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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on December 15, 2023, April 02, 2025, and November 21, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 limitations are:
…a model generation device configured to generate a prediction model… in claims 1 and 13;
…a first input data acquisition section configured to acquire first input data… in claims 1 and 13;
…a second input data acquisition section configured to acquire, as second input data that makes a pair with the first input data, resin composition characteristic input data… in claims 1 and 13;
…a first machine learning section configured to generate, on the basis of the first input data and the second input data, a first prediction model… in claims 1 and 13;
…a second machine learning section configured to generate, on the basis of the first prediction model, a second prediction model… in claims 1 and 13;
…a first algorithm execution section configured to execute a first algorithm… in claim 2;
…a second algorithm execution section configured to execute a second algorithm… in claim 2;
…a third algorithm execution section configured to execute a third algorithm… in claim 4;
…A prediction device configured to predict at least one selected from the group consisting of the following (i) to (iv)… in claim 9;
…a third input data acquisition section configured to acquire, as third input data, resin composition required characteristic data… in claims 9 and 13;
…a recommended data deriving section configured to derive recommended data… in claims 9 and 13;
…a prediction device configured to predict, with use of the prediction model generated by the model generation device, at least one selected from the group consisting of the following (i) to (iv)… in claim 13;
Regarding independent claims 1 and 13 and the above-noted three-prong test, the recited model generation device is a generic placeholder, which is used to generate a prediction model is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding independent claims 1 and 13 and the above-noted three-prong test, the recited first input data acquisition section is a generic placeholder, which is used to acquire first input data is functional language, and there is no recitation of sufficient structure to perform the acquiring.
Regarding independent claims 1 and 13 and the above-noted three-prong test, the recited second input data acquisition section is a generic placeholder, which is used to acquire, as second input data that makes a pair with the first input data, resin composition characteristic input data is functional language, and there is no recitation of sufficient structure to perform the acquiring.
Regarding independent claims 1 and 13 and the above-noted three-prong test, the recited first machine learning section is a generic placeholder, which is used to generate, on the basis of the first input data and the second input data, a first prediction model is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding independent claims 1 and 13 and the above-noted three-prong test, the recited second machine learning section is a generic placeholder, which is used to generate, on the basis of the first prediction model, a second prediction model is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claim 2 and the above-noted three-prong test, the recited first algorithm execution section is a generic placeholder, which is used to execute a first algorithm is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claim 2 and the above-noted three-prong test, the recited second algorithm execution section is a generic placeholder, which is used to execute a second algorithm is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claim 4 and the above-noted three-prong test, the recited third algorithm execution section is a generic placeholder, which is used to execute a third algorithm is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding independent claim 9 and the above-noted three-prong test, the recited prediction device is a generic placeholder, which is used to predict at least one selected from the group consisting of the following (i) to (iv) is functional language, and there is no recitation of sufficient structure to perform the predicting.
Regarding independent claims 9 and 13 and the above-noted three-prong test, the recited third input data acquisition section is a generic placeholder, which is used to acquire, as third input data, resin composition required characteristic data is functional language, and there is no recitation of sufficient structure to perform the predicting.
Regarding independent claims 9 and 13 and the above-noted three-prong test, the recited recommended data deriving section is a generic placeholder, which is used to derive recommended data is functional language, and there is no recitation of sufficient structure to perform the predicting.
Regarding independent claim 13 and the above-noted three-prong test, the recited prediction device is a generic placeholder, which is used to predict, with use of the prediction model generated by the model generation device, at least one selected from the group consisting of the following (i) to (iv) is functional language, and there is no recitation of sufficient structure to perform the predicting.
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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1-2, 4, 9, and 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claim limitations reciting a “device” or a “section” in claims 1-2, 4, 9, and 13 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 action. Therefore, it is unclear whether Applicant had possession of the claimed invention as of the effective filing date. See rejection under 35 U.S.C. 112(b) for further analysis.
Claims 3, 5-8, and 10 are further rejected for dependence, either directly or indirectly, on claims 1 and 9.
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 1-2, 4, 9, and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim limitations reciting a “device” or a “section” in claims 1-2, 4, 9, and 13 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 action.
Regarding the recitations of the “model generation device”, paragraph 82 repeats the claimed functions of the model generation device, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the model generation device in paragraphs 18, 21, 46, 83, 86, 89, and 95.
Regarding the recitations of the “prediction device”, paragraph 83 repeats the claimed functions of the prediction device, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the prediction device in paragraphs 39, 83, 86, 89, and 103.
Regarding the recitations of the “first input data acquisition section”, paragraph 21 repeats the claimed functions of the first input data acquisition section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the first input data acquisition section in paragraphs 23, 31, 95, and 107.
Regarding the recitations of the “first machine learning section”, paragraph 23 repeats the claimed functions of the first machine learning section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the first machine learning section in paragraphs 23, 27, 57-58, 95, and 107.
Regarding the recitations of the “second machine learning section”, paragraph 27 repeats the claimed functions of the second machine learning section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the second machine learning section in paragraphs 95, 98, and 107.
Regarding the recitations of the “first algorithm execution section”, paragraph 31 repeats the claimed functions of the first algorithm execution section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the first algorithm execution section in paragraphs 31, 32, 56, 60 96, and 109.
Regarding the recitations of the “second algorithm execution section”, paragraph 32 repeats the claimed functions of the second algorithm execution section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the second algorithm execution section in paragraphs 31, 60, 61, 62, 68, 109.
Regarding the recitations of the “third algorithm execution section”, paragraph 34 repeats the claimed functions of the third algorithm execution section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the third algorithm execution section in paragraphs 68, 77, 98, and 109.
Regarding the recitations of the “prediction device”, paragraph 83 repeats the claimed functions of the prediction device, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the prediction device in paragraphs 18, 39, 86, 89, 103-104, 106, and 109.
Regarding the recitations of the “third input data acquisition section”, paragraph 40 repeats the claimed functions of the third input data acquisition section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the third input data acquisition section in paragraphs 39, 103, 107, and 109.
Regarding the recitations of the “recommended data deriving section”, paragraph 42 repeats the claimed functions of the recommended data deriving section, but it does not provide an algorithm for performing the entire claimed functions, nor do the further recitations of the recommended data deriving section in paragraphs 41, 43, 46, 80-81, 103, 107, and 109.
Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. For purposes of examination, any computer software that performs the claimed functions will be deemed to read on the claims.
Applicant may:
(a) Amend the claims so that the claim limitations will no longer be interpreted as limitations 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 functions, 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 functions recited in the claims, 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 functions so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed functions, 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 functions, 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 functions. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 3, 5-8, and 10 are further rejected for dependence, either directly or indirectly, on claims 1 and 9.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate a prediction model for predicting at least one selected from the group consisting of the following (i) to (iv), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin: (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin”
“generate, on the basis of the first input data and the second input data, a first prediction model that predicts unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data”
“generate, on the basis of the first prediction model, a second prediction model that predicts at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“the model generation device comprising: a first input data acquisition section configured to…”
“a second input data acquisition section configured to…”
“a first machine learning section configured to”
“a second machine learning section configured to”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“acquire first input data that is input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin”
“acquire, as second input data that makes a pair with the first input data, resin composition characteristic input data indicating a characteristic of the resin composition”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the acquiring limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“deriving an explanatory variable corresponding to data that indicates the characteristic of the resin composition and that is obtained from the second input data”
“generating the first prediction model on the basis of the explanatory variable and the second input data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
wherein the first machine learning section includes: a first algorithm execution section configured to execute a first algorithm for…”
“a second algorithm execution section configured to execute a second algorithm for…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 2.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the second algorithm is at least one selected from the group consisting of Gaussian process regression, a support-vector machine, linear regression, a decision tree, random forest, a neural network and a gradient boosting decision tree”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating the second prediction model on the basis of the first prediction model”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the second machine learning section has a third algorithm execution section configured to execute a third algorithm for…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 4.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the third algorithm is at least one selected from the group consisting of genetic algorithm, gradient descent, grid search, and Bayesian optimization”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the inorganic filling material characteristic input data indicates, as the characteristic of the inorganic filling material, at least one selected from the group consisting of composition formula, crystallinity, specific gravity, bulk specific gravity, particle size distribution, specific surface area, pore volume, zeta potential, specific electric conductivity, dielectric constant, dielectric dissipation factor, refractive index, specific heat, thermal conductivity, linear expansion coefficient, crushing strength, sphericity, aspect ratio, moisture content, carbon content, nitrogen content, surface functional group species, surface functional group content, light absorption wavelength, light absorbance, M-value and solubility parameter of the inorganic filling material”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. Additional details that do not apply the exception in a meaningful way cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the resin characteristic input data indicates, as the characteristic of the resin, at least one selected from the group consisting of composition formula, polymerization degree, molecular weight distribution, stereoregularity, reactive functional group species, reactive functional group content, viscosity, melting point, glass transition temperature, crystallinity, elastic modulus, yield stress, breaking strength, fracture toughness, light absorption wavelength, light absorbance, specific gravity, refractive index, specific electric conductivity, dielectric constant, dielectric dissipation factor, specific heat, thermal conductivity, moisture content and solubility parameter of the resin”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. Additional details that do not apply the exception in a meaningful way cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a model generation device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the resin composition characteristic input data indicates, as the characteristic of the resin composition, at least one selected from the group consisting of viscosity, flowability, moldability, adhesiveness, transparency, color tone, strength, water absorption rate, linear expansion coefficient, elastic modulus, yield stress, breaking strength, fracture toughness, specific electric conductivity, dielectric constant, dielectric dissipation factor, thermal conductivity and stability of the resin composition”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. Additional details that do not apply the exception in a meaningful way cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a prediction device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“predict at least one selected from the group consisting of the following (i) to (iv), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin: (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin”
“a first prediction model is generated in advance, on the basis of the first input data and the second input data, the first prediction model predicting unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data”
“a second prediction model is generated in advance, on the basis of the first prediction model, the second prediction model predicting at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data”
“derive recommended data … the recommended data including at least one selected from the group consisting of (i) recommended inorganic filling material characteristic data indicating the characteristic of the inorganic filling material that satisfies the resin composition required characteristic data, (ii) recommended resin characteristic data indicating the characteristic of the resin that satisfies the resin composition required characteristic data, (iii) recommended inorganic filling material proportion data indicating a proportion of the inorganic filling material that satisfies the resin composition required characteristic data and (iv) recommended resin proportion data indicating a proportion of the resin that satisfies the resin composition required characteristic data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A prediction device configured to”
“the prediction device includes: a third input data acquisition section configured to…”
“a recommended data deriving section configured to…”
“by inputting the resin composition required characteristic data to the second prediction model”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“first input data is acquired in advance, the first input data being input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin”
“resin composition characteristic input data is acquired in advance, as second input data that makes a pair with the first input data, the resin composition characteristic input data indicating a characteristic of the resin composition”
“acquire, as third input data, resin composition required characteristic data indicating the required characteristic of the resin composition”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the acquiring limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a prediction device, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the resin composition required characteristic data indicates, as the required characteristic of the resin composition, at least one selected from the group consisting of viscosity, flowability, moldability, adhesiveness, transparency, color tone, strength, water absorption rate, linear expansion coefficient, elastic modulus, yield stress, breaking strength, fracture toughness, specific electric conductivity, dielectric constant, dielectric dissipation factor, thermal conductivity and stability of the resin composition”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. Additional details that do not apply the exception in a meaningful way cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method for generating a prediction model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating a prediction model for predicting at least one selected from the group consisting of the following (i) to (iv), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin: (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin”
“generating, on the basis of the first input data and the second input data, a first prediction model that predicts unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data”
“generating, on the basis of the first prediction model, a second prediction model that predicts at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“a first input data acquisition step of…”
“a second input data acquisition step of…”
“a first machine learning step of…”
“a second machine learning step of…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“acquiring first input data that is input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin”
“acquiring, as second input data that makes a pair with the first input data, resin composition characteristic input data indicating a characteristic of the resin composition”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the acquiring limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method for predicting, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“predicting at least one selected from the group consisting of the following (i) to (iv), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin: (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin”
“a first prediction model is generated in advance, on the basis of the first input data and the second input data, the first prediction model predicting unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data”
“a second prediction model is generated in advance, on the basis of the first prediction model, the second prediction model predicting at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data”
“deriving recommended data …the recommended data including at least one selected from the group consisting of (i) recommended inorganic filling material characteristic data indicating the characteristic of the inorganic filling material that satisfies the resin composition required characteristic data, (ii) recommended resin characteristic data indicating the characteristic of the resin that satisfies the resin composition required characteristic data, (iii) recommended inorganic filling material proportion data indicating a proportion of the inorganic filling material that satisfies the resin composition required characteristic data and (iv) recommended resin proportion data indicating a proportion of the resin that satisfies the resin composition required characteristic data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“a third input data acquisition step of…”
“a recommended data deriving step of…”
“by inputting the resin composition required characteristic data to the second prediction model”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“first input data is acquired in advance, the first input data being input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin”
“resin composition characteristic input data is acquired in advance, as second input data that makes a pair with the first input data, the resin composition characteristic input data indicating a characteristic of the resin composition”
“acquiring, as third input data, resin composition required characteristic data indicating the required characteristic of the resin composition”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the acquiring limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method for predicting, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate a prediction model for predicting at least one selected from the group consisting of the following (i) to (iv), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin: (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin”
“predict, with use of the prediction model generated by the model generation device, at least one selected from the group consisting of the following (i) to (iv), the at least one satisfying the required characteristic of the resin composition: (i) the characteristic of the inorganic filling material; (ii) the characteristic of the resin; (iii) the mixing ratio of the inorganic filling material; and (iv) the mixing ratio of the resin”
“generate, on the basis of the first input data and the second input data, a first prediction model that predicts unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data”
“generate, on the basis of the first prediction model, a second prediction model that predicts at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) 11 predicted resin proportion data, the at least one satisfying given resin composition characteristic data”
“derive recommended data … the recommended data including at least one selected from the group consisting of (i) recommended inorganic filling material characteristic data indicating the characteristic of the inorganic filling material that satisfies the resin composition required characteristic data, (ii) recommended resin characteristic data indicating the characteristic of the resin that satisfies the resin composition required characteristic data, (iii) recommended inorganic filling material proportion data indicating a proportion of the inorganic filling material that satisfies the resin composition required characteristic data and (iv) recommended resin proportion data indicating a proportion of the resin that satisfies the resin composition required characteristic data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A resin composition production system comprising: a model generation device configured to…”
“a prediction device configured to…”
“a first input data acquisition section configured to…”
“a second input data acquisition section configured to…”
“a first machine learning section configured to…”
“a second machine learning section configured to…”
“a third input data acquisition section configured to…”
“a recommended data deriving section configured to…”
“by inputting the resin composition required characteristic data to the second prediction model”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“acquire first input data that is input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin”
“acquire, as second input data that makes a pair with the first input data, resin composition characteristic input data indicating a characteristic of the resin composition”
“acquire, as third input data, resin composition required characteristic data indicating the required characteristic of the resin composition”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the acquiring limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Fujishima et al. (U.S. Patent Publication No. 2023/0252319) (“Fujishima”) in view of Nakamura et al. (U.S. Patent Publication No. 2025/0148159) (“Nakamura”).
Regarding claim 1, Fujishima teaches a model generation device configured to generate a prediction model (Fujishima [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides learning processing unit 102a2, corresponding to a model generation device, which executes a learning process of a first or second machine learning model, corresponding to generating a prediction model, wherein the execution of the learning process corresponds to the prediction model generation.) for predicting at least one selected from the group consisting of the following (i) to (iv) (Fujishima [0046] “The predicting unit 102b2 may repeatedly use the first machine learning model with a genetic algorithm to acquire the predicted composition data.”; [0047] “The predicting unit 102b2 may repeatedly use the second machine learning model with a genetic algorithm to acquire the predicted characteristic data.” Fujishima provides using the first and second machine learning models to predict resin composition data, and resin characteristic data, respectively.), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin (Fujishima [0046] “The predicting unit 102b2 may input the produced next-generation predicted composition data to the first machine learning model to acquire a plurality of pieces of characteristic data, and extract, from the predicted composition data input to the first machine learning model, the predicted composition data in which all the characteristics satisfy the target [satisfying a required characteristic of a resin composition].”; [0051] “A composition represented by the composition data may be the presence or absence of a raw material that can produce the thermosetting resin composition, or may be a compound contained in the raw material (for example, the name or structural formula of a specific compound contained in the raw material represented by a general name)... The raw material that can produce the thermosetting resin composition may contain at least one of a thermosetting resin, a thermoplastic resin [at least one resin], an inorganic filler [at least one inorganic filling material], a flame retardant, a curing accelerator, and a solvent.” Fujishima provides predicting resin composition data that satisfies target resin characteristics, wherein the composition includes at least one resin and at least one inorganic filling material): (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin (Fujishima [0047] “For example, the predicting unit 102b2 may input the target composition data (or the partial target composition data) to the second machine learning model to acquire a plurality of pieces of predicted characteristic data [(ii) a characteristic of the resin], and then perform selection for the predicted characteristic data to produce a plurality of pieces of next-generation predicted characteristic data.”; [0048] “As described above, the information processing apparatus 100 executes the learning process of the first machine learning model or the second machine learning model using the learning data. When a target characteristic is input to the first machine learning model, a composition for production of a thermosetting resin composition having the target characteristic is output. When a target composition is input to the second machine learning model, a characteristic of a thermosetting resin composition produced with the target composition is output.” Fujishima provides using the first and second machine learning models to output predictions regarding resin characteristics, corresponding to predicting (ii) a characteristic of a resin.); the model generation device comprising: a first input data acquisition section configured to acquire first input data (Fujishima [0037] “The learning data acquiring unit 102a1 acquires characteristic data and composition data as learning data.”; [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides learning data acquiring unit 102a1, corresponding to a first input data acquisition section, which acquires resin characteristic data, corresponding to the first input data.) that is input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin (Fujishima [0037] “The characteristic data includes a characteristic value regarding a characteristic of the thermosetting resin composition.”; [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides acquired characteristic data used for the learning of a first or second machine learning model, wherein the characteristic data includes (ii) resin characteristic input data indicating the characteristic of the resin.); a second input data acquisition section configured to acquire, as second input data that makes a pair with the first input data, resin composition characteristic input data indicating a characteristic of the resin composition (Fujishima [0037] “The learning data acquiring unit 102a1 acquires characteristic data and composition data as learning data… The composition data includes a composition value regarding a composition of the thermosetting resin composition.”; [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides acquired composition data, corresponding to second input data, used for the learning of the first or second machine learning model along with the acquired characteristic data, thus making a pair with the characteristic data (first input data), wherein the composition data indicates a characteristic of the resin composition); a first machine learning section configured to generate, on the basis of the first input data and the second input data, a first prediction model that predicts unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data (Fujishima [0038] “The learning processing unit 102a2 uses the characteristic data [first input data] and the composition data [second input data] acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model. When target characteristic data is input, the first machine learning model outputs predicted composition data. The target characteristic data includes a target characteristic value regarding the characteristic of the thermosetting resin composition. The predicted composition data includes a predicted composition value regarding the composition of the thermosetting resin composition. When target composition data is input, the second machine learning model outputs predicted characteristic data. The target composition data includes a target composition value regarding the composition of the thermosetting resin composition.” Fujishima provides using the acquired first and second input data to predict resin composition and characteristic data using first and second machine learning models from acquired (given) resin characteristic data, wherein the first or second machine learning model corresponds to a first prediction model.);
Fujishima fails to explicitly teach a second machine learning section configured to generate, on the basis of the first prediction model, a second prediction model that predicts at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data.
However, Nakamura teaches a second machine learning section configured to generate, on the basis of the first prediction model, a second prediction model that predicts at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data (Nakamura [0046] “The flame resistance predicting device predicts information regarding flame resistance of a polymer composite material (composite material) from material formulation information (formulation information of a material) of the polymer composite material using a machine learning technique. The material formulation information is a ratio of a resin or an additive contained in the composite material.”; [0102] “Specifically, the flame resistance predicting device may predict the flame resistance from the material formulation information using two prediction models of a first-stage prediction model [a first prediction model] that predicts the combustion information from the material formulation information and a second-stage prediction model [a second prediction model] that predicts the flame resistance from the combustion information [(ii) predicted resin characteristic data].” Nakamura provides using two predictions models, including a first and second stage, corresponding to a second prediction model on the basis of the first, which predicts flame resistance from the output of the first prediction model, and wherein the flame resistance prediction includes resin characteristic data.), the at least one satisfying given resin composition characteristic data (Nakamura [0046] “The material formulation information is a ratio of a resin or an additive contained in the composite material.”; [0107] “For example, output of toughness and rigidity of a composite resin at the same time as the flame resistance can be utilized for formulation prediction that eliminates trade-off between these characteristics and the flame resistance.” Nakamura provides predicting flame resistance which includes resin composition characteristic data.).
Fujishima and Nakamura are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to the resin related predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima with the above teachings of Nakamura. Doing so would allow for an improvement in prediction accuracy (Nakamura [0046] “In order to improve prediction accuracy, information regarding specific combustion may be added to the input of the prediction process in addition to the material formulation information.”).
Regarding claim 2, Fujishima in view of Nakamura teaches wherein the first machine learning section includes: a first algorithm execution section configured to execute a first algorithm for deriving an explanatory variable corresponding to data that indicates the characteristic of the resin composition (Nakamura [0049] “The prediction model 121 is a machine learning model using material formulation information of a composite material as an explanatory variable, and using suitability (suitable/unsuitable) of the composite material for a standard of flame resistance as an objective variable.”; [0052] “The material formulation information 132 is a ratio of a resin or an additive to be a material of a composite material, and is, for example, a weight ratio. Note that there is generally a plurality of additives.” Nakamura provides deriving an explanatory variable that indicates material formulation information which may be a ratio of a resin, corresponding to deriving an explanatory variable corresponding to data that indicates the characteristic of the resin composition.) and that is obtained from the second input data (Nakamura [0060] “In step S1l, the information acquisition unit 111 acquires material formulation information of a composite material and flame resistance of the composite material, and stores the material formulation information and the flame resistance in the material information database 130. The data of the material information database 130 is data in which the material formulation information 132 is regarded as an explanatory variable (input) and the flame resistance 133 is regarded as an objective variable (output, correct label).” Nakamura provides database 130 for obtaining material information that indicates resin composite ratio, wherein the database 130 provides the second input data.); and a second algorithm execution section configured to execute a second algorithm for generating the first prediction model on the basis of the explanatory variable and the second input data (Nakamura [0061] “In step S12, the learning unit 112 acquires a plurality of pieces of data in the material information database 130, and trains a machine learning model using these pieces of data as teacher data to generate the prediction model 121. Note that the teacher data is data in which the material formulation information 132 is regarded as an explanatory variable (input) and the flame resistance 133 is regarded as an objective variable (output, correct label).” Nakamura provides generation the prediction model 121, corresponding to the first prediction model, based on the explanatory variable and the data from material information database 130 comprising the second input data.).
Fujishima and Nakamura are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to the resin related predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima with the above teachings of Nakamura. Doing so would allow for an improvement in prediction accuracy (Nakamura [0046] “In order to improve prediction accuracy, information regarding specific combustion may be added to the input of the prediction process in addition to the material formulation information.”).
Regarding claim 3, Fujishima in view of Nakamura teaches wherein the second algorithm is at least one selected from the group consisting of Gaussian process regression, a support-vector machine, linear regression, a decision tree, random forest, a neural network and a gradient boosting decision tree (Nakamura [0057] “The learning unit 112 trains a machine learning model using the data in the material information database 130 as teacher data to generate the prediction model 121. The prediction model 121 is, for example, a machine learning model of a decision tree, but may be another model of a machine learning technique, such as a random forest, a neural network, or a support vector machine (SVM).” Nakamura provides generating the prediction model based on one of a decision tree, support vector machine, random forest, or neural network.).
Fujishima and Nakamura are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to the resin related predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima with the above teachings of Nakamura. Doing so would allow for an improvement in prediction accuracy (Nakamura [0046] “In order to improve prediction accuracy, information regarding specific combustion may be added to the input of the prediction process in addition to the material formulation information.”).
Regarding claim 4, Fujishima in view of Nakamura teaches wherein the second machine learning section has a third algorithm execution section configured to execute a third algorithm for generating the second prediction model on the basis of the first prediction model (Nakamura [0102] “Specifically, the flame resistance predicting device may predict the flame resistance from the material formulation information using two prediction models of a first-stage prediction model that predicts the combustion information from the material formulation information and a second-stage prediction model that predicts the flame resistance from the combustion information.”; [0104] “The second-stage prediction model can be generated using teacher data using the combustion information 134 as an explanatory variable and using the flame resistance 133 as an objective variable.” Nakamura provides generating a second stage prediction model using teacher data, corresponding to generating the second prediction model on the basis of the first prediction model).
Fujishima and Nakamura are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to the resin related predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima with the above teachings of Nakamura. Doing so would allow for an improvement in prediction accuracy (Nakamura [0046] “In order to improve prediction accuracy, information regarding specific combustion may be added to the input of the prediction process in addition to the material formulation information.”).
Regarding claim 5, Fujishima in view of Nakamura teaches wherein the third algorithm is at least one selected from the group consisting of genetic algorithm, gradient descent, grid search, and Bayesian optimization (Fujishima [0047] “The predicting unit 102b2 may repeatedly use the second machine learning model with a genetic algorithm to acquire the predicted characteristic data.” Fujishima provides a prediction model on the basis of a genetic algorithm.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima in view of Nakamura for the same reasons disclosed above in the rejection of claim 4.
Regarding claim 6, Fujishima in view of Nakamura teaches wherein the inorganic filling material characteristic input data indicates, as the characteristic of the inorganic filling material, at least one selected from the group consisting of composition formula, crystallinity, specific gravity, bulk specific gravity, particle size distribution, specific surface area, pore volume, zeta potential, specific electric conductivity, dielectric constant, dielectric dissipation factor, refractive index, specific heat, thermal conductivity, linear expansion coefficient, crushing strength, sphericity, aspect ratio, moisture content, carbon content, nitrogen content, surface functional group species, surface functional group content, light absorption wavelength, light absorbance, M-value and solubility parameter of the inorganic filling material (Fujishima [0070] “Examples of the inorganic filler may include silica, alumina, glass, cordierite, silicon oxide, barium sulfate, barium carbonate, talc, clay, mica powder, zinc oxide, hydrotalcite, boehmite, aluminum hydroxide, magnesium hydroxide, calcium carbonate, magnesium carbonate, magnesium oxide, boron nitride, silicon nitride, aluminum nitride, manganese nitride, aluminum borate, strontium carbonate, barium titanate, strontium titanate, calcium titanate, magnesium titanate, bismuth titanate, titanium oxide, zirconium oxide, barium zirconate titanate, barium zirconate, calcium zirconate, zirconium phosphate, and zirconium tungstate phosphate.” Fujishima provides composition formula of inorganic filler, corresponding to the characteristic of inorganic filling material consisting of composition formula.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima in view of Nakamura for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 7, Fujishima in view of Nakamura teaches wherein the resin characteristic input data indicates, as the characteristic of the resin, at least one selected from the group consisting of composition formula, polymerization degree, molecular weight distribution, stereoregularity, reactive functional group species, reactive functional group content, viscosity, melting point, glass transition temperature, crystallinity, elastic modulus, yield stress, breaking strength, fracture toughness, light absorption wavelength, light absorbance, specific gravity, refractive index, specific electric conductivity, dielectric constant, dielectric dissipation factor, specific heat, thermal conductivity, moisture content and solubility parameter of the resin (Fujishima [0094] “A characteristic represented by the characteristic data may be, for example, at least one of dimensional stability, heat resistance, elasticity, strength, extensibility, dielectric property, surface property, adhesion, electric insulation property, and flame retardance of the thermosetting resin composition.” Fujishima provides resin characteristic data including dimensional stability, heat resistance, elasticity, strength, extensibility, dielectric property, surface property, adhesion, electric insulation property, and flame retardance of the thermosetting resin composition.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima in view of Nakamura for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 8, Fujishima in view of Nakamura teaches wherein the resin composition characteristic input data indicates, as the characteristic of the resin composition, at least one selected from the group consisting of viscosity, flowability, moldability, adhesiveness, transparency, color tone, strength, water absorption rate, linear expansion coefficient, elastic modulus, yield stress, breaking strength, fracture toughness, specific electric conductivity, dielectric constant, dielectric dissipation factor, thermal conductivity and stability of the resin composition (Fujishima [0094] “characteristic represented by the characteristic data may be, for example, at least one of dimensional stability, heat resistance, elasticity, strength, extensibility, dielectric property, surface property, adhesion, electric insulation property, and flame retardance of the thermosetting resin composition.” Fujishima provides the resin composition characteristic data including dimensional stability, heat resistance, elasticity, strength, extensibility, dielectric property, surface property, adhesion, electric insulation property, and flame retardance.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima in view of Nakamura for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 9, Fujishima teaches a prediction device configured to predict at least one selected from the group consisting of the following (i) to (iv) (Fujishima [0046] “The predicting unit 102b2 may repeatedly use the first machine learning model with a genetic algorithm to acquire the predicted composition data.”; [0047] “The predicting unit 102b2 may repeatedly use the second machine learning model with a genetic algorithm to acquire the predicted characteristic data.” Fujishima provides using the first and second machine learning models to predict resin composition data, and resin characteristic data, respectively.), the at least one satisfying a required characteristic of a resin composition which contains at least one inorganic filling material and at least one resin (Fujishima [0046] “The predicting unit 102b2 may input the produced next-generation predicted composition data to the first machine learning model to acquire a plurality of pieces of characteristic data, and extract, from the predicted composition data input to the first machine learning model, the predicted composition data in which all the characteristics satisfy the target [satisfying a required characteristic of a resin composition].”; [0051] “A composition represented by the composition data may be the presence or absence of a raw material that can produce the thermosetting resin composition, or may be a compound contained in the raw material (for example, the name or structural formula of a specific compound contained in the raw material represented by a general name)... The raw material that can produce the thermosetting resin composition may contain at least one of a thermosetting resin, a thermoplastic resin [at least one resin], an inorganic filler [at least one inorganic filling material] , a flame retardant, a curing accelerator, and a solvent.” Fujishima provides predicting resin composition data that satisfies target resin characteristics, wherein the composition includes at least one resin and at least one inorganic filling material): (i) a characteristic of the inorganic filling material; (ii) a characteristic of the resin; (iii) a mixing ratio of the inorganic filling material; and (iv) a mixing ratio of the resin (Fujishima [0047] “For example, the predicting unit 102b2 may input the target composition data (or the partial target composition data) to the second machine learning model to acquire a plurality of pieces of predicted characteristic data [a characteristic of the resin], and then perform selection for the predicted characteristic data to produce a plurality of pieces of next-generation predicted characteristic data.”; [0048] “As described above, the information processing apparatus 100 executes the learning process of the first machine learning model or the second machine learning model using the learning data. When a target characteristic is input to the first machine learning model, a composition for production of a thermosetting resin composition having the target characteristic is output. When a target composition is input to the second machine learning model, a characteristic of a thermosetting resin composition produced with the target composition is output.” Fujishima provides using the first and second machine learning models to output predictions regarding resin characteristics, corresponding to predicting (ii) a characteristic of a resin.), wherein: first input data is acquired in advance (Fujishima [0037] “The learning data acquiring unit 102a1 acquires characteristic data and composition data as learning data.”; [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides learning data acquiring unit 102a1, corresponding to a first input data acquisition section, which acquires resin characteristic data, corresponding to the first input data.), the first input data being input data including at least one selected from the group consisting of (i) inorganic filling material characteristic input data indicating the characteristic of the inorganic filling material, (ii) resin characteristic input data indicating the characteristic of the resin, (iii) inorganic filling material proportion input data related to the mixing ratio of each of a plurality of the inorganic filling materials in the resin composition obtained by mixing the plurality of the inorganic filling materials in the resin and (iv) resin proportion input data related to the mixing ratio of the resin (Fujishima [0037] “The characteristic data includes a characteristic value regarding a characteristic of the thermosetting resin composition.”; [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides acquired characteristic data used for the learning of a first or second machine learning model, wherein the characteristic data includes resin characteristic input data indicating the characteristic of the resin.); resin composition characteristic input data is acquired in advance, as second input data that makes a pair with the first input data, the resin composition characteristic input data indicating a characteristic of the resin composition (Fujishima [0037] “The learning data acquiring unit 102a1 acquires characteristic data and composition data as learning data… The composition data includes a composition value regarding a composition of the thermosetting resin composition.”; [0038] “The learning processing unit 102a2 uses the characteristic data and the composition data acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model.” Fujishima provides acquired composition data, corresponding to second input data, used for the learning of the first or second machine learning model along with the acquired characteristic data, thus making a pair with the characteristic data (first input data), wherein the composition data indicates a characteristic of the resin composition); a first prediction model is generated in advance, on the basis of the first input data and the second input data, the first prediction model predicting unknown resin composition characteristic data from at least one selected from the group consisting of (i) given inorganic filling material characteristic data, (ii) given resin characteristic data, (iii) given inorganic filling material proportion data and (iv) given resin proportion data (Fujishima [0038] “The learning processing unit 102a2 uses the characteristic data [first input data] and the composition data [second input data] acquired by the learning data acquiring unit 102a1 as the learning data to execute a learning process of a first machine learning model or a second machine learning model. When target characteristic data is input, the first machine learning model outputs predicted composition data. The target characteristic data includes a target characteristic value regarding the characteristic of the thermosetting resin composition. The predicted composition data includes a predicted composition value regarding the composition of the thermosetting resin composition. When target composition data is input, the second machine learning model outputs predicted characteristic data. The target composition data includes a target composition value regarding the composition of the thermosetting resin composition.” Fujishima provides using the acquired first and second input data to predict resin composition and characteristic data using first and second machine learning models from acquired resin characteristic data, wherein the first or second machine learning model corresponds to a first prediction model.).
Fujishima fails to explicitly teach a second prediction model is generated in advance, on the basis of the first prediction model, the second prediction model predicting at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data, the at least one satisfying given resin composition characteristic data; and the prediction device includes: a third input data acquisition section configured to acquire, as third input data, resin composition required characteristic data indicating the required characteristic of the resin composition; and a recommended data deriving section configured to derive recommended data by inputting the resin composition required characteristic data to the second prediction model, the recommended data including at least one selected from the group consisting of (i) recommended inorganic filling material characteristic data indicating the characteristic of the inorganic filling material that satisfies the resin composition required characteristic data, (ii) recommended resin characteristic data indicating the characteristic of the resin that satisfies the resin composition required characteristic data, (iii) recommended inorganic filling material proportion data indicating a proportion of the inorganic filling material that satisfies the resin composition required characteristic data and (iv) recommended resin proportion data indicating a proportion of the resin that satisfies the resin composition required characteristic data.
However, Nakamura teaches a second prediction model is generated in advance, on the basis of the first prediction model, the second prediction model predicting at least one selected from the group consisting of (i) predicted inorganic filling material characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filling material proportion data and (iv) predicted resin proportion data (Nakamura [0046] “The flame resistance predicting device predicts information regarding flame resistance of a polymer composite material (composite material) from material formulation information (formulation information of a material) of the polymer composite material using a machine learning technique. The material formulation information is a ratio of a resin or an additive contained in the composite material.”; [0102] “Specifically, the flame resistance predicting device may predict the flame resistance from the material formulation information using two prediction models of a first-stage prediction model [a first prediction model] that predicts the combustion information from the material formulation information and a second-stage prediction model [a second prediction model] that predicts the flame resistance from the combustion information [predicted resin characteristic data].” Nakamura provides using two predictions models, including a first and second stage, corresponding to a second prediction model on the basis of the first, which predicts flame resistance from the output of the first prediction model, and wherein the flame resistance prediction includes resin characteristic data.), the at least one satisfying given resin composition characteristic data (Nakamura [0046] “The material formulation information is a ratio of a resin or an additive contained in the composite material.”; [0107] “For example, output of toughness and rigidity of a composite resin at the same time as the flame resistance can be utilized for formulation prediction that eliminates trade-off between these characteristics and the flame resistance.” Nakamura provides predicting flame resistance which includes resin composition characteristic data.); and the prediction device includes: a third input data acquisition section configured to acquire, as third input data, resin composition required characteristic data indicating the required characteristic of the resin composition (Nakamura [0052] “The material formulation information 132 is a ratio of a resin or an additive to be a material of a composite material, and is, for example, a weight ratio. Note that there is generally a plurality of additives.”; [0103] “As combustion information that is an objective variable of the first-stage prediction model and is an explanatory variable of the second-stage prediction model, combustion information with high importance (degree of influence) with respect to flame resistance that is a prediction result may be used. Examples of the combustion information with high importance include the number of times of drip and a drip viscosity (see FIG. 10).” Nakamura provides acquiring combustion information that includes the number of times of drip and a drip viscosity from the first stage prediction model, which includes resin information, corresponding to the third input data indicating the required characteristic of the resin composition.); and a recommended data deriving section configured to derive recommended data by inputting the resin composition required characteristic data to the second prediction model (Nakamura [0052] “The material formulation information 132 is a ratio of a resin or an additive to be a material of a composite material, and is, for example, a weight ratio. Note that there is generally a plurality of additives.”; [0102] “Specifically, the flame resistance predicting device may predict the flame resistance from the material formulation information using two prediction models of a first-stage prediction model that predicts the combustion information from the material formulation information and a second-stage prediction model that predicts the flame resistance from the combustion information.” Nakamura provides inputting the combustion information, corresponding to the third input data, into the second stage prediction model, corresponding to the second prediction model, to predict flame resistance from the combustion information, corresponding to the derived recommended data.), the recommended data including at least one selected from the group consisting of (i) recommended inorganic filling material characteristic data indicating the characteristic of the inorganic filling material that satisfies the resin composition required characteristic data, (ii) recommended resin characteristic data indicating the characteristic of the resin that satisfies the resin composition required characteristic data, (iii) recommended inorganic filling material proportion data indicating a proportion of the inorganic filling material that satisfies the resin composition required characteristic data and (iv) recommended resin proportion data indicating a proportion of the resin that satisfies the resin composition required characteristic data (Nakamura [0050] “The material information database 130 is, for example, tabular data, and one row (record) includes columns (attributes) of identification information 131 (described as ID in FIG. 2), material formulation information 132, and flame resistance 133.”; [0052] “The material formulation information 132 is a ratio of a resin or an additive to be a material of a composite material, and is, for example, a weight ratio.”; [0102] “Specifically, the flame resistance predicting device may predict the flame resistance from the material formulation information using two prediction models of a first-stage prediction model that predicts the combustion information from the material formulation information and a second-stage prediction model that predicts the flame resistance from the combustion information.” Nakamura provides predicting the flame resistance corresponding to the recommended data, which includes a ratio of a resin, corresponding to (ii) recommended resin characteristic data indicating the characteristic of the resin that satisfies the resin composition required characteristic data.).
Fujishima and Nakamura are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to the resin related predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima with the above teachings of Nakamura. Doing so would allow for an improvement in prediction accuracy (Nakamura [0046] “In order to improve prediction accuracy, information regarding specific combustion may be added to the input of the prediction process in addition to the material formulation information.”).
Regarding claim 10, Fujishima in view of Nakamura teaches wherein the resin composition required characteristic data indicates, as the required characteristic of the resin composition, at least one selected from the group consisting of viscosity, flowability, moldability, adhesiveness, transparency, color tone, strength, water absorption rate, linear expansion coefficient, elastic modulus, yield stress, breaking strength, fracture toughness, specific electric conductivity, dielectric constant, dielectric dissipation factor, thermal conductivity and stability of the resin composition (Nakamura [0052] “The material formulation information 132 is a ratio of a resin or an additive to be a material of a composite material, and is, for example, a weight ratio. Note that there is generally a plurality of additives.”; [0103] “As combustion information that is an objective variable of the first-stage prediction model and is an explanatory variable of the second-stage prediction model, combustion information with high importance (degree of influence) with respect to flame resistance that is a prediction result may be used. Examples of the combustion information with high importance include the number of times of drip and a drip viscosity (see FIG. 10).” Nakamura provides acquiring combustion information that includes the number of times of drip and a drip viscosity from the first stage prediction model, which includes resin information, corresponding to the required characteristic of the resin composition, at least one selected from the group consisting of viscosity.).
Fujishima and Nakamura are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to the resin related predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima with the above teachings of Nakamura. Doing so would allow for an improvement in prediction accuracy (Nakamura [0046] “In order to improve prediction accuracy, information regarding specific combustion may be added to the input of the prediction process in addition to the material formulation information.”).
Regarding claim 11, it is the method embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1.
Regarding claim 12, it is the method embodiment of claim 9 with similar limitations to claim 9 and is rejected using the same reasoning found above in the rejection of claim 9.
Regarding claim 13, it is the resin composition production system of claims 1 and 9 with similar limitations to claims 1 and 9 and is rejected using the same reasoning found above in the rejection of claims 1 and 9. Further, Fujishima teaches a resin composition production system (Fujishima [0020] “FIG. 2 is a table for explaining an example of target characteristic data that is input to a first machine learning model and includes a target characteristic value regarding a characteristic of a thermosetting resin composition” Fujishima provides a first machine learning model and includes a target characteristic value regarding a characteristic of a thermosetting resin composition, corresponding to a resin composition production system.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fujishima in view of Nakamura for the same reasons disclosed above in the rejection of claims 1 and 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURT NICHOLAS PRESSLY whose telephone number is (703)756-4639. The examiner can normally be reached M-F 8-4.
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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