DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-10 and 12-20 are pending.
Priority
This application claims benefit of application no. 63/388,997, filed 07/13/2022. The instant application has the effective filing date of 13 July 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/07/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings, submitted on 07/13/2023, are accepted by the examiner.
Claim Objections
Claim 20 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 17.
When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention for the following reasons.
Claim 8 recites, “two coupling atoms are of equal or different kinds,” in which “equal or different kinds” is indefinite and not defined by the specification. To overcome this rejection please clarify what is meant by “kinds.”
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 1-10 and 12-20 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-10 and 12-10 are directed to a statutory category (method).
Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claim 1: (b) generating a plurality of DFTB parameters for the reference material system; (mental process)
(c) performing an optimization routine to adjust each DFTB parameter of the plurality of DFTB parameters to improve accuracy relative to the set of benchmark parameters of the reference material system; (mental process, mathematical concept)
Claim 2: wherein generating the set of benchmark parameters includes performing a density functional theory (DFT) simulation of a subset of the reference material system. (mathematical concept)
Claim 3: optimizing a subset of the plurality of DFTB parameters to generate a second simulation output of the subset of the reference material system according to a target accuracy relative to the set of benchmark parameters. (mental process, mathematical concept)
Claim 5 and 14: assigning the optimized set of DFTB parameters to a transferability space, wherein the transferability space correlates the optimized set of DFTB parameters with one or more applicable material systems or material interfaces. (mental process)
Claim 6: wherein the plurality of DFTB parameters of the reference material system includes at least one of electronic parameters, repulsive potentials, ionic parameters, or ideal distance between coupling atoms. (mental process)
Claim 7 and 16: wherein the plurality of DFTB parameters includes repulsive potentials of the reference material system, (mental process)
wherein the optimization routine includes: (a) selecting a repulsive potential of two coupling atoms of the reference material system; (mental process)
(b) fitting the repulsive potential to the reference material system while all parameters of the set of DFTB parameters are held static to generate a fitted repulsive potential; (mathematical concept)
Claim 8: wherein the two coupling atoms are of equal or different kinds within the reference material system. (mental process)
Claim 9: transferring the optimized set of DFTB parameters to a second reference material system. (mental process)
Claim 10: (b) generating a set of DFTB parameters; (mental process)
(c) initiating a first optimization routine to generate an optimized subset of density-functional tight-binding (DFTB) parameters from a subset of the set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for a subset of the reference material system; (mental process, mathematical concept)
(d) combining the optimized subset of DFTB parameters with the set of DFTB parameters; (mental process)
(d) initiating a second optimization routine on the set of DFTB parameters to generate an optimized full set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for the reference material system. (mental process, mathematical concept)
Claim 12: wherein generating the set of benchmark parameters includes performing a density functional theory (DFT) simulation of a subset of the reference material system. (mathematical concept)
Claim 15: wherein the optimized full set of DFTB parameters of the reference material system includes at least one of electronic parameters, repulsive potentials, ionic parameters, or ideal distance between coupling atoms. (mental process, mathematical concept)
Claims 17 and 20: wherein the subset of the reference material system optimized by the first optimization routine includes the two or more materials but not the material interface. (mathematical concept)
Claim 19: (b) generating an initial set of density-functional tight-binding (DFTB) parameters; (mental process)
(c) initiating a first optimization routine to generate a first optimized set of DFTB parameters from the initial set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for a subset of the reference material system; (mental process, mathematical concept)
(d) initiating a second optimization routine on the first optimized set of DFTB parameters to generate a second optimized set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for the reference material system, wherein the first optimized set of DFTB parameters includes repulsive potentials each corresponding to two coupling atoms of the reference material system, (mental process, mathematical concept)
wherein the second optimization routine includes: (i) selecting a repulsive potential of the reference material system; (mental process)
(ii) fitting the repulsive potential to the reference material system while all parameters of the set of DFTB parameters are held static to generate a fitted repulsive potential; (mathematical concept)
Step 2A – Prong One Analysis:
Analysis techniques such as generating, adjusting, combining, or assigning parameters, requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and opinions, and fall under the mental process grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claim 6, 8, 15).
Analysis techniques such as DFT simulations, optimization routines, and generating fitted repulsive potentials recite mathematical calculations, formulas and/or relationships that fall under the mathematical concept grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claim 15 and 17).
Therefore, the claims appear to recite judicial exceptions.
[Eligibility Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering Elements:
Claim 1: (a) generating a set of benchmark parameters indicative of material properties of a reference material system through performance of at least one of a simulation of, or an experiment on, a subset of the reference material system;
Claim 4 and 13: wherein the set of benchmark parameters includes at least one of a band structure, a piezoelectric coefficient, a screening constant, a charge distribution, an optoelectronic parameter, or a mechanoelectrical parameter of the reference material system.
Claim 10: (a) generating a set of benchmark parameters indicative of material properties of a reference material system through performance of at least one of a simulation of, or an experiment on, a subset of the reference material system;
Claim 17 and 20: wherein the reference material system includes two or more materials and a material interface defined between the two or more materials
Claim 18: wherein the two or more materials includes at least one material layer and at least one defect defined by the at least one material layer.
Claim 19: (a) generating a set of benchmark parameters indicative of material properties of a reference material system through performance of at least one of a simulation of, or an experiment on, a subset of the reference material system; (iii) storing the fitted repulsive potential as a new parameter of the second optimized set of DFTB parameters.
Computational Elements:
Claim 1: (d) storing an optimized set of DFTB parameters corresponding to the material properties of the reference material system.
Claim 7: (c) storing the fitted repulsive potential as a new parameter of the set of DFTB parameters.
Step 2A – Prong Two Analysis:
The data gathering elements merely obtain data necessary to complete the judicial exceptions. Such elements are classified as insignificant extra-solution activity and thus do not integrate the judicial exceptions into practical application per MPEP 2016.05(g).
Generic computer components and implementations provide mere instructions to implement the abstract ideas onto a technological environment per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
The data gathering activities are found well-understood, routine, and conventional via Spiegelman et al. (Advances in Physics X; Vol. 5: 1; 2020).
The computational elements are further found to be well-understood, routine, and conventional per 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 for storing and retrieving information in memory and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (MPEP 2106.05 (a)).
As such, the additional elements are further found to lack inventive concept.
[Eligibility Step 2B: NO]
Therefore, claims 1-10 and 12-20 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 12-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aguirre et al. (J Chemical Theory and Computation; Vol. 16: 3; 2020) in view of Smith et al. (Scientific Data; Vol. 4: 170193; 2017).
Aguirre et al. describes the development of Density Functional Tight-Binding parameters using relative energy fitting and particle swarm optimization
Claim 1 is directed to a method of generating a computational model that includes the steps of: (a) generating a set of benchmark parameters indicative of material properties of a reference material system through a simulation of, or an experiment on, a subset of the reference material system; (b) generating a plurality of DFTB parameters for the reference material system; (c) performing an optimization routine to adjust each DFTB parameter of the plurality of DFTB parameters to improve accuracy relative to the set of benchmark parameters of the reference material system; and (d) storing an optimized set of DFTB parameters corresponding to the material properties of the reference material system.
Aguirre et al. teaches carrying out DFTB calculations on two benchmark datasets available in the literature, QM-9 and ANI-1 (page 8, column 1); using each dataset to evaluate different properties of interest (page 8, column 1); introducing the main concepts of the DFTB approximation including the specific parameters to be optimized and their physical meaning (page 4, column 1); obtaining initial guesses of these parameters that are based on DFT calculations in order to guarantee a reasonable starting point and reduce computational effort during optimization (page 4, column 1); during all optimization steps, the space boundaries of the parameters were chosen as a certain percentage of variation (100 or 50%) from the initial guesses (page 5, column 2); introducing training and testing datasets of molecules and properties based on DFT calculations that we use as a reference to optimize the parameters and evaluation datasets used to monitor the performance and transferability of the new parameterization (page 4, column 1), in which the results indicate that the new parameterization have more accurate predictions (page 8, column 1); selecting the best parameters as the model that produced the lowest testing set objective function (page 5, column 2); and showing the final variables of the parameterization, called LANL-2019 parameters, in tables 2 and 3 (page 8, column 1).
Claim 3 is directed to wherein upon generating a plurality of DFTB parameters, and before performing an optimization routine to adjust each DFTB parameter of the plurality of DFTB parameters, the method includes: optimizing a subset of the plurality of DFTB parameters intended to generate a second simulation output of the subset of the reference material system according to a target accuracy relative to the set of benchmark parameters.
Aguirre et al. teaches a first optimization step of the HC parameter optimization, where fitting is only conducted to Binding Energies and Forces (optBE,F) (page 7, table 4).
Claim 4 is directed to wherein the set of benchmark parameters includes at least one of a band structure, a piezoelectric coefficient, a screening constant, a charge distribution, an optoelectronic parameter, or a mechanoelectrical parameter of the reference material system.
Aguirre et al. teaches optimizing the parameters associated with the Hamiltonian matrix elements, pair repulsion potentials, and the Hubbard Ua (page 4, column 2), in which the pair repulsion potential Φab(Rab) mainly contains the repulsive interaction between the ions, but in practice, it contains any electronic contribution that is not captured by the other terms (page 4, column 2); and the Hubbard U parameter Ua modulates the charge transfer as a result of different electronegativities (page 4, column 2).
Claim 5 is directed to assigning the optimized set of DFTB parameters to a transferability space, wherein the transferability space correlates the optimized set of DFTB parameters with one or more applicable material systems or material interfaces.
Aguirre et al. teaches to test the transferability of the optimized DFTB parameters, we used a training set of small molecules including the most representative chemical functional groups and, as a testing set, larger systems with combinations of different functional groups (page 4, column 1); and the accuracy of current parameterization was tested on harmonic frequencies using the QM-9 dataset, by computing the zero-point vibrational energies (ZPE), in which vibrational information was not included in the objective function (page 10, column 2); therefore, this analysis provides information about the transferability of current parameterization to nonfitted properties, specifically the calculation of the second derivatives of the energy with respect to the nuclear coordinates (page 10, column 2).
Claim 6 is directed to wherein the plurality of DFTB parameters of the reference material system includes at least one of electronic parameters, repulsive potentials, ionic parameters, or ideal distance between coupling atoms.
Aguirre et al. teaches optimizing the parameters associated with the Hamiltonian matrix elements, pair repulsion potentials, and the Hubbard Ua (page 4, column 2).
Claim 9 is directed to transferring the optimized set of DFTB parameters to a second reference material system.
Aguirre et al. teaches the accuracy of current parameterization was tested on harmonic frequencies using the QM-9 dataset, by computing the zero-point vibrational energies (ZPE), in which it is important to highlight that vibrational information was not included in the objective function (page 10, column 2); therefore, this analysis provides information about the transferability of current parameterization to nonfitted properties, specifically the calculation of the second derivatives of the energy with respect to the nuclear coordinates (page 10, column 2).
Claim 10 is directed to a method of generating a computational model, comprising: (a) generating a set of benchmark parameters indicative of material properties of a reference material system through performance of at least one of a simulation of, or an experiment on, a subset of the reference material system; (b) generating a set of DFTB parameters; (c) initiating a first optimization routine to generate an optimized subset of density-functional tight-binding (DFTB) parameters from a subset of the set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for a subset of the reference material system; (d) combining the optimized subset of DFTB parameters with the set of DFTB parameters; and (d) initiating a second optimization routine on the set of DFTB parameters to generate an optimized full set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for the reference material system.
Aguirre et al. teaches carrying out DFTB calculations on two benchmark datasets available in the literature, QM-9 and ANI-1 (page 8, column 1); using each dataset to evaluate different properties of interest (page 8, column 1); introducing the main concepts of the DFTB approximation including the specific parameters to be optimized and their physical meaning (page 4, column 1); and obtaining initial guesses of these parameters that are based on DFT calculations in order to guarantee a reasonable starting point and reduce computational effort during optimization (page 4, column 1).
Aguirre et al. teaches a first optimization step of the HC parameter optimization, where fitting is only conducted to Binding Energies and Forces (optBE,F) (page 7, table 4); and for the final HCNO optimization, a training set was created by combining all training sets from steps 1–4, and the final testing set was created in the same way (page 5, column 2); and in order to assess the accuracy of our LANL-2019 parameterization, we carried out DFTB calculations on two benchmark datasets available in the literature (page 8, column 1).
Claim 13 is directed to wherein the set of benchmark parameters includes at least one of a band structure, a piezoelectric coefficient, a screening constant, a charge distribution, an optoelectronic parameter, or a mechanoelectrical parameter of the reference material system.
Aguirre et al. teaches optimizing the parameters associated with the Hamiltonian matrix elements, pair repulsion potentials, and the Hubbard Ua (page 4, column 2), in which the pair repulsion potential Φab(Rab) mainly contains the repulsive interaction between the ions, but in practice, it contains any electronic contribution that is not captured by the other terms (page 4, column 2); and the Hubbard U parameter Ua modulates the charge transfer as a result of different electronegativities (page 4, column 2).
Claim 14 is directed to assigning the optimized set of DFTB parameters to a transferability space, wherein the transferability space correlates the optimized set of DFTB parameters with one or more applicable material systems or material interfaces.
Aguirre et al. teaches to test the transferability of the optimized DFTB parameters, we used a training set of small molecules including the most representative chemical functional groups and, as a testing set, larger systems with combinations of different functional groups (page 4, column 1); and the accuracy of current parameterization was tested on harmonic frequencies using the QM-9 dataset, by computing the zero-point vibrational energies (ZPE), in which vibrational information was not included in the objective function (page 10, column 2); therefore, this analysis provides information about the transferability of current parameterization to nonfitted properties, specifically the calculation of the second derivatives of the energy with respect to the nuclear coordinates (page 10, column 2).
Claim 15 is directed to wherein the optimized full set of DFTB parameters of the reference material system includes at least one of electronic parameters, repulsive potentials, ionic parameters, or ideal distance between coupling atoms.
Aguirre et al. shows the final LANL-2019 HCNO pair potential variables (page 7, table 3).
Claims 17 and 20 are directed to wherein the reference material system includes two or more materials and a material interface defined between the two or more materials, wherein the subset of the reference material system optimized by the first optimization routine includes the two or more materials but not the material interface.
Aguirre et al. teaches using two different sets of molecular descriptors; the first set is computed from a methodology based solely on the structure of individual molecules; the second set includes properties that emerge from a collection of molecules; using binding energies and atomic forces for the first set and the success in the determination of the lowest energy isomer, the relative order of all isomers as the second set; and a first optimization step of the HC parameter optimization, where fitting is only conducted to Binding Energies and Forces (optBE,F) (page 7, table 4).
Therefore, in the first optimization routine, Aguirre et al. teaches only including the materials (individual molecule) data, but not their interface data, from the collections of molecules.
Aguirre et al. further teaches we are interested in comparing how different properties of a set of molecules compare when calculated with the approximate DFTB method with respect to a reference method like DFT (page 2, column 1).
Aguirre et al. does not explicitly teach generating the benchmark parameters via a simulation of, or an experiment on, a subset of the reference material system (claims 1 and 10); nor wherein generating the set of benchmark parameters includes performing a density functional theory (DFT) simulation of a subset of the reference material system (claims 2 and 12).
Smith et al. describes ANI- 1, one of the benchmark datasets used by Aguirre et al., which includes calculated off-equilibrium conformations for organic molecules.
Smith et al. teaches reporting a large dataset of non-equilibrium DFT total energy calculations for organic molecules (page 3, column 1); and believing it will become a new standard benchmark for comparison of current and future methods in the ML potential community (page 1, column 1).
Therefore, Smith et al. teaches a method of generating the benchmark parameters via a simulation of, or an experiment on, a subset of the reference material system applicable to the method of Aguirre et al. As such, the methods can be combined with each element merely performing the same function as they do separately to yield predictable results with a reasonable expectation of success.
Claims 7, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aguirre et al. (J Chemical Theory and Computation; Vol. 16: 3; 2020) in view of Smith et al. (Scientific Data; Vol. 4: 170193; 2017), as applied to claims 1-6, 12-15, 17, and 20 above, and in further view of Li et al. (J Chemical theory and Computation; Vol. 14; 2018).
Aguirre et al., in view of Smith et al., teach a swarm optimization procedure of DFTB parameters.
Claim 19 is directed to a method of generating a computational model, comprising: (a) generating a set of benchmark parameters indicative of material properties of a reference material system through performance of at least one of a simulation of, or an experiment on, a subset of the reference material system; (b) generating an initial set of density-functional tight-binding (DFTB) parameters; (c) initiating a first optimization routine to generate a first optimized set of DFTB parameters from the initial set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for a subset of the reference material system; and (d) initiating a second optimization routine on the first optimized set of DFTB parameters to generate a second optimized set of DFTB parameters to improve accuracy relative to the set of benchmark parameters for the reference material system.
Aguirre et al. teaches carrying out DFTB calculations on two benchmark datasets available in the literature, QM-9 and ANI-1 (page 8, column 1); using each dataset to evaluate different properties of interest (page 8, column 1); introducing the main concepts of the DFTB approximation including the specific parameters to be optimized and their physical meaning (page 4, column 1); and obtaining initial guesses of these parameters that are based on DFT calculations in order to guarantee a reasonable starting point and reduce computational effort during optimization (page 4, column 1).
Aguirre et al. teaches a first optimization step of the HC parameter optimization, where fitting is only conducted to Binding Energies and Forces (optBE,F) (page 7, table 4), the second step, which also includes Relative Energies (optRE)- (page 7, table 4); and in order to assess the accuracy of our LANL-2019 parameterization, we carried out DFTB calculations on two benchmark datasets available in the literature (page 8, column 1).
Claim 19 is further directed to wherein the first optimized set of DFTB parameters includes repulsive potentials each corresponding to two coupling atoms of the reference material system, wherein the second optimization routine includes: (i) selecting a repulsive potential of the reference material system; (ii) fitting the repulsive potential to the reference material system while all parameters of the set of DFTB parameters are held static to generate a fitted repulsive potential; and (iii) storing the fitted repulsive potential as a new parameter of the second optimized set of DFTB parameters. Claims 7 and 16 are directed to the same steps.
Regarding claims 7, 16, and 19, Aguirre et al. teaches fitting the relative energies of a set of molecules with the same stoichiometry (page 6, column 1); using this proposed parameterization approach to develop the LANL-2019 HCNO parameter set, which improves the binding and relative energies (page 11, column 1); and testing the new parameters on datasets, including the ANI-1 (page 11, column 1).
Therefore Aguirre et al. teaches fitting molecular properties of the reference system and storing the fitted properties as part of a new DFTB parameter set.
Aguirre et al. in view of Smith et al. do not teach selecting a repulsive potential of two coupling atoms; nor fitting the repulsive potential to the reference material system while all parameters of the set of DFTB parameters are held static (claims 7, 16, and 19).
Li et al. describes a Density Functional Tight Binding Layer for deep learning of chemical Hamiltonians.
Li et al. teaches the repulsive potential is typically written as a sum of interatomic potentials that are nonzero over a limited range (page 2, column 1), represented by the function,
R
Z
A
Z
B
, that depends on the elements, indicated by atomic numbers ZA, of the atoms, where A and B label atoms (page 3, column 2).
Li et al. teaches simultaneous optimization of all fitting parameters has also been done using gradient free optimization methods such as swarm optimization, simulated annealing, and pattern search (page 2, column 2); a number of recent DFTB parametrizations have, in addition to fitting the repulsive potential, empirically adjusted parameters that define the electronic Hamiltonian during the fitting process (page 2, column 2); and when only the repulsive potential R is allowed to vary, monotonic regularization is sufficient to lead to improved transfer from smaller to larger molecules (page 8, column 2); however, when the electronic Hamiltonian is also allowed to vary, monotonic regularization is not sufficient to improve transfer to larger molecules (page 8, column 2).
Claim 8 is directed to wherein the two coupling atoms are of equal or different kinds within the reference material system.
Li et al. teaches A and B label atoms (page 3, column 2) and A > B (page 3, equation 12).
Li et al. further teaches the models explored here are developed and tested on a subset of the ANI-1 data set (page 1, column 1).
Therefore Li et al. teaches selecting two different coupling atoms, based on the requirement that their interatomic potential is non-zero; and provides sufficient motivation to fit their repulsive potential, while keeping the other dftb parameters fixed in order to provide improvements to the system. It further teaches that in some embodiments, such methods of fitting the repulsive potential are combined with fitting other dftb parameters, such as electronic Hamiltonians, within swarm optimization pipelines. As such, it would be obvious to one of ordinary skill in the art to combine Aguirre et al.’s particle swarm optimization method of several dftb parameters, including Hamiltonians, with the additional, sole fitting of the repulsive potential parameter, taught by Li et al. with a reasonable expectation of success and improvement to the system.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Aguirre et al. (J Chemical Theory and Computation; Vol. 16: 3; 2020) in view of Smith et al. (Scientific Data; Vol. 4: 170193; 2017), as applied to claims 1-6, 12-15, 17, and 20 previously, and in further view of Gaus et al. (J Chemical Theory and Computation; Vol. 9; 2013) and Spiegelman et al. (Adv Phys X; Vol. 5:1; 2020).
Aguirre et al., in view of Smith et al., teach a swarm optimization procedure of DFTB parameters.
Claim 18 is directed to wherein the two or more materials include at least one material layer and at least one defect defined by the at least one material layer.
Aguirre et al. in view of Smith et al. do not teach the materials include at least one material layer and at least one defect defined by the at least one material layer.
Gaus et al. describes the parametrization and benchmark of DFTB3 for organic molecules.
Gaus et al. teaches for the parametrization of DFTB for CHNO, two distinct groups of parameters have to be optimized: the electronic parameters appearing in the first three contributions of the DFTB3 total energy (eq 1), (page 3, column 2) and the repulsive parameters, (page 3, column 2); in the current work, we have optimized both sets of parameters for generating the new DFTB3 parametrization 3OB (page 3, column 2); and benchmark our new parameters 3OB and 3OB-f with respect to experimental and high level ab initio data (page 6, column 2).
Gaus et al. further teaches DFTB is also frequently used for crystalline systems (for a review, see e.g. the recent issue of Phys. Status Solidi B, [ref 71]) (page 13, column 2); and while 3OB is expected to give reasonable results for geometric data in crystalline systems, a careful evaluation of energetic properties seems necessary when considering a specific application (page 14, column 1).
Gaus et al. does not teach the materials include at least one material layer and at least one defect defined by the at least one material layer.
Spiegelman et al. describes basic concepts and applications to molecules and clusters of Density-Functional tight-binding.
Spiegelman et al. teaches DFTB was also developed in order to study adsorption of organic molecules on carbon surfaces (page 27, column 1), for example transition metal complexes (porphyrin and porphycene) on graphene or small molecules (H2O, CH4, NH3) on defective carbon nanotubes which were all found to physisorb on the nanotubes, except NH3 which also chemisorbs (page 28, column 1); one can also find a DFTB study of graphene formation on a surface-molten copper surface (page 29, column 1); and in the latter, the authors explains the high quality of a graphene layer grown on Cu by the fact that the high mobility and rapid diffusion of surface Cu atoms induce defecthealing during graphene growth (page 29, column 1).
Therefore Gaus et al. teaches a DFTB parameterization method, analogous to that of Aguirre et al., is applicable to crystalline structures with a reasonable expectation of success; and Spiegelman et al. teaches using DFTB for at least one crystalline material layer (carbon nanotubes) that contain defects. As such, it would be obvious to one of ordinary skill in the art that the method of Aguirre et al. can be applied to material layers with at least one defect to yield predictable results with a reasonable expectation of success
Conclusion
No claims are currently allowed.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-1113. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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