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 Applicant’s argument filed May 19, 2026, regarding objections of claim 8 have been withdrawn due to the amendments made addressing the objection.
Applicant’s argument filed May 19, 2026 regarding rejection of claims 1 and 7 under 35 USC 102 have been fully considered but amended independent claims 1 and 7 require further search and consideration and have been rejected under 35 USC 103 and are unpersuasive.
Applicant’s argument filed May 19, 2026 regarding rejection of claims 3 and 9 under 35 USC 103 have been fully considered but amended claims 3 and 9 require further search and consideration and are moot.
3. Applicant argues that Bauer (US 20210065563A1), Assis (“Innovations in Tunnel Inspection Using Drones and Digital Twins for Geometric Survey”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4798917), and Qian et al. (US 20230286556A1) does not teach the features of “wherein the inspection route is a tunnel, the drone is configured to detect a track and a side wall in the environment images to calculate a first distance between the drone and the track and a second distance between the drone and the sidewall, wherein the drone is configured to control the drone to be located at a central position in the tunnel according to the first distance and the second distance” as amended in claim 1. Particularly, applicant argues that Assis determines drone position based on tunnel geometry but does not disclose detecting a track, nor calculating a distance to a track, nor using any track-based reference for navigation or positioning, and Qian determines drone position relative to a railroad track but does not disclose a tunnel, sidewalls, or any interaction between a track and tunnel boundaries. Applicant then argues that Assis and Qian are fundamentally different and incompatible positioning frameworks and that the cited references do not teach or suggest combining the two and relies on impermissible hindsight.
However, examiner argues that the previous Office Action, with regards to the amended claim aspect of claim 1, relies on Assis in view of Qian, not Assis or Qian alone. As shown, Assis is relied upon for sensing walls, floor, and ceiling of a tunnel and determining a central position within the tunnel, while Qian is relied upon for detecting railroad track and controlling a drone to move along a center of the track at a certain height (see Assis [pg 9-10] and see Qian [0064]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report of Bauer by incorporating teaching of Assis and Qian such that image sensors such as RGB camera and LiDAR of Assis determines 3D model of a tunnel and uses image of a railroad track of Qian and calculates distances between the drone and side wall as well as vertical distances of the tunnel and track to the drone to use them to find a position the drone can take to be in the center of the tunnel.
The motivation to find and position a drone in a center of a tunnel using image sensor data is that, as indicated by Assis, this would allow for avoidance of obstacles, enhances adaptability to spatial considerations of tunnel navigation, and reduce volume of irrelevant data collected (see [pg 4], [pg 5] and [pg10]).
The motivation to have an autonomous drone move along a center of a railroad track at a determined height is that, as indicated by Qian, this would allow for reduction of cost of using human pilot, reduce delay of data collection, data processing, and decision-making, and have schedules that are not dependent of a pilot to have inspection of a railroad track done when it is needed (see [0002]).
As shown above and in previous Office Action, Assis and Qian both use sensed environment to determine or control drone position and thus are complementary and would predictably provide positional information relative to both tunnel and railroad track. In addition, the rejection expressly provides reasons and motivation to combine based on the references’ own teachings. Hence, applicant’s arguments regarding lack of support in the cited references and relying on impermissible hindsight are unpersuasive.
Therefore, applicant’s arguments regarding rejection of amended independent claim 1 under 35 USC 103 are unpersuasive.
4. Applicant argues that amended independent claim 7 are allowable for the same reasons set forth above for traversal of amended independent claim 1.
However, as stated above, applicant’s arguments regarding amended independent claim 1 are unpersuasive. As such, arguments of claim 7, which recites similar amended claim of claim 1, are unpersuasive.
5. Applicant argues that amended dependent claims 3 and 9 are not taught by Bauer nor the additional references relied upon by the Office Action, especially of Assis and Qian. Therefore, rejection of claims 3 and 9 under 35 USC 103 are requested to be withdrawn.
However, examiner argues that newly added amended claim limitations to claims 3 and 9 requires further search and consideration. Upon doing so, examiner found Song et al. (US 20190003847A1) to combine with modified Bauer in view of Assis and Qian to teach amended claim limitations of amended dependent claims 3 and 9.
Song teaches that roadway attributes are longitudinally offset relative to a starting trajectory point for that current road segment, and teaches using image data from a camera, to identify a mile marker along a roadway as a vehicle travels and deciphers the numbers on the mile markers, i.e. mile markers on a track with numbers or miles to indicate a position, or a distance, relative to a starting point of a route (see [0046]-[0048]). Note also that in [0073]-[0073] and Fig. 4 that image data that includes mile marker are analyzed by interpreting text of the mile marker sign, in which the positioning system uses the interpreted text to locate the positions of the mile marker and the vehicle, i.e. identifying the numbered markers to recognize a position.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report and using the camera and image sensors to find distances from walls of a tunnel and a track to position the drone in the center of modified Bauer in view of Assis and Qian by incorporating teaching of Song such that the camera of a drone that is used to determine position of a drone above a track in a tunnel from modified Bauer in view of Assis and Qian to use the camera to identify a mile marker that indicates a distance relative to a starting point of a drone inspection route along the drone inspection route and analyze and interpret the mile marker to location positions of the mile marker and the drone, i.e. markers on track to recognize position of the drone.
The motivation to use camera or image sensor data to identify mile marker and use the information from the mile marker to locate position of a vehicle and drone is that, as indicated by Song, this would allow for improvement in localization accuracy, enable robust map matching and accurate position determination (see [0004]-[0005], [0011], and [0018]).
Therefore, applicant’s arguments regarding rejection of amended claims 3 and 9 under 35 USC 103 are moot as it required further search and consideration. The amended claims 3 and 9 are thus rejected under 35 USC 103 as being unpatentable over Bauer in view of Assis in further view of Qian in still further view of Song.
6. Applicant argues that dependent claims 2-6 and 8-12 contain all feature of amended independent claims 1 and 7 and therefore overcomes the rejection under 35 USC 103 in the previous Office Action.
However, amended independent claims 1 and 7 are fully rejected and arguments regarding claims 1 and 7 are unpersuasive. Therefore, due to its dependence to amended independent claims 1 and 7, the dependent claims 2-6 and 8-12 are rejected and applicant’s argument regarding dependent claims 2-6 and 8-12 are unpersuasive.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
7. Claim 1 and 7 are rejected under pre-35 U.S.C. 103 as being unpatentable over Bauer et al. (US 20210065563A1) in view of Assis et al. (“Innovations in Tunnel Inspection Using Drones and Digital Twins for Geometric Survey”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4798917) in further view of Qian et al. (US 20230286556A1).
Regarding claim 1, Bauer teaches a drone inspection system (see [0026] in general where a system consists of a device that receives inspection and flight plans for a UAV to perform inspection and provide images from sensors.), comprising:
a server, configured to provide an operation interface, and to receive an inspection task through the operation interface, wherein the inspection task including an inspection route (see [0074]-[0082] and Fig. 2 where a cloud system that is in communication with a user device and UAV includes a job determination engine that generates interactive user interface, through which a user provides information associated with a particular inspection job, i.e. a server receiving an inspection task through an operation interface. Note also in [0081]-[002] that inspection job includes an inspection route the user device using inspection job information from the cloud system generates flight plan for UAV to follow, such as safe altitude, property boundary, and so on.); and
a drone, communicatively connected to the server, wherein the drone comprises an image capture module (see [0026]-[0028] where UAV, or drone, provides images to a cloud system, i.e. UAV with capability to capture images and is communicatively connected to a cloud server; see further [0074] and [0082]-[0083] where UAV, i.e. drone, is in communication with cloud system and a camera, i.e. image capture module, is included in the UAV that stitches images together.),
wherein the server is configured to execute a schedule to set the drone to execute the inspection task, the image capture module is configured to continuously obtain a plurality of environment images, and the drone is configured to recognize a position of the drone according to the environment images to travel on the inspection route (see [0077]-[0080] where a job determination engine that obtains information describing jobs, which are inspections, and information describing property or environment to be inspected, also receives a time that a job is to be performed, i.e. a schedule to set a drone to execute an inspection task; see further [0032], [0082]-[0083], and [0119]-[0120] where UAV, i.e. a drone, has a camera included such that images of inspection area, i.e. plurality of environment images, are captured continuously and periodically, such as every half a second or video record, as UAV travels along a determined flight path to obtain real-world information, such as positions of the UAV, to a user device, i.e. continuous environment images to recognize position of a drone as it travels the inspection route.),
wherein in response to the drone travelling on the inspection route, the drone is configured to send the environment images to the server, the server is configured to determine whether an abnormal phenomenon occurs according to the environment images to generate an inspection result corresponding to the inspection task (see [0026]-[0028] where UAV, i.e. drone, provides images from sensors to a cloud system, i.e. sending environment images to a server, and in [0085]-[0086] where the cloud system determine types of damage from one or more visual classifiers that can operate on a received sensor information, i.e. determine an abnormal phenomenon according to environment images to generate report/result of type of damage identified, which is result of the inspection task. Note also in [0106] that user device, which is in communication with the cloud system, generates one or more interactive documents that include summary data describing the inspection.).
Bauer does not teach: wherein the inspection route is a tunnel, the drone is configured to detect a track and a sidewall in the environment images to calculate a first distance between the drone and the track and a second distance between the drone and the sidewall,
wherein the drone is configured to control the drone to be located at a central position in the tunnel according to the first distance and the second distance.
However, Assis teaches using RGB camera and LiDAR to make 3D images and model of a tunnel and calculate distances of nodes of the wall and floor and ceiling of the tunnel, all points in all direction of the tunnel and find center axis that is a position in the center in both horizontal and vertical dimensions, i.e. a first and second distance of drone height from the floor and ceiling and between a drone sidewall used to control the drone to be positioned at a central position in the tunnel (see [pg9-10]).
Further, Qian teaches a drone that uses image-based depth estimation to position a drone to move along a center of a railroad track at an appropriate height (see [0064]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report of Bauer by incorporating teaching of Assis and Qian such that image sensors such as RGB camera and LiDAR of Assis determines 3D model of a tunnel and uses image of a railroad track of Qian and calculates distances between the drone and side wall as well as vertical distances of the tunnel and track to the drone to use them to find a position the drone can take to be in the center of the tunnel.
The motivation to find and position a drone in a center of a tunnel using image sensor data is that, as indicated by Assis, this would allow for avoidance of obstacles, enhances adaptability to spatial considerations of tunnel navigation, and reduce volume of irrelevant data collected (see [pg 4], [pg 5] and [pg10]).
The motivation to have an autonomous drone move along a center of a railroad track at a determined height is that, as indicated by Qian, this would allow for reduction of cost of using human pilot, reduce delay of data collection, data processing, and decision-making, and have schedules that are not dependent of a pilot to have inspection of a railroad track done when it is needed (see [0002]).
Regarding claim 7, Bauer teaches a method for automatic inspection for a server and a drone, wherein the drone comprises an image capture module (see [0026] in general where a system consists of a device that receives inspection and flight plans for a UAV to perform inspection and provide images from sensors.), and the method comprises:
receiving an inspection task through an operation interface provided by the server, wherein the inspection task comprises an inspection route (see [0074]-[0082] and Fig. 2 where a cloud system that is in communication with a user device and UAV includes a job determination engine that generates interactive user interface, through which a user provides information associated with a particular inspection job, i.e. a server receiving an inspection task through an operation interface. Note also in [0081]-[002] that inspection job includes an inspection route the user device using inspection job information from the cloud system generates flight plan for UAV to follow, such as safe altitude, property boundary, and so on.);
executing a schedule to set the drone to execute the inspection task, wherein the image capture module is configured to continuously obtain a plurality of environment images; recognizing, by the drone, a position of the drone according to the environment images to travel on the inspection route (see [0077]-[0080] where a job determination engine that obtains information describing jobs, which are inspections, and information describing property or environment to be inspected, also receives a time that a job is to be performed, i.e. a schedule to set a drone to execute an inspection task; see further [0032], [0082]-[0083], and [0119]-[0120] where UAV, i.e. a drone, has a camera included such that images of inspection area, i.e. plurality of environment images, are captured continuously and periodically, such as every half a second or video record, as UAV travels along a determined flight path to obtain real-world information, such as positions of the UAV, to a user device, i.e. continuous environment images to recognize position of a drone as it travels the inspection route.);
sending, by the drone, the environment images to the server in response to the drone traveling on the inspection route; and determining, by the server, whether an abnormal phenomenon occurs according to the environment images to generate an inspection result corresponding to the inspection task (see [0026]-[0028] where UAV, i.e. drone, provides images from sensors to a cloud system, i.e. sending environment images to a server, and in [0085]-[0086] where the cloud system determine types of damage from one or more visual classifiers that can operate on a received sensor information, i.e. determine an abnormal phenomenon according to environment images to generate report/result of type of damage identified, which is result of the inspection task. Note also in [0106] that user device, which is in communication with the cloud system, generates one or more interactive documents that include summary data describing the inspection.).
Bauer does not teach: wherein the inspection route is a tunnel,
detecting, by a drone, a track and a sidewall in the environment images to calculate a first distance between the drone and the track and a second distance between the drone and the sidewall;
controlling, by the drone, the drone to be located at a central position in the tunnel according to the first distance and the second distance.
However, Assis teaches using RGB camera and LiDAR to make 3D images and model of a tunnel and calculate distances of nodes of the wall and floor and ceiling of the tunnel, all points in all direction of the tunnel and find center axis that is a position in the center in both horizontal and vertical dimensions, i.e. a first and second distance of drone height from the floor and ceiling and between a drone sidewall used to control the drone to be positioned at a central position in the tunnel (see [pg9-10]).
Further, Qian teaches a drone that uses image-based depth estimation to position a drone to move along a center of a railroad track at an appropriate height (see [0064]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report of Bauer by incorporating teaching of Assis and Qian such that image sensors such as RGB camera and LiDAR of Assis determines 3D model of a tunnel and uses image of a railroad track of Qian and calculates distances between the drone and side wall as well as vertical distances of the tunnel and track to the drone to use them to find a position the drone can take to be in the center of the tunnel.
The motivation to find and position a drone in a center of a tunnel using image sensor data is that, as indicated by Assis, this would allow for avoidance of obstacles, enhances adaptability to spatial considerations of tunnel navigation, and reduce volume of irrelevant data collected (see [pg 4], [pg 5] and [pg10]).
The motivation to have an autonomous drone move along a center of a railroad track at a determined height is that, as indicated by Qian, this would allow for reduction of cost of using human pilot, reduce delay of data collection, data processing, and decision-making, and have schedules that are not dependent of a pilot to have inspection of a railroad track done when it is needed (see [0002]).
8. Claim 2 and 8 are rejected under pre-35 U.S.C. 103 as being unpatentable over Bauer in view of Assis in further view of Qian in still further view of Li et al. (CN 111433828A).
Regarding claim 2, modified Bauer in view of Assis and Qian teaches the drone inspection system as claimed in claim 1, wherein the drone further sends speed, height, and the position of the drone to the server, the server is configured to determine whether a travel route of the drone complies with the inspection route (see [0033], [0062], and [0167] where UAV provides geo-spatial location to a user device that is connected to a cloud system, and data logs of UAV regarding altitude, heading, absolute or relative position, GPS coordinates, pitch, roll, yaw, ground speed, and velocity are wirelessly transmitted to the cloud system, i.e. drone sends speed, height, and position of the drone to a server; see further [0163] where contingency module, that is part of a UAV primary processing system that is a system of software in communication with one or more databases, monitors and detects contingency events that includes deviation from a flight plan, i.e. determining whether or not a travel route that the drone is taking complies with an inspection route.),
Modified Bauer in view of Assis and Qian does not teach: wherein in response to the travel route not complying with the inspection route, the server is configured to terminate the inspection task.
However, Li teaches an unmanned aerial system (UAS) that does not satisfy a route task, i.e. not complying with an inspection route, which will enter into a PathNonConform (path does not meet) state where UAS receive a stop sign and safe landing emergency plan, and after its landing, it enters MissionTerminated (task is terminated) state, i.e. terminate the inspection task (see [pg 15/118]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report of modified Bauer in view of Assis and Qian by incorporating teaching of Li such that if the drone does not follow the inspection route, i.e. not complying with the inspection route, the drone will enter into a state where it will stop and perform a safe landing emergency plan and enter into task terminated state, i.e. terminate inspection task.
The motivation to perform safe landing and terminate an inspection task when a drone is not complying with an inspection route is that, as indicated by Li, this would allow for reliable response to a drone that is beyond the visual sight (BVLOS), have emergency management, and allow for flexibility to a system to react to difficult situations such as network reliability (see [pg 11/118] and [pg 12/118]).
Regarding claim 8, modified Bauer in view of Assis and Qian teaches the method for automatic inspection as claimed in claim 7, further comprising:
sending speed, height, and the position of the drone to the serve, and determining, by the server, whether a travel route of the drone complies with the inspection route (see [0033], [0062], and [0167] where UAV provides geo-spatial location to a user device that is connected to a cloud system, and data logs of UAV regarding altitude, heading, absolute or relative position, GPS coordinates, pitch, roll, yaw, ground speed, and velocity are wirelessly transmitted to the cloud system, i.e. drone sends speed, height, and position of the drone to a server; see further [0163] where contingency module, that is part of a UAV primary processing system that is a system of software in communication with one or more databases, monitors and detects contingency events that includes deviation from a flight plan, i.e. determining whether or not a travel route that the drone is taking complies with an inspection route.).
Modified Bauer in view of Assis and Qian does not teach: if the travel route does not comply with the inspection route, terminating the inspection task.
However, Li teaches an unmanned aerial system (UAS) that does not satisfy a route task, i.e. not complying with an inspection route, which will enter into a PathNonConform (path does not meet) state where UAS receive a stop sign and safe landing emergency plan, and after its landing, it enters MissionTerminated (task is terminated) state, i.e. terminate the inspection task (see [pg 15/118]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report of Bauer by incorporating teaching of Li such that if the drone does not follow the inspection route, i.e. not complying with the inspection route, the drone will enter into a state where it will stop and perform a safe landing emergency plan and enter into task terminated state, i.e. terminate inspection task.
The motivation to perform safe landing and terminate an inspection task when a drone is not complying with an inspection route is that, as indicated by Li, this would allow for reliable response to a drone that is beyond the visual sight (BVLOS), have emergency management, and allow for flexibility to a system to react to difficult situations such as network reliability (see [pg 11/118] and [pg 12/118]).
9. Claim 3 and 9 are rejected under pre-35 U.S.C. 103 as being unpatentable over Bauer in view of Assis in further view of Qian in still further view of Song et al. (US 20190003847A1)
Regarding claim 3, modified Bauer in view of Assis and Qian teaches the drone inspection system as claimed in claim 1,
Modified Bauer in view of Assis and Qian does not teach: wherein there are markers on the track, with numbers on the markers indicating a distance between the marker and a starting point or an ending point of the inspection route, wherein the drone is configured to identify the markers on the track to recognize the position of the drone.
However, Song teaches that roadway attributes are longitudinally offset relative to a starting trajectory point for that current road segment, and teaches using image data from a camera, to identify a mile marker along a roadway as a vehicle travels and deciphers the numbers on the mile markers, i.e. mile markers on a track with numbers or miles to indicate a position, or a distance, relative to a starting point of a route (see [0046]-[0048]). Note also that in [0073]-[0073] and Fig. 4 that image data that includes mile marker are analyzed by interpreting text of the mile marker sign, in which the positioning system uses the interpreted text to locate the positions of the mile marker and the vehicle, i.e. identifying the numbered markers to recognize a position.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report and using the camera and image sensors to find distances from walls of a tunnel and a track to position the drone in the center of modified Bauer in view of Assis and Qian by incorporating teaching of Song such that the camera of a drone that is used to determine position of a drone above a track in a tunnel from modified Bauer in view of Assis and Qian to use the camera to identify a mile marker that indicates a distance relative to a starting point of a drone inspection route along the drone inspection route and analyze and interpret the mile marker to location positions of the mile marker and the drone, i.e. markers on track to recognize position of the drone.
The motivation to use camera or image sensor data to identify mile marker and use the information from the mile marker to locate position of a vehicle and drone is that, as indicated by Song, this would allow for improvement in localization accuracy, enable robust map matching and accurate position determination (see [0004]-[0005], [0011], and [0018]).
Regarding claim 9, The method for automatic inspection as claimed in claim 7,
Modified Bauer in view of Assis and Qian does not teach: wherein there are markers on the track, with numbers on the markers indicating a distance between the marker and a starting point or an ending point of the inspection route, the method further comprising:
identifying, by the drone, the markers on the track to recognize the position of the drone.
However, Song teaches that roadway attributes are longitudinally offset relative to a starting trajectory point for that current road segment, and teaches using image data from a camera, to identify a mile marker along a roadway as a vehicle travels and deciphers the numbers on the mile markers, i.e. mile markers on a track with numbers or miles to indicate a position, or a distance, relative to a starting point of a route (see [0046]-[0048]). Note also that in [0073]-[0073] and Fig. 4 that image data that includes mile marker are analyzed by interpreting text of the mile marker sign, in which the positioning system uses the interpreted text to locate the positions of the mile marker and the vehicle, i.e. identifying the numbered markers to recognize a position.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine abnormal phenomenon using camera and image sensors and generate a report and using the camera and image sensors to find distances from walls of a tunnel and a track to position the drone in the center of modified Bauer in view of Assis and Qian by incorporating teaching of Song such that the camera of a drone that is used to determine position of a drone above a track in a tunnel from modified Bauer in view of Assis and Qian to use the camera to identify a mile marker that indicates a distance relative to a starting point of a drone inspection route along the drone inspection route and analyze and interpret the mile marker to location positions of the mile marker and the drone, i.e. markers on track to recognize position of the drone.
The motivation to use camera or image sensor data to identify mile marker and use the information from the mile marker to locate position of a vehicle and drone is that, as indicated by Song, this would allow for improvement in localization accuracy, enable robust map matching and accurate position determination (see [0004]-[0005], [0011], and [0018]).
10. Claim 5 and 11 are rejected under pre-35 U.S.C. 103 as being unpatentable over Bauer in view of Assis in further view of Qian in still further view of Zhang et al. (“Reactive UAV-based automatic tunnel surface defect inspection with a field test”, https://doi.org/10.1016/j.autcon.2024.105424) in further view of Qian.
Regarding claim 5, modified Bauer in view of Assis and Qian teaches the drone inspection system as claimed in claim 1,
Bauer also teaches an abnormal phenomenon of damaged areas and generating a graphical presentation of a property with identifying where the damaged areas are as well as indicating types of damages, i.e. type and position of the abnormal phenomenon (see [0085]). Note also that types of damages includes crack in ceramic/clay tiles, gaps between shingles, and broken corners of tiles (see [0146]).
Further, Quian drone images that are screened to detect defected component of a track, i.e. track distortion (see [0056] and [0049]).
Modified Bauer in view of Assis and Qian does not particularly teach: the type comprises water seepage or track distortion.
However, Zhang teaches UAV, i.e. drone, having image data showing identified defects of water leakage, i.e. water seepage (see Fig. 13 and [pg 11]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine a damage, i.e. abnormal phenomenon, using camera and image sensors and generate a report of the type of damage and position of the damage of modified Bauer in view of Assis and Qian by further incorporating teaching of Zhang and Qian such that the damage, i.e. abnormal phenomenon not only has cracks, but also water leakage/seepage and track defect, i.e. track distortion, as well.
The motivation to include drone inspection of detecting water leakage is that, as indicated by Zhang, this would allow for regular health monitoring while keeping the cost less expensive and results less subjective than manual inspection (see [pg 1]).
The motivation to have an autonomous drone move along a railroad track to detect defected component of a track, i.e. a track distortion, as indicated by Qian, this would allow for reduction of cost of using human pilot, reduce delay of data collection, data processing, and decision-making, and have schedules that are not dependent of a pilot to have inspection of a railroad track done when it is needed (see [0002]).
Regarding claim 11, modified Bauer in view of Assis and Qian teaches the method for automatic inspection as claimed in claim 7,
Bauer also teaches an abnormal phenomenon of damaged areas and generating a graphical presentation of a property with identifying where the damaged areas are as well as indicating types of damages, i.e. type and position of the abnormal phenomenon (see [0085]). Note also that types of damages includes crack in ceramic/clay tiles, gaps between shingles, and broken corners of tiles (see [0146]).
Modified Bauer in view of Assis and Qian does not particularly teach: the type comprises water seepage or track distortion.
Further, Quian drone images that are screened to detect defected component of a track, i.e. track distortion (see [0056] and [0049]).
However, Zhang teaches UAV, i.e. drone, having image data showing identified defects of water leakage, i.e. water seepage (see Fig. 13 and [pg 11]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine a damage, i.e. abnormal phenomenon, using camera and image sensors and generate a report of the type of damage and position of the damage of modified Bauer in view of Assis and Qian by further incorporating teaching of Zhang and Qian such that the damage, i.e. abnormal phenomenon not only has cracks, but also water leakage/seepage and track defect, i.e. track distortion, as well.
The motivation to include drone inspection of detecting water leakage is that, as indicated by Zhang, this would allow for regular health monitoring while keeping the cost less expensive and results less subjective than manual inspection (see [pg 1]).
The motivation to have an autonomous drone move along a railroad track to detect defected component of a track, i.e. a track distortion, as indicated by Qian, this would allow for reduction of cost of using human pilot, reduce delay of data collection, data processing, and decision-making, and have schedules that are not dependent of a pilot to have inspection of a railroad track done when it is needed (see [0002]).
11. Claim 6 and 12 are rejected under pre-35 U.S.C. 103 as being unpatentable over Bauer in view of Assis in further view of Qian in still further view Williams (US 20180233007A1).
Regarding claim 6, modified Bauer in view of Assis and Qian teaches the drone inspection system as claimed in claim 1,
Bauer also teaches where a cloud system, a server, determine types of damage from one or more visual classifiers that can operate on a received sensor information, i.e. determine an abnormal phenomenon according to environment images to generate report/result of type of damage identified (see [0085]-[0086]).
Modified Bauer in view of Assis and Qian does not particularly teach: wherein in response to the server determining that the abnormal phenomenon occurs, the server controls the drone to hover, sends out a warning message, and receives a reply message from an external device, wherein the server is configured to determine whether to control the drone to continue the inspection task according to the reply message.
However, Williams teaches that a drone detects abnormal events such as suspicious objects, movement, and/or heat signatures, and the drone will pause and wait for instructions or stop and continue to record at an area, i.e. hover at a given area, and also send an alert, i.e. a warning message, to a user device which a user replies with commands, i.e. reply message, that causes the drone to continue its path or do additional scans (see [0079], [0084], [0104], [0107], [0109] and [0113]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine a damage, i.e. abnormal phenomenon, using camera and image sensors and generate a report of modified Bauer in view of Assis and Qian by incorporating teaching of Williams such that when a drone detects an abnormal phenomenon of a damage within an inspection route, the drone will pause and wait for instructions or stop and continue to record at an area, i.e. hover at a given area, and also send an alert, i.e. a warning message, to a user device which a user replies with commands, i.e. reply message, that causes the drone to continue its path or do additional scans.
The motivation to pause or hover at an abnormal event area and send a warning message and continue its path or do additional scan depending on a reply command is that, as indicated by William, this would allow for improvement in security, alert owners and responders of unusual events, and have real-time monitoring that is less costly (see [0003]-[0006]).
Regarding claim 12, modified Bauer in view of Assis and Qian teaches the method for automatic inspection as claimed in claim 7,
Bauer also teaches where a cloud system, a server, determine types of damage from one or more visual classifiers that can operate on a received sensor information, i.e. determine an abnormal phenomenon according to environment images to generate report/result of type of damage identified (see [0085]-[0086]).
Modified Bauer in view of Assis and Qian does not teach: in response to the server determining that the abnormal phenomenon occurs, controlling, by the server, the drone to hover, sending out a warning message, and receiving a reply message from an external device; and
determining whether to control the drone to continue the inspection task according to the reply message.
However, Williams teaches that a drone detects abnormal events such as suspicious objects, movement, and/or heat signatures, and the drone will pause and wait for instructions or stop and continue to record at an area, i.e. hover at a given area, and also send an alert, i.e. a warning message, to a user device which a user replies with commands, i.e. reply message, that causes the drone to continue its path or do additional scans (see [0079], [0084], [0104], [0107], [0109] and [0113]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a UAV, which is a drone, inspection system with a cloud system that is in communication with a drone and user interface to control the drone to an inspection route to determine a damage, i.e. abnormal phenomenon, using camera and image sensors and generate a report of modified Bauer in view of Assis and Qian by incorporating teaching of Williams such that when a drone detects an abnormal phenomenon of a damage within an inspection route, the drone will pause and wait for instructions or stop and continue to record at an area, i.e. hover at a given area, and also send an alert, i.e. a warning message, to a user device which a user replies with commands, i.e. reply message, that causes the drone to continue its path or do additional scans.
The motivation to pause or hover at an abnormal event area and send a warning message and continue its path or do additional scan depending on a reply command is that, as indicated by William, this would allow for improvement in security, alert owners and responders of unusual events, and have real-time monitoring that is less costly (see [0003]-[0006]).
Allowable Subject Matter
12. Claims 4 and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
In regards to claim 4, the claim recites:
“wherein the server is configured to predict a signal intensity of the inspection route according to a machine learning model,
wherein in response to the signal intensity of an area in the inspection route being lower than a threshold, the server reduces a speed of the drone in the area.”
No single prior art reference has been found to anticipate these limitations particularly of “wherein the server is configured to predict a signal intensity of the inspection route according to a machine learning model, wherein in response to the signal intensity of an area in the inspection route being lower than a threshold, the server reduces a speed of the drone in the area”, nor any combination of prior art references to render these limitations obvious, when viewed in the context of the remaining limitations of the claim. Therefore, claim 4 would be allowable if written in independent form including all of the limitations of the base claim and any intervening claims.
Claim 10 recites similar limitations to claim 4. Therefore, similarly no single prior art reference has been found to anticipate these limitations, nor any combination of prior art references to render these limitations obvious, when viewed in the context of the remaining limitations of the claim. As such, claim 10 would also be allowable if written in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
13. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HYANG AHN whose telephone number is (571)272-4162. The examiner can normally be reached M-F 9-5.
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/H.A./Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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