Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's remarks filed 8/20/2025 have been fully considered.
Regarding the prior art rejection of claim 1, in paragraph 8 of page 8 through paragraph 4 of page 9 of Applicant’s Remarks, Applicant’s arguments are directed to that the prior art fails to disclose, teach, or suggest the subject matter of former claim 2 which has been incorporated into amended claim 1. The arguments are further directed, in paragraphs 1-2 of page 10 of Applicant’s Remarks, towards that claim 12 has been amended similarly thus is similarly allowable.
Respectfully, the arguments are not persuasive because, upon further consideration of Barlas and Kline in light of Applicant’s arguments, it is maintained that the cited prior art does teach each limitation of amended claim 1. As it particularly pertains to selecting the wind turbine (as Applicant’s arguments emphasize the element of selecting) it is noted that Barlas's method culminates with changing control/operation of wind turbines in a wind farm based on analysis of a wind farm model; this inherently requires selecting a wind turbine for any change in control/operation.
This inherency may even be specifically identified by, for example, Barlas’s disclosure that step S208 includes “determin[ing] whether to optimize control of the selected at least one wind turbine” (para 0040) and that step S209 comprises “a power control command value of each wind turbine… is determined based on the calculated output power of each wind turbine” (para 0041), and that, ultimately “In step S210, operation of each wind turbine is controlled based on the power control command value” (para 0043). Thus, each wind turbine is selected for control changes based on the model, even if Barlas does not explicitly use the word “select” or some variation of it.
Please see mapping of amended limitations to prior art below for all details.
Regarding the new claims 21-23, please see the action below for any relevant details.
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, 8, 10-14, 16, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200362818 A1 (Barlas) in view of US 20110223006 A1 (hereinafter Loh) and US 20120065886 A1 (Kline).
Examiner’s note: All mapping below (references made to reference characters, figures, paragraphs, etc.) is with regard to the base reference Barlas unless otherwise noted.
Regarding claim 1, Barlas discloses:
A method for controlling noise generated by a wind farm,
the wind farm comprising a plurality of wind turbines arranged at a wind farm site (abstract), the method comprising:
- providing a noise propagation model (noise sound power level database, and wind turbine database, identified in paragraph 0005, combined with wind farm noise propagation database, and value of noise propagation loss, identified in paragraph 0006) related to noise propagation across the wind farm and in the vicinity of the wind farm (paragraph 0006), under various operating conditions (paragraphs 0064-0066),
the noise propagation model including the plurality of wind turbines of the wind farm as noise generators (paragraphs 0064-0066),
and the noise propagation model taking interactions among the plurality of wind turbines into account (equation 2; paragraph 0030),
including taking interference between the noise generated by the plurality of wind turbines into account,
While Barlas may not explicitly disclose that his method takes into account interference between noise generated by the plurality of wind turbines, he does disclose that the wind farm noise propagation database “is built by wind power simulation software (e.g., windpro)” (paragraph 0024), and one having ordinary skill in the art would understand that wind power simulation software such as windPRO provides capability of accounting for noise generated by a plurality of wind turbines in a wind farm (as evidenced by this archived capture of the windPRO webpage, dated Oct 22 2021, please see in particular the section “Consider New and Old Turbines; Take existing wind turbines into account when calculating the noise of planned turbines”: https://web.archive.org/web/20211022110535/https://www.emd-international.com/windpro/windpro-modules/environment-modules/decibel/). Therefore, Barlas discloses this limitation implicitly.
- for each one of the plurality of wind turbine of the wind farm,
providing a wind turbine model (paragraph 0063: “calculate a noise sound power level of each wind turbine”),
the wind turbine model relating to at least power production and noise generation of the wind turbine under the various operating conditions (paragraphs 0061-0063);
- predicting a noise level at a predefined evaluation position, based on the noise propagation model, the wind turbine models and information regarding current operating conditions (paragraph 0030),
- in a case that the predicted noise level exceeds a predefined threshold noise value,
selecting one or more wind turbines among the plurality of wind turbines of the wind farm,
and changing operation of the one or more selected wind turbines (paragraph 0067, which identifies controlling the “output power of each wind turbine” as a changing operation),
wherein selecting the one or more wind turbines among the plurality of wind turbines and changing the operation of the one or more selected wind turbines are performed using the noise propagation model and the wind turbine models (paragraph 0067), and by performing an optimisation process with the predefined threshold noise value at the predefined evaluation position as a constraint (paragraph 0067), the noise generation and the power production of the plurality of wind turbines as optimisation variables (paragraph 0067), and [a] total power production of the wind farm as an optimisation target (paragraph 0067; paragraph 0043), thereby reducing the predicted noise level at the predefined evaluation position to a level below the predefined threshold noise value while maximising [the] total power production of the wind farm (paragraph 0067; paragraph 0043).
Barlas may not explicitly disclose:
and wherein the noise propagation model further accounts for frequency components of the noise generated by the plurality of wind turbines;
However, Loh, in the same field of endeavor, wind turbines, teaches:
A method for operating a plurality of wind turbines (abstract) in a wind farm (para 0003) by considering noise levels associated with the plurality of wind turbines (para 0004). In para 0046 the method evaluates noise levels based at least in part on audio frequency, wherein measuring and/or calculating noise levels may include weighting frequency ranges based on a model of human hearing. For example, noise near 1 kilohertz (kHz) may be weighted more heavily than noise near 10 kHz is weighted.
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Loh’s teachings as described above, having the noise propagation model further account for frequency components of the noise generated by the plurality of wind turbines, in order to have Barlas’s method focus on noises which exist within the frequencies pertaining to human hearing, wherein Barlas para 0002 identifies his system is directed toward dealing with noises causing people's annoyance or affecting people's health.
Barlas may not explicitly disclose:
- providing a site specific wind flow field across the wind farm site, based on the current operating conditions;
and wherein predicting the noise level, selecting the one or more wind turbines, and changing the operation of the one or more selected wind turbines is further performed based on the site specific wind flow field.
However, Barlas does disclose providing a site specific model of the wind farm (paragraph 0034 identifies building a model of the wind farm, “a wind farm noise propagation database”, by simulation software to simulate noise propagation, at step 202 in Fig 2),
and wherein predicting the noise level, selecting the one or more wind turbines, and changing the operation of the one or more selected wind turbines is further performed based on the site specific model of the wind farm (generally identified in paragraph 0040, step S210 in Fig 2; with more specifics/elaboration identified in, for example: regarding predicting the noise level: para 0065 discusses “calculate a value of noise propagation loss of each selected wind turbine at the wind speed collected in a real-time manner according to the wind farm noise propagation database” which is done at step S205 in Fig 2; regarding selecting the wind turbine and changing the operation: this is inherent because “operation of each wind turbine is controlled”, as identified by, e.g., para 0043 and step S210 in Fig 2, is performed subsequent to the steps S202 and S205 in Fig 2, wherein controlling wind turbines in a wind farm inherently requires selecting a wind turbine for any change in control/operation. This inherency may be specifically identified by, for example, that step S208 includes “determin[ing] whether to optimize control of the selected at least one wind turbine” (para 0040) and that step S209 comprises “a power control command value of each wind turbine… is determined based on the calculated output power of each wind turbine” (para 0041), and that, ultimately “In step S210, operation of each wind turbine is controlled based on the power control command value” (para 0043). Thus, each wind turbine is selected for control changes based on the model.
Barlas does not disclose that the site specific model of the wind farm is a site specific wind flow field across the wind farm site, based on the current operating conditions.
However, Kline, in the same field of endeavor, wind farms, teaches:
A general teaching that various site specific wind flow models have been developed, for the purpose of considering wind speed and direction at wind turbine locations throughout a wind farm, and are commercially available (paragraph 0005-0006). These models utilize real time data measurements (paragraph 0005-0006) and computer software including CFD (paragraph 0007-0008).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Kline’s teachings as described above, incorporating into Barlas’s site specific model of the wind farm a wind flow field based on current operating conditions, in order to use a wind flow model as a basis for calculating the energy output from the turbines that constitute the wind farm (Kline abstract; paragraph 0005).
Note that Barlas discloses that his method utilizes real-time site specific current operating data measurements (e.g., paragraph 0035: “wind speeds and wind directions at locations of the three wind turbines need to be collected in real time”), and that his model of the wind farm “is built by wind power simulation software” (paragraph 0024). Thus, Barlas discloses his method already incorporates elements of real time data/measurement acquisition and software, which are required for utilizing Kline’s general teaching of a wind flow field based on the current operating conditions.
Regarding claim 3, Barlas, as modified above, further discloses:
the site specific wind flow field is based at least partly on a high resolution weather model.
Applicant identifies (page 8 line 13) “The high resolution weather model may, e.g., be generated by means of accurate flow models of the wind farm site, such as Computational Fluid Dynamics (CFD) models or coupled weather models, and/or based on previously measured weather data at the wind farm site.” and Barlas as modified above explicitly identifies CFD models (Kline paragraph 0007-0008).
Regarding claim 5, Barlas, as modified above, further discloses:
providing [the] site specific wind flow field is based at least partly on a wind flow model related to the wind farm site (Kline paragraph 0007-0008),
and wherein the noise propagation model is at least partly based on the wind flow model (Barlas paragraph 0064).
Regarding claim 8, Barlas, as modified above, discloses:
changing the operation of the one or more selected wind turbines comprises selecting one or more control parameter settings and adjusting the selected one or more control parameter settings in a selected manner (paragraph 0067).
Regarding claim 10, Barlas, as modified above, discloses:
updating the wind turbine models during an operation of the wind farm (paragraphs 0065-0066).
Regarding claim 11, Barlas, as modified above, discloses:
the noise propagation model is at least partly based on the wind turbine models (paragraphs 0064-0065: value of noise propagation loss calculated based on real time wind turbine data).
Regarding claim 12, Barlas discloses:
A method for controlling noise generated by a wind farm,
the wind farm comprising a plurality of wind turbines arranged at a wind farm site (abstract), the method comprising:
- providing a noise propagation model (noise sound power level database, and wind turbine database, identified in paragraph 0005, combined with wind farm noise propagation database, and value of noise propagation loss, identified in paragraph 0006) related to noise propagation across the wind farm and in the vicinity of the wind farm (paragraph 0006), under various operating conditions (paragraphs 0064-0066),
the noise propagation model including the wind turbines of the wind farm as noise generators (paragraphs 0064-0066),
and the noise propagation model taking interactions among the wind turbines into account (equation 2; paragraph 0030),
including taking interference between the noise generated by the plurality of wind turbines into account;
While Barlas may not explicitly disclose that his method takes into account interference between noise generated by the plurality of wind turbines, he does disclose that the wind farm noise propagation database “is built by wind power simulation software (e.g., windpro)” (paragraph 0024), and one having ordinary skill in the art would understand that wind power simulation software such as windPRO provides capability of accounting for noise generated by a plurality of wind turbines in a wind farm (as evidenced by this archived capture of the windPRO webpage, dated Oct 22 2021, please see in particular the section “Consider New and Old Turbines; Take existing wind turbines into account when calculating the noise of planned turbines”: https://web.archive.org/web/20211022110535/https://www.emd-international.com/windpro/windpro-modules/environment-modules/decibel/). Therefore, Barlas discloses this limitation implicitly.
- for each wind turbine of the plurality of wind turbines,
providing a wind turbine model (paragraph 0063: “calculate a noise sound power level of each wind turbine”),
the wind turbine model relating to at least power production and noise generation of the wind turbine under the various operating conditions (paragraphs 0061-0063),
- predicting a noise level at a predefined evaluation position, based on the noise propagation model, the wind turbine models and information regarding current operating conditions (paragraph 0030),
- when the predicted noise level exceeds a predefined threshold noise value,
selecting one or more wind turbines among the plurality of wind turbines of the wind farm;
and changing operation of the one or more selected wind turbines (paragraph 0067, which identifies controlling the “output power of each wind turbine” as a changing operation);
wherein selecting the one or more wind turbines among the plurality of wind turbines and changing the operation of the one or more selected wind turbines are performed using the noise propagation model and the wind turbine models (paragraph 0067), and by performing an optimisation process with a total power production of the wind farm as an optimisation target (paragraph 0067; paragraph 0043).
Barlas may not explicitly disclose:
and wherein the noise propagation model further accounts for frequency components of the noise generated by the plurality of wind turbines;
However, Loh, in the same field of endeavor, wind turbines, teaches:
A method for operating a plurality of wind turbines (abstract) in a wind farm (para 0003) by considering noise levels associated with the plurality of wind turbines (para 0004). In para 0046 the method evaluates noise levels based at least in part on audio frequency, wherein measuring and/or calculating noise levels may include weighting frequency ranges based on a model of human hearing. For example, noise near 1 kilohertz (kHz) may be weighted more heavily than noise near 10 kHz is weighted.
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Loh’s teachings as described above, having the noise propagation model further account for frequency components of the noise generated by the plurality of wind turbines, in order to have Barlas’s method focus on noises which exist within the frequencies pertaining to human hearing, wherein Barlas para 0002 identifies his system is directed toward dealing with noises causing people's annoyance or affecting people's health.
Barlas may not explicitly disclose:
- providing a site specific wind flow field across the wind farm site, based on the current operating conditions;
and wherein predicting the noise level, selecting the one or more wind turbines, and changing the operation of the one or more selected wind turbines is further performed based on the site specific wind flow field.
However, Barlas does disclose providing a site specific model of the wind farm (paragraph 0034 identifies building a model of the wind farm, “a wind farm noise propagation database”, by simulation software to simulate noise propagation, at step 202 in Fig 2),
and wherein predicting the noise level, selecting the one or more wind turbines, and changing the operation of the one or more selected wind turbines is further performed based on the site specific model of the wind farm (generally identified in paragraph 0040, step S210 in Fig 2; with more specifics/elaboration identified in, for example: regarding predicting the noise level: para 0065 discusses “calculate a value of noise propagation loss of each selected wind turbine at the wind speed collected in a real-time manner according to the wind farm noise propagation database” which is done at step S205 in Fig 2; regarding selecting the wind turbine and changing the operation: this is inherent because “operation of each wind turbine is controlled”, as identified by, e.g., para 0043 and step S210 in Fig 2, is performed subsequent to the steps S202 and S205 in Fig 2, wherein controlling wind turbines in a wind farm inherently requires selecting a wind turbine for any change in control/operation. This inherency may be specifically identified by, for example, that step S208 includes “determin[ing] whether to optimize control of the selected at least one wind turbine” (para 0040) and that step S209 comprises “a power control command value of each wind turbine… is determined based on the calculated output power of each wind turbine” (para 0041), and that, ultimately “In step S210, operation of each wind turbine is controlled based on the power control command value” (para 0043). Thus, each wind turbine is selected for control changes based on the model.
Barlas does not disclose that the site specific model of the wind farm is a site specific wind flow field across the wind farm site, based on the current operating conditions.
However, Kline, in the same field of endeavor, wind farms, teaches:
A general teaching that various site specific wind flow models have been developed, for the purpose of considering wind speed and direction at wind turbine locations throughout a wind farm, and are commercially available (paragraph 0005-0006). These models utilize real time data measurements (paragraph 0005-0006) and computer software including CFD (paragraph 0007-0008).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Kline’s teachings as described above, incorporating into Barlas’s site specific model of the wind farm a wind flow field based on current operating conditions, in order to use a wind flow model as a basis for calculating the energy output from the turbines that constitute the wind farm (Kline abstract; paragraph 0005).
Note that Barlas discloses that his method utilizes real-time site specific current operating data measurements (e.g., paragraph 0035: “wind speeds and wind directions at locations of the three wind turbines need to be collected in real time”), and that his model of the wind farm “is built by wind power simulation software” (paragraph 0024). Thus, Barlas discloses his method already incorporates elements of real time data/measurement acquisition and software, which are required for utilizing Kline’s general teaching of a wind flow field based on the current operating conditions.
Regarding claim 19, Barlas, as modified above, discloses:
the noise propagation model takes into account how operation of one wind turbine of the plurality of wind turbines affects operation of one or more other ones of the plurality of wind turbines.
Barlas discloses how operation of one wind turbine affects operation of other wind turbines because he discloses that the noise generated by one turbine affects the noise generated by another turbine (see discussion above regarding using windPRO), wherein noise generation is an element of operation of a wind turbine.
Regarding claim 20, Barlas, as modified above, discloses:
the noise propagation model takes into account how operation of one wind turbine of the plurality of wind turbines affects local operating conditions at one or more other ones of the plurality of wind turbines.
Barlas discloses how operation of one wind turbine affects local operating conditions at other wind turbines because he discloses that the noise generated by one turbine affects the noise generated by another turbine (see discussion above regarding using windPRO), wherein noise generation is an element of a local operating condition of a wind turbine.
Regarding claim 13, Barlas further discloses:
providing a site specific model of the wind farm (paragraph 0034),
and wherein predicting the noise level, selecting the one or more wind turbines and changing the operation of the one or more selected wind turbines is further performed based on the site specific model of the wind farm (paragraph 0040; step S210 in Fig 2).
Barlas does not disclose:
The site specific model of the wind farm is a site specific wind flow field across the wind farm site, based on the current operating conditions.
However, Kline, in the same field of endeavor, wind farms, teaches:
A general teaching that various site specific wind flow models have been developed, for the purpose of considering wind speed and direction at wind turbine locations throughout a wind farm, and are commercially available (paragraph 0005-0006). These models utilize real time data measurements (paragraph 0005-0006) and computer software including CFD (paragraph 0007-0008).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Kline’s teachings as described above, incorporating into Barlas’s site specific model of the wind farm a wind flow field based on current operating conditions, in order to use a wind flow model as a basis for calculating the energy output from the turbines that constitute the wind farm (Kline abstract; paragraph 0005).
Note that Barlas discloses that his method utilizes real-time site specific current operating data measurements (paragraph 0035: “wind speeds and wind directions at locations of the three wind turbines need to be collected in real time”), and that his model of the wind farm “is built by wind power simulation software” (paragraph 0024). Thus Barlas discloses his method already incorporates elements of real time data/measurement acquisition and software, which are required for utilizing Kline’s general teaching of a wind flow field based on the current operating conditions.
Regarding claim 14, Barlas, as modified above, further discloses:
the site specific wind flow field is based at least partly on a high resolution weather model.
Applicant identifies (page 8 line 13) “The high resolution weather model may, e.g., be generated by means of accurate flow models of the wind farm site, such as Computational Fluid Dynamics (CFD) models or coupled weather models, and/or based on previously measured weather data at the wind farm site.” and Barlas as modified above explicitly identifies CFD models (Kline paragraph 0007-0008).
Regarding claim 16, Barlas, as modified above, further discloses:
providing [the] site specific wind flow field is based at least partly on a wind flow model related to the wind farm site (Kline paragraph 0007-0008),
and wherein the noise propagation model is at least partly based on the wind flow model (Barlas paragraph 0064).
Claim(s) 4, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh and Kline as applied to claims 1 and 12 respectively above, and further in view of US 20160333855 A1 (hereinafter Lund).
Regarding claim 4, Barlas, as modified above, discloses all claim limitations (see above) except:
the site specific wind flow field is based at least partly on expected turbulence patterns at the wind farm site.
However, Lund, in the same field of endeavor, wind farms, teaches:
the site specific wind flow field is based at least partly on expected turbulence patterns at the wind farm site (paragraph 0071, which identifies considering “expected turbulence” within digital representation of behaviors of wind turbines).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas as modified above to include Lund’s teachings as described above, having the site specific wind flow field based at least partly on expected turbulence patterns at the wind farm site, in order to maximize overall productivity of turbine operation in the wind farm, and maximize the overall throughput and productivity under the ever-changing situations (paragraph 0071).
Regarding claim 15, Barlas, as modified above, discloses all claim limitations (see above) except:
the site specific wind flow field is based at least partly on expected turbulence patterns at the wind farm site.
However, Lund, in the same field of endeavor, wind farms, teaches:
the site specific wind flow field is based at least partly on expected turbulence patterns at the wind farm site (paragraph 0071, which identifies considering “expected turbulence” within digital representation of behaviors of wind turbines).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas as modified above to include Lund’s teachings as described above, having the site specific wind flow field based at least partly on expected turbulence patterns at the wind farm site, in order to maximize overall productivity of turbine operation in the wind farm, and maximize the overall throughput and productivity under the ever-changing situations (paragraph 0071).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh as applied to claim 1 above, and further in view of US 20160032892 A1 (hereinafter Herrig).
Regarding claim 6, Barlas discloses all claim limitations (see above) except:
at least one of providing the noise propagation model and providing the wind turbine models comprises training an artificial intelligence (AI) model.
However, Herrig, in the same field of endeavor, wind farms, teaches:
providing a noise propagation model comprising training an artificial intelligence (AI) model (paragraph 0030).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas as modified above to include Herrig’s teachings as described above, providing a noise propagation model comprising training an artificial intelligence (AI) model, in order to estimate a propagation characteristic of the sound path (paragraph 0030).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh and Herrig as applied to claim 6 above, and further in view of US 20100027469 A1 (hereinafter Gurajala).
Regarding claim 7, Barlas, as modified above, does not disclose:
training the artificial intelligence (AI) model comprises applying reinforced learning.
However, Gurajala, in the same field of endeavor, artificial intelligence, teaches:
training the artificial intelligence (AI) model comprises applying reinforced learning (paragraph 0169: “Artificial intelligence techniques typically apply advanced mathematical algorithms--e.g., decision trees, neural networks, regression analysis, principal component analysis (PCA) for feature and pattern extraction, cluster analysis, genetic algorithm, or reinforced learning--to a data set”).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas as modified above to include Gurajala’s teachings as described above, training the artificial intelligence (AI) model comprising applying reinforced learning, in order to employ a training algorithm with the AI software to make decisions to achieve the most optimal results.
Examiner’s note: The below parallel rejection of claim 15 is provided to address new claim 17.
Examiner's note: For the purposes of examining this patent application, the examiner's submitted English translation of Ikeda, submitted with this office action, is referenced hereinafter.
Claim(s) 15, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh and Kline as applied to claim 13 above, and further in view of WO 2020240657 A1 (hereinafter Ikeda).
Regarding claim 15, Barlas, as modified above, discloses all claim limitations (see above) except:
the site specific wind flow field is based at least partly on expected turbulence patterns at the wind farm site.
However, Ikeda, in the same field of endeavor, wind farms, teaches:
A device 200 which uses a combination of measurements and computational fluid dynamics (lines 237-254 on page 7) to estimate wind conditions by considering “the effects of turbulence caused by the terrain” (lines 237-254 on page 7) and also by including wind data prediction by considering the current season (lines 168-179 on page 5; lines 340-348 on page 9) “by referring to the weather information 224 stored in the storage unit 220” (lines 220-227 on page 6).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas as modified above to include Ikeda’s teachings as described above, having the site specific wind flow field based at least partly on expected turbulence patterns at the wind farm site, in order to estimate the load received by the wind power generator (lines 176-177 on page 5).
Regarding claim 17, Barlas, as modified above, further disclose:
the expected turbulence patterns at the wind farm site are generated based on known obstacles at the wind farm site, including at least one of: buildings, terrain variations, or vegetation (Ikeda teaches: A device 200 which uses a combination of measurements and computational fluid dynamics (lines 237-254 on page 7) to estimate wind conditions by considering “the effects of turbulence caused by the terrain” (lines 237-254 on page 7) and also by including wind data prediction by considering the current season (lines 168-179 on page 5; lines 340-348 on page 9) “by referring to the weather information 224 stored in the storage unit 220” (lines 220-227 on page 6).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh and Kline as applied to claim 16 above, and further in view of WO 2020240657 A1 (Ikeda et al., hereinafter “Ikeda”).
Regarding claim 18, Barlas, as modified above, discloses all claim limitations (see above) except:
the site specific wind flow field takes into account seasonal variations.
However, Ikeda, in the same field of endeavor, wind farms, teaches:
A device 200 which uses a combination of measurements and computational fluid dynamics (lines 237-254 on page 7) to estimate wind conditions by considering “the effects of turbulence caused by the terrain” (lines 237-254 on page 7) and also by including wind data prediction by considering the current season (lines 168-179 on page 5; lines 340-348 on page 9) “by referring to the weather information 224 stored in the storage unit 220” (lines 220-227 on page 6).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas as modified above to include Ikeda’s teachings as described above, having the site specific wind flow field take into account seasonal variations, in order to estimate the load received by the wind power generator (lines 176-177 on page 5).
Claim(s) 21, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh and Kline and Lund as applied to claims 4 and 15 respectively above, and further in view of CN 107315855 A (hereinafter Xue).
Examiner's note: The examiner's submitted English translation of Xue, submitted with this office action, is referenced hereinafter.
Regarding claim 21, Barlas, as modified above, discloses all claim limitations (see above) except:
the expected turbulence patterns at the wind farm site specify a turbulence intensity on a temporal scale, a spatial scale, or both.
However, Xue, in the same field of endeavor, wind farms, teaches:
That it is already known in the art to use CFD to analyze turbulence intensity in a wind farm flow field analysis. Xue identifies in, e.g., lines 107-123 and claim 6, a CFD analysis of a flow field of a wind farm model to include analyzing turbulence intensity. It is inherent that a dynamic analysis of a physical occurrence, e.g. turbulence intensity, encompasses both space and time i.e. is spatial and temporal.
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Xue’s teachings as described above, incorporating Xue’s CFD modeling which includes analyzing turbulence intensity, in order to use CFD, as is already known in the art, within a wind farm flow field analysis.
Regarding claim 23, Barlas, as modified above, discloses all claim limitations (see above) except:
the expected turbulence patterns at the wind farm site specify a turbulence intensity or a turbulence eddy on a temporal scale, a spatial scale, or both.
However, Xue, in the same field of endeavor, wind farms, teaches:
That it is already known in the art to use CFD to analyze turbulence intensity in a wind farm flow field analysis. Xue identifies in, e.g., lines 107-123 and claim 6, a CFD analysis of a flow field of a wind farm model to include analyzing turbulence intensity. It is inherent that a dynamic analysis of a physical occurrence, e.g. turbulence intensity, encompasses both space and time i.e. is spatial and temporal.
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Xue’s teachings as described above, incorporating Xue’s CFD modeling which includes analyzing turbulence intensity, in order to use CFD, as is already known in the art, within a wind farm flow field analysis.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barlas in view of Loh and Kline and Lund as applied to claim 4 above, and further in view of US 20130314694 A1 (hereinafter Tchoryk).
Regarding claim 22, Barlas, as modified above, discloses all claim limitations (see above) except:
the expected turbulence patterns at the wind farm site specify a turbulence eddy on a temporal scale, a spatial scale, or both.
However, Tchoryk, in the same field of endeavor, systems for wind farms, teaches:
An atmospheric measurement system (10; Fig 2) for a wind field (16’; Fig 1) wind farm (12; Fig 2) which utilizes “computational fluid dynamics (CFD) simulations of the associated wind field 16' ” (para 0225), wherein turbulent eddies 59 are considered, “with sufficient spatial and temporal resolution”, in characterizing the wind field 16' (para 0656) in order “to help understand realistic wind 16 patterns at a particular site and to which a prospective one or wind turbines 14 located thereat would be subjected” (para 0225).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify Barlas to include Tchoryk’s teachings as described above, incorporating Tchoryk’s CFD modeling, in order “to help understand realistic wind 16 patterns at a particular site and to which a prospective one or wind turbines 14 located thereat would be subjected” (para 0225). This modification results in teaching the limitation above.
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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Art Golik whose telephone number is (571)272-6211. The examiner can normally be reached Mon-Fri 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathaniel Wiehe can be reached at 571-272-8648. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Art Golik/Examiner, Art Unit 3745
/NATHANIEL E WIEHE/Supervisory Patent Examiner, Art Unit 3745