DETAILED ACTION
Claims 1-20 are pending in the present application.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/10/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 10 is objected to because of the following informalities:
In claim 10, line 5, the term “surface” should read as “a surface”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-7 and 11-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, there is a lack of antecedent basis for the phrase “the vehicle”. For the purpose of examination, the phrase has been read as “a vehicle”. For consistency, the phrase “a vehicle” in claim 3 has been read as “the vehicle” Additionally, and the phrase “among one or more vehicles” has been read as “among the vehicle or one or more vehicles” in claim 4.
Regarding claim 11, there is a lack of antecedent basis for the phrase “the vehicle”. For the purpose of examination, the phrase has been read as “a vehicle”. For consistency, the phrase “a vehicle” in claim 12 has been read as “the vehicle”. Additionally, and the phrase “among one or more vehicles” has been read as “among the vehicle or one or more vehicles” in claim 13.
Regarding claims 3-7 and 12-16, these claims are rejected for failing to remedy the rejection of claims 2 and 11 above under 35 U.S.C. 112(b).
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ang et al. (US PGPUB 2025/0297914 A1, hereinafter Ang).
Regarding claim 1, Ang teaches a method of inspection of a surface of a structure (see Abstract; see also Fig. 2-4, method 200 of inspection of a surface of a structure via system 300; see also [0040]-[0050], discussion of method of inspection of a surface of a structure), the method comprising: acquiring one or more images of the surface using one or image sensors disposed adjacent to the surface (see Fig. 2-4 and [0044], drone camera 304 used to acquire one or more images of the surface); determining, using the one or more images applied to a model, one or more predicted defects of the surface and corresponding location information (see Fig. 2-4 and [0045], images applied to visual AI engine (model) such that predictions of surface defects are assessed as described); controlling, using the location information, the position of one or more tactile sensors disposed adjacent to the surface to acquire dimensioning information for at least a first predicted defect of the one or more predicted defects (see Fig. 2-4 and [0046]-[0048], tactile sensors on drone positioned according to the predicted surface defect locations to acquire 3D representation of the façade, considered by the Examiner as dimensioning information); and characterizing the first predicted defect using the dimensioning information (see Fig. 2-4 and [0047], predicted defects are characterized based on the severity and type of the defects using the dimensioning information from the tactiles sensors as described).
Ang fails to specifically teach that the structure is an aerodynamic structure.
However, Ang does teach that the inspections may be focused on wind conditions around the features of the structure (see [0042]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the method and device of Ang such that aerodynamic portions of the structure were inspected. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]).
Regarding claim 2, Ang above teaches all of the limitations of claim 1.
Furthermore, Ang teaches applying the one or more images to a machine learning model to identify the one or more predicted defects and the location information (see [0045], us of AI to identify predicted defects as described); and providing, using the location information corresponding to the first predicted defect, a control signal to a vehicle to position a first tactile sensor of the one or more tactile sensors to acquire the dimensioning information for the first predicted defect (see Fig. 2-4 and [0046]-[0048], tactile sensors on drone (vehicle) positioned according to the predicted surface defect locations to acquire 3D representation of the façade, considered by the Examiner as dimensioning information).
Ang fails to specifically teach selecting, using confidence information corresponding to the one or more predicted defects, at least a first predicted defect from the one or more predicted defects.
However, Ang does teach that the inspection method determines the predicted defects based on the number, severity, and/or likelihood of defects present in the images (see [0045]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the method of Ang such that confidence information corresponding to the defects was utilized to determine a first predicted defect for further tactile analysis. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]), wherein the most critical potential defects were analyzed with priority as is known in the art.
Regarding claim 3, Ang above teaches all of the limitations of claims 1 and 2.
Furthermore, Ang teaches that a first image sensor of the one or more image sensors, used to acquire the one or more images, and the first tactile sensor are colocated on the vehicle disposed adjacent to the surface (see Fig. 4, first image sensor 304 and first tactile sensor 310 colocated on drone 302).
Regarding claim 4, Ang above teaches all of the limitations of claims 1 and 2.
Ang fails to specifically teach that selecting at least a first predicted defect comprises: selecting, based on a comparison of the confidence information with a threshold value, a set of the one or more predicted defects for tactile sensing; allocating the predicted defects of the set among the vehicle or one or more vehicles disposed adjacent to the surface; and scheduling the predicted defects of the set among the vehicle or one or more vehicles.
However, as described above, Ang does teach that the inspection method determines the predicted defects based on the number, severity, and/or likelihood of defects present in the images (see [0045]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the method of Ang such that confidence information corresponding to the defects was utilized in conjunction with threshold values to determine a set of predicted defect for scheduling tactile analysis based on, for example, the likely severity of the defect. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]), wherein the most critical potential defects were analyzed with priority as is known in the art.
Regarding claims 5 and 6, Ang above teaches all of the limitations of claims 1, 2, and 4.
Ang fails to specifically teach allocating the predicted defects of the set among the one or more vehicles comprises: applying one or more goals, wherein the one or more goals comprises one or more of balancing a workload between the one or more vehicles, and minimizing an overall inspection time; or wherein scheduling the predicted defects of the set among the one or more vehicles comprises: applying one or more goals, wherein the one or more goals comprises one or more of minimizing conflicts between the one or more vehicles, and minimizing a travel time of the one or more vehicles.
However, Ang does teach that multiple drones may be utilized (see claim 8); and wherein one of the goals of the method and device is to reduce inspection time (see [0013]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the method of Ang such that when multiple vehicles are utilized the method includes optimizing the task allocation of each drone or the conflicts between drones to minimize inspection time. This would allow for further reduction in time while still providing a comprehensive assessment of the building façade quality according to the objective as outlined by Ang (see [0013]).
Regarding claim 7, Ang above teaches all of the limitations of claims 1, 2, and 4.
Ang fails to specifically teach adjusting the threshold value; and selecting, based on a comparison of the confidence information with the adjusted threshold value, a second set of the one or more predicted defects for tactile sensing.
However, as described above, Ang does teach that the inspection method determines the predicted defects based on the number, severity, and/or likelihood of defects present in the images (see [0045]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the method of Ang such that confidence information corresponding to the defects was utilized in conjunction with multiple threshold values to determine sets of predicted defect for scheduling tactile analysis based on, for example, the likely severity of the defect. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]), wherein the most critical potential defects were analyzed with priority as is known in the art.
Regarding claim 8, Ang above teaches all of the limitations of claim 1.
Furthermore, Ang teaches that acquiring the dimensioning information for the first predicted defect comprises: determining one or more of a depth profile, a width, a length, and a sharpness of a bottom basin for the first predicted defect (see Fig. 2 and [0046], depth profile, width, and length of hollow areas within the structure may be identified).
Regarding claim 9, Ang above teaches all of the limitations of claim 1.
Ang above fails to specifically teach that characterizing the first predicted defect using the dimensioning information comprises: characterizing the first predicted defect as one of a drill run, a scratch, and a gouge.
However, Ang does teach that the tactile sensors may be utilized to determine such defects as damage in wood, failures of supports, looseness, cracking, etc. of the structure (see Fig. 2 and [0040], discussion of types of defects analyzed).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the method of Ang such that the defects characterized included any number of well-known defects under the umbrella of wood damage, including for example, scratches and gouges. This is because one of ordinary skill in the art would have been motivated to determine any number of well-known defects related to the structure of interest such that a comprehensive assessment of the structure would be provided to the user.
Regarding claim 10, Ang teaches a computer program product comprising: a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors (see Abstract; see also Fig. 2-4, method 200 of inspection of a surface of a structure via system 300 with computer program via computer 101; see also [0040]-[0050], discussion of method of inspection of a surface of a structure) to perform an operation comprising: acquiring one or more images of a surface of a structure using one or image sensors (see Fig. 2-4 and [0044], drone camera 304 used to acquire one or more images of the surface); determining, using the one or more images applied to a model, one or more predicted defects of the surface and corresponding location information (see Fig. 2-4 and [0045], images applied to visual AI engine (model) such that predictions of surface defects are assessed as described); controlling, using the location information, the position of one or more tactile sensors disposed adjacent to the surface to acquire dimensioning information for at least a first predicted defect of the one or more predicted defects (see Fig. 2-4 and [0046]-[0048], tactile sensors on drone positioned according to the predicted surface defect locations to acquire 3D representation of the façade, considered by the Examiner as dimensioning information); and characterizing the first predicted defect using the dimensioning information (see Fig. 2-4 and [0047], predicted defects are characterized based on the severity and type of the defects using the dimensioning information from the tactiles sensors as described).
Ang fails to specifically teach that the structure is an aerodynamic structure.
However, Ang does teach that the inspections may be focused on wind conditions around the features of the structure (see [0042]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the product of Ang such that aerodynamic portions of the structure were inspected. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]).
Regarding claim 11, Ang above teaches all of the limitations of claim 10.
Furthermore, Ang teaches applying the one or more images to a machine learning model to identify the one or more predicted defects and the location information (see [0045], us of AI to identify predicted defects as described); and providing, using the location information corresponding to the first predicted defect, a control signal to a vehicle to position a first tactile sensor of the one or more tactile sensors to acquire the dimensioning information for the first predicted defect (see Fig. 2-4 and [0046]-[0048], tactile sensors on drone (vehicle) positioned according to the predicted surface defect locations to acquire 3D representation of the façade, considered by the Examiner as dimensioning information).
Ang fails to specifically teach selecting, using confidence information corresponding to the one or more predicted defects, at least a first predicted defect from the one or more predicted defects.
However, Ang does teach that the inspection method determines the predicted defects based on the number, severity, and/or likelihood of defects present in the images (see [0045]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the product of Ang such that confidence information corresponding to the defects was utilized to determine a first predicted defect for further tactile analysis. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]), wherein the most critical potential defects were analyzed with priority as is known in the art.
Regarding claim 12, Ang above teaches all of the limitations of claims 10 and 11.
Furthermore, Ang teaches that a first image sensor of the one or more image sensors, used to acquire the one or more images, and the first tactile sensor are colocated on the vehicle disposed adjacent to the surface (see Fig. 4, first image sensor 304 and first tactile sensor 310 colocated on drone 302).
Regarding claim 13, Ang above teaches all of the limitations of claims 10 and 11.
Ang fails to specifically teach that selecting at least a first predicted defect comprises: selecting, based on a comparison of the confidence information with a threshold value, a set of the one or more predicted defects for tactile sensing; allocating the predicted defects of the set among the vehicle or one or more vehicles disposed adjacent to the surface; and scheduling the predicted defects of the set among the vehicle or one or more vehicles.
However, as described above, Ang does teach that the inspection method determines the predicted defects based on the number, severity, and/or likelihood of defects present in the images (see [0045]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the product of Ang such that confidence information corresponding to the defects was utilized in conjunction with threshold values to determine a set of predicted defect for scheduling tactile analysis based on, for example, the likely severity of the defect. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]), wherein the most critical potential defects were analyzed with priority as is known in the art.
Regarding claims 14 and 15, Ang above teaches all of the limitations of claims 10, 11, and 13.
Ang fails to specifically teach allocating the predicted defects of the set among the one or more vehicles comprises: applying one or more goals, wherein the one or more goals comprises one or more of balancing a workload between the one or more vehicles, and minimizing an overall inspection time; or wherein scheduling the predicted defects of the set among the one or more vehicles comprises: applying one or more goals, wherein the one or more goals comprises one or more of minimizing conflicts between the one or more vehicles, and minimizing a travel time of the one or more vehicles.
However, Ang does teach that multiple drones may be utilized (see claim 8); and wherein one of the goals of the method and device is to reduce inspection time (see [0013]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the product of Ang such that when multiple vehicles are utilized the method includes optimizing the task allocation of each drone or the conflicts between drones to minimize inspection time. This would allow for further reduction in time while still providing a comprehensive assessment of the building façade quality according to the objective as outlined by Ang (see [0013]).
Regarding claim 16, Ang above teaches all of the limitations of claims 10, 11, and 13.
Ang fails to specifically teach adjusting the threshold value; and selecting, based on a comparison of the confidence information with the adjusted threshold value, a second set of the one or more predicted defects for tactile sensing.
However, as described above, Ang does teach that the inspection method determines the predicted defects based on the number, severity, and/or likelihood of defects present in the images (see [0045]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the product of Ang such that confidence information corresponding to the defects was utilized in conjunction with multiple threshold values to determine sets of predicted defect for scheduling tactile analysis based on, for example, the likely severity of the defect. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]), wherein the most critical potential defects were analyzed with priority as is known in the art.
Regarding claim 17, Ang above teaches all of the limitations of claim 10.
Furthermore, Ang teaches that acquiring the dimensioning information for the first predicted defect comprises: determining one or more of a depth profile, a width, a length, and a sharpness of a bottom basin for the first predicted defect (see Fig. 2 and [0046], depth profile, width, and length of hollow areas within the structure may be identified).
Regarding claim 18, Ang above teaches all of the limitations of claim 10.
Ang above fails to specifically teach that characterizing the first predicted defect using the dimensioning information comprises: characterizing the first predicted defect as one of a drill run, a scratch, and a gouge.
However, Ang does teach that the tactile sensors may be utilized to determine such defects as damage in wood, failures of supports, looseness, cracking, etc. of the structure (see Fig. 2 and [0040], discussion of types of defects analyzed).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the product of Ang such that the defects characterized included any number of well-known defects under the umbrella of wood damage, including for example, scratches and gouges. This is because one of ordinary skill in the art would have been motivated to determine any number of well-known defects related to the structure of interest such that a comprehensive assessment of the structure would be provided to the user.
Regarding claim 19, Ang teaches a system (see Abstract; see also Fig. 2-4, method 200 of inspection of a surface of a structure via system 300/400; see also [0040]-[0050], discussion of method of inspection of a surface of a structure) comprising: one or more image sensors (304) disposed adjacent to a surface of an structure (see [0040]-[0050], discussion of method of inspection of a surface of a structure with system 300/400); one or more tactile sensors (310) disposed adjacent to the surface (see [0040]-[0050], discussion of method of inspection of a surface of a structure with system 300/400 and tactile sensors system 310): and one or more processors (101) configured to: acquire one or more images of a surface using one or image sensors (see Fig. 2-4 and [0044], drone camera 304 used to acquire one or more images of the surface); determine, using the one or more images applied to a model, one or more predicted defects of the surface and corresponding location information (see Fig. 2-4 and [0045], images applied to visual AI engine (model) such that predictions of surface defects are assessed as described); control, using the location information, the position of one or more tactile sensors disposed adjacent to the surface to acquire dimensioning information for at least a first predicted defect of the one or more predicted defects (see Fig. 2-4 and [0046]-[0048], tactile sensors on drone positioned according to the predicted surface defect locations to acquire 3D representation of the façade, considered by the Examiner as dimensioning information); and characterizing the first predicted defect using the dimensioning information (see Fig. 2-4 and [0047], predicted defects are characterized based on the severity and type of the defects using the dimensioning information from the tactiles sensors as described).
Ang fails to specifically teach that the structure is an aerodynamic structure.
However, Ang does teach that the inspections may be focused on wind conditions around the features of the structure (see [0042]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art, to modify the system of Ang such that aerodynamic portions of the structure were inspected. This would ensure that the applicable standards of structural integrity were maintained for all portions of a structure subject to wind and the elements as suggested by Ang (see [0002]).
Regarding claim 20, Ang above teaches all of the limitations of claim 19.
Furthermore, Ang teaches that a first image sensor of the one or more image sensors, used to acquire the one or more images, and the first tactile sensor are colocated on a vehicle disposed adjacent to the surface (see Fig. 4, first image sensor 304 and first tactile sensor 310 colocated on drone 302).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHANIEL T WOODWARD whose telephone number is (571)270-0704. The examiner can normally be reached M-F: 9:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick Assouad can be reached at (571) 272-2210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHANIEL T WOODWARD/ Primary Examiner, Art Unit 2855
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