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 .
Remarks
In response to communications sent June 30th 2022, claim(s) 1-10 are pending in this application; of these claims 1, 5, and 10 are in independent form.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawing(s) filed on June 30, 2022 are accepted by the Examiner.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: October 28, 2022.
Claim Interpretation
According to MPEP § 2111.03, “The transitional term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. See, e.g., Mars Inc. v. H.J. Heinz Co., 377 F.3d 1369, 1376, 71 USPQ2d 1837, 1843 (Fed. Cir. 2004).” (Emphasis added)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a crystal structure setting unit that determines…” in claim 10.
“a descriptor generating unit that generates…” in claim 10.
“a first-principles calculation unit that calculates…” in claim 10.
“a learning unit that performs learning…” in claim 10.
See the corresponding algorithms and hardware for these operations in Figure 12 and paragraphs [0012], [0023]-[0024], and [0136] to [0146].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 9 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.
The term “contribution to learning” in claim 9 is a relative term which renders the claim indefinite. The term “contribution to learning” 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.
In addition, claim 9 is unclear because the phrase “a descriptor” may or may not be the descriptor recited in independent claim 5.
For the purpose of compact prosecution, the Examiner assumes that the Applicant intends this to be an R-squared value contribution. For the purpose of compact prosecution, the Examiner also assumes that the Applicant intends the phrase “a descriptor” to be “the descriptor”.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental processes of preparing a descriptor (and/or adding a characteristic value), which is an abstract idea. Claim 9 and 10 recite a mathematical calculation in combination with the mental process. This judicial exception is not integrated into a practical application because the additional elements of inputting and outputting after the preparation of the descriptors are insignificant extra-solution activity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because inputting and outputting have been recognized by the precedential courts as well-understood, routine, and conventional (see MPEP 2106.05(d) “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)”).
1. A method for predicting a c-axis length of a crystal structure of a lithium compound containing cobalt, nickel, and manganese (a method is one of the four statutory classes), comprising:
a step of preparing a descriptor including n values (n is an integer greater than or equal to 0) obtained by converting a crystal structure of the lithium compound in which the manganese at any one or more of n sites is substituted by a metal atom among crystal structures of the lithium compound into binary data and a characteristic value of the metal atom (preparing a descriptor is a mental process, and the physical measurements are not interpreted to part of the step of preparation, as only the data is prepared; the manner in which the data is obtained is not a positively recited step, whereas the step of preparing a descriptor is a positively recited step);
a step of inputting the descriptor into a learned learning model (insignificant extra-solution activity of inputting; preparing or executing the machine learning model is not claimed, so the mathematics is not claimed); and
a step of outputting a predicted value of c-axis length of an optimized crystal structure and a descriptor corresponding to the optimized crystal structure as an output value of the learning model (insignificant extra-solution activity of outputting).
2. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein the learning model is built using a Gaussian process regression model (preparing the machine learning model is not a positively recited step, therefore the Gaussian process has little patentable weight on interpretation of the step of inputting).
3. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein the learning model is built using a convolutional neural network (preparing the machine learning model is not a positively recited step, therefore the Gaussian process has little patentable weight on interpretation of the step of inputting).
4. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein the crystal structure is a layered rock-salt structure (the element of the crystal structure is part of a “product-by-process” step in the independent claim that specifies the nature of the descriptor; hence it does not limit the step of preparing the descriptor in a way that changes the step from being a mental process).
5. A method for building a learning model for predicting a c-axis length of a crystal structure of a lithium compound containing cobalt, nickel, and manganese (a method is one of the four statutory classes), comprising:
a step of acquiring, as a descriptor, n values obtained by converting a crystal structure of the lithium compound in which the manganese at any one or more of n sites (n is an integer greater than or equal to 0) is substituted by a metal atom among crystal structures of the lithium compound into binary data (preparing a descriptor is a mental process, and the physical measurements are not interpreted to part of the step of preparation, as only the data is prepared; the manner in which the data is obtained is not a positively recited step, whereas the step of preparing a descriptor is a positively recited step); and
a step of adding a characteristic value of the metal atom to the descriptor (adding a characteristic value is interpreted as data that is prepared; the manner in which the data is obtained is not a positively recited step, whereas the step of preparing a descriptor is a positively recited step),
wherein a c-axis length of a crystal structure in which manganese at one of n sites is substituted by the metal atom is used as part of training data (the c-axis is part of the training data, but the training data is not limited to being used as part of the learning model; and the learning model is in the preamble rather than in a positively recited step and therefore does not have patentable weight).
6. The method for building a learning model, according to claim 5, wherein the learning model is built using a Gaussian process regression model (the learning model is in the preamble rather than in a positively recited step and therefore does not have patentable weight).
7. The method for building a learning model, according to claim 5, wherein the learning model is built using a convolutional neural network (the learning model is in the preamble rather than in a positively recited step and therefore does not have patentable weight).
8. The method for building a learning model, according to claim 5, wherein the c-axis length is calculated by first-principles calculation (the c-axis is part of the training data, but the training data is not limited to being used as part of the learning model; and the learning model is in the preamble rather than in a positively recited step and therefore does not have patentable weight).
9. The method for building a learning model, according to claim 5, wherein a descriptor having an absolute value of contribution to learning of greater than or equal to 0.001 is extracted among characteristic values of the metal atom, using a regression model (mathematical calculation).
10. A system for predicting a crystal structure (the system, described by invoking 35 U.S.C. §112(f) to recite structure, is one of the four statutory classes), comprising:
a crystal structure setting unit that determines a crystal structure containing lithium, cobalt, nickel, and manganese (insignificant extra-solution activity of setting an input value);
a descriptor generating unit that generates a descriptor including a kind of a metal atom and information on a substitution element site in a crystal structure in which manganese at m sites (m is an integer greater than or equal to 0) is substituted by the metal atom (preparing a descriptor is a mental process, and the physical measurements are not interpreted to part of the step of preparation, as only the data is prepared; the manner in which the data is obtained is not a positively recited step, whereas the step of preparing a descriptor is a positively recited step);
a first-principles calculation unit that calculates a c-axis length of a crystal structure in which the substitution element is positioned, by first-principles calculation (mathematical calculation using ab initio first principles; one example from the specification is by using Ab initio Simulation Package, which involves mathematical calculations); and
a learning unit that performs learning using a first-principles calculation result as training data (mathematical regression calculation),
wherein a learning result obtained by the learning unit includes a maximum c-axis length (mathematical regression calculation).
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.
Claim(s) 1, 4-5, 8-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Yoshida”.
The Yoshida reference is:
Yoshida, Tomohiro, Kenta Hongo, and Ryo Maezono. "First-principles study of structural transitions in LiNiO2 and high-throughput screening for long life battery." The Journal of Physical Chemistry C 123.23 (2019): 14126-14131.
As to claim 1, Yoshida teaches a method for predicting (Yoshido page 14129 bottom of column 2: performing a regression) a c-axis length (Yoshido page 14129 Figure 4: the delta d average as a predicted length measured in Angstroms) of a crystal structure of a lithium compound containing cobalt, nickel (Yoshido title: LiNiO2 structure), and manganese (Yoshido abstract: doping the LiNiO2 with other atoms, motivated by the use of Manganese in the literature, indicated in the introduction paragraph 2 on Yoshido page 14126), comprising:
a step of preparing a descriptor including n values (n is an integer greater than or equal to 0) (Yoshido page 14129 table 2 of Descriptors for the Regression of delta d average) obtained by converting a crystal structure of the lithium compound in which the manganese at any one or more of n sites is substituted by a metal atom among crystal structures of the lithium compound (Yoshido page 14128 last paragraph: 32 elements were tested for doping; the Examiner interprets that doping results in a substitution of manganese by a different metal atom, of which manganese is at once envisaged according to MPEP § 2131.02 III after being disclosed in the introduction of Yoshido) into binary data and a characteristic value of the metal atom (Yoshido page 14129 table 2 includes binary data and atomic number);
a step of inputting the descriptor into a learned learning model (Yoshido page 14129 top of second column: the equation indicates that the descriptors have been inputted into a regression model to result in the optimized parameters and benefiting from LASSO regression according to page 14129 first column last paragraph); and
a step of outputting a predicted value of c-axis length of an optimized crystal structure and a descriptor corresponding to the optimized crystal structure as an output value of the learning model (Yoshido page 14129 Figure 4 illustrates the output of the axis length as a offset delta d average measured in Angstroms and the length corresponds to the optimized output of the machine learning model).
As to claim 4, Yoshida teaches the method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein the crystal structure is a layered rock-salt structure (Yoshida Figure 1 on page 14127 illustrates a layered rock-salt structure of LiNiO2).
As to claim 5, Yoshida teaches a method for building a learning model (Yoshido page 14129 bottom of column 2: performing a regression) for predicting a c-axis length (Yoshido page 14129 Figure 4: the delta d average as a predicted length measured in Angstroms) of a crystal structure of a lithium compound containing cobalt, nickel (Yoshido title: LiNiO2 structure), and manganese (Yoshido abstract: doping the LiNiO2 with other atoms, motivated by the use of Manganese in the literature, indicated in the introduction paragraph 2 on Yoshido page 14126), comprising:
a step of acquiring, as a descriptor, n values (Yoshido page 14129 table 2 of Descriptors for the Regression of delta d average) obtained by converting a crystal structure of the lithium compound in which the manganese at any one or more of n sites (n is an integer greater than or equal to 0) is substituted by a metal atom among crystal structures of the lithium compound (Yoshido page 14128 last paragraph: 32 elements were tested for doping; the Examiner interprets that doping results in a substitution of manganese by a different metal atom, of which manganese is at once envisaged according to MPEP § 2131.02 III after being disclosed in the introduction of Yoshido) into binary data (Yoshido page 14129 table 2 includes binary data and atomic number); and
a step of adding a characteristic value of the metal atom to the descriptor (Yoshido page 14129 top of second column: the equation indicates that the descriptors have been inputted into a regression model to result in the optimized parameters and benefiting from LASSO regression according to page 14129 first column last paragraph),
wherein a c-axis length of a crystal structure in which manganese at one of n sites is substituted by the metal atom is used as part of training data (Yoshido page 14129 Figure 4 illustrates the output of the axis length as a offset delta d average measured in Angstroms and the length corresponds to the optimized output of the machine learning model; the axis length is the dependent variable for regression training).
As to claim 8, Yoshida teaches the method for building a learning model, according to claim 5, wherein the c-axis length is calculated by first-principles calculation (Yoshida abstract: the method for building the regression model was calculated by “ab initio” techniques, which means “first-principles” calculations).
As to claim 9, Yoshida teaches the method for building a learning model, according to claim 5, wherein a descriptor having an absolute value of contribution to learning of greater than or equal to 0.001 is extracted among characteristic values of the metal atom, using a regression model (Yoshida page 14129 bottom of column 2: a lasso regression technique was used to keep only the most important descriptors; the contribution to learn recited in the claim does not have units, so a general threshold is presumed).
As to claim 10, Yoshida teaches a system for predicting (Yoshido page 14129 bottom of column 2: performing a regression) a crystal structure (Yoshido title: LiNiO2 structure, including the delta d average as a predicted length measured in Angstroms; particularly when doping the LiNO2 with other atoms according to Yoshido’s abstract), comprising:
a crystal structure setting unit that determines a crystal structure containing lithium, cobalt, nickel, and manganese (Yoshido abstract: a regression involving structure when doping the LiNiO2 with other atoms, motivated by the use of Manganese in the literature, indicated in the introduction paragraph 2 on Yoshido page 14126; comparisons are made to LiCoO2 according to the introduction on page 14126);
a descriptor generating unit that generates a descriptor (Yoshido page 14129 table 2 of Descriptors for the Regression of delta d average) including a kind of a metal atom and information on a substitution element site in a crystal structure in which manganese at m sites (m is an integer greater than or equal to 0) is substituted by the metal atom (Yoshido page 14128 last paragraph: 32 elements were tested for doping; the Examiner interprets that doping results in a substitution of manganese by a different metal atom, of which manganese is at once envisaged according to MPEP § 2131.02 III after being disclosed in the introduction of Yoshido; Yoshido page 14129 table 2 includes binary data and atomic number);
a first-principles calculation unit that calculates a c-axis length of a crystal structure (Yoshido page 14129 Figure 4 illustrates the output of the axis length as a offset delta d average measured in Angstroms and the length corresponds to the optimized output of the machine learning model) in which the substitution element is positioned, by first-principles calculation (Yoshida abstract: the method for building the regression model was calculated by “ab initio” techniques, which means “first-principles” calculations); and
a learning unit that performs learning using a first-principles calculation result as training data (Yoshido page 14129 top of second column: the equation indicates that the descriptors have been inputted into a regression model to result in the optimized parameters and benefiting from LASSO regression according to page 14129 first column last paragraph),
wherein a learning result obtained by the learning unit includes a maximum c-axis length (Yoshido page 14129 Figure 4 illustrates the output of the axis length as a offset delta d average measured in Angstroms and the length corresponds to the optimized output of the machine learning model).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Yoshida” in view of US 20200364594 A1 (“Liu”).
The Yoshida reference is:
Yoshida, Tomohiro, Kenta Hongo, and Ryo Maezono. "First-principles study of structural transitions in LiNiO2 and high-throughput screening for long life battery." The Journal of Physical Chemistry C 123.23 (2019): 14126-14131.
As to claim 2, Yoshida teaches the method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, but does not teach wherein the learning model is built using a Gaussian process regression model.
Nevertheless, Liu teaches wherein the learning model is built using a Gaussian process regression model (Liu Para [0050]: using Bayesian modeling for a regression involving multiple parameters and a large search space; Liu Para [0107] provides evidence that this is in the context of lithium ion battery optimization).
Yoshida and Liu are in the same field of chemical informatics. 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 teachings of Yoshida to include the teachings of Liu because Bayesian Gaussian modeling resolves uncertainty regarding the functional form, particularly when optimizing lithium ion battery design (See Liu para [0050]). There would be reasonable expectation of success because Gaussian process modeling uses the Bayesian approach for accurate estimates.
As to claim 6, Yoshida teaches the method for building a learning model, according to claim 5, but does not teach wherein the learning model is built using a Gaussian process regression model.
Nevertheless, Liu teaches wherein the learning model is built using a Gaussian process regression model (Liu Para [0050]: using Bayesian modeling for a regression involving multiple parameters and a large search space; Liu Para [0107] provides evidence that this is in the context of lithium ion battery optimization).
Yoshida and Liu are in the same field of chemical informatics. 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 teachings of Yoshida to include the teachings of Liu because Bayesian Gaussian modeling resolves uncertainty regarding the functional form, particularly when optimizing lithium ion battery design (See Liu para [0050]). There would be reasonable expectation of success because Gaussian process modeling uses the Bayesian approach for accurate estimates.
Claim(s) 3 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Yoshida” in view of “Chen”.
The Yoshida reference is:
Yoshida, Tomohiro, Kenta Hongo, and Ryo Maezono. "First-principles study of structural transitions in LiNiO2 and high-throughput screening for long life battery." The Journal of Physical Chemistry C 123.23 (2019): 14126-14131.
The Chen reference is:
Chen, Hsin-An, and Chun-Wei Pao. "Fast and accurate artificial neural network potential model for MAPbI3 perovskite materials." ACS omega 4.6 (2019): 10950-10959.
As to claim 3, Yoshida teaches the method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, but does not teach wherein the learning model is built using a convolutional neural network.
Nevertheless, Chen teaches wherein the learning model is built using a convolutional neural network (Chen title: using a neural network for crystal prediction).
Yoshida and Chen are in the same field of chemical informatics. 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 teachings of Yoshida to include the teachings of Chen because the machine learning algorithms are a simple substitution of known machine learning methods with the advantage that a neural network can handling non-linear data with improved computational efficiency (Chen Abstract). There would be a reasonable expectation of success because the same “Ab Initio Simulation Package” software is used with Chen’s neural network as Yoshida uses for the Yoshida’s regression analysis.
As to claim 7, Yoshida teaches the method for building a learning model, according to claim 5, but does not teach wherein the learning model is built using a convolutional neural network.
Nevertheless, Chen teaches wherein the learning model is built using a convolutional neural network (Chen title: using a neural network for crystal prediction).
Yoshida and Chen are in the same field of chemical informatics. 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 teachings of Yoshida to include the teachings of Chen because the machine learning algorithms are a simple substitution of known machine learning methods with the advantage that a neural network can handling non-linear data with improved computational efficiency (Chen Abstract). There would be a reasonable expectation of success because the same “Ab Initio Simulation Package” software is used with Chen’s neural network as Yoshida uses for the Yoshida’s regression analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20090157369-A1: design of lattice; Figure 3 has atomic changes
US-20170097310-A1: same chemical elements; method of determination; Figure 3 has changing atoms
US-10734097-B2: Figure 13 has atomic substitutions
US-20220140332-A1: lithium battery analysis
US-20220199203-A1: machine learning for crystal analysis
US-20240088379-A1: Similar purpose, less machine learning
Ye, Weike, et al. "Deep neural networks for accurate predictions of crystal stability." Nature communications 9.1 (2018): 3800.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Saturday, 10-5 ET.
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/JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 March 18, 2026
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