CTNF 18/688,913 CTNF 93234 DETAILED ACTION Notice of AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-8 are rejected. Priority PCT: The current application is a 371 of the PCT application no. PCT/JP2023/010690 filled on 03/17/2023. Information Disclosure Statement The information disclosure statements (IDS) submitted on 03/04/2024 and 03/10/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner. Drawings Drawings filled on 03/04/2024 are acceptable for the examination purpose. Preliminary Amendment Preliminary amendments to claim 7 and amendments to specification are acknowledged and are being fully considered by the examiner. Specification The abstract of the disclosure is objected to because it exceeds the 150 word limit and contains total of 151 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed is directed to an abstract idea without significantly more. Step 1: Claims 1-8 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). Step 2A: The claims 1-8 fall within the judicial exception of an abstract idea. Specifically, Mental Processes such that concepts performed in the human mind or with pen and paper including observation, evaluation, judgment, opinion, and determination, and/or Mathematical Concepts such that mathematical relationships, and mathematical calculations etc. Step 2A – Prong 1: Claim 1: generate, based on the first set of operation data, a first feature map for a first target feature, and generate, based on the second set of operation data, a second feature map for the first target feature; calculate a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map; assign, based on the normalized cross-correlation result, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction, and select, from among the set of target features, a subset of target features that achieve a ranking threshold; generate, based on the subset of target features, a state prediction result that characterizes a performance difference of the second processing chamber with respect to the first processing chamber. Claim limitations describe, generating feature map, assigning ranking, selecting, and generating result. These limitations given their broadest reasonable interpretation in light of the specification is a mental process since this a concept that can be performed in the human mind and is an observation, evaluation, judgment, and determination. Further claim limitations describe, generating feature map, calculating correlation, assigning ranking. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships and mathematical calculations. Claim 2: calculate a cross-correlation result between the first feature map and the second feature map; calculate a first autocorrelation result for the first feature map; calculate a second autocorrelation result for the second feature map; generate the normalized cross-correlation result by normalizing the cross-correlation result based on a first maximum autocorrelation value from the first autocorrelation result and a second maximum autocorrelation value from the second autocorrelation result; and generate a cross-correlation graph of the normalized cross-correlation result. Claim limitations describe, calculations, and generating correlation result and graph. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships and mathematical calculations. Claim 3: identify a full width at half maximum characteristic from the cross-correlation graph; and assign the ranking to the first target feature based on the full width at half maximum characteristic. Claim limitations describe, identifying chaecteristics from graph, and assigning ranking. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships. Claim 4: extract, in a case that the first target feature achieves the ranking threshold, a feature submap from the second feature map; calculate, using a template matching technique to compare the feature submap extracted from the second feature map with the first feature map, an offset value that indicates a difference in the first target feature between the first processing chamber and the second processing chamber; and generate, based on the offset value, an operation parameter revision recommendation as the state prediction result that indicates changes to a set of operation parameters of the second semiconductor manufacturing device to reduce the performance difference of the second processing chamber with respect to the first processing chamber. Claim limitations describe, extracting feature, calculating offset, and generating/calculating parameter revision recommendation. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships and mathematical calculations. Claim 5: calculate a first average value of the first target feature in the first feature map; calculate a second average value of the first target feature in the second feature map; normalize the first feature map using the first average value; and normalize the second feature map using the second average value. Claim limitations describe, calculating values, and normalizing feature map. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships and mathematical calculations. Claim 7: generating, based on the first set of operation data, a first feature map for a first target feature; generating, based on the second set of operation data, a second feature map for the first target feature; calculating a first average value of the first target feature in the first feature map; calculating a second average value of the first target feature in the second feature map; normalizing the first feature map using the first average value; normalizing the second feature map using the second average value; calculating a cross-correlation result between the first feature map and the second feature map; calculating a first autocorrelation result for the first feature map; calculating a second autocorrelation result for the second feature map; and generating a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map by normalizing the cross-correlation result based on a first maximum autocorrelation value from the first autocorrelation result and a second maximum autocorrelation value from the second autocorrelation result; generating a correlation graph of the normalized cross-correlation result; identifying a full width at half maximum parameter from the correlation graph; assigning, based on the full width at half maximum parameter, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction; selecting, from among the set of target features, a subset of target features that achieve a ranking threshold; extracting, in a case that the first target feature achieves the ranking threshold, a feature submap from the second feature map; calculating, using a template matching technique to compare the feature submap extracted from the second feature map with the first feature map, an offset value that indicates a difference in the first target feature between the first processing chamber and the second processing chamber; and generating, based on the offset value, an operation parameter revision recommendation as a state prediction result that indicates changes to a set of operation parameters of the second semiconductor manufacturing device to reduce a performance difference of the second processing chamber with respect to the first processing chamber. Claim limitations describe, generating feature map, identifying, assigning ranking, selecting, extracting map, generating operation parameter. These limitations given their broadest reasonable interpretation in light of the specification is a mental process since this a concept that can be performed in the human mind and is observation, evaluation, judgment, and determination. Further claim limitations describe, generating feature map, calculating value, normalizing feature map, calculating correlation/autocorrelation, generating correlation result/graph, assigning ranking, calculating offset. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships. Claim 8: generate, based on the first set of operation data, a first feature map for a first target feature, and generate, based on the second set of operation data, a second feature map for the first target feature; generating a state prediction result for the semiconductor manufacturing device; calculate a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map; assign, based on the normalized cross-correlation result, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction, and select, from among the set of target features, a subset of target features that achieve a ranking threshold; generate, based on the subset of target features, a state prediction result that characterizes a performance difference of the second processing chamber with respect to the first processing chamber; Claim limitations describe, generating feature map, assigning ranking, selecting, and generating result. These limitations given their broadest reasonable interpretation in light of the specification is a mental process since this a concept that can be performed in the human mind and is an observation, evaluation, judgment, and determination. Further claim limitations describe, generating feature map, calculating correlation, assigning ranking. These limitations given their broadest reasonable interpretation in light of the specification is Mathematical Concepts such that mathematical relationships and mathematical calculations. As described above, these limitations describe Mental Process that can be performed in the human mind, or by a human using a pen and paper, and Mathematical Concepts such that mathematical relationships, and mathematical calculations etc. Step 2A – Prong 2 and Step 2B: Claims recite, a data acquisition unit, a feature management unit, a correlation calculation unit, a ranking unit, a state prediction unit (claim 1); the correlation calculation unit (claim 2); the ranking unit (claim 3); the state prediction unit (claim 4); the feature management unit (claim 5); a state prediction device, a data acquisition unit, a feature management unit, a correlation calculation unit, a ranking unit, and a state prediction unit (claim 8) that are mere instructions to implement an abstract idea on a general purpose computer ( apply it ; corresponding structure disclosed in the specification is a general purpose computer implementing the claimed functions characterized as abstract ideas above. See MPEP 2106.05(a)). The claim limitations are implemented on these generic elements such that the following are merely applying the abstract idea on a generic computer: calculating, generating, assigning, extracting, normalizing etc. The claims further recite, (claim 1): A state prediction device for a semiconductor manufacturing device, the state prediction device comprising: (claim 6): the first semiconductor manufacturing device and the second semiconductor manufacturing device are plasma etching devices. (claim 7): A state prediction method for a semiconductor manufacturing device, the state prediction method including: (claim 8): A state prediction system for a semiconductor manufacturing device, the state prediction system comprising: a semiconductor manufacturing device for manufacturing semiconductor devices; a user terminal for managing the semiconductor manufacturing device; This is generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). The claim recites the additional elements of (claims 1, 7, 8): acquire/acquiring a first set of operation data for a first processing chamber of a first semiconductor manufacturing device that achieves an operational threshold, and acquire/acquiring a second set of operation data for a second processing chamber of a second semiconductor manufacturing device that fails to achieve the operational threshold; (claim 8): output the state prediction result to the user terminal. These additional elements are recited at a high level of generality and amounts to mere data gathering which is a form of insignificant extra solution activity (MPEP 2106.05(g)). These additional elements are recited at a high level of generality and as a form of insignificant extra solution activity recognized by court as well-understood, routine, conventional activity. The acquiring set of operation data are claimed in a merely generic manner ( e.g., at a high level of generality) and as an insignificant extra solution activity for data gathering/acquiring as computer function such that data gathering are recognized by the court as well-understood, routine, and conventional (MPEP 2106.05(d)(ii)(i)). The claim recites the additional element of generating an output. This is recited at a high level of generality and is merely presenting data. This amounts to mere data gathering which is an insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. The additional elements in the claim amount to no more than insignificant extra solution activity and do not amount to significantly more than the judicial exception because acquiring data, and outputting a result are mere data gathering (MPEP 2106.05(g)). Further, the use of the claimed invention in a semiconductor manufacturing process ( the first semiconductor manufacturing device and the second semiconductor manufacturing device are plasma etching devices ) is simply an attempt to limit the use of the abstract idea to a particular technological environment (Electrical Power Group, LLC v. Alstom S.A., MPEP 2106.05(h)). The claim does not include any further additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, claims 1-8 are rejected under 35 USC 101 as being ineligible. The claims do not include additional elements, individually or combined, that are sufficient to amount to significantly more than the judicial exception. As explained above, the claim limitations are implemented on these generic elements such that he following are merely applying the abstract idea on a generic computer: calculating, generating, assigning, extracting, normalizing steps be done by at least one processor. Even when combined with all of the claim limitations as a whole, it is still directed to the abstract idea of mental process. Therefore, the claims are not patent eligible. Dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as they recite further embellishment of the judicial exception. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Claims 1-8 do not include any further additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claim(s) 1-8 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. 07-30-03-h AIA Claim Interpretation notes It is noted that, at this time, the claims 1-5 and 8 are not examined under the claim interpretation 35 USC § 112(f) and therefore the 35 USC §101 rejections are applied. See the 35 USC §101 rejections as applied above. Allowable Subject Matter Claims 1, 7 and 8 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action. Reasons for Indicating Allowable Subject Matter Claims 1, 7 and 8 are indicated to have allowable subject matter. The following is an examiner’s statement of reasons for indicating allowable subject matter: Claims 1-6: Regarding claim 1: HAO et al. (US20200243359A1, listed in the IDS 03/04/2024, #16) discloses, A state prediction device for a semiconductor manufacturing device, the state prediction device comprising: a data acquisition unit configured to: acquire a first set of operation data for a first processing chamber of a first semiconductor manufacturing device that achieves an operational threshold, and acquire a second set of operation data for a second processing chamber of a second semiconductor manufacturing device that fails to achieve the operational threshold; [¶64: FIG. 6 illustrates one embodiment of a method 600 for detecting mismatches between process chambers of the semiconductor manufacturing tool 101 for one golden chamber and one processing chambers under test of the same type as the golden chamber… ¶65: At block 605,..feeding a first set of input time-series data of one or more sensors of a first processing chamber that is within specification (e.g., a golden chamber) to the neural network 300 to produce a corresponding first set of output time-series data…. ¶66: At block 615, chamber matching analysis engine 222 feeds a second set of input time-series data from corresponding one or more sensors associated with a second processing chamber (e.g., of the manufacturing tool 101) under test to the trained neural network 300 to produce a corresponding second set of output time-series data.]; a feature management unit configured to: generate, based on the first set of operation data, a first feature map for a first target feature, and generate, based on the second set of operation data, a second feature map for the first target feature; [¶71: At block 710, the chamber matching analysis engine 222 calculates a first error between the first set of input time-series data and the corresponding first set of output time-series data. In an example, the first error is a mean square error… ¶73: At block 720, the chamber matching analysis engine 222 calculates a plurality of second errors between the plurality of second sets of input time-series data and the corresponding plurality of second sets of output time-series data. In one embodiment, the errors are mean square errors. Examiner notes that, the limitation feature map is broad, and in broadest reasonable interpretation, the feature can be any feature and map can be any data map. Accordingly, HAO teaches, first and second error are obtained from the firs chamber and second chamber data, and first and second mean square error are calculated (includes a mean, average) ]; calculate a normalized cross-correlation result… [¶54: Preprocessing includes normalizing each of the time-series trace data to the range of [0,1] (min-max normalization, (x-min)/(max-min)).]; Namiki et al. (US20220093409A1, listed in the IDS 03/04/2024, #17) discloses, calculate a normalized cross-correlation result…between the first feature map and the second feature map; [¶14: a determination unit that determines a correlation between each of a plurality of parameter values and performance values of the substrate processing apparatus, and uses a plurality of the determined correlations to determine parameter types of a plurality of parameters used by the first artificial intelligence unit as input values… ¶15: a plurality of parameters having a high level of correlation with the performance value can be selected, and the performance value can be predicted using the plurality of parameters having a high level correlation with the performance value in the second artificial intelligence unit, whereby the second artificial intelligence unit can output a combination of parameter values yielding higher performance values.]; Kawamura et al. (US20100153065A1, listed in the IDS 03/04/2024, #12) discloses, calculate a normalized cross-correlation result… [¶60: The first normalization processing unit 103 performs a normalization process of normalizing the first information. The normalization process is a process of deforming the first information and the second information that do not have a constant shape having a desired property for the operation of comparison, calculation and the like, that is, deforming the first information and the second information without a normalized form to a normalized form. As a specific example, a process of performing an average value and variance value normalization of correcting the first information such that the average is 0 and the variance is 1 is given… ¶61: The second normalization processing unit 104 performs a normalization process of normalizing the second information. The correlation calculating unit 109,…calculates the cross-correlation function of the window acquiring information,…and the second information processed by the second normalization processing unit 104. The cross-correlation function is a function used to represent similarity and the like of two signals. The second information subjected to the normalization process in the second normalization processing unit 104 may be directly inputted as is to the correlation calculating unit 109, or may be indirectly inputted through other processing units and the like.]; Ishiguro et al. (US20200064820A1, listed in the IDS 03/04/2024, #15) discloses, assign,… a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction, and select, from among the set of target features, a subset of target features that achieve a ranking threshold; [¶53: In a sixth step (S306) of abnormality detection, the feature ranking creation unit 1173 creates a ranking of features in descending order of standardized values obtained by the calculation unit 1172 in the fifth step (S305).… ¶54: In a seventh step (S307) of abnormality detection, the feature selection unit 1174 selects a predetermined number of features in descending order of the ranking created by the feature ranking creation unit 1173 in the sixth step (S306). For example, when ten features are selected, features from a first rank to a tenth rank are selected… ¶82: In a fourth step (S604) of abnormality detection, the feature ranking creation unit 2173 creates a ranking of the features in descending order of standardized values in the training data standardized by the calculation unit 2172 in the third step (S603), and the feature selection unit 2174 selects a predetermined number of features of the ranking created by the feature ranking creation unit 2173 in descending order of the rankings. For example, when ten features are selected, features from a first rank to a tenth rank are selected. The selection is performed over all samples.]. The following prior arts teach similar state prediction in the semiconductor manufacturing field, but doesn’t explicitly teach the limitations of the claim; prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Zhang et al. (US20220404712A1) describes, comparison of feature maps, finding correlation and differences, but doesn’t specifically teach all the limitations of the claim [¶22: a measured image associated with the design pattern; and determining an offset between the measured image and the first predicted measured image… ¶23: aligning the measured image with the target image by aligning the measured image with the first predicted measured image based on the offset… ¶24: the offset is determined based on a cross correlation of the measured image and the first predicted measured image. ¶25: the offset is determined as co-ordinates of the first predicted measured image where a match function indicating a match between the measured image and the first predicted measured image is the highest or exceeds a specified threshold.]; Banna (US20200110390A1) describes, comparison of features to generate variation metric to match processing chamber performance, but doesn’t specifically teach all the limitations of the claim [¶7: The predicted spatial measurements are compared with the reference spatial measurements to produce a metric indicating variation of performance between the current chamber and the reference chamber. Based on a multi-constraint optimization of the metric performed by a second machine-learning engine, adjustments for at least some of the one or more parameters of the current chamber are recommended, so that a performance of the current chamber substantially matches a performance of the reference chamber.]. O'Leary (US20060042753A1) describes regarding the claimed subject matter of optimize target chamber parameters, but doesn’t specifically teach all the limitations of the claim [¶25-31: According to the present invention there is provided a method of transferring a multi-variate process control model from one plasma processing chamber (the reference chamber) to another nominally identical plasma processing chamber (the target chamber), comprising the steps of: (a) determining a multi-variate process control model on the reference tool based upon sensor data, (b) taking a fingerprint of the reference chamber by running a designed experiment, (c) taking a fingerprint of the target chamber by running the same designed experiment, (d) determining the relationship between the reference and target chambers by comparing the results of the designed experiments and calculating a transform matrix representing differences between the two chambers, and (e) transforming the process control model from the reference chamber to the target chamber using the transform matrix obtained in step (d).] However, regarding the claim, none of the HAO et al. (US20200243359A1), Namiki et al. (US20220093409A1), Kawamura et al. (US20100153065A1), Ishiguro et al. (US20200064820A1), Zhang et al. (US20220404712A1), Banna (US20200110390A1), or O'Leary (US20060042753A1) taken either alone or in obvious combination disclose, A state prediction device, specifically including: a correlation calculation unit configured to: calculate a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map; a ranking unit configured to: assign, based on the normalized cross-correlation result, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction, and select, from among the set of target features, a subset of target features that achieve a ranking threshold; and a state prediction unit configured to: generate, based on the subset of target features, a state prediction result that characterizes a performance difference of the second processing chamber with respect to the first processing chamber. (in combination with other elements of the claim) having all the claimed features of applicant’s instant invention, including: A state prediction method for a semiconductor manufacturing device, the state prediction method including: a data acquisition unit configured to: acquire a first set of operation data for a first processing chamber of a first semiconductor manufacturing device that achieves an operational threshold, and acquire a second set of operation data for a second processing chamber of a second semiconductor manufacturing device that fails to achieve the operational threshold; a feature management unit configured to: generate, based on the first set of operation data, a first feature map for a first target feature, and generate, based on the second set of operation data, a second feature map for the first target feature; a correlation calculation unit configured to: calculate a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map; a ranking unit configured to: assign, based on the normalized cross-correlation result, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction, and select, from among the set of target features, a subset of target features that achieve a ranking threshold; and a state prediction unit configured to: generate, based on the subset of target features, a state prediction result that characterizes a performance difference of the second processing chamber with respect to the first processing chamber. Claims 2-6 are indicated to have allowable subject matter based on their dependencies on claim 1. Claim 8: Regarding Claim 8: The state prediction system of claim 8 includes similar limitations as the state prediction device of claim 1. Therefore, the state prediction system of claim 8 is indicated to have allowable subject matter for the same reasons as described above in claim 1. Claim 7 (amended): Regarding claim 7: HAO et al. (US20200243359A1, listed in the IDS 03/04/2024, #16) discloses, A state prediction method for a semiconductor manufacturing device, the state prediction method including: acquiring a first set of operation data for a first processing chamber of a first semiconductor manufacturing device that achieves an operational threshold; acquiring a second set of operation data for a second processing chamber of a second semiconductor manufacturing device that fails to achieve the operational threshold; [¶64: FIG. 6 illustrates one embodiment of a method 600 for detecting mismatches between process chambers of the semiconductor manufacturing tool 101 for one golden chamber and one processing chambers under test of the same type as the golden chamber… ¶65: At block 605,..feeding a first set of input time-series data of one or more sensors of a first processing chamber that is within specification (e.g., a golden chamber) to the neural network 300 to produce a corresponding first set of output time-series data…. ¶66: At block 615, chamber matching analysis engine 222 feeds a second set of input time-series data from corresponding one or more sensors associated with a second processing chamber (e.g., of the manufacturing tool 101) under test to the trained neural network 300 to produce a corresponding second set of output time-series data.]; generating, based on the first set of operation data, a first feature map for a first target feature; generating, based on the second set of operation data, a second feature map for the first target feature; calculating a first average value of the first target feature in the first feature map; calculating a second average value of the first target feature in the second feature map; [¶71: At block 710, the chamber matching analysis engine 222 calculates a first error between the first set of input time-series data and the corresponding first set of output time-series data. In an example, the first error is a mean square error… ¶73: At block 720, the chamber matching analysis engine 222 calculates a plurality of second errors between the plurality of second sets of input time-series data and the corresponding plurality of second sets of output time-series data. In one embodiment, the errors are mean square errors. Examiner notes that, the limitation feature map is broad, and in broadest reasonable interpretation, the feature can be any feature and map can be any data map. Accordingly, HAO teaches, first and second error are obtained from the firs chamber and second chamber data, and first and second mean square error are calculated (includes a mean, average) ]; normalizing the first feature map using the first average value; normalizing the second feature map using the second average value; [¶54: Preprocessing includes normalizing each of the time-series trace data to the range of [0,1] (min-max normalization, (x-min)/(max-min))…]; generating a normalized cross-correlation result… [¶54: Preprocessing includes normalizing each of the time-series trace data to the range of [0,1] (min-max normalization, (x-min)/(max-min)).]; Namiki et al. (US20220093409A1, listed in the IDS 03/04/2024, #17) discloses, calculating a cross-correlation result between the first feature map and the second feature map; calculating a first autocorrelation result for the first feature map; calculating a second autocorrelation result for the second feature map; [¶14: a determination unit that determines a correlation between each of a plurality of parameter values and performance values of the substrate processing apparatus, and uses a plurality of the determined correlations to determine parameter types of a plurality of parameters used by the first artificial intelligence unit as input values… ¶15: a plurality of parameters having a high level of correlation with the performance value can be selected, and the performance value can be predicted using the plurality of parameters having a high level correlation with the performance value in the second artificial intelligence unit, whereby the second artificial intelligence unit can output a combination of parameter values yielding higher performance values.]; Kawamura et al. (US20100153065A1, listed in the IDS 03/04/2024, #12) discloses, generating a normalized cross-correlation result… by normalizing the cross- correlation result based on a…autocorrelation value from the first autocorrelation result and a second…autocorrelation value from the second autocorrelation result; [¶60: The first normalization processing unit 103 performs a normalization process of normalizing the first information. The normalization process is a process of deforming the first information and the second information that do not have a constant shape having a desired property for the operation of comparison, calculation and the like, that is, deforming the first information and the second information without a normalized form to a normalized form. As a specific example, a process of performing an average value and variance value normalization of correcting the first information such that the average is 0 and the variance is 1 is given… ¶61: The second normalization processing unit 104 performs a normalization process of normalizing the second information. The correlation calculating unit 109,…calculates the cross-correlation function of the window acquiring information,…and the second information processed by the second normalization processing unit 104. The cross-correlation function is a function used to represent similarity and the like of two signals. The second information subjected to the normalization process in the second normalization processing unit 104 may be directly inputted as is to the correlation calculating unit 109, or may be indirectly inputted through other processing units and the like.]; generating a correlation graph… [¶108: FIG. 11 is a view showing the cross-correlation function calculated by the correlation calculating unit 109 in a waveform graph. In the figure, the horizontal axis indicates time, and the vertical axis indicates value of correlation.]; Ishiguro et al. (US20200064820A1, listed in the IDS 03/04/2024, #15) discloses, assigning,…a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction; selecting, from among the set of target features, a subset of target features that achieve a ranking threshold; extracting, in a case that the first target feature achieves the ranking threshold, a feature submap from the second feature map; [¶53: In a sixth step (S306) of abnormality detection, the feature ranking creation unit 1173 creates a ranking of features in descending order of standardized values obtained by the calculation unit 1172 in the fifth step (S305).… ¶54: In a seventh step (S307) of abnormality detection, the feature selection unit 1174 selects a predetermined number of features in descending order of the ranking created by the feature ranking creation unit 1173 in the sixth step (S306). For example, when ten features are selected, features from a first rank to a tenth rank are selected… ¶82: In a fourth step (S604) of abnormality detection, the feature ranking creation unit 2173 creates a ranking of the features in descending order of standardized values in the training data standardized by the calculation unit 2172 in the third step (S603), and the feature selection unit 2174 selects a predetermined number of features of the ranking created by the feature ranking creation unit 2173 in descending order of the rankings. For example, when ten features are selected, features from a first rank to a tenth rank are selected. The selection is performed over all samples.]; Zhang et al. (US20220404712A1) discloses, calculating, using a template matching technique to compare the feature submap extracted from the second feature map with the first feature map, an offset value that indicates a difference in the first target feature between the first processing chamber and the second processing chamber; [¶22: a measured image associated with the design pattern; and determining an offset between the measured image and the first predicted measured image… ¶23: aligning the measured image with the target image by aligning the measured image with the first predicted measured image based on the offset… ¶24: the offset is determined based on a cross correlation of the measured image and the first predicted measured image. ¶25: the offset is determined as co-ordinates of the first predicted measured image where a match function indicating a match between the measured image and the first predicted measured image is the highest or exceeds a specified threshold.]; Banna (US20200110390A1) discloses, generating, based on the offset value, an operation parameter revision recommendation as a state prediction result that indicates changes to a set of operation parameters…………. [¶7: The predicted spatial measurements are compared with the reference spatial measurements to produce a metric indicating variation of performance between the current chamber and the reference chamber. Based on a multi-constraint optimization of the metric performed by a second machine-learning engine, adjustments for at least some of the one or more parameters of the current chamber are recommended, so that a performance of the current chamber substantially matches a performance of the reference chamber.]. The following prior arts teach similar state prediction in the semiconductor manufacturing field, but doesn’t explicitly teach the limitations of the claim; prior art made of record and not relied upon is considered pertinent to applicant's disclosure: O'Leary (US20060042753A1) describes, regarding the claimed subject matter of optimize target chamber parameters, but doesn’t specifically teach all the limitations of the claim, [¶25-31: According to the present invention there is provided a method of transferring a multi-variate process control model from one plasma processing chamber (the reference chamber) to another nominally identical plasma processing chamber (the target chamber), comprising the steps of: (a) determining a multi-variate process control model on the reference tool based upon sensor data, (b) taking a fingerprint of the reference chamber by running a designed experiment, (c) taking a fingerprint of the target chamber by running the same designed experiment, (d) determining the relationship between the reference and target chambers by comparing the results of the designed experiments and calculating a transform matrix representing differences between the two chambers, and (e) transforming the process control model from the reference chamber to the target chamber using the transform matrix obtained in step (d).]. However, regarding the claim, none of the HAO et al. (US20200243359A1), Namiki et al. (US20220093409A1), Kawamura et al. (US20100153065A1), Ishiguro et al. (US20200064820A1), Zhang et al. (US20220404712A1), Banna (US20200110390A1), or O'Leary (US20060042753A1) taken either alone or in obvious combination disclose, A state prediction method, specifically including: generating a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map by normalizing the cross- correlation result based on a first maximum autocorrelation value from the first autocorrelation result and a second maximum autocorrelation value from the second autocorrelation result; generating a correlation graph of the normalized cross-correlation result; identifying a full width at half maximum parameter from the correlation graph; assigning, based on the full width at half maximum parameter, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction; selecting, from among the set of target features, a subset of target features that achieve a ranking threshold; extracting, in a case that the first target feature achieves the ranking threshold, a feature submap from the second feature map; calculating, using a template matching technique to compare the feature submap extracted from the second feature map with the first feature map, an offset value that indicates a difference in the first target feature between the first processing chamber and the second processing chamber; and generating, based on the offset value, an operation parameter revision recommendation as a state prediction result that indicates changes to a set of operation parameters of the second semiconductor manufacturing device to reduce a performance difference of the second processing chamber with respect to the first processing chamber. (in combination with other elements of the claim) having all the claimed features of applicant’s instant invention, including: A state prediction method for a semiconductor manufacturing device, the state prediction method including: acquiring a first set of operation data for a first processing chamber of a first semiconductor manufacturing device that achieves an operational threshold; acquiring a second set of operation data for a second processing chamber of a second semiconductor manufacturing device that fails to achieve the operational threshold; generating, based on the first set of operation data, a first feature map for a first target feature; generating, based on the second set of operation data, a second feature map for the first target feature; calculating a first average value of the first target feature in the first feature map; calculating a second average value of the first target feature in the second feature map; normalizing the first feature map using the first average value; normalizing the second feature map using the second average value; calculating a cross-correlation result between the first feature map and the second feature map; calculating a first autocorrelation result for the first feature map; calculating a second autocorrelation result for the second feature map; and generating a normalized cross-correlation result that indicates a uniformity level of the first target feature between the first feature map and the second feature map by normalizing the cross- correlation result based on a first maximum autocorrelation value from the first autocorrelation result and a second maximum autocorrelation value from the second autocorrelation result; generating a correlation graph of the normalized cross-correlation result; identifying a full width at half maximum parameter from the correlation graph; assigning, based on the full width at half maximum parameter, a ranking to the first target feature that indicates a relevance of the first target feature with respect to a set of target features with regard to processing chamber state prediction; selecting, from among the set of target features, a subset of target features that achieve a ranking threshold; extracting, in a case that the first target feature achieves the ranking threshold, a feature submap from the second feature map; calculating, using a template matching technique to compare the feature submap extracted from the second feature map with the first feature map, an offset value that indicates a difference in the first target feature between the first processing chamber and the second processing chamber; and generating, based on the offset value, an operation parameter revision recommendation as a state prediction result that indicates changes to a set of operation parameters of the second semiconductor manufacturing device to reduce a performance difference of the second processing chamber with respect to the first processing chamber. 13-03 It is for these reasons that applicant's invention defines over the prior art of the record. Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.” Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are described along with the reasons for indicating allowable subject matter and are listed in the PTO-892 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED SHAFAYET whose telephone number is (571)272-8239. The examiner can normally be reached M-F 8:30 AM-5:00 PM. 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, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /M.S./ Patent Examiner, Art Unit 2116 /KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116 Application/Control Number: 18/688,913 Page 2 Art Unit: 2116 Application/Control Number: 18/688,913 Page 3 Art Unit: 2116 Application/Control Number: 18/688,913 Page 4 Art Unit: 2116 Application/Control Number: 18/688,913 Page 5 Art Unit: 2116 Application/Control Number: 18/688,913 Page 6 Art Unit: 2116 Application/Control Number: 18/688,913 Page 7 Art Unit: 2116 Application/Control Number: 18/688,913 Page 8 Art Unit: 2116 Application/Control Number: 18/688,913 Page 9 Art Unit: 2116 Application/Control Number: 18/688,913 Page 10 Art Unit: 2116 Application/Control Number: 18/688,913 Page 11 Art Unit: 2116 Application/Control Number: 18/688,913 Page 12 Art Unit: 2116 Application/Control Number: 18/688,913 Page 13 Art Unit: 2116 Application/Control Number: 18/688,913 Page 14 Art Unit: 2116 Application/Control Number: 18/688,913 Page 15 Art Unit: 2116 Application/Control Number: 18/688,913 Page 16 Art Unit: 2116 Application/Control Number: 18/688,913 Page 17 Art Unit: 2116 Application/Control Number: 18/688,913 Page 18 Art Unit: 2116 Application/Control Number: 18/688,913 Page 19 Art Unit: 2116 Application/Control Number: 18/688,913 Page 20 Art Unit: 2116 Application/Control Number: 18/688,913 Page 21 Art Unit: 2116 Application/Control Number: 18/688,913 Page 23 Art Unit: 2116 Application/Control Number: 18/688,913 Page 24 Art Unit: 2116 Application/Control Number: 18/688,913 Page 25 Art Unit: 2116 Application/Control Number: 18/688,913 Page 26 Art Unit: 2116 Application/Control Number: 18/688,913 Page 27 Art Unit: 2116 Application/Control Number: 18/688,913 Page 28 Art Unit: 2116 Application/Control Number: 18/688,913 Page 29 Art Unit: 2116 Application/Control Number: 18/688,913 Page 30 Art Unit: 2116 Application/Control Number: 18/688,913 Page 31 Art Unit: 2116 Application/Control Number: 18/688,913 Page 32 Art Unit: 2116 Application/Control Number: 18/688,913 Page 33 Art Unit: 2116 Application/Control Number: 18/688,913 Page 34 Art Unit: 2116 Application/Control Number: 18/688,913 Page 35 Art Unit: 2116 Application/Control Number: 18/688,913 Page 36 Art Unit: 2116 Application/Control Number: 18/688,913 Page 37 Art Unit: 2116 Application/Control Number: 18/688,913 Page 38 Art Unit: 2116
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