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 .
Response to Amendment/Remarks
The 05/19/2026 Amendments are entered. Claims 1, 6, 9, and 12 are amended. Claims 2 and 13-14 are canceled. No claims are withdrawn or newly added. Claims 1, 3-11, and 15 remain pending.
Regarding the § 101 rejections, these rejections are withdrawn in light of the amendments made. Claim 1 and the other independent claims recite the limitation “trigger a preventative action” in response to determining the combination vehicle is unstable. This trigger integrates the claim into a practical application.
Regarding the prior art rejections, the examiner thanks Applicant’s representative for the productive May 13, 2026, interview, in which the 05/19/2026 Amendments were discussed. As indicated in the interview, the amendments overcome the § 102 rejection of Layfield. However, the arguments with respect to how Layfield does not read on the amended claims are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 6-9, 11-12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2013066215 A1 to Tagesson, Kristoffer et al (“Tagesson”), and further in view of US 20240051570 A1 to Foat, Jason (“Foat”).
Regarding claim 1, Tagesson teaches a computer system comprising processing circuitry configured to determine whether or not a combination vehicle is unstable, wherein the combination vehicle comprises a tractor and a plurality of trailers pulled by the tractor (FIG. 1), and wherein the plurality of trailers comprises a first trailer attached to the tractor (FIG. 1), the processing circuitry is further being configured to:
based on the predicted motion of the plurality of trailers, determine whether or not the combination vehicle is unstable; and
in response to determining that the combination vehicle is unstable, trigger a preventive action (Tagesson Abstract: “ . . . the arrangement is adapted to stabilise the at least one towed vehicle by using the determined yaw rate of the towing vehicle and the desired delay value for the at least one towed vehicle to establish a desired yaw rate for the at least one towed vehicle, and to control the steered axle and/or the individual brake of the at least one towed vehicle such that the determined yaw rate of the at least one towed vehicle corresponds to the desired yaw rate of the at least one towed vehicle.” Tagesson teaches determining the vehicle is unstable by teaching that the determined yaw rate does not match the desired yaw rate.).
Tagesson does not appear to expressly teach 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, 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 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;
based on the one or more relative positions of the first trailer, and based on the status of the combination vehicle, predict a motion of the plurality of trailers.
However, Foat teaches 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: a steering angle of the combination vehicle ([0041]: Steering angle used in the KBM. The other options are left out for the sake of brevity.);
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 ([0046]: Heading 216 reads broadly on the orientation. The status of the trailer as the first trailer (the one attached to the tractor) reads broadly on the position.), relative to the tractor (FIG. 2: Knowing the heading of the trailer and the tractor with respect to the same x-axis is at least indicative of the heading of the trailer relative to the tractor.);
based on the one or more relative positions of the first trailer, and based on the status of the combination vehicle, predict a motion of the plurality of trailers (p. 5, eqns (1)-(5); [0053]: The equations use tractor steering angle and speed to determine first hitch speed. First hitch speed is used to determine change in heading of the first trailer. Foat further discloses the process can be repeated for each successive trailer added on to the system.).
Foat does not appear to expressly teach based on the predicted motion of the plurality of trailers, determine whether or not the combination vehicle is unstable; and
in response to determining that the combination vehicle is unstable, trigger a preventive action.
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 determines a road train is unstable by measuring the actual yaw rate of each trailer and comparing it with a desired yaw rate of Tagesson with the system that uses a model to determine angular acceleration of each trailer in a road train from tractor dynamics of Foat. Doing so would have improved the accuracy of the estimation by employing a mathematical model for the prediction.
The examiner notes that it appears Foat refers to what is typically called “angular velocity” as “angular acceleration.” Support for this can be found in [0046] of Foat along with eqn. (2), among other places. In [0046], trailer heading is defined as θTrailer. In eqn. (2), angular acceleration is defined as the derivative of θTrailer with respect to time, typically known as angular velocity.
Regarding claim 6, the above combination of Foat and Tagesson teaches the computer system of claim 1.
This combination further teaches wherein the processing circuitry is further configured to predict the motion of the plurality of trailers by being configured to:
predict a motion of the first trailer, wherein the motion of the first trailer is predicted based on the one or more relative positions of the first trailer, and based on the status of the combination vehicle ([0046]: “Returning to FIG. 2, the heading θ.sub.Trailer 216 of trailer 204 and the velocity of hitch 206 can be used to determine trailer dynamics.”); and
predict the motion of the plurality of trailers based on the motion of the first trailer ([0053]: Foat teaches that eqns. (1)-(5) can be used to predict the motion of successive trailers.).
Regarding claim 7, the above combination of Foat and Tagesson teaches the computer system of claim 6.
This combination further teaches 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 ([0053]-[0055]: Understood that predicting the motion of successive trailers only occurs where there are more trailers in the train.).
Regarding claim 8, the above combination of Foat and Tagesson teaches the computer system of claim 6.
This combination further 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.”).
Regarding claim 9, the above combination of Tagesson and Foat teaches the computer system of claim 1.
This combination further teaches the processing circuitry is further configured to determine whether or not the combination vehicle is unstable by determining whether or not the predicted motion of the plurality of trailers is associated with a motion predefined as unstable (Tagesson Abstract: Tagesson teaches that the vehicle is controlled so that the estimated/measured yaw rate value matches the desired yaw rate value. APOSITA would have recognized in this combination that the control system of Tagesson would have used the KBM of Foat to perform the estimation for each trailer.).
Regarding claim 11, the above combination of Tagesson and Foat teaches 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 (Tagesson FIG. 1), and wherein the combination vehicle comprises the computer system of claim 1 (Tagesson Abstract: The arrangement broadly reads on the computer system.).
Claim 12 is rejected for similar reasons to claim 1, applied to a method.
Regarding claim 15, the above combination of Tagesson and Foat teaches 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 (Tagesson Claim 20).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Tagesson in view of Foat, further in view of US 20220017161 A1 to Layfield, Brian et al. (“Layfield”).
Regarding claim 3, the above combination of Tagesson and Foat teaches the computer system of claim 1.
This combination 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, 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 uses trailer heading to determine trailer motion of the above combination of Tagesson and Foat with the system that senses trailer angle of Layfield. Doing so would have improved the accuracy of the trailer motion by providing more data related to trailer heading.
APOSITA would have understood that the above combination of Tagesson, Foat, and Layfield further teaches the processing circuitry is 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 ([0043]: APOSITA would have understood that the KBM of Foat would have incorporated the sensor reading of Layfield in determining angular acceleration.).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Tagesson in view of Foat, further in view of US 20230227104 A1 to Pandey, Gaurav et al. (“Pandey”).
Regarding claim 4, the above combination of Tagesson and Foat teaches the computer system of claim 1.
This combination does not appear to expressly teach the processing circuitry is 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 the above combination of Tagesson and Foat 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 Tagesson in view of Foat, further in view of GB 2513616 A to Strano, Giovanni (“Strano”).
Regarding claim 5, the above combination of Tagesson and Foat teaches the computer system of claim 1.
This combination 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 the above combination of Tagesson and Foat 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 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tagesson in view of Foat, further in view of US 20230260288 A1 to Young, Jeremy et al. (“Young”).
Regarding claim 10, the above combination of Tagesson and Foat teaches the computer system of claim 1.
This combination 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HENRY R HINTON/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights