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 .
This action is in response to communications filed on 08/30/2023. Claims 1-10 are pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was filed on 08/30/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted was filed on 03/31/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (e.g. in paragraph 50). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 1, 3, and 6-10 are objected to because of the following informalities:
As per claim 1, it appears that e.g. “the material characteristics method” should be inserted before “comprising:” in line 2. It appears that the comma should be removed from line 5 (this similarly applies to claims 6 and 7). It appears that the comma should be removed from line 8 (this similarly applies to claims 6 and 7). It appears that the comma should be removed from line 10 (this similarly applies to claims 6 and 7). It appears that the comma should be removed from line 15 (this similarly applies to claims 6 and 7).
As per claim 3, it appears that a colon should be inserted after “wherein” in line 2. It appears that the commas should be removed from the 2nd to last line.
As per claim 6, it appears that “the computer to a prediction process” in line 3 should be replaced with “the computer to perform a prediction process”.
As per claim 7, it appears that e.g. “the material characteristics prediction device” should be inserted before “comprising:” in line 2. It appears that a colon should be inserted after “including” in line 4 (this similarly applies to claim 10).
As per claim 8, it appears that the commas in lines 10, 12, and 16 should be removed. This similarly applies to claims 9 and 10.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2 and 6-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iwamura (US 20200024712 A1).
As per independent claim 1, Iwamura teaches a material characteristics prediction method for predicting material characteristics of a target material, comprising:
setting a trained model acquired by machine learning of a correspondence relationship between an explanatory variable including information related to a material composition or a manufacturing condition of the target material, and an objective variable including information related to the material characteristics of the target material (e.g. in paragraphs 8, 27, and 28, “data obtaining section configured to obtain a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product [i.e. explanatory variable]; and a neural network…configured to (a) receive the plurality of parameters as input data supplied… output value which is outputted by the neural network 112 which has been trained…a property value of an aluminum product [i.e. objective variable]. Note that the property value can be any of a continuous value (e.g., a strength value), a discrete value (e.g., a value indicative of a quality or a grade), and a binary number of 0/1 (e.g., a value indicative of presence or absence of a defect)… learning data set 121 is data for use in learning carried out by the neural network 112 and includes a plurality of pieces of learning data in which a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product and respective property values of the aluminum product manufactured under those manufacturing conditions are paired”); and
predicting, including inputting an explanatory variable related to a target material whose material characteristics is to be predicted to the trained model set in the setting, and outputting an objective variable related to information of the explanatory variable, thereby predicting the material characteristics of the target material to be predicted based on the objective variable (e.g. in paragraphs 8, 27, and 28, “a property predicting device configured to output a property value indicative of a property of a product which has been manufactured under given manufacturing conditions… a neural network…configured to (a) receive the plurality of parameters as input data supplied to the input layer and (b) supply, from the output layer, a property value of the aluminum product which has been manufactured under the manufacturing conditions indicated by the plurality of parameter”),
wherein the explanatory variable further includes information related to at least one of a material characteristics evaluation temperature that is a temperature at a time of measurement of the material characteristics included in the objective variable, and an evaluation temperature holding time that is a time during which the material characteristics evaluation temperature is held until the measurement of the material characteristics (e.g. in paragraphs 62-68, “parameter indicative of a processing heat history during a manufacturing process include a parameter indicative of a temperature… a holding temperature, a holding time”).
As per claim 2, the rejection of claim 1 is incorporated and Iwamura further teaches wherein the trained model is a neural network (e.g. in paragraph 8 and 28, “a neural network”).
Claim 6 is the medium claim corresponding to method claim 1 and is rejected under the same reasons set forth and Iwamura further teaches a non-transitory computer-readable storage medium having stored therein a material characteristics prediction program for predicting which, when executed by a computer, causes the computer to a prediction process to predict material characteristics of a target material (e.g. in paragraphs 8 and 131, “property predicting device 1 includes a CPU that executes instructions of a program that is software realizing the foregoing functions; a read only memory (ROM) or a storage device (each referred to as a “storage medium”) in which the program and various kinds of data are stored so as to be readable by a computer (or a CPU); and a random access memory (RAM) in which the program is loaded”).
Claim 7 is the device claim corresponding to method claim 1 and is rejected under the same reasons set forth and Iwamura further teaches a storage device configured to store a program and a processor configured to execute the program and perform a process (e.g. in paragraphs 8 and 130-131, “property predicting device 1 includes a CPU that executes instructions of a program that is software realizing the foregoing functions; a read only memory (ROM) or a storage device (each referred to as a “storage medium”) in which the program and various kinds of data are stored so as to be readable by a computer (or a CPU); and a random access memory (RAM) in which the program is loaded”).
As per independent claim 8, Iwamura teaches a model generation method for generating a model for predicting material characteristics of a target material, the model generation method comprising:
creating a training data set including information related to a material composition or a manufacturing condition of the target material, material characteristics, and a measurement condition at a time when the material characteristics are measured (e.g. in paragraphs 25, 27-28, 51-52, 62, and 68, “learning data set 121 is data for use in learning carried out by the neural network 112 and includes a plurality of pieces of learning data in which a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product and respective property values of the aluminum product manufactured under those manufacturing conditions are paired… measure a property value during generation of learning data, a property value is preferably easy to measure for many aluminum products… tensile strength, proof stress, fracture toughness… a parameter indicative of a processing heat history during a manufacturing process include a parameter indicative of a temperature, a parameter indicative of a degree of processing, and a processing time… a holding temperature, a holding time”); and
generating a trained model by performing machine learning so that an input-output relationship of the model approaches an input-output relationship of the training data set, using the training data set created in the creating, by regarding the information related to the material composition or the manufacturing condition and the measurement condition as an input of the model, and information related to the material characteristics as an output of the model (e.g. in paragraphs 8, 27-28, and 60, “data obtaining section configured to obtain a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product [i.e. input]; and a neural network…configured to (a) receive the plurality of parameters as input data supplied… output value which is outputted by the neural network 112 which has been trained…a property value of an aluminum product [i.e. output]. Note that the property value can be any of a continuous value (e.g., a strength value), a discrete value (e.g., a value indicative of a quality or a grade), and a binary number of 0/1 (e.g., a value indicative of presence or absence of a defect)… learning data set 121 is data for use in learning carried out by the neural network 112 and includes a plurality of pieces of learning data in which a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product and respective property values of the aluminum product manufactured under those manufacturing conditions are paired [i.e. relationship]… learning and prediction with higher accuracy”, i.e. model learns the relationships of, i.e. approaches, the training data),
wherein the measurement condition includes at least one of a material characteristics evaluation temperature that is a temperature at a time of measurement of the material characteristics included in the output of the model, and an evaluation temperature holding time that is a time during which the material characteristics evaluation temperature is held until the measurement of the material characteristics (e.g. in paragraphs 62-68, “parameter indicative of a processing heat history during a manufacturing process include a parameter indicative of a temperature… a holding temperature, a holding time”).
Claim 9 is the medium claim corresponding to method claim 1 and is rejected under the same reasons set forth and Iwamura further teaches a non-transitory computer-readable storage medium having stored therein a material generation program which, when executed by a computer, causes the computer to perform a model generation process to generate a model for predicting material characteristics of a target material (e.g. in paragraphs 8, 25, and 131, “neural network 112 to carry out learning… property predicting device 1 includes a CPU that executes instructions of a program that is software realizing the foregoing functions; a read only memory (ROM) or a storage device (each referred to as a “storage medium”) in which the program and various kinds of data are stored so as to be readable by a computer (or a CPU); and a random access memory (RAM) in which the program is loaded”).
Claim 10 is the device claim corresponding to method claim 1 and is rejected under the same reasons set forth and Iwamura further teaches a storage device configured to store a program and a processor configured to execute the program and perform a process (e.g. in paragraphs 8 and 130-131, “property predicting device 1 includes a CPU that executes instructions of a program that is software realizing the foregoing functions; a read only memory (ROM) or a storage device (each referred to as a “storage medium”) in which the program and various kinds of data are stored so as to be readable by a computer (or a CPU); and a random access memory (RAM) in which the program is loaded”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Iwamura (US 20200024712 A1) in view of Chung et al. (US 6245265 B1).
As per claim 3, the rejection of claim 2 is incorporated and Iwamura further teaches wherein the material characteristics predicted in the predicting are characteristics of a metal material, a polymer material, or a glass material that vary depending on the material characteristics evaluation temperature or the evaluation temperature holding time (e.g. in paragraphs 8, 28, and 68, “a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product and respective property values of the aluminum product manufactured under those manufacturing conditions are paired… a parameter indicative of a processing heat history during a solution heat treatment step include…a holding temperature, a holding time”), and an S-shaped function, including a hyperbolic tangent function or a sigmoid function, is used as an activation function of the neural network (e.g. in paragraph 39, “activating function f can be any function that is exemplified by a sigmoid function”),
but does not specifically teach the material characteristic varying in an S-shape.
However, Chung teaches a material characteristic varying in an S-shape depending on a condition (e.g. in column 7 lines 44-63, “plot of Dimension Change (micrometers/.degree. C.) as a function of Temperature (.degree. C.) is shown in FIG. 7 for one epoxy adhesive of the present invention, the S-shaped curve A being the actual data”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Iwamura to include the teachings of Chung because one of ordinary skill in the art would have recognized the benefit of incorporated well-known shapes and/or accounting for actual data.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Iwamura (US 20200024712 A1) in view of Regani et al. (US 20200064444 A1).
As per claim 4, the rejection of claim 1 is incorporated, but Iwamura does not specifically teach wherein a kernel method is applied as a machine learning method of the trained model. However, Regani teaches a kernel method is applied as a machine learning method of a model (e.g. in paragraphs 179, 243, and 310, “trained using a dimension reduction method based on the training TSCI… PCA with different kernel… inverse sine transform… Support Vector Machine (SVM): SVM technique… use a “kernel-trick””). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Iwamura to include the teachings of Regani because one of ordinary skill in the art would have recognized the benefit of handling non-linearly separable data and/or incorporating well-known machines learning methods (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Iwamura (US 20200024712 A1) in view of Regani et al. (US 20200064444 A1) as applied above and further in view of Chung et al. (US 6245265 B1).
As per claim 5, the rejection of claim 4 is incorporated and the combination further teaches wherein the material characteristics predicted in the prediction step predicting are characteristics of a metal material, a polymer material, or a glass material that vary depending on the material characteristics evaluation temperature or the evaluation temperature holding time (e.g. Iwamura, in paragraphs 8, 28, and 68, “a plurality of parameters indicative of manufacturing conditions under which to manufacture an aluminum product and respective property values of the aluminum product manufactured under those manufacturing conditions are paired… a parameter indicative of a processing heat history during a solution heat treatment step include…a holding temperature, a holding time”), and an inverse sine function is used as a kernel function of the kernel method (e.g. Regani, in paragraphs 179, 243, and 310, “trained using a dimension reduction method based on the training TSCI… PCA with different kernel… inverse sine transform… Support Vector Machine (SVM): SVM technique… use a “kernel-trick””),
but does not specifically teach the material characteristic varying in an S-shape.
However, Chung teaches a material characteristic varying in an S-shape depending on a condition (e.g. in column 7 lines 44-63, “plot of Dimension Change (micrometers/.degree. C.) as a function of Temperature (.degree. C.) is shown in FIG. 7 for one epoxy adhesive of the present invention, the S-shaped curve A being the actual data”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Iwamura to include the teachings of Chung because one of ordinary skill in the art would have recognized the benefit of incorporated well-known shapes and/or accounting for actual data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Steingrimsson et al. (US 20200257933 A1) teaches “application of machine learning for identification of alloys or composites with desired properties of interest. For each output property of interest, we identify the corresponding driving (input) factors. These input factors may include the material composition, heat treatment… predicting the ultimate tensile strength, a model that accounts for physical dependencies, and factors in the underlying physics as a priori information. In case an artificial neural network is deemed suitable, we suggest employing custom kernel functions” (e.g. in abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.W/Examiner, Art Unit 2144
/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144 06/26/2026
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