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 .
Status of Claims
Claims 1-15 of U.S. Application No. 18/320,647 filed on 05/19/2023 have been examined.
Applicant filed remarks and amendments on Status 08/19/2025. Claims 1-3, 7 and 13 were amended, claim 9 was cancelled. Claims 1-8 and 10-15 are presently pending and presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 101: Applicant's arguments filed 08/19/2025 have been fully considered and they are persuasive. The previously given claim rejections under 35 USC 101 are withdrawn.
Regarding the claim rejections under 35 USC 103: Applicant's arguments filed 08/19/2025 with respect to Srivastava (US 20180039283 A1) in view of LAINE et al. (US 20250058766 A1) have been fully considered but they are not persuasive.
Regarding claims 1, 10 and 13, applicant argues that Srivastava does not describe using an air drag model, and in particular to use images of the vehicle combination captured after its exterior shape has changed to estimate how the projected area function depends on air-attack angle (see Response, pp. 11-12).
However, the examiner respectfully disagrees this argument is not persuasive, Srivastava teaches detecting changes in the exterior shape of a vehicle combination (e.g., attaching a trailer, which alters the overall shape and dimensions; see Srivastava, para. [0048]: “a trailer 102 may be affixed to a hitch or other connection structure at the rear of the autonomous vehicle 100 such that the vehicle 100 and the trailer 102 travel as one. As such, the vehicle 100 having the attached trailer 102 undergoes a changed shape from its original form”). Upon detection, Srivastava communicates with external sources (e.g., proximate vehicles or fixed cameras) to receive images or scans captured after the change (see Srivastava, para. [0085]: “the autonomous vehicle 100 sends scanning requests to a plurality of the proximate vehicles 302, 304, 306, and 308 for scanning of the vehicle 100… The scanning vehicles 302, 304, 306, and 308 transmit the scanned information to the requesting vehicle 100 via V2V communication”; para. [0094]: “scanning via scanners or cameras located at the user’s garage, parking locations, or on the vehicle”). While Srivastava does not explicitly detail estimating a projected area function based on air-attack angle, LAINE teaches a control unit for heavy-duty vehicles that receives ambient environment data from sensors to predict environmental impact on vehicle motion, including aerodynamic effects like air drag, and coordinates control to compensate (see LAINE, abstract: “A control unit for controlling a heavy-duty vehicle, the control unit being arranged to receive ambient environment data from one or more environment sensors on the heavy-duty vehicle, and to predict an impact of the ambient environment on motion of the heavy-duty vehicle, wherein the control unit is arranged to coordinate control of one or more of the heavy-duty vehicle to compensate for the predicted impact of the ambient environment on motion of the heavy-duty vehicle”). LAINE’s predictive framework inherently involves dependence on air-attack angle (θ) for environmental sensing and motion management in vehicle combinations (see LAINE, para. [0001]: “The present disclosure relates to vehicle motion management for heavy-duty vehicles”). It would have been obvious to one of ordinary skill in the art to modify Srivastava’s shape change detection and post-change image reception with LAINE’s predictive environmental compensation (including angle-dependent drag estimation) to improve motion control and energy efficiency in changed configurations, as both address heavy-duty vehicle combinations like trucks with trailers (motivated by LAINE’s focus on predictive control for ambient impacts; see LAINE, para. [0006]: “predict an impact of the ambient environment on motion of the heavy-duty vehicle”).
Applicant further argues that, that LAINE solves the problem of how to predict an impact of an ambient environment on the motion of a vehicle and how to use various actuators to compensate, but does not describe how the air drag coefficient is determined, how the shape of the vehicle is to be determined, etc. (see Response, p. 12).
However, the examiner respectfully disagrees this argument is not persuasive, LAINE explicitly teaches predicting environmental impacts (e.g., wind, air drag) on heavy-duty vehicle motion using sensor data and coordinating actuators for compensation, including implicit determination of shape and coefficients via environment sensing (see LAINE, para. [0001]: “relates to vehicle motion management for heavy-duty vehicles”; para. [0006]: “the control unit [is] arranged to receive ambient environment data from one or more environment sensors”). Vehicle shape is determined through sensor fusion for motion prediction (see LAINE, para. [0006]: “environment sensors on the heavy-duty vehicle” for data on “impact of the ambient environment”). The combination with Srivastava’s image-based shape assessment provides explicit post-change shape determination, rendering the overall system obvious for comprehensive drag coefficient estimation in dynamic conditions.
Applicant further argues that, that LAINE does not disclose estimating a projected area function that indicates a dependence of a projected frontal area on air-attack angle, and in particular does not teach that the estimation is to be done based on images of the vehicle, as recited (see Response, p. 13).
However, the examiner respectfully disagrees this argument is not persuasive, While LAINE does not explicitly disclose a “projected area function,” it teaches predicting the impact of ambient environment (e.g., wind direction and speed affecting air-attack angle) on vehicle motion using sensor data to estimate aerodynamic forces like drag on the frontal area (see LAINE, para. [0060]: “The VMM motion prediction sub-function 307 can now use wind data and data on expected rolling resistance to calculate the overall resistance forces which are currently acting on the vehicle 100, or which will be acting on the vehicle in the near future. The VMM motion prediction sub-function 307 can now use wind data and data on expected rolling resistance to calculate the overall resistance forces which are currently acting on the vehicle 100, or which will be acting on the vehicle in the near future.”). Estimation is based on vehicle sensors, including cameras for environmental imaging (see LAINE, para. [0037]: “a forward looking sensor system 115, such as a camera sensor arrangement or a lidar scanner, can be used to classify the road surface conditions up ahead of the vehicle, and in particular along predicted tracks of the wheels 150, 160, 170.”). Combined with Srivastava’s post-change vehicle images for shape updates (see Srivastava, para. [0085]), the combination obviously yields angle-dependent frontal area estimation for predictive control, as LAINE motivates sensor-based prediction of drag impacts (see LAINE, abstract).
Applicant further argues that, that LAINE uses environment sensors to estimate current and future impact, mentioning air drag from speed and shape only once [00063], and asserts this is the only mention of air drag (see Response, p. 13).
However, the examiner respectfully disagrees this argument is not persuasive, LAINE’s disclosure centers on predicting ambient environmental impacts, including aerodynamic forces like air drag influenced by vehicle speed, shape, and wind (see LAINE, para. [0063]: “air drag of the vehicle can be determined from vehicle speed and vehicle shape, e.g., air drag coefficient times frontal area”). This is integrated into the broader predictive framework (see LAINE, para. [0010]-[0012]), where environment sensors provide ongoing data for motion compensation. The single explicit mention supports the general teaching, and combination with Srivastava’s shape data enhances drag estimation.
Applicant further argues that, that the only “images” in LAINE are from forward-looking cameras for road analysis [0011], [0037], Claim 4, and thus references do not describe using captured images of the exterior shape to estimate the projected area function (see Response, p. 14).
However, the examiner respectfully disagrees this argument is not persuasive, LAINE’s environment sensors include cameras for ambient data, extending beyond road to vehicle-exterior impacts (see LAINE, para. [0011]: “one or more environment sensors [comprising] cameras… on the heavy-duty vehicle”; para. [0037]: camera use for environmental sensing). Claim 4 specifies sensor data for prediction. Srivastava supplies explicit exterior images post-change (see Srivastava, para. [0085]), obviously combinable for shape estimation in LAINE’s predictive system.
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.
Claims 1-7 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava (US 20180039283 A1) in view of LAINE et al. (US 20250058766 A1), hereinafter referred to as Srivastava and LAINE respectively.
Regarding claims 1, 10 and 13, Srivastava discloses A computer-implemented method of estimating air drag of a vehicle combination, the method being performed by processing circuitry of a device (“the sensors 204 may include one or more detectors 204a, 204b, 204c, and 204d located at particular locations at the autonomous vehicle 100 for receiving certain data relating to road conditions or other environmental conditions outside of the autonomous vehicle 100 or detecting parameters of the vehicle 100. In particular embodiments, the sensors 204 include a wind force detector 204a, a roof cargo detector 204b, a rear cargo detector 204c, and a weight detector 204d. In some embodiments, the wind force detector 204a is located on the body of the vehicle 100 at a location suitable for detecting wind force (or wind speed or air drag) exerted against the autonomous vehicle 100, such as, but not limited to, the hood, the roof, the side, or any other suitable external location for receiving unobstructed wind forces.” [0059]),
detecting, by the processing circuitry, a change of an exterior shape of the vehicle combination to a new exterior shape (“Similarly, the controller 202 may be configured to control or regulate operation of the autonomous vehicle 100 in response to sensing that the autonomous vehicle 100 has changed its shape from its original shape, such as by the addition of a trailer 102 or a load 104 or 106.” [0053])
communicating, by the processing circuitry, with one or more cameras to receive one or more images of the vehicle combination captured after the change of the exterior shape (“In some embodiments, the rear cargo detector 204 c is located at the rear of the vehicle 100 at a location suitable for detecting the presence of cargo (e.g., trailer 102 or load 106) that is placed (e.g., fastened) to the rear of the autonomous vehicle 100. In some embodiments, the rear cargo detector 204 c is a camera. In other embodiments, the rear cargo detector 204 c is any other suitable device for detecting the presence of an object at the rear of the vehicle 100, such as, but not limited to, an optical detector, a weight detector, an infrared detector, a device that detects physical or electrical contact, or the like.” [0063] and “the autonomous vehicle 100 includes controlling electronics 201. In some embodiments, the controlling electronics 201 may be similar to the controlling electronics 101 and include a controller 202, one or more sensors 204 connected to the controller 202, a wireless communication device 210 connected to the controller 202, and a LIDAR device 212 connected to the controller 202. The autonomous vehicle 100 further includes an engine 206 connected to the one or more sensors 204 and a user interface (UI) 208 connected to the controller 202. Such controlling electronics 201 is just one example configuration as any other suitable configurations may be implemented.” [0051]))
receiving, by the processing circuitry, predicted wind information pertinent to a particular route, and using the updated air drag model for at least one of energy management, range estimation, vehicle combination dynamics, and cruise control, of the vehicle combination along the particular route (“According to various embodiments, an autonomous (or partially autonomous) vehicle is configured to store and/or access information regarding the shape of the vehicle (e.g., height, length, width, mass, etc.). Controlling electronics within the vehicle may adjust its operation (e.g., turn radius, parking behavior, negotiating clearances, braking distance, etc.) based on its shape information. In addition, the autonomous vehicle may detect that a shape change event has occurred at the vehicle (e.g., when a user attaches a trailer or a bike rack, or stows luggage on a roof rack, etc.). As such, after detecting that a shape change event has occurred, the vehicle can trigger a scan to determine the vehicle's new shape, and the vehicle can adjust its operation based on the new shape information. “ [0042]).
Srivastava does not explicitly teach in response to detecting such a change, based on the one or more images of the vehicle combination captured after the change of the exterior shape, estimating, by the processing circuitry, a projected area function (Ap(0)) indicating a dependence of a projected frontal area of the vehicle combination having the new exterior shape on air-attack angle (0)
using, by the processing circuitry, the estimated projected area function to update a crosswind-sensitive air drag model for the vehicle combination
However, LAINE does teach in response to detecting such a change, based on the one or more images of the vehicle combination captured after the change of the exterior shape, estimating, by the processing circuitry, a projected area function (Ap(0)) indicating a dependence of a projected frontal area of the vehicle combination having the new exterior shape on air-attack angle (0) (“The one or more environment sensors can be used to estimate current and in some cases also future impact by the environment on the vehicle units. Froll can be at least in part predicted by determining a future road geometry to be travelled by the vehicle given its intended path, Fairdrag can be determined from vehicle speed and vehicle shape, such as its air drag coefficient and frontal area, Fwind can be determined based on vehicle geometry and on the output from the one or more anemometers 116, while Fslope can be determined based on output data from the one or more forward looking sensors and or from map data in combination with a GPS sensor.” [0063] and “The result of the motion estimation 305, i.e., the estimated vehicle state s (t), and the predicted future vehicle state s (t+T) is input to a force generation module 310 which determines the required global forces V=[V1, V2] for the different vehicle units to cause the vehicle 100 to move according to the requested acceleration and curvature profiles areq, Creq. This force generation module 310 is now able to account for impact by the operating environment, even if such impact has not yet resulted in a change in vehicle state.” [0064]),
using, by the processing circuitry, the estimated projected area function to update a crosswind-sensitive air drag model for the vehicle combination (“According to some aspects, the predicted impact of the ambient environment on the motion of the heavy-duty vehicle is at least in part determined based on a model of a tyre mounted onto a wheel on the heavy-duty vehicle. This tyre model makes it possible to more accurately predict an impact from a given change in, e.g., road surface conditions.”[0012] and “These sensor devices can be used for vehicle environment estimation, i.e., to obtain information regarding the surrounding environment in which the vehicle 100 is operating. This information may, e.g., comprise data related to the nature of the road surface up ahead of the vehicle, the temperature, the wind conditions, and so on.” [0036]). Both Srivastava and LAINE teach methods for using the estimated projected area function to update a crosswind-sensitive air drag model for the vehicle combination. However, LAINE explicitly teaches in response to detecting such a change, based on the one or more images of the vehicle combination captured after the change of the exterior shape, estimating, by the processing circuitry, a projected area function (Ap(0)) indicating a dependence of a projected frontal area of the vehicle combination having the new exterior shape on air-attack angle (0) and using the estimated projected area function to update a crosswind-sensitive air drag model for the vehicle combination.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air drag estimation method of Srivastava to also include in response to detecting such a change, based on the one or more images of the vehicle combination captured after the change of the exterior shape, estimating, by the processing circuitry, a projected area function (Ap(0)) indicating a dependence of a projected frontal area of the vehicle combination having the new exterior shape on air-attack angle (0) and using the estimated projected area function to update a crosswind-sensitive air drag model for the vehicle combination, as taught by LAINE, with a reasonable expectation of success. Doing so improves design of vehicles to minimize air drag (With regard to this reasoning, see at least [LAINE, 0036 - 0064]).
Regarding claim 2, Srivastava discloses The method according to claim 1, wherein the method includes initiating a capture of the one or more images in response to said detecting(“In particular embodiments, one or more of the proximate vehicles 302, 304, 306, and 308 may arrange itself around the autonomous vehicle 100 to capture a better (e.g., more complete) image or other representation of the new shape of the autonomous vehicle 100. In some embodiments, the autonomous vehicle 100 directs each of the proximate vehicles 302, 304, 306, and 308 to optimize the coverage of the scans. In other embodiments, the proximate vehicles 302, 304, 306, and 308 communicate among themselves to position themselves for optimizing scan coverage. In some embodiments, the scanning vehicles 302, 304, 306, and 308 receive GPS positioning data of the requesting vehicle 100 for locating the requesting vehicle 100 and positioning themselves around the requesting vehicle 100. For instance, the four proximate vehicles 302, 304, 306, and 308 position themselves at four corners surrounding the vehicle 100 for maximum scan coverage (e.g., as shown in FIG. 3B). The scans performed by each of the proximate vehicles 302, 304, 306, and 308 may be LIDAR scans by respective LIDAR devices of each of the proximate vehicles 302, 304, 306, and 308.” [0087]).
Regarding claim 3, Srivastava discloses The method according to claim 1, wherein the method includes receiving the one or more images from at least one camera mounted to/on the vehicle combination (“Other shape scanning procedures may include one or more of a user manually scanning the vehicle or inputting dimensions of the vehicle (e.g., via UI 208), scanning performed by scanners located at designated checkpoints along a road or other fixed scanning devices, scanning by a manned or an unmanned aerial vehicle, scanning via scanners or cameras located at the user's garage, parking locations, or on the vehicle, or the like.” [0094])
Regarding claims 4 and 12, Srivastava discloses The method according to claim 1, wherein estimating the projected area function includes estimating a side area (As) of the vehicle combination after the change of the exterior shape (“In particular embodiments, the sensors 204 include a wind force detector 204 a, a roof cargo detector 204 b, a rear cargo detector 204 c, and a weight detector 204 d. In some embodiments, the wind force detector 204 a is located on the body of the vehicle 100 at a location suitable for detecting wind force (or wind speed or air drag) exerted against the autonomous vehicle 100, such as, but not limited to, the hood, the roof, the side, or any other suitable external location for receiving unobstructed wind forces. “ [0059]) .
Regarding claims 5 and 7, Srivastava discloses The method according to claim 4, wherein the one or more images depict at least part of a side of the vehicle combination (“In particular embodiments, one or more of the proximate vehicles 302, 304, 306, and 308 may arrange itself around the autonomous vehicle 100 to capture a better (e.g., more complete) image or other representation of the new shape of the autonomous vehicle 100. In some embodiments, the autonomous vehicle 100 directs each of the proximate vehicles 302, 304, 306, and 308 to optimize the coverage of the scans. In other embodiments, the proximate vehicles 302, 304, 306, and 308 communicate among themselves to position themselves for optimizing scan coverage. In some embodiments, the scanning vehicles 302, 304, 306, and 308 receive GPS positioning data of the requesting vehicle 100 for locating the requesting vehicle 100 and positioning themselves around the requesting vehicle 100. For instance, the four proximate vehicles 302, 304, 306, and 308 position themselves at four corners surrounding the vehicle 100 for maximum scan coverage (e.g., as shown in FIG. 3B).” [0087])
Regarding claim 6, Srivastava discloses The method according to claim 4, wherein estimating the projected area function includes estimating the projected frontal area after the change of the exterior shape as a projected area of a cuboid on a plane perpendicular to air-attack (va) (“In particular embodiments, a user inputs whether cargo has been placed inside the vehicle 100 via the UI 208, which the controller 202 considers in its determination of whether a shape change event has occurred. In further embodiments, the controller 202 receives weather updates and road condition updates (e.g., via the wireless communication device 210). In yet further embodiments, the one or more sensors 204 further include a grade or slope sensor (e.g., an accelerometer, gyroscope, imaging device or the like) configured to determine an angle of incline or decline of the autonomous vehicle 100 or of the road surface ahead of the autonomous vehicle. As such, in some embodiments, the controller 202 is configured to offset the received data that contributes to false detections from readings made by the one or more sensors 204 to accurately detect whether a change shape event has occurred. “ [0075]).
Regarding claims 11 and 14, Srivastava discloses The device according to claim 10, wherein the processing circuitry is further configured to cause the device to perform the method (“In conjunction with the detecting of the parameters by the sensors 204 (and/or the detectors 204a, 204b, 204c, and 204d), the processor 202b of the controller 202 may generally determine whether or not a sensed parameter meets or exceeds a threshold or otherwise corresponds to a trigger event (e.g., a changed shape event). If the processor 202b determines that the parameter meets or exceeds a threshold or otherwise corresponds to the trigger event, in response, the processor 202b may generally control the autonomous vehicle 100 accordingly.” [0068]).
Regarding claim 15, Srivastava discloses A computer program product comprising a computer program according to claim 13, and a computer-readable storage medium on which the computer program is stored (“According to various embodiments, the various thresholds and default data associated with the sensors 204 or characteristics may be stored in the memory 202a, for access by the processor 202b. In some embodiments, the thresholds may be programmed into the memory 202a by a manufacturer or a user, and/or may be later modified by a manufacturer or a user, as desired.” [0071]).
Claim 8 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava in view of LAINE and in further view of Damon et al. (US 20190367104 A1), hereinafter referred to as Srivastava, LAINE and Damon respectively.
Regarding claim 8, Srivastava in view of LAINE discloses The method according to claim 1, wherein detecting the change of the exterior shape includes at least one of receiving a signal from a user interface of the vehicle combination (“The autonomous vehicle 100 further includes an engine 206 connected to the one or more sensors 204 and a user interface (UI) 208 connected to the controller 202.” [0051]),
Srivastava in view of LAINE does not explicitly teach receiving a signal indicative of a change in air deflector settings, and receiving a signal indicative of a trailer being either connected or detached from the vehicle combination
However, Damon does teach receiving a signal indicative of a change in air deflector settings, and receiving a signal indicative of a trailer being either connected or detached from the vehicle combination (“The pneumatic system 82 may include a valve arrangement comprising one or more controllable valves, such as one or more solenoid actuated valves, for controlling the quantity of pressurized gas entering or exiting one or more pneumatic motors, such as the pneumatic motor 28 of the side deflector 20 depicted in FIG. 5. To that end, the pneumatic system 82 may receive appropriate device specific control signals from the controller 80 for opening and/or closing one or more valves in order to, e.g.: (1) pressurize a chamber so that the configuration of a control surface of the side deflector changes from the configuration of FIG. 6A to the configuration of FIG. 6B or 6C; or (2) depressurize a chamber so that the configuration of a control surface changes from the configuration of FIG. 6C to the configuration of FIG. 6B or 6A.” [0047]). Both Srivastava in view of LAINE and Damon teach methods for using the estimated projected area function to update a crosswind-sensitive air drag model for the vehicle combination.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air drag estimation method of Srivastava in view of LAINE to also include receiving a signal indicative of a change in air deflector settings, and receiving a signal indicative of a trailer being either connected or detached from the vehicle combination, as taught by Damon, with a reasonable expectation of success. Doing so improves design of vehicles to minimize air drag (With regard to this reasoning, see at least [Damon, 0003 - 0004]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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AHMED ALKIRSHExaminer, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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