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 .
Drawings
Drawings have been reviewed and accepted.
Specification
The specification filed on 08/26/24 has been entered. Specification has been reviewed and accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted filed on 03/24/26, and 08/26/24 has been received. The submission 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.
Claim(s) 1-3, 6, and 11-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schmirler et al. (US20070276501, herein Betz).
Regarding claim 1, Yoshida teaches An abnormality detection apparatus comprising a processor configured to: acquire analysis data, the analysis data including at least operation data among the operation data and history data ([0033] The operation management apparatus 100 includes an analysis unit 110, a data storage unit 120, and a result output unit 130, [0034] The analysis unit 110 performs various kinds of processing relating to analysis of monitoring data received from the monitored system 500, [0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data), the operation data being data of a manufacturing apparatus configured to intermittently process at least one product ([0105] In a chemical manufacturing plant, reactions are accelerated and desired products are produced with high purity by, for example, heating raw materials at a predetermined temperature or applying a predetermined pressure to raw materials), the history data relating to a manufacturing history of the product processed by the manufacturing apparatus ([0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data, [0026] FIG. 8 is a diagram illustrating an example of an abnormality detection history 224 in the first example embodiment of the present invention); set an operational state of the manufacturing apparatus in the operation data based on the acquired analysis data (Fig. 4, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110)) ; and detect abnormality of the manufacturing apparatus based on the operation data and the set operational state (Fig. 4, [0078] when abnormality is detected also with the correlation models B and C at the time “15:20”, along with the correlation model A, the model switchover unit 113 determines that abnormality of the monitored system 500 is detected, [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary ).
Regarding claim 2, Yoshida teaches The abnormality detection apparatus according to claim 1, wherein the processor changes a method of detecting abnormality of the manufacturing apparatus according to the operational state (Fig. 4, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110), [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary).
Regarding claim 3, Yoshida teaches The abnormality detection apparatus according to claim 1, wherein the processor sets a first state as the operational state for each time point at which a value of the operation data is equal to or above a threshold ([0068] the analysis processing unit 112 determines that there is abnormality when the number of destructed correlations (correlation destruction) included in the correlation model or the predicted error of the correlations where correlation destruction is detected (a degree of the correlation destruction) is equal to or more than a predetermined threshold value, [0075] For example, when abnormality is detected with the correlation model A at the time “15:20”, the analysis processing unit 112 applies the monitoring data at the time “15:20” to the correlation models B and C to perform abnormality detection ) and sets a second state as the operational state for each time point at which a value of the operation data is less than the threshold, the second state differing from the first state ([0057] generates a plurality of correlation models that each includes one or more correlations between items of monitoring data on the basis of the time series of monitoring data in a period in which the monitored system 500 is normal (at the time of normality) which are stored in the monitoring data storage unit 125, [0058] plurality of operating states (processes) of the monitored system 500, the model generation unit 111 generates a correlation model by using the time series at the time of normality in the operating state (process), [0101] the operating states (processes) of the system is normal and whether the transition of the operating states (processes) is normal). (i.e first state is abnormal, second state normal)
Regarding claim 6, Yoshida teaches The abnormality detection apparatus according to claim 1, wherein the processor detects the abnormality of the manufacturing apparatus based on an order or a combination of the operational states set in the operation data (Fig. 4, Fig. 6, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110), [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary).
Regarding claim 11, Yoshida teaches An abnormality detection method comprising: acquiring analysis data, the analysis data including at least operation data among the operation data and history data ([0033] The operation management apparatus 100 includes an analysis unit 110, a data storage unit 120, and a result output unit 130, [0034] The analysis unit 110 performs various kinds of processing relating to analysis of monitoring data received from the monitored system 500, [0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data), the operation data being data of a manufacturing apparatus configured to intermittently process at least one product ([0105] In a chemical manufacturing plant, reactions are accelerated and desired products are produced with high purity by, for example, heating raw materials at a predetermined temperature or applying a predetermined pressure to raw materials), the history data relating to a manufacturing history of the product processed by the manufacturing apparatus ([0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data, [0026] FIG. 8 is a diagram illustrating an example of an abnormality detection history 224 in the first example embodiment of the present invention); setting an operational state of the manufacturing apparatus in the operation data based on the acquired analysis data (Fig. 4, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110)); and detecting abnormality of the manufacturing apparatus based on the operation data and the set operational state (Fig. 4, [0078] when abnormality is detected also with the correlation models B and C at the time “15:20”, along with the correlation model A, the model switchover unit 113 determines that abnormality of the monitored system 500 is detected, [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary ).
Regarding claim 12, Yoshida teaches A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising: acquiring analysis data, the analysis data including at least operation data among the operation data and history data ([0033] The operation management apparatus 100 includes an analysis unit 110, a data storage unit 120, and a result output unit 130, [0034] The analysis unit 110 performs various kinds of processing relating to analysis of monitoring data received from the monitored system 500, [0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data), the operation data being data of a manufacturing apparatus configured to intermittently process at least one product ([0105] In a chemical manufacturing plant, reactions are accelerated and desired products are produced with high purity by, for example, heating raw materials at a predetermined temperature or applying a predetermined pressure to raw materials), the history data relating to a manufacturing history of the product processed by the manufacturing apparatus ([0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data, [0026] FIG. 8 is a diagram illustrating an example of an abnormality detection history 224 in the first example embodiment of the present invention); setting an operational state of the manufacturing apparatus in the operation data based on the acquired analysis data (Fig. 4, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110)); and detecting abnormality of the manufacturing apparatus based on the operation data and the set operational state (Fig. 4, [0078] when abnormality is detected also with the correlation models B and C at the time “15:20”, along with the correlation model A, the model switchover unit 113 determines that abnormality of the monitored system 500 is detected, [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary ).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida et al. (US20170262561, herein Yoshida), in view of Miki et al. (US20160056064, herein Miki).
Regarding claim 4, Yoshida teaches The abnormality detection apparatus according to claim 1, wherein the history data includes a starting time and an ending time of processing performed on the product ([0035] The data storage unit 120 stores a time series of the monitoring data received from the monitored system 500 and various histories relating to analysis of the monitoring data, [0072] The analysis processing unit 112 records the usage history of the correlation model A at the time “15:10” in the model usage history 222, as in FIG. 7) , and the processor sets a processing state as the operational state for each time point of the operation data between the starting time and the ending time and …(Fig. 6, [0061] the model generation unit 111 may generate correlation models by using time series of each period of predetermined length (e.g., one day or one hour) in the time series of monitoring data at the time of normality. The predetermined length is set, for example, shorter than the length of a period in which the monitored system 500 continues each operating state). (i.e. processing state is interpreted as 15:00- 18:00, non-processing state is interpreted as before 15:00 or after 1800).
Yoshida does not teach sets a non-processing state as the operational state for each time point of the operation data not between the starting time and the ending time
Miki teaches sets a non-processing state as the operational state for each time point of the operation data not between the starting time and the ending time ([0045] FIG. 3D is a diagram illustrating content of the apparatus information acquired by the information acquiring unit 160, and a horizontal axis denotes a time. In the example illustrated in FIG. 3D, the apparatus information includes information indicating that the operational state is the in-process state (the “process” state) in each period of time of timings t1 to t3, t4 to t5, t6 to t7, and t8 to t10 and information indicating that the operational state is the non-process state (the “idle” state) in each period of time of timings t0 to t1, t3 to t4, t5 to t6, and t7 to t8)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoshida’s teaching of abnormality detection on data using time series indicating processing with Miki’s teaching of setting a non-processing state. The combined teaching provides an expected result of abnormality detection on data using time series setting processing and non-processing states. Therefore, one of ordinary skill in the art would be motivated “to improve the accuracy of anomaly detection and failure anticipation” as described by Miki [0070].
Regarding claim 5, the combination of Yoshida and Miki teach The abnormality detection apparatus according to claim 4, wherein the history data includes a work content of the processing performed on the product, and the processor sets a predetermined state corresponding to the work content as the operational state for each time point of the operation data between the starting time and the ending time (Yoshida, [0089] FIG. 10, the output screen 131 includes a model usage history display region 132, a model switchover history display region 133, and an abnormality detection history display region 134. The model usage history display region 132 indicates the usage history of main models up to the present time in the model usage history 222, [0110] the model generation unit 111 generates models for a plurality of processes of the plant, respectively, by using the time series of the respective processes at the time of normality, [0061] the model generation unit 111 may generate correlation models by using time series of each period of predetermined length (e.g., one day or one hour) in the time series of monitoring data at the time of normality. The predetermined length is set, for example, shorter than the length of a period in which the monitored system 500 continues each operating state. In this case) .
Regarding claim 7, Yoshida teaches The abnormality detection apparatus according to claim 6,
Miki teaches wherein the processor sets a non-processing state in a first period of the operation data and sets a processing state in a second period following the first period, ([0045] FIG. 3D is a diagram illustrating content of the apparatus information acquired by the information acquiring unit 160, and a horizontal axis denotes a time. In the example illustrated in FIG. 3D, the apparatus information includes information indicating that the operational state is the in-process state (the “process” state) in each period of time of timings t1 to t3, t4 to t5, t6 to t7, and t8 to t10 and information indicating that the operational state is the non-process state (the “idle” state) in each period of time of timings t0 to t1, t3 to t4, t5 to t6, and t7 to t8) and increases a detection sensitivity to the abnormality of the manufacturing apparatus in the second period to be higher than a detection sensitivity to the abnormality of the manufacturing apparatus in the first period ([0048] the cumulative condition setting unit 170 sets the weight coefficient for the in-process state (the “process” state) to Wp, and sets the weight coefficient for the non-process state (the “idle” state) to Wi (>Wp). In other words, the cumulative condition setting unit 170 sets the weight coefficient that is applied to the divergence DV of each period of time of the timings t1 to t3, t4 to t5, t6 to t7, and t8 to t10 and the weight coefficient that is applied to the divergence DV of each period of time of the timings t0 to t1, t3 to t4, t5 to t6, and t7 to t8 to Wp and Wi, respectively, according to the apparatus information).
Claim(s) 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida et al. (US20170262561, herein Yoshida), in view of Pederson et al. (US20090164933, herein Miki).
Regarding claim 8, Yoshida teaches The abnormality detection apparatus according to claim 1, wherein the processor transforms the operation data and the set operational state…, and detects the abnormality of the manufacturing apparatus based on the transformed operation data and the transformed operational state (Fig. 4, Fig. 6, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110), [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary).
Yoshida does not teach based on an amount of processing on the product
Pederson teaches based on an amount of processing on the product ([0063] The amount of fill provided by the progress bar 508 can be based on progress data that is derived using any of a number of sources. For example, progress data may be based on a number of steps (e.g., step indices) or operations to be performed for a particular process stage, a current execution time and an expected duration, a current fill level and an expected fill level, or any other type of data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoshida’s teaching of abnormality detection on data using time series indicating processing with Miki’s teaching of setting a non-processing state. The combined teaching provides an expected result of abnormality detection on data using time series setting processing and non-processing states. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by considering the overall progress as supported by Pederson [0064] By filling each of the process stage progress indicators 506 a-e as shown in the illustrated example with progress bars, a user can relatively easily determine the progress of a particular process stage and the overall progress of an overall recipe.
Regarding claim 9, the combination of Yoshida and Pederson teach The abnormality detection apparatus according to claim 8, wherein the processor sets a processing state for a predetermined period of the operation data as the operational state, and detects the abnormality of the manufacturing apparatus (Yoshida, Fig. 4, Fig. 6, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110), [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary) based on a comparison between the processing… and a length of the predetermined period ([0061] generate correlation models by using time series of each period of predetermined length (e.g., one day or one hour) in the time series of monitoring data at the time of normality).
Yoshida does not teach based on a comparison between the processing amount in the processing state and a length of the predetermined period.
Pederson teaches based on a comparison between the processing amount in the processing state and a length of the predetermined period. ([0063] The amount of fill provided by the progress bar 508 can be based on progress data that is derived using any of a number of sources. For example, progress data may be based on a number of steps (e.g., step indices) or operations to be performed for a particular process stage, a current execution time and an expected duration, a current fill level and an expected fill level, or any other type of data, [0037] to display such relative progress or performance comparisons, the batch information monitor-processor 306 may retrieve progress, status, and/or other performance information previously stored in the recipe progress history data store 308 for previous executions of those process phases or stages, and analyze progress, status, and/or other performance information of current executions of the phases or stages based on the retrieved historical progress, status, and performance information)
Regarding claim 10, the combination of Yoshida and Pederson teach The abnormality detection apparatus according to claim 9, wherein the processor normalizes the length of the predetermined period …, and detects the abnormality of the manufacturing apparatus based on …the normalized length of the predetermined period (Yoshida, [0061] the model generation unit 111 may generate correlation models by using time series of each period of predetermined length (e.g., one day or one hour) in the time series of monitoring data at the time of normality. The predetermined length is set, for example, shorter than the length of a period in which the monitored system 500 continues each operating state, Fig. 4, Fig. 6, [0077] When abnormality is detected with all the sub-models in step S109 (step S109/Y), the model switchover unit 113 determines that abnormality of the monitored system 500 is detected. The model switchover unit 113 outputs an abnormality notification to the user or the like through the result output unit 130 (step S110), [0080] When there is a sub-model with which abnormality is not detected in step S109 (step S109/N), the model switchover unit 113 determines that the present main model does not conform to the present operating state of the monitored system 500 and switching of main models is necessary).
Yoshida does not teach based on the processing amount… a comparison between the processing amount
Pederson teaches based on the processing amount… a comparison between the processing amount and the normalized length of the predetermined period ([0063] The amount of fill provided by the progress bar 508 can be based on progress data that is derived using any of a number of sources. For example, progress data may be based on a number of steps (e.g., step indices) or operations to be performed for a particular process stage, a current execution time and an expected duration, a current fill level and an expected fill level, or any other type of data, [0037] to display such relative progress or performance comparisons, the batch information monitor-processor 306 may retrieve progress, status, and/or other performance information previously stored in the recipe progress history data store 308 for previous executions of those process phases or stages, and analyze progress, status, and/or other performance information of current executions of the phases or stages based on the retrieved historical progress, status, and performance information)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Kumagae (US20140358481) discloses a system for identifying cause of abnormality.
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/YVONNE T FOLLANSBEE/
Examiner, Art Unit 2117
/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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