Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
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 Amendments
The amendment filed on 02/23/2026 has been entered and considered by the examiner. By the
amendment, claim 1-3, 8-9, 15 and 18-20 are amended. In view of amendments made, the previous 101 and 103 is modified and an explanation is given below. See office action for detail.
Applicant 101 arguments
As is noted above, the Applicant has amended the independent claims to clarify that the simulation is performed using a "finite element simulator" and that the method includes "causing fabrication of" physical samples. These amendments integrate any abstract idea into a practical application and provide significantly more than any judicial exception. The claims are integrated into a practical application of composite materials design. The method then uses these simulation results to select designs for physical fabrication, measures the fabricated samples, and reconfigures the simulator based on physical reality. This closed-loop integration of computational modeling with physical validation represents a specific technological improvement in materials design methodology. The combination of finite element simulation, physical fabrication, and feedback-based reconfiguration represents a specific technological process for materials design, not a mere abstract idea. The amended claims are directed to patent-eligible subject matter under 35 U.S.C.§ 101 and the rejection should be withdrawn.
Examiner response
Applicant’s arguments have been fully considered but are not persuasive. Accordingly, the rejection of claims 1–18 under 35 U.S.C. § 101 is maintained. Under Step 2A, Prong One, claim 1 still recites an abstract idea. Claim 1 recites determining simulated measurement data for multiple designs, selecting a subset of designs based on the simulated data, receiving physical measurements, and reconfiguring the simulator based on those measurements. These limitations describe evaluating alternatives using mathematical modeling, comparing results, selecting preferred designs options, and updating model parameters based on observed data, which constitute mathematical concepts and mental processes that can be performed conceptually or with pen and paper. The recitation of a “finite element simulator” does not remove the claim from the abstract realm, because the simulator is invoked as a tool for performing calculations and predictions rather than as an improved technology itself. Under Step 2A, Prong Two, the additional elements do not integrate the judicial exception into a practical application. Applicant argues that finite element simulation is a specific technological process involving discretization, stress-strain equations, and solving systems of equations. However, those details are not affirmatively recited in the claims. The claims merely require “using a finite element simulator” to generate simulated data. The claims do not recite any improvement to meshing, numerical solution techniques, convergence behavior, memory management, computational efficiency, or any other aspect of finite element computing. Rather, the simulator is used in its ordinary capacity to evaluate candidate designs. Applicant further argues that causing fabrication of physical samples and measuring those samples constitutes a practical application. This is not persuasive. The fabrication and measurement steps are recited generically and merely gather real-world data used to refine the underlying abstract optimization process. The claims do not recite any specific manufacturing technique, fabrication machine control, improved 3D printing process, or transformation of materials beyond routine sample creation and testing. Such data gathering and post-solution activity do not integrate the exception into a practical application. See MPEP §§ 2106.05(g) Under Step 2B, the claim does not amount to significantly more than the abstract idea. Applicant asserts that the claims improve computer functionality because the simulator is reconfigured using measured results. However, improving the accuracy of outputs from a model is not the same as improving computer functionality. The claims do not improve processor performance, storage, networking, solver operation, or any technological operation of the computer itself. Instead, they improve the quality of predictions produced by the abstract model. The claims do not apply or involve a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. The claims do not apply or perform the abstract idea with a particular machine, MPEP 2106.06b. The claims to do transform or reduce a particular article to a different state or thing (data remains data when processed by a computer), MPEP 2106.05c. The claims do not apply or use the abstract idea in a meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (i.e. a processor), such that the claims are a drafting effort to monopolize the abstract idea (i.e. the claims do not integrate the abstract idea into a practical application of the abstract idea). Accordingly, the claims are not patent eligible under 35 U.S.C. 101. Applicant also argues that finite element simulation requires specialized infrastructure and cannot be performed mentally. That a computer is useful or necessary for efficient execution of an abstract idea does not render the claim patent eligible. See Alice Corp. v. CLS Bank. The recited processor and simulator therefore amount only to generic computer implementation of the abstract idea. (See MPEP §§ 2106.05(f)) When considered as an ordered combination, the claim merely applies generic simulation, selection, fabrication, testing, and recalibration steps in an iterative workflow for optimizing microstructure designs. Considering all the limitations in combination, the claim does not show any inventive concept, such as improving the performance of a computer or any other technology. It is for these reasons that Applicants claimed subject matter is not patent eligible. Therefore, the claim does not amount to significantly more than the abstract idea itself. Thus, the 101 rejection is still maintained.
Applicant 103 arguments
The Office Action does nothing to remedy the apparent lack of a prediction step in the cited prior art. Indeed, unable to find both prediction and simulation stages in the prior art, the Office Action maps the same element from Gu to both claim limitations: determining a plurality of prediction designs, and predicting measurement data for each of the prediction designs, determining, using a physical simulator, simulated measurement data for a first plurality of simulation designs, each simulation design characterizing a structural combination of a plurality of materials, the simulated measurement data for each simulation design providing a simulation of physical qualities of said design; The mapping error reveals that the cited references simply do not disclose the claimed forward filtering architecture where prediction designs are generated, evaluated, and filtered to yield a distinct set of simulation designs.
Examiner response
Examiner respectfully disagrees. Claim 4 recites: determining a plurality of prediction designs, and predicting measurement data for each of the prediction designs, determining, using a physical simulator, simulated measurement data for a first plurality of simulation designs. Nothing in claim 4 requires the plurality of prediction designs and the plurality of simulation designs to be different physical structures, non-overlapping sets or mutually exclusively population of designs. Under the broadest reasonable sense, claim permits overlap or staged reuse of the same candidate designs. The terms “prediction design” and “simulation design” reasonably describe designs according to the manner in which they are processed. A candidate material layout may be: a prediction design when processed to obtain predicted measurement data and a simulation design when processed by a finite element simulator to obtain simulated measurement data. Thus, the same underlying structural design satisfy both recitations at different stages of the workflow. Even assuming that “prediction designs” and “simulation designs” must be different sets, Gu teaches: a larger candidate population of generated geometries, prediction/ranking of those designs using ML and evaluation of selected designs using FE code. Thus, candidate geometries subject to prediction corresponds to the claimed prediction designs and selected candidate geometries subjected to FE analysis correspond to the claimed simulation designs. Therefore, Gu teaches claim 4 under either interpretation. Claim 4 is presumed broader than dependent claim 8. Claim 1 reasonably encompasses broader arrangement, including overlapping sets. Thus, claim 4 rejection is still maintained in view of Gu.
Applicant 103 arguments
The Applicant submits that neither Gu nor Ypma, taken alone or in anu proper combination, describes or suggests: "reconfiguring the finite element simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs" as is recited by the independent claims.
Examiner response
In view of applicant amendment regarding limitation “reconfiguring the finite element simulator… according to the fabrication designs”. Examiner withdraw the Ypma reference and added the new reference Song to teach this limitation in view of Gu. See office action.
Claim Objection
a. Claim 4 is objected because of following reasons:
b. Claim 4 recites “fabrications designs” it should be fabrication designs. Typo error.
c. Claim 4 recites “set respective” it should be “set of respective”. Missing proposition.
d. Claim 4 recites “measurement a plurality” it should be “measurement of a plurality”. Missing “of”.
Claim Rejections - 35 USC §101
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-18 are directed method or process, which falls on the one of the statutory category.
Claims:19 is directed system or machine, which falls on the one of the statutory category.
Claim: 20 is directed to non-transitory computer readable storage medium storing one or more program, which falls on the one of the statutory category that is manufacture.
Regarding claim 1
Step 1 Prong one
A method for designing a structural combination of a plurality of materials, the method comprising:
determining, using a finite element simulator, simulated measurement data for a first plurality of simulation designs, each simulation design characterizing a structural combination of a plurality of materials, the simulated measurement data for each simulation design providing a simulation of physical qualities of said design; (The description of using a FEM in view of specification to generate simulated measurement data for multiple design combinations describes an abstract idea for a mathematical process. A physical simulator like FEM is inherently a mathematical process. It models the physical qualities of a design by applying complex mathematical formulas and calculations, such as differential equations, to a mesh of finite elements. Also, an engineer can mentally model the properties of a structural combination and use standard engineering principles to predict how it might behave. The computer-implemented simulation is essentially an automated version of this analytical process. The method fundamentally involves the abstract manipulation of data—inputting design data and outputting simulated measurement data. This is a classic characteristic of an abstract idea. Thus, it falls under the combination of mental process and mathematical concepts of abstract idea)
selecting a subset of the simulation designs based on the simulated measurement data for said simulation designs to yield a set of fabrication designs; (A person could, in theory, manually: Review simulation designs. Analyze the measurement data associated with each design. Use their judgment to select a subset of designs that meet certain criteria. Produce a list of the chosen fabrication designs. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas.)
reconfiguring the finite element simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs. (Reconfiguring the FEM involves applying a mathematical formula (the simulation algorithm) to a set of data (design parameters and measurements) to produce an output (the reconfigured simulation). Updating a model based on observed results is a mental process. The process relies on a human's ability to mentally compare the physical measurements of a fabricated sample to the simulated results. Based on that comparison, a human mind can then make a mental decision on how to adjust or "reconfigure" the FEM. Thus, it falls under the combination of mental process and mathematical concepts of abstract idea)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim recites the additional elements of causing fabrication of the set of fabrication design, including providing the set of fabrication designs for fabrication of a set respective physical samples is a merely gathering data—specifically, the dimensions, materials, and components of an object. If the object itself is based on an abstract principle or is conventional, the act of creating a detailed design for its fabrication viewed as merely collecting and organizing the necessary data for production and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). The additional elements of receiving a set of physical measurements for the set of physical samples, each physical measurement for a physical sample providing measurements of a plurality of physical qualities of the sample; which is recited at a high level of generality (i.e., as a general means of measuring data), and falls under the insignificant extra solution activity. (See MPEP 2106.05(g) The additional element of a non-transitory computer-readable medium comprising instructions that are executable by a processor in claim 20 are amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) Thus, claim 1, 19 and 20 are directed to abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of causing fabrication of the set of fabrication design, including providing the set of fabrication designs for fabrication of a set respective physical samples is a representation of data—specifically, the dimensions, materials, and components of an object. If the object itself is based on an abstract principle or is conventional, the act of creating a detailed design for its fabrication viewed as merely collecting and organizing the necessary data for production and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). The additional elements of receiving a set of physical measurements for the set of physical samples, each physical measurement for a physical sample providing measurements of a plurality of physical qualities of the sample; which is recited at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) The additional element of a non-transitory computer-readable medium comprising instructions that are executable by a processor in claim 20 are amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) Therefore, claims 1, 19 and 20 are directed to abstract idea and is not patent eligible.
Claim 2 further recites wherein selecting the subset of the simulation designs to yield the fabrication designs includes excluding at least one simulation design that is dominated in simulation by at least one other simulation design in the plurality of simulation designs, where a first simulation design is dominated in simulation by a second simulated design when all simulated physical qualities of the first simulated design are worse than corresponding simulated physical qualities of the second simulated design; The selection and exclusion of designs based on comparing their simulated qualities can be performed entirely in the human mind. A person could manually list and compare the performance characteristics of different designs to identify and eliminate inferior ones. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 3 further teaches wherein the set of fabrication designs includes only designs that are not dominated in physical measurement by any other design in the set of fabrication designs, where a first design is dominated in physical measurement by a second design when all measured physical qualities of the plurality of physical qualities of the first design are worse than corresponding measured physical qualities of the second design. The comparison of designs based on measured qualities is a mental process. A person could, in theory, perform this comparison mentally or with pen and paper, making it an unpatentable mental step. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Regarding claim 4
A method for designing a structural combination of a plurality of materials, the method comprising:
determining a plurality of prediction designs, and predicting measurement data for each of the prediction designs. (The prediction designs themselves are essentially mathematical models or algorithms. For instance, a design might involve using a linear regression formula or a more complex machine learning algorithm. In patent law, mathematical formulas and algorithms are fundamental concepts that cannot be monopolized. Mentally comparing the predicted measurement data from multiple designs to decide which is best is a process of comparison and evaluation that does not require a specific machine to perform. A human could perform the same steps with pen and paper.)
determining, using a physical simulator, simulated measurement data for a first plurality of simulation designs, each simulation design characterizing a structural combination of a plurality of materials, the simulated measurement data for each simulation design providing a simulation of physical qualities of said design; (The description of using a physical simulator (FEM) in view of specification to generate simulated measurement data for multiple design combinations describes an abstract idea for a mathematical process. A physical simulator like FEM is inherently a mathematical process. It models the physical qualities of a design by applying complex mathematical formulas and calculations, such as differential equations, to a mesh of finite elements. Also, an engineer can mentally model the properties of a structural combination and use standard engineering principles to predict how it might behave. The computer-implemented simulation is essentially an automated version of this analytical process. The method fundamentally involves the abstract manipulation of data—inputting design data and outputting simulated measurement data. This is a classic characteristic of an abstract idea. Thus, it falls under the combination of mental process and mathematical concepts of abstract idea)
selecting a subset of the simulation designs based on the simulated measurement data for said simulation designs to yield a set of fabrications designs; (A person could, in theory, manually: Review simulation designs. Analyze the measurement data associated with each design. Use their judgment to select a subset of designs that meet certain criteria. Produce a list of the chosen fabrication designs. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas.)
reconfiguring the physical simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs. (Reconfiguring the FEM involves applying a mathematical formula (the simulation algorithm) to a set of data (design parameters and measurements) to produce an output (the reconfigured simulation). The process relies on a human's ability to mentally compare the physical measurements of a fabricated sample to the simulated results. Based on that comparison, a human mind can then make a mental decision on how to adjust or "reconfigure" the FEM. Thus, it falls under the combination of mental process and mathematical concepts of abstract idea)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim recites the additional elements of providing the set of fabrication designs for fabrication of a set respective physical samples is a representation of data—specifically, the dimensions, materials, and components of an object. If the object itself is based on an abstract principle or is conventional, the act of creating a detailed design for its fabrication viewed as merely collecting and organizing the necessary data for production and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). The additional elements of receiving a set of physical measurements for the set of physical samples, each physical measurement for a physical sample providing measurements a plurality of physical qualities of the sample; which is recited at a high level of generality (i.e., as a general means of measuring data), and falls under the insignificant extra solution activity. (See MPEP 2106.05(g) Thus, claim 4 is directed to abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of providing the set of fabrication designs for fabrication of a set respective physical samples is a representation of data—specifically, the dimensions, materials, and components of an object. If the object itself is based on an abstract principle or is conventional, the act of creating a detailed design for its fabrication viewed as merely collecting and organizing the necessary data for production and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). The additional elements of receiving a set of physical measurements for the set of physical samples, each physical measurement for a physical sample providing measurements a plurality of physical qualities of the sample; which is recited at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) Therefore, claim 4 is directed to abstract idea and is not patent eligible.
Claim 5 further recites wherein predicting measurement data for a prediction design comprises using a neural network to process the prediction design to yield predicted measurement data. It is merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 6 further recites wherein determining the plurality of prediction designs comprises using an evolutionary procedure to iteratively generate said designs. An evolutionary algorithm is, at its core, a mathematical concept. It is a high-level description of a process that can be performed using pencil and paper, even if doing so would be time-consuming. The procedure involves the mathematical steps of iterative generation, evaluation, and selection of design options. The process of using an iterative procedure to arrive at an optimized design is a classic example of a mental process. Humans perform similar "mental trial and error" when designing solutions, albeit on a different scale. Thus, it is the combination of mental process and mathematical concepts of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 7 further recites wherein using the neural network comprises using a convolutional neural network to process a spatial specification of material of the design to yield the predicted measurement data. It is merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 8 further recites selecting a subset of the prediction designs based on the predicted measurement data for said prediction designs to yield a set of simulation designs. The prediction designs themselves are essentially mathematical models or algorithms. For instance, a design might involve using a linear regression formula or a more complex machine learning algorithm. In patent law, mathematical formulas and algorithms are fundamental concepts that cannot be monopolized. Mentally comparing the predicted measurement data from multiple designs to decide which is best is a process of comparison and evaluation that does not require a specific machine to perform. A human could perform the same steps with pen and paper. Thus, it is the combination of mental process and mathematical concepts of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 9 further recites wherein selecting the subset of the prediction designs based on the predicted measurement data includes excluding at least one prediction design that is dominated in prediction by at least one other prediction design in the plurality of prediction designs, where a prediction design is dominated in prediction by a second prediction design when all predicted values of the physical qualities of the first prediction design are worse than the corresponding predicted physical qualities of the second prediction design. The selection and exclusion of designs based on comparing their simulated qualities can be performed entirely in the human mind. A person could manually list and compare the performance characteristics of different designs to identify and eliminate inferior ones. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 10 further recites wherein each design comprises a spatial array of regions and identification of respective materials in each of the regions of the array. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 11 further recites wherein determining the simulated measurement data includes performing a finite-element simulation parameterized by physical parameters of the materials. The finite element method (FEM) itself is a mathematical technique for numerically solving partial differential equations (PDEs). It approximates the solution by dividing a complex problem into a finite number of smaller, simpler elements. As a mathematical concept, the FEM is a "building block of human ingenuity" and is not patent-eligible on its own. The simulation then uses the abstract mathematics to predict how the physical material will behave under specific conditions. So, it falls under the mathematical concepts of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 12 further recites, wherein the physical parameters include at least one of Young's modulus and Poisson's ratio. The simulation is driven by equations that describe the relationship between physical properties and the resulting deformation or stress. Young's modulus and Poisson's ratio are mathematical expressions of scientific truths about material behavior under stress. So, it falls under the mathematical concepts of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 13 further recites wherein the method of claim 1 is repeated a plurality of times. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 14 further recites in which steps of claim 4 are repeated a plurality of times for each repetition of the steps of claim 1. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. See also claim 4 interpretations. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 15 further recites determining an output plurality of designs specifying structural combinations of the plurality of materials. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 16 further recites wherein the plurality of output designs includes only designs that are not dominated in physical measurement by another design of the plurality of output designs. The comparison of designs based on measured qualities is a mental process. A person could, in theory, perform this comparison mentally or with pen and paper, making it an unpatentable mental step. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 17 further recites wherein the plurality of output designs includes only designs that are not dominated in simulated measurement by another design of the plurality of output designs. The comparison of designs based on measured qualities is a mental process. A person could, in theory, perform this comparison mentally or with pen and paper, making it an unpatentable mental step. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 18 further recites fabricating the set of fabrication designs yielding the set of respective physical samples; and measuring the set of physical samples to yield the physical measurements for the physical samples. The physical acts of fabricating and measuring samples are often well-understood, routine, and conventional in many technical fields. The use of standard fabrication techniques and ordinary measuring instruments is not, by itself, an inventive concept. In Intellectual Ventures I LLC v. Capital One Bank, the court found that claims related to tracking financial transactions and transmitting data were directed to the abstract idea of budgeting. The additional elements, such as mere data gathering, were considered insignificant. In Content Extraction and Transmission v. Wells Fargo Bank, the Federal Circuit held that a claim involving receiving data, recognizing data fields, and storing that data was directed to the abstract idea of "data collection, recognition, and storage," and that the computer components were performing routine functions. In cases like Mayo Collaborative Services v. Prometheus Laboratories, Inc., the collection of data (e.g., measuring the level of a biomarker in blood) was deemed a preparatory step, while the central claim was directed toward a natural law. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. 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.
9. 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.
10. Claims 1, 4-5, 7, 10-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gu, et al. ("De novo composite design based on machine learning algorithm." Extreme Mechanics Letters 18 (2018): 19-28.) in view of Song et al., (PUB NO: US20210118530A1).
Regarding claim 1
Gu teaches a method for designing a structural combination of a plurality of materials, (see abstract-To demonstrate the application of machine learning to composite design, we optimize a large-scale system not tractable by an exhaustive brute force approach and show that it is a promising tool towards composite design. This work offers a new perspective in the exploration of design spaces and accelerating the discovery of new functional, customizable composites.) the method comprising:
determining, using a finite element simulator, simulated measurement data for a first plurality of simulation designs, each simulation design characterizing a structural combination of a plurality of materials, the simulated measurement data for each simulation design providing a simulation of physical qualities of said design; (see section 2.1 finite element model and fig 1- An FEM model is executed to solve for mechanical properties such as toughness and strength of 2-D composites. In this work, FEM results are considered as the ground truth when comparing to ML results. A brute force algorithm is implemented to generate all possible combinations of 2-D composites, which have an edge crack of 25% of the specimen width in the y-direction (Supporting Information Fig. S1). The toughness of a 2-D composite is defined as the area underneath its stress–strain curve; this area is proportional to the energy needed to initiate crack propagation. The strain at the crack tip is used to calculate the toughness and strength; once the strain reaches the failure strain of the element, the toughness and strength of the composite can be determined. See also section 2.2- Each combination is an 8 by 4 matrix flattened to an array with 32 binary values (i.e., 0 for soft material and 1 for stiff material) denoting the type of material in each element. We randomly choose 30,000 combinations among 35,960 combinations as the training data and the remaining 5,960 combinations as the testing data)
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Examiner note: The "Finite Element Code" box represents a physical simulator used to "Calculate toughness & strength" for "all input data," which consists of various geometries ("Generate all possible geometries" from the "Brute Force Generator"). The code calculates physical qualities such as "toughness & strength" for each design. The "Brute Force Generator" creates "all possible geometries," which can be interpreted as a plurality of simulation designs. These designs represent structural combinations of materials that are then fed into the simulator.
selecting a subset of the simulation designs based on the simulated measurement data for said simulation designs to yield a set of fabrications designs (see fig 1 -Ranking & labels (binary)-Top 17,980 →good Other 17,980 →bad and section 3.4.1-The top candidates are generated based on the weights of elements outputted from the ML model. The weights are used in the ML model to determine whether a design is “good” or “bad”. By using the weight information, we generate roughly 1 million top candidate geometries)
causing fabrication of the set of fabrication designs, including providing the set of fabrication designs for fabrication of a set respective physical samples (see section introduction-with the advances of additive manufacturing, it is now possible to print multiple materials, enabling the manufacturing of composites that vary in material and property in three spatial directions, and with virtually any geometry and complex combinations of distinct materials. See section discussion-These designs can now be experimentally reproduced with current 3D-printing techniques.)
receiving a set of physical measurements for the set of physical samples, each physical measurement for a physical sample providing measurements a plurality of physical qualities of the sample;(see section Discussion- the experimentally measured properties of 3D-printed samples can be used as an input to a computational design algorithm for improving the prediction accuracy. see abstract- mechanical properties including toughness and strength.)
. (see section introduction-with the advances of additive manufacturing, it is now possible to print multiple materials, enabling the manufacturing of composites that vary in material and property in three spatial directions, and with virtually any geometry and complex combinations of distinct materials. See section discussion-These designs can now be experimentally reproduced with current 3D-printing techniques. The experimentally measured properties of 3D-printed samples can be used as an input to a computational design algorithm for improving the prediction accuracy. We show that ML can learn the patterns of composites and rebuild the detailed performances of the designs, even when this information is lost in the training process. Additionally, our model can be improved to possess better accuracy by incorporating more complex architectures.)
In the related field of invention, Song teaches reconfiguring the finite element simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs. (see para 45- Steps (1)-(3) are respectively calculated for the properties of independent or separate metal and ceramic phases in multiphase composites; according to the Poisson's ratio obtained from step (1), Young's modulus and thermal expansion coefficient of metal and ceramic phase at particular temperature obtained in step (2), yield strength and hardening coefficient of metal phase with particular grain size at specific temperature obtained in step (3), parameter model in finite element model of the ceramic and metal phase at different temperature is built; Reading the geometric model in finite element model of composite material structure constructed in step (4), and endowing the parameter model of ceramic phase and metal phase with different temperature to the geometric model of composite material microstructure, defining boundary conditions and constraints; applying load to geometric model in finite element model of composite material microstructure and calculating the complex stress; stress-strain relationship, stress distribution and its evolution, plastic deformation and other mechanical behaviors of the composite under different temperature and complex stress conditions are further quantitatively analyzed. See also para 65-66- The internal statistical stress-strain response of Co phase in compression process is calculated and compared with the test results of neutron diffraction method in the literature, which is shown in FIG. 5. The simulation results of this model show a similar trend to the stress-strain curves of the experimental results, which verifies the rationality of this model.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include reconfiguring the finite element simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs as taught by Song in the system of Gu in order to monitor or control the patterning processes based on measurement structures formed on the substrate develop a multi-scale computing model for the intrinsic relationship between material composition, structure, process and performance, so as to realize the integrated design of material and the efficient and accurate prediction of material property and analysis. (See para 002-003, Song)
Regarding claim 19
Gu teaches a system configured to perform steps, (see abstract-To demonstrate the application of machine learning to composite design, we optimize a large-scale system not tractable by an exhaustive brute force approach and show that it is a promising tool towards composite design. This work offers a new perspective in the exploration of design spaces and accelerating the discovery of new functional, customizable composites. And see fig 1) the system comprising:
The rest of claim 19 is rejected for the same reasons as Claim 1, as they share the same elements.
Regarding claim 20
Gu teaches a non-transitory machine-readable medium comprising instruction stored thereon, the instructions when executed by a processor cause the processor to perform steps comprising in claim 20: (see section 4 discussion- As for the ML computational cost, the training period of the linear model is around 200 s using the same computer, and then it is able to predict mechanical properties of 1 million geometries in less than one second. And fig 1)
The rest of claim 20 is rejected for the same reasons as Claim 1, as they share the same elements.
Regarding claim 4
Gu teaches a method for designing a structural combination of a plurality of materials, (see abstract-To demonstrate the application of machine learning to composite design, we optimize a large-scale system not tractable by an exhaustive brute force approach and show that it is a promising tool towards composite design. This work offers a new perspective in the exploration of design spaces and accelerating the discovery of new functional, customizable composites.) the method comprising:
determining a plurality of prediction designs, and predicting measurement data for each of the prediction designs. (see section 2 and fig 1- see section 2 and fig 1-predicted labels of testing data, predicted labels of all data predicted ranking of all data)) determining, using a physical simulator, simulated measurement data for a first plurality of simulation designs, each simulation design characterizing a structural combination of a plurality of materials, the simulated measurement data for each simulation design providing a simulation of physical qualities of said design; (see section 2.1 finite element model and fig 1- An FEM model is executed to solve for mechanical properties such as toughness and strength of 2-D composites. In this work, FEM results are considered as the ground truth when comparing to ML results. A brute force algorithm is implemented to generate all possible combinations of 2-D composites, which have an edge crack of 25% of the specimen width in the y-direction (Supporting Information Fig. S1). The toughness of a 2-D composite is defined as the area underneath its stress–strain curve; this area is proportional to the energy needed to initiate crack propagation. The strain at the crack tip is used to calculate the toughness and strength; once the strain reaches the failure strain of the element, the toughness and strength of the composite can be determined. See also section 2.2- Each combination is an 8 by 4 matrix flattened to an array with 32 binary values (i.e., 0 for soft material and 1 for stiff material) denoting the type of material in each element. We randomly choose 30,000 combinations among 35,960 combinations as the training data and the remaining 5,960 combinations as the testing data)
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Examiner note: Gu discloses a common plurality of candidate composite designs. These designs represent structural combinations of materials that are then fed into the simulator. Prediction designs are those when processed by ML model for predicted label/rankings and simulation designs are those when processed by FE CODE for calculating toughness and strength.
selecting a subset of the simulation designs based on the simulated measurement data for said simulation designs to yield a set of fabrications designs (see fig 1 -Ranking & labels (binary)-Top 17,980 →good Other 17,980 →bad and section 3.4.1-The top candidates are generated based on the weights of elements outputted from the ML model. The weights are used in the ML model to determine whether a design is “good” or “bad”. By using the weight information, we generate roughly 1 million top candidate geometries)
providing the set of fabrication designs for fabrication of a set respective physical samples (see section introduction-with the advances of additive manufacturing, it is now possible to print multiple materials, enabling the manufacturing of composites that vary in material and property in three spatial directions, and with virtually any geometry and complex combinations of distinct materials. See section discussion-These designs can now be experimentally reproduced with current 3D-printing techniques.)
receiving a set of physical measurements for the set of physical samples, each physical measurement for a physical sample providing measurements a plurality of physical qualities of the sample;(see section Discussion- the experimentally measured properties of 3D-printed samples can be used as an input to a computational design algorithm for improving the prediction accuracy. see abstract- mechanical properties including toughness and strength.)
. (see section introduction-with the advances of additive manufacturing, it is now possible to print multiple materials, enabling the manufacturing of composites that vary in material and property in three spatial directions, and with virtually any geometry and complex combinations of distinct materials. See section discussion-These designs can now be experimentally reproduced with current 3D-printing techniques. The experimentally measured properties of 3D-printed samples can be used as an input to a computational design algorithm for improving the prediction accuracy. We show that ML can learn the patterns of composites and rebuild the detailed performances of the designs, even when this information is lost in the training process. Additionally, our model can be improved to possess better accuracy by incorporating more complex architectures.)
In the related field of invention, Song teaches reconfiguring the physical simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs. (see para 45- Steps (1)-(3) are respectively calculated for the properties of independent or separate metal and ceramic phases in multiphase composites; according to the Poisson's ratio obtained from step (1), Young's modulus and thermal expansion coefficient of metal and ceramic phase at particular temperature obtained in step (2), yield strength and hardening coefficient of metal phase with particular grain size at specific temperature obtained in step (3), parameter model in finite element model of the ceramic and metal phase at different temperature is built; Reading the geometric model in finite element model of composite material structure constructed in step (4), and endowing the parameter model of ceramic phase and metal phase with different temperature to the geometric model of composite material microstructure, defining boundary conditions and constraints; applying load to geometric model in finite element model of composite material microstructure and calculating the complex stress; stress-strain relationship, stress distribution and its evolution, plastic deformation and other mechanical behaviors of the composite under different temperature and complex stress conditions are further quantitatively analyzed. See also para 65-66- The internal statistical stress-strain response of Co phase in compression process is calculated and compared with the test results of neutron diffraction method in the literature, which is shown in FIG. 5. The simulation results of this model show a similar trend to the stress-strain curves of the experimental results, which verifies the rationality of this model.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include reconfiguring the physical simulator using a set of fabrication designs in association with respective physical measurements of physical samples fabricated according to the fabrication designs as taught by Song in the system of Gu in order to monitor or control the patterning processes based on measurement structures formed on the substrate develop a multi-scale computing model for the intrinsic relationship between material composition, structure, process and performance, so as to realize the integrated design of material and the efficient and accurate prediction of material property and analysis. (See para 002-003, Song)
Regarding claim 5
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 4. Gu further teaches the method of claim 4, wherein predicting measurement data for a prediction design comprises using a neural network to process the prediction design to yield predicted measurement data. (see section 2.2 machine learning approach and fig 1- Stochastic training is implemented to train our ML models. In the 8 by 8 system, we randomly choose 30,000 combinations among 35,960 combinations as the training data and the remaining 5,960 combinations as the testing data. A schematic of this process is shown in Fig. 1. To study how the amount of training data affects the prediction accuracy, we train ML models with different amounts of training data from 100 to 30,000 combinations corresponding to the training data densities from 0.28% to 83%. The testing data is the same no matter what amount of training data is used in the training process. Additionally, various numbers of training loops from 100 to 1,000,000 are selected to study the influence on the prediction accuracy. In each loop, a batch of combinations from the training data are randomly chosen. Different batch sizes from 10 to 1,000 combinations are considered to study their influence on the prediction accuracy. The effects of these training parameters are shown in Fig. 2.)
Examiner note: "All input data" (geometries) are processed to create "Training data" and "Testing data" for the neural network such as "Machine Learning Algorithm" and "ML Classifier Model." The "ML Classifier Model" produces "Predicted labels of testing data" and "Predicted labels of all data. The "Probability Extractor" and "ML Classifier Model" work together to produce a "Predicted ranking of all data," which is a form of predicted measurement data.
Regarding claim 7
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 4. Gu further teaches the method of claim 5, wherein using the neural network comprises using a convolutional neural network to process a spatial specification of material of the design to yield the predicted measurement data. (see section 2.3- Two ML models are considered in this work: the linear model and the Convolutional Neural Networks (CNN) model. In addition to the linear model, we also explore the use of convolution neural networks, a popular deep learning method for image recognition and other image related tasks. Due to the small size of systems considered in this work, our CNN model consists of only one convolutional layer. The convolutional layer computes 32 features using a 3 by 3 patch. See section 3.2- To understand the effects of using a more complex ML model, we also incorporate the CNN model to the same problem. The accuracies of the CNN model come out to be above 98% for both properties, which are at least 2% higher than the linear model with accuracies of 96% and 95% for toughness and strength, respectively. The predicted rankings of composite performance generated by the CNN model are depicted in Fig. 4a and 4b and zoomed-in figures for the top 2,000 ranked results are shown in Fig. 4c and 4d. In the CNN model, the NRMSD values are 0.0532 and 0.0474 for toughness and strength, respectively.)
Regarding claim 10
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. Gu further teaches wherein each design comprises a spatial array of regions and identification of respective materials in each of the regions of the array. (see fig 5, 8 and section 3.3- the top 12 geometries for achieving highest toughness and strength obtained from FEM and the ML models are shown in Fig. 5 for linear model)
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Examiner note: Figure 5 displays "Geometric patterns for high-toughness designs" in a grid format. These grids are composed of a spatial array of individual regions or cells, which are either pink or black. The figure legend for Figure 5 explicitly identifies the materials represented by the colors. It states that "Pink color refers to the stiff material and black color refers to the soft material". This indicates that the image provides a method for identifying the material in each region of the array.
Regarding claim 11
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. Gu further teaches wherein determining the simulated measurement data includes performing a finite-element simulation parameterized by physical parameters of the materials. (see section 2.1- An FEM model is executed to solve for mechanical properties such as toughness and strength of 2-D composites. Four-node elements are adopted in the FEM model. As 2-D elements, each node has two degrees of freedom (i.e., x and y). The four-node elements used in this work are linear elastic with a stiffness ratio between the stiff and soft materials of 0.1. Displacement boundary conditions are applied in the x direction shown in Supporting Information Fig. S1. The Young’s modulus of the stiff material is set to 1 GPa with a failure strain of 10%. Additionally, the toughness of the bulk stiff and soft materials is set to equal as we aim to study geometry effects rather than material effects on the composite properties. Thus, the Young’s modulus of the soft material is set to 0.1 GPa with a failure strain of 100%.)
Regarding claim 12
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. Gu further teaches wherein the physical parameters include at least one of Young's modulus. (see section 2.1-The four-node elements used in this work are linear elastic with a stiffness ratio between the stiff and soft materials of 0.1. Displacement boundary conditions are applied in the x direction shown in Supporting Information Fig. S1. The Young’s modulus of the stiff material is set to 1 GPa with a failure strain of 10%. Additionally, the toughness of the bulk stiff and soft materials is set to equal as we aim to study geometry effects rather than material effects on the composite properties. Thus, the Young’s modulus of the soft material is set to 0.1 GPa with a failure strain of 100%.)
Gu does not teach wherein the physical parameters include at least one of Poisson's ratio.
In the related field of invention, Song teaches wherein the physical parameters include at least one of Poisson's ratio. (see para 45-Steps (1)-(3) are respectively calculated for the properties of independent or separate metal and ceramic phases in multiphase composites; according to the Poisson's ratio obtained from step (1))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include wherein the physical parameters include at least one of Poisson's ratio as taught by Song in the system of Gu in order to monitor or control the patterning processes based on measurement structures formed on the substrate develop a multi-scale computing model for the intrinsic relationship between material composition, structure, process and performance, so as to realize the integrated design of material and the efficient and accurate prediction of material property and analysis. (See para 002-003, Song)
Regarding claim 13
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. Gu teaches the method of claim 1, wherein the method is repeated a plurality of times. (See section 2.2-Additionally, various numbers of training loops from 100 to 1,000,000 are selected to study the influence on the prediction accuracy. In each loop, a batch of combinations from the training data are randomly chosen. Different batch sizes from 10 to 1,000 combinations are considered to study their influence on the prediction accuracy.)
Regarding claim 14
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 4. Gu teaches the method of claim 4, in which steps of claim 4 are repeated a plurality of times for each repetition of the steps of claim 1. (See fig 1 and section 2.2-Additionally, various numbers of training loops from 100 to 1,000,000 are selected to study the influence on the prediction accuracy. In each loop, a batch of combinations from the training data are randomly chosen. Different batch sizes from 10 to 1,000 combinations are considered to study their influence on the prediction accuracy.)
Regarding claim 15
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. Gu further teaches determining an output plurality of designs specifying structural combinations of the plurality of materials. (See fig 1 and section 3.3- Additionally, the best mechanical performance obtained from FEM is one of the top performers obtained from the ML models for both properties. These results show that the ML models can accurately predict the optimal designs for both properties.)
Examiner note: Calculated properties like "toughness & strength" for these designs is used to train a machine learning model, which learns the relationship between the structural designs (geometries) and their properties. The model is then used to predict the properties of all possible designs. Final output is a "Predicted ranking of all data," which effectively provides a plurality of designs along with their predicted properties, allowing for the identification of optimal designs.
Regarding claim 18
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. Gu further teaches fabricating the set of fabrication designs yielding the set of respective physical samples; and measuring the set of physical samples to yield the physical measurements for the physical samples. see section introduction-with the advances of additive manufacturing, it is now possible to print multiple materials, enabling the manufacturing of composites that vary in material and property in three spatial directions, and with virtually any geometry and complex combinations of distinct materials. See section discussion-These designs can now be experimentally reproduced with current 3D-printing techniques. the experimentally measured properties of 3D-printed samples can be used as an input to a computational design algorithm for improving the prediction accuracy. see abstract- mechanical properties including toughness and strength.)
11. Claims 2-3, 8-9 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Gu, et al. ("De novo composite design based on machine learning algorithm." Extreme Mechanics Letters 18 (2018): 19-28.) in view of Song et al., (PUB NO: US20210118530A1) and further in view of Chen et al. ("Machine learning for composite materials." MRs Communications 9.2 (2019): 556-566.)
Regarding claim 2
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. The combination of Gu and Song does not teach wherein selecting the subset of the simulation designs to yield the fabrication designs includes excluding at least one simulation design that is dominated in simulation by at least one other simulation design in the plurality of simulation designs, where a first simulation design is dominated in simulation by a second simulated design when all simulated physical qualities of the first simulated design are worse than corresponding simulated physical qualities of the second simulated design;
In the related field of invention, Chen teaches wherein selecting the subset of the simulation designs to yield the fabrication designs includes excluding at least one simulation design that is dominated in simulation by at least one other simulation design in the plurality of simulation designs, where a first simulation design is dominated in simulation by a second simulated design when all simulated physical qualities of the first simulated design are worse than the corresponding simulated the physical qualities of the second simulated design; (see fig 4(a)(b))
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Examiner note: The figure shows the creation of a "Pareto frontier" or "Pareto curve" to represent the optimal trade-off between two physical qualities: Young's modulus and toughness. A design is considered "dominated" if another design exists that is better in all objective qualities, in this case, having a higher Young's modulus and/or a higher toughness. The Pareto frontier is the set of all non-dominated solutions, where no one objective can be improved without making another objective worse. The process depicted in the figure, particularly in graph 4(a), shows the "Metamodel output" as a large cloud of blue points representing all the simulated designs. The red curve on this graph is the "Pareto frontier." This curve is the set of designs from the larger cloud that are not dominated by any other design in the simulation. By identifying and selecting the designs that lie on this frontier, the process effectively excludes all the dominated designs that fall below the curve, as they are considered suboptimal for at least one of the two qualities. This allows for focusing on a smaller, more optimal set of choices for potential fabrication.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu and Song to include wherein selecting the subset of the simulation designs to yield the fabrication designs includes excluding at least one simulation design that is dominated in simulation by at least one other simulation design in the plurality of simulation designs, where a first simulation design is dominated in simulation by a second simulated design when all simulated physical qualities of the first simulated design are worse than the corresponding simulated the physical qualities of the second simulated design as taught by Chen in the system of Gu and Song in order to design and optimize composites for the next generation of materials with unprecedented properties. (See ABSTRACT, Chen)
Regarding claim 3
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. The combination of Gu and Song does not teach the method of claim 2, wherein the set of fabrication designs includes only designs that are not dominated in physical measurement by any other design in the set of fabrication designs, where a first design is dominated in physical measurement by a second design when all measured physical qualities of the plurality of physical qualities of the first design are worse than corresponding measured physical qualities of the second design.
In the related field of invention, Chen teaches the method of claim 2, wherein the set of fabrication designs includes only designs that are not dominated in physical measurement by any other design in the set of fabrication designs, where a first design is dominated in physical measurement by a second design when all measured physical qualities of the plurality of physical qualities of the first design are worse than corresponding measured physical qualities of the second design. (see fig 4(a)(b))
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Examiner note: Figure 4(a) and 4(b) explicitly show a "Pareto frontier" or "Pareto curve," which represents the set of optimal designs. The designs on the Pareto frontier are those where it is impossible to improve one physical quality (e.g., Young's modulus) without making another physical quality (e.g., toughness) worse. This aligns with the definition of non-dominated designs provided in the query. Designs not on this curve are "dominated" because there are other designs with better or equal values for both physical properties. The figure uses Young's modulus and toughness as the two physical qualities being optimized. The Pareto frontier shown in the graphs represents the best possible trade-offs between these two properties.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include wherein the set of fabrication designs includes only designs that are not dominated in physical measurement by any other design in the set of fabrication designs, where a first design is dominated in physical measurement by a second design when all measured physical qualities of the plurality of physical qualities of the first design are worse than corresponding measured physical qualities of the second design as taught by Chen in the system of Gu and Song in order to design and optimize composites for the next generation of materials with unprecedented properties. (See ABSTRACT, Chen)
Regarding claim 8
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 4. The combination of Gu and Song does not teach selecting a subset of the prediction designs based on the predicted measurement data for said prediction designs to yield a set of simulation designs.
In the related field of invention, Chen teaches selecting a subset of the prediction designs based on the predicted measurement data for said prediction designs to yield a set of simulation designs. (see fig 4(a)(b))
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Examiner note: An initial set of Coarse-Grained Molecular Dynamics (CG-MD) simulations (blue dots) is used to build a metamodel. The metamodel is then used to predict the properties of a large number of designs, from which a Pareto frontier is identified (the red curve in figure (b)). The Pareto frontier represents the optimal trade-offs between Young's modulus and toughness. A small subset of designs from this Pareto curve (the purple squares in figure 4 (b)) is then selected for validation. The selected designs are validated by running actual CG-MD simulations (the green diamonds in figure 4(b)) and the results are compared to the metamodel's predictions in figures 4(c) and 4(d). This is a validation step, where the subset of designs selected from the metamodel's predictions is tested with actual simulations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include selecting a subset of the prediction designs based on the predicted measurement data for said prediction designs to yield a set of simulation designs as taught by Chen in the system of Gu and Song in order to design and optimize composites for the next generation of materials with unprecedented properties. (See ABSTRACT, Chen)
Regarding claim 9
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 4. The combination of Gu and Song does not teach wherein selecting the subset of the prediction designs based on the predicted measurement data includes excluding at least one prediction design that is dominated in prediction by at least one other prediction design in the plurality of prediction designs, where a prediction design is dominated in prediction by a second prediction design when all predicted values of the physical qualities of the first prediction design are worse than the corresponding predicted physical qualities of the second prediction.
In the related field of invention, Chen teaches wherein selecting the subset of the prediction designs based on the predicted measurement data includes excluding at least one prediction design that is dominated in prediction by at least one other prediction design in the plurality of prediction designs, where a prediction design is dominated in prediction by a second prediction design when all predicted values of the physical qualities of the first prediction design are worse than the corresponding predicted physical qualities of the second prediction. (see fig 4(a)(b))
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Examiner note: The figure displays a Pareto frontier, which is a key concept in multi-objective optimization. The Pareto frontier (the red curve in Figure 4a) represents the set of optimal trade-offs between two or more competing objectives. In this case, the objectives are maximizing Young's modulus and toughness. A prediction design is considered "dominated" if there is at least one other design that is superior in all measured qualities. The metamodel output (the blue shaded area) represents the plurality of all possible prediction designs. The Pareto frontier is formed by selecting only the non-dominated designs from this set. All the designs within the blue shaded area but not on the red curve are dominated by at least one design on the Pareto frontier. Therefore, the figure shows the process of excluding dominated designs to identify the most optimal set of solutions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include selecting the subset of the prediction designs based on the predicted measurement data includes excluding at least one prediction design that is dominated in prediction by at least one other prediction design in the plurality of prediction designs, where a prediction design is dominated in prediction by a second prediction design when all predicted values of the physical qualities of the first prediction design are worse than the corresponding predicted physical qualities of the second prediction as taught by Chen in the system of Gu and Song in order to design and optimize composites for the next generation of materials with unprecedented properties. (See ABSTRACT, Chen)
Regarding claim 16
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. The combination of Gu and Song does not teach wherein the plurality of output designs includes only designs that are not dominated in physical measurement by another design of the plurality of output designs.
In the related field of invention, Chen teaches wherein the plurality of output designs includes only designs that are not dominated in physical measurement by another design of the plurality of output designs. (See fig 4 and see page 563- Figure 4 shows the modulus and toughness obtained from the metamodel by sampling 1 million assembled hairy NPs with different combinations of input variables. As can be seen in the figure, the predicted modulus and toughness obtained from the metamodel and those obtained from the CG-MD simulations are very close.)
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Examiner note: The Pareto frontier in the figure 4(a)(b) illustrates the optimal trade-off between Young's modulus and toughness for the materials being analyzed by the metamodel. Any design on this red curve (the Pareto frontier) is a solution where neither property can be improved without making the other one worse. Designs that are "dominated" would be located away from this frontier, as they would be worse in at least one objective and not better in any others compared to a design on the frontier Validation: Figure 4(b) shows the Pareto curve along with validation points, confirming the existence of this non-dominated set of designs.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include wherein the plurality of output designs includes only designs that are not dominated in physical measurement by another design of the plurality of output designs as taught by Chen in the system of Gu and Song in order to design and optimize composites for the next generation of materials with unprecedented properties. (See ABSTRACT, Chen)
Regarding claim 17
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 1. The combination of Gu and Song does not teach wherein the plurality of output designs includes only designs that are not dominated in simulated measurement by another design of the plurality of output designs.
In the related field of invention, Chen teaches wherein the plurality of output designs includes only designs that are not dominated in simulated measurement by another design of the plurality of output designs. (see fig 4(a)(b))
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Examiner note: The red curve labeled "Pareto frontier" and "Pareto curve" in these figures represents the set of designs that are not dominated by any other designs in the simulated measurement space.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include wherein the plurality of output designs includes only designs that are not dominated in simulated measurement by another design of the plurality of output designs as taught by Chen in the system of Gu and Song in order to design and optimize composites for the next generation of materials with unprecedented properties. (See ABSTRACT, Chen)
12. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Gu, et al. ("De novo composite design based on machine learning algorithm." Extreme Mechanics Letters 18 (2018): 19-28.) in view of Song et al., (PUB NO: US20210118530A1) and further in view of Gu et al. ("Optimization of composite fracture properties: method, validation, and applications." Journal of Applied Mechanics 83.7 (2016): 071006.)
Regarding claim 6
Gu in view of Song as shown in the rejection above, discloses the limitations of claim 4. The combination of Gu and Song does not teach wherein determining the plurality of prediction designs comprises using an evolutionary procedure to iteratively generate said designs.
In the related field of invention, Gu1 teaches wherein determining the plurality of prediction designs comprises using an evolutionary procedure to iteratively generate said designs. (see fig 2)
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Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for designing a structural combination of a plurality of materials as disclosed by Gu to include wherein determining the plurality of prediction designs comprises using an evolutionary procedure to iteratively generate said designs as taught by Gu1 in the system of Gu and Song in order to architect composites with excellent material properties compared to its constituents, which themselves often have contrasting mechanical behavior. (See ABSTRACT, Gu1)
Conclusion
12. Claims 1-20 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20210342501-A1 Carpenter et al.
Discussing the systems for the operation of: performing one or more simulations of a simulated physical object that contains one or more materials associated with the calculated optimum material coefficients, where the calculated optimum material coefficients are used in the one or more simulations to determine the behavior of the one or more materials in the simulation of the physical object that contains or includes the one or more materials. In one embodiment, the simulations can provide one or more results that indicate one or more of: a performance of the physical object; failure data about the physical object; thermo-mechanical fatigue data about the physical object; or data specifying an estimated useful life of the physical object. In one embodiment, after the one or more simulations, the method can further include the operation of: revising the design of the physical object based upon the one or more results of the one or more simulations, and this revised design can be further simulated or tested or prepared for fabrication or manufacture..
Gu, Grace X., et al. "Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment." Materials Horizons 5.5 (2018): 939-945.
ii. Discussing method t0 demonstrate a new machine learning-based design approach for hierarchical materials. The new designs created by our machine learning model, which is trained with a database of hundreds of thousands of geometries from finite element analysis, are validated using additive manufacturing and experimentation.
13. 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.
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/PURSOTTAM GIRI/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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