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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to claim 1:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
an analysis unit configured to execute first analysis for calculating a predicted value of the objective variable from the explanatory variable group and calculating prediction accuracy of the predicted value on the basis of the target value of the objective variable and second analysis for deriving a degree of contribution of the explanatory variable to the objective variable, for each learning model (recites mental process steps involving mathematical calculations to evaluate/analyze the data to determine accuracy of predictions as well as statistical analysis to determine how important particular attributes/features are when forming a decision/predicted output);
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
A model setting support device comprising: a model learning unit having a plurality of learning models including a plurality of types of explanatory variables indicating at least a material or design matter of the product (recites field of use limitations describing the intended meaning of the data to be used, see MPEP 2106.05(h)) and configured to learn each of the plurality of learning models using training data including a dataset including the explanatory variable group and a target value of the objective variable (recites apply-it type limitations of using the computer components such as a machine learning model as a tool to perform the judicial exception, see MPEP 2106.05(f));
and a learning result processing unit configured to output learning result information indicating the prediction accuracy and the contribution degree for each learning model (recites insignificant extrasolution activity of transmitting information, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite additional elements that merely describe at a high-level of generality the usage of machine learning models to perform various calculation on a broad recitation of particular desired data as well as transmitting/outputting the calculated results and adds no meaningful limitation beyond that of the abstract idea.
Step 2B:
Below is the analysis of the claims:
A model setting support device comprising: a model learning unit having a plurality of learning models including a plurality of types of explanatory variables indicating at least a material or design matter of the product (recites field of use limitations describing the intended meaning of the data to be used, see MPEP 2106.05(h)) and configured to learn each of the plurality of learning models using training data including a dataset including the explanatory variable group and a target value of the objective variable (recites apply-it type limitations of using the computer components such as a machine learning model as a tool to perform the judicial exception, see MPEP 2106.05(f));
and a learning result processing unit configured to output learning result information indicating the prediction accuracy and the contribution degree for each learning model (recites well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite additional elements that merely describe at a high-level of generality the usage of machine learning models to perform various calculation on a broad recitation of particular desired data as well as transmitting/outputting the calculated results and adds no meaningful limitation beyond that of the abstract idea.
With regard to claim 2, this claim recites wherein the model learning unit learns the plurality of learning models for each objective variable (recites apply-it limitations describing training a machine learning model that amounts to high-level of generality recitation of usage of a computer as a tool to implement the judicial exception, see MPEP 2106.05(f)),
wherein the analysis unit executes the first analysis and the second analysis for each objective variable (recites mental process steps of analyzing data),
and wherein the learning result processing unit outputs the learning result information for each objective variable (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 3, this claim recites a learning setting unit configured to select a learning model for predicting the objective variable in accordance with an input manipulation, wherein the model learning unit learns the selected learning model without learning an unselected learning model (recites mental process step of evaluation and selection/decision of a model to use/train and other models to not use).
With regard to claim 4, this claim recites a learning setting unit configured to select an explanatory variable in accordance with an input manipulation, wherein the model learning unit learns the learning model including an explanatory variable selected from the training data without including an unselected explanatory variable (recites mental process steps of testing/evaluating and analyzing data including changing variable quantities to see how that affects the outcome/output, i.e. scientific method).
With regard to claim 5, this claim recites a learning setting unit configured to retrieve a hyperparameter for learning the learning model with higher prediction accuracy for each learning model (recites insignificant extrasolution activity of receiving information which amounts to well-understood, routine, and conventional activity of receiving information, see MPEP 2106.05(d)).
With regard to claim 6, this claim recites wherein the product is a battery (recites field of use limitations describing the preferred data, see MPEP 2106.05(h)).
With regard to claim 7, this claim recites a computer-readable non-transitory storage medium storing a program (recites generic computer hardware to implement the abstract idea on a computer, see MPEP 2106.05(f)) for causing a computer to function as the model setting support device according to claim 1 (see claim 1’s mapping above).
With regard to claim 8, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
Claim Interpretation
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 model learning unit…configured to learn…”; “an analysis unit configured to execute first analysis…and second analysis…”; and “a learning result processing unit configured to output learning result information…” 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, and 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Maejima et al [WO 2022/168163 A1] in view of Shirane et al [US 2020/0341065 A1].
With regard to claim 1, Maejima teaches a model setting support device comprising: a model learning unit having a plurality of learning models for estimating an objective variable indicating performance objective variables and explanatory variables to determine/predict an output associated with the input data;
“FIG. 1 is a diagram for explaining the outline of the first embodiment. As shown in FIG. 1, input data is input to each of a plurality of learning models (learning models A and B in the example of FIG. 1), prediction processing is performed in each learning model, and output as a prediction result Data is output. The prediction accuracy of the learning model is obtained by comparing the output data, which is the prediction result, with the correct answer of the output data.”, first paragraph in section <First Embodiment>;
“The material data collection unit 21 collects material data used as materials for constructing a learning data set used to create a learning model. Material data is data that can be used as objective variables and explanatory variables, and is data that has a unique data identifier for distinguishing each data. The material data collection unit 21 receives an input of a data identifier designated by a user, and collects material data having the received data identifier. The material data collection unit 21 may collect, for example, sensor values output from sensors, data stored in an external or internal storage device, etc., as material data. The material data collection unit 21 stores the collected material data in the material data storage unit 22 .”, first whole paragraph on page 5);
an analysis unit configured to execute first analysis for calculating a predicted value of the objective variable from the explanatory variable group (see first paragraph in section <First Embodiment> on page 2; the system can form a predicted output value) and calculating prediction accuracy of the predicted value on the basis of the target value of the objective variable and second analysis for deriving a degree of contribution of the explanatory variable to the objective variable, for each learning model (see fifth paragraph on page 6; the system is able to calculate predictions and evaluate such predictions to determine prediction accuracy as well as degree of contribution of the explanatory variables;
“The acquisition unit 29 acquires the prediction accuracy of each learning model and the degree of contribution of at least one type of feature quantity specified by the user to the prediction result of the learning model. Specifically, the acquisition unit 29 acquires the prediction accuracy based on the error between the output data of the learning model when the explanatory variables included in the verification data are input to the learning model and the objective variable included in the verification data. do. The prediction accuracy may be, for example, a value based on RMSE (Root Mean Square Error). In addition, the acquisition unit 29 acquires the degree of contribution for each type of feature quantity forming the learning data using the verification data. The contribution may be, for example, a value based on the SHAP value. The acquisition unit 29 outputs the prediction accuracy and the degree of contribution acquired for each learning model to the generation unit 30 .”);
and a learning result processing unit configured to output learning result information indicating the prediction accuracy and the contribution degree for each learning model (see fifth paragraph on page 6; “The acquisition unit 29 outputs the prediction accuracy and the degree of contribution acquired for each learning model to the generation unit 30 .”).
Maejima does not appear to explicitly teach:
a model learning unit having a plurality of learning models for estimating an objective variable indicating performance of a product from an explanatory variable group including a plurality of types of explanatory variables indicating at least a material or design matter of the product and configured to learn each of the plurality of learning models using training data including a dataset including the explanatory variable group and a target value of the objective variable.
Shirane teaches performance of a product… indicating at least a material or design matter of the product (see paragraphs [0127]-[0128], and [0047]-[0051]; the system can utilize machine learning algorithms/models to perform battery/product design and evaluation/assessment).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the model evaluation system of Maejima by evaluating the models with respect to particular data such as battery design as taught by Shirane in order to utilize the evaluation system to allow users of the system to evaluate and compare different models as well as utilize the models for particular purposes that the user wants to achieve including evaluations of designs thus allowing the user to be able to not only see designs but also how different characteristics/attributes of designs affect design performance so that the users of the system can make more informed decisions regarding their design process earlier in the design steps without having to manually modify and test/simulate their designs on their own thus saving the users time and effort.
Maejima in view of Shirane teach a model learning unit having a plurality of learning models for estimating an objective variable indicating performance of a product from an explanatory variable group including a plurality of types of explanatory variables indicating at least a material or design matter of the product (see Shirane, paragraphs [0127]-[0128], and [0047]-[0051]; see Maejima, see first paragraph in section <First Embodiment> on page 2; see first whole paragraph on page 5 discussing “The material data collection unit 21”; the system can utilize various variables/inputs including objective variables and explanatory variables to determine/predict an output associated with the input data which relates to product designs)
and configured to learn each of the plurality of learning models using training data including a dataset including the explanatory variable group and a target value of the objective variable (see Shirane, paragraph [0085]; the system uses the various training data to allow the various models to undergo machine learning, i.e. training, using training data).
With regard to claim 2, Maejima in view of Shirane teach wherein the model learning unit learns the plurality of learning models for each objective variable, wherein the analysis unit executes the first analysis and the second analysis for each objective variable, and wherein the learning result processing unit outputs the learning result information for each objective variable (see Shirane, paragraphs [0127]-[0128], and [0047]-[0051]; see Maejima, see first whole paragraph on page 5 discussing “The material data collection unit 21”; see fifth paragraph on page 6; the system allows the respective models to be trained/learn for the various objective variables for the system as well as be able to perform calculations including prediction accuracy and degree of contribution too and has means to output that calculated information).
With regard to claim 4, Maejima in view of Shirane teach a learning setting unit configured to select an explanatory variable in accordance with an input manipulation, wherein the model learning unit learns the learning model including an explanatory variable selected from the training data without including an unselected explanatory variable (see Maejima, third paragraph on page 8 and second to last paragraph on page 7; the system allows users to select the data/variables/features to be used by the respective system).
With regard to claim 5, Maejima in view of Shirane teach a learning setting unit configured to retrieve a hyperparameter for learning the learning model with higher prediction accuracy for each learning model (see Maejima, last paragraph on page 13; the system has means to search for hyperparameters that maximize the performance the respective learning model).
With regard to claim 6, Maejima in view of Shirane teach wherein the product is a battery (see Shirane, paragraphs [0127]-[0128], and [0047]-[0051]; the system is designing and evaluating a battery).
With regard to claim 7, Maejima in view of Shirane teach a computer-readable non-transitory storage medium storing a program for causing a computer to function as the model setting support device according to claim 1 (see first whole paragraph on page 4 through third whole paragraph on page 4; also, see rejection of claim 1 above).
With regard to claim 8, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Maejima et al [WO 2022/168163 A1] in view of Shirane et al [US 2020/0341065 A1] in further view of Drevo et al [US 2016/0132787 A1].
With regard to claim 3, Maejima in view of Shirane teach all the claim limitations of claims 1 and 2 as discussed above.
Maejima in view of Shirane do not appear to explicitly teach:
a learning setting unit configured to select a learning model for predicting the objective variable in accordance with an input manipulation, wherein the model learning unit learns the selected learning model without learning an unselected learning model.
Drevo teaches a learning setting unit configured to select a learning model for predicting the objective variable in accordance with an input manipulation (see paragraph [0156]; the system has means to allow a user to select a model or models to be evaluated).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the model evaluation system of Maejima in view of Shirane by providing means for the user to be able to select candidate models to be evaluated as taught by Drevo in order to provide greater control to the user on which models to evaluate thus saving total computation time and effort by not having to evaluate all models when the user knows or wants only some subset of models to be used.
Maejima in view of Shirane in further view of Drevo teach wherein the model learning unit learns the selected learning model without learning an unselected learning model (see Drevo, paragraph [0156]; Shirane, paragraph [0085]; the system can train/learn the selected models).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Miyamoto [US 2021/0157959 A1] teaches at paragraphs [0013], [0023]-[0024], [0030], [0058], and [0075] that the system can utilize machine learning algorithms/models to perform battery/product design and evaluation/assessment.
Hulsmann et al, “Local Interpretable Explanations of Energy Systems Design” teaches at section 3 energy system designs with using LIME for machine learning (local interpretable model-agnostic explanation” that can determine the most important interpretable features (i.e. explanation) for the task being evaluated.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC S SOMERS whose telephone number is (571)270-3567. The examiner can normally be reached M-F 11-8 EST.
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, Ann Lo can be reached at 5712729767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARC S SOMERS/Primary Examiner, Art Unit 2159 8/11/2026
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