additive Manufacturing based on in-situ sensor data is unconventional.
This argument is unpersuasive.
§101 subject matter eligibility step 2B relating to whether or not a limitation is well-understood, routine, or conventional is an analysis of the “additional” limitations. It is irrelevant whether or not limitations comprising the abstract idea itself are well-understood, routine, or conventional. Here, the Lippmann-Schwinger equation is explicit mathematical equations comprising the identified abstract idea itself.
Applicant remarks page 11 further argues:
Standard practice in AM involves post-process validation or open-loop control due to the computational cost of microstructure simulation (see the Application, Example 6). Claim 1 enables closed-loop, microstructure-aware control. This specific arrangement provides an inventive concept
This argument is unpersuasive. Claim 1 does not recite any specific control steps nor “closed-loop, microstructure-aware control” as argued. Accordingly, Applicant is arguing limitations or specificity which not currently recited in the claims.
Applicant remarks page 11 further argues:
The claim does not preempt all uses ….
This argument is unpersuasive. Preemption is evaluated by performing the multi-step §101 analysis. See Examiner’s entire §101 analysis for the considerations related to preemption.
Applicant remarks page 12 further argues:
Therefore, claim 1 is directed to a specific technological process for improving additive manufacturing. It integrates mathematical calculations into a practical application by using them to control a physical machine in real-time based on physical sensor data. It imposes meaningful limits on any judicial exception (MPEP § 2106.05(±)) and provides an inventive concept through its unconventional combination of steps (Step 2B).
This argument is unpersuasive.
Claim 1 is not limited to a control. Claim 1 last clause recites control in the alternative along with other non-control alternatives. However, Examiner notes that even if the other alternatives were struck from the claim, the control is currently recited in a non-specific manner. The control merely must be done “with the response prediction.” No particular manner or method for performing the control is recited.
Regarding §102/103:
Applicant remarks page 12 argues:
The cited references fail to teach "additively manufactured (AM) metal" with "in-situ" data for "live control"
This argument 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 further view of US patent 11,933,747 B2 Zavalij, et al. [herein “Zavalij”].
Furthermore, the additional references Saunders, R., et al. “A Method to Determine Local Stress Fields in Microstructure Features Produced by Additive Manufacturing” Proceedings of ASME 2017 Int'l Mech. Engineering Congress & Expo. (2017) [herein “Saunders”], and Hernández, J.A., et al. “High-performance model reduction techniques in computational multiscale homogenization” Computational Methods Applied Engineering, vol. 276, pp. 149-189 (2014) [herein “Hernandez”] are newly added necessitated by limitations not specifically argued within Applicant’s Remarks.
Applicant remarks page 14 further argues:
The cited references fail to teach solving for ''fatigue loading response" to "control fabrication parameters"
This argument is partially unpersuasive and partially persuasive.
Regarding the fatigue loading response, Bessa page 319 section 1 teach “Ideally, efficient and accurate predictions of the macroscopic behavior of heterogeneous materials should be uniquely obtained from the constitutive behavior of each separate constituent (material phase) and from the information about the material microstructure.” Bessa page 320 first paragraph teaches “capturing highly localized microstructure-induced nonlinear material behaviors, such as plasticity, damage and fatigue.” Predicting macroscopic behavior to capture a fatigue or inelastic deformation corresponds with a physical response prediction of a fatigue loading response.
Bessa page 336 figure 10 teaches “Response of the reduced RUC for the fiber-reinforced composite material under the three step loading path.” Displaying a figure of a respective loading response is outputting the predicted response information.
Regarding control fabrication parameter controlling, Bessa does not explicitly disclose controlling during a live material fabrication; however, in analogous art of monitoring microstructure of metal 3D printing parts, Zavalij column 2 lines 32-37 teach “generating, in real-time, a comprehensive analysis result for the printing object quality, which can be feedback, in real-time, to the 3-D printer to adjust the 3-D printing process control parameters (if needed) to in-situ correct (or prevent) deficiencies and to provide high quality of the 3-D printing objects.”
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 Bessa, Ma, Saunders, and Zavalij. One having ordinary skill in the art would have found motivation to use real-time feedback into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of “correct (or prevent) deficiencies and to provide high quality of the 3-D printing objects.” See Zavalij column 2 lines 32-37.
Claim Interpretation
The claims are read in light of the Specification. All claim terms are read in light of the Specification even while Examiner provides specific clarification here regarding particular claim terms.
The acronym and phrase microstructure volume element (MVE) is interpreted in light of the Specification. Specification page 2 last paragraph states “Microstructure Volume Element” and “When done with finite elements.” This shows finite elements are an example of MVE. Furthermore, figures 4 and 5 shows examples of MVE using finite elements. Accordingly, Microstructure Volume Elements are interpreted as encompassing at least the specific example of finite elements.
Specification page 55 lines 19-22 recites:
Early efforts to apply machine learning to mechanics used a computation and knowledge representation paradigm, which is actually a type of feed forward neural network, to directly ‘learn’ material behavior by training from analytic and experimental data. One early work applied a backpropagation neural network to model the behavior of concrete.
Accordingly, applying machine learning is interpreted as encompassing at least the specific example of training a neural network with the data.
Furthermore, claim 1 recite “for design optimization and performance prediction of a physical material system ….” This recitation is interpreted as an intended use recitation. See MPEP §2111.02.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13, 15, 35, and 36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
See MPEP §2106.
Step 2A is a two prong inquiry. MPEP §2106.04(II)(A). Under 2A(i), the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP §2106.04(a)(2). Under 2A(ii), the second prong, examiners determine whether any additional limitations integrates the judicial exception into a practical application. MPEP §2106.04(d).
Claim 1 step 2A(i):
The claim(s) recite:
1. A computer-implemented method for design optimization and performance prediction of a physical material system comprising an additively manufactured (AM) metal, the method, …, comprising:
generating, …, a multi-scale digital representation of the physical material system, wherein the multi-scale digital representation at each scale comprises microstructure volume elements (MVEs) that are computational building blocks of the physical material system at that scale, each MVE being associated with a corresponding physical portion of the physical material system, wherein the building blocks are defined by measured material properties and structural descriptors extracted from the sensor-generated experimental data and encoded in a domain-decomposed data structure identifying material phases and spatial relationships;
…
applying, …, machine learning based clustering to the collected response field data to generate clusters that minimize a distance between points in a defined response space metric, each cluster representing a reduced-order physical behavior of a plurality of material points;
computing, …, an interaction tensor representing physical interactions of each cluster with each of the other clusters, wherein the collected response-field data, the generated clusters, and the computed interaction tensors are persistently stored in one or more material-system databases;
manipulating, …, a governing partial differential equation (PDE) that governs physical behavior of the physical material system using Green's function to form a generalized Lippmann-Schwinger integral equation;
solving, …, the generalized Lippmann-Schwinger integral equation in real time during fabrication or operational use of the physical material system by retrieving the precomputed interaction tensors and clustered response- field data from the one or more material-system databases, thereby avoiding full-order numerical re-simulation;
….
For designing a respective material system is a recitation of intended use which is not given patentable weight. See “Claim Interpretation” section above. Furthermore, designing and prediction steps correspond with mental processes of evaluation, judgment, and/or opinion.
Microstructure Volume Elements (MVEs) are interpreted as encompassing at least the specific example of finite elements. See “Claim Interpretation” section above. Finite elements are a mathematical construct. Accordingly, generating a representation comprising MVEs is a recitation to perform mathematical operations to create the mathematical data structure. The material properties which define the MVE building blocks are numerical values regardless of their source/origin. Accordingly, the MVE which is generated is a mathematical construct of numerical values.
Applying machine learning based clustering to the collected data to generate clusters that minimize a distance is interpreted as encompassing training a neural network with the data. See “Claim Interpretation” section above. This claim recitation is similar to analysis in the “July 2024 Subject Matter Eligibility Examples” for example #47. That guidance analyzes example #47 step (c) stating:
Step (c) recites training an ANN using a selected algorithm. The training algorithm is a backpropagation algorithm and a gradient descent algorithm. When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient descent algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.
The instant claim recitation of applying machine learning to the collected data is analogous to this example. Similarly, Examiner finds applying machine learning to the collected data to generate clusters that minimize a distance is a recitation of mathematical calculations.
Computing an interaction tensor is a recitation to perform respective mathematical calculations.
Manipulating the governing PDE using Green’s function to form a generalized Lippmann-Schwinger integral equation is an explicit recitation of mathematical operations.
Solving the generalized Lippmann-Schwinger integral equation is an explicit recitation of performing mathematical calculations to determine a numerical value.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 1 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
…, being performed by one or more processors executing instructions stored on a non-transitory computer-readable medium, …
acquiring, …, sensor-generated experimental data obtained from physical characterization of the physical material system during fabrication or qualification, the sensor-generated experimental data comprising at least one of x-ray diffraction (XRD) data, transmission electron microscope (TEM) image data, and in-situ process sensor data;
…
collecting, by the one or more processors, numerical response-field data of the MVEs computed from a physics-based material model of the physical material system over predefined sets of material properties, material constituents, and boundary conditions, the response-field data representing physical stress, strain, deformation, or thermal behavior of the physical material system;
…;
outputting, by the one or more processors, a physical response prediction comprising a fatigue loading response of the AM metal; and
automatically controlling, optimizing, or validating fabrication parameters or operational limits of the physical material system during live material fabrication or operation with the response prediction.
The processor(s), computer-readable media, and database(s) are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Acquiring sensor experimental data of respective types in a non-specific manner is insignificant extra solution activity in the form of data gathering. See MPEP §2106.05(g).
Collecting the data response fields of the MVEs computed from the material model is storing respective data. Storing data and/or data gathering in a non-specific collection is insignificant extra solution activity. See MPEP §2106.05(g).
Outputting a response prediction of calculated fatigue loading response is insignificant extra solution activity in the form of an insignificant outputting of the result of the abstract idea. See MPEP §2106.05(g).
Automatically controlling, or optimizing, or validating the fabrication parameters or operating limits during a fabrication or operation with the result of the abstract idea amounts to mere instruction to ‘apply it’. See MPEP §2106.05(f). In particular, the generality of the recited limitation is not restricted to any particular physical operation and does not indicate how the fabrication parameters or operating limits should be changed for any particular fatigue loading result. Accordingly, considering the particularity or generality of the application weighs towards a finding that this limitation is mere instruction to apply an exception. See MPEP §2106.05(f)(3).
Claim 1 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Limitations analyzed under MPEP §2106.05(b) and (f) in step 2A(ii) above are analyzed the same here under step 2B.
For limitations analyzed under MPEP §2106.05(g) above, MPEP §2106.05(d) provides the examples:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
These data gathering examples are encompassed by the generic recitation of data gathering recited by the claim. Transmitting data encompasses an “outputting” of calculation results. Accordingly, acquiring, storing and retrieving a collection of data and outputting a calculated result fails to amount to significantly more than the judicially excepted abstract idea. Similarly, performing the repetitive calculations in real-time corresponds with using the computer for its most basic function of performing the calculations of solving the integral equations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 2 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
2. The method of claim 1, further comprising 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.
Iterating the calculation process is recitation of additional calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 2 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 2 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 3 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
3. The method of claim 1, wherein the building blocks are defined by material properties and structural descriptors further obtained by modeling.
Modeling material properties and structural descriptors is manipulating respective numerical values via mental process evaluation, judgment, or opinion, or mathematical concepts of respective mathematical calculation.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 3 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 3 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 4 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
4. The method of claim 3, wherein the structural descriptors comprise characteristic length and geometry.
Defining length and geometry is identifying respective mathematical relationships. Geometry is mathematical.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 4 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 4 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 5 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
5. The method of claim 1, wherein the boundary conditions are chosen to satisfy the Hill-Mandel condition.
The Hill-Mandel condition is a mathematical condition. Satisfying the mathematical condition is further recitation of mathematical concepts.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 5 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 5 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 6 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 6 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
6. The method of claim 1, wherein the collected data of response fields comprise a strain concentration tensor, a deformation concentration tensor, stress tensor including PK-I stress and Cauchy stress tensors, plastic strain tensor, or thermal gradient.
Defining the response fields as respective data is selecting a particular data source or type to be manipulated. See MPEP §2106.05(g). Similarly, the selection of collected data is a general recitation of a field of use for the calculations. See MPEP §2106.05(h).
Claim 6 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
MPEP §2106.05(d) provides the example:
iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Accordingly, gathering statistics of the respective response field data is a generic recitation of gathering statistics. Thus, defining the collected data with respective data fails to amount to significantly more than the judicially excepted abstract idea.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 7 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
7. The method of claim 1, wherein the machine learning comprises unsupervised machine learning and/or supervised machine learning.
Training of either unsupervised machine learning or supervised machine learning encompasses performing respective mathematical calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 7 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 7 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 8 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
8. The method of claim 1, wherein the machine learning is performed with a self-organizing mapping (SOM) method, or a k-means clustering method.
Performing self-organizing mapping and/or k-means clustering is a recitation to perform respective mathematical calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 8 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 8 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 9 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
9. The method of claim 1, wherein the clusters are generated by marking all material points that have the same response field within the representation of the material system with a unique ID and grouping material points with the same ID.
Determining that respective points have a ‘same’ response field is a mathematical evaluation. Marking them with a corresponding unique ID and same ID is creating the respective clusters. The groups correspond with the mathematically created clusters.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 9 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 9 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 10 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
10. The method of claim 9, wherein the generated clusters are a reduced representation of the material system, which reduces the number of degrees of freedom required to represent the material system.
A reduction in the number of degrees of freedom is a mathematical condition for the mathematical representation of the system.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 10 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 10 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 11 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
11. The method of claim 10, wherein the generated clusters are a reduced order MVE of the material system.
The order of the system is a mathematical condition. Ensuring the generated clusters are a reduced order is a recitation of a mathematical condition for the mathematical construction of the system.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 11 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 11 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 12 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
12. The method of claim 1, wherein the computed interaction tensor is for all pairs of the clusters.
The computer interaction tensor is a mathematical calculation regardless of the scope of pairs to which it is calculated.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 12 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 12 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 13 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
13. The method of claim 1, wherein said computing the interaction tensor is performed with fast Fourier transform (FFT), numerical integration, or finite element method (FEM).
FFT, numerical integration, and finite element methods to computer the interaction tensor are computations of the mathematical calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 13 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 13 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 15 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
15. The method of claim 14, wherein said solving the PDE with the LS equation is performed with arbitrary boundary conditions and material properties.
Solving the PDE with LS equation is a recitation of performing mathematical calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 15 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 15 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 35 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 35 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
35. A non-transitory tangible computer-readable medium storing instructions which, when executed by the one or more processors, cause a system to perform a method for design optimization and performance prediction of a physical material system, wherein the method is in accordance with claim 1.
The non-transitory tangible computer-readable medium and processor are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Claim 35 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The generic computer implementation is analyzed the same under step 2B as under step 2A(ii) above.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 36 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 36 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
36. A computational system for design optimization and performance prediction of a physical material system, comprising
one or more computing devices comprising the one or more processors; and
a non-transitory tangible computer-readable medium storing instructions which, when executed by the one or more processors, cause the one or more computing devices to perform a method for design optimization and performance prediction of a physical material system, wherein the method is in accordance with claim 1.
The non-transitory tangible computer-readable medium and processor are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Claim 36 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The generic computer implementation is analyzed the same under step 2B as under step 2A(ii) above.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
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.
Claims 1, 3, 4, 6-13, 15, 35, and 36
Claims 1, 3, 4, 6-13, 15, 35, and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Liu, Z. & Bessa, M. & Liu, W.K. “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials” Computer Methods in Applied Mechanics & Engineering, vol. 306, pp. 319-341 (2016) [herein “Bessa”] in view of US 2019/0278880 A1 Ma, et al. [herein “Ma”], Saunders, R., et al. “A Method to Determine Local Stress Fields in Microstructure Features Produced by Additive Manufacturing” Proceedings of ASME 2017 Int'l Mech. Engineering Congress & Expo. (2017) [herein “Saunders”], US patent 11,933,747 B2 Zavalij, et al. [herein “Zavalij”], and Hernández, J.A., et al. “High-performance model reduction techniques in computational multiscale homogenization” Computational Methods Applied Engineering, vol. 276, pp. 149-189 (2014) [herein “Hernandez”].
Claim 1 recites “1. A computer-implemented method for design optimization and performance prediction of a physical material system comprising an additively manufactured (AM) metal.” Bessa page 319 section 1 first paragraph discloses “Ideally, efficient and accurate predictions of the macroscopic behavior of heterogeneous materials should be uniquely obtained from the constitutive behavior of each separate constituent (material phase) and from the information about the material microstructure.” Predicting macroscopic behavior is predicting performance of the material system.
Bessa does not explicitly disclose an additively manufactured metal; however, in analogous art of predicting microstructure stress fields of materials, Saunders title teaches “additive manufacturing.” Saunders abstract teaches “metal [powder]-based additive manufacturing (PAM) processes typically result in microstructures …, which affect the mechanical properties of the material.”
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 Bessa, Ma, and Saunders. One having ordinary skill in the art would have found motivation to use microstructure performance prediction of an additively manufactured metal into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of “helps with the prediction of defects and void formation in the material.” See Saunders abstract.
Claim 1 recites “the method, being performed by one or more processors executing instructions stored on a non-transitory computer-readable medium” and recites a plurality of times “by one or more processors.” Bessa page 319 section 1 first paragraph discloses “computational modeling of macroscopic structures.”
But Bessa does not explicitly disclose a computer or computer-readable media; however, in analogous art of computational material design, Ma paragraph 20 teaches “any code or program that can be in a processor of a host computer, regardless of whether the implementation is in hardware, firmware or as a software computer product available on a disc, a memory storage device.”
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 Bessa and Ma. One having ordinary skill in the art would have found motivation to use a computer into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of software implementation of the analysis. See Ma paragraphs 19-20.
Claim 1 further recites “comprising: acquiring, …, sensor-generated experimental data obtained from physical characterization of the physical material system during fabrication or qualification, the sensor-generated experimental data comprising at least one of x-ray diffraction (XRD) data, transmission electron microscope (TEM) image data, and in-situ process sensor data.” From the above list of alternatives the Examiner is selecting “x-ray diffraction (XRD) data.”
Bessa page 319 last paragraph to page 320 first paragraph discloses “Traditional phenomenological constitutive relations [1–3] characterize the average behaviors of the material … These laws regard materials as ‘black boxes,’ implying the need for burdensome experimental characterization and tedious calibration.”
Bessa does not explicitly disclose x-ray diffraction (XRD) data; however, in analogous art of monitoring microstructure of metal 3D printing parts, Zavalij column 1 lines 48-55 teach:
an in-situ XRD based system and method adapted for real-time measurements and analysis of various microstructure properties of printed materials, including, but not limited to, crystal phase composition, texture, crystal lattice structure, etc., for a broad range of materials, including, but not limited to, metals, semiconductors, polymers, nanomaterials, etc., performed during the printing process.
In-situ XRD for real-time measurement of microstructure properties corresponds with acquired XRD sensor generated data of the physical system during printing.
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 Bessa, Ma, Saunders, and Zavalij. One having ordinary skill in the art would have found motivation to use acquiring XRD real-time sensor data into the system of self-consistent clustering analysis of heterogeneous materials because “Crystalline phase composition is critical to the quality of metal 3D printing parts.” See Zavalij column 2 lines 66-67.
Claim 1 further recites “generating, …, a multi-scale digital representation of the physical material system, wherein the multi-scale digital representation at each scale comprises microstructure volume elements (MVEs) that are computational building blocks of the physical material system at that scale, each MVE being associated with a corresponding physical portion of the physical material system, wherein the building blocks are defined by measured material properties and structural descriptors extracted from the sensor-generated experimental data and encoded in a domain-decomposed data structure identifying material phases and spatial relationships.” Bessa title teaches “multi-scale.”
Bessa page 319 last paragraph to page 320 first paragraph discloses “Traditional phenomenological constitutive relations [1–3] characterize the average behaviors of the material … These laws regard materials as ‘black boxes,’ implying the need for burdensome experimental characterization and tedious calibration.” Bessa page 320 fourth paragraph discloses “Analytical micromechanical methods [14–17] are very efficient because they describe the heterogeneous material by several microstructural descriptors.” Experimental characterization and microstructural descriptors correspond with material properties and structural descriptors.
Bessa title discloses “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials.” Multiscale is representing a number of plural scales.
Bessa page 320 section 2 first paragraph discloses “The starting point is a high-fidelity Representative Unit Cell (RUC) of the material, i.e. a representative domain of the microstructure of a material.” The representative unit cell (RUC) of the material representing the microstructure of a material is a microstructure volume representation of building blocks of the material system. The unit cells are building blocks of the material system. These representative unit cells representative of the microstructure of the material corresponds with a building block defined by respective microstructure descriptors and material characterization.
Bessa does not explicitly disclose the experimental characterization defines the building blocks with respective measure material properties; however, in analogous art of computational reduced order prediction of multiscale mechanical properties, Hernandez page 153 section 2.1 teach “In microstructures that exhibit statistical homogeneity, this domain receives the name of Representative Volume Element (RVE).” RVEs are a microstructure volume element associated with the physical material system.
Hernandez page 156 section 3.2.1 teach “The Proper Orthogonal Decomposition is nothing but a multidimensional data fitting procedure intended to obtain a sequence of orthogonal basis functions whose span best approximate the space of snapshots. As such, the POD is a purely data-driven process — it is ‘agnostic’ to the physical origin of the data.” POD being purely data driven is the building blocks being defined by measured sensor data. POD is a domain decomposition. Hernandez page 155 section 3.1 second paragraph teach “the constitutive laws that govern the mechanical behavior of the distinct phases in the RVE.5 This means that the solution of the finite element equilibrium equation (3) for given values of the macro-strain tensor
ϵ
M
actually lives in a smaller subspace.” The constitutive laws and phases of the RVE are an encoding of material phase and spatial relationship information.
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 Bessa, Ma, Saunders, Zavalij and Hernandez. One having ordinary skill in the art would have found motivation to use empirical POD into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of following a decomposition method with a “speedup factor is over three orders of magnitude.” See Hernandez abstract last sentence.
Claim 1 further recites “collecting, …, numerical response-field data of the MVEs computed from a physics-based material model of the physical material system over predefined sets of material properties, material constituents, and boundary conditions, the response-field data representing physical stress, strain, deformation, or thermal behavior of the physical material system.” From the above list of alternatives the Examiner is selecting “strain.”
Bessa page 321 section 2.1 discloses “reference material with stiffness
C
0
.” The material stiffness is a material property of the material system.
Bessa page 322 below equation (8) discloses “In order to solve
ε
x
in the integral equation (8), constraints are needed from the macroscopic boundary conditions.” The macroscopic boundary conditions are boundary conditions of the material system. Bessa page 321 footnote 2 discloses “For example, if the variable of interest is local strain.”
Bessa page 321 equation (3) discloses “the following cluster averaging relationship can be written, [equation (3)] where
∙
signifies any quantity of interest to be averaged in the cluster domain
Ω
I
.” Quantities of interest over the cluster domain are corresponding material properties.
Bessa page 338 Appendix A lines 1-2 disclose “In order to generate the database with the strain concentration tensors
A
x
, we implemented the DNS method based on fast Fourier transforms.” Generating the database of strain concentration tensors is collecting respective response field data. Strain is data representing a strain.
For purposes of combination with Hernandez relating to subsequently recited limitations which derive antecedent to the response-field data, it is relevant to establish that Hernandez also teaches similar strain response-field data.
Hernandez page 155 section 3.1 second paragraph teach “the constitutive laws that govern the mechanical behavior of the distinct phases in the RVE.5 This means that the solution of the finite element equilibrium equation (3) for given values of the macro-strain tensor
ϵ
M
actually lives in a smaller subspace.” The strain tensor is a response-field data representing strain. Hernandez page 153 last line teaches “the boundary conditions (BCs) prescribed on the RVE.” The boundary conditions are boundary conditions of the material system.
Hernandez page 155 footnote 4 teaches “In the modified Voigt’s notation, both stress
σ
and strain
ϵ
tensors are represented as column vectors ({
σ
} and {
ϵ
}, respectively).”
Claim 1 further recites “applying, …, machine learning based clustering to the collected response field data to generate clusters that minimize a distance between points in a defined response space metric, each cluster representing a reduced-order physical behavior of a plurality of material points.” Bessa page 324 section 2.2.1 second paragraph discloses “By definition, the similarity between two data points is characterized by the difference between their strain concentration tensors.”
Bessa page 326 “Remark 3” discloses “Clustering depends on specific choices of data type (in this case, strain concentration tensor), distance definition (Frobenius norm) and clustering algorithm (k-means clustering).”
Bessa page 325 first paragraph discloses “The next step is to perform the domain decomposition by grouping similar data points using a clustering algorithm called k-means clustering [43]. Note that the data points grouped by the k-means clustering do not need to be adjacent to each other. These points belong to the same cluster because their strain concentration tensor is approximately the same.” Performing k-means clustering is applying machine learning to the collected data to generate clusters that minimize a distance between points. Clustering points because their strain concentration tensor is approximately the same is generating clusters that minimize a distance in a nominal response space. The strain concentration tensor corresponds with response field data.
Claim 1 further recites “computing, …, an interaction tensor representing physical interactions of each cluster with each of the other clusters.” Bessa page 326 section 2.2.2 titled “Computing the interaction tensors” first paragraph and equation (22) discloses “The interaction tensor
D
I
J
represents the influence of the stress in the Jth cluster on the strain in the Ith cluster.” The interaction tensor representing the influence between J and Ith clusters is a computed interaction tensor between each respective cluster.
Claim 1 further recites “wherein the collected response-field data, the generated clusters, and the computed interaction tensors are persistently stored in one or more material-system databases.” Bessa page 338 Appendix A lines 1-2 disclose “In order to generate the database with the strain concentration tensors
A
x
, we implemented the DNS method based on fast Fourier transforms.” The strain concentration tensors correspond with the data response fields. The generated database is saving the data in a material system database.
Bessa page 338 Appendix A third paragraph discloses “1. Assignment step: Each data point is assigned to the cluster whose mean is nearest to the data point.” The assigned clusters for the data points within the database is storing generated cluster information in the database.
Claim 1 further recites “manipulating, …, a governing partial differential equation (PDE) that governs physical behavior of the physical material system using Green's function to form a generalized Lippmann-Schwinger integral equation.” Bessa page 326 section 2.2.2 first paragraph discloses “the interaction tensor can be written as an integral of Green’s function in a RUC domain
Ω
with periodic boundary conditions.” Writing the interaction tensor as an integral of Green’s function is manipulating the PDE using Green’s function.
Bessa page 323 “Remark 1” discloses “This closed system of equations [(17) and (18)] arises from the averaging of the Lippmann–Schwinger equation for each cluster.”
Claim 1 further recites “solving, …, the generalized Lippmann-Schwinger integral equation in real time during fabrication or operational use of the physical material system by retrieving the precomputed interaction tensors and clustered response-field data from the one or more material-system databases, thereby avoiding full-order numerical re-simulation.” Bessa page 327 section 2.3 “Remark 4” discloses “The solution of the discrete Lippmann–Schwinger equations when finding the local strain in each cluster by solving Eqs. (17) and (18).” Solving the discrete Lippmann–Schwinger equations for at least one local strain is solving the Lippman-Schwinger integral equation(s) using the clusters and interaction tensors to result in a response prediction of the material system.
Bessa page 332 section 3.1 last paragraph discloses “applying the proposed method to microstructure-based property design.” Microstructure-based property design is a design optimization.
Bessa page 319 section 1 first paragraph discloses “Ideally, efficient and accurate predictions of the macroscopic behavior of heterogeneous materials should be uniquely obtained from the constitutive behavior of each separate constituent (material phase) and from the information about the material microstructure.” Predicting macroscopic behavior is predicting performance of the material system.
Bessa teaches an offline (section 2.2) and online (section 2.3) solving of the continuous Lippmann-Schwinger equations. Values computing in the offline stage correspond with pre-computed values. Bessa page 323 figure 2 shows “Online or predictive stage.” The online stage is a real-time stage. Bessa page 323 figure 2 shows the online stage is described in “Section 2.3.” An online stage is in real-time.
Bessa page 327 section 2.3 “Remark 4” discloses “The solution of the discrete Lippmann–Schwinger equations when finding the local strain in each cluster by solving Eqs. (17) and (18).” Thus, the teaching of solving the discrete LS equation is part of section 2.3 corresponding with the online stage. Bessa page 326 section 2.2.2 teaches computing the interaction tensor
D
I
J
as a part of the offline stage. However, Bessa fails to teach using the interaction tensor
D
I
J
as precomputed values within the online stage. Bessa page 330 Box II step 2 teaches “update the interaction tensor
D
I
J
and the stiffness tensor within an iterative framework.
But Bessa does not explicitly disclose pre-computing interaction tensors (i.e. strain tensors); however, in analogous art of computational reduced order prediction of multiscale mechanical properties, Hernandez page 165 below equation (73) teaches “it is independent of the input parameter, the macro-strain
ϵ
M
— and, therefore, it can be pre-computed offline.” Pre-computing the macro-strain is precomputing an interaction tensor to avoid full-order numerical simulation in the online stage. Hernandez page 172 Box 8.2 teaches “Online stage (solution of the hyperreduced-order RVE equilibrium problem for given macroscopic strains).” An online stage corresponds with a real-time computation.
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 Bessa, Ma, Saunders, Zavalij and Hernandez. One having ordinary skill in the art would have found motivation to use empirical POD into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of following a decomposition method with a “speedup factor is over three orders of magnitude.” See Hernandez abstract last sentence.
Claim 1 further recites “outputting, by the one or more processors, a physical response prediction comprising a fatigue loading response of the AM metal.” Bessa page 319 section 1 teach “Ideally, efficient and accurate predictions of the macroscopic behavior of heterogeneous materials should be uniquely obtained from the constitutive behavior of each separate constituent (material phase) and from the information about the material microstructure.” Bessa page 320 first paragraph teaches “capturing highly localized microstructure-induced nonlinear material behaviors, such as plasticity, damage and fatigue.” Predicting macroscopic behavior to capture a fatigue or inelastic deformation corresponds with a physical response prediction of a fatigue loading response.
Bessa page 336 figure 10 teaches “Response of the reduced RUC for the fiber-reinforced composite material under the three step loading path.” Displaying a figure of a respective loading response is outputting the predicted response information.
Claim 1 further recites “and automatically controlling, optimizing, or validating fabrication parameters or operational limits of the physical material system during live material fabrication or operation with the response prediction.” From the above list of alternatives the Examiner is selecting “controlling” and “live material fabrication.”
Bessa page 332 third paragraph teach “This shows the potential of applying the proposed method to microstructure-based property design of heterogeneous material system, such as nanostructured polymers.” A property design of the material system is an optimizing of the physical system with the respective predicted properties.
Bessa does not explicitly disclose controlling during a live material fabrication; however, in analogous art of monitoring microstructure of metal 3D printing parts, Zavalij column 2 lines 32-37 teach “generating, in real-time, a comprehensive analysis result for the printing object quality, which can be feedback, in real-time, to the 3-D printer to adjust the 3-D printing process control parameters (if needed) to in-situ correct (or prevent) deficiencies and to provide high quality of the 3-D printing objects.”
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 Bessa, Ma, Saunders, and Zavalij. One having ordinary skill in the art would have found motivation to use real-time feedback into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of “correct (or prevent) deficiencies and to provide high quality of the 3-D printing objects.” See Zavalij column 2 lines 32-37.
Claim 3 further recites “3. The computer-implemented method of claim 1, wherein the building blocks are defined by material properties and structural descriptors further obtained by modeling.” From the above list of alternatives the Examiner is selecting “modelling.”
Bessa page 338 Appendix A discloses:
In order to generate the database with the strain concentration tensors
A
x
, we implemented the DNS method based on fast Fourier transforms [19], which iteratively solves the full Lippmann–Schwinger equation with periodic boundary conditions. The finite element method could also be used to calculate
A
x
.
Using DNS based on fast Fourier transforms or using a finite element method to calculate
A
x
is modelling respective material properties.
Claim 4 further recites “4. The computer-implemented method of claim 3, wherein the structural descriptors comprise characteristic length and geometry.” Bessa page 325 second paragraph discloses “in this example the finite element mesh of the high-fidelity RUC that had three hundred and sixty thousand elements.” Bessa page 324 section 2.2.1 third paragraph discloses “for a 3-dimensional (3D) material.” A 3D finite element mesh has characteristic length and 3D geometry.
Claim 6 further recites “6. The computer-implemented method of claim 1, wherein the collected response-field data comprise a strain concentration tensor, a deformation concentration tensor, stress tensor including PK-I stress and Cauchy stress tensors, plastic strain tensor, or thermal gradient.” From the above list of alternatives the Examiner is selecting “a strain concentration tensor.”
Bessa page 325 first paragraph discloses “The next step is to perform the domain decomposition by grouping similar data points using a clustering algorithm called k-means clustering [43]. Note that the data points grouped by the k-means clustering do not need to be adjacent to each other. These points belong to the same cluster because their strain concentration tensor is approximately the same.”
Claim 7 further recites “7. The computer-implemented method of claim 1, wherein the machine learning comprises unsupervised machine learning and/or supervised machine learning.” From the above list of alternatives the Examiner is selecting “unsupervised machine learning.”
Bessa page 325 first paragraph discloses “The next step is to perform the domain decomposition by grouping similar data points using a clustering algorithm called k-means clustering [43]. Note that the data points grouped by the k-means clustering do not need to be adjacent to each other. These points belong to the same cluster because their strain concentration tensor is approximately the same.” K-means clustering is an unsupervised machine learning.
Claim 8 further recites “8. The computer-implemented method of claim 1, wherein the machine learning is performed with a self-organizing mapping (SOM) method, or a k-means clustering method.” From the above list of alternatives the Examiner is selecting “k-means clustering method.”
Bessa page 325 first paragraph discloses “The next step is to perform the domain decomposition by grouping similar data points using a clustering algorithm called k-means clustering [43]. Note that the data points grouped by the k-means clustering do not need to be adjacent to each other. These points belong to the same cluster because their strain concentration tensor is approximately the same.”
Claim 9 further recites “9. The computer-implemented method of claim 1, wherein the clusters are generated by marking all material points that have the same response field within the representation of the physical material system with a unique ID and grouping material points with the same ID.” Bessa page 325 first paragraph discloses “The next step is to perform the domain decomposition by grouping similar data points using a clustering algorithm called k-means clustering [43]. Note that the data points grouped by the k-means clustering do not need to be adjacent to each other. These points belong to the same cluster because their strain concentration tensor is approximately the same.” Clustering points with approximately the same strain concentration tensor is generating clusters with points having a same response field.
Bessa page 323 figure 2 shows a “Reduced Representative Unit Cell” wherein 8 different clusters are color coded and labeled according to the legend. The color coding and labeling is a marking of respective points of the clusters.
Claim 10 further recites “10. The computer-implemented method of claim 9, wherein the generated clusters are a reduced representation of the physical material system, which reduces the number of degrees of freedom required to represent the physical material system.” Bessa page 320 section 2 second paragraph discloses “to decompose the high-fidelity RUC into a group of large subdomains, obtaining a reduced RUC.” Decomposing the high-fidelity RUC into a reduced RUC is generating a reduced representation of the material system. Bessa page 324 “Remark 2” discloses “the reduction of the number of degrees of freedom by forming material clusters.”
Claim 11 further recites “11. The computer-implemented method of claim 10, wherein the generated clusters are a reduced order MVE of the physical material system.” Bessa page 320 section 2 second paragraph discloses “to decompose the high-fidelity RUC into a group of large subdomains, obtaining a reduced RUC.” Decomposing the high-fidelity RUC into a reduced RUC is generating a reduced order MVE of the material system.
Claim 12 further recites “12. The computer-implemented method of claim 1, wherein the computed interaction tensor is for all pairs of the clusters.” Bessa page 326 section 2.2.2 titled “Computing the interaction tensors” first paragraph and equation (22) discloses “The interaction tensor
D
I
J
represents the influence of the stress in the Jth cluster on the strain in the Ith cluster.” The interaction tensor representing the influence between J and Ith clusters is a computed interaction tensor between each respective cluster. Integrating over the volume fraction of the Ith cluster and Jth cluster is computing all pairs of the clusters.
Claim 13 further recites “13. The computer-implemented method of claim 1, wherein said computing the interaction tensor is performed with fast Fourier transform (FFT), numerical integration, or finite element method (FEM).” From the above list of alternatives the Examiner is selecting “fast Fourier transform (FFT).”
Bessa page 338 Appendix A discloses:
In order to generate the database with the strain concentration tensors
A
x
, we implemented the DNS method based on fast Fourier transforms [19], which iteratively solves the full Lippmann–Schwinger equation with periodic boundary conditions. The finite element method could also be used to calculate
A
x
.
Using DNS based on fast Fourier transforms or using a finite element method to calculate
A
x
is modelling respective material properties to computer the interaction tensor
A
x
.
Claim 15 further recites “15. The computer-implemented method of claim 1, wherein said solving the PDE with the LS equation is performed with arbitrary boundary conditions and material properties.” Bessa page 321 equation (3) discloses “the following cluster averaging relationship can be written, [equation (3)] where
∙
signifies any quantity of interest to be averaged in the cluster domain
Ω
I
.” Any quantity of interest is an arbitrary material property of interest.
Bessa page 326 section 2.2.2 first paragraph discloses “the interaction tensor can be written as an integral of Green’s function in a RUC domain
Ω
with periodic boundary conditions.” Bessa page 322 below equation (8) discloses “In order to solve
ε
x
in the integral equation (8), constraints are needed from the macroscopic boundary conditions.” Bessa does not teach being limited to specific boundary conditions or that the periodic boundary conditions are the only boundary conditions which are suitable to use as the macroscopic boundary conditions. Therefore, a person of ordinary skill in the art would understand arbitrary or other macroscopic boundary conditions could be used.
Claim 35 further recites “35. A non-transitory tangible computer-readable medium storing instructions which, when executed by the one or more processors, cause a system to perform a method for design optimization and performance prediction of a physical material system, wherein the method is in accordance with claim 1.” Bessa page 319 section 1 first paragraph discloses “computational modeling of macroscopic structures.”
But Bessa does not explicitly disclose a computer or computer-readable media; however, in analogous art of computational material design, Ma paragraph 20 teaches “any code or program that can be in a processor of a host computer, regardless of whether the implementation is in hardware, firmware or as a software computer product available on a disc, a memory storage device.”
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 Bessa and Ma. One having ordinary skill in the art would have found motivation to use a computer into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of software implementation of the analysis. See Ma paragraphs 19-20.
Claim 36 further recites “36. A computational system for design optimization and performance prediction of a physical material system.” From the above list of alternatives the Examiner is selecting “performance prediction.”
Bessa page 319 section 1 first paragraph discloses “Ideally, efficient and accurate predictions of the macroscopic behavior of heterogeneous materials should be uniquely obtained from the constitutive behavior of each separate constituent (material phase) and from the information about the material microstructure.” Predicting macroscopic behavior is predicting performance of the material system.
Claim 36 further recites “comprising one or more computing devices comprising the one or more processors; and a non-transitory tangible computer-readable medium storing instructions which, when executed by the one or more processors, cause the one or more computing devices to perform a method for design optimization and performance prediction of a physical material system, wherein the method is in accordance with claim 1.” Bessa page 319 section 1 first paragraph discloses “computational modeling of macroscopic structures.”
But Bessa does not explicitly disclose a computer or computer-readable media; however, in analogous art of computational material design, Ma paragraph 20 teaches “any code or program that can be in a processor of a host computer, regardless of whether the implementation is in hardware, firmware or as a software computer product available on a disc, a memory storage device.”
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 Bessa and Ma. One having ordinary skill in the art would have found motivation to use a computer into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of software implementation of the analysis. See Ma paragraphs 19-20.
Dependent Claim 2
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Bessa, Ma, Saunders, Zavalij, and Hernandez as applied to claim 1 above, and further in view of Liu, W.K. & McVeigh, C. “Predictive multiscale theory for design of heterogeneous materials” Comput. Mech., vol. 42, pp. 147-170 (2008) (Published online April 2007) [herein “McVeigh”].
Claim 2 further recites “2. The computer-implemented method of claim 1, further comprising 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.” Bessa title discloses “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials.” Multiscale is representing a number of plural scales.
Bessa does not explicitly disclose passing a result of a response prediction to a next coarser scale and iterating; however, in analogous art of multiscale prediction of heterogeneous materials, McVeigh page 147 section 1 second paragraph teaches:
This average response is often derived via a hierarchical technique [21–24] or ‘bottom-up’ approach. That is, the known material response at the finest scale is used to derive the behavior at the next coarsest scale, and so on until the macroscale constitutive behavior is defined in terms of the distinct physics observed at each scale e.g., dislocation movement, damage nucleation and grain boundary formation. The resulting constitutive law represents the macroscale effects in terms of the microscale causes.
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 Bessa, Ma, Saunders, Zavalij, and Hernandez, and McVeigh. One having ordinary skill in the art would have found motivation to use the finest scale to define behavior at the next coarsest scale into the system of self-consistent clustering analysis of heterogeneous materials for the advantageous purpose of implementing a multiscale approach and to include microscale information of the microstructure. See McVeigh page 148 right column third paragraph.
Dependent Claim 5
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Bessa, Ma, Saunders, Zavalij, and Hernandez as applied to claim 1 above, and further in view of Tuijl, R., et al. “Integration efficiency for model reduction in micro-mechanical analyses” Comput. Mech., vol. 62, pp. 151-169 (2018) (Published online Nov. 2017) [herein “Tuijl”].
Claim 5 further recites “5. The computer-implemented method of claim 1, wherein the boundary conditions are chosen to satisfy the Hill-Mandel condition.” Examiner relies upon an ordinary and customary meaning in the art to define the claim language “Hill-Mandel condition.” Tuijl page 154 left column section 2.3 defining “The second principle is the Hill–Mandel condition [43] which prescribes that the virtual work performed per unit volume at the macroscale should equal the volume average of the virtual work at the microscale.” Here, the Tuijl reference is relied upon solely as a dictionary reference to define the claim term “Hill-Mandel condition.” See MPEP §2111.01(III) third paragraph (“It is also appropriate to look to how the claim term is used in the prior art, which includes prior art patents, published applications, trade publications, and dictionaries.”).
Bessa page 324 lines 2-3 disclose “the global average of the local strains and stresses are constrained to coincide with the macroscopic applied strain or stress.” Constraining the average of strains and stresses of clusters to coincide with macroscopic strain or stress is satisfying the Hill-Mandel condition as defined by Tuijl.
Conclusion
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/Jay Hann/Primary Examiner, Art Unit 2186 4 April 2026