DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 Arguments
2. Applicant's arguments received 08/13/2025 have been considered but are moot in view of the new ground(s) of rejection.
Amended claims 12-33, 36-45 and 47-48 are rejected as new prior art reference SASAKI et al. (JP 2004152091 A) has been found to teach, in combination with other cited prior art references, the claimed inventions. Detailed response is given in sections 3-5 as set forth below in this Office Action.
Claim Rejections - 35 USC § 103
3. 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 of this title, 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.
4. Claims 12-23, 25-29, 31-33, 36-45 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (CN 107300927 A, machine translation) in view of DITTBERNER et al. (US 20180292374 A1), SASAKI et al. (JP 2004152091 A, machine translation) and TABARIN (RU 130711 U1, machine translation).
Regarding claims 12, 13 and 42, Liu discloses a method and apparatus for planning aerial inspection of a plurality of facilities in a geographical region (Abstract; para. 0012-0016), the method comprising: a) storing data that represents the plurality of facilities (i.e., target points) in the geographical region and data that represents at least one base in the geographical region, the at least one base supporting aerial inspection of the plurality of facilities in the geographical region (para. 0043); b) selecting a particular base in the geographical region (para. 0044: i.e., select the base station with the highest number of target points); c) performing a clustering method on the data of a) to define cluster data representing a set of facility clusters in the geographical region that are associated with the particular base of b) (Abstract; para. 0045); and d) processing the cluster data of c) (e.g., via the “CW save search algorithm”) and flight time costs (Abstract; para. 0062-0064) to determine flight path data representing flight path segments that form a trip, the trip including: an origination at the particular base, traveling to a sequence of facility clusters, scanning each facility in each facility cluster (para. 0046-0049: checking whether there are not reconnaissance/inspection of the target point, if there is then transferring to Step 2, if does not enter the Step 6; para. 0084: it can minimize the cost of UAV base station site selection and patrol paths, greatly reduce UAV patrol costs, and provide a better site selection path planning solution; see also para. 0095, 0098, 0108), and returning back to the particular base, the sequence of facility clusters of the trip corresponding to the set of facility clusters represented by the cluster data of c) (para. 0046-0049; see also discussion of Figs. 4-6), the flight time costs being based on at least one operational parameter of an airborne sensor, the operational parameter comprising at least one of: a cost or a limit of detection (para. 0095: “the drone body needs to have sufficient load capacity and be able to carry various mission loads, including batteries, cameras and other sensors. … Costs associated with construction and drone use”; para. 0098: “ … the drone needs to carry a simple video imaging system including a camera. The controller in the base station can obtain clear and accurate real-time image information by adjusting the direction and focal length of the camera”; see also para. 0119-0121). Liu further discloses: wherein the data of a) is stored in computer memory, and operations c) and d) are performed by at least one processor (para. 0184-0185; see also discussion of Fig. 2); wherein the plurality of facilities includes at least an upstream facility (para. 0004: “border areas” reads on “upstream facilities”).
Liu does not mention explicitly: e) performing a first aerial inspection in accordance with the trip of d), wherein performing the first aerial inspection comprises scanning, with a first sensor type as the airborne sensor, each facility in each facility cluster; f) identifying, based on data collected by the airborne sensor during the first aerial inspection, at least one facility from which no airborne gas is emitted; g) identifying, based on data collected by the airborne sensor during the first aerial inspection, at least one target facility from which airborne gas is emitted; h) performing a second aerial inspection of only the at least one target facility to confirm the presence of airborne gas emissions using a second sensor type as the airborne sensor; i) determining whether confirmed airborne gas emissions from the second aerial inspection are known temporarily-allowed gas emissions, based at least on a time of the second aerial inspection directly corresponding to a time of the known temporarily-allowed emissions; j) in response to the confirmed airborne gas emissions from the second aerial inspection not being the known temporarily-allowed gas emissions at the time of known allowed emissions, identifying the at least one target facility for component-level inspection and repair; k) identifying, during an inspection of the at least one target facility, at least one component that has caused airborne gas emissions from the at least one target facility to not correspond to allowed gas emissions; l) repairing the at least one component; and m) following repair of the at least one component, verifying that the repair has reduced the airborne gas emissions.
DITTBERNER discloses a method, and an apparatus for practicing the method, for inspecting airborne gas emissions (e.g., gas leaks) in a geographical region (e.g., 204 Fig. 3; see also Abstract and para. 0033), the method comprising:
e) performing a first aerial inspection in accordance with a planned trip (e.g., the initial flight path 308 Fig. 4A; para. 0018: “The drone flies along a first flight path determined based on the three-dimensional model, through the geographic area and collects high-level information that indicates the presence of a gas leak”) to sense airborne gas emissions (e.g., 416 Fig. 5) from a plurality of facilities (para. 0035: “The drone collects data regarding the presence of gas while flying the initial flight path 308 and identifies one or more regions in the geographic area that include higher than expected concentrations of gas”; para. 0048-0049), wherein performing the first aerial inspection comprises scanning, with a first sensor type (e.g., a camera, an infrared camera and one or more chemical sensors that are used to capture data …) as the airborne sensor, each of said plurality of facilities (para. 0018, 0021, 0035, 0048-0049);
f) identifying, based on data collected by the airborne sensor during the first aerial inspection, at least one facility from which no airborne gas is emitted (para. 0035 renders it obvious that the initial flight path 308 includes flying along the grid pattern through the geographic area 304 which identifies at least one facility from which no airborne gas is emitted as well as at least one target facility from which airborne gas is emitted);
g) identifying, based on data collected by the airborne sensor during the first aerial inspection, at least one target facility from which airborne gas is emitted (para. 0035);
h) performing a second aerial inspection (e.g., 314 Fig. 4A) of only the at least one target facility that has the sensed airborne gas emissions to confirm the presence of the airborne gas emissions using a second sensor type as the airborne sensor (para. 0035: “Secondary flight paths 314 are then created for the one or more regions with the detected gas and the drone flies along the secondary flight paths 314”; para. 0036: “the drone is configured to capture more detailed information about the presence of gas while flying the secondary flight plan that the initial flight plan, this can include … sampling the gas concentration with high accuracy sensors”; see also Fig. 7 and related text);
i) determining details of the confirmed airborne gas emissions from the second aerial inspection based at least on a time of the second aerial inspection (para. 0035-0036: “the drone is configured to capture more detailed information about the presence of gas while flying the secondary flight plan that the initial flight plan, this can include sampling the gas concentration more frequently, sampling the gas concentration with high accuracy sensors, or a combination of both”; para. 0039: “Additionally the plumes may be used to … determine its leak rate using an inversion model”);
j) in response to the determined details of the confirmed airborne gas emissions from the second aerial inspection, identifying the at least one target facility for component-level inspection (para. 0038: “… to predict the location of a possible gas leak”; para. 0039: “ … the plumes may be used to localize the leak …”; para. 0041: “ … will associate chemical to the individual sources by acquiring methane plume maps at different heights. Furthermore, the model 400 can include an indication 420 of the detected concentrations of the gas disposed at the corresponding locations”; para. 0046: “identifying one or more regions with potential gas leaks in the geographical area based on the plurality of infrared images”; see also para. 0050) and repair (para. 0003: “leaks can be easily fixed if monitoring and tracking technologies are in place to detect and alert owner about the size and locations of the leaks”); and
k) identifying, during an inspection of the at least one target facility (e.g., e.g., 406 Fig. 5), at least one source that has caused airborne gas emissions from the at least one target facility to not correspond to allowed gas emissions (para. 0042: “Using differential measurements the drone will detect the mixing ration of the two chemical sources and will associate chemical to the individual sources by acquiring methane plume maps at different heights”; para. 0046: “… and at the same time minimize the amount of flight while detecting the leak sources”).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate DITTBERNER’s teaching of two-steps scanning strategy into Liu’s method to arrive the claimed invention. Doing so would allow to obtain detailed gas emissions concentration data while improve the accuracy of the inspection/scanning (DITTBERNER, para. 0019, 0036).
The combination of Liu and DITTBERNER is silent on: said determining the details of the confirmed airborne gas emissions from the second aerial inspection comprising: determining whether confirmed airborne gas emissions from the second aerial inspection are known temporarily-allowed gas emissions, based at least on a time of the second aerial inspection directly corresponding to a time of the known temporarily-allowed emissions; wherein the identified at least one source is a particular component that has caused airborne gas emissions to not correspond to allowed gas emissions; and l) repairing the particular component; and m) following repair of the component, verifying that the repair has reduced the airborne gas emissions.
SASAKI discloses a technique of inspecting airborne gas emissions in a geographical region (Abstract), comprising: monitoring gas emissions emitted by each gas emitting business (para. 0009: “monitors the amount of greenhouse gas emitted by each gas emission company (for example, a manufacturing plant, building or office, etc.)” ); determining whether monitored gas emissions are known temporarily-allowed gas emissions based at least on a time of the monitoring directly corresponding to a time of the known temporarily-allowed emissions (para. 0002: “ … It is a right that arises from the excess or deficiency of the specified emission allowance and the current gas emission”; para. 0021, 0027, 0031, 0044, 0049-0054).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate SASAKI’s emission amount certification method into the combination of Liu and DITTBERNER for handling emission allowances related to gas emissions. Doing so would provide the Liu/DITTBERNER combination with an additional step of inspection data validation through which one can verify whether the confirmed gas emissions are permitted for the time period of the inspection and/or whether necessary actions should be taken to remedy the anomalous emissions, therefore the troublesome work of the gas emission business can be greatly reduced (SASAKI, para. 0053).
The combination of Liu/DITTBERNER/SASAKI is still silent on: wherein the identified at least one source is a particular component that has caused airborne gas emissions to not correspond to allowed gas emissions; and l) repairing the particular component; and m) following repair of the component, verifying that the repair has reduced the airborne gas emissions.
TABARIN discloses a technique of inspecting airborne gas emissions in a geographical region (Abstract), comprising: identifying, during an inspection of the at least one target facility (pipeline 7 Fig.), at least one source that has caused airborne gas emissions (gas cloud 8) from the at least one target facility to not correspond to allowed gas emissions, wherein said at least one source is a physical component at the target facility, and repairing the at least one component (page 4, 4th paragraph: “A "spot" of green color on the ground from the semiconductor laser 3 enables the helicopter pilot or aircraft and correctly follow the route of the pipeline. But, the main thing is that this “spot” allows you to accurately identify the place of gas leakage, since when a signal from the electronic unit 14 indicates a leak from the gas pipeline 7, this place, due to satellite orientation, is marked on the map of the gas pipeline’s route and is “linked” to the characteristic signs of the terrain or on the technological scheme of the MG, which allows ground repair services to accurately determine the place of leakage”).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate TABARIN’s teaching of component-level inspection and ground repair services into the combination of Liu/DITTBERNER/SASAKI to arrive the claimed invention. Doing so would allow to accurately identify the place of gas leakage and facilitate the ground repair services (TABARIN, page 4, 4th paragraph).
As to the limitation of verifying the repair, Examiner takes official notice that pipeline leakage repairing services including, following the repair, verifying that the repair has revolved the leakage is a common practice provided by a professional plumber. It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate such a common practice into the combination of Liu/DITTBERNER/SASAKI/TABARIN to arrive the claimed invention. One of ordinary skill in the art would have recognized that the results of such a modification were predictable for pipeline leakage repair since the use of that common practice provides the rationale to arrive at a conclusion of obviousness.
Regarding claims 14 and 43, Liu discloses: wherein: in d), the flight path data representing the flight segments of the trip is determined by minimizing the flight time costs for the trip (Abstract; para. 0062-0064, 0084).
Regarding claim 15, Liu discloses: storing flight vehicle data that represents operational parameters for at least one flight vehicle, and storing sensor data that represents operational parameters for the airborne sensor (para. 0091: control personnel can also obtain the drone returned information and images, including geographic location of unmanned aerial vehicle, flying height, speed, supply voltage, and real-time image returned by the unmanned aerial vehicle and so on); wherein, in d) the flight time costs for the trip are based on the flight vehicle data and the sensor data (para. 0064; see also the section of “summary of the invention”).
Regarding claim 16, Liu discloses: repeating operations c) and d) for at least one additional base in the geographic region (see the section of “3.1.2 neighborhood search”; see also the discussion of Fig. 8).
Regarding claim 17, Liu discloses: repeating operations c) and d) for different combinations of flight vehicle and airborne sensor (e.g., camera sensor) that could be used for the aerial inspection (para. 0093-0095, see also the sections “2.1 base station addressing and patrol route optimization problem description”, “2.2 Problem model construction”, and “(3) task load” which discuss the impact of task payload on the construction of the flight-path optimization model; in particular, Eq. (2.1) reads on “repeating the operations of c) and d) for different combinations of flight vehicle and airborne sensor ….”).
Regarding claim 18, Liu discloses: wherein: the different combinations of flight vehicle and airborne sensor have different flight vehicles (para. 0116: “The drones in all base stations constitute a drone set, which is recorded as V = {1, 2, L, V} ”; see also the section of “(3) task load”).
Regarding claims 19 and 20, Liu discloses: wherein the different combinations of flight vehicle and airborne sensor have different airborne sensors (para. 0096-0098: see the discussion about “task load”); wherein the different combinations of flight vehicle and airborne sensor have both different flight vehicles and different airborne sensors (para. 0096-0098; see also discussion of the Eq. (2.1), para. 0123-0140).
Regarding claims 21-23, Liu discloses: using the flight path data of d) to determine overall costs for the different combinations of flight vehicle and airborne sensor (para. 0097-0098 discusses optimizing the total costs based on the flight path data and for the different combinations of flight vehicle and airborne sensor); wherein: the overall costs for the different combinations of flight vehicle and airborne sensor are based on financial parameters for the different combinations of flight vehicle and airborne sensor (para. 0022, 0095, 0097-0098; see also para. 0116 and discussion of Eq. (2.1), para. 0123-0140: “The objective function (2.1) minimizes the overall cost of base station construction, fixed use and flight of UAVs”); and using the particular combination of flight vehicle and airborne sensor and the flight path data of d) for the particular combination of flight vehicle and airborne sensor to perform the aerial inspection of the facilities in the geographical region (para. 0121, 0168-0177).
Regarding claim 25, Liu discloses: wherein: wherein: the clustering method of c) is applied to a filtered set of facilities that are associated with the particular base (para. 0019, 0047).
Regarding claim 26, Liu discloses: wherein: the processing of d) uses a computer-implemented (para. 0182) vehicle routing problem (VRP) solver to determine the flight path data (para. 0143-0145).
Regarding claim 27, Liu discloses: wherein: the VRP solver employs a graph with sets of facility clusters defined as vertices of the graph (Figs. 4 and 5), time to travel between the sets of facility clusters at flight vehicle cruising speed defined as edge costs in the graph, scan times for scanning each facility in the sets of facility clusters (para. 0114: “The UAV needs to hover for a certain period of time at each reconnaissance target …”) embedded as vertex costs in the graph, and vehicle range limits imposed as capacity constraints (para. 0021-0023, 0064, 0090-0091, 0114, 0126-0140).
Regarding claim 28, Liu discloses: wherein: no-fly zone restrictions (e.g., restrictions on the flying height of drones) and possibly other limitations are defined by a set of constraints that are added as penalties on non-compliant edges of the graph (para. 0095, 0140, 0150-0151, 0166).
Regarding claim 29, Liu discloses: storing data representing a template scan pattern (e.g., the cluster allocation and heuristic search design model) which is intended to be used in scanning one or more facilities in a respective cluster (para. 0036, 0141-0144); wherein the flight time costs include scanning (i.e., video imaging) costs for scanning the respective cluster which is based on the data representing the template scan pattern (para. 0091, 0098, 0095: “Costs associated with construction and drone use”, para. 0116: “Because drones require corresponding maintenance before and after use, a fixed cost of using drones F<sub>k</sub>(k∈V) will be generated”).
Regarding claim 31, discloses: wherein: the flight time costs include scanning costs (i.e., UAV patrols costs) for scanning one or more facilities in a respective cluster (para. 0108: “minimizing the overall costs such as patrol costs and base station establishment costs is the objective function, taking into account constraints such as UAV endurance and the number of UAVs in the control base station, construct The model is shown in Figure 4”; para. 0114, 0140), which is based on optimization of a flight pattern (para. 0140: “Constraint (2.3) indicates that each target point must be visited and can only be visited once, that is, it must and can only be assigned to the flight loop of one UAV”) for the one or more facilities of the respective cluster to minimize flight times for scanning the one or more facilities of the respective cluster (para. 0116: “the sum of the flight time and service time of the drone visiting multiple targets cannot exceed its maximum endurance time”; para. 0140: “Constraint (2.3) indicates that each target point must be visited and can only be visited once, that is, it must and can only be assigned to the flight loop of one UAV. Constraint (2.4) means that the flight time and service time of each UAV must not exceed its endurance capacity”).
Regarding claim 32, Liu discloses: storing data representing flight vehicle scan speed which is intended to be used in carrying out scanning one or more facilities in a respective cluster (para. 0114: “The UAV needs to hover for a certain period of time at each reconnaissance target i (i∈N) to complete the reconnaissance mission of the target area, which is recorded as the service time s<sub>i</sub> of target i. The flight distance between any two reconnaissance targets (or reconnaissance targets and base stations) i,j (i,j∈MUN) is known, recorded as d<sub>ij</sub>”; para. 0116: “the UAV always flies at a constant speed during patrol, and the flight distance between any two reconnaissance targets (or reconnaissance targets and base stations) i,j (i,j∈MUN) is known, the flight distance between any two points can be obtained Time, recorded as t<sub>ij</sub>”; para. 0169: “The reconnaissance service time for each target point is set to 0.2 hours”; by inherency, “flight vehicle scan speed” must be defined to be dij / (si + tij ) ); wherein the flight time costs include scanning costs for scanning one or more facilities in a respective cluster, which is based on flight vehicle scan speed in carrying out the scanning (para. 0121, 126-140).
Regarding claim 33, Liu discloses: storing data representing flight vehicle cruise speed (para. 0091, 0095); wherein the flight time costs are based on the flight vehicle cruise speed for the flight segments of the trip between the base to the sequence of facility clusters, between facility clusters, and back to the base (para. 0091, 0094-0095, 0140).
Regarding claim 36, Liu discloses: wherein: the airborne sensor comprises a video imaging system including a camera (para. 0086, 0098). Liu is silent on: said video imaging system comprises a laser-based sensor. However, Embry teaches a laser-based sensors (para. 0008: “the lidar device can be in the form of … pulsed laser lidar”; see also para. 0048). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate such a well-known laser-based sensor into the Liu method to arrive the claimed invention. The skilled person would apply such modification without needing inventive skill but depending on practical considerations and according to the dictates of the circumstances.
Regarding claims 37 and 38, Liu discloses: wherein: the flight time costs are based on at least one operational parameter of a flight vehicle, wherein the at least one operational parameter is selected from the group consisting of cruise speed, and turn rate (para. 0095, 0116).
Regarding claim 39, Liu discloses: wherein the flight vehicle is a drone (para. 0002, 0021).
Regarding claims 40 and 44, Liu does not but DITTBERNER teaches: wherein the aerial inspection scans a plurality of facilities in the geographical region for fugitive emission of methane (para. 0003-0004). As such, the combination of Liu/DITTBERNER/SASAKI/TABARIN renders the claimed invention obvious.
Regarding claim 41, Liu discloses: wherein the plurality of facilities includes at least an upstream facility (para. 0004: “border areas” reads on “upstream facilities”).
Regarding claim 45, Liu discloses a method for aerial inspection of a plurality of facilities in a geographical region, the method comprising: a) storing data that represents the plurality of facilities in the geographical region and data that represents at least one base in the geographical region, wherein the at least one base supports aerial inspection of the plurality of facilities in the geographical region, and wherein the plurality of facilities include at least an upstream facility; b) selecting a particular base in the geographical region; c) performing a clustering method on the data of a) to define cluster data representing a set of facility clusters in the geographical region that are associated with the particular base of b); d) processing the cluster data of c) to determine flight path data representing flight path segments that form a trip, wherein the trip originates at the particular base, travels to a sequence of facility clusters and scans each facility in each facility cluster, and returns back to the particular base, wherein the sequence of facility clusters of the trip corresponds to the set of facility clusters represented by the cluster data of c) (see discussions for claims 12 and 42 above); and e) flying a flight vehicle equipped with a sensor (e.g., camera sensor) in accordance with the trip of d) (para. 0091, 0095, 0140).
Liu does not mention explicitly: e) performing a first aerial inspection in accordance with the trip of d), wherein performing the first aerial inspection comprises scanning, with a first sensor type as the airborne sensor, each facility in each facility cluster; f) identifying, based on data collected by the airborne sensor during the first aerial inspection, at least one facility from which no airborne gas is emitted; g) identifying, based on data collected by the airborne sensor during the first aerial inspection, at least one target facility from which airborne gas is emitted; h) performing a second aerial inspection of only the at least one target facility to confirm the presence of airborne gas emissions using a second sensor type as the airborne sensor; i) determining whether confirmed airborne gas emissions from the second aerial inspection are known temporarily-allowed gas emissions, based at least on a time of the second aerial inspection directly corresponding to a time of the known temporarily-allowed emissions; j) in response to the confirmed airborne gas emissions from the second aerial inspection not being the known temporarily-allowed gas emissions at the time of known allowed emissions, identifying the at least one target facility for component-level inspection and repair; k) identifying, during an inspection of the at least one target facility, at least one component that has caused airborne gas emissions from the at least one target facility to not correspond to allowed gas emissions; l) repairing the at least one component; and m) following repair of the at least one component, verifying that the repair has reduced the airborne gas emissions.
However, as discussed for claims 12 and 42 above, the combination of Liu/DITTBERNER/SASAKI/TABARIN teaches or renders obvious the above-noted deficiencies of Liu (see discussion for claims 12 and 42 above).
Regarding claim 47, Liu does not but DITTBERNER teaches: the inspection of the at least one target facility includes a component-level inspection of the at least one target facility (para. 0044) using portable equipment (a drone equipped with a sensor like an ultrasound sensor, laser, and/or infrared cameras reads on “portable equipment”), and providing necessary repair to reduce the airborne gas emissions (para. 0003: “leaks can be easily fixed if monitoring and tracking technologies are in place to detect and alert owner about the size and locations of the leaks”).
The combination of Liu/DITTBERNER is silent on: said verifying comprises using the portable equipment to verify a quality of the repair.
However, as discussed for claims 12 above, Examiner takes official notice that it is common practice for professional plumbers to verify their repair work has resolved a pipeline leak by employing various methods to ensure a successful repair. Since DITTBERNER teaches monitoring and tracking the (repaired or fixed) leaks (para. 0003) using portable equipment such as a drone (para. 0044), it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to conceive and practice said verification through after-repair monitoring/tracking using a portable equipment such as a drone. It has been held that the mere application of a known technologies to a specific instance by those skilled in the art would have been obvious.
5. Claims 24, 30 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. in view of DITTBERNER et al., SASAKI et al. and TABARIN, further in view of MULLAN et al. (US 20180224854 A1).
Regarding claim 24, Liu does not mention explicitly: wherein: the clustering method of c) is a hierarchical multilevel clustering method.
MULLAN discloses a method for planning aerial inspection of a plurality of facilities (e.g., pipelines) in a geographical region (para. 0014, 0064), comprising: storing data that represents the plurality of facilities in the geographical region and data that represents at least one base in the geographical region (para. 0070: “… based on the launch of the UV 104 …”), wherein the at least one base supports aerial inspection of the plurality of facilities in the geographical region (para. 0069-0070); performing a clustering method on the data to define cluster data representing a set of facility clusters in the geographical region, wherein the clustering method includes a hierarchical multilevel clustering method (para. 0071).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate MULLAN’s teaching of hierarchical multilevel clustering method into the combination of Liu/DITTBERNER/SASAKI/TABARIN to arrive the claimed invention wherein the hierarchical multilevel clustering method is adapted to define cluster data representing a set of facility clusters in the geographical region that are associated with the particular base of UAVs. Doing so would provide for reduction of the computational complexity of the above clustering and filtering to limit the search space to areas close to the facility to be inspected (MULLAN, para. 0071).
Regarding claim 30, Liu does not mention explicitly: wherein: the scanning costs for scanning the respective cluster is further based on parameters of a bounding box that covers the one or more facilities in the respective cluster.
The teaching of MULLAN includes: evaluating the costs (e.g., the computational complexities) for scanning the respective cluster based on parameters of a bounding box (para. 0071: “Using the extracted color patches, the position of the color patches relative to the entire frame may be set as the bounded search space for the subsequent steps”; para. 0073: “the size of the box that bounds the whole patch …”) that covers the one or more facilities in the respective cluster (para. para. 0071-0074).
Since Liu teaches the need to consider the costs for scanning the respective cluster which is based on the data representing the scan pattern (see discussion for claim 29 above), it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Liu/DITTBERNER/SASAKI/TABARIN to incorporate MULLAN’s teaching of evaluating the costs for scanning the respective cluster based on parameters of a bounding box that covers the one or more facilities in the respective cluster to arrive the claimed invention. Doing so would provide for assessment of the computational (and/or data transmission) complexity of the clustering and reduction of the computational complexity of the above clustering and filtering to limit the search space to areas close to the facility to be inspected (MULLAN, para. 0071).
Regarding claim 48, the combination of Liu/DITTBERNER/SASAKI/TABARIN does not mention explicitly: wherein the component-level inspection comprises inspection of at least one of: a valve, a flange, or a tank of the facility.
The teaching of MULLAN includes: performing component-level inspection of a plurality of facilities (e.g., pipelines) in a geographical region (para. 0014, 0064), wherein the component-level inspection comprises inspection of a tank of the facility (para. 0021).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Liu/DITTBERNER/SASAKI/TABARIN to incorporate MULLAN’s teaching of component-level inspection to arrive the claimed invention. It has been held that the mere application of a known technologies to a specific instance by those skilled in the art would have been obvious.
Conclusion
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Citation of Relevant Prior Art
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references cited show related teachings in the art.
THORPE et al. (US 20170097274 A1) discloses a method and system of gas leak detection and monitoring (Abstract; para. 0002), which can be implemented using unmanned aircraft (para. 0056). THORPE teaches: “Once a specific leak site is identified, the system can follow up with gas quantification measurements and a high-resolution measurement of the equipment demonstrating the leak. This process can give site managers actionable information. For example, a dispatch engineer may know which part needs to be repaired or replaced before ever visiting the site” (para. 0115).
Furry (US 20060091310 A1) discloses a method and system of gas leak detection and monitoring (Abstract; para. 0002), comprising: performing component-level inspection via sensors mounted on a helicopter flying over a plurality of facilities in a geographical region in real-time (para. 0079).
Contact Information
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIUQIN SUN whose telephone number is (571)272-2280. The examiner can normally be reached 9:30am-6:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/X.S/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857