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
This action is in response to amendments and remarks filed on 03/26/2026. Claims 1-12 are considered in this office action. Claims 1-12 are pending examination.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
David does not disclose storing in a memory a model comprising a modeled tool position and heading at the initial time.
Backman does not disclose (a) storing in a memory a model comprising: (i) a modeled vehicle position and heading (pvn-1, hvn-1) at an initial time; (d) updating the model by calculating: (ii) a modeled tool position and heading (ptn, htn) at the future time, wherein ptn is based on (ptn-1, htn-1) and (pvn, hyn), and wherein htn is based on htn-1 and (pvn, hvn); and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn.
Applicant’s argument A, with respect David has been fully considered and is not persuasive. In David, the claimed "modeled tool position and heading" is the implement angle (θ) because David models the implement using coordinates and an angular orientation. David then estimates the implement's east/north position (eI,nI) and determines the implement angle (θ) from inverse kinematics based on the implement position, tractor position, tractor heading, and tow-pin geometry as seen in Table 1.
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Under the broadest reasonable interpretation (BRI) an implement heading is a direction/orientation of the implement in the horizontal plane, and the disclosed implement angle (θ) represents that orientation relative to the tractor/field coordinate system. Therefore, the disclosed implement position together with the implement angle reasonably correspond to a modeled tool position and heading, that is stored via the control processor.
Applicant’s argument B, with respect Backman has been fully considered and is not persuasive. Regarding the argument that Backman does not disclose (a) storing in a memory a model comprising: (i) a modeled vehicle position and heading (pvn-1, hvn-1) at an initial time. David discloses this part of the limitation where David explains that the estimated implement states include east position, north position, and implement angle, and further discloses that the implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (el,nl), the tractor position (ET,NT), tractor heading as well as the position of the tow pin relative to the implement and tractor positions (DESCRIPTION OF THE PREFERRED EMBODIMENT, 6. The implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (eI, nI), the tractor position (ET,NT), tractor heading (ψ) as well as the position of the tow pin relative to the implement and tractor positions (a, L, c), as shown in FIG. 2). Thus, to the extent that Backman’s articulation angle is argued not to be the claimed tool heading, David discloses the missing modeled implement heading/orientation.
Furthermore, Backman teaches the predictive updating process where the implement heading state would be incorporated from David. Backman states that (2.1 Kinematic model of the tractor-trailer system , the model of the tractor-trailer system is needed for the estimation and the control purposes. The NMPC uses the kinematic model to estimate the future in the optimization process). Backman further teaches that (3.1 The Nonlinear Model Predictive Controller, the basic idea of the NMPC is to predict the future and to minimise the cost function. The future is predicted with the mathematical model of the controlled system). Backman also identifies tractor position heading, vehicle speed, trailer articulation, and seed-coulter position as model state variables, and its tractor kinematic equations update tractor position and heading using vehicle speed and heading dynamics. Therefore, Backman provides the future state propagation of the tractor trailer model while David supplies the express implement heading/angle state and inverse kinematics relationship. Accordingly the rejection is maintained.
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 1-2, 4-6, 8-10, and 12 are all rejected under 35 U.S.C. 103 as being unpatentable over David et al (US6434462B1), in view of Backman et al. (Nonlinear Model Predictive Trajectory Control in Tractor-Trailer System for Parallel Guidance in Agricultural Field Operations).
Regarding claim 1, David discloses a computer-implemented method for controlling an agricultural tool towed by a pivotally attached vehicle (1. A control system for a work vehicle towing a towed implement…), the method comprising the steps of:(a) storing in a memory a model (DESCRIPTION OF THE PREFERRED EMBODIMENT, the control processor 54 also includes an Extended Kalman Filter (EKF) which includes a measurement update and time update, which is performed at each time step (k)) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the EKF provides estimates of all the states in Table I at every time step), comprising: (i) a modeled vehicle position and heading (pvn-1, hvn-1) at an initial time (DESCRIPTION OF THE PREFERRED EMBODIMENT, 1. The tractor has position, velocity, heading, yaw, yaw rate, yaw acceleration, heading bias, steer angle, steering angle slew rate, steer angle bias, gyro bias and radar bias parameters as shown in FIG. 1 and as indicated in Table I), and (ii) a modeled tool position (DESCRIPTION OF THE PREFERRED EMBODIMENT, 7. Control processor 54 executes an estimation algorithm which determines the 16 states listed in Table I so that the implement 16 can be a accurately controlled) Table 1 Implement Positioning States.
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and heading (ptn-1, htn-1) (DESCRIPTION OF THE PREFERRED EMBODIMENT, the implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (eI, nI)), at the initial time; (b) at or after the initial time (DESCRIPTION OF THE PREFERRED EMBODIMENT, the EKF provides estimates of all the states in Table I at every time step), (c) calculating a vehicle velocity (vv) and a rate of change in heading per unit time (rv) based on the positional data received from the first GPS receiver (DESCRIPTION OF THE PREFERRED EMBODIMENT, the tractor has position, velocity, heading, yaw, yaw rate, yaw acceleration, heading bias, steer angle, steering angle slew rate, steer angle bias, gyro bias and radar bias parameters as shown in FIG. 1 and as indicated in Table I.) … (a plurality of vehicle GPS antennas 36-42 are mounted on the tractor 10. A third GPS receiver 44 is coupled to one of the vehicle GPS antennas 42 and generates vehicle position data. A known vector unit 46, such as Part No. 27760-00, manufactured by Trimble Navigation Limited, is coupled to the vehicle GPS antennas 36-42 and generates a vehicle attitude signal including tractor yaw (ψ), roll (φ) and pitch (λ) data); (ii) a modeled tool position and heading (ptn, htn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, 7. Control processor 54 executes an estimation algorithm which determines the 16 states listed in Table I so that the implement 16 can be a accurately controlled) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (eI, nI)), at the future time, wherein ptn is based on (ptn-1, htn-1) and (pvn, hyn), and wherein htn is based on htn-1 and (pvn, hvn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, the EKF provides estimates of all the states in Table I at every time step),
However, David does not explicitly disclose, receiving positional data from a first GPS receiver attached to the vehicle so as to move in unison with the vehicle; (d) updating the model by calculating: (i) a modeled vehicle position and heading (pvn, hvn) at a future time, wherein pvn is based on pvn-1 and vv, and wherein hvn is based on hvn-1 and rv and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn.
Nevertheless, Backman who is in the same field of endeavor of model predictive trajectory discloses, receiving positional data from a first GPS receiver attached to the vehicle so as to move in unison with the vehicle (Introduction, Here the GPS positioning device is installed on tractor and the local sensor in the trailer, which is seed drill), (d) updating the model by calculating: (i) a modeled vehicle position and heading (pvn, hvn) at a future time, wherein pvn is based on pvn-1 and vv, and wherein hvn is based on hvn-1 and rv (2.1 Kinematic model of the tractor-trailer system, the model of the tractor-trailer system is needed for the estimation and the control purposes. The NMPC uses the kinematic model to estimate the future in the optimization process) … (2.1 Kinematic model of the tractor-trailer system , the differential equation of the tractor’s kinematic model is:); See figure 1.
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Fig. 1
and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn (2. TEST CONFIGURATION, Because there were two actuators which affected the position of the seed drill, the problem was a multivariable control problem. Nonlinear Model Predictive Controller (NMPC) is a natural way to accomplish these kinds of tasks).
One of ordinary skill in art prior to the effective filing date of the given invention would
have been motivated to combine David’s disclosure with Backman’s. David’s disclosure offers maintaining a state model for tractor position and heading using gps and kinematic relationships. Backman teaching of an NMPC cost function to generate control inputs to minimize implement tracking error would have been a predictable use of prior art functions. This would allow for controller updates based on the modeled vehicle and tool positions and heading. Therefore, one in the art would have found it obvious to combine the two disclosures to yield more precise and accurate results.
Justification for combining David’s disclosure with Backman not only comes
from the state of the art but from David (it is understood that many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, this invention is intended to embrace all such alternatives, modifications and variations which fall within the spirit and scope of the appended claims).
Regarding claim 2, David and Backman disclose the method of claim 1, as discussed supra. Additionally, David discloses, wherein in step (e), the control signal is further based on htn (1. control processor generating the steering control signal as a function of the actual implement position data, the vehicle position data, the implement angle signal, the steering angle signal and the desired implement position signal).
Regarding claim 4, David and Beckman disclosed, the method of claim 1, as discussed supra. Additionally, David discloses, wherein in step (d)(i), pvn is further based on a vehicle position (pv)determined from the positional data received from the first GPS receiver (BACKGROUND OF THE INVENTION, Larsen, W. E., Nielsen, G. A., Tyler, D. A., “Precision Navigation with GPS,” in Computers and Electronics in Agriculture, Vol. 11, 1995, pp. 85-95, suggest that GPS can be used to navigate a tractor and implement along a predetermined path, and appears to describe a model which, based on the geometry of the tractor and implement, determines or calculates the position of the implement). However David alone does not explicitly disclose that the hvn is further based on a vehicle heading (hv) determined from the positional data received from the first GPS receiver.
Nevertheless, Beckman discloses that the hvn is further based on a vehicle heading (hv) determined from the positional data received from the first GPS receiver Introduction, the system combines two sensors systems used in commercial parallel guidance systems, both GPS and
local sensors. Here the GPS positioning device is installed on tractor and the local sensor in the trailer, which is seed drill. The local sensor detects an edge of the adjacent swath. The hypothesis is that a nonlinear model predictive control (NMPC) is powerful approach to realize the trajectory following).
Regarding claim 5, David discloses, A system for controlling an agricultural tool towed by a pivotally attached vehicle, the system comprising: a processor operatively connected to the first GPS receiver to receive positional data therefrom (Summary of Invention, A first processor generates an implement position signal as a function of the implement position data and the reference position data. A second processor generates a vehicle position signal as a function of the vehicle position data and the reference position data), and a tangible, non-transitory computer readable medium storing instructions readable by the processor to implement a method (DESCRIPTION OF THE PREFERRED EMBODIMENT, A first processor 48 is coupled to GPS receiver 26 and to wireless receiver 34 and generates an implement position signal as a function of the implement position data and the reference position data), comprising the steps of:(a) storing in a memory a model comprising: (i) a modeled vehicle position and heading (pvn-1, hvn-1)at an initial time; and (ii) a modeled tool position and heading (ptn-1, htn-1) at the initial time; (b) at or after the initial time (BACKGROUND OF THE INVENTION, Larsen, W. E., Nielsen, G. A., Tyler, D. A., “Precision Navigation with GPS,” in Computers and Electronics in Agriculture, Vol. 11, 1995, pp. 85-95, suggest that GPS can be used to navigate a tractor and implement along a predetermined path, and appears to describe a model which, based on the geometry of the tractor and implement, determines or calculates the position of the implement) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the control processor 54 also includes an Extended Kalman Filter (EKF) which includes a measurement update and time update, which is performed at each time step (k)), and (c) calculating a vehicle velocity (vv) and a rate of change in heading per uit time (rv) based on the positional data received from the first GPS receiver (DESCRIPTION OF THE PREFERRED EMBODIMENT, the tractor has position, velocity, heading, yaw, yaw rate, yaw acceleration, heading bias, steer angle, steering angle slew rate, steer angle bias, gyro bias and radar bias parameters as shown in FIG. 1 and as indicated in Table I.) … (a plurality of vehicle GPS antennas 36-42 are mounted on the tractor 10. A third GPS receiver 44 is coupled to one of the vehicle GPS antennas 42 and generates vehicle position data. A known vector unit 46, such as Part No. 27760-00, manufactured by Trimble Navigation Limited, is coupled to the vehicle GPS antennas 36-42 and generates a vehicle attitude signal including tractor yaw (ψ), roll (φ) and pitch (λ) data), (ii) a modeled tool position and heading (ptn, htn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, 7. Control processor 54 executes an estimation algorithm which determines the 16 states listed in Table I so that the implement 16 can be a accurately controlled) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (eI, nI)), at the future time, wherein ptn is based on (ptn-1, htn-1) and (pvn, hyn), and wherein htn is based on htn-1 and (pvn, hvn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, the EKF provides estimates of all the states in Table I at every time step). However, David does not explicitly disclose, receiving positional data from a first GPS receiver attached to the vehicle so as to move in unison with the vehicle; and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn.
Nevertheless, Backman discloses, receiving positional data from a first GPS receiver attached to the vehicle so as to move in unison with the vehicle (Introduction, Here the GPS positioning device is installed on tractor and the local sensor in the trailer, which is seed drill), and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn (2. TEST CONFIGURATION, Because there were two actuators which affected the position of the seed drill, the problem was a multivariable control problem. Nonlinear Model Predictive Controller (NMPC) is a natural way to accomplish these kinds of tasks).
Regarding claim 6, David and Beckman disclose the system of claim 5 as discussed supra. Additionally, David discloses, wherein in step (e), the control signal is further based on htn (1. control processor generating the steering control signal as a function of the actual implement position data, the vehicle position data, the implement angle signal, the steering angle signal and the desired implement position signal).
Regarding claim 8, David and Beckman disclosed, the system of claim 5, as discussed supra. Additionally, David discloses, wherein in step (d)(i), pvn is further based on a vehicle position (pv)determined from the positional data received from the first GPS receiver (BACKGROUND OF THE INVENTION, Larsen, W. E., Nielsen, G. A., Tyler, D. A., “Precision Navigation with GPS,” in Computers and Electronics in Agriculture, Vol. 11, 1995, pp. 85-95, suggest that GPS can be used to navigate a tractor and implement along a predetermined path, and appears to describe a model which, based on the geometry of the tractor and implement, determines or calculates the position of the implement). However David alone does not explicitly disclose that the hvn is further based on a vehicle heading (hv) determined from the positional data received from the first GPS receiver.
Nevertheless, Beckman discloses that the hvn is further based on a vehicle heading (hv) determined from the positional data received from the first GPS receiver Introduction, the system combines two sensors systems used in commercial parallel guidance systems, both GPS and
local sensors. Here the GPS positioning device is installed on tractor and the local sensor in the trailer, which is seed drill. The local sensor detects an edge of the adjacent swath. The hypothesis is that a nonlinear model predictive control (NMPC) is powerful approach to realize the trajectory following).
Regarding claim 9, David discloses, A computer program product for controlling an agricultural tool towed by a pivotally attached vehicle the computer program product comprising a tangible, non-transitory computer readable medium storing instructions readable by a processor (Summary of Invention, A first processor generates an implement position signal as a function of the implement position data and the reference position data. A second processor generates a vehicle position signal as a function of the vehicle position data and the reference position data), to implement a method comprising the steps of: (a) storing in a memory a model comprising: (i) a modeled vehicle position and heading (pvn-1, hvn-1) at an initial time; comprising the steps of:(a) storing in a memory a model comprising: (i) a modeled vehicle position and heading (pvn-1, hvn-1)at an initial time; and (ii) a modeled tool position and heading (ptn-1, htn-1) at the initial time; (b) at or after the initial time (BACKGROUND OF THE INVENTION, Larsen, W. E., Nielsen, G. A., Tyler, D. A., “Precision Navigation with GPS,” in Computers and Electronics in Agriculture, Vol. 11, 1995, pp. 85-95, suggest that GPS can be used to navigate a tractor and implement along a predetermined path, and appears to describe a model which, based on the geometry of the tractor and implement, determines or calculates the position of the implement) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the control processor 54 also includes an Extended Kalman Filter (EKF) which includes a measurement update and time update, which is performed at each time step (k)), and (c) calculating a vehicle velocity (vv) and a rate of change in heading per uit time (rv) based on the positional data received from the first GPS receiver (DESCRIPTION OF THE PREFERRED EMBODIMENT, the tractor has position, velocity, heading, yaw, yaw rate, yaw acceleration, heading bias, steer angle, steering angle slew rate, steer angle bias, gyro bias and radar bias parameters as shown in FIG. 1 and as indicated in Table I.) … (a plurality of vehicle GPS antennas 36-42 are mounted on the tractor 10. A third GPS receiver 44 is coupled to one of the vehicle GPS antennas 42 and generates vehicle position data. A known vector unit 46, such as Part No. 27760-00, manufactured by Trimble Navigation Limited, is coupled to the vehicle GPS antennas 36-42 and generates a vehicle attitude signal including tractor yaw (ψ), roll (φ) and pitch (λ) data), (ii) a modeled tool position and heading (ptn, htn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, 7. Control processor 54 executes an estimation algorithm which determines the 16 states listed in Table I so that the implement 16 can be a accurately controlled) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (eI, nI)), at the future time, wherein ptn is based on (ptn-1, htn-1) and (pvn, hyn), and wherein htn is based on htn-1 and (pvn, hvn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, the EKF provides estimates of all the states in Table I at every time step) (ii) a modeled tool position and heading (ptn, htn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, 7. Control processor 54 executes an estimation algorithm which determines the 16 states listed in Table I so that the implement 16 can be a accurately controlled) … (DESCRIPTION OF THE PREFERRED EMBODIMENT, the implement angle (θ) is calculated from inverse kinematics using known geometrical relationships, the implement position (eI, nI)), at the future time, wherein ptn is based on (ptn-1, htn-1) and (pvn, hyn), and wherein htn is based on htn-1 and (pvn, hvn) (DESCRIPTION OF THE PREFERRED EMBODIMENT, the EKF provides estimates of all the states in Table I at every time step). However, David does not explicitly disclose, receiving positional data from a first GPS receiver attached to the vehicle so as to move in unison with the vehicle; and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn.
Nevertheless, Backman discloses, receiving positional data from a first GPS receiver attached to the vehicle so as to move in unison with the vehicle (Introduction, Here the GPS positioning device is installed on tractor and the local sensor in the trailer, which is seed drill), and (e) generating a control signal for controlling an actuator associated with the tool, wherein the control signal is based on ptn (2. TEST CONFIGURATION, Because there were two actuators which affected the position of the seed drill, the problem was a multivariable control problem. Nonlinear Model Predictive Controller (NMPC) is a natural way to accomplish these kinds of tasks).
Regarding claim 10, David and Beckman disclose the computer program product of claim 9, as discussed supra. Additionally, David discloses, wherein in step (e), the control signal is further based on htn (1. control processor generating the steering control signal as a function of the actual implement position data, the vehicle position data, the implement angle signal, the steering angle signal and the desired implement position signal).
Regarding claim 12, David and Beckman disclosed, the computer program product of claim 9 as discussed supra. Additionally, David discloses, wherein in step (d)(i), pvn is further based on a vehicle position (pv)determined from the positional data received from the first GPS receiver (BACKGROUND OF THE INVENTION, Larsen, W. E., Nielsen, G. A., Tyler, D. A., “Precision Navigation with GPS,” in Computers and Electronics in Agriculture, Vol. 11, 1995, pp. 85-95, suggest that GPS can be used to navigate a tractor and implement along a predetermined path, and appears to describe a model which, based on the geometry of the tractor and implement, determines or calculates the position of the implement). However David alone does not explicitly disclose that the hvn is further based on a vehicle heading (hv) determined from the positional data received from the first GPS receiver.
Nevertheless, Beckman discloses that the hvn is further based on a vehicle heading (hv) determined from the positional data received from the first GPS receiver Introduction, the system combines two sensors systems used in commercial parallel guidance systems, both GPS and
local sensors. Here the GPS positioning device is installed on tractor and the local sensor in the trailer, which is seed drill. The local sensor detects an edge of the adjacent swath. The hypothesis is that a nonlinear model predictive control (NMPC) is powerful approach to realize the trajectory following).
Claims 3, 7, and 11 are all rejected under 35 U.S.C. 103 as being unpatentable over David et al (US6434462B1), in view of Backman et al. (Nonlinear Model Predictive Trajectory Control in Tractor-Trailer System for Parallel Guidance in Agricultural Field Operations), further in view of Medagoda et al. (US20170144701A1).
Regarding claim 3, David and Beckman disclosed the method of claims 1 or 2, as discussed supra. Additionally, David discloses, the method further comprises, at or after the initial time, receiving positional data from a second GPS receiver attached to the tool so as to move in unison with the tool (2. an implement GPS antenna is mounted on the implement and a GPS receiver is coupled to the implement GPS antenna and generates the actual implement position data) and in step (d)(ii), ptn is further based on a tool position (pt) determined from the positional data received from the second GPS receiver (2. an implement GPS antenna is mounted on the implement and a GPS receiver is coupled to the implement GPS antenna and generates the actual implement position data). However, David does not explicitly disclose that htn, is further based on a tool heading (ht) determined from the positional data received, from the second GPS receiver.
Nevertheless, Medagoda who is in the same field of endeavor of implement steering discloses that the htn, is further based on a tool heading (ht) determined from the positional data (0058, where ψt,0 is the initial trailer heading. Equation 3.2 provides an analytic representation of trailer heading over time. A derivation of Equation 3.2 is described below. The analytic solution assumes that basic vehicle information is available (V, δS and ψv), which can be used to determine the subsequent trailer heading after a given period of time) … (15. measure the trailer heading error and cross-track error based on readings from a global positioning system (GPS) receiver and inertial sensor located on the trailer), received, from the second GPS receiver (0054, an implement GPS receiver 152A and implement inertial sensors 152B are installed on implement 104. GPS receiver 152A and inertial sensors 152B may generate and send navigation states for implement 104 to guidance system 120 via wired or wireless connections).
One of ordinary skill in art prior to the effective filing date of the given invention would have been motivated to combine the combination of David and Backman with Medagoda. This would make for a more accurate tool-pose estimation for curved paths and variable conditions by using Medagoda’s GPS data for the tool heading calculation. Therefore, one in the art would have found it obvious to combine the three disclosures to yield more precise and accurate results for suboptimal field conditions or arrangements.
Justification for combining the combination of David and Backman not only comes from the state of the art but from David (it is understood that many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, this invention is intended to embrace all such alternatives, modifications and variations which fall within the spirit and scope of the appended claims).
Regarding claim 7, David and Beckman disclosed the system of claims 5 or 6, as discussed supra. Additionally, David discloses, the system further comprises, a second GPS receiver attached to the tool so as to move in unison with the tool (2. an implement GPS antenna is mounted on the implement and a GPS receiver is coupled to the implement GPS antenna and generates the actual implement position data) and in step (d)(ii), ptn is further based on a tool position (pt) determined from the positional data received from the second GPS receiver (2. an implement GPS antenna is mounted on the implement and a GPS receiver is coupled to the implement GPS antenna and generates the actual implement position data). However, David does not explicitly disclose that htn, is further based on a tool heading (ht) determined from the positional data received, from the second GPS receiver.
Nevertheless, Medagoda discloses that the htn, is further based on a tool heading (ht) determined from the positional data (0058, where ψt,0 is the initial trailer heading. Equation 3.2 provides an analytic representation of trailer heading over time. A derivation of Equation 3.2 is described below. The analytic solution assumes that basic vehicle information is available (V, δS and ψv), which can be used to determine the subsequent trailer heading after a given period of time) … (15. measure the trailer heading error and cross-track error based on readings from a global positioning system (GPS) receiver and inertial sensor located on the trailer), received, from the second GPS receiver (0054, an implement GPS receiver 152A and implement inertial sensors 152B are installed on implement 104. GPS receiver 152A and inertial sensors 152B may generate and send navigation states for implement 104 to guidance system 120 via wired or wireless connections).
Regarding claim 11, David and Beckman disclosed the computer program product of claims 9 or 10, as discussed supra. Additionally, David discloses, the processor is operatively connected to a second GPS receiver attached to the tool so as to move in unison with the tool (2. an implement GPS antenna is mounted on the implement and a GPS receiver is coupled to the implement GPS antenna and generates the actual implement position data) and in step (d)(ii), ptn is further based on a tool position (pt) determined from the positional data received from the second GPS receiver (2. an implement GPS antenna is mounted on the implement and a GPS receiver is coupled to the implement GPS antenna and generates the actual implement position data). However, David does not explicitly disclose that htn, is further based on a tool heading (ht) determined from the positional data received, from the second GPS receiver.
Nevertheless, Medagoda discloses that the htn, is further based on a tool heading (ht) determined from the positional data (0058, where ψt,0 is the initial trailer heading. Equation 3.2 provides an analytic representation of trailer heading over time. A derivation of Equation 3.2 is described below. The analytic solution assumes that basic vehicle information is available (V, δS and ψv), which can be used to determine the subsequent trailer heading after a given period of time) … (15. measure the trailer heading error and cross-track error based on readings from a global positioning system (GPS) receiver and inertial sensor located on the trailer), received, from the second GPS receiver (0054, an implement GPS receiver 152A and implement inertial sensors 152B are installed on implement 104. GPS receiver 152A and inertial sensors 152B may generate and send navigation states for implement 104 to guidance system 120 via wired or wireless connections).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/S.E.D./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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