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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/27/2026 has been entered.
Status of Claims
Applicant filed remarks and amendments on 01/27/2026. Claims 1, 5, 10 and 13 were amended, claim 4, 6 and 11 were cancelled. Claims 1-3, 5, 7-8 and 10, 12-15 are presently pending and presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 103: Applicant's arguments filed 01/27/2026 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 “Paragraph [0059] of Srivastava states to use a ‘wind force detector 204’ located on e.g. a body of the vehicle. The functioning of this wind force detector is further (and only) described in [0060], wherein it is stated that it is only configured to ‘receive wind and measure the wind speed’, which is obviously not enough to estimate the side area of the vehicle (combination).”
Response:
The Examiner respectfully disagrees, Srivastava discloses:
paragraph [0059]: “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.”
paragraph [0060]: “As such, the wind force detector 204a may be configured to receive wind and measure the wind speed. In some embodiments, the wind force detector 204a is connected to the one or more sensors 204, which measure the wind speed exerted on the wind force detector 204 and report the measurements to the controller 202.”
paragraph [0083]: “the autonomous vehicle 100 detects the changed shape event caused by the added trailer (e.g., using the one or more sensors 204 and the controller 202).”
These paragraphs establish that the wind force detector is one of several sensors used in detecting a changed exterior shape (including trailer attachment). The detection of the shape-change event triggers image/scan-based determination of the vehicle’s updated dimensions (length, width, and height). See also paragraph [0007] and claim 4: “the parameter corresponds to one or more of a weight, wind drag, or engine torque of the vehicle.”
paragraph [0096]:
“Accordingly, in various embodiments, because of the scanning procedure performed on the vehicle 100 having a changed shape, the vehicle 100 is configured to determine the updated dimensions of its body including the added cargo (e.g., trailer). For example, the vehicle 100 may be configured to determine a precise length, width, and height of added cargo, and the precise overall length, width, and height of the overall shape of the vehicle 100 having the changed shape.”
The width and length dimensions obtained after the change provide the information from which side area of the vehicle combination can be estimated.
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 1-3, 5, 7, 10 and 12-15 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 12-13, 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] and “the autonomous vehicle 100 is a transport vehicle that is designed to tow one or more trailers (e.g., a semi-trailer truck)” see at least [0047]),
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] and “the autonomous vehicle 100 may be towing a trailer 102, resulting in a changed shape event of the autonomous vehicle 100 due to the addition of the trailer 102 to the vehicle 100. As such, in some embodiments, the autonomous vehicle 100 detects the changed shape event caused by the added trailer (e.g., using the one or more sensors 204 and the controller 202).” see at least [0083])
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.” see at least [0051]))
i) estimating, 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), as a projected area of a cuboid with the estimated side area on a plane perpendicular to air-attack (v) (“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. “ see at least [0075]),
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. “ see at least [0042]).
Srivastava does not explicitly teach updating, by the processing circuitry and in response to detecting such a change, a crosswind- sensitive air drag model for the vehicle combination based on an estimate of a side area of the vehicle combination obtained from the one or more images of the vehicle combination captured after the change of the exterior shape;
wherein said updating comprises at least one of: ii) using the estimated side area to update a side area-dependent drag area function ([CdA](0)) of the air drag model.
However, Laine does teach updating, by the processing circuitry and in response to detecting such a change, a crosswind- sensitive air drag model for the vehicle combination based on an estimate of a side area of the vehicle combination obtained from the one or more images of the vehicle combination captured after the change of the exterior shape (“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.” see at least [0036] and
“models may be based on physics of the given vehicle, potentially adapted based on, e.g., current vehicle load. Alternatively, or as a complement, models which describe how the environment impacts motion of a given vehicle can be trained on-line by correlating obtained environment data from the environment sensors 280 with estimated or measured vehicle state. Thus, the impact of various changes in the environment is observed, and models are constructed after the impacts have been observed. The models can then be used to predicted future impact based on measurement data from the environment sensors 280.” see at least [0048] and
“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.” see at least [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.” see at least [0064]),
ii) using the estimated side area to update a side area-dependent drag area function ([CdA](0)) of the air drag model (“One or more anemometers 116 can also be arranged on the truck 110 and/or on the trailer 120 to measure current wind speed and direction. A shift in wind speed or direction can therefore be compensated for with low latency. Since the MSD compensation for the change in wind speed or direction may be applied rapidly, the significant vehicle mass of a heavy-duty vehicle like that in FIG. 1 will not have had time to change state due to the change in wind conditions, and the overall vehicle control actions can therefore be of smaller magnitude compared to the case where large errors in vehicle state have had time to develop which need to be overcome by resolute action by the MSDs.” see at least [0038] and
“The estimated operating environment may, e.g., comprise information related to an upcoming change in rolling resistance for one or more wheels of the vehicle 100 detected by the forward looking sensors 115 and/or wind conditions detected by the one or more anemometers 116.” see at least [0046] and
“The control unit may also be arranged to account for one or more predetermined dynamic properties of an MSD by changing the set-point of said MSD ahead of the predicted impact. Thus, the predicted change in operating conditions is compensated by an actuation, which in itself comprises a prediction of how the MSD will react to a given request, or update in set-point. The future MSD behavior is then matched to the future operating conditions, thereby allowing a further increase in vehicle motion management accuracy.” see at least [0014] and
“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.” see at least [0063]. 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 teaches updating, by the processing circuitry and in response to detecting such a change, a crosswind- sensitive air drag model for the vehicle combination based on an estimate of a side area of the vehicle combination obtained from the one or more images of the vehicle combination captured after the change of the exterior shape; ii) using the estimated side area to update a side area-dependent drag area function ([CdA](0)) of the air drag model.
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 updating, by the processing circuitry and in response to detecting such a change, a crosswind- sensitive air drag model for the vehicle combination based on an estimate of a side area of the vehicle combination obtained from the one or more images of the vehicle combination captured after the change of the exterior shape; ii) using the estimated side area to update a side area-dependent drag area function ([CdA](0)) of the air drag model, 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, 0014, 0036, 0038, 0046, 0048 and 0063 - 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.” see at least [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.” see at least [0094]).
Regarding claims 5 and 7, Srivastava discloses The method according to claim 1, 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).” see at least [0087]).
Regarding claim 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.” see at least [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.” see at least [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.” see at least [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.” see at least [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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED ALKIRSH whose telephone number is (703) 756-4503. The examiner can normally be reached M-F 9:00 am-5:00 pm EST.
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/A.A./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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