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 the applicant’s arguments
A new independent claim 16 is provided and a new reference was found that is relevant to claim 1, 15 and 16.
In regard to claim 1, 15 and 16, He discloses “….16. A surveillance system for an offshore wind turbine comprising:
an unmanned aerial vehicle for inspecting the offshore wind turbine;
an unmanned surface vessel (USV) comprising: (see paragraph 4 where the drone can be used for wind turbine blade inspection)
o an environmental sensor system comprising a lidar configured to measure one or
more environmental parameters, wherein the one or more environmental (see paragraph 35-40 where the drone can include a lidar sensor and the lidar sensor can capture an image of the wind turbine blades to observe the frequency of operation of the wind turbine blades being based on a normal frequency or a reduced power output frequency of rotation of the wind turbine blade such as when they have ice on them in FIG. 7a to 7b)
parameters includes wind speed; (see paragraph 34 where the drone can include sensors to also detect humidity and the wind speed )
o a carrying area to carry the unmanned aerial vehicle; and
o a controller arranged at the unmanned aerial vehicle and configured to:
■ obtain the one or more environmental parameters from the lidar
[[environmental sensor system]];
■ obtain one or more operational parameters from the lidar [[of the offshore
Infrastructure]]; (see paragraph 35-40 where the drone can include a lidar sensor and the lidar sensor can capture an image of the wind turbine blades to observe the frequency of operation of the wind turbine blades being based on a normal frequency or a reduced power output frequency of rotation of the wind turbine blade such as when they have ice on them in FIG. 7a to 7b)
■ determine an expected power output of the offshore wind turbine based on the wind speed measured by the lidar; (See paragraph 45-60 where the expected power output should be a high value but it is lower since the blades are damaged and stiffer and rotating less frequently due to the damage and ice and the power output is lower)
■ compare an actual power output of the offshore wind turbine to each of the operational parameters to [[an]]the expected power output (see paragraph 45-60 and 77 where the drone can determine that the power output is very high and do nothing or if it is very low then there is damage since the frequency of rotation of lower due to ice, or blade damage)
■ determine, based on the comparison, to inspect the offshore
wind turbine with the unmanned aerial vehicle; and (see paragraph 35-39 and FIG. 7a to 7b where the power output of the wind turbine can appear to be lower than expected due to the rotor blades being damaged and the drone can also observe that the wind turbine blades are having ice on them and this is changing the expected frequency of operation of the wind turbine blades; As shown at (104), the method 100 may also include monitoring at least one operational parameter of the wind turbine 10. For example, in an embodiment, the operational parameter(s) may include at least one of power output, wind speed, rotor speed, generator speed, or generator torque. As such, in an embodiment, the method 100 may only implement a blade damage control scheme 108 when the operational parameter(s) exceeds an operational threshold. In other words, the controller 32 may only implement the control scheme 108 if the power output exceeds a certain power threshold (as shown at (106)).
Accordingly, as shown at (110), the control scheme 108 may include converting the electrical condition(s) of the pitch system 70 into a frequency domain. For example, as shown in FIG. 8 , a graph 150 of one embodiment of pitch motor current 152 in the frequency domain according to the present disclosure is illustrated. Thus, as shown, the control scheme 108 may include determining one or more peaks 154 of the frequency domain around a frequency component related to a natural frequency of the rotor blade(s) 22. As used herein, the frequency component related to the natural frequency of the rotor blade(s) 22 may include, as an example, a natural frequency of the rotor blade 22 directly or a natural frequency of the rotor blade(s) 22 modulated by control schemes. In addition, it should be under that the natural frequency of the rotor blade(s) 22 can by affected by temperature and/or operating conditions.)_
■ instruct the unmanned aerial vehicle to inspect the offshore wind turbine when the controlled determines to inspect the offshore wind turbine. (See paragraph 45-65 where the drone can detect that the power is off and inspect the blades to determine if the blades are damaged and stiff)
The applicant states as follows:
“[t]he Office action concedes that the primary reference, Collins, does not teach comparing each of the operational parameters to an expected value, and determining, based on the comparison, to inspect the offshore infrastructure with the unmanned vehicle. Instead, col. 9, lines 10-40 and FIG. 13 of Salman are cited as teaching this feature. Salman discloses a system that can assess image data "to detect one or more conditions that may warrant action as to status of one or more pieces of subsea equipment", i.e. a damaged pump, leaking valve, etc. Salman at col. 9, I. 33-38. However, there is no teaching or suggestion in Salman that this system compares these conditions with an expected value. Accordingly, Salman also does not teach determining, based on the comparison, to inspect the offshore infrastructure with the unmanned vehicle”.
The office does not agree. The drone has a camera. The camera can determine if the pump is in good form or if a valve is leaking. Inherently there is come comparison to a pump that is a functioning pump or a VALVE that is a functioning valve and then to see if there is a discrepancy. This is a comparison to an expected value and if the pump is not as expected then the drone can determine that there is damage. The applicant states with regard to claim 1 that an expected power value is not shown but this is not claimed. The applicant is arguing limitations that are not claimed. Further He discloses this limitation that is cited. In regard to claim 1, 15 and 16, He discloses “….16. A surveillance system for an offshore wind turbine comprising:
an unmanned aerial vehicle for inspecting the offshore wind turbine;
an unmanned surface vessel (USV) comprising: (see paragraph 4 where the drone can be used for wind turbine blade inspection)
o an environmental sensor system comprising a lidar configured to measure one or
more environmental parameters, wherein the one or more environmental (see paragraph 35-40 where the drone can include a lidar sensor and the lidar sensor can capture an image of the wind turbine blades to observe the frequency of operation of the wind turbine blades being based on a normal frequency or a reduced power output frequency of rotation of the wind turbine blade such as when they have ice on them in FIG. 7a to 7b)
parameters includes wind speed; (see paragraph 34 where the drone can include sensors to also detect humidity and the wind speed )
o a carrying area to carry the unmanned aerial vehicle; and
o a controller arranged at the unmanned aerial vehicle and configured to:
■ obtain the one or more environmental parameters from the lidar
[[environmental sensor system]];
■ obtain one or more operational parameters from the lidar [[of the offshore
Infrastructure]]; (see paragraph 35-40 where the drone can include a lidar sensor and the lidar sensor can capture an image of the wind turbine blades to observe the frequency of operation of the wind turbine blades being based on a normal frequency or a reduced power output frequency of rotation of the wind turbine blade such as when they have ice on them in FIG. 7a to 7b)
■ determine an expected power output of the offshore wind turbine based on the wind speed measured by the lidar; (See paragraph 45-60 where the expected power output should be a high value but it is lower since the blades are damaged and stiffer and rotating less frequently due to the damage and ice and the power output is lower)
■ compare an actual power output of the offshore wind turbine to each of the operational parameters to [[an]]the expected power output (see paragraph 45-60 and 77 where the drone can determine that the power output is very high and do nothing or if it is very low then there is damage since the frequency of rotation of lower due to ice, or blade damage)
■ determine, based on the comparison, to inspect the offshore
wind turbine with the unmanned aerial vehicle; and (see paragraph 35-39 and FIG. 7a to 7b where the power output of the wind turbine can appear to be lower than expected due to the rotor blades being damaged and the drone can also observe that the wind turbine blades are having ice on them and this is changing the expected frequency of operation of the wind turbine blades; As shown at (104), the method 100 may also include monitoring at least one operational parameter of the wind turbine 10. For example, in an embodiment, the operational parameter(s) may include at least one of power output, wind speed, rotor speed, generator speed, or generator torque. As such, in an embodiment, the method 100 may only implement a blade damage control scheme 108 when the operational parameter(s) exceeds an operational threshold. In other words, the controller 32 may only implement the control scheme 108 if the power output exceeds a certain power threshold (as shown at (106)).
Accordingly, as shown at (110), the control scheme 108 may include converting the electrical condition(s) of the pitch system 70 into a frequency domain. For example, as shown in FIG. 8 , a graph 150 of one embodiment of pitch motor current 152 in the frequency domain according to the present disclosure is illustrated. Thus, as shown, the control scheme 108 may include determining one or more peaks 154 of the frequency domain around a frequency component related to a natural frequency of the rotor blade(s) 22. As used herein, the frequency component related to the natural frequency of the rotor blade(s) 22 may include, as an example, a natural frequency of the rotor blade 22 directly or a natural frequency of the rotor blade(s) 22 modulated by control schemes. In addition, it should be under that the natural frequency of the rotor blade(s) 22 can by affected by temperature and/or operating conditions.)_
■ instruct the unmanned aerial vehicle to inspect the offshore wind turbine when the controlled determines to inspect the offshore wind turbine. (See paragraph 45-65 where the drone can detect that the power is off and inspect the blades to determine if the blades are damaged and stiff)
The applicant also states that a Camera is not an environmental sensor but on page 4 they list an environmental sensor can be a camera in the specification. The applicant seems to be claiming a lidar sensor however this is not recited in claim 1.
This is not understood and is well known in the art. The drone can take an image. Then the image is classified in the neural network which has data that is “functioning and working” or not as expected and the pump is broken and cut in half. Then the drone and ai element can determine that the device requires maintenance without any human inspection. . (see Col. 9, lines 10-40 where the drone can use the image data to compare the image data to the artificial neural network data and then classify this as a damaged pump and a leaking valve; see FIG. 13 where the inspected data can be predicted to be damage and relevant in blocks 1330 or the images and sensor data can be not determined with success and then problematic and then a further review of the problematic data can be made in block 1320)”.
It is not understood how normal is considered the expected value and damaged is considered the non-expected value. This is well known in the art for machine learning outputs.
The applicant continues that “...the Office action further concedes that the primary reference, Collins, does not teach an unmanned surface vessel (USV) comprising an environmental sensor system to measure one or more environmental parameters. As defined in the as-filed specification, an unmanned surface vessel is a boat or ship that operates on the surface of the water without a crew. As-filed specification at p. 3, I. 3-4. While Peng Cheng does appear to disclose a surface vessel in FIG. 3, the Office action appears to rely on a depiction of an unmanned aerial drone (UAV) in FIG. 6 as teaching a USV with an environmental sensor system. The Office action argues that FIG. 6 of Peng Cheng depicts an unmanned aerial drone (UAV) with a camera. We note that FIG. 6 contains no label indicating the presence of a camera on the UAV, and the specification does not ppear to discuss the existence of a camera on the UA V. However, even if FIG. 6 of Peng Cheng disclosed a camera, the camera would not be located on a USV, since FIG. 6 depicts an aerial drone, not a boat or ship that operates on the surface of the water. Furthermore, there is no teaching nor suggestion in Peng Cheng that a camera is being used to measure one or more environmental parameters. Therefore, Peng Cheng does not teach an unmanned surface vessel (USV) comprising an environmental sensor system to measure one or more environmental parameters. The Office action cites ,I 0012 of Collins as teaching obtaining one or more operational parameters of the offshore infrastructure. While ,I 0012 of Collins discloses the inspection of wind turbines with a UAV it does not disclose obtaining operational parameters of the wind turbine(such as power output or yaw misalignment). Accordingly, Collins fails to disclose obtaining one or more operational parameters of the offshore infrastructure”. The applicant is arguing the references individually which is improper. The drone that inspects a wind turbine can be used to inspect other elements in a marine area with success. This is based on a combination of references and is not a 102 rejection. The office states that the field of use of the drone is not patentable. The drone can inspect a wind turbine, a marine environment and a city environment or a desert environment. There is not structure in claim 1 that is missing from the rejection. (see ship 300 and a deck area to obtain a landing area of the drone and a spool to lower the underwater drone into the water 100)”.
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of DENG with a reasonable expectation of success since DENG teaches that a ship can include a landing area for the drone and a second underwater drone 200, 100. The drone 200 can land on an area and the second underwater drone can be stored in a storage area of the boat and released for an underwater surveillance using a retracting cable to bring the underwater drone back into the boat. In this manner, and over the air and underwater surveillance capability can be realized. See paragraph 1-20.
The drone that inspects the wind turbine and the underwater marine drone can also provide data to the ai device to determine if components are working or visibly damaged. Using drones for inspecting components is well known in the art. There is no structure in the claims argued other than the field of use of the drone.
The applicant also argues that this cannot be combined with success. This is false. These are both drones and can be combined.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 12-13 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Deng and assigned to Peng Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Deng and Peng Cheng”) and in view of U.S. Patent No.: US12283197B2 to Salman that was filed in 2020.
PNG
media_image1.png
596
838
media_image1.png
Greyscale
In regard to claim 1, and 12-13, Collins discloses “1. (Original) A surveillance system for an offshore infrastructure comprising: (See Fig. 1 where a drone can inspect an offshore wind turbine platform)
an unmanned vehicle for inspecting the offshore infrastructure; (see paragraph 5 where a drone can inspect a vessel from an offshore platform)”.
PNG
media_image2.png
520
1116
media_image2.png
Greyscale
The primary reference is silent but Peng Cheng teaches “...an unmanned surface vessel (USV) comprising: (see surface vessel 300 and an underwater drone 100 and an UAV 200 )
— an environmental sensor system to measure one or more environmental parameters; (see Fig. 6 where the drone has a camera)
PNG
media_image3.png
578
1054
media_image3.png
Greyscale
A carrying area to carry the unmanned vehicle; and a controller configured to: (see ship 300 and a deck area to obtain a landing area of the drone and a spool to lower the underwater drone into the water 100)”.
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of DENG with a reasonable expectation of success since DENG teaches that a ship can include a landing area for the drone and a second underwater drone 200, 100. The drone 200 can land on an area and the second underwater drone can be stored in a storage area of the boat and released for an underwater surveillance using a retracting cable to bring the underwater drone back into the boat. In this manner, and over the air and underwater surveillance capability can be realized. See paragraph 1-20.
Collins discloses “...— obtain the one or more environmental parameters from the environmental sensor system; (see paragraph 18 where the surface vehicle or ground vehicle can include sensors to determine a problem and status of the platform of the wind turbine)
— obtain [[the]] one or more operational parameters of the offshore infrastructure; (see paragraph 12 where the drone can inspect the wind turbine and then repeat the process of inspection)
PNG
media_image4.png
754
678
media_image4.png
Greyscale
Collins is silent but Salman teaches “— compare each of the operational parameters to an expected value; and
— determine, based on the comparison, to inspect the offshore infrastructure with the unmanned vehicle”. (see Col. 9, lines 10-40 where the drone can use the image data to compare the image data to the artificial neural network data and then classify this as a damaged pump and a leaking valve; see FIG. 13 where the inspected data can be predicted to be damage and relevant in blocks 1330 or the images and sensor data can be not determined with success and then problematic and then a further review of the problematic data can be made in block 1320)”.
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of SALAMAN with a reasonable expectation of success since SALAMAN teaches that an image detection can be used for detection of subsea equipment including a damaged pump. However, if the detection is not able to identify the issue, then an annotation is made for further action. This can improve the detection accuracy of the images.
Claim 2 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of U.S. Patent No.: US12283197B2 to Salman that was filed in 2020 and in view of Chinese Patent Pub. No.: CN111776148B to Shanghai Jiao Tong University filed n 2020.
PNG
media_image5.png
824
730
media_image5.png
Greyscale
Shanghai Jiao teaches “...2. (Currently Amended) [[A]]The surveillance system according to any of claim 1, wherein the controller is arranged at the unmanned surface vessel”. (see Fig. 3-9 where the underwater drone has a controller for inspection purposes; As shown in Figure 3, the tethered drone inspection system 1 includes a multi-rotor drone platform 1.1, a dual light source camera 1.2, a drone tethered cable 1.3, and a drone landing platform 1.4, Tethered integrated control box 1.5, generator 1.6. The multi-rotor UAV platform 1.1 is a multi-rotor UAV, the lower part of which is equipped with a dual-light source camera 1.2, and the multi-rotor UAV platform 1.1 has a ring-shaped tripod, which is used to connect with the UAV landing platform 1.4. Cooperate to play the role of fixing the multi-rotor UAV platform 1.1; the UAV tethered cable 1.3 contains power supply lines, communication lines and reinforcing materials. One end of the UAV tethered cable 1.3 is not connected to the multi-rotor. The lower end of the man-machine platform 1.1 is connected to the lower end, and the other end is connected to the mooring integrated control box 1.5 after passing the UAV landing platform 1.4 in line and reverse direction; the mooring integrated control box 1.5 is fixedly placed in the artificial cockpit 4.4, and the system The outside of the integrated control box 1.5 is reinforced with a steel frame structure. The integrated control box 1.5 contains a cable retracting device and a rectifier inside. The retractable speed of the man-machine tethered cable 1.3 is automatically adjusted, and the tethered integrated control box 1.5 can convert the high-voltage alternating current generated by the generator 1.6 into direct current to supply the multi-rotor UAV platform 1.1.)”
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of JIAO with a reasonable expectation of success since JIAO teaches that a ship can include an integrated submersible inspection system. This is a low-cost device that can provide an underwater inspection with a GPS location for an underwater inspection at a low cost.
Claim 3 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of U.S. Patent No.: US12283197B2 to Salman that was filed in 2020.
Collins discloses “...3(Currently Amended) [[A]]The surveillance system according to any of claims claim 1[[ — 2]], wherein the environmental sensor system comprises a lidar, an ocean sensor, a sonar, a temperature sensor, an air density sensor, an infrared sensor or camera”. (see paragraph 90 where the drones can includes a lidar sensor)
Claim 4 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of United States Patent No.: US11217104B2 to Ivanov filed in 2019.
Ivanov teaches“4. Currently Amended) [[A]]The surveillance system according to any of claims claim 1[[ — 3]], wherein the one or more environmental parameters comprises at least one of: wind speed, wind direction, wind turbulence, air density, visibility, temperature, sea current speed, sea current direction, wave height, wave direction, wavelength, sea salinity, water quality and sea level.” (see col. 3, line 14 to col., 4, line 30).
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of IVANOV with a reasonable expectation of success since IVANOV teaches that a mobile device can detect a windspeed value and provide this data to the cloud for an index in FIG. 1-11. This can provide an improved route being based on the windspeed value.
Claims 5-7 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of United States Patent Application Pub. No.: US20190072082A1 to Lysgaard et al. that was filed in 2016.
Lysgaard teaches “...5. (Currently Amended) [[A]]The surveillance system according to any of claims claim 1[[ — 4]], wherein the one or more operational parameters of the offshore infrastructure comprises: leakage level of the offshore infrastructure; or degradation level of the offshore infrastructure; or a pitch angle of an offshore wind turbine, when the offshore infrastructure comprises the offshore wind turbine or yaw angle of an offshore wind turbine, when the offshore infrastructure comprises the offshore wind turbine; a power output of an offshore wind turbine, when the offshore infrastructure comprises the offshore wind turbine; or
a temperature of an offshore electrical substation, when the offshore infrastructure comprises the offshore electrical substation;
a temperature of a submarine power cable, when the offshore infrastructure comprises the submarine power cable”. (see paragraph 306, 319, 400-406 and FIG. 20a-20b where based on the windspeed the yaw misalignment can be detected)
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of LYSGAARD with a reasonable expectation of success since LYSGAARD teaches that sensor can detect a yaw misalignment and a wind speed and an alarm can be provided. A condition monitoring system combined with a reliable communication system can then provide an instant alarm from and communication to the wind turbine for the receiver to receive earliest possible any instant alarm signal from the wind turbine and for the receiver to be able to stop the operation of the wind turbine remotely when needed to prevent the wind turbine to operate with misalignments, failures to develop or even to prevent catastrophic failures.
The primary reference is silent but Lysgaard et al. teaches “...6 (Currently Amended) [[A]]The surveillance system according to any of claims claim 1[[ — 5]], wherein the controller is further configured to compare at least one of the one or more environmental parameters to a threshold; and wherein to determine to inspect the offshore infrastructure is further based on the comparison of the at least one of the one or more environmental parameters to the threshold”. (see paragraph 306, 319, 400-406 and FIG. 20a-20b where based on the windspeed the yaw misalignment can be detected)
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of LYSGAARD with a reasonable expectation of success since LYSGAARD teaches that sensor can detect a yaw misalignment and a wind speed and an alarm can be provided. A condition monitoring system combined with a reliable communication system can then provide an instant alarm from and communication to the wind turbine for the receiver to receive earliest possible any instant alarm signal from the wind turbine and for the receiver to be able to stop the operation of the wind turbine remotely when needed to prevent the wind turbine to operate with misalignments, failures to develop or even to prevent catastrophic failures.
The primary reference is silent but Lysgaard et al. teaches “...7 (Currently Amended) [[A]]The surveillance system according to any of claims claim 1I[ - 6]], wherein the controller is further configured to instruct the environmental sensor system to continue measuring the one or more environmental parameters or to travel towards another location determine when not to inspect the offshore infrastructure (see claims 1-12 where the turbine or the drone can provide an alarm and then a manual inspection for continued operation of the wind turbine generator based on the drone detecting that the turbine is misaligned);
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of LYSGAARD with a reasonable expectation of success since LYSGAARD teaches that sensor can detect a yaw misalignment and a wind speed and an alarm can be provided. A condition monitoring system combined with a reliable communication system can then provide an instant alarm from and communication to the wind turbine for the receiver to receive earliest possible any instant alarm signal from the wind turbine and for the receiver to be able to stop the operation of the wind turbine remotely when needed to prevent the wind turbine to operate with misalignments, failures to develop or even to prevent catastrophic failures.
Claim 8 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of U.S. Patent No.: US12283197B2 to Salman that was filed in 2020.
Salman teaches “...8. (Currently Amended) [[A]]The surveillance system according to any of claims claim 1I[ - 7]], wherein the controller is further configured to instruct the unmanned vehicle to inspect the offshore infrastructure determine when to inspect the offshore infrastructure ”. (see col.18, lines 1-35 where there are confident observations of damage and then other observations that are to be continued for labeling for a human annotation in block 658)
See motivation statement above.
Claims 9-11 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of U.S. Patent No.: US12283197B2 to Salman that was filed in 2020.
Salman teaches “...9. (Currently Amended) [[A]]The surveillance system according to claim 8, wherein the controller is further configured to receive, from the unmanned vehicle, data about the inspection of the offshore infrastructure”. (see col. 9, lines 1-60)
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of SALAMAN with a reasonable expectation of success since SALAMAN teaches that an image detection can be used for detection of subsea equipment including a damaged pump. However, if the detection is not able to identify the issue, then an annotation is made for further action. This can improve the detection accuracy of the images.
PNG
media_image6.png
672
1006
media_image6.png
Greyscale
Salman teaches “...10. (Currently Amended) [[A]]The surveillance system according to claim 9, wherein the controller is further configured to send the data about the inspection of the offshore infrastructure to an onshore controller”. (see Col. 15, lines 12-col. 16, lines 50 and FIG. 4 where there may be further training for the images offshore with a cloud computing arrangement ).
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of SALAMAN with a reasonable expectation of success since SALAMAN teaches that an image detection can be used for detection of subsea equipment including a damaged pump. However, if the detection is not able to identify the issue, then an annotation is made for further action. This can improve the detection accuracy of the images.
PNG
media_image7.png
760
786
media_image7.png
Greyscale
Collins discloses offshore wind turbines and is silent as to Salaman teaches “..11. (Currently Amended) [[A]]The surveillance system according to any of claims claim 1[[ — 10]], wherein the offshore infrastructure comprises an offshore wind turbine, an oil and gas equipment, an offshore electrical substation and/or a submarine power cable”. (see Fig. 1 where the device includes a oil and gas infrastructure 111 and electrical station 112 and submarine 125 with underwater cables 132)
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of SALAMAN with a reasonable expectation of success since SALAMAN teaches that an image detection can be used for detection of subsea equipment including a damaged pump. However, if the detection is not able to identify the issue, then an annotation is made for further action. This can improve the detection accuracy of the images.
Claims 14-15 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of U.S. Patent No.: US12283197B2 to Salman that was filed in 2020.
Salman teaches “...14. (Currently Amended) [[A]]The surveillance method according to any of claims claim 12[[ - 13]], further comprising moving the unmanned vehicle towards the offshore infrastructure when inspecting the offshore infrastructure is determined”. (see col.18, lines 1-35 where there are confident observations of damage by the drone’s observation and then other observations that are to be continued for labeling for a human annotation in block 658)
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of SALAMAN with a reasonable expectation of success since SALAMAN teaches that an image detection can be used for detection of subsea equipment including a damaged pump. However, if the detection is not able to identify the issue, then an annotation is made for further action. This can improve the detection accuracy of the images.
Salman teaches “..15. (Currently Amended) [[A]]The surveillance method according to claim 14, further comprising inspecting and acquiring, with the unmanned vehicle, data about the inspection of the offshore infrastructure. . (see col 18 and Fig. 1 where the device includes an oil and gas infrastructure 111 and electrical station 112 and submarine 125 with underwater cables 132)
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of SALAMAN with a reasonable expectation of success since SALAMAN teaches that an image detection can be used for detection of subsea equipment including a damaged pump. However, if the detection is not able to identify the issue, then an annotation is made for further action. This can improve the detection accuracy of the images.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 15 and 16 are rejected under 35 U.S.C. sec. 102(a)(2) as being anticipated by States Patent Application Pub. No.; US20230011028A1 to He et al. that was filed in 2019 and assigned to General Electric Verona (hereinafter “HE”).
PNG
media_image8.png
722
530
media_image8.png
Greyscale
PNG
media_image9.png
712
544
media_image9.png
Greyscale
In regard to claim 1, 15 and 16, He discloses “….16. A surveillance system for an offshore wind turbine comprising:
an unmanned aerial vehicle for inspecting the offshore wind turbine;
an unmanned surface vessel (USV) comprising: (see paragraph 4 where the drone can be used for wind turbine blade inspection)
o an environmental sensor system comprising a lidar configured to measure one or
more environmental parameters, wherein the one or more environmental (see paragraph 35-40 where the drone can include a lidar sensor and the lidar sensor can capture an image of the wind turbine blades to observe the frequency of operation of the wind turbine blades being based on a normal frequency or a reduced power output frequency of rotation of the wind turbine blade such as when they have ice on them in FIG. 7a to 7b)
parameters includes wind speed; (see paragraph 34 where the drone can include sensors to also detect humidity and the wind speed )
o a carrying area to carry the unmanned aerial vehicle; and
o a controller arranged at the unmanned aerial vehicle and configured to:
■ obtain the one or more environmental parameters from the lidar
[[environmental sensor system]];
■ obtain one or more operational parameters from the lidar [[of the offshore
Infrastructure]]; (see paragraph 35-40 where the drone can include a lidar sensor and the lidar sensor can capture an image of the wind turbine blades to observe the frequency of operation of the wind turbine blades being based on a normal frequency or a reduced power output frequency of rotation of the wind turbine blade such as when they have ice on them in FIG. 7a to 7b)
■ determine an expected power output of the offshore wind turbine based on the wind speed measured by the lidar; (See paragraph 45-60 where the expected power output should be a high value but it is lower since the blades are damaged and stiffer and rotating less frequently due to the damage and ice and the power output is lower)
■ compare an actual power output of the offshore wind turbine to each of the operational parameters to [[an]]the expected power output (see paragraph 45-60 and 77 where the drone can determine that the power output is very high and do nothing or if it is very low then there is damage since the frequency of rotation of lower due to ice, or blade damage)
■ determine, based on the comparison, to inspect the offshore
wind turbine with the unmanned aerial vehicle; and (see paragraph 35-39 and FIG. 7a to 7b where the power output of the wind turbine can appear to be lower than expected due to the rotor blades being damaged and the drone can also observe that the wind turbine blades are having ice on them and this is changing the expected frequency of operation of the wind turbine blades; As shown at (104), the method 100 may also include monitoring at least one operational parameter of the wind turbine 10. For example, in an embodiment, the operational parameter(s) may include at least one of power output, wind speed, rotor speed, generator speed, or generator torque. As such, in an embodiment, the method 100 may only implement a blade damage control scheme 108 when the operational parameter(s) exceeds an operational threshold. In other words, the controller 32 may only implement the control scheme 108 if the power output exceeds a certain power threshold (as shown at (106)).
Accordingly, as shown at (110), the control scheme 108 may include converting the electrical condition(s) of the pitch system 70 into a frequency domain. For example, as shown in FIG. 8 , a graph 150 of one embodiment of pitch motor current 152 in the frequency domain according to the present disclosure is illustrated. Thus, as shown, the control scheme 108 may include determining one or more peaks 154 of the frequency domain around a frequency component related to a natural frequency of the rotor blade(s) 22. As used herein, the frequency component related to the natural frequency of the rotor blade(s) 22 may include, as an example, a natural frequency of the rotor blade 22 directly or a natural frequency of the rotor blade(s) 22 modulated by control schemes. In addition, it should be under that the natural frequency of the rotor blade(s) 22 can by affected by temperature and/or operating conditions.)_
■ instruct the unmanned aerial vehicle to inspect the offshore wind turbine when the controlled determines to inspect the offshore wind turbine. (See paragraph 45-65 where the drone can detect that the power is off and inspect the blades to determine if the blades are damaged and stiff)
Claims 17-19 are cancelled.
Claim 20 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of International Patent Pub. No.: WO2018236903A1 to Collins et al. filed in 2018 (hereinafter “Collins”) and in view of Chinese Patent Pub. No.: CN113086137A to Pend Cheng Laboratory that was filed on 4-14-21 which is prior to the effective filing date of 12-2-21 (hereinafter “Peng Cheng”) and in view of United States Patent Application Pub. No.: US20190072082A1 to Lysgaard et al. that was filed in 2016.
Collins discloses “..19. (New) The surveillance system according to claim 18, wherein the unmanned vehicle is an unmanned aerial vehicle”. (see abstract).
Claim 20 depends from claim 20 which is 112 issue and is objected to. However, this is believed to be dependent on claim 1.
Ivanov teaches “...20. (New) The surveillance system according to claim 20, wherein the controller is configured to: receive a wind speed from the Lidar; compare the wind speed with a wind speed threshold; and wherein determining to inspect the offshore wind turbine is further based on the comparison of the wind speed with the wind speed threshold. (see col. 3, line 14 to col., 4, line 30).
It would have been obvious for one of ordinary skill in the art to combine the disclosure of COLLINS with the teachings of IVANOV with a reasonable expectation of success since IVANOV teaches that a mobile device can detect a windspeed value and provide this data to the cloud for an index in FIG. 1-11. This can provide an improved route being based on the windspeed value.
Claim 20 is rejected under 35 USC sec. 112b as a claim cannot depend on itself. IT is believed to be intended to be dependent on claim 1.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN PAUL CASS whose telephone number is (571)270-1934. The examiner can normally be reached Monday to Friday 7 am to 7 pm; Saturday 10 am to 12 noon.
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, Scott A. Browne can be reached at 571-270-0151. 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.
/JEAN PAUL CASS/Primary Examiner, Art Unit 3666
Prosecution Insights