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 .
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 recites “wherein the performance measure is based the F1 score …”; it should recite “wherein the performance measure is based on the F1 score.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “where each Bayesian network is learned by another learning method.” It is unclear whether “another” means each Bayesian network is learned by a different learning method as compared to all other Bayesian networks, or if each Bayesian network is learned successively by an additional learning method, which may or may not be the same as a previous or subsequent learning method. As a result of this ambiguity, the precise boundary of the claim cannot be determined, and therefore the claim is rejected as indefinite under 35 U.S.C. 112(b).
Claims 2-12 depend from claim 1, and are therefore are also rejected due to their dependency.
Claim 10 recites “generated beforehand by the method”. The precise meaning of this limitation is unclear. That is, claim 10 already recites “the method processes the prediction model which is generated by the method according to claim 1” and immediately following, in the alternative, recites “or which is generated beforehand by the method.” Since the limitation “the method processes the prediction model which is generated by the method according to claim 1” does not include the act of generating the model, only processing it, the model must have been already generated. As such, further reciting “or which is generated beforehand by the method” renders the meaning of that limitation unclear.
In addition, in the recitation of “or which is generated beforehand by the method,” it is not clear what “the method” refers to; claim 10 previously referred to itself as “the method” (“the method processes the prediction model”), but this recitation could also refer to the method of claim 1.
As a result of these ambiguities, the precise boundary of the claim cannot be determined, and therefore the claim is rejected as indefinite under 35 U.S.C. 112(b).
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.
Regarding claim 12, under the broadest reasonable interpretation, this claim is directed to a computer program only. Claim 12 is a computer program with program code for carrying out a method according to claim 1 when the program code is executed on a computer. Because Applicant's specification does not define a computer program to include hardware, examiner uses the broadest reasonable interpretation to interpret it as software alone. “Computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs, are not physical 'things.' They are neither computer components nor statutory processes, as they are not 'acts' being performed.” MPEP §2106.01 I. Because this claim recites only abstractions that are neither “things” nor “acts,” the claim is not within one of the four statutory classes of invention. Because the claim is not within one of the four statutory classes of invention, the claim is rejected under 35 U.S.C. §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-7, 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Scutari et al., “Package ‘bnlearn’”, published March 18, 2022, in view of Zhe, CN 111198099, hereinafter “BnLearn Manual” and “Zhe,” respectively.
Regarding claim 1, BnLearn Manual discloses a computer-implemented method for generating a prediction model, wherein the method processes previously acquired data, wherein the method comprises the following: (see at least p. 3 “bnlearn implements key algorithms covering all stages of Bayesian network modelling: data preprocessing, structure learning combining data and expert/prior knowledge, parameter learning, and inference (including causal inference via do-calculus).”);
a) discretizing the values of those variables which are numerical variables, resulting in modified data sets; (see at least pp. 45-47 disclosing pre-processing data including discretization using Hartemink’s algorithm);
b) structure learning of a plurality of Bayesian networks based on the modified data sets, where each Bayesian network is a probabilistic directed acyclic graph comprising the variables as nodes and defining the probabilistic dependencies between nodes by directed edges and where each Bayesian network is learned by another learning method; (see at least pp. 104-106 disclosing Bayesian network structure learning algorithms including PC, Grow-Shrink, Incremental Association, Fast Incremental Association, Interleaved Incremental Association, Hill Climbing, and Tabu Search; p. 22 disclosing an exemplary use of bn.cv for “comparing algorithms with multiple runs of cross-validation” teaching users to use multiple structure learning algorithms);
c) determining an optimum Bayesian network out of the plurality of Bayesian networks based on a performance measure reflecting the prediction quality of a respective Bayesian network, where the optimum Bayesian network has the best performance measure (see at least pp. 19-23 disclosing bn.cv for cross-validation a respective Bayesian network and the exemplary use of bn.cv for “comparing algorithms with multiple runs of cross-validation” teaching users to determine the optimum Bayesian network produced by the different structure learning algorithms);
d) parameter learning of the optimum Bayesian network based on the modified data sets, resulting in conditional probabilities between variables representing nodes linked by respective directed edges the optimum Bayesian network, where the optimum Bayesian network in combination with the conditional probabilities is the prediction model. (see at least pp. 23-26 disclosing bn.fit for fitting the parameters of the Bayesian network).
BnLearn Manual fails to disclose applying the package to generate a prediction model for predicting rotor blade damages of a wind turbine, wherein the method processes previously acquired data, the data comprising data sets for a plurality of wind turbines, where each data set refers to a specific wind turbine and comprises respective values of variables, the variables including one or more turbine variables defining characteristics of the specific wind turbine, one or more weather variables defining weather conditions averaged over the operation time of the specific wind turbine and one or more damage variables defining damages having occurred on at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine.
Zhe teaches generating a prediction model for predicting rotor blade damages of a wind turbine, wherein the method processes previously acquired data, the data comprising data sets for a plurality of wind turbines, where each data set refers to a specific wind turbine and comprises respective values of variables, the variables including one or more turbine variables defining characteristics of the specific wind turbine, one or more weather variables defining weather conditions averaged over the operation time of the specific wind turbine and one or more damage variables defining damages having occurred on at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine. (see at least Abstract; pp. 5-6 disclosing establishing a model to monitor the health of wind turbines using SCADA historical data including wind speed, output power, and engine speed).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the methods disclosed in BnLearn Manual to generate a prediction model for predicting rotor blade damages of a wind turbine, since it was well known in the art to use Bayesian networks to monitor the health of wind turbines (Zhe) and BnLearn Manual is a “one-stop shop for Bayesian networks in R, providing the tools needed for learning and working with discrete Bayesian networks” (BnLearn Manual, p. 3).
Regarding claim 2, Zhe teaches wherein the one or more turbine variables comprise one or more of the following variables: a turbine variable specifying the generator type used in the specific wind turbine; a turbine variable specifying the rotational speed for which the rotor of the specific wind turbine is configured; a turbine variable specifying the rotor diameter of the specific wind turbine; a turbine variable specifying a category to which the specific wind turbine belongs; a turbine variable specifying the altitude of the specific wind turbine; a turbine variable specifying the type of the specific wind turbine; a turbine variable specifying the rotor tip speed for which the specific wind turbine is configured; a turbine variable specifying the age of the specific wind turbine; a turbine variable specifying the total amount of time where the specific wind turbine experiences a wind speed over a predetermined value during its operation; and a turbine variable specifying whether the specific wind turbine is an onshore or offshore wind turbine. (see at least pp. 5-6 disclosing establishing a model to monitor the health of wind turbines using SCADA historical data including wind speed, output power (e.g. wind turbine category), and engine speed (e.g. rotational speed, wind turbine category)).
Regarding claim 3, Zhe teaches wherein the one or more weather variables comprise one or more of the following variables: a weather variable specifying the average wind speed at the location of the specific wind turbine during the operation time of the specific wind turbine; a weather variable specifying the average air humidity at the location of the specific wind turbine during the operation time of the specific wind turbine; a weather variable specifying the average lightning density which is the average number of lightning strikes per time unit and per area unit around the location of the specific wind turbine during the operation time of the specific wind turbine; a weather variable specifying the average precipitation per time unit and per area unit around the location of the specific wind turbine during the operation time of the specific wind turbine. (see at least pp. 5-6 disclosing establishing a model to monitor the health of wind turbines using SCADA historical data including wind speed, output power, and engine speed).
Regarding claim 5, BnLearn Manual discloses wherein the discretization step a) is based on the Hartemink's information-preserving algorithm. (see at least pp. 45-47 disclosing pre-processing data including discretization using Hartemink’s algorithm).
Regarding claim 6, BnLearn Manual discloses wherein the learning methods used in step b) comprise one or more of the following methods: PC algorithm, Grow-Shrink; Incremental Association Markov Blanket; Fast Incremental Association; Interleaved Incremental Association; Hill Climbing; Tabu Search. (see at least pp. 104-106 disclosing Bayesian network structure learning algorithms including PC, Grow-Shrink, Incremental Association, Fast Incremental Association, Interleaved Incremental Association, Hill Climbing, and Tabu Search; p. 22 disclosing an exemplary use of bn.cv for “comparing algorithms with multiple runs of cross-validation” teaching users to use multiple structure learning algorithms).
Regarding claim 7, BnLearn Manual wherein in step c) one or more cross validations are performed for each Bayesian network of the plurality of Bayesian networks, where in each cross validation a parameter learning of the respective Bayesian network based on a first part of the modified data sets is performed, resulting in conditional probabilities between variables representing nodes linked by respective directed edges in the respective Bayesian network, where a prediction of the values of the one or more damage variables of a second part of the modified data sets being different from the first part is performed by the respective Bayesian network in combination with the conditional probabilities based on the values of the one or more turbine variables and weather variables of the second part of the modified data sets, where a prediction quality parameter is determined for the respective cross validation by comparing the predicted values with the actual values of the one or more damage variables of the second part of the modified data sets, where the prediction quality parameter is the performance measure in case of a single cross validation and where the average of the prediction quality parameters over the cross validations is the performance measure in case of several cross validations. (see at least pp. 19-23 disclosing bn.cv for cross-validation a respective Bayesian network and the exemplary use of bn.cv for “comparing algorithms with multiple runs of cross-validation” teaching users to determine the optimum Bayesian network produced by the different structure learning algorithms; cross-validation strategies include k-fold and custom-folds).
Regarding claim 11, BnLearn Manual discloses a computer program product comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method with program code, which is stored on a non-transitory machine-readable carrier, for carrying out a method according to claim 1 when the program code is executed on a computer. (see claim 1 above).
Regarding claim 12, BnLearn Manual discloses a computer program with program code for carrying out a method according to claim 1 when the program code is executed on a computer. (see claim 1 above).
Claims 4, 10 are rejected under 35 U.S.C. 103 as being unpatentable over BnLearn Manual in view of Zhe, and further in view of Eisenberg et al. “Leading Edge Protection Lifetime Prediction Model Creation and Validation”, published 2016, hereinafter “Eisenberg.”
Regarding claim 4, BnLearn Manual in view of Zhe fails to specifically disclose wherein the one or more damage variables comprise one or more of the following variables: one or more erosion occurrence variables, each specifying the number of erosion occurrences with a respective severity level and/or of a respective type in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; one or more erosion area variables each specifying the total area of all erosions with a respective severity level and/or of a respective type occurred in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; one or more superficial damage occurrence variables, each specifying the number of superficial damage occurrences with a respective severity level and/or of a respective type in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; one or more superficial damage area variables, each specifying the total area of all superficial damages with a respective severity level and/or of a respective type occurred in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a crack variable specifying the number of cracks occurred on at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine an accessory loss variable specifying the number of accessory parts lost from at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a contamination occurrence variable specifying the number of contamination occurrences in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a contamination area variable specifying the total area of all contaminations occurred in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a lightning protection system failure variable specifying the number of failures of the lightning protection system of at least one rotor blade of the specific wind turbine which occurred during the operation time of the specific wind turbine.
BnLearn Manual in view of Zhe does disclose diagnosing the health of a wind turbine, which includes damage to the wind turbine.
Eisenberg teaches wherein the one or more damage variables comprise one or more of the following variables: one or more erosion occurrence variables, each specifying the number of erosion occurrences with a respective severity level and/or of a respective type in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; one or more erosion area variables each specifying the total area of all erosions with a respective severity level and/or of a respective type occurred in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; one or more superficial damage occurrence variables, each specifying the number of superficial damage occurrences with a respective severity level and/or of a respective type in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; one or more superficial damage area variables, each specifying the total area of all superficial damages with a respective severity level and/or of a respective type occurred in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a crack variable specifying the number of cracks occurred on at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine an accessory loss variable specifying the number of accessory parts lost from at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a contamination occurrence variable specifying the number of contamination occurrences in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a contamination area variable specifying the total area of all contaminations occurred in a predetermined region of at least one rotor blade of the specific wind turbine during the operation time of the specific wind turbine; a lightning protection system failure variable specifying the number of failures of the lightning protection system of at least one rotor blade of the specific wind turbine which occurred during the operation time of the specific wind turbine. (see at least one or more erosion occurrence variables specifying the number of occurrences (as seen on scatterplot) and severity (radius)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify BnLearn Manual in view of Zhe to include one or more damage variables as taught by Eisenberg, since erosion was a known measure of wind turbine health (Siemens, “The aerodynamic impact of the erosion has also been modeled and been used to determine the expected sectional efficiency loss of the damaged airfoils. Combining the leading edge erosion forecast model with the efficiency loss model, AEP loss over time on different sites due to rain induced leading edge erosion can be predicted.”).
Regarding claim 10, BnLearn Manual in view of Zhe discloses a computer-implemented method for predicting health of a wind turbine, where the method processes the prediction model which is generated by the method according to claim 1, or which has been generated beforehand by the method, where the health is predicted based on known values of at least one turbine variable and at least one weather variable valid for the wind turbine. (see claim 1 above).
BnLearn in view of Zhe fails to specifically disclose the health in terms of rotor blade damages where the value of at least one damage variable is predicted.
Eisenberg teaches the health in terms of rotor blade damages where the value of at least one damage variable is predicted (see at least one or more erosion occurrence variables specifying the number of occurrences (as seen on scatterplot) and severity (radius)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify BnLearn Manual in view of Zhe to include wind turbine health in terms of rotor blade damages where the value of at least one damage variable is predicted as taught by Eisenberg, since erosion was a known measure of wind turbine health (Siemens, “The aerodynamic impact of the erosion has also been modeled and been used to determine the expected sectional efficiency loss of the damaged airfoils. Combining the leading edge erosion forecast model with the efficiency loss model, AEP loss over time on different sites due to rain induced leading edge erosion can be predicted.”).
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over BnLearn Manual in view of Zhe, and further in view of Zizi et al. US2022/0197986, hereinafter “Zizi.”
Regarding claim 8, BnLearn Manual discloses a performance measure of the Bayesian network (see claim 1 above).
BnLearn Manual fails to specifically disclose the performance measure is based the F1 score or the Precision or the Recall.
Zizi teaches the performance measure is based the F1 score or the Precision or the Recall (see at least ¶ [0285] “various metrics such as precision, recall or F1 score”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify BnLearn Manual in view of Zhe to include a performance measure based the F1 score or the Precision or the Recall, as these were known techniques applied to a known method ready for improvement to yield predictable results.
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over BnLearn Manual in view of Zhe, and further in view of Official Notice.
Regarding claim 9, BnLearn Manual discloses where the steps a) to d) are performed for the previously acquired data additionally comprising the newly acquired data sets, thus resulting in an updated prediction model. (see claim 1 above for generating a data driven prediction model).
BnLearn Manual fails to disclose wherein newly acquired data sets are added to the previously acquired data.
Examiner takes Official Notice that adding new data to existing data is well known in the art.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify that data set disclosed by BnLearn Manual in view of Zhe to include adding new data to existing data in order to improve the accuracy of the prediction model by including a greater amount of data.
Conclusion
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/COURTNEY D HEINLE/Supervisory Patent Examiner, Art Unit 3745