DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim(s) 1-5 are pending for examination. This action is Non-Final.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 2 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Starr et al. (US 2021/0173414 A1).
Regarding Claim 1;
Starr discloses a system for infrastructure inspection and localization (Abstract) comprising:
a motor vehicle ([0034] - ...unmanned autonomous aerial vehicle (UAV) for power line monitoring...) including;
a stereo camera ([0047]-[0048] - ...CCTV camera...a stereo camera, infrared camera...);
an inertial measurement unit ([0047]-[0048] - ...gyroscopes and IMUs, magnetometers...);
a high-resolution camera ([0047]-[0048] - ...CCTV camera...a stereo camera, infrared camera...);
a global positioning system (GPS) receiver ([0047]-[0048] - ...a global positioning sensor (GPS) unit...); and
a computer system including a processor ([0047]-[0048] - This includes but is not limited to a processor package 501 comprising at least one central processing unit (CPU) and at least one graphics processing unit (GPU)), the processor configured at least in part to perform real-time utility pole detection ([0033]-[0034] - The system would consist of both UAVs operating continuously in order to perform inspections, as well as stationary docking stations mounted on, along, adjacent to, or nearby transmission lines, providing real-time data through communication networks to prevent failure and identify potential hazards);
perform utility pole GPS location determination and mapping ([0047] - For example, for a given received task, UAV 100 uses processors 501, on-board sensors 503-512, and wireless communication resources to construct a task list and a map of where to go. More detailed information on how these inputs are received and integrated is provided in the discussion of FIG. 6 and FIG. 7 and [0009] - analyzing, using a processor, the sensor data using a trained model, wherein the trained model is trained using one or more of path planning algorithms, GPS data, LIDAR point clouds; performing one or more of: identifying, using a processor, a type of path for effectively navigating to objects for purposes of sensor data collection or live data communication to other UAVs or locations for recommended interventions and identifying a high-level task strategy for data collection on objects, and dissemination of commands to a collective of UAVs; and moving the UAV using the path and flight strategy to identify and inspect the object and [0061] - With knowledge of the individual UAV locations, docking stations, telemetry and the geography of the power line system subject to inspection, the infrastructure platform can disperse the UAVs throughout the system in order to make condition assessments in its entirety according to a preset schedule and [0066]); perform real-time infrastructure detection ([0033]-[0034] - ... providing real-time data through communication networks to prevent failure and identify potential hazards); and perform vegetation overgrowth detection ([0069]) - For example, if data on vegetation encroachment is desired, the system can show the entire system and mark areas with imminent vegetation risks in red, near term vegetation risks in yellow, and far field vegetation risks in green) and utility pole damage detection and utility pole response ([0033] - ... providing real-time data through communication networks to prevent failure and identify potential hazards).
Regarding Claim 2;
Starr discloses the system of claim 1
Starr further discloses wherein the processor is configured to perform the real-time utility pole detection ([0033] and [0047]-[0048]), utility pole GPS location determination and mapping ([0047] - For example, for a given received task, UAV 100 uses processors 501, on-board sensors 503-512, and wireless communication resources to construct a task list and a map of where to go. More detailed information on how these inputs are received and integrated is provided in the discussion of FIG. 6 and FIG. 7 and [0009] - analyzing, using a processor, the sensor data using a trained model, wherein the trained model is trained using one or more of path planning algorithms, GPS data, LIDAR point clouds; performing one or more of: identifying, using a processor, a type of path for effectively navigating to objects for purposes of sensor data collection or live data communication to other UAVs or locations for recommended interventions and identifying a high-level task strategy for data collection on objects, and dissemination of commands to a collective of UAVs; and moving the UAV using the path and flight strategy to identify and inspect the object and [0061] - With knowledge of the individual UAV locations, docking stations, telemetry and the geography of the power line system subject to inspection, the infrastructure platform can disperse the UAVs throughout the system in order to make condition assessments in its entirety according to a preset schedule and [0066]), real-time infrastructure detection ([0033]-[0034] - ... providing real-time data through communication networks to prevent failure and identify potential hazards), and perform vegetation overgrowth detection ([0064] - The UAVs may then proceed to accomplish the specified task 612, whether that be visual inspection of cables, scanning vegetation around the power lines, or some other task not listed here and [0069]) - For example, if data on vegetation encroachment is desired, the system can show the entire system and mark areas with imminent vegetation risks in red, near term vegetation risks in yellow, and far field vegetation risks in green) and utility pole damage detection and utility pole response ([0033] - ... providing real-time data through communication networks to prevent failure and identify potential hazards) while the motor vehicle is moving ([0061]-[0062] and [0064] - The UAVs may then proceed to accomplish the specified task 612, whether that be visual inspection of cables, scanning vegetation around the power lines, or some other task not listed here).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starr et al. (US 2021/0173414 A1) in view of Davis, II (US 2022/0131375 A1) (hereinafter, Davis).
Regarding Claim 3;
Starr discloses the system of claim 2.
Starr fails to explicitly disclose discloses further comprising an artificial intelligence (Al) engine configured to evaluate the operating condition of an electrical grid including the utility poles.
However, in an analogous art, Davis teaches further comprising an artificial intelligence (Al) engine configured to evaluate the operating condition of an electrical grid including the utility poles ([0015] - In an embodiment, an automated inspection algorithm may be a trained machine learning algorithm or artificial intelligence algorithm. Such machine learning algorithm or artificial intelligence algorithm may be trained, for example, by reference to a plurality of correlated lidar data sets and grid infrastructure and [0196] - Collection of power line imaging data 900 may be performed, for example, by a Lidar imaging system carried on a flight platform such as a helicopter, plane, or unmanned aerial vehicle (UAV) flown along the power line route and [0201] - Classifier 950 may include a trained classification algorithm 960 executed by a processor 530, in run-time mode, to compare the collected power line imaging data 900 to reference classification information. ...It will be understood that the trained classification algorithm may be a suitable machine learning algorithm or artificial intelligence algorithm. In an embodiment, the trained classification algorithm, executing the training mode during the training period, may develop the reference classification information in relation to receiving reference power line imaging data 900 in a reference view of infrastructure including a utility asset, where the reference view may be a virtual view, a real-world image, or both.).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Davis to the system of Starr to include further comprising an artificial intelligence (Al) engine configured to evaluate the operating condition of an electrical grid including the utility poles.
One would have been motivated to combine the teachings of Davis to Starr to do so as it provides / allows improved systems, apparatus and methods for electrical power grid inspection, resiliency and post-storm recovery (Davis, [0010]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starr et al. (US 2021/0173414 A1) in view of Davis, II (US 2022/0131375 A1) (hereinafter, Davis) and further in view of Fujita et al. (US 2020/0364848 A1).
Regarding Claim 4;
Starr in view of Davis discloses the system of claim 3.
Starr in view of Davis fail to explicitly disclose wherein the processor is configured to determine relative angles between the vehicle and a utility pole.
However, in an analogous art, Fujita teaches wherein the processor is configured to determine relative angles between the vehicle and a utility pole (FIG. 4 and [0030] - The object detecting unit 130 detects a relative distance and a relative angle to the object to be inspected. and [0045] - At this time, the object detecting unit 130 detects a relative angle θw of the overhead power wire W with respect to a reference line G extending in the vertical direction of the unmanned aerial vehicle 100)
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Fujita to the system of Starr and Davis to include wherein the processor is configured to determine relative angles between the vehicle and a utility pole
One would have been motivated to combine the teachings of Fujita to Starr and Davis to do so as it provides / allows easily [determination] whether a re-image capture operation of an object to be inspected is required (Fujita, [0008]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starr et al. (US 2021/0173414 A1) in view of Davis, II (US 2022/0131375 A1) (hereinafter, Davis) and Fujita et al. (US 2020/0364848 A1) and further in view of Durand et al. US (20170337524 A1).
Regarding Claim 5;
Starr in view of Davis and Fujita discloses the system of claim 4.
Starr in view of Davis and Fujita fail to explicitly disclose wherein the processor is configured to identify pole mounted instruments mounted on the utility pole.
However, in an analogous art, Durand teaches wherein [a] processor is configured to identify pole mounted instruments mounted on the utility pole ([0020] - For example, separate and independent detailed assessments may be performed on the pole, power line support (e.g., cross arms, alley arms standoffs, etc.), and power line in which the details of asset components are thoroughly analyzed to determine the extent of damage to the asset and [0024]-][0027] - For example, a more detailed assessment may identify the condition of the asset's components (e.g., the pole, the power lines connected to the asset, a power line support of the asset, etc.). As noted the components taught in Durand are consistent to Applicant’s Specification describing pole mounted instruments, see Applicant’s Specification ⁋⁋[0031]-[0043].
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Durand to the system and specifically the processor of Starr in view of Davis and Fujita to include wherein the processor is configured to identify pole mounted instruments mounted on the utility pole
One would have been motivated to combine the teachings of Durand to Starr in view of Davis and Fujita to do so as it provides / allows utilizing sensor and imagery data to automatically assess infrastructure damage (Durand, [0001]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fernandes (US 4818990 A) discusses A monitoring system using a unique remotely piloted drone with dual counter rotating propellers and carrying electric field sensing, thermal infra-red imaging, video imaging, acoustic and corona discharge sensing equipment. The compact remotely piloted drone flies along a power corridor and is maintained at a fixed distance from an outer phase conductor using on board electric field detection circuitry, video/infra-red imagery and an RF/laser altimeter. The counter rotating, twin-turbo driven configuration for the propellers mounted on coaxial vertical shafts provides a highly stable platform, unlike conventional manned helicopters presently used for routine right-of-way patrols. Dual, counter-rotating saucer-shaped auxiliary propellers provide a degree of stability far superior to a conventional helicopter, particularly in gusty winds. On board sensors and video cameras would permit electric utilities an economic approach to right-of-way monitoring, inspection of frayed conductors or deteriorated splices through infra-red sensing, detection of cracked insulators through acoustic/corona sensors, monitoring of critical, thermally limiting spans and other monitoring functions. (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JESSICA LEMIEUX can be reached at (571)270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626