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 .
Response to Arguments
Applicant's arguments filed 08/28/2025 have been fully considered but they are not persuasive.
Regarding the argument that Maeda et al. (US PGPUB 2012/0166142 A1) does not disclose, teach, or suggest, “annotating outputs from the computer-based model to generate annotated analytic outputs.” The Examiner respectfully notes that ¶45 of Maeda et al. discloses, “Selection of learning data (completeness evaluation) and anomaly diagnosis/prognosis may be performed using event data (such as alarm information) other than sensor data. Further, a more robust anomaly detection can be realized via identification using a plurality of classifiers.” (emphasis added) which shows that annotation of the data for ‘more robust anomaly detection’ occurs.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maeda et al. (US PGPUB 2012/0166142 A1) in view of Weitkamp (US PGPUB 2010/0013227 A1).
Regarding claim 1, Maeda et al. discloses a method for controlling a wind turbine ([0003], “wind mills in wind power plants”), the method comprising:
detecting, via a controller (119, [0089]), a plurality of analytic outputs from a plurality of analytics (Figs. 1, 2; [0040]-[0042]);
generating, via the controller, at least one computer-based model of the wind turbine ([0074], “The target facility can be models by collecting such sensor signals for each case.”) using at least a portion of analyzed plurality of analytic outputs ([0056], [0074, [0095]; Figs. 8, 18, and 19);
annotating outputs from the computer-based model to generate annotated analytic outputs (“Selection of learning data (completeness evaluation) and anomaly diagnosis/prognosis may be performed using event data (such as alarm information) other than sensor data. Further, a more robust anomaly detection can be realized via identification using a plurality of classifiers.” [0045]),
training, via the controller, the at least one computer-based model of the wind turbine using the annotated analytic outputs of the wind turbine ([0095], “a learning data composed of mainly normal data”, “a model generation section for modeling learning data”);
checking the plurality of analytic outputs for anomalies using the at least one computer-based model ([0012]);
However, while Maeda et al. discloses displaying a detected anomaly in [0099], they do not explicitly disclose, “detecting at least one anomaly in the plurality of analytic outputs and implementing a control action in response thereto comprising one of: shutting down the wind turbine, derating the wind turbine, or uprating the turbine.”
Weitkamp teaches, in the field of operating wind turbines, “A safety shutdown of a wind power plant can be triggered above all due to excess rotation speeds, vibrations, errors in the control hardware and/or control software and in the case of excess cable twisting in the tower head. ([0015])” (emphasis added).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the controller of Maeda et al. to cause a safety shutdown of the wind power plant to be triggered when errors (e.g., anomalies) in the control hardware and/or control software are detected as taught by Weitkamp as both references are in the same field of endeavor, and one of ordinary skill would appreciate that, “The safety shutdown device is a device that is logically superordinate to the operating control system, which monitors compliance with safety-critical threshold values of the wind power plant independently of other operating controls and triggers a safety shutdown when one of these threshold values is exceeded. This is, in particular, required when the operating control system of the wind power plant during serious failures is not in the position to maintain the wind power plant in normal operating range. The safety shutdown device also keeps the wind power plant in a safe system state in the case of a failure of the operating control system. ([0014])”.
Regarding claim 2, the combination of Maeda et al. and Weitkamp teach all of claim 1 as above, wherein the analytic outputs of the wind turbine comprises at least two of the following: condition- based monitoring system data or events (Maeda et al., [0061]-[0062]), one or more environmental conditions (Maeda et al., [0061]), sensor data (Maeda et al., [0061]), alerts (Maeda et al., [0099] “the result of anomaly diagnosis/prognosis is displayed), or events (Maeda et al., [0062)).
Regarding claim 3, the combination of Maeda et al. and Weitkamp teach all of claim 1 as above, wherein analyzing the plurality of analytic outputs of wind turbine further comprises: filtering the plurality of analytic inputs (Maeda et al., [0041]-[0042]).
Regarding claim 4, the combination of Maeda et al. and Weitkamp teach all of claim 1 as above, wherein analyzing the plurality of analytic outputs of the wind turbine further comprises:
using at least one of principal component analysis (Maeda et al., [0047]) or factorization to reduce a number of dimensions in the plurality of analytic components (Maeda et al., [0046]).
Regarding claim 5, the combination of Maeda et al. and Weitkamp teach all of claim 1 as above, wherein training the at least one computer-based model of the wind turbine using annotated analytic outputs further comprises:
at least one of machine learning the at least one computer-based model (Maeda et al., [0095], “a model generation section for modeling learning date”) using the annotated analytic outputs of the wind turbine or using a rules engine (Maeda et al., [0050], “construction of determination conditions (rules) according to various types of anomalies”) on the plurality of analytic outputs of the wind turbine.
Regarding claim 6, the combination of Maeda et al. and Weitkamp teach all of claim 5 as above, wherein training the at least one computer-based model of the wind turbine using the annotated analytic outputs further comprises
utilizing association rule mining for determining fusion-based rules based on the co-occurrence of anomalies (Maeda et al.; [0049]-[0051], Fig. 5 shows that the method is able to determine the difference between ‘design-based anomaly detection’ and ‘case-based anomaly detection’).
Regarding claim 9, Maeda et al. discloses a method for controlling a wind turbine ([0003], “wind mills in wind power plants”), the method comprising:
detecting, via a controller (119, [0089]), a plurality of analytic outputs from a plurality of analytics (Figs. 1, 2; [0040]-[0042]);
generating, via the controller, at least one computer-based model of the wind turbine ([0074], “The target facility can be models by collecting such sensor signals for each case.”) using at least a portion of analyzed plurality of analytic outputs ([0056], [0074, [0095]; Figs. 8, 18, and 19);
annotating outputs from the computer-based model to generate annotated analytic outputs (“Selection of learning data (completeness evaluation) and anomaly diagnosis/prognosis may be performed using event data (such as alarm information) other than sensor data. Further, a more robust anomaly detection can be realized via identification using a plurality of classifiers.” [0045]),
training, via the controller, the at least one computer-based model of the wind turbine using annotated analytic outputs of the wind turbine ([0095], “a learning data composed of mainly normal data”, “a model generation section for modeling learning data”);
checking the plurality of analytic outputs for anomalies using the at least one computer-based model ([0012]) and identifying a plurality of anomalies (Abstract “also appropriately executing setting and control of threshold values for highly sensitive, early, and clearly visible detection of anomalies”);
further comprising performing one or more of: combining anomalies of the plurality of anomalies that are from a condition-based monitoring system ([0055], “a global anomaly measurement is determined by via fusion (global anomaly measure) 14”); and
determining fusion-based rules based on the combined anomalies by association rule mining of historical databased of wind turbine data ([0049]-[0051], Fig. 5 shows that the method is able to determine the difference between ‘design-based anomaly detection’ and ‘case-based anomaly detection’; [0013] “The case-based anomaly detection performs modeling of the learning data via a subspace classifier, and detects an anomaly candidate based on the distance relationship of observation data and subspace.”)
However, while Maeda et al. discloses displaying a detected anomaly in [0099], they do not explicitly disclose, “implementing a control action in response to one or more of the plurality of anomalies comprising one of: shutting down the wind turbine, derating the wind turbine, or uprating the turbine.”
Weitkamp teaches, in the field of operating wind turbines, “A safety shutdown of a wind power plant can be triggered above all due to excess rotation speeds, vibrations, errors in the control hardware and/or control software and in the case of excess cable twisting in the tower head. ([0015])” (emphasis added).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the controller of Maeda et al. to cause a safety shutdown of the wind power plant to be triggered when errors (e.g., anomalies) in the control hardware and/or control software are detected as taught by Weitkamp as both references are in the same field of endeavor, and one of ordinary skill would appreciate that, “The safety shutdown device is a device that is logically superordinate to the operating control system, which monitors compliance with safety-critical threshold values of the wind power plant independently of other operating controls and triggers a safety shutdown when one of these threshold values is exceeded. This is, in particular, required when the operating control system of the wind power plant during serious failures is not in the position to maintain the wind power plant in normal operating range. The safety shutdown device also keeps the wind power plant in a safe system state in the case of a failure of the operating control system. ([0014])”.
Regarding claim 11, the combination of Maeda et al. and Weitkamp teach all of claim 1 as above, wherein the at least on computer based-model comprises a support vector machine ([0086)).
Regarding claim 12, Maeda et al. discloses a system (abstract) for controlling a wind turbine ({0003], “wind mills in wind power plants”), the system comprising:
a plurality of analytics for generating a plurality of different analytics (Figs. 1, 2; [0040] -[0042]) of the wind turbine;
a controller (119, [0089]) communicatively coupled to the plurality of analytics, the controller configured to a plurality of operations, the plurality of operations comprising:
analyzing the plurality of analytic outputs of the wind turbine (Figs. 1, 2; [0040]-[0042]);
generating at least one computer-based model of the wind turbine ([0074], “The target facility can be models by collecting such sensor signals for each case.”) using at least a portion of analyzed plurality of analytic outputs ([0056], [0074, [0095]; Figs. 8, 18, and 19);
annotating outputs from the computer-based model to generated annotated analytic outputs (“Selection of learning data (completeness evaluation) and anomaly diagnosis/prognosis may be performed using event data (such as alarm information) other than sensor data. Further, a more robust anomaly detection can be realized via identification using a plurality of classifiers.” [0045]);
training the at least one computer-based model of the wind turbine using annotated analytic outputs of the wind turbine ([0095], “a learning data composed of mainly normal data”, “a model generation section for modeling learning data”);
checking the plurality of analytic outputs for anomalies using the at least one computer-based model ([0012]); and
detecting at least one anomaly in the plurality of analytic outputs ([0012]) and implementing a control action (Fig. 6, the detected anomaly is displayed).
However, while Maeda et al. discloses displaying a detected anomaly in [0099], they do not explicitly disclose, “detecting at least one anomaly in the plurality of analytic outputs and implementing a control action in response thereto comprising one of: shutting down the wind turbine, derating the wind turbine, or uprating the turbine.”
Weitkamp teaches, in the field of operating wind turbines, “A safety shutdown of a wind power plant can be triggered above all due to excess rotation speeds, vibrations, errors in the control hardware and/or control software and in the case of excess cable twisting in the tower head. ([0015])” (emphasis added).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the controller of Maeda et al. to cause a safety shutdown of the wind power plant to be triggered when errors (e.g., anomalies) in the control hardware and/or control software are detected as taught by Weitkamp as both references are in the same field of endeavor, and one of ordinary skill would appreciate that, “The safety shutdown device is a device that is logically superordinate to the operating control system, which monitors compliance with safety-critical threshold values of the wind power plant independently of other operating controls and triggers a safety shutdown when one of these threshold values is exceeded. This is, in particular, required when the operating control system of the wind power plant during serious failures is not in the position to maintain the wind power plant in normal operating range. The safety shutdown device also keeps the wind power plant in a safe system state in the case of a failure of the operating control system. ([0014])”.
Regarding claim 13, the combination of Maeda et al. and Weitkamp teach all of claim 12 as above, wherein the analytic outputs of the wind turbine comprises at least two of the following: condition-based monitoring system data or events (Maeda et al.; [0061]-[0062]), one or more environmental conditions (Maeda et al.; [0061]), sensor data (Maeda et al.; [0061]), anomalies (Maeda et al., [0061]), alerts (Maeda et al.; [0099] “the result of anomaly diagnosis/prognosis is displayed), or events (Maeda et al., [0062)).
Regarding claim 14, the combination of Maeda et al. and Weitkamp teach all of claim 12 as above, wherein analyzing the plurality of analytic outputs of wind turbine further comprises:
filtering the plurality of analytic inputs (Maeda et al., [0041]-[0042]).
Regarding claim 15, the combination of Maeda et al. and Weitkamp teach all of claim 12 as above, wherein analyzing the plurality of analytic outputs of the wind turbine further comprises:
using at least one of principal component analysis (Maeda et al., [0047]) or factorization to reduce a number of dimensions in the plurality of analytic components (Maeda et al., [0046]).
Regarding claim 16, the combination of Maeda et al. and Weitkamp teach all of claim 12 as above, wherein training the at least one computer-based model of the wind turbine using annotated analytic outputs further comprises:
at least one of machine learning the at least one computer-based model (Maeda et al.; [0095], “a model generation section for modeling learning date”) using the annotated analytic outputs of the wind turbine or using a rules engine (Maeda et al.; [0050], “construction of determination conditions (rules) according to various types of anomalies”) on the plurality of analytic outputs of the wind turbine.
Regarding claim 18, Maeda et al. discloses a system (abstract) for controlling a wind turbine ([0003], “wind mills in wind power plants”), the system comprising:
a plurality of analytics for generating a plurality of different analytics (Figs. 1, 2; [0040]-[0042]) of the wind turbine;
a controller (119, [0089]) communicatively coupled to the plurality of analytics, the controller configured to a plurality of operations, the plurality of operations comprising:
analyzing the plurality of analytic outputs of the wind turbine (Figs. 1, 2; [0040]-[0042]);
generating at least one computer-based model of the wind turbine ([0074], “The target facility can be models by collecting such sensor signals for each case.”) using at least a portion of analyzed plurality of analytic outputs ([0056], [0074, [0095]; Figs. 8, 18, and 19);
annotating outputs from the computer-based model to generated annotated analytic outputs (“Selection of learning data (completeness evaluation) and anomaly diagnosis/prognosis may be performed using event data (such as alarm information) other than sensor data. Further, a more robust anomaly detection can be realized via identification using a plurality of classifiers.” [0045]);
training the at least one computer-based model of the wind turbine using annotated analytic outputs of the wind turbine ([0095], “a learning data composed of mainly normal data”, “a model generation section for modeling learning data”);
checking the plurality of analytic outputs for anomalies using the at least one computer-based model ([0012]); and identifying a plurality of anomalies (Abstract “also appropriately executing setting and control of threshold values for highly sensitive, early, and clearly visible detection of anomalies”);
further comprising performing one or more of: combining anomalies of the plurality of anomalies that are from a condition-based monitoring system ([0055], “a global anomaly measurement is determined by via fusion (global anomaly measure) 14”); and
determining fusion-based rules based on the combined anomalies by association rule mining of historical databased of wind turbine data ([0049]-[0051], Fig. 5 shows that the method is able to determine the difference between ‘design-based anomaly detection’ and ‘case-based anomaly detection’; [0013] “The case-based anomaly detection performs modeling of the learning data via a subspace classifier, and detects an anomaly candidate based on the distance relationship of observation data and subspace.”)
However, while Maeda et al. discloses displaying a detected anomaly in [0099], they do not explicitly disclose, “implementing a control action in response to one or more of the plurality of anomalies comprising one of: shutting down the wind turbine, derating the wind turbine, or uprating the turbine.”
Weitkamp teaches, in the field of operating wind turbines, “A safety shutdown of a wind power plant can be triggered above all due to excess rotation speeds, vibrations, errors in the control hardware and/or control software and in the case of excess cable twisting in the tower head. ([0015])” (emphasis added).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the controller of Maeda et al. to cause a safety shutdown of the wind power plant to be triggered when errors (e.g., anomalies) in the control hardware and/or control software are detected as taught by Weitkamp as both references are in the same field of endeavor, and one of ordinary skill would appreciate that, “The safety shutdown device is a device that is logically superordinate to the operating control system, which monitors compliance with safety-critical threshold values of the wind power plant independently of other operating controls and triggers a safety shutdown when one of these threshold values is exceeded. This is, in particular, required when the operating control system of the wind power plant during serious failures is not in the position to maintain the wind power plant in normal operating range. The safety shutdown device also keeps the wind power plant in a safe system state in the case of a failure of the operating control system. ([0014])”.
Regarding claim 19, the combination of Maeda et al. and Weitkamp teach all of claim 12 as above, wherein implementing the control action when the anomaly is detected further comprises generating an alarm or alert (Maeda et al., (0091]-[0092]).
Regarding claim 20, Maeda et al. discloses a wind farm ([0003]), comprising:
a plurality of wind turbines ([(0003]) each comprising a turbine controller (119);
a farm-level controller (abstract, [0092] “the number of facilities can be more than one.”) coupled to perform a plurality of operations comprising:
receiving a plurality of analytics for generating a plurality of analytics (Figs. 1, 2; [0040] - [0042]) of the wind turbine;
a controller (119, [0089]) communicatively coupled to the plurality of analytics, the controller configured to a plurality of operations, the plurality of operations comprising:
analyzing the plurality of analytic outputs of the wind turbine (Figs. 1, 2; [0040]-[0042]):
generating at least one computer-based model of the wind turbine ([0074], “The target facility can be models by collecting such sensor signals for each case.”) using at least a portion of analyzed plurality of analytic outputs ([0056], [0074, [0095]; Figs. 8,18, and 19);
annotating outputs from the computer-based model to generated annotated analytic outputs (“Selection of learning data (completeness evaluation) and anomaly diagnosis/prognosis may be performed using event data (such as alarm information) other than sensor data. Further, a more robust anomaly detection can be realized via identification using a plurality of classifiers.” [0045]);
training the at least one computer-based model of the wind turbine using annotated analytic outputs of the wind turbine ([0095], “a learning data composed of mainly normal data”, “a model generation section for modeling learning data”);
checking the plurality of analytic outputs for anomalies using the at least one computer-based model ([0012]); and
detecting at least one anomaly in the plurality of analytic outputs ([0012]) and implementing a control action (Fig. 6, the detected anomaly is displayed).
However, while Maeda et al. discloses displaying a detected anomaly in [0099], they do not explicitly disclose, “detecting at least one anomaly in the plurality of analytic outputs and implementing a control action in response thereto comprising one of: shutting down the wind turbine, derating the wind turbine, or uprating the turbine.”
Weitkamp teaches, in the field of operating wind turbines, “A safety shutdown of a wind power plant can be triggered above all due to excess rotation speeds, vibrations, errors in the control hardware and/or control software and in the case of excess cable twisting in the tower head. ([0015])” (emphasis added).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the controller of Maeda et al. to cause a safety shutdown of the wind power plant to be triggered when errors (e.g., anomalies) in the control hardware and/or control software are detected as taught by Weitkamp as both references are in the same field of endeavor, and one of ordinary skill would appreciate that, “The safety shutdown device is a device that is logically superordinate to the operating control system, which monitors compliance with safety-critical threshold values of the wind power plant independently of other operating controls and triggers a safety shutdown when one of these threshold values is exceeded. This is, in particular, required when the operating control system of the wind power plant during serious failures is not in the position to maintain the wind power plant in normal operating range. The safety shutdown device also keeps the wind power plant in a safe system state in the case of a failure of the operating control system. ([0014])”.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Maeda et al. (US PGPUB 2012/0166142 A1) and Weitkamp (US PGPUB 2010/0013227 A1) as applied to claim 1 above, and further in view of Lavid et al. (WO 2017/139046 A1).
Regarding claim 7, the combination of Maeda et al. and Weitkamp teach all of claim 1 as above.
However, the combination Maeda et al. and Weitkamp do not explicitly teach, “performing a root cause analysis of the annotated analytic outputs of the wind turbine.”
Lavid et al. teaches in the field of root cause analysis for machine failures using unsupervised machine learning [0026], “that root causes of machine failures may be determined through modeling of sensory inputs and detecting indicators in the sensory inputs [0027]” and “Based on the indicators, root causes of the machine failures may be determined [0027]”
It would have been obvious to one of ordinary skill in the art before the effective filing date to use the analytics of Maeda et al. and Weitkamp to determine root causes of failure through unsupervised machine learning as taught by Lavid et al., as both references are in the same field of endeavor (detecting anomalies in machine data), and one of ordinary skill in the art would appreciate that “The unsupervised machine learning analysis includes at least detecting anomalies in the analyzed sensory inputs. Based on the unsupervised machine learning analysis, an attribution dataset including at least the sensory inputs leading to the failure is determined. Based on the attribution dataset, one or more analytics including a root cause of the failure is determined. In some embodiments, a recommendation for avoiding re- occurrence of the failure may be generated. [0026]”.
Regarding claim 8, the combination of Maeda et al., Weitkamp, and Lavid et al. teach all of claim 7 as above, further comprising storing the root cause analysis of the annotated analytic outputs for future use and/or providing the root cause analysis to the at least one computer-based model of the wind turbine (Lavid et al., [0026]).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Maeda et al. (US PGPUB 2012/0166142 A1) and Weitkamp (US PGPUB 2010/0013227 A1) as applied to claim 12 above, and further in view of Lavid et al. (WO 2017/139046 A1).
Regarding claim 17, the combination of Maeda et al. and Weitkamp teach all of claim 12 as above.
However, the combination Maeda et al. and Weitkamp do not explicitly teach, “performing a root cause analysis of the annotated analytic outputs of the wind turbine.”
Lavid et al. teaches in the field of root cause analysis for machine failures using unsupervised machine learning [0026], “that root causes of machine failures may be determined through modeling of sensory inputs and detecting indicators in the sensory inputs [0027]” and “Based on the indicators, root causes of the machine failures may be determined [0027]”
It would have been obvious to one of ordinary skill in the art before the effective filing date to use the analytics of Maeda et al. and Weitkamp to determine root causes of failure through unsupervised machine learning as taught by Lavid et al., as both references are in the same field of endeavor (detecting anomalies in machine data), and one of ordinary skill in the art would appreciate that “The unsupervised machine learning analysis includes at least detecting anomalies in the analyzed sensory inputs. Based on the unsupervised machine learning analysis, an attribution dataset including at least the sensory inputs leading to the failure is determined. Based on the attribution dataset, one or more analytics including a root cause of the failure is determined. In some embodiments, a recommendation for avoiding re- occurrence of the failure may be generated. [0026]”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.C.C./Examiner, Art Unit 3745
/COURTNEY D HEINLE/Supervisory Patent Examiner, Art Unit 3745
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