NON-FINAL REJECTION, FIRST DETAILED ACTION
L31Status of Prosecution
The present application, 18/404,273 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on January 4, 2024 and claims benefit to Korean application KR10-2023-0083652, filed June 28, 2023.
Claims 1-12 are pending. Claims 1, 11 and 12 are independent.
Status of Claims
Claims 1-10 are rejected under § 112(b) as indefinite.
Claims 1-3, 5, 7-8 and 10-12 are rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wang et al. (“Wang”), “Machine-Learning Approach for Predicting the Discharing Capacities of Doped Lithium Nickel-Cobalt-Manganese Cathode Materials in Li-Ion Batteries, published 2021 in view of Jahnke et al., (“Jahnke”) United States Patent Application Publication 2024/0222641, published on July 4, 2024.
Claims 4 and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Jahnke and in further view of Martins et al. (“Martins”), United States Patent 2022/0223235, published on July 14, 2022.
Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Jahnke and in further view of Hosoumi et al. (“Hosoumi”), United States Patent 2022/0277815, published on Sep. 1, 2022.
Claim Interpretation -- § 112(f)
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 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: database constructing unit, pre-processing unit, prediction model generating unit and a candidates generating unit in claim 1.
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(b)
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.
Claims 1-10 are rejected under § 112(b) as indefinite.
Claim limitations “prediction model generating unit” and “candidates generating unit “ invoke 35 U.S.C. § 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The Specification discusses calculations and use of ratios, but there is no sufficient disclosure as to any algorithm or means to generate the models. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. As the dependent claims inherit this deficiency and are not otherwise cured, they are also rejected.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 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.
A.
Claims 1-3, 5, 7-8 and 10-12 are rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wang et al. (“Wang”), “Machine-Learning Approach for Predicting the Discharing Capacities of Doped Lithium Nickel-Cobalt-Manganese Cathode Materials in Li-Ion Batteries, published 2021 in view of Jahnke et al., (“Jahnke”) United States Patent Application Publication 2024/0222641, published on July 4, 2024.
As to Claim 1, Wang teaches: An apparatus for screening cathode active material candidates for secondary batteries, the apparatus comprising:
a database constructing unit configured to receive a data-set labeled with properties of a cathode active material structure for secondary batteries (Wang:, pg. 1552, “These promising
results further encouraged us to curate a more high quality discharge performance data set for the layered NCM cathode and implement ML to reveal the complex structure-property relationship.”);
a pre-processing unit configured to pre-process a part of the data-set to a learning data-set (Wang: Fig. 1, data cleaning and filters are applied before the data set is fed into the model for training);
a prediction model generating unit configured to generate a cathode active material prediction model for predicting performance indicators of target materials (Wang: Sec. 2.2, pg. 1553, “Within the model, 20 covariate variables were selected to predict the initial and 50th cycle discharge capacities of each material”).
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Wang may not explicitly teach: a prediction model generating unit configured to generate a cathode active material prediction model for predicting performance indicators of target materials that may be arranged to fit a predetermined structure based on the learning data-set; and
a candidates generating unit configured to generate cathode active material candidates for secondary batteries based on a result of the cathode active material prediction model.
Jahnke teaches in general concepts related to optimizing material properties of components of a battery (Jahnke: Abstract). Specifically, Jahnke teaches a simulation model is correlated with an experimental structure (Jahnke: par. 0050, 52, a fiber network structure is established). Parameters are fed into the simulation model which is then used for training the artificial intelligence (AI) model (Jahnke: Fig. 13, par. 0221). Candidates for the batteries of the resulting prediction model are output (Jahnke: par. 0012).
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It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wang disclosures and teachings to allow for structures to be considered in the output result of candidates as taught and suggested by Jahnke. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the optimizing material properties of a battery with an efficient simulation process (Jahnke: par. 0004).
As to Claim 2, Wang and Jahnke teach the elements of claim 1.
Jahnke further teaches: wherein the pre-processing unit configured to: perform a performance evaluation on at least one cathode active material prediction model through a verification data-set excluding the learning data-set from the data-set (Jahnke: par. 0021, “The AI model attempts to predict the simulation result data of the corresponding material parameter data and compares its prediction with the actual simulation result data (i.e. verification data-set used to obtain the verification result data) to generate an error (i.e. performance evaluation) which is used to determine whether the AI model is well-trained.”).
As to Claim 3, Wang and Jahnke teach the elements of claim 2.
Wang and Jahnke may not explicitly teach: wherein the pre-processing unit configured to: determine a ratio between the learning data-set and the verification data-set according to a result of the performance evaluation.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have further modified the Wang-Jahnke combination by calculating the error as a ratio between the actual simulation result data and the prediction simulation data as part of the performance evaluation. Such a person would have done so with an expectation for success, because a ratio of expected and actual values is a mathematical expression of an error.
As to Claim 5, Wang and Jahnke teach the elements of claim 1.
Jahnke further teaches: wherein the predetermined structure is a layered structure of the cathode active material including fixed particles (Jahnke: par. 0187, a copper foil layer on the bottom of a 2D electrode).
As to Claim 7, Wang and Jahnke teach the elements of claim 1.
Wang further teaches: wherein the prediction model generating unit configured to: select materials of represented by Chemical Formula 1 below to target materials:
LiNi0.85MxNyO2 (1)
(where, x + y = 0.15, M and N are any one of Al, Mg, W, Sb, Ta, Y, B, Ga, Si, Ti, V, Nb, Zr, Zn, Co, Mn, La, Tb, As, Cl, Tm, Ge, Ho, Fe, Cr, Sn, Sc, Cu, Re, Mo, Se, Te, and Tl, and M and N are not overlapped) (Wang: Introduction, the Quniary oxides that are listed would be within the range, as well as the NCM333, NCM523 and NCM811 oxides).
As to Claim 8, Wang and Jahnke teach the elements of claim 3.
Wang and Jahnke may not explicitly teach: wherein the prediction model generation unit configured to: regenerate the cathode active material prediction model according to the ratio between the learning data-set and the verification data-set.
Jahnke however does teach that the training of the prediction model is done iteratively until the comparison between the actual uncertainty factor and the predefined threshold uncertainty factor is satisfied to determine whether retraining is needed (Jahnke: par. 0221).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have further modified the Wang-Jahnke combination by basing the need to retrain on the ratio as part of the performance evaluation. Such a person would have done so with an expectation for success, to take into account the performance metric of the model.
As to Claim 10, Wang and Jahnke teach the elements of claim 1.
Wang further teaches: wherein the candidates generating unit configured to: generate the cathode active material candidates for secondary batteries by excluding candidate material corresponding to a result value of the cathode active material prediction model that does not satisfy a predetermined criterion (Wang: Fig. 1, several filters may be applied to the data and the materials for consideration before training the model).
As to Claim 11, it is rejected for similar reasons as claim 1.
As to Claim 12, it is rejected for similar reasons as claims 1 and 11.
B.
Claims 4 and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang et al. (“Wang”), “Machine-Learning Approach for Predicting the Discharing Capacities of Doped Lithium Nickel-Cobalt-Manganese Cathode Materials in Li-Ion Batteries, published 2021 in view of Jahnke et al., (“Jahnke”) United States Patent Application Publication 2024/0222641, published on July 4, 2024 and in further view of Martins et al. (“Martins”), United States Patent 2022/0223235, published on July 14, 2022.
As to Claim 4, Wang and Jahnke teach the elements of claim 2.
Wang and Jahnke may not explicitly teach: wherein the pre-processing unit configured to: determine data to be excluded from the data set according to a result of the performance evaluation.
Martins teaches in general concepts related to training a neural network to classify chemical spectra data via training machine learning models that are test utilizing a validation dataset (Martins: Abstract). Specifically, Martins teaches the accuracy of feature, data element, or other parameters of a test dataset may be considered and excluded in future runs to optimize the models (Martins: par. 0032, after running the various scenarios, the changes may be made to optimize the models in a next iteration of the process).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wang-Jahnke combination by excluding the data as determined by performance evaluation as taught and suggested by Martins. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the optimizing the models.
As to Claim 9, Wang and Jahnke teach the elements of claim 1.
Wang and Jahnke may not explicitly teach teaches: wherein the prediction model generating unit configured to: generate a plurality of cathode active material prediction models corresponding to each of performance indicators to perform prediction on each of the plurality of performance indicators.
Martins teaches in general concepts related to training a neural network to classify chemical spectra data via training machine learning models that are test utilizing a validation dataset (Martins: Abstract). Specifically, Martins teaches the accuracy of feature, data element, or other parameters of a test dataset may be considered and excluded in future runs to optimize the models (Martins: par. 0032, after running the various scenarios, the changes may be made to optimize the models in a next iteration of the process). Several models are trained with each one having different characteristics (Martins: Fig. 5, steps [406], [408] indicate that the models are different and test for at least one chemical).
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It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wang-Jahnke combination by allowing for multiple models to be created for different parameters as taught and suggested by Martins. Such a person would have been motivated to do so with a reasonable expectation of success to allow for giving a pool of models for different uses to a user.
C.
Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang et al. (“Wang”), “Machine-Learning Approach for Predicting the Discharing Capacities of Doped Lithium Nickel-Cobalt-Manganese Cathode Materials in Li-Ion Batteries, published 2021 in view of Jahnke et al., (“Jahnke”) United States Patent Application Publication 2024/0222641, published on July 4, 2024 and in further view of Hosoumi et al. (“Hosoumi”), United States Patent 2022/0277815, published on Sep. 1, 2022.
As to Claim 6, Wang and Jahnke teach the elements of claim 5.
Wang and Jahnke may not explicitly teach: wherein the prediction model generating unit configured to: determine a ratio between substitute particles disposed between the fixed particles and select target materials.
Hosoumi teaches in general concepts related to a property prediction system (Hosoumi: Abstract). Specifically, Hosoumi teaches for light-emitting devices, the materials that are layered may have the concentration ratio of the materials be considered and used for the teacher data in a machine learning model (Hosoumi: par. 0125, the information and properties are used in the teacher data 53_1 to 53_m for the layers; par. 0128, the concentration ration 23(1) to (23(n).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wang-Jahnke combination by considering the material ratio for each of the layers of the battery as taught and suggested by Hosoumi. Though Housomi is directed towards light-emitting devices, both Hosoumi and the Wang-Jahnke combination are directed towards machine learning models applied to material sciences. Such a person would have thus been motivated to do so with a reasonable expectation of success to allow for giving a user of the model more information about the properties of the chemical construct of the battery.
Conclusion
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147
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