DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/21/2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is 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 use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “a temperature type selection means” in claim 1.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. [Lee] (US PGPub 2025/0005451) in view of Nishiuchi et al. [Nishiuchi] (US PGPub 2023/0041536).
As to claim 1
Lee discloses a material data processing device (composition search system 100, see Fig. 1) using a computer (computer; see paragraph 0074, line 4), comprising:
a regression model creation processing unit (learning device 110/learning unit 112, see Fig. 1) that performs machine learning using, out of process data including information on manufacturing conditions for manufacturing individual samples, composition data including information on composition of the individual samples, characteristics data including information on characteristics of the individual samples (training data; see paragraph 0060, line 2) (see paragraph 0060, lines 1-8), and creates a regression model (prediction model/learned model; see paragraph 0060, lines 1-2) representing a correlation between respective data (see paragraph 0060, lines 5-8);
an estimation processing unit (predicting device 120/predicting unit 122, see Fig. 1) that estimates, by using the regression model, the process data, the composition data, or the characteristics data, having been used for machine learning (see paragraphs 0064 and 0065).
Though Lee discloses the material data processing device comprising the learning device and prediction device for using the prediction model to estimate the data; Lee fails to specifically disclose the data to include microstructure data including information on microstructure of the individual samples, two or more data including the microstructure data. Further, Lee fails to specifically disclose estimating the data having been used for machine learning, wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during heating, and a feature amount based on a magnetization temperature dependence during cooling. Lee also fails to specifically disclose the material data processing device comprising: a temperature type selection means that selects use of either one or both of the feature amount based on the magnetization temperature dependence during heating or the feature amount based on the magnetization temperature dependence during cooling as the structure data to be used for the machine learning.
Nishiuchi discloses a material data processing device (material data processing system 1000, see Fig. 17) creating a regression model (math model; see Fig. 17) representing a correlation between data (composition data, processing data, microstructure data, property data; see Fig. 17) including microstructure data (microstructure data) including information on microstructure of individual samples (sample 12, see Fig. 2) (see paragraph 0138, lines 5-11). Nishiuchi also discloses estimating, by using the resgression model, the data wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during cooling (temperature-decreasing period) (see paragraph 0117, lines 1-8 and paragraph 0138, lines 1-11). Further, Nishiuchi discloses the material data processing device comprising: a temperature type selection means (data processing device 200, see Fig. 17) that selects use of either one or both of the feature amount based on the magnetization temperature dependence during heating or the feature amount based on the magnetization temperature dependence during cooling as the structure data to be used for the machine learning (see paragraph 0109, lines 1-21).
Lee and Nishiuchi are analogous art because they are from the same field of endeavor which is material processing. At the time of the invention it would have been obvious to one of ordinary skill in the art to modify Lee’s invention with Nishiuchi’s in order to use machine learning to estimate data including complex microstructure data, since doing so would help better achieve desired material properties (see Nishiuchi paragraph 0002).
As to claim 2
Lee discloses the material data processing device according to claim 1, wherein the regression model creation processing unit creates the regression model with the characteristics data as objective variable data and data other than the characteristics data as explanatory variable data (see paragraph 0060, lines 1-8).
As to claim 3
Nishiuchi discloses the material data processing device according to claim 1, further comprising:
a feature amount extraction processing unit extracts, based on measured data of the individual samples, the feature amount based on the magnetization temperature dependence during heating and the feature amount based on the magnetization temperature dependence during cooling,
wherein the feature amount extraction processing unit divides the measured data into measured data during heating and measured data during cooling, and extracts the feature amount based on the magnetization temperature dependence during heating from the measured data during heating and the feature amount based on the magnetization temperature dependence during cooling from the measured data during cooling (see paragraphs 0067-0069).
As to claim 4
Nishiuchi discloses the material data processing device according to claim 1, wherein the feature amount based on the magnetization temperature dependence is a feature amount related to magnetic phase transition (see paragraph 0064, lines 1-12).
As to claim 5
Nishiuchi discloses the material data processing device according to claim 1, wherein the feature amount based on the magnetization temperature dependence includes at least one of Curie temperature and Neel temperature (see paragraph 0011, lines 1-3).
As to claim 6
Nishiuchi discloses the material data processing device according to claim 1, wherein the composition data includes the types of elements contained in the individual samples and composition ratios of the elements, and the process data includes parameters defining heat treatment conditions (see paragraph 0012, lines 1-6).
As to claim 7
Nishiuchi discloses the material data processing device according to claim 1, wherein the characteristics data includes at least one of residual flux density, coercive force, saturation magnetization, and magnetic permeability (see paragraph 0013, lines 1-5).
As to claim 8
Nishiuchi discloses the material data processing device according to claim 1, wherein the microstructure data includes a parameter defining a crystal structure of a main phase (see paragraph 0014, lines 1-3).
As to claim 9
Lee discloses a computer-performed material data processing method, comprising:
performing machine learning using, out of process data including information on manufacturing conditions for manufacturing individual samples, composition data including information on composition of the individual samples, characteristics data including information on characteristics of the individual samples (training data; see paragraph 0060, line 2) (see paragraph 0060, lines 1-8), and creating a regression model (prediction model/learned model; see paragraph 0060, lines 1-2) representing a correlation between respective data (see paragraph 0060, lines 5-8);
estimating, by using the regression model, the process data, the composition data, or the characteristics data, having been used for machine learning (see paragraphs 0064 and 0065).
Though Lee discloses the computer-performed material data processing method comprising using the prediction model to estimate the data; Lee fails to specifically disclose the data to include microstructure data including information on microstructure of the individual samples, two or more data including the microstructure data. Further, Lee fails to specifically disclose estimating the data having been used for machine learning, wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during heating, and a feature amount based on a magnetization temperature dependence during cooling. Lee also fails to specifically disclose the computer-performed material data processing method comprising: selecting use of either one or both of the feature amount based on the magnetization temperature dependence during heating or the feature amount based on the magnetization temperature dependence during cooling as the structure data to be used for the machine learning.
Nishiuchi discloses a computer-performed material data processing method creating a regression model (math model; see Fig. 17) representing a correlation between data (composition data, processing data, microstructure data, property data; see Fig. 17) including microstructure data (microstructure data) including information on microstructure of individual samples (sample 12, see Fig. 2) (see paragraph 0138, lines 5-11). Nishiuchi also discloses estimating, by using the resgression model, the data wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during cooling (temperature-decreasing period) (see paragraph 0117, lines 1-8 and paragraph 0138, lines 1-11). Further, Nishiuchi discloses the computer-performed material data processing method comprising selecting use of either one or both of the feature amount based on the magnetization temperature dependence during heating or the feature amount based on the magnetization temperature dependence during cooling as the structure data to be used for the machine learning (see paragraph 0109, lines 1-21).
Lee and Nishiuchi are analogous art because they are from the same field of endeavor which is material processing. At the time of the invention it would have been obvious to one of ordinary skill in the art to modify Lee’s invention with Nishiuchi’s in order to use machine learning to estimate data including complex microstructure data, since doing so would help better achieve desired material properties (see Nishiuchi paragraph 0002).
As to claim 10
Lee discloses a material data processing device (composition search system 100, see Fig. 1) using a computer (computer; see paragraph 0074, line 4), comprising:
a regression model creation processing unit (learning device 110/learning unit 112, see Fig. 1) that performs machine learning using, out of process data including information on manufacturing conditions for manufacturing individual samples, composition data including information on composition of the individual samples, characteristics data including information on characteristics of the individual samples (training data; see paragraph 0060, line 2) (see paragraph 0060, lines 1-8), and creates a regression model (prediction model/learned model; see paragraph 0060, lines 1-2) representing a correlation between respective data (see paragraph 0060, lines 5-8); and
an estimation processing unit (predicting device 120/predicting unit 122, see Fig. 1) that estimates, by using the regression model, the process data, the composition data, or the characteristics data, having been used for machine learning (see paragraphs 0064 and 0065).
Though Lee discloses the material data processing device comprising the learning device and prediction device for using the prediction model to estimate the data; Lee fails to specifically disclose the data to include microstructure data including information on microstructure of the individual samples, two or more data including the microstructure data. Further, Lee fails to specifically disclose estimating the data having been used for machine learning, wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during heating, and a feature amount based on a magnetization temperature dependence during cooling.
Nishiuchi discloses a material data processing device (material data processing system 1000, see Fig. 17) creating a regression model (math model; see Fig. 17) representing a correlation between data (composition data, processing data, microstructure data, property data; see Fig. 17) including microstructure data (microstructure data) including information on microstructure of individual samples (sample 12, see Fig. 2) (see paragraph 0138, lines 5-11). Nishiuchi also discloses estimating, by using the resgression model, the data wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during cooling (temperature-decreasing period) (see paragraph 0117, lines 1-8 and paragraph 0138, lines 1-11).
Lee and Nishiuchi are analogous art because they are from the same field of endeavor which is material processing. At the time of the invention it would have been obvious to one of ordinary skill in the art to modify Lee’s invention with Nishiuchi’s in order to use machine learning to estimate data including complex microstructure data, since doing so would help better achieve desired material properties (see Nishiuchi paragraph 0002).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J. Brown whose telephone number is (571)272-5932. The examiner can normally be reached Monday-Thursday from 5:30am-4:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Brown/
Primary Examiner, Art Unit 2115
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