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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 7, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220410955 A1 (Gao).
Regarding claims 1, 15, and 20: Gao teaches a method comprising: receiving, at a computing system (computing devices 106), sensor data from a plurality of sensors (sensor 120, load receiving element 112, force sensor 116); determining, based on the sensor data, acceleration data and force data corresponding to the vehicle as the vehicle moves (“In one embodiment, a vibration sensor (an example form of motion sensor 124) may be installed on the load receiving element 112 and configured to generate a voltage signal that is proportional to the amount of acceleration or vibration of the load receiving element 112. One example device may be an accelerometer-based micro-electro-mechanical system (MEMS) device, which can detect acceleration in two or three axis.”, Paragraph [0030]), wherein the force data represents a quantity of force applied to vehicle to cause the vehicle to move (“additional sensors and circuitry may be used to detect different effects along different force vectors, including horizontal forces, bending moment, lateral forces, etc.”, Paragraph [0022]); and estimating, based on the acceleration data and force data for the vehicle, a mass of the vehicle (“weighing system 100 may include at least one force sensor 116 configured to measure shear forces applied to the top surface of load receiving element 112, thereby measuring the mass of the merchandise in the vehicle 102.”, Paragraph [0021]).
Gao doesn’t directly teach that the vehicle is a railway vehicle moving along a track. However, Gao teaches that the vehicle is a shopping cart, which is also a vehicle for transporting freight.
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the vehicle of Gao with a railway vehicle. This is because they are both vehicles for transporting goods.
Claim 15 is a system to accomplish the method of claim 1 comprising substantially the same limitations. Claim 20 is non-transitory computer readable medium with instructions to accomplish the method of claim 1 comprising substantially the same limitations with the addition of one or more processors, which is also taught by Gao (processor 114).
Therefore, the rejection of claim 1 also applies to claims 15 and 20 mutatis mutandis.
Regarding claim 7: Gao teaches the method of claim 1 (see above), further comprising: adjusting a control strategy for the railway vehicle based on the estimated mass of the railway vehicle (“The processor 114 of the weighing system 100 may generate a signal to the force sensor 116 and/or the angle/orientation sensor 118 in order to temporarily inhibit a weight measurement to be taken by the weighing system 100. Simultaneously, the processor 114 may be configured to generate signals to an interface module 122 by blanking a c01Tesponding digital weight indicator (i.e., displaying non-metro-logically significant readings) of weighing system 100, or returning values to any interfaced system or device that cannot be interpreted as a measurement, or inhibiting a weight measurement from being recorded for a predetermined period of time.”, Paragraph [0027]).
Claim(s) 2-3, 9, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220410955 A1 (Gao) as applied to claims 1, 7, 15, and 20 above, and further in view of DE 102021127654 A1 (Dieckmann).
Regarding claim 2: Gao teaches the method of claim 1 (see above), wherein the plurality of sensors comprises an inertial measurement unit (motion sensor 124), wherein the inertial measurement unit is configured to measure acceleration of the railway vehicle (“weighing system 100 may use a motion sensor 124 (e.g., 1-axis, 2-axis, 3-axis or multi-axis accelerometer,”, Paragraph [0029]).
Gao dose not teach a torque sensor, wherein the torque sensor is configured to measure a torque output by a motor coupled to the railway vehicle.
However, Dieckmann teaches a torque sensor (unnamed, but referred to in "The sensors used for this are an acceleration sensor, with which the acceleration a of the vehicle can be measured in the longitudinal direction of the vehicle, and a sensor, with which the driving force currently acting on the vehicle can be measured directly or indirectly."), wherein the torque sensor is configured to measure a torque output by a motor coupled to the railway vehicle (“the torque M of the drive motor can be called up from the on-board computer in real time”).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the sensor system of Gao with the torque sensing of Dieckmann. This is because they are both systems to measure the weight of a vehicle. This is important to calculate the vehicle mass.
Regarding claim 3: Modified Gao teaches the method of claim 2, but does not directly teach that the motor is coupled to an axle of the railway vehicle via a bearing adapter.
Applicant has not disclosed that a bearing adapter provides an advantage, is used for a particular purpose, or solves a stated problem other than the well-known and unsurprising function of connecting the motor to the axle.
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the motor and axle system of Gao with a bearing adapter. This is because one of ordinary skill in the art would have expected a bearing adapter to be one of several straightforward ways of connecting a motor to an axle.
Regarding claim 9: Gao teaches the method of claim 1 (see above), wherein but does not directly teach that the vehicle is a freight railway vehicle having a motor coupled to an axle.
However, Dieckmann teaches a railway vehicle that carries a payload (“the mass or the weight of the complete vehicle 18 plus its current payload) having a motor (drive motor) coupled to an axle (“The value of the force F required to determine the total mass m resting on all vehicle axles using the formula m = F/a can be determined using the torque of the vehicle's drive motor.”).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the vehicle of Gao with the freight railway vehicle of Dieckmann. This is because they are both vehicles that carry a payload. This is important in order to apply the sensing system to a vehicle not propelled by a human.
Regarding claim 16: Gao teaches the system of claim 15, but does not teach that it further comprises: a motor coupled to an axle of the railway vehicle via a bearing adapter; and wherein the computing device is configured to: determine the force data based on sensor data representing torque generated by the motor as the railway vehicle moves along the track.
However, Dieckmann teaches a torque sensor (unnamed, but referred to in "The sensors used for this are an acceleration sensor, with which the acceleration a of the vehicle can be measured in the longitudinal direction of the vehicle, and a sensor, with which the driving force currently acting on the vehicle can be measured directly or indirectly."), wherein the torque sensor is configured to measure a torque output by a motor coupled to the railway vehicle (“the torque M of the drive motor can be called up from the on-board computer in real time”).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the sensor system of Gao with the torque sensing of Dieckmann. This is because they are both systems to measure the weight of a vehicle. This is important to calculate the vehicle mass.
Additionally, Applicant has not disclosed that a bearing adapter provides an advantage, is used for a particular purpose, or solves a stated problem other than the well-known and unsurprising function of connecting the motor to the axle.
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the motor and axle system of Gao with a bearing adapter. This is because one of ordinary skill in the art would have expected a bearing adapter to be one of several straightforward ways of connecting a motor to an axle.
Regarding claim 17: Modified Gao teaches the system of claim 16, wherein the computing device is further configured to: adjust one or more control parameters of the motor based on the estimated mass of the railway vehicle (Gao: “The processor 114 of the weighing system 100 may generate a signal to the force sensor 116 and/or the angle/orientation sensor 118 in order to temporarily inhibit a weight measurement to be taken by the weighing system 100. Simultaneously, the processor 114 may be configured to generate signals to an interface module 122 by blanking a c01Tesponding digital weight indicator (i.e., displaying non-metro-logically significant readings) of weighing system 100, or returning values to any interfaced system or device that cannot be interpreted as a measurement, or inhibiting a weight measurement from being recorded for a predetermined period of time.”, Paragraph [0027]).
Claim(s) 4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220410955 A1 (Gao) as applied to claim 1 above, and further in view of US 8188385 B2 (Wolfgang).
Regarding claim 4: Gao teaches the method of claim 1 (see above), further comprising: generating a profile corresponding to the railway vehicle, wherein the profile represents at least the acceleration data and force data corresponding to the railway vehicle as the railway vehicle moves along the track and a predefined mass of the railway vehicle (“computing devices 106 may be configured to provide functionalities for any connected devices such as sharing data or provisioning resources among multiple client devices, or performing computations for each connected client device.”, Paragraph [0015]).
Gao does not directly teach that the predefined mass of the railway vehicle represents a weight of the railway vehicle in an empty state.
However, Wolfgang teaches that “by subtraction of a known unladen vehicle weight or a total weight determined earlier, the weight of the load or the load change can be determined”.
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the data processing of Modified Gao with the unladen vehicle weight variable of Wolfgang. This is because they are both methods of determining the weight of a vehicle. This is important in order to calculate the mass of a vehicle and its payload.
Regarding claim 6: Gao teaches the method of claim 4, but does not directly teach that it is further comprising: comparing the estimated mass of the railway vehicle to the predefined mass of the railway vehicle to determine a weight of cargo carried by the railway vehicle.
However, Wolfgang teaches that “by subtraction of a known unladen vehicle weight or a total weight determined earlier, the weight of the load or the load change can be determined”.
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the data processing of Modified Gao with the unladen vehicle weight variable of Wolfgang. This is because they are both methods of determining the weight of a vehicle. This is important in order to calculate the mass of a vehicle and its payload.
Claim(s) 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220410955 A1 (Gao) and DE 102021127654 A1 (Dieckmann) as applied to claims 16-17 above, and further in view of US 8188385 B2 (Wolfgang).
Regarding claim 18: Modified Gao teaches the system of 16, but does not directly teach that the computing device is further configured to: compare the estimated mass of the railway vehicle to a predefined mass of the railway vehicle to determine a weight of cargo being carried by the railway vehicle, wherein the predefined mass of the railway vehicle represents a weight of the railway vehicle in an empty state.
However, Wolfgang teaches that “by subtraction of a known unladen vehicle weight or a total weight determined earlier, the weight of the load or the load change can be determined”.
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the data processing of Modified Gao with the unladen vehicle weight variable of Wolfgang. This is because they are both methods of determining the weight of a vehicle. This is important in order to calculate the mass of a vehicle and its payload.
Regarding claim 19: Modified Gao teaches the system of claim 18, wherein the computing device is further configured to: communicate the weight of cargo being carried by the railway vehicle to a remote computing device via a wireless network (Gao: communication network 108 may include a wireless data network (Paragraph [0016]).
Allowable Subject Matter
Claims 5, 8, and 10-14 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 5: Modified Gao teaches the method of claim 4 (see above), but does not teach further comprising: processing sensor data from the plurality of sensors using a Kalman filter; and wherein generating the profile corresponding to the railway vehicle comprises: generating the profile corresponding to the railway vehicle based on processing the sensor data from the plurality of sensors using the Kalman filter.
Neither reference of Modified Gao teaches a sensor data processing method using a Kalman filter. Additionally, no reference previously relied upon or later cited as relevant to the claimed invention teaches a filter similar to the process of a Kalman filter.
Therefore, the limitations of claim 5 are novel and non-obvious.
Regarding claim 8: Gao teaches the method of claim 1 (see above), but does not teach further comprising: causing the railway vehicle to move along the track according to a predefined trajectory, wherein the predefined trajectory comprises moving in a first direction along the track for a first distance and subsequently moving in a second direction along the track for a second distance, wherein the second direction is opposite of the first direction; and wherein determining acceleration data and force data corresponding to the railway vehicle as the railway vehicle moves along the track comprises: determining an average acceleration of the railway vehicle and an average force applied to the railway vehicle using sensor data obtained as the railway vehicle moved along the track according to the predefined trajectory.
Gao does not teach a predefined trajectory, a first direction, a second direction, or determining an average acceleration or an average force. Additionally, while a track taught by prior art may be interpreted as a predetermined trajectory, no reference previously relied on or later cited as relevant art teaches a first or second direction or determining an average acceleration and an average force.
Therefore, the limitations of claim 8 are novel and non-obvious.
Regarding claim 10: Modified Gao teaches the method of claim 9 (see above), but does not teach further comprising: determining, based on the sensor data, the railway vehicle is located proximate a drop zone, wherein the drop zone is a target destination for deploying cargo carried by the railway vehicle; and triggering, based on determining that the railway vehicle is located proximate the drop zone, an automatic release of the cargo carried by the railway vehicle at the drop zone.
Neither reference in Modified Gao, nor any other reference cited as relevant prior art, teaches determining a drop zone and deploying cargo automatically based on the location of the railway vehicle.
Therefore, the limitations of claim 10 are novel and non-obvious.
Claims 11-14 would also be allowable for the same reasons due to their dependence on claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BR 112020012799 B1 teaches a railway vehicle and a method for inspecting a track section by means of a railway vehicle.
WO 2023007273 A1 teaches a system and method for diagnosing abnormal bending in railway rails.
KR 20220138721 A teaches a system and a method for controlling a cabin lamp of a railroad vehicle, and a recording medium recording a computer-readable program for executing the method for controlling a cabin lamp of a railroad vehicle.
JP 2019049095 A teaches a deflection measuring device for a railroad bridge including an acceleration sensor and an arithmetic unit.
KR 20170134086 A teaches a lateral force measurement device of a railroad vehicle.
EP 2647543 B1 teaches a system for detecting characteristics of passing rail vehicles on a rail track, wherein at least one measuring unit is provided for measuring the temperature of wheelsets of a passing rail vehicle.
EP 2602168 B1 teaches a system of diagnosing railway bogies problems or defects, such as wheel flats, and the analysis and communication of the findings.
JP 5547791 B2 teaches a railway vehicle that includes a vehicle body, a wheel that can rotate so as to move the vehicle body, an axle that rotatably supports the wheel, and an axle box that supports the axle. The axle box has an acceleration measuring device for measuring the acceleration of the vehicle body.
JP 2012225883 A teaches a wireless magnetic field measurement device in a railway vehicle capable of promptly measuring a magnetic field in the railway vehicle.
KR 20110124054 A teaches a load measuring system and method for a railway vehicle to measure the load of a railway vehicle in real time from information transmitted from sensors and measuring instruments installed in the railway vehicle.
GB 2443646 A teaches an automated system for inspecting sets of railway tracks for defects is mounted on a railway vehicle for travelling along a set of rails.
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/JULIA FITZPATRICK/Examiner, Art Unit 2855
/LAURA MARTIN SWEENEY/Supervisory Patent Examiner, Art Unit 2855
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