DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-60 are pending.
Election/Restrictions
Applicant’s election without traverse of Group I (claims 1-40) in the reply filed on 12/22/2025 is acknowledged.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-9, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mulligan (US Publication No. 2018/0273066).
Mulligan teaches:
Re claim 1. A mobile railway asset monitoring apparatus comprising:
a sensor to gather data associated with a vibration of a mobile railway asset (paragraph [0023]: “a microphone or vibration sensor pack”);
a processor operably coupled to the sensor, the processor configured to calculate a position of at least a portion of the mobile railway asset relative to the sensor based at least in part on the data (paragraphs [0014, 0026 and 0087]: “a suitably configured computing device”; “means to organize and correlate the sensor information and associate relevant parts of the sensor information with a particular wheel or railcar at a particular time and location”; and “the acoustic signature for each wheelset is segregated and then signatures from each microphone in the array are consolidated using digital signal processing techniques.”); and
the processor configured to determine at least one parameter of the mobile railway asset based at least in part on the data from the sensor (paragraphs [0030 and 0087]: “means to provide the operator information, which may be an alert, to the operator that a particular wheel of a particular railcar is anomalous, and that the anomaly, being the difference in sensor information identified at step i) correlates meaningfully with the indication of step h) of a failure precursor, indicating that a particular maintenance or operational action is recommended, and may include in the failure precursor operator information an indication of remaining serviceable life of the particular wheel.”; and “Bearing defects affect both the frequency and amplitude of the patterns which appear in calculated time-frequency visualization maps. Using machine learning classification techniques and reference databases, changes in the time-frequency harmonics are associated to specific bearing defects”).
Re claim 2. Wherein the processor is configured to utilize deconvolution with the data to calculate the position of the at least a portion of the mobile railway asset (paragraph [0062]: “When deployed, each of the above detectors provide data relatable and relevant to a particular wheel on a particular individual axle and side of a particular railcar for all measured train passings.”; paragraph [0063]: “correlation methods can be used to link a measured sensed event at a location with a particular wheel and car. This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”; paragraph [0064]: “the matching of sensor output and wheel can be performed virtually based on available AEI or similar consist information from another source or repository and mapped or correlated and associated with, sensor location, sensed event timing, and sensor reading. For virtual AEI, matching can be performed using any of, but not limited to: passing time, passing date, and number of axles and/or railcars in a train or consist.”; and paragraph [0087]: “the acoustic signature for each wheelset is segregated and then signatures from each microphone in the array are consolidated using digital signal processing techniques. The time-frequency map of each aggregated time signal for each wheelset is then calculated.”).
Re claim 3. Wherein the processor is configured to determine a signal amplitude of the data (paragraph [0087]: “the acoustic signature for each wheelset is segregated and then signatures from each microphone in the array are consolidated using digital signal processing techniques. The time-frequency map of each aggregated time signal for each wheelset is then calculated. Bearing defects affect both the frequency and amplitude of the patterns which appear in calculated time-frequency visualization maps. Using machine learning classification techniques and reference databases, changes in the time-frequency harmonics are associated to specific bearing defects”); and
wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the sensor based at least in part on the signal amplitude (paragraph [0062]: “When deployed, each of the above detectors provide data relatable and relevant to a particular wheel on a particular individual axle and side of a particular railcar for all measured train passings.”; paragraph [0063]: “correlation methods can be used to link a measured sensed event at a location with a particular wheel and car. This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”; paragraph [0064]: “the matching of sensor output and wheel can be performed virtually based on available AEI or similar consist information from another source or repository and mapped or correlated and associated with, sensor location, sensed event timing, and sensor reading.”).
Re claim 5. Wherein the at least a portion of the mobile railway asset includes a hand brake of the mobile railway asset (paragraphs [0036 and 0037]: “hand brake”); and
wherein the at least one parameter of the mobile railway asset includes an orientation of the mobile railway asset (paragraph [0063]: “This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”).
Re claim 6. Wherein the at least a portion of the mobile railway asset includes a brake shoe of the mobile railway asset (paragraph [0036]: “brake beam, brake cylinder, brake side frame liner, brake rigging, brake control valve, or hand brake applied”); and
wherein the at least one parameter of the mobile railway asset includes a brake shoe engagement status (paragraph [0036]).
Re claim 7. Wherein the sensor includes at least one of a microphone, an accelerometer, a strain gauge, and a gyroscope (paragraph [0023]: “a microphone or vibration sensor pack”).
Re claim 8. Wherein the processor is configured to estimate the position of the at least a portion of the mobile railway asset by utilizing at least one of:
a doppler effect technique;
a time-of-flight technique; and
a pattern recognition technique (paragraph [0087]: “acoustic signature”).
Re claim 9. Wherein the processor is operable to receive data indicative of a direction of movement of the mobile railway asset along a track (paragraph [0087]: “direction of the movements”);
wherein the vibration includes a vibration or a change of vibration caused by a track anomaly (track anomalies will inherently cause changes in vibrations of a train passing over the anomalous section of track); and
wherein the at least one parameter includes an orientation of the mobile railway asset (paragraph [0063]: This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”).
Re claim 20. Wherein the processor is configured to estimate the position of the at least a portion of the mobile railway asset in response to a mobile railway asset event (paragraphs [0062]: “When deployed, each of the above detectors provide data relatable and relevant to a particular wheel on a particular individual axle and side of a particular railcar for all measured train passings.”).
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 4, 12, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as applied to claim 1 above, and further in view of Dick et al. (US Publication No. 2020/0317239).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 4) wherein the processor is configured to determine a first time value of a first event of the data and a second time value of a second event of the data that is different than the first time value; and
wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the sensor based at least in part on the difference between the first time value and the second time value; and
(re claim 12) wherein the sensor includes a first sensor and a second sensor;
wherein the data gathered by the sensor includes first data gathered via the first sensor and second data gathered via the second sensor, the second data being different than the first data;
wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the first and second sensors based at least in part on differences between the first and second data; and
wherein the processor is configured to determine the at least one parameter of the mobile railway asset based at least in part on at least one of the first data and the second data of the first and second sensors.
Dick teaches, at paragraph [0045], determining the location of a wheel with a wheel anomaly based on a time difference of arrival algorithm used with sensor devices connected to railroad cars. Triangulating the location of an anomaly allows for such anomalies to be located and corrected more quickly.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 4) wherein the processor is configured to determine a first time value of a first event of the data and a second time value of a second event of the data that is different than the first time value; and wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the sensor based at least in part on the difference between the first time value and the second time value; and (re claim 12) wherein the sensor includes a first sensor and a second sensor; wherein the data gathered by the sensor includes first data gathered via the first sensor and second data gathered via the second sensor, the second data being different than the first data; wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the first and second sensors based at least in part on differences between the first and second data; and wherein the processor is configured to determine the at least one parameter of the mobile railway asset based at least in part on at least one of the first data and the second data of the first and second sensors, with a reasonable expectation of success, since Dick teaches determining the location of a wheel with a wheel anomaly based on a time difference of arrival algorithm used with sensor devices connected to railroad cars. Triangulating the location of an anomaly allows for such anomalies to be located and corrected more quickly.
Mulligan fails to specifically teach: (re claim 14) wherein at least one of the first and second sensors includes a microphone.
Dick teaches, at paragraph [0045], the sensor devices sense sounds, and these sensed sounds can be used to triangulate the location of an anomaly.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 14) wherein at least one of the first and second sensors includes a microphone, with a reasonable expectation of success, since Dick teaches, at paragraph [0045], the sensor devices sense sounds, and these sensed sounds can be used to triangulate the location of an anomaly.
Mulligan fails to specifically teach: (re claim 19) wherein the first sensor includes a first accelerometer and a first microphone; and wherein the second sensor includes a second accelerometer and a second microphone.
Dick teaches, at paragraph [0031], including, in the sensor devices, a sound sensor to detect sounds that are audible to a human ear and an accelerometer to detect vibrations that are not audible to the human ear. This allows for capturing a more complete range of vibrations.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 19) wherein the first sensor includes a first accelerometer and a first microphone; and wherein the second sensor includes a second accelerometer and a second microphone, with a reasonable expectation of success, since Dick teaches including, in the sensor devices, a sound sensor to detect sounds that are audible to a human ear and an accelerometer to detect vibrations that are not audible to the human ear. This allows for capturing a more complete range of vibrations.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as applied to claim 1 above, and further in view of Bradley (US Publication No. 2019/0236859).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 10) wherein the vibration includes a vibration caused by a track anomaly; and wherein the at least one parameter includes an interwheel spacing of the mobile railway asset; and (re claim 11) wherein the at least one parameter of the mobile railway asset includes a ground speed of the mobile railway asset; and wherein the processor is configured to determine the ground speed of the mobile railway asset based at least in part on the interwheel spacing and the data.
Bradley teaches, at paragraph [0051], during calibration, the wheelbase d of a vehicle may be estimated using the delay between acceleration peaks
τ
as a vehicle’s front and rear wheels pass over imperfections, multiplied by the speed
v
of the vehicle. (
d
=
τ
×
v
). During use, the vehicle speed
v
may be determined by dividing the determined wheelbase d by the delay between acceleration peaks
τ
. (
v
=
d
/
τ
). This allows for more useful data to be derived from such acceleration sensors.
In view of Bradley’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 10) wherein the vibration includes a vibration caused by a track anomaly; and wherein the at least one parameter includes an interwheel spacing of the mobile railway asset; and (re claim 11) wherein the at least one parameter of the mobile railway asset includes a ground speed of the mobile railway asset; and wherein the processor is configured to determine the ground speed of the mobile railway asset based at least in part on the interwheel spacing and the data, with a reasonable expectation of success, since Bradley teaches during calibration, the wheelbase d of a vehicle may be estimated using the delay between acceleration peaks
τ
as a vehicle’s front and rear wheels pass over imperfections, multiplied by the speed
v
of the vehicle. During use, the vehicle speed
v
may be determined by dividing the determined wheelbase d by the delay between acceleration peaks
τ
. This allows for more useful data to be derived from such acceleration sensors.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Dick et al. (US Publication No. 2020/0317239) as applied to claim 12 above, and further in view of Hassler et al. (US Publication No. 2015/0362407).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 13) wherein the processor is configured to determine a first signal amplitude of the first data and a second signal amplitude of the second data that is different than the first signal amplitude; and wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the first and second sensors based at least in part on the difference between the first signal amplitude and the second signal amplitude.
Hassler teaches, at paragraphs [0019-0020], a plurality of sensors distributed on a driven axle system can detect the position of an origin of a vibration based on different vibrations being present with amplitudes that differ depending on the position. This allows for the origin of a vibration to be concluded based on the detected vibration characteristics.
In view of Hassler’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 13) wherein the processor is configured to determine a first signal amplitude of the first data and a second signal amplitude of the second data that is different than the first signal amplitude; and wherein the processor is configured to calculate the position of the at least a portion of the mobile railway asset relative to the first and second sensors based at least in part on the difference between the first signal amplitude and the second signal amplitude, with a reasonable expectation of success, since Hassler teaches a plurality of sensors distributed on a driven axle system can detect the position of an origin of a vibration based on different vibrations being present with amplitudes that differ depending on the position. This allows for the origin of a vibration to be concluded based on the detected vibration characteristics.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Dick et al. (US Publication No. 2020/0317239) as applied to claim 12 above, and further in view of Hogg et al. (US Publication No. 2022/0063690).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 15) wherein the first sensor includes a first strain sensor to be associated with a first bogie of the mobile railway asset on a first side thereof, the first strain sensor configured to gather first load data; wherein the second sensor includes a second strain sensor to be associated with a second bogie of the mobile railway asset on an opposite, second side thereof, the second sensor configured to gather second load data; and wherein the processor is configured to determine shifting of a load of the mobile railway asset based at least in part on the first load data and the second load data.
Hogg teaches, at paragraphs [0101 and 0105], strain gauges mounted at each wheel may be used to detect a partially dumped wagon or if a wagon is unbalanced. This provides data on the loading of a train wagon.
In view of Hogg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 15) wherein the first sensor includes a first strain sensor to be associated with a first bogie of the mobile railway asset on a first side thereof, the first strain sensor configured to gather first load data; wherein the second sensor includes a second strain sensor to be associated with a second bogie of the mobile railway asset on an opposite, second side thereof, the second sensor configured to gather second load data; and wherein the processor is configured to determine shifting of a load of the mobile railway asset based at least in part on the first load data and the second load data, with a reasonable expectation of success, since Hogg teaches strain gauges mounted at each wheel may be used to detect a partially dumped wagon or if a wagon is unbalanced. This provides useful data on the loading of a train wagon.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Dick et al. (US Publication No. 2020/0317239) as applied to claim 12 above, and further in view of LeFebvre et al. (US Publication No. 2016/0325767).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 16) further comprising a body to be mounted to a bogie of the mobile railway asset; and wherein the first and second sensors are supported by the body; and (re claim 17) further comprising a first body including the first sensor, the first body to be mounted to a first bogie of the mobile railway asset at a side of the mobile railway asset; a second body including the second sensor, the second body to be mounted to a second bogie of the mobile railway asset at an opposite side of the mobile railway asset.
LeFebvre teaches, at Figs. 2 and 4, and paragraph [0055], wireless sensor nodes 104 may be mounted to each bogie of a railcar, and each wireless sensor node comprises a housing 400 to protect the sensors such as accelerometer 404. Such sensors mounted on a railcar allow for the condition of bearings and wheels to be assessed when not in range of wayside detectors, as mentioned at paragraphs [0003-0004].
In view of LeFebvre’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 16) further comprising a body to be mounted to a bogie of the mobile railway asset; and wherein the first and second sensors are supported by the body; and (re claim 17) further comprising a first body including the first sensor, the first body to be mounted to a first bogie of the mobile railway asset at a side of the mobile railway asset; a second body including the second sensor, the second body to be mounted to a second bogie of the mobile railway asset at an opposite side of the mobile railway asset, with a reasonable expectation of success, since LeFebvre teaches wireless sensor nodes 104 may be mounted to each bogie of a railcar, and each wireless sensor node comprises a housing 400 to protect the sensors such as accelerometer 404. Such sensors mounted on a railcar allow for the condition of bearings and wheels to be assessed when not in range of wayside detectors.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Dick et al. (US Publication No. 2020/0317239) as applied to claim 12 above, and further in view of LeFebvre et al. (US Publication No. 2016/0272228, hereinafter LeFebvre ‘228).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 18) further comprising a body including at least one of the first and second sensors, the body includes a bracket configured to be attached to a bolster of the mobile railway asset.
Dick teaches, at paragraph [0031], sensor device 60 may include a housing 61 to protect the internal components of the sensor device 60.
LeFebvre ‘228 teaches, at the table on page 6, using an accelerometer fixed to a bolster to measure the respective roll, pitch, and yaw angles with respect to fixed-earth coordinates. This provides additional useful data regarding the railcar.
In view of LeFebvre ‘228’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Mulligan, (re claim 18) further comprising a body including at least one of the first and second sensors, the body includes a bracket configured to be attached to a bolster of the mobile railway asset, with a reasonable expectation of success, since Dick teaches sensor device 60 may include a housing 61 to protect the internal components of the sensor device 60; and LeFebvre ‘228 teaches using an accelerometer fixed to a bolster to measure the respective roll, pitch, and yaw angles with respect to fixed-earth coordinates. This provides additional useful data regarding the railcar.
Claims 21-23, 25-29, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) in view of Hogg et al. (US Publication No. 2022/0063690).
Mulligan teaches:
Re claim 21. A method of monitoring a mobile railway asset, the method comprising:
gathering data associated with a vibration of a mobile railway asset (paragraph [0023]: “a microphone or vibration sensor pack”);
calculating a position of at least a portion of the mobile railway asset relative to the sensor based at least in part on the data (paragraphs [0026, 0062, and 0087]: “means to organize and correlate the sensor information and associate relevant parts of the sensor information with a particular wheel or railcar at a particular time and location”; “When deployed, each of the above detectors provide data relatable and relevant to a particular wheel on a particular individual axle and side of a particular railcar for all measured train passings.” and “the acoustic signature for each wheelset is segregated and then signatures from each microphone in the array are consolidated using digital signal processing techniques.”); and
determining at least one parameter of the mobile railway asset based at least in part on the data from the sensor (paragraphs [0030 and 0087]: “means to provide the operator information, which may be an alert, to the operator that a particular wheel of a particular railcar is anomalous, and that the anomaly, being the difference in sensor information identified at step i) correlates meaningfully with the indication of step h) of a failure precursor, indicating that a particular maintenance or operational action is recommended, and may include in the failure precursor operator information an indication of remaining serviceable life of the particular wheel.”; and “Bearing defects affect both the frequency and amplitude of the patterns which appear in calculated time-frequency visualization maps. Using machine learning classification techniques and reference databases, changes in the time-frequency harmonics are associated to specific bearing defects”).
Mulligan fails to specifically teach: (re claim 21) gathering, via a sensor of a bogie of the mobile railway asset, data associated with a vibration of a mobile railway asset.
Hogg teaches, at Figs. 3 and 4, and paragraphs [0102, 104, and 0106], sensor modules 126, including an accelerometer and acoustic sensor, may be located at the bogie of a rail car. Paragraphs [0083-0087] teach such bogie mounted monitoring systems are an improvement over trackside detection systems as they avoid high installation costs, the monitoring system is not fixed to a particular location, allows for at least semi-continuous or real-time monitoring of rail infrastructure, and provides track monitoring.
In view of Hogg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 21) gathering, via a sensor of a bogie of the mobile railway asset, data associated with a vibration of a mobile railway asset, with a reasonable expectation of success, since Hogg teaches sensor modules 126, including an accelerometer and acoustic sensor, may be located at the bogie of a rail car. Such bogie mounted monitoring systems are an improvement over trackside detection systems as they avoid high installation costs, the monitoring system is not fixed to a particular location, allows for at least semi-continuous or real-time monitoring of rail infrastructure, and provides track monitoring.
Mulligan further teaches:
Re claim 22. Wherein calculating the position of the at least a portion of the mobile railway asset relative to the sensor includes utilizing deconvolution with the data (paragraph [0062]: “When deployed, each of the above detectors provide data relatable and relevant to a particular wheel on a particular individual axle and side of a particular railcar for all measured train passings.”; paragraph [0063]: “correlation methods can be used to link a measured sensed event at a location with a particular wheel and car. This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”; paragraph [0064]: “the matching of sensor output and wheel can be performed virtually based on available AEI or similar consist information from another source or repository and mapped or correlated and associated with, sensor location, sensed event timing, and sensor reading. For virtual AEI, matching can be performed using any of, but not limited to: passing time, passing date, and number of axles and/or railcars in a train or consist.”; and paragraph [0087]: “the acoustic signature for each wheelset is segregated and then signatures from each microphone in the array are consolidated using digital signal processing techniques. The time-frequency map of each aggregated time signal for each wheelset is then calculated.”).
Re claim 23. Further comprising determining a signal amplitude of the data (paragraph [0087]: “the acoustic signature for each wheelset is segregated and then signatures from each microphone in the array are consolidated using digital signal processing techniques. The time-frequency map of each aggregated time signal for each wheelset is then calculated. Bearing defects affect both the frequency and amplitude of the patterns which appear in calculated time-frequency visualization maps. Using machine learning classification techniques and reference databases, changes in the time-frequency harmonics are associated to specific bearing defects”); and
wherein calculating the position of the at least a portion of the mobile railway asset relative to the sensor includes calculating the position based at least in part on the signal amplitude (paragraph [0062]: “When deployed, each of the above detectors provide data relatable and relevant to a particular wheel on a particular individual axle and side of a particular railcar for all measured train passings.”; paragraph [0063]: “correlation methods can be used to link a measured sensed event at a location with a particular wheel and car. This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”; paragraph [0064]: “the matching of sensor output and wheel can be performed virtually based on available AEI or similar consist information from another source or repository and mapped or correlated and associated with, sensor location, sensed event timing, and sensor reading.”).
Re claim 25. Wherein the at least a portion of the mobile railway asset includes a hand brake (paragraphs [0036 and 0037]: “hand brake”); and
wherein the at least one parameter of the mobile railway asset includes an orientation of the mobile railway asset (paragraph [0063]: “This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”).
Re claim 26. wherein the at least a portion of the mobile railway asset includes a hand brake of the mobile railway asset (paragraphs [0036 and 0037]: “hand brake”); and
wherein the at least one parameter of the mobile railway asset includes a brake shoe engagement status (paragraph [0036]: “brake beam, brake cylinder, brake side frame liner, brake rigging, brake control valve, or hand brake applied”).
Re claim 27. Wherein gathering, via the sensor, the data includes gathering data utilizing at least one of a microphone, an accelerometer, a strain gauge, and a gyroscope (paragraph [0023]: “a microphone or vibration sensor pack”).
Re claim 28. Wherein estimating the position of the at least a portion of the mobile railway asset relative to the sensor includes utilizing at least one of:
a doppler effect technique;
a time-of-flight technique; and
a pattern recognition technique (paragraph [0087]: “acoustic signature”).
Re claim 29. Further comprising receiving data indicative of a direction of movement of the mobile railway asset along a track (paragraph [0087]: “direction of the movements”);
wherein the vibration includes a vibration or vibration change caused by a track anomaly (track anomalies will inherently cause changes in vibrations of a train passing over the anomalous section of track);
wherein the at least one parameter includes an orientation of the mobile railway asset (paragraph [0063]: This information is used to correlate raw axle and side sensor information with individual wheel locations on a particular railcar, based on inferred railcar orientation within a consist being measured by the particular sensors passed.”); and
wherein determining the at least one parameter of the mobile railway asset includes determining the orientation of the mobile railway asset based at least in part on the direction of movement of the mobile railway asset and the data (paragraph [0063]).
Re claim 40. A non-transitory computer readable medium having instructions which, when executed by a processor, cause the processor to perform operations including the method of claim 21 data (paragraph [0014]: “a suitably configured computing device”).
Claims 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Hogg et al. (US Publication No. 2022/0063690) as applied to claim 21 above, and further in view of Bradley (US Publication No. 2019/0236859).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 30) wherein the vibration includes a vibration caused by a track imperfection; and wherein the at least one parameter includes an interwheel spacing of the mobile railway asset; and (re claim 31) wherein the at least one parameter of the mobile railway asset includes a ground speed of the mobile railway asset; wherein determining the at least one parameter of the mobile railway asset includes determining the ground speed of the mobile railway asset based at least in part on the interwheel spacing and the data.
Bradley teaches, at paragraph [0051], during calibration, the wheelbase d of a vehicle may be estimated using the delay between acceleration peaks
τ
as a vehicle’s front and rear wheels pass over imperfections, multiplied by the speed
v
of the vehicle. (
d
=
τ
×
v
). During use, the vehicle speed
v
may be determined by dividing the determined wheelbase d by the delay between acceleration peaks
τ
. (
v
=
d
/
τ
). This allows for more useful data to be derived from such acceleration sensors.
In view of Bradley’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 30) wherein the vibration includes a vibration caused by a track imperfection; and wherein the at least one parameter includes an interwheel spacing of the mobile railway asset; and (re claim 31) wherein the at least one parameter of the mobile railway asset includes a ground speed of the mobile railway asset; wherein determining the at least one parameter of the mobile railway asset includes determining the ground speed of the mobile railway asset based at least in part on the interwheel spacing and the data, with a reasonable expectation of success, since Bradley teaches during calibration, the wheelbase d of a vehicle may be estimated using the delay between acceleration peaks
τ
as a vehicle’s front and rear wheels pass over imperfections, multiplied by the speed
v
of the vehicle. During use, the vehicle speed
v
may be determined by dividing the determined wheelbase d by the delay between acceleration peaks
τ
. This allows for more useful data to be derived from such acceleration sensors.
Claims 24, 32, and 34-39 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Hogg et al. (US Publication No. 2022/0063690) as applied to claim 21 above, and further in view of Dick et al. (US Publication No. 2020/0317239).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 24) further comprising determining a first time value of a first event in the data and a second time value of a second event in the data that is different than the first time value; and wherein estimating the position of the at least a portion of the mobile railway asset relative to the sensor includes calculating the position based at least in part on the difference between the first time value and the second time value; (re claim 32) wherein the sensor includes a first sensor and a second sensor and the data gathered by the sensor includes first data gathered via the first sensor and second data gathered via the second sensor, the second data being different than the first data; wherein calculating the position of the at least a portion of the mobile railway asset relative to the sensor includes calculating the position of the at least a portion of the mobile railway asset relative to the first and second sensor based at least in part on the differences between the first data and the second data; and wherein determining the at least one parameter of the mobile railway asset includes determining the at least one parameter based at least in part on at least one of the first data and the second data of the first and second sensors; and (re claim 34) further comprising determining a first time value of the first data and a second time value of the second data that is different than the first time value; and wherein calculating the position of the at least a portion of the mobile railway asset relative to the first and second sensors includes calculating the position based at least in part on the difference between the first time value and the second time value.
Dick teaches, at paragraph [0045], determining the location of a wheel with a wheel anomaly based on a time difference of arrival algorithm used with sensor devices connected to railroad cars. Triangulating the location of an anomaly allows for such anomalies to be located and corrected more quickly.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 24) further comprising determining a first time value of a first event in the data and a second time value of a second event in the data that is different than the first time value; and wherein estimating the position of the at least a portion of the mobile railway asset relative to the sensor includes calculating the position based at least in part on the difference between the first time value and the second time value; (re claim 32) wherein the sensor includes a first sensor and a second sensor and the data gathered by the sensor includes first data gathered via the first sensor and second data gathered via the second sensor, the second data being different than the first data; wherein calculating the position of the at least a portion of the mobile railway asset relative to the sensor includes calculating the position of the at least a portion of the mobile railway asset relative to the first and second sensor based at least in part on the differences between the first data and the second data; and wherein determining the at least one parameter of the mobile railway asset includes determining the at least one parameter based at least in part on at least one of the first data and the second data of the first and second sensors; and (re claim 34) further comprising determining a first time value of the first data and a second time value of the second data that is different than the first time value; and wherein calculating the position of the at least a portion of the mobile railway asset relative to the first and second sensors includes calculating the position based at least in part on the difference between the first time value and the second time value, with a reasonable expectation of success, since Dick teaches determining the location of a wheel with a wheel anomaly based on a time difference of arrival algorithm used with sensor devices connected to railroad cars. Triangulating the location of an anomaly allows for such anomalies to be located and corrected more quickly.
Mulligan fails to specifically teach: (re claim 35) wherein at least one of the first and second sensors includes a microphone.
Dick teaches, at paragraph [0045], the sensor devices sense sounds, and these sensed sounds can be used to triangulate the location of an anomaly.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 35) wherein at least one of the first and second sensors includes a microphone, with a reasonable expectation of success, since Dick teaches, at paragraph [0045], the sensor devices sense sounds, and these sensed sounds can be used to triangulate the location of an anomaly.
Mulligan fails to specifically teach: (re claim 36) wherein at least one of the first and second sensors includes an accelerometer.
Dick teaches, at paragraph [0031], the sensor devices include an accelerometer to detect sounds/vibrations that are not audible to the human ear. These sensed sounds/vibrations can be used to triangulate the location of an anomaly.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 36) wherein at least one of the first and second sensors includes an accelerometer, with a reasonable expectation of success, since Dick teaches the sensor devices include an accelerometer to detect sounds/vibrations that are not audible to the human ear. These sensed sounds/vibrations can be used to triangulate the location of an anomaly.
Mulligan fails to specifically teach: (re claim 37) wherein the first sensor includes a first strain sensor associated with a first bogie of the mobile railway asset on a first side thereof, the first strain sensor configured to gather first load data; wherein the second sensor includes a second strain sensor associated with a second bogie of the mobile railway asset on an opposite, second side thereof, the second sensor configured to gather second load data; and determining shifting of a load of the mobile railway asset based at least in part on the first load data and the second load data; and (re claim 38) wherein the first sensor is associated with a first bogie of the mobile railway asset at a side of the mobile railway asset and the second sensor is associated with a second bogie of the mobile railway asset at an opposite side of the mobile railway asset.
Hogg teaches, at paragraphs [0101 and 0105], strain gauges mounted at each wheel may be used to detect a partially dumped wagon or if a wagon is unbalanced. This provides data on the loading of a train wagon.
In view of Hogg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 37) wherein the first sensor includes a first strain sensor associated with a first bogie of the mobile railway asset on a first side thereof, the first strain sensor configured to gather first load data; wherein the second sensor includes a second strain sensor associated with a second bogie of the mobile railway asset on an opposite, second side thereof, the second sensor configured to gather second load data; and determining shifting of a load of the mobile railway asset based at least in part on the first load data and the second load data; and (re claim 38) wherein the first sensor is associated with a first bogie of the mobile railway asset at a side of the mobile railway asset and the second sensor is associated with a second bogie of the mobile railway asset at an opposite side of the mobile railway asset, with a reasonable expectation of success, since Hogg teaches strain gauges mounted at each wheel may be used to detect a partially dumped wagon or if a wagon is unbalanced. This provides useful data on the loading of a train wagon.
Mulligan fails to specifically teach: (re claim 39) wherein gathering the first data includes gathering the first data via an first accelerometer and a first microphone of the first sensor; and wherein gathering the second data includes gathering the second data via a second accelerometer and a second microphone of the second sensor.
Dick teaches, at paragraph [0031], including, in the sensor devices, a sound sensor to detect sounds that are audible to a human ear and an accelerometer to detect vibrations that are not audible to the human ear. This allows for capturing a more complete range of vibrations.
In view of Dick’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 39) wherein gathering the first data includes gathering the first data via an first accelerometer and a first microphone of the first sensor; and wherein gathering the second data includes gathering the second data via a second accelerometer and a second microphone of the second sensor, with a reasonable expectation of success, since Dick teaches including, in the sensor devices, a sound sensor to detect sounds that are audible to a human ear and an accelerometer to detect vibrations that are not audible to the human ear. This allows for capturing a more complete range of vibrations.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Mulligan (US Publication No. 2018/0273066) as modified by Hogg et al. (US Publication No. 2022/0063690) and Dick et al. (US Publication No. 2020/0317239) as applied to claim 32 above, and further in view of Hassler et al. (US Publication No. 2015/0362407).
The teachings of Mulligan have been discussed above. Mulligan fails to specifically teach: (re claim 33) further comprising determining a first signal amplitude of the first data and a second signal amplitude of the second data that is different from the first signal amplitude; and wherein estimating the position of the at least a portion of the mobile railway asset relative to the first and second sensors includes calculating the position based at least in part on the difference between the first signal amplitude and the second signal amplitude.
Hassler teaches, at paragraphs [0019-0020], a plurality of sensors distributed on a driven axle system can detect the position of an origin of a vibration based on different vibrations being present with amplitudes that differ depending on the position. This allows for the origin of a vibration to be concluded based on the detected vibration characteristics.
In view of Hassler’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Mulligan, (re claim 33) further comprising determining a first signal amplitude of the first data and a second signal amplitude of the second data that is different from the first signal amplitude; and wherein estimating the position of the at least a portion of the mobile railway asset relative to the first and second sensors includes calculating the position based at least in part on the difference between the first signal amplitude and the second signal amplitude, with a reasonable expectation of success, since Hassler teaches a plurality of sensors distributed on a driven axle system can detect the position of an origin of a vibration based on different vibrations being present with amplitudes that differ depending on the position. This allows for the origin of a vibration to be concluded based on the detected vibration characteristics.
Conclusion
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/SPENCER D PATTON/ Primary Examiner, Art Unit 3656