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 .
Status of Claims
• This action is in reply to the Application Number 18/039,752 filed on 06/01/2023.
• Claims 11-18 are currently pending and have been examined.
• This action is made FINAL in response to the “Amendment” and “Remarks” filed on 02/17/2026.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed in Application No. 18/039,752 filed on 06/01/2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/01/2023 and 07/07/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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) 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu (CN110209200A) in view of NURK FPV (Flight of the Year // Trains, Bridges, Rapids, Mountains, Sunset, Gapping, Perching, Powerlooping, https://www.youtube.com/watch?v=nQDcDZ6rmGE, 09/20/2017).
Regarding Claims 11 and 15:
Zhu teaches:
A method for operating a rail vehicle, wherein: before travel of the rail vehicle is started, capturing an environment outside of the rail vehicle by at least one sensor on board a flying unmanned aerial vehicle (Zhu, para [08], “A train track obstacle detection method based on a drone and a vehicle-mounted controller”, and para [10], “When a train stops in a section… the onboard controller starts the drone”, and para [68], “by installing a visual navigation camera 12 at the bottom of the drone, the drone will be able to …detect the type and distance of obstacles”, para [13], “The UAV identifies the obstacle and obtains the outline size, first spatial position, and second spatial position of the obstacle, … and transmits the obstacle information to the onboard controller;”) Examiner note: Zhu teaches a train is stopped on a track and only allows the train to travel after the UAV’s image-based evaluation shows that the track is free of obstacles, which is a “before travel is started” condition under the broadest reasonable interpretation.
and generating sensor signals corresponding to the environment, (Zhu, para [13], “The UAV identifies the obstacle and obtains the outline size, first spatial position, and second spatial position of the obstacle, … and transmits the obstacle information to the onboard controller;”)
performing an automated digital evaluation of the sensor signals so as to obtain a result; (Zhu, para [68], “The UAV control system can build a train obstacle detection unit 13 through a high performance embedded board, analyze and process the image data collected by the camera in real time, obtain candidate frames containing the features of the detected objects in the image through the currently advanced object detection algorithm based on deep neural networks, and classify the above candidate frames to screen out … obstacles”)
automatedly determining from the result of the automated digital evaluation whether or not to release the rail vehicle for travel, (Zhu, para [10], “When a train stops in a section… the onboard controller starts the drone”, para [74], “If no obstacles are detected, the drone will guide the onboard controller to control the train forward at a fixed speed limit”)
wherein the rail vehicle is released for travel only if the automated digital evaluation determines that the environment is free from any obstacle to the rail vehicle proceeding along the track, free from any loose part, or free from any defective part, or any combination thereof. (Zhu, para [10], “When a train stops in a section… the onboard controller starts the drone”, para [74], “If no obstacles are detected, the drone will guide the onboard controller to control the train forward at a fixed speed limit”)
the rail vehicle and a track on which the rail vehicle is standing, (Zhu, Fig. 3 depicts a train standing on a track)
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Zhu does not explicitly teach, but NURK FPV teaches:
while the unmanned aerial vehicle is flying in a space underneath the rail vehicle between the rail vehicle and a track (NURK FPV shows a video of an UAV flied underneath a rail vehicle between the rail vehicle and a track, and the UAV captured the video of environment outside of the rail vehicle as depicted the screenshots below.) Examiner note: NURK FPV demonstrates that a UAV can be flown through the confined space underneath a moving freight train, between the undercarriage and the track, while capturing video of the environment outside the rail vehicle. NURK FPV provides concrete, real-world evidence that under-train flight in that narrow region is technically feasible and was known in the art.
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Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a train rail obstacle detection method of Zhu to include these above teachings from NURK FPV in order to include while the unmanned aerial vehicle is flying in a space underneath the rail vehicle between the rail vehicle and a track. One of ordinary skill in the art would have been motivated to make this modification in order to inspect the environment of the rail vehicle below the rail vehicle between the rail vehicle and the track so that the personnel of the rail vehicle are relieved of the task of preparing for the travel and do not have to take a look at the entire environment of the rail vehicle.
Regarding Claims 12 and 16:
Zhu in view of NURK FPV, as shown in the rejection above, discloses the limitations of claims 11, 15. Zhu teaches:
The method according to claim 11, wherein the unmanned aerial vehicle is initially coupled to the rail vehicle, the unmanned aerial vehicle is uncoupled from the rail vehicle, and the at least one sensor captures the environment of the rail vehicle after the uncoupling. (Zhu, and para [10], “When a train stops in a section… the onboard controller starts the drone”, and para [71], “The drone will continue to fly forward to detect obstacles ahead.”, and para [68], “by installing a visual navigation camera 12 at the bottom of the drone, the drone will be able to …detect the type and distance of obstacles”)
Regarding Claims 13 and 17:
Zhu in view of NURK FPV, as shown in the rejection above, discloses the limitations of claims 11, 15. Zhu teaches:
The method according to claim 11, further comprising, on board the unmanned aerial vehicle performing the automated digital evaluation of the sensor signals and, (Zhu, para [68], “The UAV control system can build a train obstacle detection unit 13 through a high performance embedded board, analyze and process the image data collected by the camera in real time, obtain candidate frames containing the features of the detected objects in the image through the currently advanced object detection algorithm based on deep neural networks, and classify the above candidate frames to screen out … obstacles”)
depending on the result of the automated digital evaluation; automatedly generates a release signal, and transmitting the releasing signal to the rail vehicle and releases the travel of rail vehicle. (Zhu, para [74], “If no obstacles are detected, the drone will guide the onboard controller to control the train forward at a fixed speed limit”)
Regarding Claims 14 and 18:
Zhu in view of NURK FPV, as shown in the rejection above, discloses the limitations of claims 11, 15. Zhu teaches:
The method according to claim 11, wherein a control system of the rail vehicle receives the sensor signals and/or sensor data generated by processing the sensor signals, (Zhu, para [13], “The UAV identifies the obstacle and obtains the outline size, first spatial position, and second spatial position of the obstacle, … and transmits the obstacle information to the onboard controller;”)
and wherein the control system performs the automated digital evaluation of the received sensor signals and/or the sensor data and, (Zhu, para [68], “The UAV control system can build a train obstacle detection unit 13 through a high performance embedded board, analyze and process the image data collected by the camera in real time, obtain candidate frames containing the features of the detected objects in the image through the currently advanced object detection algorithm based on deep neural networks, and classify the above candidate frames to screen out … obstacles”)
depending on the result of the automated digital evaluation, generates a release signal which releases or prohibits travel of the rail vehicle. (Zhu, para [74], “If no obstacles are detected, the drone will guide the onboard controller to control the train forward at a fixed speed limit”)
RESPONSE TO ARGUMENTS
35 USC § 103 rejection. Applicant argues that the combination of Zhu and NURK FPV fails to render claim 11, as amended, obvious for the reasons listed below,
No Under-Vehicle Capture "While ... Standing" Before Travel Starts;
No Use of Under-Vehicle Data to Gate a Pre-Start Release Decision;
Different Inspection Targets: Ahead-of-Track vs. Under-Carriage/Vehicle Condition;
Temporal and Operational Mismatch with the Claimed Pre-Release Check;
Absence of Any Suggestion to Repurpose NURKFPV's Stunt Flight for a Safety Release Workflow;
Failure to Disclose Simultaneous Under-Vehicle Flight and Sensing in the Claimed Space;
No Disclosure of Release Conditioned on "Loose" or "Defective" Parts;
The Proposed Modification Would Alter Zhu's Principle of Operation;
The Proposed Combination Requires an Impermissible Amount of Hindsight;
Even Combining the References Does Not Bridge the Essential Claim Tie-In.
In response to A. Examiner respectfully disagree. Zhu teaches launching the UAV when the train is stopped and only allows the train to move again after the UAV’s image-based evaluation shows that the path is clear, which is a “before travel is started” condition under the broadest and reasonable interpretation. NURK FPV shows that a UAV can fly in the under-train space as claimed in claim 11 while capturing images, and flying under a stationary train is much easier than flying under the moving train shown in the video. If such a maneuver can be successfully performed with a moving train, a person of ordinary skill in the art would have found it straightforward to perform the same flight path when the rail vehicle is standing on a track, where relative motion is eliminated and the flight can be performed nice and easy. Thus, NURK FPV demonstrates that under-train flight in within the ordinary skill in the art, and applying the same flight path in the easier case of a stationary rail vehicle, combined with Zhu’s automated digital evaluation system , would have been a routine and predictable modification for improving rail vehicle safety inspection so that the personnel of the rail vehicle are relieved of the task of preparing for the travel and do not have to take a look at the under-train environment of the rail vehicle.
In response to B. Examiner respectfully disagree. Zhu already teaches using automated digital evaluation of UAV’s image data to decide whether the train may proceed after a stop. NURK FPV shows that under-train video can be captured while flying a UAV through the space under the train, and it would be obvious for a skilled person to use that under-train video to check for obstacles in that region and then apply Zhu’s existing evaluation logic to decide whether to release the train or not.
In response to C. Examiner respectfully disagree. The claim 11 recites that release occurs only if the environment is free from “any obstacle to the rail vehicle proceeding along the track, free from any loose part, or free from any defective part, or any combination thereof”, which the examiner reasonably interprets it as a disjunctive “or” limitation- i.e., the prior art only need teach one of these limitations. Zhu already teaches using automated digital evaluation of UAV’s image data to decide whether there are any obstacles preventing the rail vehicle from proceeding along the track, so this alone satisfies at least the “obstacle” portion of the limitation.
In response to D. Examiner respectfully disagree. Zhu teaches launching the UAV when the train is stopped and the train moves only after the UAV’s evaluation is favorable, which fits a “pre-release” check from a standing condition. NURK FPV shows that a UAV can fly in the under-train space as claimed in claim 11 while capturing images, and flying under a stationary train is much easier than flying under the moving train shown in the video. In view of this, it would be obvious and predictable design choice for a skilled person to adapt the known under-train flight maneuver from NURK FPV to perform a safety-inspection when the rail vehicle is stationary.
In response to E. Examiner respectfully disagree. NURK FPV teaches that a UAV can fly in the under-train space as claimed in claim 11 while capturing images, and flying under a stationary train is much easier than flying under the moving train shown in the video. In view of this, it would be obvious and predictable design choice for a skilled person to adapt the known under-train flight maneuver from NURK FPV to perform a safety-inspection when the rail vehicle is stationary. Zhu already teaches the safety motivation and inspection workflow to using image-based evaluation feature to detect hazards and control train operation, combining NURK FPV’s under-train captured video with Zhu’s automated digital evaluation feature, would have been a routine and predictable modification for improving rail-vehicle safety inspection.
In response to F. Examiner respectfully disagree. NURK FPV teaches that a UAV can fly in the under-train space as claimed in claim 11 while capturing images, and flying under a stationary train is much easier than flying under the moving train shown in the video. In view of this, it would be obvious and predictable design choice for a skilled person to adapt the known under-train flight maneuver from NURK FPV to perform a safety-inspection when the rail vehicle is stationary. Zhu already teaches the safety motivation and inspection workflow to using image-based evaluation feature to detect hazards and control train operation, combining NURK FPV’s under-train captured video with Zhu’s automated digital evaluation feature, would have been a routine and predictable modification for improving rail-vehicle safety inspection.
In response to G. Examiner respectfully disagree. The claim 11 recites that release occurs only if the environment is free from “any obstacle to the rail vehicle proceeding along the track, free from any loose part, or free from any defective part, or any combination thereof”, which the examiner reasonably interprets it as a disjunctive “or” limitation- i.e., the prior art only need teach one of these limitations. Zhu already teaches using automated digital evaluation of UAV’s image data to decide whether there are any obstacles preventing the rail vehicle from proceeding along the track, so this alone satisfies at least the “obstacle” portion of the limitation.
In response to H. Examiner respectfully disagree. Zhu’s fundamental principle is using UAV to capture images and video, and using automated digital evaluation of UAV’s image data to control train movement based on the evaluation result. Including the under-train video data from NURK FPV in the automated digital evaluation is a straightforward and predictable modification for improving rail-vehicle safety inspection.
In response to I. Examiner respectfully disagree. NURK FPV teaches that a UAV can fly in the under-train space as claimed in claim 11 while capturing images, and flying under a stationary train is much easier than flying under the moving train shown in the video. In view of this, it would be obvious and predictable design choice for a skilled person to adapt the known under-train flight maneuver from NURK FPV to perform a safety-inspection when the rail vehicle is stationary. Zhu already teaches the safety motivation and inspection workflow to using image-based evaluation feature to detect hazards and control train operation, combining NURK FPV’s under-train captured video with Zhu’s automated digital evaluation feature, would have been a routine and predictable modification for improving rail-vehicle safety inspection, rather than a hindsight reconstruction.
In response to J. Examiner respectfully disagree. Zhu’s fundamental principle is using UAV to capture images and video, and using automated digital evaluation of UAV’s image data to control train movement based on the evaluation result. NURK FPV provides the under-train flight path for capturing under-train images. A person of ordinary skill would recognize that using Zhu’s evaluation and control on under vehicle images obtained via the know flight path is a straightforward way to decide whether to allow or prevent the start of train travel, therefore close the gap.
Conclusion
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAI NMN WANG whose telephone number is (571)270-5633. The examiner can normally be reached Mon-Fri 0800-1700.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rachid Bendidi can be reached on (571) 272-4896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KAI NMN WANG/ Examiner, Art Unit 3664
/REDHWAN K MAWARI/ Primary Examiner, Art Unit 3664
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