DETAILED ACTION
Claims 1-5 and 7 are pending.
Claim 6 is canceled.
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 .
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.
Claims 1-5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Griffin, James M., et al. "Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools." The International Journal of Advanced Manufacturing Technology 116.1 (2021): 579-596 (hereinafter Griffin) in view of You, Changyu. An artificial intelligence model to simulate strip flatness in a tandem cold rolling mill. Diss. University of Wollongong, 2008. (hereinafter You).
Regarding claims 1-5 and 7, Griffin teaches
Claim 1. A quality abnormality factor analysis support system comprising: data storage circuitry; quality abnormality factor estimation/calculation circuitry; and display information generation circuitry, wherein the data storage circuitry configured to store manufacturing conditions for products manufactured in a sheet metal plant, setting conditions for facilities and equipment installed in the sheet metal plant, and time-series data at time of manufacture as record data acquired from measurement equipment installed in the sheet metal plant; the quality abnormality factor estimation/calculation circuitry configured to execute calculating quality evaluation values from quality data about product qualities of the products selected from the time-series data at the time of manufacture, regarding such products or a product group that the calculated quality evaluation values exceed a tolerance as abnormal products, obtaining quality abnormality positions of the abnormal products at which the tolerance is exceeded (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN); registering the time-series data, the manufacturing conditions, and the setting conditions about a normal product group that the quality evaluation values are within the tolerance as normal data, constructing a quality abnormality factor estimation model based on the time- series data registered as the normal data or based on the manufacturing conditions and the setting conditions registered as the normal data, the quality abnormality factor estimation model using machine learning or a statistical method, estimating factor candidates of the quality abnormality from the time-series data or from the manufacturing conditions and the setting conditions, based on the constructed quality abnormality factor estimation model, and calculating degrees of relationship of the estimated factor candidates with the quality abnormality; and the display information generation circuitry configured to generate unit generating information for displaying, on a display, the quality data of the abnormal products, the quality abnormality positions, the time-series data, or the manufacturing conditions and the setting conditions, and the factor candidates and the degrees of relationship estimated and calculated by the quality abnormality factor estimation/calculation circuitry (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN).
Claim 2. The quality abnormality factor analysis support system according to claim 1, wherein the display information generation circuitry generates information for displaying the manufacturing conditions, the setting conditions, the quality evaluation values, and the quality abnormality positions, for the abnormal products, in a list (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN).
Claim 3. The quality abnormality factor analysis support system according to claim 1, wherein the display information generation circuitry generates information for displaying trend charts of the quality data of the abnormal products, a trend chart of the time- series data corresponding to the factor candidate with a highest degree of relationship among the factor candidates estimated by the quality abnormality factor estimation/calculation circuitry, and the quality abnormality positions in each of the trend charts (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN).
Claim 4. The quality abnormality factor analysis support system according to claim 1, wherein the display information generation circuitry generates information for displaying trend charts of the quality data of the abnormal products, the plurality of factor candidates estimated by the quality abnormality factor estimation/calculation circuitry, the degree of relationship of each factor candidate, a trend chart of the time-series data corresponding to each factor candidate, and the quality abnormality positions in each of the trend charts (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN).
Claim 5. The quality abnormality factor analysis support system according to claim 1, wherein the display information generation circuitry generates, for the abnormal products the quality data and quality abnormality positions of which are the same, information for displaying the setting conditions as the factor candidates and degrees of relationships of the setting conditions with the quality abnormality (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN).
Claim 7. The quality abnormality factor analysis support system according to claim 1, wherein the quality abnormality factor estimation/calculation circuitry reconstructs the quality abnormality factor estimation model each time each of the products is manufactured, and, when a predicted error of the reconstructed quality abnormality factor estimation model is smaller than a predicted error of the quality abnormality factor estimation model before the reconstruction, updates the quality abnormality factor estimation model (Abstract, Fig 5 and Fig 6 - - creating a model and determining how accuracy of the model in terms of abnormality classification, wherein equipment setting and conditions are considered. Different classes are utilized for classification purposes and acoustic sensors are used to measure galling. The accuracy of the prediction is determined using classification, CART, and NN).
Griffin fails to clearly specify quality control in a rolling plant.
However, You teaches quality control in a rolling mill,
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to quality control.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above machine learning control system, as taught by Griffin, and incorporating the concept of quality control in a rolling plant, as taught by You.
One of ordinary skill in the art would have been motivated to do this modification in order to provide improved flatness control in rolling processes , as suggested by You ( See Abstract).
Citation of Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Imanari et al., US Patent Application Publication No. 2021/0232131 relates to – abnormality determination in a sheet rolling system.
Giurgiu, Ioana, and Anika Schumann. "Additive explanations for anomalies detected from multivariate temporal data." Proceedings of the 28th acm international conference on information and knowledge management. 2019.
Mizan, Mubasshira, Laila Sumiya Khan Nilo, and Mosrika Momin Tuli. Transfer learning based industrial steel plates fault diagnosis using industrial fault signals. Diss. Brac University, 2021.
Chen, Zheyuan, et al. "Multi-faceted modelling for strip breakage in cold rolling using machine learning." International Journal of Production Research 59.21 (2021): 6347-6360.
Antwarg, Liat, et al. "Explaining anomalies detected by autoencoders using Shapley Additive Explanations." Expert systems with applications 186 (2021): 115736.
Conclusion
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/CARLOS R ORTIZ RODRIGUEZ/ Primary Examiner, Art Unit 2119
