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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/05/2026 has been entered.
Response to Arguments
Applicant's arguments filed 05/05/2026 have been fully considered but they are not persuasive. The applicant argues that the prior art disclosed does not teach or disclose that the target location is provided as at least one input into the trained prediction model. The applicant argues that the wind speed prediction is based on meteorological measurements at the location and does not use the location as an input for the measurements. The examiner disagrees with this assertion. Chapter 4 section A of Ehsan describes that the data from a specific location is used, so the location selected and thereby a target location is inherently an input for the trained prediction model. The claim limitations do not describe the specific location data or how the data is used, so because the weather data is measured at a specific location, the location data is thereby used as an input into the trained prediction model. Chapter 1 further describes that the system uses a location and height in the system and that a certain location is selected for the determination of the wind speed parameters, and as such a location is an input into the model. The applicant argues that no latitude, longitude, or gps info is used in the system but these limitations are not required by claim 1. As a specific location must be selected by the user or the automated system in order to scrape and choose the meteorological data used in the predictive model, the location data is an input to run and operate the model. The applicant even discusses in the arguments that the speed prediction is based on meteorological measurements at a location but then states that the location information itself is not used. It is not clear how this is possible when a location to predict the wind speed must be selected in order to determine or estimate the wind speed at that location. As such, the location data is an input to the system. Once again, the applicant argues that Ehsan does not describe the customer relationship management system without pointing out what the limitations of such a system must be or why the structure pointed out previously does not meet these limitations. The arguments are not found to be convincing and updated rejections are presented below.
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.
Claim(s) 1, 4, 12, and 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks”, hereafter referred to as “Ehsan”.
Regarding claim 1, Ehsan discloses A method for providing wind data at a location (abstract describes using a predictive model to provide wind data at a location), comprising
providing training data sets for a plurality of installation locations for wind turbines, wherein the training data sets are obtained from public databases,
preparing the training data sets for machine learning by transforming the training data sets into features (Chapter 4 Sections B and C describes preparing training data sets by transforming them by splitting them into separate sets and analyzing them with algorithms),
training a prediction model for predicting at least one statistical wind condition at the location based on the features (Chapter 4 section A describes using data from specific locations along a three month period),
obtaining a target location (Chapter 4 section A describes using data from specific locations, specifically it describes that data from a specific station is used, meaning it provides data from one location. Further, Chapter 1 describes the placement of the wind turbine being the desired goal, so the estimation is provided for a certain location), in particular from a Customer Relationship Management (CRM) system (Optional limitation, but no structure is given to what a CRM is nor is there any known term common in the art, so the term is being interpreted broadly as any system that can accept or deliver data, as is described in Chapter 4),
predicting the at least one statistical wind condition at the target location using the trained prediction model (Chapter 4 section C describes the simulation results providing a wind speed wind condition using the model), wherein the target location is provided as at least one input into the trained prediction model for predicting the at least one statistical wind condition (Chapter 4 section A of Ehsan describes that the data from a specific location is used, so the location selected and thereby a target location is inherently an input for the trained prediction model. The claim limitations do not describe the specific location data or how the data is used, so because the weather data is measured at a specific location, the location data is thereby used as an input into the trained prediction model. Chapter 1 further describes that the system uses a location and height in the system and that a certain location is selected for the determination of the wind speed parameters, and as such a location is an input into the model. As a specific location must be selected by the user or the automated system in order to scrape and choose the meteorological data used in the predictive model, the location data is an input to run and operate the model), and
providing wind data including the predicted at least one statistical wind condition, to the CRM system (abstract describes using the predicted data for planning and feasibility studies, which both meet the limitations of a CRM system as they are used for customer planning).
Regarding claim 4, Ehsan discloses that the wind data at the target location is further predicted by indicating a target hub height of the wind turbine (Chapter I describes the desired height being 80m and the remainder of the document describes predicting the data at this location).
Regarding claim 12, Ehsan discloses that transforming the training data sets into features comprises: transforming the training data sets into a subset of the available features having at most 25 features (Chapter IV Section A describes using 17 predictors).
Regarding claim 13, Ehsan discloses that the prediction model comprises at least one of a decision tree, a random forest, or a boosted forest algorithm (Chapter IV, Section C).
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.
Claim(s) 2, 3, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks” in view of Steisdal (EP 2148225).
Regarding claim 2, Ehsan discloses the limitations of claim 1 and that the at least one statistical wind condition comprises an average wind speed (Chapter V describes wind speed being estimated and the values used were provided over a timeframe, making the estimate an average). However, Ehsan does not explicitly disclose the wind condition also comprising a turbulence intensity. Steisdal and Ehsan are analogous prior art because both describe forecasting wind conditions for wind turbine locations.
Steisdal teaches providing a turbulence estimate based on wind speed values (Par. 0040) and measuring the wind speed at multiple height locations to describe the local wind-shear (Par. 0017) for accurate determination of all the useful wind properties for wind turbine operation (Par. 0009). Ehsan Chapter I describes the importance of the wind characteristics for designing and choosing the wind turbine location, so the addition of the turbulence parameter would provide another piece of data to determine wind turbine feasibility. Thereby, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the turbulence estimation of Steisdal and the multiple height measurements and wind shear estimation of Steisdal into the predictive model of Ehsan because it provides another parameter to determine the wind turbine feasibility and Steisdal describes that the turbulence is a useful wind property for wind turbine operation (Par. 0009).
Regarding claim 3, Ehsan in view of Steisdal teaches that the at least one statistical wind condition comprises in addition to the average wind speed and the turbulence intensity further includes statistical wind conditions, including an average wind shear (Steisdal Par. 0017).
Regarding claim 8, Ehsan in view of Steisdal teaches that the training data sets includes wind speeds at different altitudes and the features include a wind shear determined from the wind speeds at different altitudes (Steisdal Par. 0017). The reasons for obviousness are the same as the reasons provided in the rejection of claim 2 above and can be seen in detail there.
Claim(s) 5-6, 9-11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks” in view of Zhang (CN 110555538).
Regarding claim 5, Ehsan discloses the limitations of claim 1 as set forth in the above 102 rejection and that the location is characterized by a plurality of location-specific features (Chapter IV section A describes that location is used in the values). However, Ehsan does not explicitly disclose that the features are extracted from publicly accessible resources that include European Reanalysis (ERAS), New European Wind Atlas (NEWA), Global Wind Atlas (GWA) and Shuttle Radar Topography Mission (SRTM30). Ehsan and Zhang are analogous prior art because both describe prediction systems for wind turbines.
Zhang teaches finding location and topographical data from a SRTM (Pg. 2, lines 49-53, pg. 5, lines 42-60) and overlaying it with the wind speed data. Zhang describes that the topographical map provides higher-resolution wind speed prediction information (Pg. 1, lines 19-37). As the goal of Ehsan is to provide accurate wind speed prediction information and Zhang describes that adding the SRTM topographical map information to the system provides a higher-resolution wind speed data set. Thereby, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the SRTM data and topographical data mapping of Zhang in the wind speed prediction method of Ehsan because it provides higher-resolution wind speed prediction information (Pg. 1, lines 19-37).
Regarding claim 6, Ehsan in view of Zhang teaches that the location-specific features are determined using a time series data set, with hourly resolution of weather-related variables (Ehsan Chapter IV section A describes the data being provided with hourly average instances, which creates an hourly resolution of the variables).
Regarding claim 9, Ehsan in view of Zhang teaches that the training data sets include altitude information (Ehsan Chapter IV Section A describes the data sets being based on the height measured) based on satellite measurements (Zhang Pg. 2, lines 49-53, pg. 5, lines 42-60 describes satellite measurements being used), around the prediction location, and wherein the features derived from altitude information that reflect an altitude difference between an installation location and a reference location (Ehsan Chapter IV Sections A and C describes the difference in data heights at 2m and 5m and the installation location being at 50m which showcases a difference in altitude. The reasons for obviousness are the same as the reasons provided in the rejection of claim 5 above and can be seen in detail there.
Regarding claim 10, Ehsan in view of Zhang teaches that the reference location is arranged at a predetermined distance in a) a predetermined direction (Ehsan Chapters I and IV describe measuring and predicting the values at a specific location. The limitations do not describe any details of the location or distance or direction, so as long as any location is chosen the limitations are met).
Regarding claim 11, Ehsan in view of Zhang teaches that the features derived from the altitude information include a surface roughness (Ehsan Chapter IV section A).
Regarding claim 16, Ehsan in view of Zhang teaches that the location-specific features are formed based on one or more of mean values, standard deviation, normalization and maximum values of the weather-related variables (Ehsan Chapter IV Section A describes average wind direction and average wind speed being used which are both mean values).
Claim(s) 7 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks” in view of Fraser (US 20220397097).
Regarding claim 7, Ehsan discloses that the features include a direction-dependent wind speed (Ehsan Chapter IV section A describes that average wind direction is used with the wind speed which creates a direction dependency of the wind speed when analyzed). However, Ehsan does not explicitly disclose a distribution of the wind speed. Ehsan and Fraser are analogous prior art because both describe turbine wind determination systems.
Fraser teaches the use of a wind rose to determine a wind distribution (Par. 0072) that provide more accurate wind speed and direction mechanisms (Par. 0005-0007). Ehsan already describes determining the wind direction and information from the data so the addition of a more accurate wind rose would provide more accurate information for the system. Thereby, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the wind speed prediction system of Ehsan to include the wind rose system of Fraser because it provides more accurate wind speed and direction mechanisms (Par. 0005-0007).
Regarding claim 17, Ehsan in view of Fraser teaches that the direction-dependent wind speed distribution includes a plurality of sectors each including at least 30° or at least 60°. Fraser paragraph 0072 describes that there can be 8 zones which would provide 45° zones.
Regarding claim 18, Ehsan in view of Fraser teaches that the direction-dependent wind speed distribution does not contain a directional sector. Fraser Paragraphs 0021, 0073, and 0076 describes that the sectors can be weighted, meaning that some sectors can be not included in the wind speed distribution.
Regarding claim 19, Ehsan in view of Fraser teaches that the direction-dependent wind speed includes a larger number of sectors in a main wind direction than in a wind direction other than the main wind direction. No limitations are given for what determines the main wind direction so as the main wind direction can change there can be more sectors that meet the main wind direction than others when the wind is changing consistently.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks”.
Regarding claim 14, Ehsan discloses the limitations of claim 1 as set forth in the above 102 rejection. However, Ehsan does not explicitly disclose that the provided wind data includes a predicted average wind speed expressed in 0.5 m/s increments. The Applicant has not disclosed that the increments of 0.5 m/s solves any stated problem or is for any particular purpose and thus it appears that the accuracy provided would perform equally well with the specified structure as claimed by applicant or any wind speed tolerance and accuracy. The specification of the instant application has not provided any criticality to the specific accuracy claimed. It would have been an obvious matter of design choice to modify the accuracy to have the increments be of 0.5 m/s as claimed and one of ordinary skill in the art would be motivated to do so as it would allow the structure to have a specific tolerance of wind speed values.
Claim(s) 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks” in view of Davoust (US 20210239094).
Regarding claim 15, Ehsan discloses the limitations of claim 1 as set forth in the above 102 rejection and that the wind turbine is part of a wind farm that includes a plurality of wind turbines (Chapter V). However, Ehsan does not explicitly disclose predicting a load of the wind turbine based on the wind data; predicting an annual yield of the wind turbine based on the wind data; predicting one or more of a life span and maintenance interval of at least one component of the wind turbine based on at least one of the load and the annual yield of the wind turbine. Ehsan and Davoust are analogous prior art because both describes methods for determining wind intensity on a wind turbine. Davoust teaches using reference wind speed data to determine fatigue or extreme loads on the wind turbine (Par. 0050 and 0204-0205), determining annual energy production and power (Pars. 0213-0216), and using the load determination to estimate the lifespan of the wind turbine (Pars. 0047, 0123, and 0128). As described in paragraph 0050, the adjustment and estimations allow for better power production, the reduction of fatigue loads, and a longer lifespan of the wind turbine.
Ehsan already describes using the wind speed estimations to determine the feasibility of a location for a wind turbine farm (Chapter V) but does not describe specifically how these are used. As described in Davoust, these wind speed estimations can predict these values and each can be taken into account for the lifespan, the loads, and the power of the turbine, which all are essential to determining if the wind turbine location will be effective. Thereby, the determination of the loads, power production, and lifespan as provided in Davoust would provide predictable results if added to the method of Ehsan. Thereby, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Ehsan to include the determination of the loads, power production, and lifespan as described in Davoust because they allow for a more detailed estimation of the wind turbine effectiveness at a specific location with the use of the data of Ehsan.
Regarding claim 20, Ehsan in view of Davoust teaches optimizing a wind farm configuration of the wind farm based on at least one of the load or the annual yield of the wind turbine. As described in the rejection of claim 15 above, the system seeks to provide an ideal location for a wind turbine, so those configurations would be modified based on the results.
Conclusion
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/THEODORE C RIBADENEYRA/ Examiner, Art Unit 3745
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