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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/06/2026 has been entered.
Response to Arguments
Applicant’s arguments, see Remarks, filed 07/06/2026, with respect to the rejection(s) of claims 1 and 15 under 35 U.S.C. 103 in view of McKenney (US 20220242465 A1) and Wojtech (Object Detection using LiDAR; 2020) have been fully considered and are persuasive in light of the amendments. McKenney (US 20220242465 A1) teaches motion analysis to determine direction and speed of an object using lidar and also teaches determining if an object has come to rest in the monitored area which is viewed as a transition of state. However, they do not explicitly teach determining, from the three-dimensional object location data acquired at multiple times, a state of a detected object within a region of interest and detecting a transition of the detected object between states within the region of interest. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of McKenney (US 20220242465 A1) and Robinson (US 20230054759 A1).
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.
Claims 1-7, 9, 10, 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over McKenney (US 20220242465 A1) in view of Robinson (US 20230054759 A1).
With respect to claims 1 & 15, McKenney teaches, providing one or more LIDAR sensors operable to detect one or more objects of interest (Para. [0023] “the system can comprise some number of intrusion detection sensor nodes which make use of more than one sensing modality to provide verification and/or refinement of intrusion detection; these sensing modalities may be visible light, infrared, microwaves, lidar, radar, acoustic, or any other means of sensing.) in a field of interest (Abstract teaches “One or more sensing devices can be used in combination with computer processing capabilities to monitor an area for intrusions or obstacles.” (i.e. area is the field of interest.))
And providing logic and control circuitry in communication with the one or more LIDAR sensors, (Para. [0051] “Embodiments of intrusion detection sensors 100 are depicted in the form of a vibration sensor 200, a visible and/or long wave infrared (LWIR) camera 205, a short-wave infrared (SWIR) light detection and ranging (LIDAR) system 210, and a microwave radio detection and ranging (RADAR) sensor 215. Sensor signals captured by the intrusion detection sensors 100 can be delivered in parallel to two or more redundant controllers 220. The use of redundant controllers 220 provides for continued operation even if one controller fails.” (i.e. the redundant controllers are considered logic and control circuitry.)
operating the logic and control circuitry to: generate three-dimensional object location data for each detected object from image data captured by the LIDAR sensors; (Para. [0074] teaches “The first step 1210 uses object recognition to identify the object based on the 2D image or 3D point cloud;” Para. [0076] teaches “For example, the object's location can be analyzed in step 1230 to determine whether the blob is within a geo-fence defining at least one intrusion zone;”)
And determine information associated with the one or more detected objects within the field of interest, including tracking moving objects as they traverse through the field of interest and deriving one or more parameters including speed and direction of movement based on the three-dimensional object location data. (Para. [0074] teaches “The first step 1210 uses object recognition to identify the object based on the 2D image or 3D point cloud” Para. [0075] teaches “The identified and sized object then undergoes motion analysis in step 1220 to determine its speed and direction;” (i.e. object recognition, speed, and direction are considered information associated with a detected object.))
McKenney does not explicitly teach,
determining, from the three-dimensional object location data acquired at multiple times, a state of a detected object within a region of interest and detecting a transition of the detected object between states within the region of interest, and deriving one or more parameters including speed and direction of movement based on the three-dimensional object location data acquired at multiple times.
Robinson teaches,
determining, from the three-dimensional object location data acquired at multiple times, a state of a detected object within a region of interest and detecting a transition of the detected object between states within the region of interest, and deriving one or more parameters including speed and direction of movement based on the three-dimensional object location data acquired at multiple times. (Para. [003] teaches “In contrast to these conventional approaches, the current systems and methods are capable of tracking a velocity state of detected objects or obstacles using LiDAR data. For example, using LiDAR data alone, an iterative closest point (ICP) algorithm may be used to determine a current state of detected objects for a current frame and a Kalman filter may be used to maintain a tracked state of the one or more objects detected over time. Embodiments of the present disclosure include a system configured to estimate velocity for one or more detected objects, compare the estimated velocity to one or more previous tracked states for previously detected objects, determine that the detected objects corresponds to a certain previously detected object, and update the tracked state for the previously detected object with the estimated velocity.”(i.e. velocity includes direction of travel) Para. [0018] teaches “. For example, one or more LiDAR sensors may generate 3D LiDAR data representative of their field of view or sensory field—e.g., the LiDAR data may represent a standard spherical projection or other projection type.” (i.e. 3d object data in a field of view is viewed as a region of interest.) Para. [0043] teaches “Once the corresponding tracked state is identified, the tracked state manager 112 (or other component) may update, using the estimated velocity, the tracked object state corresponding to the previously detected object that has the highest probability of corresponding to the object to generate an updated tracked object state.” (i.e. updated state is viewed as transition between states.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify McKenney with determining, from the three-dimensional object location data acquired at multiple times, a state of a detected object within a region of interest and detecting a transition of the detected object between states within the region of interest, and deriving one or more parameters including speed and direction of movement based on the three-dimensional object location data acquired at multiple times such as that of Robinson.
One of ordinary skill would have been motivated to modify McKenney, because the motion analysis as seen in McKenney Para. [0075] would require multiple frames/images of the object to be taken at multiple times to determine the speed and direction of the object as seen in Robinson. Furthermore, McKenney determines when an object comes to rest thus determining when an object changes from an active to an at rest state. Lastly, determining this information would prevent collisions in the region of interest. Therefore, it would be obvious to combine the references because McKenney suggests determining a speed and a change of state of an object as done in Robinson and to prevent collisions in the region of interest.
With respect to claims 2 & 16, McKenney further teaches, the system of claim 1 & the method of claim 15, wherein the field of interest comprises one or more areas of interest. (Para. [0024] “These sensing systems may be stationary, mounted to observe some portion of a railyard, or mobile, mounted upon various vehicles, to provide safety and/or security information to the vehicle and/or its operators” (i.e. the sensors observe a portion of the railyard where the portion is considered the field of interest and the railyard is the area of interest.)),
With respect to claims 3 & 17, McKenney further teaches, the system of claim 2 & the method of claim 16, wherein respective fields of view of the one or more LIDAR sensors overlap with the one or more areas of interest. (Para. [0024] teaches “These sensing systems may be stationary, mounted to observe some portion of a railyard, or mobile, mounted upon various vehicles, to provide safety and/or security information to the vehicle and/or its operators” (i.e. Fig. 16 shows lidar systems 1620 overlooking the railyard.)
With respect to claim 4 & 18, McKenney further teaches, the system of claim 1 & the method of claim 16, wherein the field of interest comprises an at-grade crossing, a station, a platform, a bridge, a tunnel, other railroad right-of-way area, area adjacent to a railroad right-of-way area, or combinations thereof. (Fig(s). 7-10 show that the lidar sensors can have up to 360-degree views and Fig. 16 shows a bridge/tunnel, a railroad right of way area and an area adjacent to a railroad right-of-way area.)
With respect to claim 5 & 19, McKenney further teaches the system of claim 1 & the method of claim 15, wherein the information associated with the one or more detected objects comprises behaviors of interest. (Para. [0075] teaches “The identified and sized object then undergoes motion analysis in step 1220 to determine its speed and direction;” (i.e. speed, and direction are considered behaviors of interest of the object.))
With respect to claim 6, McKenney further teaches the system of claim 5, wherein behaviors of interest comprises velocity, direction, location, duration, or combinations thereof. (Para. [0075] teaches “The identified and sized object then undergoes motion analysis in step 1220 to determine its speed and direction;” Para. [0076] teaches “the object's location can be analyzed in step 1230 to determine whether the blob is within a geo-fence defining at least one intrusion zone; if the blob is not in any intrusion zone the analysis completes with a negative result (e.g., not violated) provided to the fuzzy expert system.”
With respect to claim 7, McKenney further teaches, the system of claim 1, wherein the logic and control circuitry is further operable to generate alerts, generate notifications, generate an event log, or combinations thereof upon detection of an object. (Para. [0002] teaches “In an embodiment, a sensor system is provided with multiple solutions to detect intrusions of people and/or objects into a railroad right-of-way and alert appropriate persons of such events.”)
With respect to claim 9, McKenney further teaches, the system of claim 1, wherein the logic and control circuitry is configured to detect a vehicle incursion into the field of interest. (Abstract teaches “An illustrative area is part of a railyard or railroad track, which can be monitored for intrusions by foot, vehicle, or even by inanimate objects such as falling rocks from a track cut.”)
With respect to claim 10, McKenney further teaches, the system of claim 1, wherein the logic and control circuitry is configured to detect an object entering and/or exiting the field of interest. (Para. [0002] teaches “In an embodiment, a sensor system is provided with multiple solutions to detect intrusions of people and/or objects into a railroad right-of-way and alert appropriate persons of such events.” (i.e. intrusions are seen as an object entering a field of view))
With respect to claim 13, McKenney further teaches, the system of claim 1, wherein the logic and control circuitry is configured to detect loitering of an object within the field of interest. (Para. [0056] teaches “This allows detection of dynamic intrusions as objects actively enter the restricted space, as well as static intrusions, once the objects come to rest.” (i.e. a static intrusion is seen as the loitering of an object.)
With respect to claim 14, the system of claim 1, wherein the logic and control circuitry is configured to detect a suspicious object within the field of interest. (Para. [0051] teaches “depicts one embodiment of a remote IDS node 105, in this case an intrusion (e.g., falling objects, vehicle, animal, people, etc.) activity monitoring node.” (i.e. an animal is seen as a suspicious object because as seen in Para. [0077] of the specification a suspicious object is a non-train non-person object.)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over McKenney (US 20220242465 A1) and Robinson (US 20230054759 A1) as applied to claim 1 above, and further in view of Hilleary (US 20160200334 A1).
With respect to claim 8, McKenney does not explicitly teach, wherein the logic and control circuitry is configured to detect a crossing gate malfunction.
Hilleary teaches, wherein the logic and control circuitry is configured to detect a crossing gate malfunction. (Para. [0044] teaches “The video analytic system 70 can use the same detection techniques described above to detect and signal error conditions or malfunctions of the crossing gates 80.” (i.e. the analytic system 70 is viewed as logic and control circuitry))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify McKenney wherein the logic and control circuitry is configured to detect a crossing gate malfunction such as that of Hilleary. One of ordinary skill would have been motivated to modify McKenney, because as seen in Para. [0044] of Hilleary detecting a crossing gate malfunction would allow for the proper maintenance to be performed on the crossing gate to get the crossing back up and running properly after a breakdown.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over McKenney (US 20220242465 A1) and Robinson (US 20230054759 A1) as applied to claim 1 above, and further in view of Lucas (US 20210107540 A1).
With respect to claim 11,
McKenney does not explicitly teach, wherein the logic and control circuitry is configured to detect a near-miss of a train with an object.
Lucas teaches, wherein the logic and control circuitry is configured to detect a near-miss of a train with an object. (Para. [0013] teaches “A warning signal is communicated from the wayside cameras responsive to the wayside cameras detecting the presence of an obstruction in the crossing. This warning signal can be communicated to a centralized control center (e.g., a back office server), which then communicates the warning to rail vehicle systems heading toward the location of the obstruction. The rail vehicle systems having positive train control systems and headed toward the crossing receive the warning and the positive train control systems automatically apply brakes of the rail vehicle systems. This prevents the rail vehicle systems from entering the crossing having the obstruction and colliding with the obstruction.” (i.e. This is seen as a near miss as the vehicles are controlled to avoid the object in the crossing.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify McKenney wherein the logic and control circuitry is configured to detect a near-miss of a train with an object such as that of Lucas. One of ordinary skill would have been motivated to modify McKenney, because detecting a near miss can prevent further collisions as seen in para. [0044] of Lucas “For example, the positive train control system onboard a vehicle can receive the warning bulletin and automatically engage brakes to slow or stop movement of the vehicle (and prevent a collision with the detected obstruction). As another example, another vehicle control system may engage brakes of the vehicle, may change which route the vehicle is traveling on, or the like, to avoid collision with the detected obstruction.”
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over McKenney (US 20220242465 A1) and Robinson (US 20230054759 A1) as applied to claim 1 above, and further in view of Dolberg (US 20210279488 A1).
With respect to claim 12, McKenney does not explicitly teach, wherein the logic and control circuitry is configured to detect a collision of a train with an object.
Dolberg teaches, wherein the logic and control circuitry is configured to detect a collision of a train with an object. (Para. [0212] teaches “(b) improved recording and alarm provisions including accident, collision, derailment and damage assessment utilizing object and obstacle detection, tracking and classification, based on some embodiments of the present invention;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify McKenney wherein the logic and control circuitry is configured to detect a collision of a train with an object such as that of Dolberg. One of ordinary skill would have been motivated to modify McKenney, because detecting a collision can allow for an alarm or notification to assist the parties involved. Furthermore, it can allow for better data management as seen in para. [0212] of Dolberg “(c) the smart storage used selectively for logging true alarms and false alarms and upload them according to train position and discard it when not relevant through a FIFO mechanism.”
Conclusion
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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