DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-27 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/23/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6, 12, 14-15, 23-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Morisawa et al. USPGPUB 2013/0173042 (hereinafter “Morisawa”).
Regarding claim 1, Morisawa teaches an information processing method comprising, by an information processing apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”):
acquiring monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0102], wherein examiner interpreted acquiring output of equipment controllers and the in-processing sensors, recording it as equipment data as acquiring monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus, wherein examiner interpreted manufacturing equipment as target apparatus);
generating a dimensional compression model for compressing the number of dimensions of the monitoring data (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph as generating a dimensional compression model for compressing the number of dimensions of the monitoring data);
inputting the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph as inputting the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation); and
outputting the monitoring data converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as outputting the monitoring data converted into the low-dimensional representation).
Regarding claim 2, Morisawa teaches wherein the monitoring data includes a plurality of items classified into a plurality of categories (Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0104] “FIG. 29 shows a data configuration example of the equipment data DB 132. In a case where the equipment data DB 132 is connected to the network to form it as a common database storing equipment data of a plurality of semiconductor manufacturing equipment, as a directory for classifying the equipment data to be stored, a semiconductor manufacturing equipment ID 2901 is included. Further, the equipment data is classified by a directory composed of data record of the lot or wafer ID 2902 processed in this semiconductor manufacturing equipment, a manufacturing station ID 2903, a manufacturing recipe ID 2904, and manufacturing date and time 2905. Then in this semiconductor manufacturing equipment, while manufacturing process of this lot or wafer is carried out, the equipment data is stored in a predetermined sampling cycle for each signal”, wherein examiner interpreted recording equipment data into equipment data wherein equipment data includes classifying the data record of wafer processed in manufacturing equipment as wherein the monitoring data includes a plurality of items classified into a plurality of categories), and
the information processing apparatus generates the dimensional compression model for each category (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph based on manufacturing data as the information processing apparatus generating the dimensional compression model for each category), and
inputs the monitoring data into a dimensional compression model corresponding to each category to convert the monitoring data into a low-dimensional representation for each category (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph based on manufacturing data as inputting the monitoring data into a dimensional compression model corresponding to each category to convert the monitoring data into a low-dimensional representation for each category).
Regarding claim 6, Morisawa teaches wherein the information processing apparatus acquires monitoring data about a plurality of the target apparatuses (Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0104] “FIG. 29 shows a data configuration example of the equipment data DB 132. In a case where the equipment data DB 132 is connected to the network to form it as a common database storing equipment data of a plurality of semiconductor manufacturing equipment, as a directory for classifying the equipment data to be stored, a semiconductor manufacturing equipment ID 2901 is included. Further, the equipment data is classified by a directory composed of data record of the lot or wafer ID 2902 processed in this semiconductor manufacturing equipment, a manufacturing station ID 2903, a manufacturing recipe ID 2904, and manufacturing date and time 2905. Then in this semiconductor manufacturing equipment, while manufacturing process of this lot or wafer is carried out, the equipment data is stored in a predetermined sampling cycle for each signal”, wherein examiner interpreted recording equipment data into equipment data wherein equipment data which includes data from various manufacturing equipment as wherein the information processing apparatus acquires monitoring data about a plurality of the target apparatuses),
converts the monitoring data about the plurality of target apparatuses into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph based on manufacturing data as converting the monitoring data about the plurality of target apparatuses into a low-dimensional representation),
selects one or more monitoring data groups based on the monitoring data converted into the low-dimensional representation, converts the monitoring data into a low-dimensional representation for each selected monitoring data group (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph based on manufacturing data as selecting one or more monitoring data groups based on the monitoring data converted into the low-dimensional representation, converts the monitoring data into a low-dimensional representation for each selected monitoring data group), and
outputs the monitoring data for each monitoring data group converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as outputting the monitoring data for each monitoring data group converted into the low-dimensional representation).
Regarding claim 12, Morisawa teaches wherein the information processing apparatus acquires time series data from the target apparatus (Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0104] “FIG. 29 shows a data configuration example of the equipment data DB 132. In a case where the equipment data DB 132 is connected to the network to form it as a common database storing equipment data of a plurality of semiconductor manufacturing equipment, as a directory for classifying the equipment data to be stored, a semiconductor manufacturing equipment ID 2901 is included. Further, the equipment data is classified by a directory composed of data record of the lot or wafer ID 2902 processed in this semiconductor manufacturing equipment, a manufacturing station ID 2903, a manufacturing recipe ID 2904, and manufacturing date and time 2905. Then in this semiconductor manufacturing equipment, while manufacturing process of this lot or wafer is carried out, the equipment data is stored in a predetermined sampling cycle for each signal”, wherein examiner interpreted recording equipment data into equipment data wherein equipment data includes classifying the data record within different times of that wafer that is processed in manufacturing equipment as wherein the information processing apparatus acquires time series data from the target apparatus),
divides the process performed by the target apparatus into a plurality of periods, and calculates statistical values in each period of the acquired time series data, receives a selection of the period based on the calculated statistical values, and stores the statistical values of the periods during which the selection is received, as the monitoring data (Paragraph [0104] “FIG. 29 shows a data configuration example of the equipment data DB 132. In a case where the equipment data DB 132 is connected to the network to form it as a common database storing equipment data of a plurality of semiconductor manufacturing equipment, as a directory for classifying the equipment data to be stored, a semiconductor manufacturing equipment ID 2901 is included. Further, the equipment data is classified by a directory composed of data record of the lot or wafer ID 2902 processed in this semiconductor manufacturing equipment, a manufacturing station ID 2903, a manufacturing recipe ID 2904, and manufacturing date and time 2905. Then in this semiconductor manufacturing equipment, while manufacturing process of this lot or wafer is carried out, the equipment data is stored in a predetermined sampling cycle for each signal. The equipment data is stored in a data record format composed of data items including a signal ID 2906, observation starting date and time 2907, observation ending date and time 2908, a sampling interval 2909, and time-series equipment data 2910”, wherein examiner interpreted storing equipment data of a plurality of semiconductor manufacturing equipment in predetermined sampling cycle for each signal as divides the process performed by the target apparatus into a plurality of periods, and calculates statistical values in each period of the acquired time series data, receives a selection of the period based on the calculated statistical values, and stores the statistical values of the periods during which the selection is received, as the monitoring data).
Regarding claim 6, Morisawa teaches wherein the information processing apparatus outputs the monitoring data converted into the low-dimensional representation using a histogram, a box and whisker plot, a violin chart, a scattering diagram, a density contour diagram, or a heat map ([Abstract] “calculating principal component scores by principal component analysis; processing of comparing magnitudes of the eigen values of the principal components, arranging the eigen values in descending order to display a list thereof; and processing of displaying a scatter diagram where the principal component scores of the respective signals are plotted in a feature space selecting the principal component corresponding to the eigen value having a contribution ratio”, and FIGS. 11A-11B, 20A-20C).
Regarding claim 15, Morisawa teaches an information processing method comprising, by an information processing apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”):
acquiring monitoring data which includes a plurality of items related to a state of each process performed by an analysis target apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0102], wherein examiner interpreted acquiring output of equipment controllers and the in-processing sensors, recording it as equipment data as acquiring monitoring data which includes a plurality of items related to a state of each process performed by an analysis a target apparatus, wherein examiner interpreted manufacturing equipment as target apparatus);
acquiring analysis information which includes monitoring data related to an analyzed apparatus or a dimensional compression model generated based on the monitoring data (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph based on equipment data as acquiring analysis information which includes monitoring data related to an analyzed apparatus or a dimensional compression model generated based on the monitoring data);
inputting the monitoring data of the acquired analysis target apparatus into the dimensional compression model generated based on the acquired monitoring data included in the analysis information or a dimensional compression model included in the analysis information, to convert the monitoring data into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph as inputting the monitoring data of the acquired analysis target apparatus into the dimensional compression model generated based on the acquired monitoring data included in the analysis information or a dimensional compression model included in the analysis information, to convert the monitoring data into a low-dimensional representation); and
outputting the monitoring data converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as outputting the monitoring data converted into the low-dimensional representation).
Regarding claim 23, Morisawa teaches wherein the monitoring data of the analysis target apparatus converted into the low-dimensional representation and the monitoring data of the analyzed apparatus converted into the low-dimensional representation are output together (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map for various equipment data as outputting the monitoring data converted into the low-dimensional representation wherein the monitoring data of the analysis target apparatus converted into the low-dimensional representation and the monitoring data of the analyzed apparatus converted into the low-dimensional representation are output together).
Regarding claim 24, Morisawa teaches a non-transitory recording medium in which a computer program is recorded, the computer program for causing a computer to perform a process comprising (Paragraph [0167] “Note that the equipment data monitoring device 133 does not necessarily have to be installed, for example, near the equipment, and may be installed in a building such as a data center. Outputting of the processing results does not have to be performed at the screen-user interface 112, but may be performed at an office PC (personal computer) terminal screen-user interface”):
acquiring monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0102], wherein examiner interpreted acquiring output of equipment controllers and the in-processing sensors, recording it as equipment data as acquiring monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus, wherein examiner interpreted manufacturing equipment as target apparatus);
generating a dimensional compression model for compressing the number of dimensions of the monitoring data (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph as generating a dimensional compression model for compressing the number of dimensions of the monitoring data);
inputting the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph as inputting the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation); and
outputting the monitoring data converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as outputting the monitoring data converted into the low-dimensional representation).
Regarding claim 25, Morisawa teaches a non-transitory recording medium in which a computer program is recorded, the computer program for causing a computer to perform a process comprising (Paragraph [0167] “Note that the equipment data monitoring device 133 does not necessarily have to be installed, for example, near the equipment, and may be installed in a building such as a data center. Outputting of the processing results does not have to be performed at the screen-user interface 112, but may be performed at an office PC (personal computer) terminal screen-user interface”):
acquiring monitoring data which includes a plurality of items related to a state of each process performed by an analysis target apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0102], wherein examiner interpreted acquiring output of equipment controllers and the in-processing sensors, recording it as equipment data as acquiring monitoring data which includes a plurality of items related to a state of each process performed by an analysis a target apparatus, wherein examiner interpreted manufacturing equipment as target apparatus);
acquiring analysis information which includes monitoring data related to an analyzed apparatus or a dimensional compression model generated based on the monitoring data (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph based on equipment data as acquiring analysis information which includes monitoring data related to an analyzed apparatus or a dimensional compression model generated based on the monitoring data);
inputting the monitoring data of the acquired analysis target apparatus into the dimensional compression model generated based on the acquired monitoring data included in the analysis information or a dimensional compression model included in the analysis information, to convert the monitoring data into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph as inputting the monitoring data of the acquired analysis target apparatus into the dimensional compression model generated based on the acquired monitoring data included in the analysis information or a dimensional compression model included in the analysis information, to convert the monitoring data into a low-dimensional representation); and
outputting the monitoring data converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as outputting the monitoring data converted into the low-dimensional representation).
Regarding claim 26, Morisawa teaches an information processing apparatus comprising (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0167] “Note that the equipment data monitoring device 133 does not necessarily have to be installed, for example, near the equipment, and may be installed in a building such as a data center. Outputting of the processing results does not have to be performed at the screen-user interface 112, but may be performed at an office PC (personal computer) terminal screen-user interface”):
an acquirer configured to acquire monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0102], wherein examiner interpreted acquiring output of equipment controllers and the in-processing sensors, recording it as equipment data as an acquirer configured to acquire monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus, wherein examiner interpreted manufacturing equipment as target apparatus);
a generator configured to generate a dimensional compression model for compressing the number of dimensions of the monitoring data (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph as a generator configured to generate a dimensional compression model for compressing the number of dimensions of the monitoring data);
a converter configured to input the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph as a converter configured to input the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation); and
an outputter configured to output the monitoring data converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as an outputter configured to output the monitoring data converted into the low-dimensional representation).
Regarding claim 27, Morisawa teaches an information processing apparatus comprising (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0167] “Note that the equipment data monitoring device 133 does not necessarily have to be installed, for example, near the equipment, and may be installed in a building such as a data center. Outputting of the processing results does not have to be performed at the screen-user interface 112, but may be performed at an office PC (personal computer) terminal screen-user interface”):
a monitoring data acquirer configured to acquire monitoring data which includes a plurality of items related to a state of each process performed by an analysis target apparatus (Paragraph [0028] “To address the problem described above, one aspect of the present invention refers to semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing”, Paragraph [0102], wherein examiner interpreted acquiring output of equipment controllers and the in-processing sensors, recording it as equipment data as a monitoring data acquirer configured to acquire monitoring data which includes a plurality of items related to a state of each process performed by an analysis target apparatus);
an analysis information acquirer configured to acquire analysis information which includes monitoring data related to an analyzed apparatus or a dimensional compression model generated based on the monitoring data (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, wherein examiner interpreted principle component analysis PCA calculating scores to graph based on equipment data as an analysis information acquirer configured to acquire analysis information which includes monitoring data related to an analyzed apparatus or a dimensional compression model generated based on the monitoring data);
a converter configured to input the monitoring data of the acquired analysis target apparatus into the dimensional compression model generated based on the acquired monitoring data included in the analysis information or a dimensional compression model included in the analysis information, to convert the monitoring data into a low-dimensional representation (Paragraph [0109] “In the inter-time-point principal component analysis IT-PCA, a correlation matrix between time points is first calculated in the correlation matrix calculation section 704. Then eigen values and eigen vectors are calculated in the eigen value calculation section 706, and principal component scores are calculated in the principal component score calculation section 707”, Paragraph [0067] “This principal component analysis PCA is a method of identifying signal similarity by obtaining principle components (eigen values, eigen vectors) of a correlation matrix R obtained by a formula below”, Paragraph [0079] “The principal component analysis PCA using this correlation matrix between the time points is called inter-time-point principal component analysis IT-PCA. A calculation method itself of the principal component analysis PCA is identical for the formulae 3 to 5. For one signal, a principal component score of the number n.sub.time of time points is obtained. In a space where the principal component scores are arranged, difference between the signals can be detected and identified”, Paragraph [0109] “For results of calculation processing, a graph of principal component scores is displayed at the screen-user interface 112 in the feature space (principle component score, MSD map) display section 711, and the principle component vectors are displayed in a graph at the characteristic signal waveform (principle component vector, MDS score) display section 712 with processing time plotted in order of vector device numbers at a horizontal axis and with the vector values plotted at a vertical axis”, Paragraph [0101] “The calculator-storage equipment 111, in manufacturing processing, acquires the output of the equipment controllers and the in-processing sensors, records it as equipment data into the equipment data DB 132 via the equipment controller-outside communication equipment 109, and in processing of monitoring the semiconductor manufacturing equipment, reads out the corresponding equipment data from the equipment data DB 132 via the equipment controller-outside communication equipment 109 and performs analysis to thereby execute the equipment monitoring processing. Moreover, the calculator-storage equipment 111 is connected to the screen-user interface 112 and displays for the user information such as a status of equipment monitoring and a fault occurrence alarm, principal component scores as analysis results, a graph of the MDS map, a graph of a signal waveform, and a list of eigen values (contributions)”, Paragraph [0102], wherein examiner interpreted equipment monitoring data used to analyze and calculate MDS scores to graph as a converter configured to input the monitoring data of the acquired analysis target apparatus into the dimensional compression model generated based on the acquired monitoring data included in the analysis information or a dimensional compression model included in the analysis information, to convert the monitoring data into a low-dimensional representation); and
an outputter configured to output the monitoring data converted into the low-dimensional representation (Paragraph [0110] “A graph of the MDS map is displayed at the screen-user interface 112 in the feature space (principal component score, MDS map) display section 711. A list of eigen values (contribution ratios) is also displayed in the eigen value list display section 710”, wherein examiner interpreted displaying a graph of MDS map as an outputter configured to output the monitoring data converted into the low-dimensional representation).
Allowable Subject Matter
Claims 3-5, 7-11, 13, 16-22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
MAKI et al. [USPGPUB 2022/0223443] teaches a state determination device determines the state of a drive mechanism configured to operate while holding a substrate in a substrate processing apparatus.
OTSU et al. [USPGPUB 2020/0026262] teaches a machine-tool-state determination system configured to perform spectral analysis with time series data of the state value, and clustering with feature-amount data.
HORIWAKI et al. [USPGPUB 2020/0159197] teaches a manufacturing condition specifying system uses manufacturing condition data and quality data at a plurality of time points from a manufacturing flow to build models for each manufacturing state change in each manufacturing process of the flow.
Matsukura et al. [USPGPUB 2024/0339308] teaches an apparatus diagnostic apparatus that performs a computation process using data acquired from a semiconductor apparatus manufacturing apparatus.
Conclusion
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/D.P./ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119
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