DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2 June 2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-5 and 7-20 are pending in this application.
Claim 6 is cancelled.
Claims 1, 11, and 15 are amended.
Claims 1-5 and 7-20 are presented for examination.
Response to Applicant’s Amendments
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1) in view of Miki et al. (US Publication 2020/0191824 A1) and in further view of Graves (US Publication 2025/0012576 A1).
Regarding claim 1, Sorrells teaches a system comprising: an antenna (Sorrells: Para. 14; antenna); an accelerometer disposed on a machine (Sorrells: Para. 21; accelerometer positioned on the machine); a remote operating station (ROS) including a processor (Sorrells: Para. 23; computer at the remote office may additionally be programmed); at least one electronic control module (ECM) in communication with the antenna and the accelerometer (Sorrells: Para. 14, 21; an antenna, receiver and processor interface the main control module; accelerometer signals are transmitted to the main control module); and a non-transitory computer-readable media having stored thereon computer-executable instructions that, when executed, cause the at least one ECM (Sorrells: Para. 13; main control module includes a processor portion and a memory portion; memory portion provides a storage location for programming) to: receive vertical acceleration data of the machine from the accelerometer as the machine traverses a surface (Sorrells: Para. 21; accelerometer signals are transmitted to the main control module; accelerometer produces electronic signals related to the machines' position and rate of change of position, related to each of a longitudinal axis, lateral axis and a vertical axis); determine, based at least in part on the vertical acceleration data, a series of road surface quality (RSQ) index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event) ………… ; send, as a wireless signal via the antenna, the series of RSQ index values to the ROS (Sorrells: Para. 23; the location and severity of the event can be recorded by at least one of the main control module or remote office); ……. ; autonomously implement, based at least in part on the command, the change in speed of the machine (Sorrells: Para. 17-19; engine control module additionally sends signals to the engine related to desired engine speed; automatic retarder system actuates the service brake, or down shifts the transmission to slow the machine); ………. ; and autonomously implement the second change in speed of the machine (Sorrells: Para. 17-19; engine control module additionally sends signals to the engine related to desired engine speed; automatic retarder system actuates the service brake, or down shifts the transmission to slow the machine).
Sorrells doesn’t explicitly teach receive, from the ROS, a command to implement a change in speed of the machine based at least in part on the series of RSQ index values.
However, Sorrells is deemed to disclose an equivalent teaching. Sorrells teaches a site map that is continuously updated to reflect the location and severity of detected events, such as potholes. These events are expected to be adverse conditions of the road that would be detected again as other machines pass over the location. As a work machine approaches a known adverse road condition, the system can send instructions of appropriate corrective measure to prevent another event (Sorrells: Para. 23) The prior art teaches a step of causing the work machine to slow down prior to reaching said portion of the road that contains the adverse condition (Sorrells: Claim 17). Therefore the prior art receives the upcoming road condition and would slow down the vehicle as it approached a noted pothole on the site map.
It would have been obvious to one of ordinary skill before the effective filing date to have implement a change of speed based on any upcoming road features taught in Sorrells with a reasonable expectation of success because prior to reaching a location of a known pothole causing the work vehicle to slow down to reduce the effect of the pothole on the work vehicle (Sorrells: Para. 23, Claim 17).
Sorrells doesn’t explicitly teach by determining a moving average of a square of the vertical acceleration data.
However Miki, in the same field of endeavor, teaches by determining a moving average of a square of the vertical acceleration data (Miki: Para. 49, 108; moving average of square (square of absolute values), with regard to the waveform of the impact acceleration; detecting impact acceleration in a direction perpendicular to the bridge floor; impact acceleration in a vertical direction which is a direction of gravity acceleration).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108) with a reasonable expectation of success because acceleration signal processing improves the ration between the impact acceleration and other noises in order to detect the waveform of the impact acceleration by the axle (Miki: Para. 68).
Sorrells and Miki don’t explicitly teach receive, from the ROS, a second command to implement a second change in speed of the machine, wherein the second command is based at least in part on a second series of RSQ index values generated by a second machine on a portion of the surface untraversed by the machine.
However Graves, in the same field of endeavor, teaches receive, from the ROS, a second command to implement a second change in speed of the machine, wherein the second command is based at least in part on a second series of RSQ index values generated by a second machine on a portion of the surface untraversed by the machine (Graves: Para. 5, 48; a road height profile may be obtained from the wheel's vertical height data (e.g., as determined by measuring acceleration of the wheel) by applying a notch filter or low-pass filter; the current road-surface profile at least based on a running average of the squares of the data points).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108) and the running average of the squares and low-pass filter (Graves: Para. 5, 48) with a reasonable expectation of success because comparing the degree of similarity between at least the first reference road-surface profile and the current road-surface profile at least based on a running average of the squares of the data points in each of the first reference road-surface profile and the current road-surface profile can determine the vehicle’s current position (Graves: Para. 5).
Regarding claim 2, Sorrells teaches the system of claim 1, wherein the computer-executable instructions, when executed, cause the at least one ECM to: receive, from the ROS, a third command to implement a third change in operation of the machine, wherein the third command is based at least in part on the second series of RSQ index values (Sorrells: Para. 23; as machines travel the road and approach a known adverse road condition, a warning may be relayed to the machine operator, prior to an event and instructions can be displayed on the message center, advising the operator of an appropriate corrective measure to prevent another event); and implement the change in operation of the machine (Sorrells: Para. 17, 23; advising the operator of an appropriate corrective measure to prevent another event; engine control module 108 additionally sends signals to the engine related to desired engine speed).
Regarding claim 8, Sorrells teaches the system of claim 1, further comprising: a second accelerometer (Sorrells: Para. 21; accelerometer), wherein the computer-executable instructions, when executed, cause the at least one ECM (Sorrells: Para. 13; main control module includes a processor portion and a memory portion; memory portion provides a storage location for programming) to: receive, from the second accelerometer, second vertical acceleration data as the machine traverses the surface (Sorrells: Para. 21; accelerometer signals are transmitted to the main control module; accelerometer produces electronic signals related to the machines' position and rate of change of position, related to each of a longitudinal axis, lateral axis and a vertical axis); determine, based at least in part on the second vertical acceleration data, a second series of RSQ index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event); and send, as a second wireless signal and via the antenna, the second series of RSQ index values to the ROS (Sorrells: Para. 23; the location and severity of the event can be recorded by at least one of the main control module or remote office).
Sorrells doesn’t explicitly teach wherein the command to implement the change in speed is based at least in part on the second series of RSQ index values.
However, Sorrells is deemed to disclose an equivalent teaching. Sorrells teaches a site map that is continuously updated to reflect the location and severity of detected events, such as potholes. These events are expected to be adverse conditions of the road that would be detected again as other machines pass over the location. As a work machine approaches a known adverse road condition, the system can send instructions of appropriate corrective measure to prevent another event (Sorrells: Para. 23) The prior art teaches a step of causing the work machine to slow down prior to reaching said portion of the road that contains the adverse condition (Sorrells: Claim 17). A work machine will slow down as it approaches and passes a pothole. Then the work machine would increase back to its previous speed after the pothole has been passed.
It would have been obvious to one of ordinary skill before the effective filing date to have implement a change of speed based on any upcoming road features taught in Sorrells with a reasonable expectation of success because prior to reaching a location of a known pothole causing the work vehicle to slow down to reduce the effect of the pothole on the work vehicle (Sorrells: Para. 23, Claim 17).
Regarding claim 9, Sorrells teaches the system of claim 1, wherein the ROS is configured to: receive the series of RSQ index values (Sorrells: Para. 23; the location and severity of the event can be recorded by at least one of the main control module or remote office).
Sorrells doesn’t explicitly teach wherein the first zone is associated with a slower operating speed than the second zone; ……….. send, to the second machine, a command for the second machine to speed up.
However, Sorrells is deemed to disclose an equivalent teaching. Sorrells teaches a site map that is continuously updated to reflect the location and severity of detected events, such as potholes. These events are expected to be adverse conditions of the road that would be detected again as other machines pass over the location. As a work machine approaches a known adverse road condition, the system can send instructions of appropriate corrective measure to prevent another event (Sorrells: Para. 23) The prior art teaches a step of causing the work machine to slow down prior to reaching said portion of the road that contains the adverse condition (Sorrells: Claim 17). Therefore the prior art receives the upcoming road condition and would slow down the vehicle as it approached a noted pothole on the site map. A work machine will travel slower as it approaches and passes a pothole. Then the work machine would increase back to its previous speed after the pothole has been passed.
It would have been obvious to one of ordinary skill before the effective filing date to have implement a change of speed based on any upcoming road features taught in Sorrells with a reasonable expectation of success because prior to reaching a location of a known pothole causing the work vehicle to slow down to reduce the effect of the pothole on the work vehicle (Sorrells: Para. 23, Claim 17).
Sorrells and Miki don’t explicitly teach determine, based at least in part on the series of RSQ index values, that a first location at a worksite is associated with a first zone; determine, based at least in part on the series of RSQ index values, that a second location at the worksite is associated with a second zone, ……… determine that a second machine is to travel from the first location to the second location; and ………. responsive to the second machine traveling from the first location to the second location.
However Graves, in the same field of endeavor, teaches determine, based at least in part on the series of RSQ index values, that a first location at a worksite is associated with a first zone (Graves: Para. 30; currently measured road surface profile may then be compared with a previously determined reference road surface profile(s)); determine, based at least in part on the series of RSQ index values, that a second location at the worksite is associated with a second zone (Graves: Para. 30; based at least in part on how well a portion of the currently measured road surface profile matches a portion of the reference profile, the current position of the vehicle may be determined ), ……… determine that a second machine is to travel from the first location to the second location (Graves: Para. 28; properties and features of a road surface may be characterized and mapped to provide forward-looking or preview information about the road surface features located along a path of travel of a vehicle); and ………. responsive to the second machine traveling from the first location to the second location (Graves: Para. 28; properties and features of a road surface may be characterized and mapped to provide forward-looking or preview information about the road surface features located along a path of travel of a vehicle).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108) and the running average of the squares and low-pass filter (Graves: Para. 5, 48) with a reasonable expectation of success because comparing the degree of similarity between at least the first reference road-surface profile and the current road-surface profile at least based on a running average of the squares of the data points in each of the first reference road-surface profile and the current road-surface profile can determine the vehicle’s current position (Graves: Para. 5).
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1) in view of Miki et al. (US Publication 2020/0191824 A1), Graves (US Publication 2025/0012576 A1), and in further view of Kunzel et al. (Foreign Reference EP3382342A1).
Regarding claim 3, Sorrells, Miki, and Graves don’t explicitly teach generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data.
However Kunzel, in the same field of endeavor, teaches generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data (Kunzel: Para. 35; a singularity will generally affect a front and rear axle (and will be perceived twice in the vehicle interior)).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108), the running average of the squares and low-pass filter (Graves: Para. 5, 48), and the down-sampled vertical acceleration data (Kunzel: Para. 35) with a reasonable expectation of success because the front and the back wheels can both produce acceleration date for the same pothole and there is only one pothole, counted twice (Kunzel: Para. 35).
Regarding claim 10, Sorrells teaches the system of claim 9, wherein the ROS is configured to: determine that a third machine is enroute to the first zone (Sorrells: Para. 23; as machines travel the road and approach a known adverse road condition, a warning may be relayed to the machine operator, prior to an event and instructions can be displayed on the message center, advising the operator of an appropriate corrective measure to prevent another event).
Sorrells, Miki, and Graves don’t explicitly teach send, to the third machine, a third command to reroute the third machine to avoid entering the first zone.
However Kunzel, in the same field of endeavor, teaches send, to the third machine, a third command to reroute the third machine to avoid entering the first zone (Kunzel: Para. 28; signal s can be issued as a notice, for example an alert which can be indicative of a hazardous situation; based on the received or processed data, the at least one determined route can at least partially be updated, altered, changed).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108), the running average of the squares and low-pass filter (Graves: Para. 5, 48), and the down-sampled vertical acceleration data (Kunzel: Para. 35) with a reasonable expectation of success because the front and the back wheels can both produce acceleration date for the same pothole and there is only one pothole, counted twice (Kunzel: Para. 35).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1) in view of Miki et al. (US Publication 2020/0191824 A1), Graves (US Publication 2025/0012576 A1), and in further view of Song (US Publication 2004/0153226 A1).
Regarding claim 4, Sorrells, Miki, and Graves don’t explicitly teach generate low-pass filtered vertical acceleration data by applying an anti-aliasing filter to the vertical acceleration data.
However Song, in the same field of endeavor, teaches generate low-pass filtered vertical acceleration data by applying an anti-aliasing filter to the vertical acceleration data (Song: Para. 24, 29; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108), the running average of the squares and low-pass filter (Graves: Para. 5, 48), and the anti-aliasing filter (Song: Para. 24) with a reasonable expectation of success because an anti-aliasing filter is a known filter that ensures clean and proper signals improving sensed information from accelerometers (Song: Para. 24).
Regarding claim 5, Sorrells, Miki, and Graves don’t explicitly teach wherein the anti-aliasing filter includes a low pass filter to filter out vertical acceleration data with frequencies exceeding 25 Hz.
However Song, in the same field of endeavor, teaches wherein the anti-aliasing filter includes a low pass filter to filter out vertical acceleration data with frequencies exceeding 25 Hz (Song: Para. 24, 29, 31; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers; frequency that ranges from 10 to 12 Hz).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108), the running average of the squares and low-pass filter (Graves: Para. 5, 48), and the anti-aliasing filter (Song: Para. 24) with a reasonable expectation of success because an anti-aliasing filter is a known filter that ensures clean and proper signals improving sensed information from accelerometers (Song: Para. 24).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1) in view of Miki et al. (US Publication 2020/0191824 A1), Graves (US Publication 2025/0012576 A1), Kunzel et al. (Foreign Reference EP3382342A1), and in further view of Song (US Publication 2004/0153226 A1).
Regarding claim 7, Sorrells teaches the system of claim 1, wherein the computer-executable instructions, when executed, cause the at least one ECM to: ……. Includes generating the RSQ index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event).
Sorrells doesn’t explicitly teach wherein determining a moving average of a square of the vertical acceleration data ……… by determining a moving average of a square.
However Miki, in the same field of endeavor teaches wherein determining a moving average of a square of the vertical acceleration data (Miki: Para. 49, 108; moving average of square (square of absolute values), with regard to the waveform of the impact acceleration; detecting impact acceleration in a direction perpendicular to the bridge floor; impact acceleration in a vertical direction which is a direction of gravity acceleration) ……… by determining a moving average of a square (Miki: Para. 49, 108; moving average of square (square of absolute values), with regard to the waveform of the impact acceleration; detecting impact acceleration in a direction perpendicular to the bridge floor; impact acceleration in a vertical direction which is a direction of gravity acceleration).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108) with a reasonable expectation of success because acceleration signal processing improves the ration between the impact acceleration and other noises in order to detect the waveform of the impact acceleration by the axle (Miki: Para. 68).
Sorrells, Miki, and Graves don’t explicitly teach generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data.
However Kunzel, in the same field of endeavor, teaches generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data (Kunzel: Para. 35; a singularity will generally affect a front and rear axle (and will be perceived twice in the vehicle interior)).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108), the running average of the squares and low-pass filter (Graves: Para. 5, 48), and the down-sampled vertical acceleration data (Kunzel: Para. 35) with a reasonable expectation of success because the front and the back wheels can both produce acceleration date for the same pothole and there is only one pothole, counted twice (Kunzel: Para. 35).
Sorrells. Miki, Graves, and Kunzel don’t explicitly teach generate anti-alias filtered and down-sampled vertical acceleration data by applying an anti-aliasing filter to the down-sampled vertical acceleration data ……. of the anti-alias filtered and down-sampled vertical acceleration data.
However Song, in the same field of endeavor, teaches generate anti-alias filtered and down-sampled vertical acceleration data by applying an anti-aliasing filter to the down-sampled vertical acceleration data (Song: Para. 24, 29; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers) ……. of the anti-alias filtered and down-sampled vertical acceleration data (Song: Para. 24, 29, 57; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers; ADDERs receive 4 output control signals; signals for each ADDER are combined to create one output control signal).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the moving average of the square (Miki: Para. 108), the running average of the squares and low-pass filter (Graves: Para. 5, 48), and the down-sampled vertical acceleration data (Kunzel: Para. 35) and the anti-aliasing filter (Song: Para. 24) with a reasonable expectation of success because an anti-aliasing filter is a known filter that ensures clean and proper signals improving sensed information from accelerometers (Song: Para. 24).
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1) in view of Kunzel et al. (Foreign Reference EP3382342A1), Song (US Publication 2004/0153226 A1), and in further view of Miki et al. (US Publication 2020/0191824 A1).
Regarding claim 11, Sorrells teaches a method comprising: receiving, from an accelerometer of a machine and by an electronic control module (ECM) of the machine, acceleration data of the machine (Sorrells: Para. 21; accelerometer positioned on the machine; accelerometer signals are transmitted to the main control module; accelerometer produces electronic signals related to the machines' position and rate of change of position, related to each of a longitudinal axis, lateral axis and a vertical axis); …… ; generating, by the ECM, road surface quality (RSQ) index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event) …..….. ; sending, by the ECM and to a remote operating station (ROS), the RSQ index values (Sorrells: Para. 23; the location and severity of the event can be recorded by at least one of the main control module or remote office); ……… ; and autonomously implementing, by the ECM, the change in the route of the machine (Sorrells: Para. 17-18; engine control module additionally sends signals to the engine related to desired engine speed).
Sorrells doesn’t explicitly teach generating, by the ECM and based at least in part on the acceleration data, a down-sampled acceleration data by deleting one or more individual ones of the acceleration data; ………. receiving, by the ECM and from the ROS, a command to change a route of the machine, wherein the command is based at least in part on the RSQ index values.
However Kunzel, in the same field of endeavor, teaches generating, by the ECM and based at least in part on the acceleration data, a down-sampled acceleration data by deleting one or more individual ones of the acceleration data (Kunzel: Para. 35; a singularity will generally affect a front and rear axle (and will be perceived twice in the vehicle interior)); ……… receiving, by the ECM and from the ROS, a command to change a route of the machine, wherein the command is based at least in part on the RSQ index values (Kunzel: Para. 28; signal s can be issued as a notice, for example an alert which can be indicative of a hazardous situation; based on the received or processed data, the at least one determined route can at least partially be updated, altered, changed).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the down-sampled vertical acceleration data (Kunzel: Para. 35) with a reasonable expectation of success because the front and the back wheels can both produce acceleration date for the same pothole and there is only one pothole, counted twice (Kunzel: Para. 35).
Sorrells and Kunzel don’t explicitly teach generating, by the ECM, anti-alias filtered and down-sampled acceleration data by applying an anti-aliasing filter to the down-sampled acceleration data …….. of the anti-alias filtered and down-sampled acceleration data.
However Song, in the same field of endeavor, teaches generating, by the ECM, anti-alias filtered and down-sampled acceleration data by applying an anti-aliasing filter to the down-sampled acceleration data (Song: Para. 24, 29; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers) …….. of the anti-alias filtered and down-sampled acceleration data (Song: Para. 24, 29, 57; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers; ADDERs receive 4 output control signals; signals for each ADDER are combined to create one output control signal).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the down-sampled vertical acceleration data (Kunzel: Para. 35) and the anti-aliasing filter (Song: Para. 24) with a reasonable expectation of success because an anti-aliasing filter is a known filter that ensures clean and proper signals improving sensed information from accelerometers (Song: Para. 24).
Sorrells, Kunzel, and Song don’t explicitly teach by determining a moving average of a square.
However Miki, in the same field of endeavor, teaches by determining a moving average of a square (Miki: Para. 49, 108; moving average of square (square of absolute values), with regard to the waveform of the impact acceleration; detecting impact acceleration in a direction perpendicular to the bridge floor; impact acceleration in a vertical direction which is a direction of gravity acceleration).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the down-sampled vertical acceleration data (Kunzel: Para. 35) and the moving average of the square (Miki: Para. 108) with a reasonable expectation of success because acceleration signal processing improves the ration between the impact acceleration and other noises in order to detect the waveform of the impact acceleration by the axle (Miki: Para. 68).
Regarding claim 12, Sorrells teaches the method of claim 11, further comprising: receiving, by the ECM and from the ROS, a second command to change an operation of the machine, wherein the second command is based at least in part on second RSQ index values associated with a second machine (Sorrells: Para. 23; as machines travel the road and approach a known adverse road condition, a warning may be relayed to the machine operator, prior to an event and instructions can be displayed on the message center, advising the operator of an appropriate corrective measure to prevent another event); and implementing, by the ECM, the change in the operation of the machine (Sorrells: Para. 17, 23; advising the operator of an appropriate corrective measure to prevent another event; engine control module 108 additionally sends signals to the engine related to desired engine speed).
Regarding claim 13, Sorrells teaches the method of claim 11, wherein sending the RSQ index values comprises continuously streaming the RSQ index values to the ROS (Sorrells: Para. 14, 23; at any given time the main control module and the remote office can determine the location of the work machine within 1/2 meter; the location and severity of the event can be recorded by at least one of the main control module or remote office).
Regarding claim 14, Sorrells teaches the method of claim 11, further comprising: receiving, from a second accelerometer of the machine and by the ECM, second acceleration data (Sorrells: Para. 21; accelerometer signals are transmitted to the main control module; accelerometer produces electronic signals related to the machines' position and rate of change of position, related to each of a longitudinal axis, lateral axis and a vertical axis); generating, by the ECM and using the second acceleration data, second RSQ index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event); and sending, by the ECM and to the ROS, the second RSQ index values (Sorrells: Para. 23; the location and severity of the event can be recorded by at least one of the main control module or remote office).
Claims 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1).
Regarding claim 15, Sorrells teaches a machine comprising: an antenna (Sorrells: Para. 14; antenna); an accelerometer disposed on the machine (Sorrells: Para. 21; accelerometer positioned on the machine); an electronic control module (ECM) in communication with the antenna and the accelerometer (Sorrells: Para. 14, 21; an antenna, receiver and processor interface the main control module; accelerometer signals are transmitted to the main control module); and non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the ECM (Sorrells: Para. 13; main control module includes a processor portion and a memory portion; memory portion provides a storage location for programming) to: receive vertical acceleration data from the accelerometer as the machine traverses a surface, the vertical acceleration data indicating a vertical acceleration of the machine (Sorrells: Para. 21; accelerometer signals are transmitted to the main control module; accelerometer produces electronic signals related to the machines' position and rate of change of position, related to each of a longitudinal axis, lateral axis and a vertical axis); determine, based at least in part on the vertical acceleration data, a series of road surface quality (RSQ) index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event) ………. ; autonomously implement the change of at least one of the speed or the route (Sorrells: Para. 17-18; engine control module additionally sends signals to the engine related to desired engine speed); ……… , wherein the command is based at least in part on a second series of RSQ index values generated by a second machine on a portion of the surface untraversed by the machine (Sorrells: Para. 23, Claim 17; using the GPS system the location and severity of the event can be recorded by at least one of the main control module or remote office; a warning may be relayed to the machine operator, prior to an event and instructions can be displayed on the message center, advising the operator of an appropriate corrective measure to prevent another event); and autonomously implement the second change in operation of the machine (Sorrells: Para. 17-19; engine control module additionally sends signals to the engine related to desired engine speed; automatic retarder system actuates the service brake, or down shifts the transmission to slow the machine).
Sorrells doesn’t explicitly teach determine, based at least in part on the series of RSQ index values, that the machine is to change at least one of speed or route ………. receive, from a remote operating station (ROS), a command to implement a second change of at least one of speed or route.
However, Sorrells is deemed to disclose an equivalent teaching. Sorrells teaches a site map that is continuously updated to reflect the location and severity of detected events, such as potholes. These events are expected to be adverse conditions of the road that would be detected again as other machines pass over the location. As a work machine approaches a known adverse road condition, the system can send instructions of appropriate corrective measure to prevent another event (Sorrells: Para. 23) The prior art teaches a step of causing the work machine to slow down prior to reaching said portion of the road that contains the adverse condition (Sorrells: Claim 17). Therefore the prior art receives the upcoming road condition and would slow down the vehicle as it approached a noted pothole on the site map.
It would have been obvious to one of ordinary skill before the effective filing date to have implement a change of speed based on any upcoming road features taught in Sorrells with a reasonable expectation of success because prior to reaching a location of a known pothole causing the work vehicle to slow down to reduce the effect of the pothole on the work vehicle (Sorrells: Para. 23, Claim 17).
Regarding claim 16, Sorrells teaches the machine of claim 15, wherein the computer-executable instructions, when executed, cause the ECM to: determining a location where the at least one of the speed or the route are to be changed (Sorrells: Para. 23; if the event was cause by a pothole, it would be expected that the severity of the event would increase, as the pothole becomes enlarged; as machines travel the road and approach a known adverse road condition); and communicate the location to a second machine (Sorrells: Para. 23; a warning may be relayed to the machine operator, prior to an event and instructions can be displayed on the message center, advising the operator of an appropriate corrective measure to prevent another event).
Regarding claim 17, Sorrells teaches the machine of claim 16, wherein the computer-executable instructions, when executed, cause the ECM to: store the location in an immutable ledger, wherein the immutable ledger is accessible by the second machine (Sorrells: Para. 14; site map is stored in electronic form in the memory portion or remote office).
Regarding claim 18, Sorrells teaches the machine of claim 17, wherein the computer-executable instructions, when executed, cause the ECM to: receive, from the immutable ledger, a command to decrease speed (Sorrells: Para. 23; as machines travel the road and approach a known adverse road condition, a warning may be relayed to the machine operator, prior to an event and instructions can be displayed on the message center, advising the operator of an appropriate corrective measure to prevent another event); and implement, the decrease in speed (Sorrells: Para. 17-18; engine control module additionally sends signals to the engine related to desired engine speed).
Regarding claim 19, Sorrells teaches the machine of claim 15, wherein the computer-executable instructions, when executed, cause the ECM to: transmit, to the ROS via the antenna, the series of RSQ index values (Sorrells: Para. 23; the location and severity of the event can be recorded by at least one of the main control module or remote office).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Sorrells (US Publication 2004/0122580 A1) in view of Kunzel et al. (Foreign Reference EP3382342A1), Song (US Publication 2004/0153226 A1), and in further view of Miki et al. (US Publication 2020/0191824 A1).
Regarding claim 20, Sorrells teaches the machine of claim 15, wherein the computer-executable instructions that, when executed, cause the ECM to: ………. ; and generate the RSQ index values (Sorrells: Para. 23; main control module further evaluates data from at least one of the inclinometer, vibration meter, and accelerometer to verify the severity of the event).
Sorrells doesn’t explicitly teach generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data.
However Kunzel, in the same field of endeavor, teaches generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data (Kunzel: Para. 35; a singularity will generally affect a front and rear axle (and will be perceived twice in the vehicle interior)).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the down-sampled vertical acceleration data (Kunzel: Para. 35) with a reasonable expectation of success because the front and the back wheels can both produce acceleration date for the same pothole and there is only one pothole, counted twice (Kunzel: Para. 35).
Sorrells and Kunzel don’t explicitly teach generate anti-alias filtered and down-sampled vertical acceleration data by applying an anti-aliasing filter to the down-sampled vertical acceleration data ……. of the anti-alias filtered and down-sampled vertical acceleration data.
However Song, in the same field of endeavor, teaches generate anti-alias filtered and down-sampled vertical acceleration data by applying an anti-aliasing filter to the down-sampled vertical acceleration data (Song: Para. 24, 29; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers) ……. of the anti-alias filtered and down-sampled vertical acceleration data of the anti-alias filtered and down-sampled vertical acceleration data (Song: Para. 24, 29, 57; the RC anti-aliasing filter improves the sensing information from accelerometers; vertical wheel accelerations from accelerometers; ADDERs receive 4 output control signals; signals for each ADDER are combined to create one output control signal).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the down-sampled vertical acceleration data (Kunzel: Para. 35) and the anti-aliasing filter (Song: Para. 24) with a reasonable expectation of success because an anti-aliasing filter is a known filter that ensures clean and proper signals improving sensed information from accelerometers (Song: Para. 24).
Sorrells, Kunzel, and Song don’t explicitly teach by determining a moving average of a square.
However Miki, in the same field of endeavor, teaches by determining a moving average of a square (Miki: Para. 49, 108; moving average of square (square of absolute values), with regard to the waveform of the impact acceleration; detecting impact acceleration in a direction perpendicular to the bridge floor; impact acceleration in a vertical direction which is a direction of gravity acceleration).
It would have been obvious to one having ordinary skill in the art to modify the site map of known adverse road conditions (Sorrells: Para. 23) with the down-sampled vertical acceleration data (Kunzel: Para. 35) and the moving average of the square (Miki: Para. 108) with a reasonable expectation of success because acceleration signal processing improves the ration between the impact acceleration and other noises in order to detect the waveform of the impact acceleration by the axle (Miki: Para. 68).
Response to Arguments
Applicant’s arguments, filed 2 June 2026, with respect to the rejection of claims 1-5 and 7-20 under 35 U.S.C. 103 have been fully considered, but they are not persuasive.
The applicant’s attorney argues that “an accelerometer disposed on a machine” is not taught by the prior arts.
In response to the applicant’s argument above, Sorrells teaches the road analysis includes a three-axis accelerometer positioned on the machine (Sorrells: Para. 21).
The applicant next argues that “receive vertical acceleration data of the machine from the accelerometer as the machine traverses a surface” is not taught by the prior arts.
In response to the applicant’s argument above, Sorrells teaches an accelerometer on the machine that produces electronic signals related to the machine’s position and the rate of change of positions related to each of a longitudinal axis, lateral axis, and a vertical axis. Those signals are sent to and received by the main control module (Sorrells: Para. 21). The system analyzes the vehicle data, including from the accelerometer, to determine an adverse road condition event and its severity with its GPS location to the main control module and/or remote office (Sorrells: Para. 23). Sorrells’ systems receives vertical acceleration data as the vehicle travels.
The applicant argues that “determine, based at least in part on the vertical acceleration data, a series of road surface quality (RSQ) index values by determining a moving average of a square of the vertical acceleration data” are not taught by the prior arts.
In response to the applicant’s argument above, Sorrells teaches events related to rack, pitch, and torque can be analyzed to determine adverse road conditions. Then the accelerometer data is evaluated to verify the severity of the event. The main control module or remote office records the location and severity of the event (Sorrells: Para. 23).
Miki teaches detecting impact acceleration perpendicular to the road surface, vertical acceleration. Miki teaches that with a stream of acceleration data that the moving average of square of the vertical acceleration data creates a smooth vertical acceleration waveform that is helpful in analysis of vertical acceleration data in a road/vehicle field of transportation. Miki teaches how one of ordinary skill in the art could use the moving average of the square vertical acceleration to improve acceleration analysis (Miki: Para. 49, 108).
The applicant next argues that “automatically implement the change in speed of the machine” is not taught by the prior arts.
In response to the applicant’s argument above, Sorrells teaches collecting location and severity of adverse road conditions, such as a pothole map (Sorrells: Para. 23). Sorrells teaches causing the machine to slow down prior to reaching the location of the pothole (Sorrells: Claim: 17). Sorrells teaches an automatic retard system that actuates a brake to slow down the machine (Sorrells: Para. 17-19). Sorrells’ system automatically brakes the vehicle, slowing the speed, before the vehicle reaches the known location of a pothole.
The applicant next argues that “receive, from the ROS, a second command to implement a second change in speed of the machine, wherein the second command is based at least in part on a second series of RSQ index values generated by a second machine on a portion of the surface untraversed by the machine” are not taught by the prior arts.
In response to the applicant’s argument above, Graves teaches comparing the current road surface profile with the first reference road surface profile (Graves: Para. 5) The first reference road surface profile would be the vertical acceleration data from a first vehicle creating a surface profile for that area. The current road profile is the vertical acceleration from a second vehicle, the current vehicle, creating a second surface profile for that area. Graves compares the road surface profile from the fist vehicle with the road surface profile from the second vehicle and compares similarities to maintain and update the road surface profile map for that area (Graves: Para. 5, 48). Once Graves has confirmed the vehicle’s location, vehicle is controlled based on the determined location and the road surface profile of the road segment ahead of the vehicle (Graves: Para. 82). The vehicle controlled based on the road surface profile of the road segment ahead is a second command on a portion of the surface untraversed by the machine.
The applicant next argues that the prior arts are non-analogous.
In response to applicant's argument that Sorrells, Miki, and Graves are nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, each prior art is in the field of vertical acceleration and its analysis for transportation purposes.
The applicant next argues that there is no motivation to combine the prior arts.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Sorrells teaches a road analysis system that saves adverse events, location and severity, used in movement of vehicles along a route (Sorrells: Para. 21, 23, Claim. 17). Miki teaches that with a stream of acceleration data that the moving average of square of the vertical acceleration data creates a smooth vertical acceleration waveform that is helpful in analysis of vertical acceleration data in a road/vehicle field of transportation. Miki teaches how one of ordinary skill in the art could use the moving average of the square vertical acceleration to improve acceleration analysis (Miki: Para. 49, 108). Graves uses vehicle vertical acceleration data and current road-surface profile to correlate the vehicle’s current position with its position on its reference road surface profile (Graves: Para. 5, 48). Once Graves has confirmed the vehicle’s location, vehicle is controlled based on the determined location and the road surface profile of the road segment ahead of the vehicle (Graves: Para. 82). Each prior art is in the field of vertical acceleration and its analysis for transportation purposes.
The applicant next argues that the examiner has used hindsight reasoning.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The applicant next argues that the combination of Miki and Graves would render the disclosure in Graves inoperable.
In response to applicant's argument that the combination of the prior art would be inoperable. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Sorrells and Graves both have remotely known road surface profiles for an area. Both use that road surface map by using it to control a vehicle based on upcoming road surface conditions. Graves teaches a running average of the squares of the data points for the road surface profile. Miki teaches that with a stream of acceleration data that the moving average of square of the vertical acceleration data creates a smooth vertical acceleration waveform that is helpful in analysis of vertical acceleration data in a road/vehicle field of transportation. Real-time accelerometer data, with a high sampling rate, could have noise problems. The running average of the squares smooths out the real-time accelerometer data. As seen in Miki and Graves, one of ordinary skill in the art of vehicle transportation would know about mathematical techniques to improve data for analysis.
The applicant next argues that claim 3’s “generate down-sampled vertical acceleration data by deleting one or more individual ones of the vertical acceleration data” is not taught by the prior arts.
In response to the applicant’s argument above, Kunzel teaches anomalies in the data. These outlier data points give false negatives and can be neglected (Kunzel: Para. 35). It is obvious to one of ordinary skill in the art to identify outliers that are not useful or relevant, and neglect these data points by way of deleting the outliers. The applicant’s claim defines down-sampled vertical acceleration data as deleting one or more individual points of vertical acceleration data. Kunzel’s outlier detection and neglecting is an example of that.
The applicant next argues that claim 11’s “generating, by the ECM, road surface quality (RSQ) index values by determining a moving average of a square of the anti-alias filtered and down-sampled acceleration data” is not taught by the prior arts.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. The applicant’s arguments to claim 1 are responded to above, and the replies would similarly apply to claim 11.
The applicant next argues that claim 15’s “receive, from a remote operating station (ROS), a command to implement a second change of at least one of speed or route, wherein the command is based at least in part on a second series of RSQ index values generated by a second machine on a portion of the surface untraversed by the machine; and automatically implement the second change in operation of the machine” is not taught by the prior arts.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. The applicant’s arguments to claim 1 are responded to above, and the replies would similarly apply to claim 15.
The applicant argues that claims 2-5, 7-10, 12-14, and 16-20 depend from independent claims 1, 11, and 15, thus are allowable at least based on their dependencies.
In response to the applicant’s argument above, claims 1, 11, and 15 are rejected. Therefore claims 2-5, 7-10, 12-14, and 16-20 are rejected at least based on their dependencies.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Sorrells’ map of location and severity of road events with Miki’s moving average of the square acceleration and Graves’ change of speed based on the severity of the road-surface profile reads on applicant’s system and method for machine control using road surface quality data. The rejection is maintained.
Conclusion
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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