DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1, & 3-5 have been presented for examination based on the application filed on 10/13/2025.
Claims 2, 6-11 are cancelled.
Claims 1, & 3-5 are amended.
Claim objection is presented for claim 1.
Claim Interpretation is presented for claim 1.
Claims 1, & 3-5 are rejected under 35 U.S.C. 101.
Claim(s) 1, & 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over JP 2019074969 A by NAKAHARA RYO et al., in view of US 20170297072 A1 by Kuyama; Shuji et al.
This action is made Final.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 1 is objected to because of multiple recitations of “and”. The connector “and” is recited after first recitation of “a storage step…”, “a step of fixing…”, “a step of changing… and wherein…”. The “and” in the subsequent indented step is appropriate. To correct this first recitation of “a storage step…”, & “a step of fixing…” should be removed.
Claim Interpretation
Claim 1 recites:
a step of fixing a manufacturing condition confirmed during manufacturing, and predicting quality of a metal material manufactured under the fixed manufacturing condition for each of predetermined areas by the quality prediction model;
The fixing is understood as proposed solution to be run in the prediction model so as to predict if the fixed manufacturing condition during manufacturing would be fixed. The claim does not disclose what is done to fix, and performs the predicting to fix manufacturing condition. Also what is considered as stopping condition or what is considered as fixed is not claimed. In view of above the limitation is broadly read as iterative prediction process to achieve claimed fixing broadly.
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Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.5-6:
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(Response 1) The performance gain is based on idea of solution (MPEP 2106.05(f)(1)) identified in the fixing step and correcting steps. The claims do not recite what is done (fixed) to attain the material gains. Its unclear whether the fixing is done for the training purposes or process improvement purpose. Further argument regarding the sensor-based acquisition, is extra-solution activity (MPEP 2106.05(g)) and can also be shown as WRC (under MPEP 2106.05(d)).
Further arguments made against prior art Nakahara are considered and new limitations are mapped with Nakahara and Kuyama to address these arguments. Kuyama addresses quality evaluation and estimation over entire length of the steep strip (as mapped & shown in Fig.2, 3, 5-7). Under new grounds of rejection, Examiner for at least this reason respectfully maintains the rejection.
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, 3-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 1 & 10:
Step 1: the claims 1 & 10 are drawn to a method and system respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: Taking claim 1 as representative, however analysis is applicable to claim 10 as well. The claim 1 limitations recite (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
1.(Original) A quality prediction model generation method for a metal material manufactured through one or more processes, the method comprising:
a first collection step of collecting a manufacturing condition of each of the processes for each of predetermined areas of the metal material, the manufacturing condition including an actual measured value collected by a sensor installed in each of the processes;
a second collection step of evaluating and collecting quality of the metal material manufactured through each process for each of the predetermined areas;
a storage step of storing the manufacturing condition of each process and the quality of the metal material manufactured under the manufacturing condition in association with each other for each of the predetermined areas;
and
a model generation step of generating a quality prediction model that predicts quality of the metal material for each of the predetermined areas based on the stored manufacturing condition for each of the predetermined areas in each process; and
a step of fixing a manufacturing condition confirmed during manufacturing, and
predicting quality of a metal material manufactured under the fixed manufacturing condition for each of predetermined areas by the quality prediction model; and
a step of changing the manufacturing condition of a subsequent process based on a predicted result such that the quality of the manufactured material in every predetermined area included over entire length of the metal material is within a predetermined control range,
wherein each of the predetermined areas is determined based on a travel distance of the metal material in a conveyance direction in each process
and
the storage step includes
converting the manufacturing condition and the quality collected in units of time into data in units of a length of the metal material and in a fixed cycle by performing interpolation to align granularity of the data, and
storing the converted manufacturing condition and the converted quality in association with each other for each of the predetermined areas
See Step 2A Prong 2 and 2B.
See Step 2A Prong 2 and 2B.
See Step 2A Prong 2 and 2B.
Abstract Idea/Mathematical Concept/Mental Process: The model generation step of generating a quality prediction model recites mathematical concept (as in MPEP 2106.04(a)(2)(I)(C)). The model is a mathematical model based on algorithm. See specification ¶[0036].
The prediction model as recited can also be a mental process based on observation (manufacturing condition) to form an opinion/judgement or evaluation (of quality of output). (See as in MPEP 2106.04(a)(2)(III)(A)).
Abstract Idea/Mathematical Concept/Mental Process: The fixing is considered as mental step based on observation (condition confirmed during manufacturing). This part of the step may also be rejected under Step 2A Prong 2. The predicting quality for each of predetermined area is mathematical concept as identified above in the model generation step. Performing iteratively for each predetermined area is exercise in repeat computation.
See Step 2A Prong 2 & 2B.
Abstract Idea/Mathematical Concept/Mental Process: The determination of predetermined area based on travel distance and then length determination, is a mathematical concept based on observed speed of travel. The converting aspect is also based on speed and evaluation for quality is based on mathematical model based on algorithm. See specification ¶[0036].
See Step 2A Prong 2 & 2B.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. That is, nothing in the claim element precludes the step from practically being performed in the mind or with the aid of pencil and paper but for the recitation of generic computer components. Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper.
Step 2A, Prong 2: In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
1.(Original) A quality prediction model generation method for a metal material manufactured through one or more processes, the method comprising:
a first collection step of collecting a manufacturing condition of each of the processes for each of predetermined areas of the metal material;
a second collection step of evaluating and collecting quality of the metal material manufactured through each process for each of the predetermined areas;
a storage step of storing the manufacturing condition of each process and the quality of the metal material manufactured under the manufacturing condition in association with each other for each of the predetermined areas; and
a model generation step of generating a quality prediction model that predicts quality of the metal material for each of the predetermined areas based on the stored manufacturing condition for each of the predetermined areas in each process
a step of fixing a manufacturing condition confirmed during manufacturing, and
predicting quality of a metal material manufactured under the fixed manufacturing condition for each of predetermined areas by the quality prediction model; and
a step of changing the manufacturing condition of a subsequent process based on a predicted result such that the quality of the manufactured material in every predetermined area included over entire length of the metal material is within a predetermined control range,
wherein each of the predetermined areas is determined based on a travel distance of the metal material in a conveyance direction in each process
and
the storage step includes
converting the manufacturing condition and the quality collected in units of time into data in units of a length of the metal material and in a fixed cycle by performing interpolation to align granularity of the data, and
storing the converted manufacturing condition and the converted quality in association with each other for each of the predetermined areas
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the this is mere data gathering.
Under MPEP 2106.05(g)/(f): At best, The use of generic computer component to perform storage with different types of datum as claimed is mere instruction to apply and extra-solution activity.
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
In this case the problem (generating a quality prediction model) only based on stored data without any restriction on how the data leads to the solution.
See Step 2A Prong 1 above.
Under MPEP 2106.05(f)(1) the fixing can be considered as idea of solution also as there are no steps disclosed how the fixing (presumably of the model to replicate the issue or to correct the issue) is performed to confirm the manufacturing condition.
Under MPEP 2106.05(g) this is considered as extra-solution/post-solution activity based on idea of solution. There is no details how and what is changed. For this reason this is also an field of use (being applied to metal sheet manufacturing).
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
MPEP 2106.05(g) this is considered as extra-solution/post-solution activity as no details of what and how it is stored for the converted condition.
The claim does not disclose any additional elements which integrate the abstract idea into practical application.
Step 2B: As discussed above with respect to integration of the abstract idea into a practical application, the elements also do not add significantly more to the abstract idea. Further, the preamble as claimed amount to generally linking the use of the judicial exception to a particular environment of field of use which does not integrate the judicial exception into a practical application or provide significantly more than the abstract idea (See MPEP 2106.05(h)) because the steps may lead to better algorithm to quality prediction model, but it does not indicate actual manufacturing is improved or the performance of manufacturing is improved based on the algorithm.
Further, even is sensor based acquisition is considered additional element adding significantly more, such is well-known, routine and conventional (WRC) as shown in US 20170297072 A1 by Kuyama; Shuji et al. at least for length of the sheet metal (See Fig.1, 5-7). The claim 1 is therefore considered to be patent ineligible.
Claims 3 recites comprising, before the storage step, a third collection step of collecting at least one or more of whether a leading end and a trailing end of the metal material have been interchanged in each process, whether a front face and a back face of the metal material have been interchanged in each process, and a cutting position of the metal material in each process, wherein the storage step identifies the predetermined areas by taking into account, on the metal material in each process, at least one or more of whether the leading end and the trailing end have been interchanged, whether the front face and the back face have been interchanged, and the cutting position, and stores the manufacturing condition of each process and the quality of the metal material produced under the manufacturing condition in association with each other for each of the predetermined areas generally is considered as extra-solution activity of data gathering and storage (using generic computer components as best) (See MPEP 2106.05(f)/(g)). The claims do not disclose any additional limitations that integrate the judicial exception into practical element or add significantly more.
Claims 4 recites wherein in a case where a shape of the metal material is deformed by going through each process, the storage step identifies the predetermined area by evaluating a volume of the metal material from the leading end, and stores the manufacturing condition of each process and the quality of the metal material manufactured under the manufacturing condition in association with each other for each of the predetermined areas is considered as extra-solution activity of specific type of data gathering and storage (using generic computer components as best) (See MPEP 2106.05(f)/(g)). No special mechanism to log the deformation is part of the claim. The claims do not disclose any additional limitations that integrate the judicial exception into practical element or add significantly more.
Claims 5 recites wherein the model generation step generates the quality prediction model using machine learning including linear regression, local regression, principal component regression, PLS regression, a neural network, a regression tree, a random forest, and XGBoost. This limitation add to the abstract idea/mathematical concept (See MPEP 2106.04(a)(2)(1)(C) and (III)) of using generic mathematical techniques/algorithms for generating the model. The claims do not disclose any additional limitations that integrate the judicial exception into practical element or add significantly more.
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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 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.
Claim(s) 1, & 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over JP 2019074969 A by NAKAHARA RYO et al., in view of US 20170297072 A1 by Kuyama; Shuji et al.
Regarding Claim 1
Nakahara teaches (Claim 1). A quality prediction model generation method for a metal material manufactured through one or more processes (Nakahara: Pg.3 §Overview ¶1-2) , the method comprising:
a first collection step of collecting a manufacturing condition of (Nakahara : Pg. 2 "... The data identification unit, the forecast target data acquisition unit for acquiring the operation performance data of the quality prediction target product, and the operation performance data as the explanatory variable and the quality performance data as the objective variable are acquired for the identified product for learning. For the learning data generation unit that generates learning data, and the operation result data of the product for learning..." Pg.5 § [3-1 . Learning data generation process) "... In the present embodiment, when constructing a quality prediction model for predicting the quality of the quality prediction target product, latest actual data of each quality prediction target product is used as learning data. That is, as shown in FIG. 4, the actual data of the coils of the first number of data manufactured within a predetermined period close to the manufacturing time of one quality prediction target product is acquired as learning data. Products manufactured within a predetermined period close to the manufacturing time of the quality prediction target product are manufactured under operating conditions and operating environments similar to the situation in which the quality prediction target product was manufactured. By using actual product data of such products as learning data, it is possible to construct a quality prediction model based on the operating conditions and operating environment similar to the situation in which the quality prediction target product was manufactured, and the accuracy of the quality prediction Can be enhanced...." Pg. 6 "... The component of the steel plate is a value whose representative value is determined for each charge of the steel making process, and for example, the representative value of the component content of C, Si, etc. is used. The operation conditions of the hot rolling process include, for example, a heating charging temperature, a roll cooling water temperature, and a finishing temperature, and the intermediate index obtained in the hot rolling process includes, for example, Ar3 transformation temperature and scale thickness.... The outside air temperature is input as an index that represents the operating environment. The outside temperature is assumed to be acquired at a predetermined timing (for example, every hour). ") each of the processes for each of predetermined areas of the metal material (Nakahara: Pg. 6 "... Next, the coordinate calculation unit 123 converts the coordinate position on the steel plate of the defect acquired by the quality record data acquisition unit 121 into the coordinate position on the steel plate in each process (S120). The coordinate position on the steel plate of the defect of the quality result data is represented on a different scale from the acquisition timing with the operation result data. For this reason, it is necessary to convert the coordinate position on the steel plate of the defect of quality performance data into the coordinate position at the acquisition timing of operation performance data. Coordinate conversion by the coordinate calculation unit 123 can be specified from the rolling conditions of the steel plate performed in each of the hot rolling process and the cold rolling process. In..." - the coordinates are predetermined areas for each of the processes (such as hot rolling process and cold rolling process) the manufacturing condition including an actual measured value collected (Nakahara: 6 ¶5 "... The learning data generation processing unit 125 acquires chart data such as finishing temperature and scale thickness from the operation result data storage unit 40 that stores chart data, and a representative value storage unit 50 that stores representative values. The representative values such as the inlet temperature, the roll cooling water temperature, the Ar3 transformation temperature, and the outside air temperature are acquired...." Pg. 6¶7 "... From the various operation result data and quality result data measured in each process, data for learning that can easily construct a quality prediction model is generated....");
a second collection step of evaluating and collecting quality of the metal material manufactured through each process for each of the predetermined areas (Nakahara: Pg. 2 "... The data identification unit, the forecast target data acquisition unit for acquiring the operation performance data of the quality prediction target product, and the operation performance data as the explanatory variable and the quality performance data as the objective variable are acquired for the identified product for learning. For the learning data generation unit that generates learning data, and the operation result data of the product for learning...") ;
a storage step of storing the manufacturing condition of each process and the quality of the metal material manufactured under the manufacturing condition in association with each other for each of the predetermined areas (Nakahara: Pg.4 "... The quality result data acquisition unit 121 acquires the coil No. for learning data. The quality record data used as the objective variable is acquired from the quality record data storage unit 20 based on the above... The quality performance data storage unit 20 stores the presence or absence of a defect on the surface of the steel plate, and the coordinate position on the steel plate if there is a defect, as an inspection result in the purification inspection step") ; and
a model generation step of generating a quality prediction model that predicts quality of the metal material for each of the predetermined areas based on the stored manufacturing condition for each of the predetermined areas in each process (Nakahara: Pg. 6 "... Next, among the learning data generated by the learning data generation unit 120, the learning data selection unit 133 selects learning data used to construct a quality prediction model. In the present embodiment, learning data is sorted by Just-In-Time modeling. Just-in-time modeling extracts the operation result data of the product highly relevant to the quality prediction target product as the neighborhood data each time the quality prediction is performed, builds a local model based on the neighborhood data, and makes the quality prediction target It is a modeling method to obtain the output of the product. By using operation performance data highly relevant to the quality prediction target product, even if operation performance data of a product manufactured within a predetermined period close to the production timing of the quality prediction target product is less than the first data number, A relatively accurate model can be built....") .
a step of fixing a manufacturing condition confirmed during manufacturing, and predicting quality of a metal material manufactured under the fixed manufacturing condition for each of predetermined areas by the quality prediction model (Nakahara: Pg.7 ¶8 "... The prediction process by the prediction unit 130 according to the present embodiment has been described above. The prediction process of FIG. 6 is repeatedly executed until quality prediction of all the coils of the quality prediction target product is finished, the quality prediction model is constructed for the coils of each quality prediction target product, and the occurrence probability of defects is calculated..."- here the repeatedly performing prediction until the target is achieved is performed in the prediction model) ; and
wherein each of the predetermined areas is determined based on a travel distance of the metal material in a conveyance direction in each process (Nakahara: Pg. 6 "... More specifically, when a defect appears along the
longitudinal direction of the steel plate, usually, in the refining inspection step, a predetermined inspection of the steel plate S in the longitudinal direction is performed as shown in FIG. Only the position is inspected for the presence of defects. The quality record data storage unit 20 stores the presence or absence of a defect on the surface of the steel plate at each inspection position, and if there is a defect, stores the coordinate position on the steel plate of the inspection position as an inspection result. The quality record data acquisition unit 121 outputs the coordinate position on the steel plate of the defect acquired from the quality record data storage unit 20 to the coordinate calculation unit 123...." – predetermined area as coordinates on the steel plate; Pg. 6 "... Next, the coordinate calculation unit 123 converts the coordinate position on the steel plate of the defect acquired by the quality record data acquisition unit 121 into the coordinate position on the steel plate in each process (S120). The coordinate position on the steel plate of the defect of the quality result data is represented on a different scale from the acquisition timing with the operation result data. For this reason, it is necessary to convert the coordinate position on the steel plate of the defect of quality performance data into the coordinate position at the acquisition timing of operation performance data. Coordinate conversion by the coordinate calculation unit 123 can be specified from the rolling conditions of the steel plate performed in each of the hot rolling process and the cold rolling process. In..." ), and
the storage step includes
storing the (Nakahara: Pg.4 "... The quality result data acquisition unit 121 acquires the coil No. for learning data. The quality record data used as the objective variable is acquired from the quality record data storage unit 20 based on the above... The quality performance data storage unit 20 stores the presence or absence of a defect on the surface of the steel plate, and the coordinate position on the steel plate if there is a defect, as an inspection result in the purification inspection step").
Nakahara does not explicitly teach the limitation crossed out above. Specifically changing step and how the data is stored.
Kuyama teaches the manufacturing condition including an actual measured value collected by a sensor installed in each of the processes (Kuyama: [0037] "... This actual value collecting device 5 is connected, via the transmission bus 7, to the measuring devices in the manufacturing process 100, such as the component measuring device 101 of the refining process 10, the thickness meter 111 of the casting process 11, the thermometer 123 of the reheating process 12, the thickness and width meters 133 and 135 and the measuring roll 137 of the rolling process 13, the thermometers 143, 145, and 147 of the cooling process 14, and the tester 151 of the testing process 15, which have been described above...." sensor as measuring device).
Kuyama also (already mapped to Nakahara above) teaches a step of fixing a manufacturing condition confirmed during manufacturing (Kuyama: Fig.4 step a20 [0062]-[0063] where the correction term represents fixing a manufacturing condition; [0062] "... Specifically, by using the property estimating unit 23 as an inversion calculation unit to calculate manufacturing conditions from a given material-property-value, the manufacturing condition determining unit 28 calculates manufacturing conditions, by which the material properties match required specifications, and adds a difference between the calculated manufacturing conditions and the set manufacturing conditions, as a set correction term of the manufacturing conditions of the steel-strip product planned to be manufactured next, to the manufacturing conditions...") , and predicting quality of a metal material manufactured under the fixed manufacturing condition for each of predetermined areas by the quality prediction model (Kuyama: [0062]-[0063]. [0067]-[0068], see Nakahara teaching predicting being a iterative process also).
Kuyama teaches a step of changing the manufacturing condition of a subsequent process based on a predicted result (Kuyama: [0062]-[0063], [0070] correction to the process, followed by threshold processing [0069] to achieve "... the decrease in the yield is able to be reduced..." ) such that the quality of the manufactured material in every predetermined area included over entire length of the metal material is within a predetermined control range (Kuyama: See Figs. 5-7 [0068]"... Thereby, material properties of a steel-strip product are able to be estimated accurately over the entire region thereof...").
Kuyama teaches the storage step includes
converting the manufacturing condition and the quality collected in units of time into data in units of a length of the metal material (Kuyama : [0023]-[0028] show the data measurements are made in time and then storeed in form of length elements as shown in Fig.2 - showing the “Manufacturing Conditions” stored in units of length (“Mesh No” column with values for each element in length e.g. P01_001) associated with quality (Chemical composition, thickness, width, temperature) of each unit (e.g. P01_001)
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for entire length (Fig.3 below), associated with each element (such as P1_001):
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and in a fixed cycle by performing interpolation to align granularity of the data (Kuyama: [0058] showing interpolation for any given cycle as in Fig.4) , and
storing the converted manufacturing condition and the converted quality in association with each other for each of the predetermined areas (Kuyama: Fig.2).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuyama to Nakahara to determine and use the cutting position also as input datum to the quality assessment/prediction process/model to better model the process (Kuyama: [0061][0003]; Nakahara: Pg.3). Further motivation to combine would have been that Kuyama & Nakahara are analogous art in the field of monitoring steel strip manufacturing and modeling (Kuyama: [0061][0003]
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; & Nakahara: Pg.3).
Regarding Claim 3
Nakahara teaches comprising, (Nakahara: Pg.3 "... Further, as in the conventional case, also in the case of predicting the occurrence of defects after completion of the hot rolling step which is the cause step and after completion of the refining inspection step, if there is a defect portion predicted to have a defect The defective portion can be cut from the steel plate and can be shipped. The plated steel plate from which the defect portion has been cut is re-inspected and shipped as a product if no defect is detected. As described above, the quality prediction apparatus according to the present embodiment can predict the occurrence of a defect even before the defect becomes apparent....") – as this is apparent the defect location determination is important for cutting to happen – hence the location where the cut would need to happen has to stored and collection of such data would also be important).
Nakahara does not specifically teach a third collection step of collecting at least one or more of whether a leading end and a trailing end of the metal material have been interchanged in each process, whether a front face and a back face of the metal material have been interchanged in each process, and a cutting position of the metal material in each process.
Kuyama teaches a third collection step of collecting at least one or more of whether a leading end and a trailing end of the metal material have been interchanged in each process, whether a front face and a back face of the metal material have been interchanged in each process, and a cutting position of the metal material in each process (Kuyama: [0003] "... In general, since the quality of a steel-strip product is not stable at end portions thereof, the quality of the whole product is maintained by cut-off positions being determined from results of the quality assessment and the end portions being cut off...."); [0061] "... [0061] Subsequently, the cut-off position determining unit 27 determines a cut-off position of the target steel-strip product (Step a19). Specifically, the cut-off position determining unit 27 firstly performs threshold processing of the property value of each mesh of the target steel-strip product by using, as a threshold, a permissible value of material-property-value defined according to required quality in advance, and distinguishes between a qualified portion where the material properties are not less than a threshold, and a disqualified portion. The cut-off position determining unit 27 then determines a boundary between the distinguished qualified portion and disqualified portion as the cut-off position. The cut-off position determining unit 27 transmits information related to the determined cut-off position to a steel-strip cutting device 152 in the testing process 15. The steel-strip cutting device 152 uncoils the steel-strip arranged at an uncoiler 153 up to the cut-off position by use of the uncoiler 153. The steel-strip cutting device 152 cuts the steel-strip at the cut-off position by transmitting a cut-off order command to a cutter 154....") .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuyama to Nakahara to determine and use the cutting position also as input datum to the quality assessment/prediction process/model to better model the process (Kuyama: [0061][0003]; Nakahara: Pg.3). Further motivation to combine would have been that Kuyama & Nakahara are analogous art in the field of monitoring steel strip manufacturing and modeling (Kuyama: [0061][0003]; Fig.1 & Nakahara Fig.3).
Regarding Claim 4 (Updated 1/6/2025)
Nakahara teaches the method according to claim 1, wherein in a case where a shape of the metal material is deformed by going through each process (Nakahara: Pg.3 ¶Overview ¶3 Fig.1 "... More specifically, as shown in FIG. 1, the plated steel sheet is manufactured through a hot rolling process, a cold rolling process, and a plating process, and the quality of the manufactured product is inspected in the purification inspection process....") , the storage step identifies the predetermined area by evaluating a volume of the metal material from the leading end (Nakahara: Pg. 6 ¶5 "... The scale thickness is a value calculated according to the composition of the steel plate, the finishing temperature, the time, the oxidation rate coefficient, and the like.
Therefore, the scale thickness is also set to different values in the sheet width direction at each position in the longitudinal direction of the steel sheet. The..." – thickness as representation of area) , and stores the manufacturing condition of each process and the quality of the metal material manufactured under the manufacturing condition in association with each other for each of the predetermined areas (Nakahara: Pg. 6 ¶5 "... The learning data generation processing unit 125 acquires chart data such as finishing temperature and scale thickness from the operation result data storage unit 40 that stores chart data, and a representative value storage unit 50 that stores representative values. The representative values such as the inlet temperature, the roll cooling water temperature, the Ar3 transformation temperature, and the outside air temperature are acquired....") .
Regarding Claim 5 (Updated 1/6/2025)
Nakahara teaches the method according to claim 1, wherein the model generation step generates the quality prediction model using machine learning including linear regression, local regression, principal component regression, PLS regression, a neural network, a regression tree, a random forest, and XGBoost (Nakahara: Pg.7 ¶5"... Thereafter, the prediction model construction unit 135 constructs a quality prediction model based on the learning data selected by the learning data selection unit 133 (S250). In the present embodiment, a quality prediction model is constructed using random forest. The random forest is one of the ensemble learning methods, and is a method performed using a plurality of decision trees which calculate the probability belonging to the objective variable by a combination of a plurality of explanatory variables. For example, ten explanatory variables are randomly selected from the fifty explanatory variables to construct a plurality of decision trees....").
Relevant Prior Art
US PGPUB No. 20090083206 A1 by Shigemori; Hiroyasu (Inventor) shows the prediction model constructing means determines a parameter of a prediction model corresponding to the manufacturing condition of the prediction target. This prior art may be used in future rejections as well.
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Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Tuesday, January 6, 2026