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 1-16 are pending, of which claims 1, 15, and 16 are independent claims. Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for European Application No. 2216273.7 filed on December 23, 2022. Information Disclosure Statement The references cited in the information disclosure statement (IDS) submitted on 12/22/2023 has been considered by the examiner. Claim Objections The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes: Claim 4, line 3, insert “and” before “wherein”. Claim 7, line 1, replace “determining” with “the determining of”. Claim 8, line 1, replace “providing” with “the providing of”. Claim 10, line 1, insert “further” before “comprising”. Claim 11, line 1, insert “further” before “comprising”. Claim 12, line 2, insert “and” before “wherein”. Claim 13, line 1, insert “further” before “comprising”. Appropriate correction is respectfully requested. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Independent claim 1 recites, “... determining, based on the operational data, a set of candidate root causes associated with the fault, by applying a machine learning model to the operational data, wherein the machine learning model is configured to classify and/or locate one or more candidate root causes.. .” Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0041], [0050], [0083], and [0084], for instance, the determining and classifying limitations are mental processes that can be performed through observation, evaluation and judgement. Therefore, a person may perform, through observation, evaluation and judgement, the features enunciated above. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of, “- obtaining operational data associated with operation of a wind turbine system in response to a fault of the wind turbine system; … - providing, based on the set of candidate root causes, output data indicative of at least one root cause of the fault of the wind turbine system”. The obtaining limitation is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo , 566 U.S. at 79, 101 USPQ2d at 1968. In accord with MPEP 2105(g), “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.” The additional features including “the wind turbine system”, as recited in the claim that is configured to carry out the additional and abstract idea limitations may be tools that are used as recited in claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or machine. Implementing an abstract idea on a generic electronic or computer component as a tool to perform an abstract idea is not indicative of integration into a practical application. The providing limitation in response to the judicial exception identified is not applying or using the judicial exception in some meaningful way beyond outputting the at least one airflow parameter that does not integrate the abstract idea into a practical application and it is an insignificant extra-solution activity to the judicial exception MPEP 2106.05(g). In view of the foregoing, the additional limitations , individually or combined, are not sufficient to demonstrate integration of a judicial exception into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The obtaining limitation represents a function that is recognized as well-understood, routine, and conventional. For instance, US Patent Publication No. 2022/0099532 A1 to Zhang et al. describes in paragraph [0052] “Referring particularly to FIG. 4, in an embodiment, the controller 200 of the system 300 may be configured to receive a plurality of operational data sets 304. The operational data sets 304 may be 0 of a performance anomaly 306 for the power generating asset 100.” US Patent Publication No. 2022/0292666 A1 to Zhang et al. describes in paragraph [0036] “Operational data 305 might be received directly or indirectly from the wind turbines, such as a database storing the data and/or a service provider that might aggregate or otherwise collect the operational data. For example, data processing & data filtering component 315 might operate to exclude turbine downtime data received in operational data 305 since such data may not be needed in some embodiments herein.” US Patent Publication No. 2018/0038346 A1 to Booth et al. describes in the Abstract “…in response to the transmission of the polling signal, receiving current configuration data for the wind turbine component from the identification sensor. The method may also include comparing the current configuration data received from the identification sensor to last-known configuration data for the wind turbine component and automatically updating one or more parameter settings associated with operating the wind turbine based on any differences identified between the current configuration data and the last-known configuration data.” The additional features including “the wind turbine system”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used for the functions recited in claim 17, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or machine. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”) The providing limitation represents a function that is recognized as well-understood, routine, and conventional. For instance, US Patent Publication No. 2022/0099532 A1 to Zhang et al. describes in Paragraph [0074] “In an embodiment, the system 300 may implement a control action 368 based on the determined actual root cause 350. For example, in an embodiment, the control action 368 may include generating an alarm. The generation of the alarm may facilitate the scheduling of a maintenance event in order to address the actual root cause 350 of the performance anomaly 306. Accordingly, the alarm may include an auditory signal, a visual signal, an alert, a notification, a system input, and/or any other system which may identify the root cause to an operator. Therefore, the additional claimed features, individually or combined, do not amount to significantly more and the claim is not patent eligible.” US Patent Publication No. 2022/0292666 A1 to Zhang et al. describes in paragraph [0038] “For example, deep learning model building and validation component 325 may operate to develop (i.e., generate) a deep learning classification model that builds connections (e.g., transfer functions, algorithms, etc.) between the scatter plots based on the operational data and root causes for anomalies in the operational data by processing an input of high-dimensional images including data pixels corresponding to the scatter plots to generate an output including root cause labels associated with one or more anomalies derived from data patterns in the images.” US Patent Publication No. 2022/0245801 A1 to Gietzen et al. describes in paragraph [0133] “The process continues in the second stage by accessing the production image input feature for the bad image (step 7) and providing the input features to the trained root cause classifier 171 (step 8). Each decision tree in the root cause classifier 171 votes for the input image features by applying the one-vs-the-rest classification. In this case, the classification determines whether the image belongs to one of the six failure class versus the rest of the five failure classes.” Thus, in view of the foregoing, independent claim 1 is not patent eligible. Regarding claims 2-6 and 14, these claims are also directed to further defining the abstract idea as recited in independent claim 1. There are no additional limitations in the claims to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 2-6 and 14 are not patent eligible. Regarding claim 7, this claim recites “determining the set of candidate root causes associated with the fault comprises determining a probability that a candidate root cause of the set of candidate root causes is associated with the fault”. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0083], [0084], [0093], and [0094], for instance, the determining limitation is a mental process that can be performed through observation, evaluation and judgement. Therefore, a person may perform, through observation, evaluation and judgement, the features enunciated above. There are no additional limitations in the claim to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claim is not more than a drafting effort designed to monopolize the exception. The claim also does not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claim 7 is not patent eligible. Regarding claim 8, this claim recites “ providing the output data indicative of at least one root cause of the fault comprises filtering the candidate root causes of the set based on their respective probabilities ”. The limitation in response to the judicial exception identified is not applying or using the judicial exception in some meaningful way beyond outputting the at least one airflow parameter that does not integrate the abstract idea into a practical application and it is an insignificant extra-solution activity to the judicial exception MPEP 2106.05(g). Also, the limitation represents a function that is recognized as well-understood, routine, and conventional. For instance, US Patent Publication No. 2022/0099532 A1 to Zhang et al., paragraphs [0057], [0058], and [0071]; and US Patent Publication No. 2022/0245801 A1 to Gietzen et al., paragraphs [0045] and [0088]. Thus, claim 8 is not patent eligible. Regarding claims 9, 12, and 13, these claims are also directed to further defining the operational data obtained through data gathering as recited in independent claim 1. There are no additional limitations in the claims to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 9, 12, and 13 are not patent eligible. Regarding claim 10, this claim recites “determining a difference between a first configuration of the wind turbine system and a second configuration of the wind turbine system”. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0096]-[0099], for instance, the determining limitation is a mental process that can be performed through observation, evaluation and judgement. Therefore, a person may perform, through observation, evaluation and judgement, the features enunciated above. There are no additional limitations in the claim to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limitation on the judicial exception. The claim is not more than a drafting effort designed to monopolize the exception. The claim also does not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claim 10 is not patent eligible. Regarding claim 11, this claim recites “determining, based on historical operational data and/or the operational data, one or more relations between the difference and the fault”. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0098]-[0099], for instance, the determining limitation is a mental process that can be performed through observation, evaluation and judgement. Therefore, a person may perform, through observation, evaluation and judgement, the features enunciated above. The claim does not include additional elements that integrate the judicial exceptions into a practical application and are not sufficient to amount to significantly more than the judicial exception. The function “generating a data structure representative of the one or more relations” in response to the judicial exception is not applying or using the judicial exception in some meaningful way beyond outputting the at least one airflow parameter that does not integrate the abstract idea into a practical application and it is an insignificant extra-solution activity to the judicial exception MPEP 2106.05(g). Also, the limitation represents a function that is recognized as well-understood, routine, and conventional. For instance, US Patent Publication No. 2021/0184462 A1 to Gupta et al., paragraphs [0014]-[0017]; US Patent Publication No. 2011/0182712 A1 to Nayebi et al., paragraphs [0015], [0019], [0021], and [0022]; and US Patent Publication No. 2020/0309099 A1 to Zhang et al., paragraphs [0031], [0032], [0047], and [0050]. Thus, claim 11 is not patent eligible. Regarding independent claim 15, the functions of independent claim 15 are implemented by similar functions as those of the method of independent claim 1 with substantially the same limitations. Therefore, the rejection applied to independent claim 15 above also applies to independent claim 15. Independent claim 15 further recites “A root cause analysis system comprising: a memory circuitry; a processor circuitry communicatively coupled to the memory circuitry”. The claim does not include additional elements that integrate the judicial exceptions into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional limitations may be tools that are used as recited in independent claim 15, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application. In addition, the additional limitations may be tools that are used for the functions recited in claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”) Accordingly, i ndependent claim 15 is not deemed patent eligible. Regarding independent claim 16, the functions of independent claim 16 are implemented by similar functions as those of the method of independent claim 1 with substantially the same limitations. Therefore, the rejection applied to independent claim 16 above also applies to independent claim 16. Independent claim 16 further recites “A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device cause the electronic device to perform an operation”. The claim does not include additional elements that integrate the judicial exceptions into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional limitations may be tools that are used as recited in independent claim 16, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application. In addition, the additional limitations may be tools that are used for the functions recited in claim 1, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”) Accordingly, i ndependent claim 16 is not deemed patent eligible. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1-4, 7-9, 12, and 14-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (US Patent Publication No. 2022/0099532 A1) (“ Zhang ”). Regarding independent claim 1, Zhang teaches: A method, performed by a root cause analysis system, the method comprising: Zhang : Paragraph [0001] (“…systems and methods for operating power generating assets by determining an actual root cause of a performance anomaly.”) - obtaining operational data associated with operation of a wind turbine system in response to a fault of the wind turbine system; Zhang : Paragraph [0052] (“Referring particularly to FIG. 4, in an embodiment, the controller 200 of the system 300 may be configured to receive a plurality of operational data sets 304. The operational data sets 304 may be indicative of a performance anomaly 306 for the power generating asset 100 . The operational data sets 304 may, in an embodiment, include multiple parameters associated with the performance and/or health of a component of the power generating asset 100, the environmental data set 302, and/or data indicative of a performance parameter of the power generating asset 100 which is subject to monitoring . For example, in an embodiment, the performance parameter may comprise the power outputs of the wind turbine 114. In an embodiment, the performance parameter may be a tip speed ratio, a pitch setpoint, a yawing moment, and/or a bending moment. In other words, the plurality of operational data sets 304 may include a plurality of performance analytics, which generally refer to collected and analyzed data associated with the performance and/or health of the power generating asset 100, which may be characterized, stored, and/or analyzed to study various trends or patterns in the data.”) Zhang : Paragraph [0074] (“In an embodiment, the system 300 may implement a control action 368 based on the determined actual root cause 350. For example, in an embodiment, the control action 368 may include generating an alarm. The generation of the alarm may facilitate the scheduling of a maintenance event in order to address the actual root cause 350 of the performance anomaly 306.”) - determining, based on the operational data, a set of candidate root causes associated with the fault, by applying a machine learning model to the operational data, wherein the machine learning model is configured to classify and/or locate one or more candidate root causes; and Zhang : Paragraph [0054] (“In an embodiment, the potential root causes 308 of the performance anomaly 306 and a number of corresponding probabilities 310 for each of the potential root cases 308 may be determined by the controller 200 via the execution of a plurality of predictive models 312 (e.g. root-cause predictive models). The predictive models 312 may, for example, include feature-based AI models, image-based AI models, time-series-based AI models, and/or any other suitable AI model configured to analyze at least a portion of the operational data set 304 and identify potential root causes 308 of the performance anomaly 306 . In other words, in an embodiment, the predictive models 312 may, for example, be classification models (e.g. a neural network classification model). Each classification model may be configured to determine and refine an optimal mapping (e.g. transfer function) between a set of inputs (e.g. the portion of the operational data set 304) and the outputs (e.g. the probability 310 that an identified root cause 308 is the actual root cause 350 of the performance anomaly 306).”) Zhang : Paragraph [0055] (“Upon detecting the performance anomaly 306, a first predictive model 318 may analyze the first operational data set 314 in order to develop a first plurality 320 of potential root causes 308 and corresponding probabilities 310. Additionally, a second predictive model 322 may analyze the second operational data set 316 in order to develop a second plurality 324 of potential root causes 308 and corresponding probabilities 310. Therefore, the first plurality 320 of potential root causes 308 may correlate to the first operational data set 314, while the second plurality 324 of potential root causes 308 may correlate to the second operational data set 316.”) - providing, based on the set of candidate root causes, output data indicative of at least one root cause of the fault of the wind turbine system. Zhang : Paragraph [0074] (“In an embodiment, the system 300 may implement a control action 368 based on the determined actual root cause 350. For example, in an embodiment, the control action 368 may include generating an alarm. The generation of the alarm may facilitate the scheduling of a maintenance event in order to address the actual root cause 350 of the performance anomaly 306. Accordingly, the alarm may include an auditory signal, a visual signal, an alert, a notification, a system input, and/or any other system which may identify the root cause to an operator . It should be appreciated that the control action 368 as described herein may further include any suitable command or constraint by the controller 200. For example, in an embodiment, the control action 368 may include temporarily de-rating the power generating asset 100. Additionally, in an embodiment, the control action 368 may include limiting an operation of at least one component of the power generating asset. For example, the control action 368 may limit a pitching of a rotor blade 112 and/or a yawing of the nacelle 106 of the wind turbine 114.”) [The generation of the alarm associated with the root case reads on “providing, based on the set of candidate root causes, output data”.] Regarding claim 2, Zhang teaches all the claimed features of claim 1, from which claim 2 depends. Zhang further teaches: The method according to claim 1, wherein the machine learning model comprises one or more of: a first machine learning model, a second machine learning model, and a third machine learning model. Zhang : Paragraphs [0054] and [0055] [As described in claim 1.] Zhang : Paragraph [0057] (“It should be appreciated that any number of predictive models 312 may be generated, such that a separate predictive model 312 may be created for subsets of feature sets such that the absence of one or more feature analytics will not prevent the algorithm from operating properly.”) Zhang : Paragraph [0058] (“For example, in an embodiment, the determination of the performance anomaly signature 354 as reflected in the first operational data set 314 may facilitate the detection of the performance anomaly 306 in the data points 356 of the second operational data set 316 and/or a third operational data set 360 . It should be appreciated that the performance anomaly signature 354 may be a pattern, value, concentration, photograph example, and/or other suitable arrangements of the data points 356 which may serve to identify the presence, or potential presence of the performance anomaly 306.”) Zhang : Paragraph [0059] (“Since each of the predictive models 312 may be configured to evaluate somewhat different portions of the operational data set 304, it may be desirable to ensure that the data points 356 evaluated by each of the predictive model 312 correlates to the performance anomaly 306. As such, in an embodiment, the controller 200 may define an anomaly range 362 within at least one of the operational data sets 304. By applying the anomaly range 362 to each of the remaining operational data sets 304, the corresponding potential root causes 308 and probabilities 310 may then be determined by each corresponding predictive model 312 based on the data points 356 which fall within the anomaly range 362. For example, in an embodiment, such as depicted in FIG. 5, the first, second, and third operational data sets 314, 316, 360 may be time-series data sets. As such, the anomaly range 362 may define a time-based sampling interval. The corresponding predictive models 312 may then evaluate the respective data points 356 falling within the time-based sampling interval. However, in an additional embodiment, the anomaly range 362 may vary across the operational data sets 304 based on the data type of each of the operational data sets 304. For example, while the first operational data set 314 may include time-series data, the second operational data set 316 may include feature-based data, while the third operational data set 360 may be an image-based data set. Accordingly, the anomaly range 362 for the second and third operational data sets 316, 360 may be selected to correlate to the sampling period of the first operational data set 314 but may be configured as appropriate for the differing data types.”) [The corresponding predictive models for the first operational data set, the second operational data set, and the third operational data set reads on “a first machine learning model, a second machine learning model, and a third machine learning model”.] Regarding claim 3, Zhang teaches all the claimed features of claim 2, from which claim 3 depends. Zhang further teaches: The method according to claim 2, wherein the first machine learning model has a first order of execution, wherein the second and the third machine learning model have a second order of execution lower than the first order. Zhang : Paragraphs [0058] and [0059] [As described in claim 2.] Regarding claim 4, Zhang teaches all the claimed features of claim 2, from which claim 4 depends. Zhang further teaches: The method according to claim 2, wherein the first machine learning model is a first classifier configured to classify the operational data into one or more first root cause categories; wherein the second machine learning model is a second classifier configured to classify the operational data into one or more second root cause categories. Zhang : Paragraphs [0058] and [0059] [As described in claim 2.] Zhang : Paragraph [0034] (“Each of the predictive models may be configured to analyze/monitor different systems of the power generating asset and may therefore utilize different portions of the operational data sets to accomplish this analysis/monitoring. As each of the predictive models may be analyzing/monitoring different systems and may be considering somewhat different data sets, each of the predictive models may generate a different combination of potential root causes and corresponding probabilities thereof. In other words, different artificial intelligence (AI) techniques may be leveraged to identify multiple diagnostic patterns of the performance anomaly in the operational data.”) [The first predictive model reads on “a first classifier” and the second predictive model reads on “a second classifier”.] Regarding claim 7, Zhang teaches all the claimed features of claim 1, from which claim 7 depends. Zhang further teaches: The method according to claim 1, wherein determining the set of candidate root causes associated with the fault comprises determining a probability that a candidate root cause of the set of candidate root causes is associated with the fault. Zhang : Paragraph [0006] (“The method may also include determining, via a plurality of predictive models implemented by the controller, a plurality of potential root causes of the performance anomaly and a plurality of corresponding probabilities for each of the plurality potential root causes based on the plurality of operational data sets . The method may include generating, via the controller, a consolidation model classifying the plurality of potential root causes of the performance anomaly and the plurality of corresponding probabilities for each of the plurality of potential root causes. Additionally, the method may include training, via the controller, the consolidation model via a training data set to correlate the plurality of potential root causes and the plurality of corresponding probabilities to an actual root cause for the performance anomaly. The method may also include determining, via the consolidation model implemented by the controller, the actual root cause of the performance anomaly based on the plurality of potential root causes of the performance anomaly and the plurality of corresponding probabilities. Further, the method may include implementing a control action based on the determined actual root cause.”) Regarding claim 8, Zhang teaches all the claimed features of claim 7, from which claim 8 depends. Zhang further teaches: The method according to claim 7, wherein providing the output data indicative of at least one root cause of the fault comprises filtering the candidate root causes of the set based on their respective probabilities. Zhang : Paragraphs [0054] and [0057]-[0058] [As described in claim 1.] Zhang : Paragraph [0071] (“By way of further illustration, in FIG. 6, the analysis of an exemplary historical anomaly case is described in line 346. As depicted, the analysis of the historical anomaly case by the first predictive model 318 may result in a determination of a 70% likelihood that potential cause A is the root cause of the performance anomaly 306. An analysis of the same historical anomaly by the second predictive model 322 may result in a determination that there exists a 60% likelihood of potential cause B being the root cause of the performance anomaly 306. Utilizing at least these determinations as inputs, the consolidation model 328, may determine that for the conditions described in the historical anomaly case, the output of the second predictive model 322 should be given a greater weight. This, in turn, may result in the consolidation model 328 determining that there exists a 70% likelihood that cause B is the actual root cause 350. It should therefore be appreciated that, in an embodiment, the actual root cause 350 as determined by the consolidation model 328 may not be the potential root cause 308 having the highest single potential root cause probability 310 as determined by the predictive models 312. Rather, the consolidation model 328 fuses and consolidates the outputs of each of the predictive model 312 to determine the optimal mapping between the actual root cause 350 and the operational anomaly 306 under the monitored operating parameters.”) [The identification of the performance anomaly based on the analysis using the predictive models reads on “filtering the candidate root causes of the set based on their respective probabilities”.] Regarding claim 9, Zhang teaches all the claimed features of claim 1, from which claim 9 depends. Zhang further teaches: The method according to claim 1, wherein the operational data associated with the wind turbine system comprises one or more of: one or more logs, telemetry data, and configuration data. Zhang : Paragraph [0052] (“The operational data sets 304 may, in an embodiment, include multiple parameters associated with the performance and/or health of a component of the power generating asset 100, the environmental data set 302, and/or data indicative of a performance parameter of the power generating asset 100 which is subject to monitoring. For example, in an embodiment, the performance parameter may comprise the power outputs of the wind turbine 114. In an embodiment, the performance parameter may be a tip speed ratio, a pitch setpoint, a yawing moment, and/or a bending moment. In other words, the plurality of operational data sets 304 may include a plurality of performance analytics, which generally refer to collected and analyzed data associated with the performance and/or health of the power generating asset 100, which may be characterized, stored, and/or analyzed to study various trends or patterns in the data.” Which reads on “telemetry data” and “configuration data”. ) Regarding claim 12, Zhang teaches all the claimed features of claim 1, from which claim 12 depends. Zhang further teaches: The method according to claim 1, wherein the operational data comprises fault data associated with the fault of the wind turbine system, Zhang : Paragraphs [0052] and [0074] [As described in claim 1.] wherein the fault data comprises information indicative of the wind turbine associated with the fault. Zhang : Paragraphs [0054] and [0055] [As described in claim 1.] Regarding claim 14, Zhang teaches all the claimed features of claim 1, from which claim 14 depends. Zhang further teaches: The method according to claim 1, wherein the machine learning model is trained based on one or more of: fault data, root causes associated with the fault data, relational data associated with the wind turbine, operational data, historical fault data, historical operational data associated with the historical fault data, and root causes associated with the historical fault data. Zhang : Paragraph [0012] (“…the method may include incorporating the plurality of operational data sets, the plurality of potential root causes and corresponding probabilities, and the actual root cause determined by the consolidation model into the training data set so as to establish a training feedback loop.”) Zhang : Paragraph [0068] (“ In an embodiment, as shown at 334, training the consolidation model 328 may include developing a plurality of correlations between the historical root causes 338 and a plurality of operational parameters as reflected by the operational data sets 304. For example, in an embodiment, the controller 200 may include a supervised machine learning algorithm which may apply what has been learned in the past to new data using labeled data to determine the actual root cause 350 of the anomaly 306. Starting from the model build, the learning algorithm may produce an inferred function to make a determination about the output values. As such, the controller 200 may be able to provide targets for any new input after sufficient training. The learning algorithm may also compare its output with the correct, intended output to find errors in order to modify the model accordingly. Thus, as shown at 334, the training of the consolidation model 328 may facilitate the determination of the optimal transfer function which fuses and consolidates the potential root causes 308 and corresponding probabilities 310 developed by the predictive models 312 in order to determine the actual root cause 350 of the performance anomaly 306.”) Regarding independent claim 15, Zhang teaches: A root cause analysis system comprising: a memory circuitry; a processor circuitry communicatively coupled to the memory circuitry and configured to perform an operation comprising: Zhang : Paragraph [0050] (“As shown particularly in FIG. 3, a schematic diagram of one embodiment of suitable components that may be included within the controller 200 is illustrated. For example, as shown, the controller 200 may include one or more processor(s) 206 and associated memory device(s) 208 configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, calculations and the like and storing relevant data as disclosed herein).”) The remaining recitations of the claim recite similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale. Regarding independent claim 16, Zhang teaches: A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device cause the electronic device to perform an operation, comprising: Zhang : Paragraph [0051] (“As used herein, the term “processor” refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, and other programmable circuits. Additionally, the memory device(s) 208 may generally comprise memory element(s) including, but not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), a floppy disk, a compact disc-read only memory (CD-ROM), a magneto-optical disk (MOD), a digital versatile disc (DVD) and/or other suitable memory elements. Such memory device(s) 208 may generally be configured to store suitable computer-readable instructions that, when implemented by the processor(s) 206, configure the controller 200 to perform various functions including, but not limited to, determining the actual root cause of a performance anomaly based on a plurality of potential root causes of the performance anomaly and the plurality of corresponding probabilities as described herein, as well as various other suitable computer-implemented functions.”) The remaining recitations of the claim recite similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale. It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. 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 5, 6, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang , in view of Zhang et al. (US Patent Publication No. 2022/0292666 A1) (“ Zhang ‘666 ”). Regarding claim 5, Zhang teaches all the claimed features of claim 2, from which claim 5 depends. Although Zhang describes in paragraph [0054] that the predictive models may include image-based AI models, Zhang does not expressly indicate that the image-based AI model includes a graph convolutional network. However, Zhang ‘666 describes a system and method including receiving historical time series sensor data associated with operation of an industrial asset. Zhang ‘666 teaches: The method according to claim 2, wherein the third machine learning model comprises a graph convolutional network configured to locate a candidate root cause based on the operational data. Zhang ‘666 : Paragraph [0027] (“In some embodiments, a convolutional neural network model is developed and used to recognize patterns in images of the scatter plots and to classify the training cases with particular root causes. Further, cross validation is performed to ensure robustness of the generated model. In some embodiments, the model may be used for real-time anomaly prediction of operational assets. Some embodiments might include a feedback loop to, for example, track model accuracy, facilitate the continuous updating of the training data, model improvement, and combinations thereof.”) Zhang ‘666 : Paragraph [0038] (“ The deep learning model building and validation component 325 or functionality of deep learning model system 310 may operate to convert or transform the scatter plots (or other representations of wind turbine operational data 305) into visual representation images of the scatter plots (or other representations of the operational data). For example, deep learning model building and validation component 325 may operate to develop (i.e., generate) a deep learning classification model that builds connections (e.g., transfer functions, algorithms, etc.) between the scatter plots based on the operational data and root causes for anomalies in the operational data by processing an input of high-dimensional images including data pixels corresponding to the scatter plots to generate an output including root cause labels associated with one or more anomalies derived from data patterns in the images . The deep learning model herein is a deep learning classification model developed to build a connection between scatter plots including data representations of wind turbine anomalies and the corresponding root causes thereof. In some aspects, a convolutional neural network (CNN) model is developed to capture and process pixel data to recognize the complex data patterns in images of the scatter plots and to further classify anomaly cases in the training set as being associated with a particular root cause for the determined anomaly classification . Deep learning model building and validation component325 may include functionality to perform one or more types of cross validation on the developed and trained model to ensure robustness of the model.”) Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Zhang and Zhang ‘666 before them, for the third machine learning model to comprise a graph convolutional network configured to locate a candidate root cause based on the operational data because the references are in the same field of endeavor as the claimed invention and they are focused on detecting and assessing anomalies in a wind turbine. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would ensure robustness of the model and improve the model to detect anomalies. Zhang ‘666 Paragraphs [0038] and [0039]. Regarding claim 6, Zhang and Zhang ‘666 teach all the claimed features of claim 5, from which claim 6 depends. Zhang ‘666 further teaches; The method according to claim 5, wherein the graph convolutional network is associated with the first classifier and/or the second classifier. Zhang ‘666 : Paragraph [0057] (“Continuing to operation 620, a deep learning model and more particularly a convolutional neural network (CNN) model is trained using a first subset of the labeled images and tested based on a second subset of the labeled images applied to the trained model to evaluate the performance of the trained model. In some aspects, the first subset of the labeled images is referred to a training set of data and the second subset of the labeled images that is applied to the trained model is referred to as test data, where the first and second subsets of images are distinct from each other.”) The motivation to combine Zhang and Zhang ‘666 as provided in claim 5 is incorporated herein. Regarding claim 13, Zhang teaches all the claimed features of claim 1, from which claim 13 depends. Zhang does not expressly teach the features of claim 13. However, Zhang ‘666 describes a system and method including receiving historical time series sensor data associated with operation of an industrial asset. Zhang ‘666 teaches: The method according to claim 1, the method comprising pre-processing the operational data for application of the machine learning model. Zhang ‘666 : Paragraph [0036] (“ In some embodiments, data processing & data filtering component 315 might process, condition, pre-process, or “clean” the operational data 305 so that it is configured in an expected manner and format for efficient processing by deep learning model system 310.”) Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Zhang and Zhang ‘666 before them, for the method of Zhang to include preprocessing of the operational data for application of the machine learning model because the references are in the same field of endeavor as the claimed invention and they are focused on detecting and assessing anomalies in a wind turbine. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to ensure data quality and data validity, such as, for example, to process the operational data to execute an air density correction for wind speed measurements included in the operational data 305.. Zhang ‘666 Paragraph [0036]. Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang , in view of Zhang et al. (US Patent Publication No. 2020/0309099 A1) (“ Zhang ‘099 ”). Regarding claim 10, Zhang teaches all the claimed features of claim 1, from which claim 10 depends. Zhang does not expressly teach the features of claim 10. However, Zhang ‘099 describes predicting wind turbine fault. Zhang ‘099 teaches: The method according to claim 1, the method comprising determining a difference between a first configuration of the wind turbine system and a second configuration of the wind turbine system. Zhang ‘099 : Paragraph [0031] (“…the variation of the wind turbine model parameters is further considered when updating the wind turbine model parameters in each period. In this case, during each period, the updated wind turbine model parameters enable the wind turbine state parameters outputted by the wind turbine model into which the current environment condition is inputted to be as close to current actual wind turbine state parameters as possible, meanwhile, the weighted result of the variation of the updated wind turbine model parameters relative to the last updated wind turbine model parameters (i.e. the wind turbine model parameters updated in the last period) i