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 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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 13 is a machine type claim. Therefore, claims 13-25 are directed to either a process, machine, manufacture or composition of matter.
With regard to claim 13,
2A Prong 1:
“calculate first difference information being a difference between the first output and a first simulation result with respect to the first atomic structure generated by the atomic simulation corresponding to the first label information” The user mentally or with a pen and paper can perform the mathematics required to calculate the difference between the output and simulation result.
“calculate second difference information being a difference between the second output and a second simulation result with respect to the second atomic structure generated by the atomic simulation corresponding to the second label information” The user mentally or with a pen and paper can perform the mathematics required to calculate the difference between the output and simulation result.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“acquire a first output from a neural network model based on information related to a first atomic structure and first label information related to an atomic simulation” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
“acquire a second output from the neural network model based on information related to a second atomic structure and second label information related to the atomic simulation” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“acquire a first output from a neural network model based on information related to a first atomic structure and first label information related to an atomic simulation” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving step is well-understood, routine, conventional activity is supported under Berkheimer).
“acquire a second output from the neural network model based on information related to a second atomic structure and second label information related to the atomic simulation” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)). (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving step is well-understood, routine, conventional activity is supported under Berkheimer).
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 10, 24, and 30 are 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.
The terms “functional” and “basis function” in claim 10 is a relative term which renders the claim indefinite. The terms “functional” and “basis function” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification does not establish a definition of what qualifies as a “functional” and a “basis function” when it comes to label information related to an atomic simulation as recited in the claims. The specification fails to describe or define what is meant by the terms “functional” and “basis function”. In particular, it is unclear what metrics or standards qualify as the information on a “functional” or the information on a “basis function” in claims 10, 24, and 30. Due to the confusion these claims were not able to be examined further.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-9, 12-17, 19-23, 26-29, and 32 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Watanabe et al, NPL (High-dimensional neural network atomic potentials for examining energy materials: some recent simulations) published December 15, 2020.
With regard to independent claim 1,
Watanabe teaches “An inferring device comprising: at least one memory;” (Section 1, pg.1, paragraph 1; EN: This denotes that use of various novel devices such as information devices and resistive switching memory devices). “and at least one processor configured to: acquire an output from a neural network model based on information related to an atomic structure” (Section 2, pg. 2, paragraph 2; EN: This denotes a neural network can take atomic structural information as an input and output the energy related to the atomic structure). “and label information related to an atomic simulation,” (Section 2, pg. 2, paragraph 3-4; EN: This denotes the use of a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure. The examiner notes that while labels typically refer to the target class or ground truth of an output, the specification uses “labels” as way of embedding or conditioning the input. E.g., [0067] of spec as filed. Therefore, the examiner finds the BRI of this term to include descriptors). “wherein the neural network model is trained to infer a simulation result with respect to the atomic structure generated by the atomic simulation corresponding to the label information” (Section 2, pg. 2-3, paragraph 2-6; EN: This denotes that HD NNPs is a representation of the total energy as a sum of the atomic energies and utilizes the first-principles calculation).
With regard to dependent claim 2,
Watanabe teaches “The inferring device according to claim 1, wherein the at least one processor is configured to: acquire the output by inputting the information related to the atomic structure and the label information into the neural network model” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure).
With regard to dependent claim 3,
Watanabe teaches “The inferring device according to claim 2, wherein the at least one processor is configured to: input the label information into at least one of an intermediate layer or an output layer of the neural network model” (Section 2, Pg. 2, paragraph 2; EN: This denotes that the NNP has multiple different layers that are connected to adjacent layers and each node has a value calculated from the values of nodes in the previous layer)
With regard to dependent claim 4,
Watanabe teaches “The inferring device according to claim 1, wherein: the neural network model is configured to generate a plurality of outputs;” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes using on-the-fly sampling, which reoptimizes a NNP when an uncertainty threshold is higher than a certain threshold and adding the acquired data to the training dataset). “and the at least one processor is configured to: acquire the output by selecting one of the plurality of outputs from the neural network model based on the label information” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes that during on-the-fly sampling for an NNP model, the model will constantly reoptimize itself as long as the model uncertainty or variance is higher than a certain threshold).
With regard to dependent claim 5,
Watanabe teaches “The inferring device according to claim 1, wherein the at least one processor is configured to: input the information related to the atomic structure into a first neural network model decided based on the label information;” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure). “and acquire the output by inputting an output from the first neural network model into the neural network model” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 6,
Watanabe teaches “The inferring device according to claim 1, wherein the label information includes at least one of information on software to be used for the atomic simulation, information on a calculation technique to be used for the atomic simulation, information on a function to be used for the atomic simulation, information on a parameter to be used for the atomic simulation, information on a calculation condition to be used for the atomic simulation, or information on an arithmetic mode to be used for the atomic simulation” (Section 2, Pg. 2-3, paragraph 4-5; EN: This denotes The NNP can use symmetry functions (SF), smooth overlap of atomic positions (SOAPs), bispectrum, and Chebyshev and Zernike radial distributions to be used as the descriptor set).
With regard to dependent claim 7,
Watanabe teaches “The inferring device according to claim 1, wherein the neural network model is a model relating to NNP (Neural Network Potential)” (Section 2, Pg. 2, paragraph 2; EN: This denotes the use of NNP).
With regard to dependent claim 8,
Watanabe teaches “The inferring device according to claim 1, wherein the atomic simulation is executed using a first-principles calculation” (Section 2, Pg. 3, paragraph 6; EN: This denotes that the output of the NNP corresponds to the first-principles calculations).
With regard to dependent claim 9,
Watanabe teaches “The inferring device according to claim 1, wherein the atomic simulation is executed using a DFT (Density Function Theory) calculation” (Section 2, Pg. 3, paragraph 6; EN: This denotes that the NNP can produce density functional theory (DFT) calculation data).
With regard to dependent claim 12,
Watanabe teaches “The inferring device according to claim 1, wherein the atomic simulation is at least one of a simulation to be executed using two or more different pieces of software, a simulation to be executed using two or more different calculation techniques, a simulation to be executed using two or more different functions, a simulation to be executed using two or more different parameters, a simulation to be executed using two or more different calculation conditions, or a simulation to be executed using two or more different arithmetic modes” (Section 2, Pg. 2-3, paragraph 2-6; EN: This denotes the use of multiple calculations, which are being used in regards to the various layers, descriptor sets, and DFT for the NNP model).
With regard to independent claim 13,
Watanabe teaches “A training device comprising: at least one memory;” (Section 1, pg.1, paragraph 1; EN: This denotes that use of various novel devices such as information devices and resistive switching memory devices). “and at least one processor configured to: acquire a first output from a neural network model based on information related to a first atomic structure and first label information related to an atomic simulation;” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure). “calculate first difference information being a difference between the first output and a first simulation result with respect to the first atomic structure generated by the atomic simulation corresponding to the first label information;” (Section 2, Pg. 3, paragraph 6; EN: This denotes that the NNP will minimalize the loss function during training). “acquire a second output from the neural network model based on information related to a second atomic structure and second label information related to the atomic simulation;” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold). “calculate second difference information being a difference between the second output and a second simulation result with respect to the second atomic structure generated by the atomic simulation corresponding to the second label information;” (Section 2, Pg. 3, paragraph 6; EN: This denotes that the NNP will minimalize the loss function during training). “and update a parameter of the neural network model based on the first difference information and the second difference information” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 14,
Watanabe teaches “The training device according to claim 13, wherein: the at least one processor is configured to: acquire the first output by inputting the information related to the first atomic structure and the first label information into the neural network model;” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure). “and acquire the second output by inputting the information related to the second atomic structure and the second label information into the neural network model” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 15,
Watanabe teaches “The training device according to claim 13, wherein: the neural network model is configured to generate an output with respect to the first label information and an output with respect to the second label information;” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold). “and the at least one processor is configured to: acquire the output with respect to the first label information as the first output based on the first label information;” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure). “and acquire the output with respect to the second label information as the second output based on the second label information” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 16,
Watanabe teaches “The training device according to claim 13, wherein the at least one processor is configured to: input the information related to the first atomic structure into a first neural network model decided based on the first label information;” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure). “acquire the first output by inputting an output from the first neural network model into the neural network model;” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold). “input the information related to the second atomic structure into a second neural network model decided based on the second label information;” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold). “acquire the second output by inputting an output from the second neural network model into the neural network model;” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold). “update a parameter of the first neural network model based on the first difference information” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold). “and update a parameter of the second neural network model based on the second difference information” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 17,
Watanabe teaches “The training device according to claim 13, wherein the first atomic structure and the second atomic structure include the same or almost the same atomic structure” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 19,
This claim is similar in scope to claim 12 and is rejected under a similar rationale.
With regard to dependent claim 20,
Watanabe teaches “The training device according to claim 13, wherein: the first label information includes at least one of information on first software to be used for the atomic simulation, information on a first calculation technique to be used for the atomic simulation, information on a first function to be used for the atomic simulation, information on a first parameter to be used for the atomic simulation, information on a first calculation condition to be used for the atomic simulation, or information on a first arithmetic mode to be used for the atomic simulation;” (Section 2, Pg. 2-3, paragraph 4-5; EN: This denotes The NNP can use symmetry functions (SF), smooth overlap of atomic positions (SOAPs), bispectrum, and Chebyshev and Zernike radial distributions to be used as the descriptor set). “and the second label information includes at least one of information on second software to be used for the atomic simulation, information on a second calculation technique to be used for the atomic simulation, information on a second function to be used for the atomic simulation, information on a second parameter to be used for the atomic simulation, information on a second calculation condition to be used for the atomic simulation, or information on a second arithmetic mode to be used for the atomic simulation” (Section 3, Subsection 3.3.2, Pg. 5, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
With regard to dependent claim 21,
This claim is similar in scope to claim 7 and is rejected under a similar rationale.
With regard to dependent claim 22,
This claim is similar in scope to claim 8 and is rejected under a similar rationale.
With regard to dependent claim 23,
This claim is similar in scope to claim 9 and is rejected under a similar rationale.
With regard to independent claim 26,
Watanabe teaches “A method for inferring by one or more processors, comprising: acquiring an output from a neural network model based on information related to an atomic structure and label information related to an atomic simulation” (Section 2, pg. 2, paragraph 2-4; EN: This denotes that a NNP can take atomic structural information as an input and output the energy related to the atomic structure, while also using a descriptor set, where the values of the input nodes are the values of descriptors corresponding to a specific atomic structure). “wherein the neural network model is trained to infer a simulation result with respect to the atomic structure generated by the atomic simulation corresponding to the label information” (Section 2, pg. 2-3, paragraph 2-6; EN: This denotes that the HDNNP is a representation of the total energy as a sum of the atomic energies and utilizes the first-principles calculation).
With regard to dependent claim 27,
This claim is similar in scope to claim 2 and is rejected under a similar rationale.
With regard to dependent claim 28,
This claim is similar in scope to claim 6 and is rejected under a similar rationale.
With regard to dependent claim 29,
This claim is similar in scope to claim 9 and is rejected under a similar rationale.
With regard to dependent claim 32,
This claim is similar in scope to claim 12 and is rejected under a similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 11, 18, 25, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al, NPL (High-dimensional neural network atomic potentials for examining energy materials: some recent simulations), published December 15, 2020 as applied to claims 1-9, 12-17, 19-23, 26-29, and 32 above, and further in view of Selvaratnam et al, NPL (Prediction of optoelectronic properties of Cu2O using neural network potential), published July 14, 2020.
With regard to dependent claim 11,
Watanabe teaches “The inferring device according to claim 9, wherein: the label information includes at least one of information on a first condition of the DFT calculation or information on a second condition of the DFT calculation;” (Section 5, Pg.12, paragraph 4; EN: This denotes that the DFT calculation for the NNP model can be used on perfect crystals). “… DFT calculation higher in accuracy than under the second condition is executable, under a periodic boundary condition;” (Section 5, Pg.12, paragraph 4; EN: This denotes that the NNP can investigate the atomic energy mapping of perfect crystals and the atomic energies of the crystals could deviate from the reference DFT atomic energies). “DFT calculation higher in accuracy” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
However, Watanabe fails to explicitly disclose “the first condition is a condition …” and “and the second condition is a condition under which the … than under the first condition is executable, under a free boundary condition”.
Selvaratnam teaches “the first condition is a condition …” (Section 2.3; EN: This denotes the use of the VASP software). “and the second condition is a condition under which the … than under the first condition is executable, under a free boundary condition” (Section 2, paragraph 2; EN: This denotes the use of Gaussian parameters).
Watanabe and Selvaratnam are considered to be analogous art to the claimed invention due to the fact that they both disclose the use of NNPs in regards to the configuration of atoms. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the HD NNPs of Watanabe with the prediction of Fermi energy, band edges, and partial density of states of Cu2O of Selvaratnam. One would be motivated to do so to improve the calculations and accuracy of NNPs.
With regard to dependent claim 18,
Selvaratnam teaches “The training device according to claim 13, wherein: the atomic simulation is executed using two or more different pieces of software;” (Section 2; EN: This denotes that the NN-PES model can and does utilize various types of software, such as VASP, Gaussian, RuNNer, and Atomistic machine learning package (AMP)). “and the atomic simulation corresponding to the first label information and the atomic simulation corresponding to the second label information are executed using the same software or different pieces of software” (Section 2; EN: This denotes that the NN-PES model can be retrained if the DFT energy is greater than a specific threshold. It also shows that the NN-PES model can and does utilize various types of software, such as VASP, Gaussian, RuNNer, and Atomistic machine learning package (AMP)).
With regard to dependent claim 25,
Watanabe teaches “The training device according to claim 23, wherein: the first label information includes information on a first condition of the DFT calculation;” (Section 5, Pg.12, paragraph 4; EN: This denotes that the DFT calculation for the NNP model can be used on perfect crystals). “… DFT calculation higher in accuracy than under the second condition is executable, under a periodic boundary condition;” (Section 5, Pg.12, paragraph 4; EN: This denotes that the NNP can investigate the atomic energy mapping of perfect crystals and the atomic energies of the crystals could deviate from the reference DFT atomic energies). “DFT calculation higher in accuracy” (Section 3, Subsection 3.3.2, Pg. 5-6, paragraph 1-3; EN: This denotes reoptimizing a NNP when an uncertainty threshold is higher than a certain threshold).
However, Watanabe fails to explicitly disclose “the second label information includes information on a second condition of the DFT calculation;”.
Selvaratnam teaches “the second label information includes information on a second condition of the DFT calculation;” (Section 2; EN: This denotes that the NN-PES model can be retrained if the DFT energy is greater than a specific threshold and that it also utilizes Gaussian parameters). “the first condition is a condition …” (Section 2.3; EN: This denotes the use of the VASP software). “and the second condition is a condition under which the … than under the first condition is executable, under a free boundary condition” (Section 2, paragraph 2; EN: This denotes the use of Gaussian parameters).
With regard to dependent claim 31,
This claim is similar in scope to claim 11 and is rejected under a similar rationale.
Conclusion
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/SETH CAPRIANO-UMARI SHELTON/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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