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 .
Claim Objections
Claim 1 is objected to because of the following informalities: The claim cites “the rotor blades” (on the last line) with insufficient antecedent basis requirement. Appropriate correction is required.
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.
Claim(s) 1, 3, 5, 7, 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Geisler et al. (US 10815967) in view of Ravindra et al. (US 2016/0146190) and Evans et al. (US 2020/0056589).
Regarding to claims 1, 3:
Geisler et al. discloses a method for controlling a wind farm, comprising:
reading-in data from at least one first wind power plant of the wind farm, wherein the read-in data comprises mechanical load data (FIG. 1: The wind sensor 17 reads the wind data from the wind turbine 14 and provides the wind data to the prediction model 28. FIG. 6, step 110: The current values of the wind speed and direction of the first wind turbine read on the claimed mechanical load data);
supplying of the read-in data of the at least one first wind power plant to a statistical prediction model (FIG. 1, element 28) for the control of at least one second wind power plant of the wind farm, based on the read-in data of the at least one first wind power plant (column 10, lines 29-39: Said prediction model (28), using the current wind speed and direction of the first wind turbine (14) to predict a future time point to generate a control signal to alter a pitch angle of a rotor blade of the second wind turbine); and
using the statistical prediction model to control the at least one second wind power plant (column 5, line 67 to column 6, line 9: The wind prediction is performed in the central computing facility (21) for the entire wind park, then from there, the wind prediction is transmitted to the control units of the plurality of individual wind turbines to control the wind turbines individually),
wherein the statistical prediction model takes into account a distance between the at least one first wind power plant and the at least one second wind power plant, a wind direction, and a wind speed (column 1, line 47 to column 2, line 7: The prediction model takes into account the distance between the wind turbines because such distance affects the wake area of the second wind turbine. The prediction model also considers the wind speed and direction in the wake of the second wind turbine), and
wherein the mechanical load is a mechanical loading on the rotor blades (column 6, lines 45-55: The air-mass flow acting upon the entire load reads on the claimed mechanical loading on the rotor blades. Column 4, lines 23-27: The loading sensors of a wind turbine may comprise strain gauges in the rotor blades to measure the elastic deformation of the rotor blades due to the loading of the wind on the blades).
Geisler et al. however does not teach wherein the read-in data comprises electrical power data, and wherein the statistical prediction model makes prediction for an electrical power and mechanical load to be anticipated for the at least one second wind power plant.
Ravindra et al. discloses a system/method for optimizing operation of a wind farm having a plurality of wind turbines (FIG. 1, elements 104) comprising an upstream wind turbine and a downstream wind turbine (FIGs. 4-5, elements WTup and WTdown), wherein the method comprises of reading data from the upstream wind turbine (such as rotor speed, pitch angle, wind speed, power output) and forwarding the reading data to Estimator 144 (FIG. 3) to predict/estimate the downstream wind turbine dynamics such as rotor speed, turbulence, pitch angle, and power output (paragraph [0037]).
Therefore, it would have been obvious for one having ordinary skill in the art at the time of the filing date to modify Geisler’s system to include predict/estimate the data dynamics of the downstream wind turbine from the data read from the upstream wind turbine for optimizing the operation of the wind farm as taught by Ravindra et al. (Abstract).
Geisler et al. also is silent wherein the reading-in data is also from meteorological sensors and supplying read-in data from meteorological sensors to the statistical prediction model.
Evans et al. discloses a method for controlling operation of a wind turbine comprising obtaining data from meteorological sensors (FIG. 5, element 546: Atmospheric condition sensor) and providing data from the meteorological sensors to a machine learning model to estimate power output from a wind turbine (paragraph [0064]: Applying the recent environment data to the machine learning model to estimate power output from a wind turbine).
Therefore, it would have been obvious for one having ordinary skill in the art at the time of the filing date to modify Geisler’s system to include meteorological sensors to provide atmospheric/environment data to the machine learning model to predict/estimate the power output of a wind turbine and apply settings the wind turbine accordingly to optimize the wind turbine operation as taught by Evans et al. (paragraphs [0063]-[0064]).
Geisler et al. also discloses the following claims:
Regarding to claims 5, 7: wherein the statistical prediction model has a machine learning method (column 4, lines 28-30: The prediction model can be improved in an automatic learning process), wherein the data is read in at a high sampling rate of 1 Hz or more (It would have been obvious to one having ordinary skill in the art at the time the invention was made to set the reading rate in the range as claimed, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F. 2d 272, 205 USPQ 215 (CCPA 1980)).
Regarding to claims 11, 14-15: external data from meteorological sensors is read in and supplied to the statistical prediction model in order to control the at least one second wind power plant, which in particular can be a Bayesian hybrid model that learns continuously and improves its predictions over time (It is conventional that reading data from a meteorological sensor is provided for further training a machine learning model using Bayesian regression as disclosed in Evans et al. (US 2020/0056589, paragraphs [0026], [0061]).
Regarding to claims 12-13: comprising: at least one first wind power plant (FIG. 1, element 14), at least one second wind power plant (FIG. 1, element 14), and a control module (FIG. 1, element 21) for the control of the at least one first and/or second wind power plant.
Response to Arguments
Applicant's arguments filed 1/7/2026 have been fully considered but they are not persuasive.
In response to Applicant’s Remarks, the Examiner cites that Geisler et al., as the base reference, teaches wherein the mechanical load is a mechanical loading on the rotor blades. As disclosed in column 6, lines 45-55, Geisler et al. teaches the air-mass flow acting upon the entire rotor, this load reads on the claimed mechanical loading on the rotor blades. In addition, in column 4, lines 23-27, Geisler et al. teaches that the loading sensors of a wind turbine may comprise strain gauges in the rotor blades to measure the elastic deformation of the rotor blades due to the loading of the wind on the blades.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853