DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 6-12, and 14-15 are rejected under 35 U.S.C. 101 because the claims are (1) not one of the four judicial categories, and (2) are further directed to an abstract idea without significantly more.
The Examiner will now proceed through the two-prong test laid out in MPEP § 2106 on claim 1 (the present claim) to illustrate how the broadest reasonable interpretation of the claims is directed toward the judicial exception. However, the other independent and dependent claims are also directed to a judicial exception unless otherwise specified.
Firstly, the broadest reasonable interpretation (BRI) of the present claim appears a to be a computer system for determining whether the trailers pulled by a tractor are unstable.
Regarding Step 1, the present claim is directed to a machine because it describes a computer system and the function it performs. However, Per MPEP 2106.03, “[p]roducts that do not have a physical or tangible form” are not directed to any statutory category. For example, a machine is “a concrete thing, consisting of parts, or of devices and combination of devices,” a manufacture is “a tangible article . . . given new form,” and a composition of matter comprises multiple substances in combination with one another. MPEP 2106.03. While a product claim need not fit one specific category of invention to fall into one of the statutory categories, it must “fall . . . into at least one category.” Id..
Here, claim 1 only positively recites the tangible item of “a computer system comprising processing circuitry.” This appears to be a recited machine because the computer system comprising processing circuitry is a concrete thing. However, as claimed, the system does not consist of any other parts, devices, or device combinations. The preamble of the claim further states “the combination vehicle comprises a tractor and a plurality of trailers pulled by the tractor,” but this merely provides context for the computer system to perform the operations rather than positively reciting more tangible parts. Thus, as written, the claim does not fall into one of the statutory categories. The examiner notes that incorporation of claim 11, or other recitation of additional concrete, tangible parts appears to get over this rejection.
Even if the claim properly recited a system, the analysis then proceeds to Step 2A.
Regarding Step 2A, the present claim recites a judicial exception because it is (1) directed to an abstract idea; and (2) it does not recite additional elements that integrate the judicial exception into a practical application.
The present claim is (1) directed to an abstract idea, particularly a mental process. A mental process is any concept that could be interpreted as being performed by the human mind or by a human mind with a physical aid. MPEP § 2106.04(a)(2)(III). In the present claim, the claim limitations, when broadly interpreted, do not preclude a human from, in their mind or with a pen and piece of paper, “based on the one or more relative positions of the first trailer, and based on the status of the combination vehicle, estimat[ing] the position of the plurality of trailers; and based on the estimated motion of the plurality of trailers, determin[ing] whether or not the combination vehicle is unstable.” Therefore, the claim is directed to a mental process because of the high level of generality with which the limitations are recited, and analysis proceeds to step (2).
The present claim also (2) fails to integrate the judicial exception into a practical application. In a computing environment, a mental process may be integrated into a practical application where the claim goes “beyond generally linking the use of the judicial exception to a particular technological environment . . . .” MPEP § 2106.04(d)(1). Here, the generic recitation of a computer system and condition signals does not appear to the Examiner as more than generally linking the mental process defined in step 2A to being generally performed by a computer. Therefore, the present claim does not integrate the mental process into a practical application.
The present claim reciting to a mental process generically applied on computer hardware, the analysis proceeds to Step 2B.
Regarding Step 2B, the claim does not recite additional elements that amount to significantly more than the judicial exception. Additional elements of computer components to an abstract idea do not amount to significantly more than the judicial exception when, considered as a whole, the claim appears to be “[s]imply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.” MPEP § 2106.05(I)(A). The additional elements of a generically-recited obtaining data, estimating motion based on the obtained data, and determining whether the vehicle is unstable in the claim appear to be appending the well-understood, routine, conventional activity of performing processes on a computer, at a high level of generality, to the mental process of the present claim. Therefore, the present claim does not recite significantly more than the judicial exception.
The Examiner notes that while the above analysis was applied to claim 1 in particular, further steps recited in the other independent and dependent claims all feature similar issues that bar them from being considered eligible subject matter unless specified below.
The Examiner notes that claims 2 and 13 appear to overcome the rejection by reciting a controlling step that integrates the mental process into a practical application. Claim 11 appears to overcome the rejection by reciting another component of the system, fitting Claim 1 into one of the statutory categories.
Furthermore, the Examiner notes that claim 14 alone is not directed to one of the statutory categories because it claims mere program code.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6-7, 9, and 11-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20220017161 A1 to Layfield, Brian et al. (“Layfield”).
Regarding claim 1, Layfield discloses a computer system comprising processing circuitry configured to determine whether or not a combination vehicle is unstable (Layfield [0100]: “ . . . controller 502 that communicate with equipment, such as sensors 40, that may be used for, among other applications, assistance with steering and stability.”), wherein the combination vehicle comprises a tractor and a plurality of trailers pulled by the tractor, and wherein the plurality of trailers comprises a first trailer attached to the tractor (Layfield FIG. 1), the processing circuitry is further being configured to:
obtain one or more vehicle condition signals indicative of a status of the combination vehicle, the one or more vehicle condition signals being indicative of any one or more out of: an acceleration (Layfield [0100]: “The active converter dolly apparatus 14 may further include a plurality of . . . sensors 40, that may be used for, among other applications, assistance with steering and stability.” See for example FIG. 5 and the description in [0116]. Layfield discloses sensors 40 including IMUs. IMUs understood as measuring acceleration or deceleration.), a deceleration (Layfield [0100]: See previous citation.), a steering angle, at least one pedal position, a throttle status (Layfield [0112]: “”The communication interface 68 may be configured to receive various types of data from the towing vehicle 13 . . . [t]his data may include the throttle level of the main tractor . . . .”), at least one axle load, a suspension status, and a position of the combination vehicle;
obtain one or more relative positions of the first trailer, the one or more relative position respectively being indicative of a position and orientation of the first trailer, relative to the tractor (Layfield [0101]: “Furthermore, in some embodiments, sensors may be used to help identify the relative position of the converter apparatus 14 to other elements or components of the tractor-trailer 10.”; Layfield [0116]: “ . . . the set of sensors 40, such as but not limited to, a global navigation satellite system (GNSS) tracking devices . . . .” Understood that the location of the converter apparatus 14, by merit of being attached to the first trailer, is at least indicative of a position and orientation of the first trailer relative to the tractor. See [0194] of Layfield- the angle of misalignment is determined based on the relative position. Therefore, both the position and orientation of the trailer is measured. See also [0199], where the described method is taught as able to be applied between the primary trailer and the towing vehicle.);
based on the one or more relative positions of the first trailer, and based on the status of the combination vehicle, estimate a motion of the plurality of trailers (Layfield [0192], [0194]: Layfield discloses detecting jack-knifing conditions via use of accelerometers, CAN bus data from the towing vehicle, and an angle of misalignment based on the relative positions of the various vehicles. Layfield [0199]: “In other embodiments, the forward jack-knifing detection system may be used by one or more other vehicles in a tractor-trailer vehicle configuration or a road train, to detect jack-knifing risk conditions as between two adjacent vehicles. . . . Thus, each coupling between two vehicles in a drive train (of arbitrary length) may be monitored for risk of jack-knifing, either as part of the front or rear vehicle with respect to that coupling.” A person of ordinary skill in the art would have understood that estimating a jack-knifing motion of the drive train would at least be based on the relative position of the first trailer to the tractor and status of the combination vehicle as part of the entirety of the estimation for the whole train.); and
based on the estimated motion of the plurality of trailers, determine whether or not the combination vehicle is unstable (Layfield [0199]: “The forward jack-knifing detection system in at least embodiment described above may be configured to deactivate all motors in the vehicle to the rear of the coupling where the jack-knifing risk is detected.” Deactivation of the motors upon the determination the risk of jack-knifing is high (based upon the estimated motion of the vehicles discussed above) is taken as a determination that the combination vehicle is unstable.).
Regarding claim 2, Layfield discloses the computer system of claim 1, wherein the processing circuitry is further configured to:
in response to determining that the combination vehicle is unstable, trigger a preventive action
(Layfield [0199]: “The forward jack-knifing detection system in at least embodiment described above may be configured to deactivate all motors in the vehicle to the rear of the coupling where the jack-knifing risk is detected.”).
Regarding claim 6, Layfield discloses the computer system of claim 1, wherein the processing circuitry is further configured to estimate the motion of the plurality of trailers by being configured to:
estimate a motion of the first trailer, wherein the motion of the first trailer is estimated based on the one or more relative positions of the first trailer, and based on the status of the combination vehicle (Layfield [0192], [0194]: Layfield discloses detecting jack-knifing conditions via use of accelerometers, CAN bus data from the towing vehicle, and an angle of misalignment based on the relative positions of the various vehicles.); and
predict the motion of the plurality of trailers based on the motion of the first trailer (Layfield [0199]: “In other embodiments, the forward jack-knifing detection system may be used by one or more other vehicles in a tractor-trailer vehicle configuration or a road train, to detect jack-knifing risk conditions as between two adjacent vehicles. . . . Thus, each coupling between two vehicles in a drive train (of arbitrary length) may be monitored for risk of jack-knifing, either as part of the front or rear vehicle with respect to that coupling.” A person of ordinary skill in the art would have understood that estimating a jack-knifing motion of the drive train would at least be based on the relative position of the first trailer to the tractor and status of the combination vehicle as part of the entirety of the estimation for the whole train.).
Regarding claim 7, Layfield discloses the computer system of claim 6, wherein the processing circuitry is further configured to predict the motion of the plurality of trailers at least partly based on a quantity of trailers in the plurality of trailers (Layfield [0199]: “Thus, each coupling between two vehicles in a drive train (of arbitrary length) may be monitored for risk of jack-knifing, either as part of the front or rear vehicle with respect to that coupling.” The term “each” implies that depending on the number of vehicles in drive train, more or less couplings will be monitored. Thus, the motion prediction is at least partly based on the quantity of trailers.).
Regarding claim 9, Layfield discloses the computer system of claim 1, wherein the processing circuitry is further configured to determine whether or not the combination vehicle is unstable by determining whether or not the estimated motion of the plurality of trailers is associated with a motion predefined as unstable (Layfield [0197]: “In one embodiment, the determination of jack-knifing risk is binary and is determined to be present when the angle of misalignment of the rear and front vehicles exceeds a predetermined angle. In other embodiments, this angle may be modified based on predetermined or dynamically detected characteristics of the vehicles, the road conditions, or other information.”).
Regarding claim 11, Layfield discloses a combination vehicle comprising a tractor and a plurality of trailers pulled by the tractor, and wherein the plurality of trailers comprises a first trailer attached to the tractor (Layfield FIG. 1), and wherein the combination vehicle comprises the computer system of claim 1 (Layfield [0209]: “The coding of software for carrying out the above-described methods described for execution by a controller (or processor) of the dolly apparatus 14 or other apparatus is within the scope of a person of ordinary skill in the art having regard to the present disclosure. Machine readable code executable by one or more processors of one or more respective devices to perform the above-described method may be stored in a machine readable medium such as the memory of the data manager.”).
Claim 12 is rejected over similar reasons to claim 1, applied to a method.
Claim 13 is rejected over similar reasons to claim 2, applied to a method.
Regarding claim 14, Layfield discloses a computer program product comprising program code for performing, when executed by the processing circuitry, the method of claim 12 (Layfield [0209]: “The coding of software for carrying out the above-described methods described for execution by a controller (or processor) of the dolly apparatus 14 or other apparatus is within the scope of a person of ordinary skill in the art having regard to the present disclosure. Machine readable code executable by one or more processors of one or more respective devices to perform the above-described method may be stored in a machine readable medium such as the memory of the data manager.”).
Regarding claim 15, Layfield discloses a non-transitory computer-readable storage medium comprising instructions, which when executed by the processing circuitry, cause the processing circuitry to perform the method of claim 12 (Layfield [0211]: “Accordingly, the technical solution of the present disclosure may be embodied in a non-volatile or non-transitory machine readable medium (e.g., optical disk, flash memory, etc.) having stored thereon executable instructions tangibly stored thereon that enable a processing device (e.g., a data manager) to execute examples of the methods disclosed herein.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over US 20220017161 A1 to Layfield, Brian et al. (“Layfield”), further in view of a second embodiment of Layfield.
Regarding claim 3, Layfield teaches the computer system of claim 1.
Layfield does not appear to expressly teach wherein the processing circuitry is further configured to obtain the one or more relative positions of the first trailer by being configured to:
obtain sensor data indicative of a status of a kingpin of the tractor, wherein the first trailer is attached to the kingpin.
However, another embodiment of Layfield teaches wherein the processing circuitry is further configured to obtain the one or more relative positions of the first trailer by being configured to:
obtain sensor data indicative of a status of a kingpin of the tractor, wherein the first trailer is attached to the kingpin (Layfield [0193]: “ . . . the detection of a forward jack-knifing risk condition may be based on detection of an angle of misalignment between the dolly and the primary trailer. . . . The angle of misalignment may be detected directly by, e.g., a rotation sensor situated at the coupling between the dolly and the primary trailer.”; Layfield [0199]: “ . . . a further jack-knifing detection system could be installed on the towing vehicle 2512 to detect jack-knifing between the primary trailer 2510 and the towing vehicle 2512.” The coupling between vehicles is taken as the kingpin. A person of ordinary skill in the art would have recognized from [0199] that Layfield teaches a rotation sensor may be placed at the coupling between the towing vehicle and the primary trailer.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that measures trailer position using GPS and accelerometers as part of a jack-knife prevention system taught by Layfield with the system that measures trailer angle of misalignment using an angle sensor in the coupling between towing vehicle and trailer taught by another embodiment of Layfield. Doing so would have improved the reliability of the estimation by providing a redundant means by which trailer position can be measured.
One of ordinary skill in the art would have recognized this combination further teaches the system processor configured to predict the one or more relative positions of the first trailer based on the status of the combination vehicle, and the obtained sensor data (Layfield [0192], [0194]: Layfield discloses detecting jack-knifing conditions via use of accelerometers, CAN bus data from the towing vehicle, and an angle of misalignment based on the relative positions of the various vehicles.).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US 20220017161 A1 to Layfield, Brian et al. (“Layfield”), further in view of US 20230227104 A1 to Pandey, Gaurav et al. (“Pandey”).
Regarding claim 4, Layfield teaches the computer system of claim 1.
Layfield does not appear to expressly teach the computer system further configured to predict the one or more relative positions of the first trailer based on a first statistical model, wherein the first statistical model is trained on one or more vehicle training signals indicative of any one or more out of: an acceleration, a deceleration, a steering angle, at least one pedal position, a throttle status, at least one axle load, a suspension status, and a position of a first training combination vehicle, and trained on sensor data indicative of a relative position of a training trailer attached to a tractor of the first training combination vehicle.
However, Pandey teaches a computer system further configured to predict the one or more relative positions of the first trailer based on a first statistical model (Pandey [0069]: “Referring now to FIG. 5, an exemplary process diagram of a trailer angle detection routine 56 is shown. In general, the trailer angle detection routine involves (i) employing a first process 113a to identify a first estimated trailer angle γ based on the image data; (ii) simultaneously employing a second process 113b to identify a second estimated trailer angle γ based on the steering angle data and the vehicle speed data; and (iii) employing a third process 113c to produce a final, more accurate trailer angle γ estimate based on the first and second estimated trailer angles γ.”), wherein the first statistical model is trained on one or more vehicle training signals indicative of any one or more out of: an acceleration, a deceleration, a steering angle, at least one pedal position, a throttle status, at least one axle load, a suspension status, and a position of a first training combination vehicle, and trained on sensor data indicative of a relative position of a training trailer attached to a tractor of the first training combination vehicle (Pandey FIG. 6: Pandey teaches training a set of neural networks, taken as the statistical models, to use steering angle, speed, and camera data to determine trailer angle. To implement this training, a trailer angle detection apparatus 102 is used to detect the actual trailer angle. This is taken as the neural networks being trained on sensor data indicative of a relative position of a training trailer.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that estimates a trailer relative position to detect and prevent jack-knifing scenarios taught by Layfield with the system that estimates a trailer relative position using a trained neural network taught by Pandey. Doing so would have “improve[d] the reliability and accuracy of the identified trailer angle” as suggested in [0044] of Pandey.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over US 20220017161 A1 to Layfield, Brian et al. (“Layfield”), further in view of GB 2513616 A to Strano, Giovanni (“Strano”).
Regarding claim 5, Layfield teaches the computer system of claim 1.
Layfield does not appear to expressly teach wherein the processing circuitry is further configured to obtain the one or more relative positions of the first trailer by at least partly measuring the one or more relative positions of the first trailer using at least two position sensors mounted at different locations of the tractor.
However, Strano teaches wherein the processing circuitry is further configured to obtain the one or more relative positions of the first trailer by at least partly measuring the one or more relative positions of the first trailer using at least two position sensors mounted at different locations of the tractor (Strano p. 7: “ . . . the vehicle 12 is provided with additional sensors, such as ultrasonic sensors, which also detect a value for the yaw angle θ of the trailer 14. . . . [S]uch sensors are typically deployed at spaced locations across the rear of the vehicle 12, e.g. at optimal positions along a rear bumper of the vehicle 12. Each ultrasonic sensor may therefore be used to detect changes in the distance to an adjacent portion of the trailer 14 as the trailer 14 oscillates from side to side about the neutral position.” Measurements of distance understood as sensing of at least one-dimensional position.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that detects a jack-knifing condition for a vehicle comprising a tractor and multiple trailers in part by determining an angle of misalignment taught by Layfield with the system that determines the yaw of a trailer in part by using multiple distance sensors on the bumper of the pulling vehicle taught by Strano. Doing so would have improved the reliability of the system by providing an alternative means to calculate trailer angle if one means fails.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over US 20220017161 A1 to Layfield, Brian et al. (“Layfield”), further in view of US 20240051570 A1 to Foat, Jason (“Foat”).
Regarding claim 8, Layfield teaches the computer system of claim 6.
Layfield does not appear to expressly teach wherein the processing circuitry is further configured to predict the motion of the plurality of trailers at least partly based on a second statistical model trained on training data comprising a relative motion of one or more second training trailers of one or more second training combination vehicles, a number of trailers in the one or more second training combination vehicles, and motion sensor data of respective one or more training trailers in the one or more second training combination vehicles.
However, Foat teaches wherein the processing circuitry is further configured to predict the motion of the plurality of trailers at least partly based on a second statistical model (Foat [0062]: “AV 102 may include one or more machine learning models that may be associated with prediction stack 112. In some cases, simulated vehicle dynamics associated with tractor 202 and/or trailer 204 can be used to train machine learning models that predict movement of an articulated vehicle.”; Foat [0053]: “In some examples, system 500 may include an articulated vehicle having a tractor 502 and multiple trailers (e.g., trailer 504 and trailer 506).”) trained on training data comprising a relative motion of one or more second training trailers of one or more second training combination vehicles (Foat [0062]: “In some aspects, the process 600 can include training a machine learning model associated with an autonomous vehicle to predict movement of one or more articulated vehicles based on the second simulated movement.”), a number of trailers (Foat [0055]: “In some cases, additional trailers can be added to system 500. In some instances, the systems and techniques described herein can be used to determine vehicle dynamics for any number of trailers.” Understood the kinematics models defined by Foat could be modified to include the motion of more trailers, making any machine learning model trained on the kinematics model dependent on the number of trailers.) in the one or more second training combination vehicles (Foat [0062]: “AV 102 may include one or more machine learning models that may be associated with prediction stack 112. In some cases, simulated vehicle dynamics associated with tractor 202 and/or trailer 204 can be used to train machine learning models that predict movement of an articulated vehicle.”), and motion sensor data of respective one or more training trailers in the one or more second training combination vehicles (Foat [0062]: “AV 102 may include one or more machine learning models that may be associated with prediction stack 112. In some cases, simulated vehicle dynamics associated with tractor 202 and/or trailer 204 can be used to train machine learning models that predict movement of an articulated vehicle.” Understood that if the machine learning model is trained using simulated vehicle dynamics, then motion sensor data representing the dynamics of a real vehicle is used in inference time to predict movement of the articulated vehicle. See for example (Foat [0048]: “In some cases, the perception stack 112 may identify parameters or dimensions corresponding to an articulated vehicle that can be used to predict movement of the articulated vehicle. For instance, the prediction stack 116 can use data obtained by the perception stack 112 to predict movement of an articulated vehicle based on simulation data (e.g., simulated dynamics of tractor 202 and/or trailer 204).”; Foat [0022]: “The perception stack 112 can detect and classify objects and determine their current locations, speeds, directions, and the like.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system for predicting the motion of a multi-trailer system taught by Layfield with the system that predicts the motion of a multi-trailer system using a trained machine learning model trained on simulated vehicle dynamics taught by Foat. Doing so would have improved “AV operations . . . by providing a simulation environment that accurately models real-world environments” as suggested in [0014] of Foat, improving the simulation accuracy of the motion predicting system to conform better to reality.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over US 20220017161 A1 to Layfield, Brian et al. (“Layfield”), further in view of US 20230260288 A1 to Young, Jeremy et al. (“Young”).
Regarding claim 10, Layfield teaches the computer system of claim 1.
Layfield does not appear to expressly teach wherein the processing circuitry is further configured to determine whether or not the combination vehicle is unstable based on a third statistical model, the third statistical model being trained on one or more third training trailer motions of one or more third training combination vehicles, and trained by labelling one or more motions of the training trailer motions as unstable.
However, Young teaches wherein the processing circuitry is further configured to determine whether or not the combination vehicle is unstable based on a third statistical model (Young [0044]: “The assessment platform 125 receives sensor data representing a series of movement of the vehicle 105 and/or the trailer 113 over a period of time. Such sensor data may be input to a machine learning model, and in response, the machine learning model may output data indicating whether the trailer 113 is being impacted by trailer sway.”), the third statistical model being trained on one or more third training trailer motions of one or more third training combination vehicles, and trained by labelling one or more motions of the training trailer motions as unstable (Young [0044]: “The machine learning model may be trained to identify trailer sway based on historical data of past events in which trailers were impacted by trailer sway. The historical data may include sensor data, such as image data, indicating series of movements associated with said trailers during said past events.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system for measuring trailer motion and identifying times where the trailer requires stability assistance (ex. [0180]) taught by Layfield with the system that measures trailer motion and determines when the trailer is being affected by sway using a machine learning model taught by Young. Doing so would have “enable[d] a system to reliably detect potential trailer sway events based on sensor data, thereby preventing occurrences of trailer sway and improving safety” as taught by [0094] of Young.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Masters, Nathan et al.. US 20010054524 A1. Robotic Vehicle That Tracks The Path Of A Lead Vehicle.
Cebon, David et al.. WO 2019202317 A1. Method and System of Articulation Angle Measurement.
Zhao, Shi-jie. CN 108871338 B. Trailer system pose prediction method and device and storage medium.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY RICHARD HINTON whose telephone number is (703)756-1051. The examiner can normally be reached Monday-Friday 7:30-4:30.
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/HENRY R HINTON/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665