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 .
This action is in response to Application and Preliminary Amendment filed on 05/21/2025.
Claims 3, 6, 8, 11 and 13 have been amended, and no claim has been added or canceled. Currently, claims 1-15 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed by Applicant on 05/21/2025 has been considered. A copy of the considered IDS is enclosed with this Office action.
Drawings
The drawings are objected to because they fail to show necessary textual labels of features or symbols in Figs. 1-7 as described in the specification. For example, placing a label, "Tower", with element 102 of Fig. 1, would give the viewer necessary detail to fully understand this element at a glance. A descriptive textual label for each numbered element in these figures would be needed to better understand these figures without substantial analysis of the detailed specification. Any structural detail that is of sufficient importance to be described should be labeled in the drawing. Optionally, the applicant may wish to include a table next to the present figure to fulfill this requirement. See 37 CFR 1.84(n)(o), recited below:
"(n) Symbols. Graphical drawing symbols may be used for conventional elements when appropriate. The elements for which such symbols and labeled representations are used must be adequately identified in the specification. Known devices should be illustrated by symbols which have a universally recognized conventional meaning and are generally accepted in the art. Other symbols which are not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.
(o) Legends. Suitable descriptive legends may be used, or may be required by the Examiner, where necessary for understanding of the drawing, subject to approval by the Office. They should contain as few words as possible."
Specification
The abstract of the disclosure is objected to because acronyms have been used without initially spelling out (e.g. “LPE” in line 2, “LEP” in line 4, and “SCADA” in line 3). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 3, 5 and 11 are objected to because of the following informalities:
Regarding claim 3, the term “includes” in line 2 should be “include”.
Regarding claim 5, the term “includes” in line 1 should be “include”.
Regarding claim 11, the term “includes in line 2 should be “include”.
Appropriate correction is required.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because:
With respect to claim 15, the term “computer-readable storage medium" is not limited to tangible/physical media (e.g., memory or storage devices) that are given as examples in Applicant’s specification (see page 21, lines 14-21), which does not explicitly exclude a carrying/transmission medium (i.e., signal or wave), not in and of itself a tangible medium or being directed to a statutory category of invention (e.g., process, machine, manufacture or composition of matter). Replacing the term “A computer-readable storage medium” by “A non-transitory computer-readable storage medium” is effective to overcome this rejection.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining categorizations/classifications of a lost production event at a wind turbine based on received data.
The claims recite an abstract idea of determining categorizations/classifications of a lost production event at a wind turbine based on received data based on broadly recited steps of determining, which are broadly recited steps/concepts that can be performed in the human mind or with the aid of pencil and paper and directed to mental processes grouping of abstract ideas.. This judicial exception is not integrated into a practical application because other additional elements including insignificant extra-solution activity (e.g., mere data gathering) and genetic computer and/or generic computer components for implementing the abstract idea are not sufficient to integrate the abstract idea into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements include only generic computer and/or generic computer components (e.g., memory, processor, program instructions, etc.) for performing generic computer functions (e.g., accessing, storing, displaying, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering), which are not sufficient to amount to significantly more than the recited abstract idea.
Abstract idea analysis as follows:
Step 1:
According to the first part of the analysis, in the instant claims, claims 1-8 are directed to a method comprising a series of steps (i.e. a process), and claims 9-14 are directed to a system comprising one or more processor and memory (i.e., a machine), and claim 15 is directed to a computer-readable storage medium having computer-readable program code (e.g., signal or wave carrying program code). Thus, each of the claims 1-14 falls within one of the four statutory categories (i.e. process, machine, manufacture or composition of matter).
Step 2a Prong 1 (claims 1, 9 and 15):
The following limitations recited in claims 1, 9 and 15 are abstract ideas that fall under mental processes:
determining categorizations of the LPE using a plurality of machine learning (ML) models, wherein each of the plurality of ML models corresponds to one of the different types of data (this step of determining categorizations of the LPE based on the different types of data as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and/or opinion, wherein the recitation of using a plurality of machine learning (ML) model as broadly recited without specifying how the ML models function can provide nothing more than mere instructions to implement or apply the abstract idea on a generic computer); and
determining a final categorization of the LPE using the categorizations generated by the plurality of ML models and the initial categorization (this step of determining a final categorization of the LPE as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and/or opinion).
All the limitations above are mental steps that can be performed in the human mind or with the aid of pencil and paper.
Step 2a Prong 2 (Claims 1, 9 and 15):
The following limitations in claims 1, 9 and 15 are additional elements:
receiving different types of data from a wind turbine (this step of receiving recited at high level of generality is directed to mere data gathering or insignificant extra-solution activity);
receiving an initial categorization of a lost production event (LPE) that occurred at the wind turbine, wherein the LPE occurred while the different types of data were measured at the wind turbine (this step of receiving recited at high level of generality is directed to mere data gathering or insignificant extra-solution activity),
a system (this element is directed to generic computer),
one or more processors (these elements are directed to generic computer components),
memory configured to store an application which when executed by any combination of the one or more processors performs an operation, the operation comprising (these elements are directed to generic computer components or mere instructions for implementing or applying the abstract idea), and
a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to (these elements are directed to generic computer components and/or mere instructions for implementing or applying the abstract idea).
These are a generic computer and/or generic computer components used to perform generic computer functions or insignificant extra-solution activity for implementing or applying the abstract. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2b (Claims 1, 9 and 15):
The following limitations in claims 1, 9 and 15 are additional elements:
receiving different types of data from a wind turbine (this step of receiving recited at high level of generality is directed to mere data gathering or insignificant extra-solution activity or well-understood, routine, conventional activity);
receiving an initial categorization of a lost production event (LPE) that occurred at the wind turbine, wherein the LPE occurred while the different types of data were measured at the wind turbine (this step of receiving recited at high level of generality is directed to mere data gathering or insignificant extra-solution activity or well-understood, routine, conventional activity),
a system (this element is directed to generic computer),
one or more processors (these elements are directed to generic computer components),
memory configured to store an application which when executed by any combination of the one or more processors performs an operation, the operation comprising (these elements are directed to generic computer components or mere instructions for implementing or applying the abstract idea), and
a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to (these elements are directed to generic computer components and/or mere instructions for implementing or applying the abstract idea).
These are a generic computer and/or generic computer components used to perform generic computer functions or well-understood, routine, conventional activity, and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 2 and 10, claims 2 and 10 depend on claims 1 and 9 respectively. As such, claims 2 and 10 recite the abstract idea as presented in claims 1 and 9.
In addition, claims 2 and 10 include additional elements:
wherein determining the final categorization is performed using a categorization Al system that receives as inputs the categorizations generated by the plurality of ML models and the initial categorization (this step of determining final categorizations of the LPE based on the the categorizations and the initial categorization as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and/or opinion, wherein the recitation of using a categorization AI system as broadly recited without specifying how the categorization AI system function (i.e., the process/steps of determining the final categorization) can provide nothing more than mere instructions to implement or apply the abstract idea on a generic computer).
These are additional elements directed to mere instructions for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 3 and 11, claims 3 and 11 depend on claims 1 and 9 respectively. As such, claims 3 and 11 recite the abstract idea as presented in claims 1 and 9.
In addition, claims 3 and 11 include additional elements:
wherein the different types of data includes data generated by a Supervisory Control and Data Acquisition (SCADA) system associated with the wind turbine, maintenance activities on the wind turbine, and weather data at the wind turbine (this element specifying the different types of data, which is directed to mere data).
These are additional elements directed to mere data/information for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 4 and 12, claims 4 and 12 depend on claims 3 and 11 respectively. As such, claims 4 and 12 recite the abstract idea as presented in claims 3 and 11.
In addition, claims 4 and 12 include additional elements:
wherein the data generated by the SCADA system comprises 10-minute signal data and event data (this element specifying the data generated by the SCADA system, which is directed to mere data).
These are additional elements directed to mere data/information for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 5, claim 5 depends on claim 3. As such, claim 5 recites the abstract idea as presented in claim 3.
In addition, claim 5 includes additional elements:
wherein the different types of data also includes vibrational data associated with the wind turbine (this element specifying the different types of data, which is directed to mere data).
These are additional elements directed to mere data/information for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 6 and 13, claims 6 and 13 depend on claims 1 and 9 respectively. As such, claims 6 and 13 recite the abstract idea as presented in claims 1 and 9.
In addition, claims 6 and 13 include additional elements:
generating, using a large language model, a textual description explaining why the final categorization of the LPE is different from the initial categorization (the step of generation a textual description as recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and/or opinion; wherein the recitation of using a large language model as broadly recited without specifying how the large language model function (i.e., the process/steps of generating the textual description) can provide nothing more than mere instructions to implement or apply the abstract idea on a generic computer).
These are additional elements directed to mere instructions for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 7 and 14, claims 7 and 14 depend on claims 6 and 13 respectively. As such, claims 7 and 14 recite the abstract idea as presented in claims 6 and 13.
In addition, claims 7 and 14 include additional elements:
receiving feedback indicating that the final categorization of the LPE was incorrect (this step of receiving feedback recited at high level of generality being directed to mere data gathering or insignificant extra-solution activity); and
retraining the plurality of ML models based on the feedback (this feature of training/retraining a model based on feedback is a common function in the art (see Zhang et al. (EP 3974390 A1), [0066] for a training feedback loop; see Biessmann et al. (U.S. Patent No. 9,892,133), [column 14, lines 7-14]; also see Heo et al. (U.S. Publication No. 2020/0264005), [0163]).
These are additional elements directed to mere instructions for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 8, claim 8 depends on claim 1. As such, claim 8 recites the abstract idea as presented in claim 1.
In addition, claim 8 includes additional elements:
wherein the initial categorization of the LPE is done using only data measured by the wind turbine (this element specifying data being used for the initial categorization, which is directed to mere data).
These are additional elements directed to mere data/information for implementing or application the abstract idea on a generic computer, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 8-10 and 15 (effective filing date 06/03/2024) are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (EP 3974930 A1, Publication date 03/30/2022).
As to claim 1, Zhang et al. teaches:
“A method” (see Zhang et al., Abstract), comprising:
“receiving different types of data from a wind turbine” (see Zhang et al., [0027] and [0046] for receiving a number of operational data sets from a power generating asset (e.g., a wind turbine); also see [0038]-[0041] for collecting data);
“receiving an initial categorization of a lost production event (LPE) that occurred at the wind turbine, wherein the LPE occurred while the different types of data were measured at the wind turbine” (see Zhang et al., [0027] and [0046] wherein the operation data sets may be indicative of a performance anomaly, wherein the indicated performance anomaly can be interpreted as equivalent to a lost production event and/or an initial categorization of a lost production event as broadly recited);
“determining categorizations of the LPE using a plurality of machine learning (ML) models, wherein each of the plurality of ML models corresponds to one of the different types of data” (see Zhang et al., [0048] for determining the potential root causes of the performance anomaly and a number of corresponding probabilities for each of the potential root causes using a plurality of predictive models (i.e., a plurality of ML models), wherein the potential root causes of the performance anomaly can be interpreted as categorizations of the LPE as recited, and wherein each predictive model evaluates/uses a corresponding portion of the operational data sets; also see [0049]-[0050]); and
“determining a final categorization of the LPE using the categorizations generated by the plurality of ML models and the initial categorization” (see Zhang et al., [0058] for determining the actual root cause for the performance anomaly based on the potential root causes and the corresponding probabilities generated by the predictive models, wherein the actual root cause of the performance anomaly can be interpreted as a final categorization of the LPE as recited).
As to claim 2, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following:
Zhang et al. teaches:
“wherein determining the final categorization is performed using a categorization Al system that receives as inputs the categorizations generated by the plurality of ML models and the initial categorization” (see Zhang et al., [0058] for determining the actual root cause for the performance anomaly (i.e., final categorization) using the consolidation model (i.e., a categorization AI system) based on the potential root causes (i.e., the categorizations) and the corresponding probabilities generated by the predictive models, wherein the actual root cause of the performance anomaly is obviously based on the performance anomaly (i.e., initial categorization)).
As to claim 8, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following:
Zhang et al. teaches:
“wherein the initial categorization of the LPE is done using only data measured by the wind turbine” (see Zhang et al., [0046] for the operation data sets indicative of a performance anomaly (i.e., the initial categorization of the LPE), wherein the operational data sets can include performance parameter(s) of a wind turbine).
As to claim 9, Zhang et al. teaches:
“A system” (see Zhang et al., Abstract), comprising:
“one or more processors” (see Zhang et al., [0020]) ; and
“memory configured to store an application which when executed by any combination of the one or more processors performs an operation, the operation comprising” (see Zhang et al., [0020]):
“receiving different types of data from a wind turbine” (see Zhang et al., [0027] and [0046] for receiving a number of operational data sets from a power generating asset (e.g., a wind turbine); also see [0038]-[0041] for collecting data);
“receiving an initial categorization of a lost production event (LPE) that occurred at the wind turbine, wherein the LPE occurred while the different types of data were measured at the wind turbine” (see Zhang et al., [0027] and [0046] wherein the operation data sets may be indicative of a performance anomaly, wherein the indicated performance anomaly can be interpreted as equivalent to a lost production event and/or an initial categorization of a lost production event as broadly recited);
“determining categorizations of the LPE using a plurality of machine learning (ML) models, wherein each of the plurality of ML models corresponds to one of the different types of data” (see Zhang et al., [0048] for determining the potential root causes of the performance anomaly and a number of corresponding probabilities for each of the potential root causes using a plurality of predictive models (i.e., a plurality of ML models), wherein the potential root causes of the performance anomaly can be interpreted as categorizations of the LPE as recited, and wherein each predictive model evaluates/uses a corresponding portion of the operational data sets; also see [0049]-[0050]); and
“determining a final categorization of the LPE using the categorizations generated by the plurality of ML models and the initial categorization” (see Zhang et al., [0058] for determining the actual root cause for the performance anomaly based on the potential root causes and the corresponding probabilities generated by the predictive models, wherein the actual root cause of the performance anomaly can be interpreted as a final categorization of the LPE as recited).
As to claim 10, this claim is rejected based on the same arguments as above to reject claim 9 and is similarly rejected including the following:
Zhang et al. teaches:
“wherein determining the final categorization is performed using a categorization Al system that receives as inputs the categorizations generated by the plurality of ML models and the initial categorization” (see Zhang et al., [0058] for determining the actual root cause for the performance anomaly (i.e., final categorization) using the consolidation model (i.e., a categorization AI system) based on the potential root causes (i.e., the categorizations) and the corresponding probabilities generated by the predictive models, wherein the actual root cause of the performance anomaly is obviously based on the performance anomaly (i.e., initial categorization)).
As to claim 15, Zhang et al. teaches:
“A computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to” (see Zhang et al., Abstract and [0020]):
“receive different types of data from a wind turbine” (see Zhang et al., [0027] and [0046] for receiving a number of operational data sets from a power generating asset (e.g., a wind turbine); also see [0038]-[0041] for collecting data);
“receive an initial categorization of a lost production event (LPE) that occurred at the wind turbine, wherein the LPE occurred while the different types of data were measured at the wind turbine” (see Zhang et al., [0027] and [0046] wherein the operation data sets may be indicative of a performance anomaly, wherein the indicated performance anomaly can be interpreted as equivalent to a lost production event and/or an initial categorization of a lost production event as broadly recited);
“determine categorizations of the LPE using a plurality of machine learning (ML) models, wherein each of the plurality of ML models corresponds to one of the different types of data” (see Zhang et al., [0048] for determining the potential root causes of the performance anomaly and a number of corresponding probabilities for each of the potential root causes using a plurality of predictive models (i.e., a plurality of ML models), wherein the potential root causes of the performance anomaly can be interpreted as categorizations of the LPE as recited, and wherein each predictive model evaluates/uses a corresponding portion of the operational data sets; also see [0049]-[0050]); and
“determine a final categorization of the LPE using the categorizations generated by the plurality of ML models and the initial categorization” (see Zhang et al., [0058] for determining the actual root cause for the performance anomaly based on the potential root causes and the corresponding probabilities generated by the predictive models, wherein the actual root cause of the performance anomaly can be interpreted as a final categorization of the LPE as recited).
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 3-5 and 11-12 (effective filing date 06/03/2024) are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (EP 3974930 A1, Publication date 03/30/2022), and further in view of Visweswariah et al. (U.S. Publication No. 2021/0203157, Publication date 07/01/2021).
As to claims 3 and 11, Zhang et al. teaches all limitations of claims 1 and 9 respectively including different types of data (see Zhang et al., [0046] for operational data sets, environmental data set, etc.).
In addition, Zhang et al. teaches:
“wherein the different types of data include ….weather data at the wind turbine” (see Zhang et al., [0038] for monitoring environment data set including wind condition data (i.e., weather data)).
However, Zhang et al. does not explicitly teach:
“wherein the different types of data includes data generated by a Supervisory Control and Data Acquisition (SCADA) system associated with the wind turbine, maintenance activities on the wind turbine”.
On the other hand, Visweswariah et al. explicitly teach:
“wherein the different types of data includes data generated by a Supervisory Control and Data Acquisition (SCADA) system associated with the wind turbine, maintenance activities on the wind turbine” (see Visweswariah et al., [0270]-[0271] for data generated by a SCADA system associated with a wind turbine; also see [0272] for log data regarding maintenance activities).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Visweswariah et al.’s teaching to Zhang et al.’s system by incorporating different types of data to include data generated by a SCADA system and maintenance activity data. Ordinarily skilled artisan would have been motivated to do because SCADA system associated with a wind turbine is well-known and well-used in the art for collecting and managing data associated with a wind turbine. In addition, both of the references (Zhang et al. and Visweswariah et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, a system for using machine learning models to predict failure/anomaly associated with power generating asset (e.g., a wind turbine) based on monitored data. This close relation between both of the references highly suggests an expectation of success when combined.
As to claims 4 and 12, these claims are rejected based on the same arguments as above to reject claims 3 and 11 and are similarly rejected including the following:
Zhang et al. as modified by Visweswariah et al. teaches:
“wherein the data generated by the SCADA system comprises 10-minute signal data and event data” (see Visweswariah et al., [0271]-[0272] and [0284] for signal and event data from the SCADA system; also see [0325] for receiving time series data (e.g., 10 Minute sampled time series data from the real-time operation SCADA data)).
As to claim 5, this claim is rejected based on the same arguments as above to reject claim 3 and is similarly rejected including the following:
Zhang et al. as modified by Visweswariah et al. teaches:
“wherein the different types of data also includes vibrational data associated with the wind turbine” (see Zhang et al., [0041] wherein collected data includes vibration data of a power generating asset (e.g. a wind turbine); also see [0030]).
Claims 6-7 and 13-14 (effective filing date 06/03/2024) are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (EP 3974930 A1, Publication date 03/30/2022), and further in view of Monteiro Paes et al. (U.S. Publication No. 2025/0284728, effectively filed date 03/05/2024).
As to claims 6 and 13, Zhang et al. teaches all limitations of claims 1 and 9 respectively including determining the final categorization of the LPE and the initial categorization (see Zhang et al., [0046] for determining a performance anomaly (i.e., initial categorization), and see [0058] for determining the actual root cause of the performance anomaly (i.e., final categorization)).
However, Zhang et al. does not explicitly teach a feature for using a LLM to provide an explanation of differences between data items as equivalently recited as follows:
“generating, using a large language model, a textual description explaining why the final categorization of the LPE is different from the initial categorization”.
On the other hand, Monteiro Paes et al. explicitly teach a feature for using a LLM to provide an explanation of differences between data items/outputs (see Monteiro Paes et al., [0022] for using a target LLM to generate an output to explain the difference).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Monteiro Paes et al.’s teaching to Zhang et al.’s system by implementing a feature for using a LLM to provide an explanation of differences between data items/outputs. Ordinarily skilled artisan would have been motivated to do to provide Zhang et al.’s system with an effective way to evaluate categories/classifications regarding a performance anomaly. In addition, a large language model has been well-known and well-used in the art for generating data/knowledge based on processing/analyzing data.
As to claims 7 and 14, these claims are rejected based on the same arguments as above to reject claims 6 and 13 respectively, and are similarly rejected including the following:
Zhang et al. as modified by Monteiro Paes et al. teaches:
“receiving feedback indicating that the final categorization of the LPE was incorrect” (see Zhang et al., [0067] for receiving data/feedback based on validating the actual root cause of the performance anomaly (i.e., the final categorization of the LPE), wherein validating means to evaluate whether it is viable (i.e., correct/incorrect); also see [0049] for user feedback); and
“retraining the plurality of ML models based on the feedback” (see Zhang et al., [0066] for a training feedback loop (i.e., training/retraining based on result feedback)).
Prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Verma et al. (“A Reliable Framework for Predicting Wind Turbine Failures Utilising SCADA and Alarm Date”, 2023) teaches a system for training and developing a classifier for predicting faults or failures in the SCADA data from wind turbines and previous alarm data.
Conclusion
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/Phuong Thao Cao/Primary Examiner, Art Unit 2164
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