DETAILED ACTION
Claims 1, 2, 5-12 and 14-17 are currently pending. Claims 1, 2, 5-12 and 14-17 are maintained in rejection despite the Applicant’s amendments/arguments filed 02/06/2026. A response to Applicant’s arguments can be found at the end of this Office action.
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.
Claim(s) 1, 2, 5-12 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kernwein (US 2020/0207385 A1) in view of Singh (US 2017/0066459 A1).
Referring to Claim 1: Kernwein teaches an end of train device (EOT) suitable of use on a railway vehicle comprising:
a mounting unit for installation on a car (202) of a railway vehicle (Para. [0098]),
an enclosure (130 or 230) housing a plurality of electronic components (Fig. 2), and
a LiDAR sensor (140 or 240a, 240b) configured to illuminate a section of a surrounding area of the railway vehicle, to receive reflected light and to measure time for the reflected light to return to the LiDAR sensor for monitoring purposes of the surrounding area (Para. [0071] reciting LIDAR as an embodiment of sensor 140 and [0096] reciting sensors 240a and 240b as being the same sensor as 140),
a processing unit (234) integrated within the EOT (230) (Fig. 2) (Para. [0098]) and configured to
process data and signals provided by the LiDAR sensor (Para. [0090]) (see also processor 304 used with LiDAR sensor 310 as seen in Fig. 3 and discussed in Para. [0104-0107]),
create an image of the surrounding area (Para. [0082], last sentence)
transmit the image to a head of train device (HOT), remote server or external monitoring system (Para. [0091]).
Kernwein teaches a LIDAR sensor and a processor used to create an image of the surrounding area to be transmitted to the HOT, as noted above, but does not specifically teach creating and transmitting a point cloud in real time. However, Singh teaches a rail track asset survey system, having a laser device (16) configured to create and transmit a point cloud for analysis (Para. [0015], [0096-0097] and [0173]) and a processor configured to process the images in real-time to (Para. [0042]), or in near real-time depending on the computational cost (Para. [0044]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for Kernwein to create and transmit a point cloud from the LIDAR data in real time, as taught by Singh, in order to provide detailed three dimensional track data to the system and allow the user to view track assets in real-time, and thereby increase safety, with a reasonable expectation of success.
Referring to Claim 2: Kernwein teaches the EOT of claim 1, wherein the illumination device comprises a transmitter and a receiver, wherein the transmitter is configured to emit light, and wherein the receiver is configured to receive the reflected light and to measure the time for the reflected light to return to the receiver (“LIDAR”, Para. [0071]).
Referring to Claim 4: Kernwein teaches the EOT of claim 2, wherein the illumination device comprises a LiDAR sensor (Para. [0071]).
Referring to Claim 5: Kernwein teaches the EOT of claim 1, comprising a global positioning system (GPS) receiver (Para. [0072]) or a global navigation satellite system (GNSS) receiver to determine position and orientation of the illumination device (see also Para. [0099] and [0101]).
Referring to Claim 6: Kernwein teaches the EOT of claim 1, comprising a communication module configured to transmit data including the measured time and position data to a head of train device (HOT), a remote server or an external monitoring system for further processing (Para. [0008], [0016] and [0092]) (claim 14).
Referring to Claim 7: Kernwein does not teach that the transmitted data include raw or partially processed time and position data, and wherein the HOT, remote server or external monitoring system is configured to process the time and position data, and to create a point cloud, image and/or reconstruction of the surrounding area. However, Singh teaches a rail track asset survey system, wherein the transmitted data includes raw or partially processed time and position data, and a processor (80), separate from the laser device (16), processes the time and position data in real-time or from a memory (86) (Para. [0155], [0159-0160]) to generate a three dimensional point cloud (Para. [0173]) (see also [0096] and [0097]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for Kernwein to create a point cloud from the LIDAR data using a separate remote processor, as taught by Singh, in order to provide detailed three dimensional track data to the system and user from a convenient processing location with a reasonable expectation of success.
Referring to Claim 8: Kernwein teaches the EOT of claim 6, wherein the communication module (130 or 232) comprises a radio module configured to transmit the data via a radio-based telemetry link (“radio frequency (RF) interface”) to the HOT (120 or 220) (Para. [0078]) (Figs. 1 and 2).
Referring to Claim 9: Kernwein teaches a monitoring system for a railway vehicle, the monitoring system comprising:
a first monitoring device (130 or 230) configured as end of train device (EOT) (Fig. 2),
a second monitoring device (120 or 220),
a communication link (232, 222) between the EOT and the second monitoring device,
wherein the EOT comprises a LiDAR sensor (140 or 240a, 240b) configured to illuminate a section of a surrounding area of the railway vehicle, and to receive reflected light (Para. [0071] reciting LIDAR as an embodiment of sensor 140 and [0096] reciting sensors 240a and 240b as being the same sensor as 140), and
wherein the EOT comprises a processing unit (234) configured to
process data and signals provided by the LiDAR sensor (Para. [0090]) (see also processor 304 used with LiDAR sensor 310 as seen in Fig. 3 and discussed in Para. [0104-0107]),
create an image of the surrounding area (Para. [0082], last sentence)
wherein the EOT is configured to communicate the data and signals, provided by the LiDAR sensor, to the second monitoring device for monitoring of the surrounding area (Para. [0071], [0091] and [0101]).
Kernwein teaches a LIDAR sensor and a processor used to create an image of the surrounding area to be transmitted to the HOT, as noted above, but does not specifically teach creating and transmitting a point cloud in real time. However, Singh teaches a rail track asset survey system, having a laser device (16) configured to create and transmit a point cloud for analysis (Para. [0015], [0096-0097] and [0173]) and a processor configured to process the images in real-time to (Para. [0042]), or in near real-time depending on the computational cost (Para. [0044]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for Kernwein to create and transmit a point cloud from the LIDAR data in real time, as taught by Singh, in order to provide detailed three dimensional track data to the system and allow the user to view track assets in real-time, and thereby increase safety, with a reasonable expectation of success.
Referring to Claim 10: Kernwein teaches the monitoring system of claim 9, wherein the second monitoring device (120 or 220) is configured as a head of train device (HOT), and wherein the EOT (130 or 230) and HOT (120 or 220) are configured to communicate via a radio-based telemetry link (“radio frequency (RF) interface” or 222, 232) (Para. [0078]) (Fig. 1).
Referring to Claim 11: Kernwein teaches the monitoring system of claim 9, wherein the second monitoring device comprises a remote server (Para. [0008], [0016] and [0092]) (claim 14), a remote monitoring system, or an external monitoring system.
Referring to Claim 12: Kernwein teaches the monitoring system of claim 9, wherein the LiDAR sensor comprises a transmitter and a receiver, wherein the transmitter is configured to emit light, and wherein the receiver is configured to receive the reflected light and to measure time for the reflected light to return to the receiver (“LIDAR”, Para. [0071]).
Referring to Claim 14: Kernwein does not teach that the transmitted data include raw or partially processed time and position data, and wherein the HOT, remote server or external monitoring system is configured to process the time and position data, and to create a point cloud, image and/or reconstruction of the surrounding area. However, Singh teaches a rail track asset survey system, wherein the transmitted data includes raw or partially processed time and position data, and a processor (80), separate from the laser device (16), processes the time and position data in real-time or from a memory (86) (Para. [0155], [0159-0160]) to generate a three dimensional point cloud (Para. [0173]) (see also [0096] and [0097]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for Kernwein to create a point cloud from the LIDAR data using a separate remote processor, as taught by Singh, in order to provide detailed three dimensional track data to the system and user from a convenient processing location with a reasonable expectation of success.
Referring to Claim 15: Kernwein further teaches the monitoring system of claim 14, further comprising a processing unit (304) configured to receive and process the raw or partially processed signals and data, wherein the processing unit is included in the second monitoring device (120 or 220) (Para. [0104-0105]) or is a separate processing device (Para. [0008], [0016] and [0092]) (claim 14).
Referring to Claim 16: Kernwein further teaches the monitoring system of claim 9, wherein the first monitoring device comprises a global positioning system (GPS) receiver (Para. [0072]) or a global navigation satellite system (GNSS) receiver to determine position and orientation of the illumination device (see also Para. [0099] and [0101]).
Referring to Claim 17: Kernwein teaches a display screen (e.g., Para. [0082]), but fails to specifically teach a display for visualizing and displaying the point cloud, image and/or reconstruction of the surrounding area in real time. However, Singh teaches a rail track asset survey system, having a display (46) for visualizing and displaying the point cloud (Para. [0015]) (see also [0096] and [0097]), image and/or reconstruction of the surrounding area in real time (Para. [0040]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for Kernwein to create a point cloud from the LIDAR data and display the point cloud in real time, as taught by Singh, in order to provide detailed three dimensional track data to the system, and thereby provide useful inspection results in real time with a reasonable expectation of success (see Singh, Para. [0010]).
Response to Arguments
Regarding amended claims 1 and 9, Applicant argues that Kernwein in view of Singh fails to teach a processing unit integrated within the EOT that is capable of creating a point cloud in real time for detecting objects, accidents or approaching trains for the following reasons.
First, Applicant argues that Kernwein is used for sensing motion of objects and providing indications to an operator while the train is moving in reverse, such as for coupling operations or moving through switches (Kernwein, Para. [0068]). Examiner responds that this reasonably satisfies detecting objects or approaching trains behind the railway vehicle, as claimed. Coupling while moving in reverse involves both detecting objects (e.g., couplers) and detecting approaching trains (e.g., railcars and locomotives having available couplers).
Second, Applicant argues that Singh fails to cure the deficiencies of Kernwein because Singh is not for real-time safety monitoring during train operation and requires docking for continuing data analysis, and therefore, Singh has fundamentally different architecture. Examiner responds that Singh explicitly teaches real-time safety monitoring as follows, “At least one processor may be arranged to process the captured images and/or laser data so as to analyse asset status. The asset surveying system may output asset identification, location and/or status in real-time, e.g. such that there is substantially no delay or negligible delay between when the assets are visible within the sensor field of view, and their detection by the automated asset analyser system. This allows for the operator to view track asset data as the vehicle approaches or passes those assets in real-time.” (emphasis added) (Para. [0042]). While paragraph [0190] of Singh discusses an embodiment for logging real-time image data for subsequent analysis, Singh, taken as a whole, reasonably teaches real-time monitoring.
Third, Applicant argues that Singh’s purpose of asset labeling of fixed infrastructure is distinct from the real-time detection of objects, accidents, or approaching trains as recited in amended claims 1 and 9. Examiner responds that picking out the asset labeling aspect of Singh amounts to a piecemeal analysis of this reference, when Singh also teaches that pixel clusters may be identified as objects based on what the software tool has been trained to recognize (Para. [0164]). Taking the teachings of Kernwein and Singh as a whole, it would suggest training the software to identify a variety of objects, based on the intended application, not necessarily only identifying fixed infrastructure.
Fourth, Applicant argues that the motivation to combine Kernwein and Singh does not address the real-time detection of objects, accidents, or approaching trains as recited in amended claims 1 and 9. Examiner responds that the current rejection in view of Kernwein and Singh addresses this amended language because the primary reference Kernwein discusses identifying objects such as railcars (e.g., Para. [0005] and [0113-0115]) and also discusses identifying trains behind the railway vehicle since moving backward to couple to separate railcars (e.g., Para. [0075]) may be reasonably interpreted as identifying approaching trains.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZACHARY L KUHFUSS whose telephone number is (571)270-7858. The examiner can normally be reached Monday - Friday 10:00am to 6:00 pm CDT.
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/ZACHARY L KUHFUSS/Primary Examiner, Art Unit 3617