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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “data acquisition module” in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 6-8, 10, 11, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (US 20240078654 A1) in view of Kommareddy et al. (WO 2024/160958 A1) and further in view of Barrett et al. (US 6549820 B1).
Regarding claim 1, a method for improving quality of a rotor blade of a wind turbine (see para [0001]; “a wind turbine blade inspection system for inspecting a wind turbine blade shell part for defects and a method for inspecting a wind turbine blade shell part for defects”, see also para [0006]; “there is a need for automated processes that more efficiently can identify defects and suggest a proper repair method to remedy the defect and which are less prone to errors”), the method comprising: receiving, via a data acquisition module of a controller, image data relating to the rotor blade (see para [0063]; “The wind turbine blade inspection system 60 further comprises a processing unit or computing unit 70, which is configured to image process captured images from the image capturing devices 62, 64a, 64b”, see also para [0065]; “The processing unit 70 comprises a data instigation module (DM) 72, a stitching module (SM) 74, a filter module (FM) 76, and an analytics module (AM) 78”, and [0070]; “The data instigation module 72 consists of an interface to read either infrared images, optical images, infrared video or optical video collected from the image capturing devices 62, 64a, 64b”), the image data collected during or after manufacturing of the rotor blade before the rotor blade is placed into operation on the wind turbine (see para [0018]; “method preferably relates to the inspection during the manufacture of a wind turbine blade. Accordingly, the system is preferably configured to inspecting the wind turbine blade part, such as a wind turbine blade shell or a spar cap, during the manufacture of the blade. Thus, the system is configured to be used at the manufacturing facilities. Similarly, the inspection method is preferably used during the manufacturing of a wind turbine blade, e.g. after curing of a wind turbine blade shell or a spar cap”, see also para [0066]; “The wind turbine blade inspection system 60 may preferably be used immediately after curing of the wind turbine blade shell part 38”); identifying, via a processor of the controller, an anomaly on the rotor blade using the image data relating to the rotor blade (see para [0008]; “an image processing unit configured to identify an anomaly in the plurality of acquired images and identify whether the anomaly relates to a defect in the wind turbine blade part”); determining, via the processor, a location of the anomaly of the rotor blade (see para [0025]; “the system is configured to provide a full image of the wind turbine blade part, which provides a better feedback as to the positions of the possible anomalies and defect”, see also para [0035]; “the system further comprises a pointer unit, which is configured to illuminate one or more areas on the wind turbine blade part to identify defect locations for repair work on the wind turbine blade part”); displaying, via the processor, the location of the anomaly of the rotor blade (see para [0034]; “The original image data, region boundaries (polygons) and their corresponding defect category for each image along with the meta information related to inspection may then be passed to the visualization module for the inspector to verify, validate and take repair action”, see also para [0050]; “FIG. 7 discloses a pointer system for illuminating areas to identify defect locations for repair”). However, Miller et al. does not teach using a combination of at least two of the following: an estimated location of an imaging device when the image data was collected, a known location of a pixel as represented by multiple angles that describe a location of the pixel and the anomaly within the image data as projected onto a spherical shell, Light Detection and Ranging (LIDAR) data of a cross section of the rotor blade at a time and location when the image data was collected, a specific internal cavity that the imaging device is in when the image data was collected, or a computer-aided design (CAD) model of the rotor blade; and implementing, via the processor, a corrective action for a subsequent manufacturing process of another rotor blade based on the location of the anomaly of the rotor blade .
In the same field of endeavor, Kommareddy et al. teaches using a combination of at least two of the following: an estimated location of an imaging device when the image data was collected, a known location of a pixel as represented by multiple angles that describe a location of the pixel and the anomaly within the image data as projected onto a spherical shell, Light Detection and Ranging (LIDAR) data of a cross section of the rotor blade at a time and location when the image data was collected, a specific internal cavity that the imaging device is in when the image data was collected, or a computer-aided design (CAD) model of the rotor blade (see page 15, 9th para; “the positioning sensor may provide the position of the wind turbine blade inspection system 100 relative to the length of the wind turbine blade 7. The controller 140 may obtain, from the positioning sensor, the location of the wind turbine blade inspection system 100. Based on the location of the wind turbine blade inspection system 100, the controller 140 may also be configured to localize a defect if a defect is detected in the inspection surface 200”, see also page 4, Clause 12; “ obtain a CAD model of the blade shell part; overlay images received from the image-capturing devices containing a defect on the CAD file; and generate data comprising the location and the type of defect” Note; disclose at least two of the claimed localization inputs). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kommareddy et al. in order to optimize process parameters and reduce defects (see page 15, 9th para). However, the combination of Miller et al. and Kommareddy et al. as a whole does not specifically teach and implementing, via the processor, a corrective action for a subsequent manufacturing process of another rotor blade based on the location of the anomaly of the rotor blade.
In the same field of endeavor, Barrette et al. teaches and implementing, via the processor, a corrective action for a subsequent manufacturing process of another rotor blade based on the location of the anomaly of the rotor blade (see col. 2, lines 59-64; “Because defect and repair information is gathered on parts as they are manufactured, manufacturing process steps can be modified in real time in order to avoid or eliminate defects in subsequent composite parts as they are manufactured”). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kommareddy et al. and further in view of a method for inspection of parts and, more particularly, to the non-destructive inspection parts of Barrette et al. in order to identify significant problems or anomalies in the part, and transmit the resulting information to other manufacturing personnel so that prompt corrective action can be taken (see col. 2, lines 59-64).
Regarding claim 6, the rejection of claim 1 is incorporated herein.
Kommareddy et al. in the combination further teach wherein displaying the location of the anomaly of the rotor blade further comprises generating a report that includes the location of the anomaly of the rotor blade (see page 19, last para; “The method may further comprise generating data including the location and the type of defect. This data may include an inspection report”).
Regarding claim 7, the rejection of claim 1 is incorporated herein.
Miller et al. in the combination further teach wherein the processor of the controller is configured to implement one or more machine-learned models for determining the location of the anomaly of the rotor blade (see para [0031]; “the module may be trained using machine learning or artificial intelligence to identify the anomaly and the related defect type”).
Regarding claim 8, the rejection of claim 7 is incorporated herein.
Miller et al. in the combination further teach wherein the one or more machine-learned models comprise at least one of an unsupervised learning-based model, a supervised learning-based model, or a self-supervised learning-based model (see para [0073]; “The network may be trained using the input and output pair provided by an operator in a supervised learning”, see also para [0078]; “There are several neural network architectures based on auto-encoder and generative adversarial network, which performs learning in an unsupervised setting”).
Regarding claim 10, the rejection of claim 1 is incorporated herein.
Barrett et al. in the combination further teach wherein implementing the corrective action for the subsequent manufacturing process of another rotor blade based on the location of the anomaly of the rotor blade further comprises at least one of scheduling a repair to correct the anomaly, modifying a manufacturing parameter, or halting production of subsequent rotor blades until the anomaly is corrected (see col. 2, lines 59-64; “Because defect and repair information is gathered on parts as they are manufactured, manufacturing process steps can be modified in real time in order to avoid or eliminate defects in subsequent composite parts as they are manufactured”, see also col. 14, lines 28-37; “modify the data in the REPAIR RECORD form. Like the QA DATA RECORD window, the REPAIR RECORD window 358 includes a plurality of data entry boxes, some of which include drop-down lists. The data entry boxes of the exemplary REPAIR RECORD window 358 illustrated in FIG. 9A include a Repair Date box 364; a Repair Operator box 366; a Defect Type Found box (drop down list) 368; a TKR # box 370; a Repair NCR # box 372; a Defect Location box (drop down list) 374”).
Regarding claim 11, the scope of claim 11 is fully encompassed by the scope of claim 1, accordingly, the rejection analysis of claim 1 is equally applicable.
Regarding claim 16, the rejection of claim 11 is incorporated herein.
Kommareddy et al. in the combination further teach wherein displaying the location of the anomaly of the rotor blade further comprises generating a report that includes the location of the anomaly of the rotor blade (see page 19, last para; “The method may further comprise generating data including the location and the type of defect. This data may include an inspection report”).
Regarding claim 17, the rejection of claim 11 is incorporated herein.
Miller et al. in the combination further teach wherein the processor of the controller is configured to implement one or more machine-learned models for determining the location of the anomaly of the rotor blade (see para [0031]; “the module may be trained using machine learning or artificial intelligence to identify the anomaly and the related defect type”).
Regarding claim 18, the rejection of claim 17 is incorporated herein.
Miller et al. in the combination further teach wherein the one or more machine-learned models comprise at least one of an unsupervised learning-based model, a supervised learning-based model, or a self-supervised learning-based model (see para [0073]; “The network may be trained using the input and output pair provided by an operator in a supervised learning”, see also para [0078]; “There are several neural network architectures based on auto-encoder and generative adversarial network, which performs learning in an unsupervised setting”).
Regarding claim 20, the rejection of claim 11 is incorporated herein.
Barrett et al. in the combination further teach wherein implementing the corrective action for the subsequent manufacturing process of another rotor blade based on the location of the anomaly of the rotor blade further comprises at least one of scheduling a repair to correct the anomaly, modifying a manufacturing parameter, or halting production of subsequent rotor blades until the anomaly is corrected (see col. 2, lines 59-64; “Because defect and repair information is gathered on parts as they are manufactured, manufacturing process steps can be modified in real time in order to avoid or eliminate defects in subsequent composite parts as they are manufactured”, see also col. 14, lines 28-37; “modify the data in the REPAIR RECORD form. Like the QA DATA RECORD window, the REPAIR RECORD window 358 includes a plurality of data entry boxes, some of which include drop-down lists. The data entry boxes of the exemplary REPAIR RECORD window 358 illustrated in FIG. 9A include a Repair Date box 364; a Repair Operator box 366; a Defect Type Found box (drop down list) 368; a TKR # box 370; a Repair NCR # box 372; a Defect Location box (drop down list) 374”).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. and Kommareddy et al. in view of Barrett et al. as applied in claim 1 above, and further in view of Bowyer et al. (US 20220375351 A1).
Regarding claim 2, the rejection of claim 1 is incorporated herein. The combination of Miller et al., Kommareddy et al., and Barrett et al. as a whole does not teach wherein the image data relating to the rotor blade further comprises high resolution image data comprising at least one of one or more 3600 videos of the rotor blade during manufacturing, one or more 180° videos of the rotor blade during manufacturing, one or more 360° images of the rotor blade, or one or more 180° images of the rotor blade.
In the same field of endeavor, Bowyer et al. teach wherein the image data relating to the rotor blade further comprises high resolution image data comprising at least one of one or more 3600 videos of the rotor blade during manufacturing, one or more 180° videos of the rotor blade during manufacturing, one or more 360° images of the rotor blade, or one or more 180° images of the rotor blade (see para [0076]; “use the high resolution (360 deg) digital cameras to log locally and then download this video data later”, see also para [0142]; “The flight control system may be one wherein the captured images are captured assuming rotation of the wind turbine blades …The flight control system may be one in which the wind turbine includes a rotor”). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kummareddy et al. and further in view of a method for inspection of parts and, more particularly, to the non-destructive inspection parts of Barrette et al. and flight control systems, to ground-based control centres, to Remotely Piloted Aircraft of Bowyer et al. in order to communicate between the RPA and the ground-based control centre, based on the determined operation risk (see para [0143]).
Regarding claim 12, the rejection of claim 11 is incorporated herein.
Bowyer et al. in the combination further teach wherein the image data relating to the rotor blade further comprises high resolution image data comprising at least one of one or more 3600 videos of the rotor blade during manufacturing, one or more 180° videos of the rotor blade during manufacturing, one or more 360° images of the rotor blade, or one or more 180° images of the rotor blade (see para [0076]; “use the high resolution (360 deg) digital cameras to log locally and then download this video data later”, see also para [0142]; “The flight control system may be one wherein the captured images are captured assuming rotation of the wind turbine blades …The flight control system may be one in which the wind turbine includes a rotor”). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kummareddy et al. and further in view of a method for inspection of parts and, more particularly, to the non-destructive inspection parts of Barrette et al. and flight control systems, to ground-based control centres, to Remotely Piloted Aircraft of Bowyer et al. in order to communicate between the RPA and the ground-based control centre, based on the determined operation risk (see para [0143]).
Claims 3-5, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. and Kommareddy et al. in view of Barrett et al. as applied in claim 1 above, and further in view of Oliver et al. (US 20250215858 A1).
Regarding claim 3, the rejection of claim 1 is incorporated herein.
Miller et al. in the combination further teach during or after manufacturing thereof before the rotor blade is placed into operation on the wind turbine (see para [0018]; “the system is preferably configured to inspecting the wind turbine blade part, such as a wind turbine blade shell or a spar cap, during the manufacture of the blade. Thus, the system is configured to be used at the manufacturing facilities. Similarly, the inspection method is preferably used during the manufacturing of a wind turbine blade, e.g. after curing of a wind turbine blade shell or a spar cap”). However, the combination of Miller et al., Kommareddy et al., and Barrett et al. as a whole does not teach further comprising operating an intelligent vehicle across a surface of the rotor blade to collect the image data relating to the rotor blade.
In the same field of endeavor, Oliver et al. teaches further comprising operating an intelligent vehicle across a surface of the rotor blade to collect the image data relating to the rotor blade (see para [0053]; “FIG. 4A illustrates a perspective view of the wind turbine 400. FIGS. 4B and 4C illustrate cross-sectional views of an unmanned ground vehicle 406 inspecting a rotor blade 402”, see also para [0056]; “The UGV 406 can include two tracks 412, which can be used to navigate the UGV 406 along a length of the rotor blade 402”). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kummareddy et al. and further in view of a method for inspection of parts and, more particularly, to the non-destructive inspection parts of Barrette et al. and method for performing external inspections and internal inspections on wind turbine blades of Oliver et al. in order to assess a risk level corresponding to the defect based on a combination of the external blade data and the internal blade data (see para [0053]).
Regarding claim 4, the rejection of claim 3 is incorporated herein.
Kommareddy et al. in the combination further teach the known location of the pixel as represented by the multiple angles that describe the location of the pixel and the anomaly within the image data as projected onto the spherical shell (see page 18, 12th para; “The controller may estimate a position of a defect by converting or correlating the pixels of the image into an estimation of the position of the defect in the blade shell. This correlation may also be used to determine the shape and/or the dimensions of the defect of the blade shell. The correlation may include converting pixels of the images to mm”), and the CAD model of the rotor blade (see page 14, Clause 12; “wherein the controller is configured to: obtain a CAD model of the blade shell part; overlay images received from the image-capturing devices containing a defect on the CAD file”).
Oliver et al. in the combination further teach further comprising determining the location of the anomaly of the rotor blade using the estimated location of the imaging device when the image data was collected (see para [0079]; “the displays 500a and 500b, can have correct scaling, distances, and relative locations, and locations of any defects 330, 430, 510 can be accurately determined, viewed, and correlated…. Positions of defects on the exterior surface of the rotor blade can be determined based on relative positions of the defects along the length of the rotor blade, relative positions of the defects between the leading edge and the trailing edge, and the like. The defects may be located on any of the leading edge, the trailing edge, the suction side, or the pressure side of the rotor blade”), the LIDAR data of the cross section of the rotor blade at the time and location when the image data was collected (see para [0060]; “the distance sensors 410 can include LiDAR”, see also para [0062]; “The distance sensors 410 can be used to determine the width and height of a cavity of the rotor blade 402”), the specific internal cavity that the imaging device is in when the image data was collected (see para [0067]; “a first cavity between the leading edge and a first shear web, a second cavity between the first shear web and a second shear web, and a third cavity between the second shear web and the trailing edge”).
Regarding claim 5, the rejection of claim 4 is incorporated herein.
Kommareddy et al. in the combination further teach wherein determining the location of the anomaly of the rotor blade further comprises: correcting the estimated location of the imaging device within the rotor blade with respect to the CAD model when the image data was collected (see page 16, 8th para; “The position of the directional light sources may thus be adjusted to the shape of the blade shell part”, see also page 18, 12th para “The correlation may include converting pixels of the images to mm. In addition, from the CAD profile of the blade shell and the location of the image-capturing device with regard to the surface,
these pixel-to-mm conversions can be pre-programmed for each image-capturing device and at every location of the blade shell”); and calculating a three-dimensional location of the pixel by projecting through the spherical shell to the CAD model (see page 21, 7th para; “Based on the geometry of the blade shell part, the pixel may be converted to dimensions, e.g. to millimeters, as represented at block 620. Using this correlation, the shape of and/or the dimensions of a defect may be determined. Furthermore, the position of the defect may be
determined. This conversion may be used for overlaying an image containing a defect onto the CAD model of the blade shell part”).
Regarding claim 13, the rejection of claim 11 is incorporated herein.
Miller et al. in the combination further teach during or after manufacturing thereof before the rotor blade is placed into operation on the wind turbine (see para [0018]; “the system is preferably configured to inspecting the wind turbine blade part, such as a wind turbine blade shell or a spar cap, during the manufacture of the blade. Thus, the system is configured to be used at the manufacturing facilities. Similarly, the inspection method is preferably used during the manufacturing of a wind turbine blade, e.g. after curing of a wind turbine blade shell or a spar cap”).
Oliver et al. in the combination further teach further comprising operating an intelligent vehicle across a surface of the rotor blade to collect the image data relating to the rotor blade (see para [0053]; “FIG. 4A illustrates a perspective view of the wind turbine 400. FIGS. 4B and 4C illustrate cross-sectional views of an unmanned ground vehicle 406 inspecting a rotor blade 402”, see also para [0056]; “The UGV 406 can include two tracks 412, which can be used to navigate the UGV 406 along a length of the rotor blade 402”). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kummareddy et al. and further in view of a method for inspection of parts and, more particularly, to the non-destructive inspection parts of Barrette et al. and method for performing external inspections and internal inspections on wind turbine blades of Oliver et al. in order to assess a risk level corresponding to the defect based on a combination of the external blade data and the internal blade data (see para [0053]).
Regarding claim 14, the rejection of claim 13 is incorporated herein.
Kommareddy et al. in the combination further teach the known location of the pixel as represented by the multiple angles that describe the location of the pixel and the anomaly within the image data as projected onto the spherical shell (see page 18, 12th para; “The controller may estimate a position of a defect by converting or correlating the pixels of the image into an estimation of the position of the defect in the blade shell. This correlation may also be used to determine the shape and/or the dimensions of the defect of the blade shell. The correlation may include converting pixels of the images to mm”), and the CAD model of the rotor blade (see page 14, Clause 12; “wherein the controller is configured to: obtain a CAD model of the blade shell part; overlay images received from the image-capturing devices containing a defect on the CAD file”).
Oliver et al. in the combination further teach wherein the plurality of operations further comprise determining the location of the anomaly of the rotor blade using the estimated location of the imaging device when the image data was collected (see para [0079]; “the displays 500a and 500b, can have correct scaling, distances, and relative locations, and locations of any defects 330, 430, 510 can be accurately determined, viewed, and correlated…. Positions of defects on the exterior surface of the rotor blade can be determined based on relative positions of the defects along the length of the rotor blade, relative positions of the defects between the leading edge and the trailing edge, and the like. The defects may be located on any of the leading edge, the trailing edge, the suction side, or the pressure side of the rotor blade”), the LIDAR data of the cross section of the rotor blade at the time and location when the image data was collected (see para [0060]; “the distance sensors 410 can include LiDAR”, see also para [0062]; “The distance sensors 410 can be used to determine the width and height of a cavity of the rotor blade 402”), the specific internal cavity that the imaging device is in when the image data was collected (see para [0067]; “a first cavity between the leading edge and a first shear web, a second cavity between the first shear web and a second shear web, and a third cavity between the second shear web and the trailing edge”).
Regarding claim 15, the rejection of claim 14 is incorporated herein.
Kommareddy et al. in the combination further teach wherein determining the location of the anomaly of the rotor blade further comprises: correcting the estimated location of the imaging device within the rotor blade with respect to the CAD model when the image data was collected (see page 16, 8th para; “The position of the directional light sources may thus be adjusted to the shape of the blade shell part”, see also page 18, 12th para “The correlation may include converting pixels of the images to mm. In addition, from the CAD profile of the blade shell and the location of the image-capturing device with regard to the surface,
these pixel-to-mm conversions can be pre-programmed for each image-capturing device and at every location of the blade shell”); and calculating a three-dimensional location of the pixel by projecting through the spherical shell to the CAD model (see page 21, 7th para; “Based on the geometry of the blade shell part, the pixel may be converted to dimensions, e.g. to millimeters, as represented at block 620. Using this correlation, the shape of and/or the dimensions of a defect may be determined. Furthermore, the position of the defect may be
determined. This conversion may be used for overlaying an image containing a defect onto the CAD model of the blade shell part”).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. and Kommareddy et al. in view of Barrett et al. as applied in claim 1 above, and further in view of Manikani et al. (US 20240202907 A1).
Regarding claim 9, the rejection of claim 7 is incorporated herein. The combination of Miller et al., Kommareddy et al., and Barrett et al. as a whole does not teach further comprising continuously training and updating the one or more machine-learned models using the location of the anomaly of the rotor blade as well as historical and new locations of anomalies.
In the same field of endeavor, Manikani et al. teach further comprising continuously training and updating the one or more machine-learned models using the location of the anomaly of the rotor blade as well as historical and new locations of anomalies (see para [0083]; “The model is trained to recognize distinct types of defects, their characteristics, and typical appearances … generated by the CNN. The training process can be iterative, continuously refining the model based on its performance in identifying known defects…. the defect analysis model is continually trained as new images are ingested”) see also para [0060]; “one or more embodiments may be applied to automatically discovering wind turbine blade image defects”). Accordingly, it would have been obvious to one ordinary of the skill I the art before the effective filling date of the invention of the use of a wind turbine blade inspection system for inspecting a wind turbine blade part for defects of Miller et al. in view of a wind turbine blade inspection system for detecting defects in a wind turbine blade of Kummareddy et al. and further in view of a method for inspection of parts and, more particularly, to the non-destructive inspection parts of Barrette et al. and a method provided for defect detection in industrial inspections of Manikani et al. in order to recognize new defects when those defects are labeled and used to train the model (see para [0069]).
Regarding claim 19, the rejection of claim 17 is incorporated herein.
Manikani et al. in the combination further teach wherein the plurality of operations further comprise continuously training and updating the one or more machine-learned models using the location of the anomaly of the rotor blade as well as historical and new locations of anomalies (see para [0083]; “The model is trained to recognize distinct types of defects, their characteristics, and typical appearances … generated by the CNN. The training process can be iterative, continuously refining the model based on its performance in identifying known defects…. the defect analysis model is continually trained as new images are ingested”), see also para [0060]; “one or more embodiments may be applied to automatically discovering wind turbine blade image defects”).
Conclusion
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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
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