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 July 31, 2026, claim(s) 1-3 and 11-17 is/are pending in this application; of these claim(s) 1 is/are in independent form. Claim(s) 4-10 is/are cancelled. Claims 11-17 are new.
Response to Arguments
Applicant’s arguments, see page 5 lines 10-12, filed July 31, 2026, with respect to claim 9 have been fully considered and are persuasive. The rejection of claim 9 mailed April 1, 2026 has been withdrawn.
Regarding 35 U.S.C. § 101: Applicant's arguments filed July 31, 2026 have been fully considered but they are not persuasive. The rejection of record to the independent claim notes that the claim recites a mental process, not a mathematical calculation. Applicant argues that the high-dimensional lattice configurations and physical parameter matrices to be inputted into a computer-implemented technical data conversion cannot be tracked and vectorized in the human mind. The Examiner respectfully disagrees for the instances in which the lattice configurations and matrices are sufficiently small. Note that vector information is routinely contemplated in the human mind by mathematicians and conversions are part of the case law regarding abstract ideas. See Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972).
Applicant’s arguments, see page 7 line 24 to page 8 line 32, filed July 31, 2026, with respect to claim 1 have been fully considered and are persuasive. The rejection of claim 1, mailed April 1, 2026 has been withdrawn. (Claims 4, 5, and 8-10 have been cancelled.)
Applicant’s arguments, see page 9 lines 1-6, filed July 31, 2026, with respect to claims 2, 3, 6, and 7 have been fully considered and are persuasive. The rejections of claims 2, 3, 6, and 7 sent April 1, 2026 have been withdrawn.
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.
Claims 1-3 and 11-17 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.
Claim 1’s preamble recites “a crystal structure of a lithium compound containing cobalt, nickel, and manganese”. It is the structure for which a c-axis length is predicted. The body of the claim, claim 1 recites “an optimized crystal structure” as an output of the machine learning model. However, it is unclear whether the crystal structure in the preamble is the same as the optimized crystal structure. A reader of the claim would expect them to be different, because the crystal structure in the preamble is an initial crystal structure whereas the optimized crystal structure has a manganese atom replace with another atom relative to the initial crystal structure. In view of this interpretation of the method, wouldn’t this be a method for predicting an optimized crystal structure? For the purpose of examination, the Examiner assumed that the intended use of the method is to predict the optimized crystal structure, and the description of “a crystal structure of a lithium compound containing cobalt, nickel, and manganese” describes an initial crystal structure before optimization. The Examiner suggests amending the claim to resolve this ambiguity between the preamble and the body of the claim.
Claims 2-3 and 11-17 are rejected because they depend from a rejected base claim and incorporate the limitations from base claim 1.
Allowable Subject Matter
Claim 11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding 35 U.S.C. § 101, claim 11 is integrated into a practical application because the preparation for the machine learning steps and the output of the machine learning steps result in a corresponding material output that is specified by the output and therefore is a particular transformation to form a material.
The novelty is because manganese is being replaced by other atoms, in the context of algorithmic approaches to optimize c-axis length of a crystal structure of a lithium compound. In addition, the characteristic values are all claimed, but only some of the characteristic values are in the cited art.
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 1-3 and is 12-17 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. Claims 16-17 also recite mathematics 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 six values obtained by converting a crystal structure of the lithium compound in which the manganese at any one or more of six sites is substituted by a metal atom among crystal structures of the lithium compound into binary data and characteristic values 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 machine 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 as an output value of the learning model (insignificant extra-solution activity of outputting),
wherein the crystal structure is a layered rock-salt structure (a limitation to the representation that still requires a mental process for the preparation of the representation), and
wherein the characteristic values of the metal atom are valence, atomic weight, electronegativity, first ionization energy, atomic radius, and ionic radius (a limitation to the characteristic values that still require a mental process for the preparation of the values).
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).
12. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein the metal atom substituted at any one or more of the six sites is selected from the group consisting of Mg, Al, Sc, Ti, V, Cr, Fe, Cu, Zn, Ga, and Ge (the substitution is contemplated for the purpose of preparing a descriptor, which is a limitation to the mental process; the actual substitution does not occur in an actual material).
13. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein the metal atom is titanium (Ti), and wherein the descriptor corresponds to an optimized crystal structure having titanium substituted at four of the six sites Ge (the substitution is contemplated for the purpose of preparing a descriptor, which is a limitation to the mental process; the actual substitution does not occur in an actual material).
14. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein outputting the predicted value of the c-axis length comprises outputting a mean value of predicted c-axis length, a prediction dispersion variance, or a prediction suggestion score corresponding to the optimized crystal structure (insignificant extra-solution activity of outputting),.
15. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, further comprising identifying a specific three-dimensional spatial distribution of the substituted metal atoms within the layered rock-salt structure based on the binary data in the outputted descriptor corresponding to the optimized crystal structure (a mental process of identification).
16. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein inputting the descriptor into the learned machine learning model comprises: processing the descriptor through a first regression model employing sparse modeling to extract contributing descriptor parameters having an absolute contribution value greater than or equal to a predetermined threshold (a regression is a mathematical calculation); and inputting the extracted descriptor parameters into a second regression model to generate the predicted value (a regression is a mathematical calculation).
17. The method for predicting a c-axis length of a crystal structure of a lithium compound, according to claim 1, wherein preparing the descriptor comprises subjecting the binary data and the characteristic values of the metal atom to linear interpolation and normalization prior to inputting into the learned machine learning model (interpolation and normalization are mathematical calculations).
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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 - Friday, 10-5 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 October 9, 2026
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