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, 8, 10, 11, 18 and 20 are amended.
Claims 2-6, 12-16 are cancelled.
Claims 1,7-11 and 17-20 are pending.
Response to arguments
With respect to Applicant’s remarks filed on 12/19/2025; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant remarks:
Argument should overcome 35 U.S.C. 112(b).
Kawamata does not disclose goal point, the amended claims explained the differentiation of initial goal point and modified goal point.
Eichorn does not teach determining whether the grain cart will make a sharp turn or not.
Office Response:
Argument overcomes 35 U.S.C. 112(b).
Please see new mapping for the independent and dependent claims for both arguments regarding Eichorn and Kawamata.
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 1,7-11 and 17-20 are rejected under 35 U.S.C. 103 as being obvious over Kusumakar et al., INTRALOG – intelligent autonomous truck applications in logistics; single and double articulated autonomous rearward docking onDCs, IET Intelligent Transport SystemsSpecial Issue: Selected Papers from the 25th ITS World Congress Copenhagen 2018. (herein after “Kusumakar”).
Regarding claim 1, Kusumakar teaches A system for controlling a grain cart relative to a grain truck (see abstract in-vehicle intelligent systems are focused on driver support, opening opportunities such as truck platooning. ) , wherein the grain truck includes a side edge extending between a front end and a rear end, the system comprising:
a monitoring device configured to obtain an image of the grain truck; (See Step 2 In step 3, the vehicle arrives near the receiving dock gate, which is first localised also by means of dedicated markers by UAV's cameras and accurate mutual position of the vehicle and its destination is being determined. Subsequently, a path to reach the destination is proposed, which will respect kinematic constraints of the vehicle combination.)
a controller configured to use the image from the monitoring device to identify a location of the grain truck (See Kusumakar para 3.2 Vehicle anchoring-video guidance, images support the driver and guidance to a particular receiving dock such as small boats which assist big container ships at the harbours); and
a ranging device configured to identify a position and orientation of the side edge relative to the grain cart based on the location identified by the controller; (see Kusumakar Step 4 : Autonomous path following
In this case, the driver is being instructed by haptic or visual interface how the tractor should be controlled to bring the semi-trailer to the desired position and orientation at the docking gate. It is expected that the altitude of the UAV will be controlled according to the distance between the destination point and semi-trailer. It enables to increase the accuracy of the position and orientation estimate during the last meters before reaching the destination, because of the smaller field of view covered with the fixed resolution of the camera)
wherein the controller is configured to:
derive a truck line by fitting a first order model to the side edge; (see Kusumakar The second method was the pure pursuit method which also used a geometric method of fitting circular arcs between the rear axle and the goal point and determining the angle between them, Pure pursuit model is considered a first-order path following algorithm)
determine a path line parallel to the side edge, wherein the path line is a first predetermined distance from the side edge; (see Kusumakar similar to reference line, see figures 5 and 6)
identify an initial goal point based on the path line, wherein the initial goal point is a second predetermined distance from the one of the front and the rear end of the side edge, wherein the second predetermined distance is greater than the first predetermined distance; (it is possible to extend the edge of the trailer to the reference path and initial goal point as intersection points between edge and reference path)
identify an initial perpendicular line that is perpendicular to the truck line wherein the initial perpendicular line extends through the one of the front and the rear end of the side edge and wherein the initial goal point aligns with the initial perpendicular line;
determine whether the grain cart will make a sharp turn from the initial goal point to the path line (controller can consider the turn is sharp turn or not) , and if the controller determines that the grain cart will make the sharp turn (see Kusumakar para 4.2 Simulation results The common manoeuvres in a docking yard would be mostly a 45°, 60° or 90° turn., 90 degree is considered to a sharp turn), the controller is further configured to:
determine an offset point that is a third predetermined distance from the one of the front and the rear end of the side edge, wherein the offset point lies along the truck line; (see Kusumakar 2.3.1 reversing controller, 2.3.2 forward controller)
identify an offset perpendicular line that is perpendicular to the truck line and going through the offset point; and
move the initial goal point on the initial perpendicular line to a modified goal point on the offset perpendicular line; and (see Kusumakar figures 5 and 6)
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Figure 5 of Kusumakar
plan a path for the grain cart to the modified goal point; (see Step 3 : Identify, measure and plan the path Finally, in step 4, the vehicle combination will be navigated along the planned path either autonomously by directly actuating the steering angle and the reverse speed of the tractor by the controller or semi-autonomously)
drive the grain cart along the path to the modified goal point (See Step 4 : Autonomous path following In this case, the driver is being instructed by haptic or visual interface how the tractor should be controlled to bring the semi-trailer to the desired position and orientation at the docking gate); and
drive the grain cart from the modified goal point to the path line (when cart reach to the goal point, it is possible to drive the cart from the goal point to path line)
determine whether the grain cart will make a sharp turn from the initial goal point to the path line (See 4.2 Simulation results: Hence, two test cases were developed based on the requirements which were first being a 90° curve, meaning the simulation result is based on sharp turn which identified using the controller),
However, Kusumaka fails to explicitly mentioned/defined distance as below in the art:
identify an initial goal point based on the path line, wherein the initial goal point is a second predetermined distance from the one of the front and the rear end of the side edge,
wherein the second predetermined distance is greater than the first predetermined distance, identify an initial perpendicular line that is perpendicular to the truck line wherein the initial perpendicular line extends through the one of the front and the rear end of the side edge and wherein the initial goal point aligns with the initial perpendicular line;
identify an offset perpendicular line that is perpendicular to the truck line and going through the offset point; and
move the initial goal point on the initial perpendicular line to a modified goal point on the offset perpendicular line.
However, the aforementioned controller functionality is encompassed and/or at least suggested by the teachings and/or context per Kusumaka such that the aforementioned controller functionality relates to one of the particular means via which one of ordinary skill in the art would identify a initial point on the reference path (see figure 5) and define the distance between initial point to edges as a second predetermined distance as second predetermined distance is greater that first predetermined distance, identify the offset perpendicular line that is perpendicular to the truck/path line(in prior art reference path) and consider a initial goal point on that line which need to modified to find actual goal point, determine a line parallel to the side edge of the trailer and defined the distance between line and edge as first predetermined distance.
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(All the definition and length identified in figure 5 of Kusumaka as present in the invention)
The modified goal point can be considered based on the location of the trailer (in reference the trailer is behind/parallel of the tractor and in the invention the trailer is perpendicular) as below since path line is parallel to truck line, (next figure is drawn only changing the location of trailer based on modified goal point)
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Using the aforementioned controller in the art, it would be obvious to determine if there is a sharp turn (90 degree) or not.
As such, it would have been obvious to one of ordinary skill in the art and/or merely involve routine skill in the art to accordingly have the controller/system per Kusumaka specifically configured to achieve the particular determination of distance and identifying the initial goal points, which is simple and obvious mathematical calculation.
Regarding claim 7, Kusumakar teaches wherein the controller is configured to use one of a pure pursuit algorithm (see Kusumakar controller perspective This pure geometric method follows the pure pursuit method of path tracking as used in [18].) and a model predictive control to drive the cart along the path.
Regarding claim 8, Kusumakar teaches wherein the controller is configured to drive the grain cart along the path line while the grain cart unloads crop material into the grain truck. (see Kusumakar step 3 Finally, in step 4, the vehicle combination will be navigated along the planned path either autonomously by directly actuating the steering angle and the reverse speed of the tractor by the controller or semi-autonomously- in 3.2 Vehicle anchoring).
Regarding claim 9, Kusumakar teaches wherein the controller is configured to use one of a Stanley controller(see Kusumakar para 2.2 Controller perspective - The first was the Stanley method which used the geometric path tracking algorithm to determine the steering angle to the nearest goal point. ), a rear wheel based feedback method(See Kusumakar ,Moreover, the third was a model-based state feedback control method. ),, and a model predictive control to drive the cart along the path. (See Kusumakar,para 2.2 Controller perspective - Moreover, the third was a model-based state feedback control method).
Regarding claim 10, Kusumakar teaches wherein the controller is configured to use Dubins Path to plan the path for the grain cart to the modified goal point. (see Kusumakar,This approach uses the Dubin's curve to determine the shortest kinematically viable path to the destined gates to dock the trailer. Figure 18.).
Regarding claim 11, Kusumakar teaches A method for a controller on a grain cart to control the grain cart relative to a grain truck(see abstract in-vehicle intelligent systems are focused on driver support, opening opportunities such as truck platooning. ), wherein the grain truck includes a side edge extending between a front end and a rear end, the method comprising the steps of: obtaining, by a monitoring device mounted on the grain cart, an image of the grain truck; (See Step 2 In step 3, the vehicle arrives near the receiving dock gate, which is first localised also by means of dedicated markers by UAV's cameras and accurate mutual position of the vehicle and its destination is being determined. Subsequently, a path to reach the destination is proposed, which will respect kinematic constraints of the vehicle combination.)
using the image from the monitoring device to identify a location of the grain truck; (See para 3.2 Vehicle anchoring-video guidance, images support the driver and guidance to a particular receiving dock such as small boats which assist big container ships at the harbours);
determining, by a ranging device mounted on the grain cart, a position and orientation of the side edge relative to the grain cart based on the location; (see Step 4 : Autonomous path following In this case, the driver is being instructed by haptic or visual interface how the tractor should be controlled to bring the semi-trailer to the desired position and orientation at the docking gate. It is expected that the altitude of the UAV will be controlled according to the distance between the destination point and semi-trailer. It enables to increase the accuracy of the position and orientation estimate during the last meters before reaching the destination, because of the smaller field of view covered with the fixed resolution of the camera)
deriving a truck line by fitting a first order model to the side edge; (see The second method was the pure pursuit method which also used a geometric method of fitting circular arcs between the rear axle and the goal point and determining the angle between them, Pure pursuit model is considered a first-order path following algorithm )
determining a path line parallel to the side edge, wherein the path line is a first predetermined distance from the side edge; (see similar to reference line)
identifying an initial goal point based on the path line, wherein the initial goal point is a second predetermined distance from one of the front and the rear end of the side edge, wherein the second predetermined distance is greater than the first predetermined distance; (it is possible to extend the edge of the trailer to the reference path and initial goal point as intersection points between edge and reference path)
identifying an initial perpendicular line that is perpendicular to the truck line wherein the initial perpendicular line extends through the one of the front and the rear end of the side edge and wherein the initial goal point aligns with the initial perpendicular line;
determining whether the grain cart will make a sharp turn from the initial goal point to the path line, and if it is determined that the grain cart will make the sharp turn, (see para 4.2 Simulation results The common manoeuvres in a docking yard would be mostly a 45°, 60° or 90° turn., 90 degree is considered to a sharp turn),
the method further comprises the steps of:
determining an offset point that is a third predetermined distance from the one of the front and the rear end of the side edge wherein the offset point lies along the truck line; ; (see Kusumakar 2.3.1 reversing controller, 2.3.2 forward controller)
identifying an offset perpendicular line that is perpendicular to the truck line and going through the offset point; and (see Kusumakar figures 5 and 6)
moving the initial goal point on the initial perpendicular line to a modified goal point on the offset perpendicular line; (see Kusumakar figures 5 and 6) and planning a path for the grain cart to the modified goal point; (see Step 3 : Identify, measure and plan the path
Finally, in step 4, the vehicle combination will be navigated along the planned path either autonomously by directly actuating the steering angle and the reverse speed of the tractor by the controller or semi-autonomously)
driving the grain cart along the path to the modified goal point(See Step 4 : Autonomous path following
In this case, the driver is being instructed by haptic or visual interface how the tractor should be controlled to bring the semi-trailer to the desired position and orientation at the docking gate);; and
driving the grain cart from the modified goal point to the path line. (when cart reach to the goal point, it is possible to drive the cart from the goal point to path line).
However, Kusumaka fails to explicitly teach:
identify an initial goal point based on the path line, wherein the initial goal point is a second predetermined distance from the one of the front and the rear end of the side edge, wherein the second predetermined distance is greater than the first predetermined distance, identify an initial perpendicular line that is perpendicular to the truck line wherein the initial perpendicular line extends through the one of the front and the rear end of the side edge and wherein the initial goal point aligns with the initial perpendicular line; determine whether the grain cart will make a sharp turn from the initial goal point to the path line, identify an offset perpendicular line that is perpendicular to the truck line and going through the offset point; and move the initial goal point on the initial perpendicular line to a modified goal point on the offset perpendicular line.
However, the aforementioned controller functionality is encompassed and/or at least suggested by the teachings and/or context per Kusumaka such that the aforementioned controller functionality relates to one of the particular means via which one of ordinary skill in the art would identify a initial point on the reference path (see figure 5) and define the distance between initial point to edges as a second predetermined distance as second predetermined distance is greater that first predetermined distance, identify the offset perpendicular line that is perpendicular to the truck/path line(in prior art reference path) and consider a initial goal point on that line which need to modified to find actual goal point, determine a line parallel to the side edge of the trailer and defined the distance between line and edge as first predetermined distance.
Using the aforementioned controller in the art, it would be obvious to determine if there is a sharp turn(90 degree) or not.
As such, it would have been obvious to one of ordinary skill in the art and/or merely involve routine skill in the art to accordingly have the controller/system per Kusumaka specifically configured to achieve the particular determination of distance and identifying the initial goal points, which is simple and obvious mathematical calculation.
Regarding claim 17, Kusumakar teaches further comprising the step of using one of a pure pursuit algorithm (see Kusumakar controller perspective This pure geometric method follows the pure pursuit method of path tracking as used in [18].) and a model predictive control to drive the cart along the path. (See Kusumakar,para 2.2 Controller perspective - Moreover, the third was a model-based state feedback control method).
Regarding claim 18, Kusumakar teaches further comprising the step of driving the grain cart along the path line while the grain cart unloads crop material into the grain truck. (see Kusumakar step 3 Finally, in step 4, the vehicle combination will be navigated along the planned path either autonomously by directly actuating the steering angle and the reverse speed of the tractor by the controller or semi-autonomously- in 3.2 Vehicle anchoring).
Regarding claim 19, Kusumakar teaches wherein the controller uses one of a Stanley controller (see Kusumakar para 2.2 Controller perspective - The first was the Stanley method which used the geometric path tracking algorithm to determine the steering angle to the nearest goal point. ), a rear wheel based feedback method (See Kusumakar, Moreover, the third was a model-based state feedback control method. ), and a model predictive control to drive the cart along the path. (See Kusumakar, para 2.2 Controller perspective - Moreover, the third was a model-based state feedback control method).
Regarding claim 20, Kusumakar teaches further comprising the step of using Dubins Path to plan the path for the grain cart to the modified goal point. (see Kusumakar, This approach uses the Dubin's curve to determine the shortest kinematically viable path to the destined gates to dock the trailer. Figure 18.).
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 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.
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/NAZIA AFRIN/ Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666
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