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
This action is in response to amendments and remarks filed on 04/27/2026. Claim(s) 1 and 10 have been amended. Claim(s) 3-6, 12-15, 21, and 24 have been cancelled. Claim(s) 1-2, 7-11, 16-20, 22-23, and 25-26 are pending examination. This action is made final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 103 rejection of independent claims 1 and 10 is improper. Applicant asserts that the prior art fails to teach all claim limitations as amended.
Applicant's arguments filed 04/27/2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument A, the examiner respectfully disagrees. Applicant’s arguments appear to be a broad claim of hindsight reasoning and that each reference individually fails to teach the invention as claimed.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant broadly argues that the results obtained by the examiner are based on hindsight reasoning and then attacks each reference individually. Applicant’s arguments do not convince the examiner that the prior art doesn’t render the claims as obvious. Applicant’s broad assertions and subsequent broad allegations that the prior art doesn’t work together do not convince the examiner that the claims are not obvious in view of the prior art.
Accordingly independent claims 1 and 10, which is substantially similar to claim 1, would continue to be rejected under 35 USC 103. Dependent claims 2, 8-9, 11, and 16-20, 22-23, and 25-26 would be rejected at least due to their dependence on rejected subject matter. For more detailed mapping and explanation see the section below titled, “Claim Rejections – 35 USC 103.”
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-2, 10-11, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakkar (US PG Pub 2024/0147888) in view of Morimoto (US PG Pub 2024/0099176) Tahiliani (US PG Pub 2022/0374019), and Senneff (US PG Pub 2009/0037041).
Regarding claim 1, Kakkar teaches a computer-implemented method of guidance and/or automation for a self-propelled work vehicle operating within a defined work area, the method comprising: determining at least a first non-passable exterior boundary ([0080] teach a non-passable boundary) and (Fig. 7 item 704 and [0080] teach the system receiving a boundary of a work area) ; and
for each of the plurality of passes by the work vehicle: ([0025]-[0027] and [0072]-[0073] which teach a series of sensors that can be used to track the position and movement information of the vehicle and the vehicle work implement. This would be analogous to the work vehicle kinetic as it is tracking vehicle kinetic information. [0054]-[0055] teach the vehicle using sensor information to determine the current and future pathing of the vehicle as it moves through the environment.)
based on the at least one or more vehicle motion characteristic monitored for a current pass of the work vehicle determining a current vehicle path ([0055]-[0056] teach monitoring the vehicle movement information for a current path to determine a current vehicle pathing through the environment)
automatically and dynamically generating a revised vehicle path ([0077]-[0079] teach a revised vehicle pathing dynamically generated based on the sensed vehicle movement information) for (([0025]-[0027] and [0072]-[0073] which teach a series of sensors that can be used to track the position and movement information of the vehicle and the vehicle work implement. This would be analogous to the work vehicle kinetic as it is tracking vehicle kinetic information. [0054]-[0055] teach the vehicle using sensor information to determine the current and future pathing of the vehicle as it moves through the environment. [0070] further teaches inputting vehicle motion characteristics, [0077]-[0079] teach adjusting the trajectory of the vehicle path based on the intersection of the boundary along the current vehicle pathing and the vehicle motion characteristics), and
producing one or more output signals corresponding to the revised vehicle path; ([0074] teaches the system having an ECU that can output controls associated with vehicle movement) and
a controlled intervention in at least an advance of the work vehicle with respect to the portion of the at least one boundary, responsive to the produced one or more output signals ([0074] teaches the system having an ECU that can output controls associated with vehicle movement, [0057] teaches a control module that can then control the vehicle’s movement)
comprising automating implementation of the revised vehicle path via automatically controlled operating parameters for the work vehicle. ([0074] teaches the system as automatically implementing vehicle controls to avoid an intersection with the boundary)
Kakkar does not teach a first passable boundary internal with respect to the first non-passable exterior, wherein the work area coverage associated with an operation is defined by work performed during a plurality of passes across an interior defined by the first passable boundary, a current pass for the work vehicle, upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering; transition between the current pass and a subsequent pass, based on the at least first non-passable boundary and the at least first passable boundary, and a best fit analysis with respect to contours of the work area, and current vehicle operating conditions and one or more of cost parameters, time parameters and/or operator parameters to maximize an amount of work area coverage with respect to a minimal number of passes and transitions in the operation.
However, Morimoto teaches “a first passable boundary internal with respect to the first non-passable exterior,” (10 AL, [0037], and [0063] teach the setting of an internal avoidance route AL which is an internal border with respect to the boundary) “wherein the work area coverage associated with an operation is defined by work performed during a plurality of passes across an interior defined by the first passable boundary,” (Fig. 2 shows a work area, i.e. a farm field, defined by a series of linear travel routes which are analogous to the passes defined here, [0030]-[0031] teach the passes a performed with reference to passable/impassable boundaries) “determining a current vehicle path along a current pass for the work vehicle,” (Fig. 2 and [0030]-[0031] teach the work path as defined by the linear travel routes) and “transition between the current pass and a subsequent pass, based on the at least first non-passable boundary and the at least first passable boundary,” (Figs. 2, 3, and 12; and [0030]-[0033] teach the work vehicle as transitioning from the current to a subsequent pass)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar and Morimoto; and have a reasonable expectation of success. Both relate to the control of vehicles as they traverse a work zone. Both also teach the idea of boundaries to restrict access between areas. Morimoto [0006]-[0007] teaches the ideas of avoiding a boundary line and this avoidance being based on the movement of a vehicle. In [0037] the teaching of an avoidance line (AL) is set. This avoidance line allows for the system to determine that it is nearing a boundary line. Morimoto also teaches using the boundaries to determine the number and shape of pass transitions. As taught in [0004]-[0008] the crux of Morimoto allows for smooth path transitions in a restricted environment. This restriction is caused by boundary lines of the work area. Ensuring a smooth transition between them ensures optimal work flow and a maximum work efficiency.
Kakkar and Morimoto fail to teach on a best fit analysis with respect to contours of the work area, and current vehicle operating conditions and one or more of cost parameters, time parameters and/or operator parameters to maximize an amount of work area coverage with respect to a minimal number of passes and transitions in the operation; upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.
However, Tahiliani teaches “a best fit analysis with respect to contours of the work area, and current vehicle operating conditions and one or more of cost parameters, time parameters and/or operator parameters to maximize an amount of work area coverage with respect to a minimal number of passes and transitions in the operation.” (Figs. 3A-7B, [0032], and [0041]-[0052] teach a system that can determine the shape of a work area, determine an optimal number of tracks to take over the area to maximize work area coverage while minimizing a plethora of values, including total distance traveled. The examiner finds the tracks to be analogous to the current claims passes. Additionally, the minimizing of total distance travelled would be the result of a minimal number of passes. Furthering the idea of contours [0038]-[0039] teach the boundary area can have an irregular shape, while [0069] and [0076] teach the system knowing the location of various static obstacles to avoid. The irregular shape and obstacle avoidance would be analogous to various contours of the working area. Additionally, Fig. 19 and [0085]-[0088] teach the system able to dynamically adapt the route in the event of any changes necessary to the route. This allows the system to add stops and new routes in an optimal way. The system can dynamically change the way the vehicle is operating based on a series of operating conditions in view of the total cost, time, or other applicable restriction.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar and Morimoto with Tahiliani; and have a reasonable expectation of success. All relate to the control of work vehicles in a specific work environment. As Tahiliani teaches in [0004] that there is a need for a system that can precisely and optimally make a track for the vehicle to follow. The system would maximize efficiency for the vehicle, preventing unnecessary turns, passes, and/or other movements. [0032] furthers this teaching by ensuring that a work area coverage is maximized for the lowest distance travelled. This would result in both fuel and cost savings and provide a more efficient way moving across the work area. Additionally, As Tahiliani teaches in [0087] there is a need to dynamically adapt a route for a work vehicle. By being able to constrain it based on the operational parameters of the vehicle it prevents cost overruns and excessive time being dedicated to the vehicle making long or unnecessary movements to adjusted route parameters.
The combination of Kakkar, Morimoto, and Tahiliani does not teach upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.
However, Senneff teaches “upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.” (Figs. 2, 4, and 5; and [0031]-[0032] and [0035]-[0048], teach the vehicle can determine the location of the vehicle and based on this location determination it can affect the control of the vehicle. When the vehicle is determined to have crossed the boundary the system takes some automatic actions. These include monitoring the vehicle position, and motion as well as the implement of the vehicle. The system can then, based on the monitoring, adjust the turning of the vehicle in order to better match an “ideal path.” This is triggered by the vehicle crossing a traversable boundary. The vehicle is automatically controlled by the determination of crossing the boundary and in relation to this the vehicle can turn around in the headland region.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, and Tahiliani with Senneff; and have a reasonable expectation of success. All relate to the control of work vehicles in a specific work environment. As established in [0002]-[0003] the end turn portion of agriculture vehicle travel is very important, and may be difficult to ensure that the end turn matches an ideal end turn. The automatic monitoring and adjustment of this turning ensures the most ideal pathing is followed for the vehicle.
Regarding claim 2, Kakkar teaches the method of claim 1, wherein the one or more vehicle motion characteristics comprise an available minimum turn radius ([0070] teaches the vehicle having a minimum turning radius used to classify its motion) and/or a wheel angle rate determined with respect to the work vehicle. ([0044] teaches the wheel angle of the vehicle used to classify its motion)
Regarding claim 10, Kakkar teaches a system for guidance and/or automation of a self-propelled work vehicle operating within a defined work area, the system comprising: data storage having stored thereon one or more vehicle motion characteristics; ([0068] teaches the system having a storage unit which saves the relevant information such as boundaries and motion profiles)
one or more sensors configured to detect at least one of a position, motion, configuration, or kinetic of the work vehicle; ([0025]-[0027] and [0072]-[0073] which teach a series of sensors that can be used to track the position and movement information of the vehicle and the vehicle work implement. This would be analogous to the work vehicle kinetic as it is tracking vehicle kinetic information. [0054]-[0055] teach the vehicle using sensor information to determine the current and future pathing of the vehicle as it moves through the environment.)
at least one computing device functionally linked to the data storage and configured to direct the performance of operations ([0065] teaches the use of a computing system to execute the control and linked to the storage system) comprising:
determining at least a first non-passable exterior boundary ([0080] teach a non-passable boundary) and for the defined work area,(Fig. 7 item 704 and [0080] teach the system receiving a boundary of a work area)
for each of the plurality of passes by the work vehicle: ([0025]-[0027] and [0072]-[0073] which teach a series of sensors that can be used to track the position and movement information of the vehicle and the vehicle work implement. [0054]-[0055] teach the vehicle using sensor information to determine the current and future pathing of the vehicle as it moves through the environment.)
based on at least one of the one or more vehicle motion characteristic monitored for a current pass of the work vehicle determining a current vehicle path ([0055]-[0056] teach monitoring the vehicle movement information for a current path to determine a current vehicle pathing through the environment)
automatically and dynamically generating a revised vehicle path ([0077]-[0079] teach a revised vehicle pathing dynamically generated based on the sensed vehicle movement information) for (([0025]-[0027] and [0072]-[0073] which teach a series of sensors that can be used to track the position and movement information of the vehicle and the vehicle work implement. This would be analogous to the work vehicle kinetic as it is tracking vehicle kinetic information. [0054]-[0055] teach the vehicle using sensor information to determine the current and future pathing of the vehicle as it moves through the environment. [0070] further teaches inputting vehicle motion characteristics, [0077]-[0079] teach adjusting the trajectory of the vehicle path based on the intersection of the boundary along the current vehicle pathing and the vehicle motion characteristics), and
producing one or more output signals corresponding to the revised vehicle path; ([0074] teaches the system having an ECU that can output controls associated with vehicle movement) and
a controlled intervention in at least an advance of the work vehicle with respect to the portion of the at least one boundary, responsive to the produced one or more output signals ([0074] teaches the system having an ECU that can output controls associated with vehicle movement, [0057] teaches a control module that can then control the vehicle’s movement)
comprising automating implementation of the revised vehicle path via automatically controlled operating parameters for the work vehicle. ([0074] teaches the system as automatically implementing vehicle controls to avoid an intersection with the boundary)
Kakkar does not teach a first passable boundary internal with respect to the first non-passable exterior, wherein the work area coverage associated with an operation is defined by work performed during a plurality of passes across an interior defined by the first passable boundary, the current pass, transition between the current pass and a subsequent pass, based on the at least first non-passable boundary and the at least first passable boundary, and an optimization routine for maximizing an amount of work area coverage with respect to a minimal number of passes and transitions in the operation; and upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.
However, Morimoto teaches “a first passable boundary internal with respect to the first non-passable exterior,” (10 AL, [0037], and [0063] teach the setting of an internal avoidance route AL which is an internal border with respect to the boundary) “wherein the work area coverage associated with an operation is defined by work performed during a plurality of passes across an interior defined by the first passable boundary,” (Fig. 2 shows a work area, i.e. a farm field, defined by a series of linear travel routes which are analogous to the passes defined here, [0030]-[0031] teach the passes a performed with reference to passable/impassable boundaries) “determining a current vehicle path along a current pass,” (Fig. 2 and [0030]-[0031] teach the work path as defined by the linear travel routes) and “transition between the current pass and a subsequent pass, based on the at least first non-passable boundary and the at least first passable boundary.” (Figs. 2, 3, and 12; and [0030]-[0033] teach the work vehicle as transitioning from the current to a subsequent pass)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar and Morimoto; and have a reasonable expectation of success. Both relate to the control of vehicles as they traverse a work zone. Both also teach the idea of boundaries to restrict access between areas. Morimoto [0006]-[0007] teaches the ideas of avoiding a boundary line and this avoidance being based on the movement of a vehicle. In [0037] the teaching of an avoidance line (AL) is set. This avoidance line allows for the system to determine that it is nearing a boundary line. Morimoto also teaches using the boundaries to determine the number and shape of pass transitions. As taught in [0004]-[0008] the crux of Morimoto allows for smooth path transitions in a restricted environment. This restriction is caused by boundary lines of the work area. Ensuring a smooth transition between them ensures optimal work flow and a maximum work efficiency.
Kakkar and Morimoto fail to teach on a best fit analysis with respect to contours of the work area, and current vehicle operating conditions and one or more of cost parameters, time parameters and/or operator parameters to maximize an amount of work area coverage with respect to a minimal number of passes and transitions in the operation; and upon the work vehicle crossing the first passable boundary, automatically triggering; and upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.
However, Tahiliani teaches “a best fit analysis with respect to contours of the work area, and current vehicle operating conditions and one or more of cost parameters, time parameters and/or operator parameters to maximize an amount of work area coverage with respect to a minimal number of passes and transitions in the operation.” (Figs. 3A-7B, [0032], and [0041]-[0052] teach a system that can determine the shape of a work area, determine an optimal number of tracks to take over the area to maximize work area coverage while minimizing a plethora of values, including total distance traveled. The examiner finds the tracks to be analogous to the current claims passes. Additionally, the minimizing of total distance travelled would be the result of a minimal number of passes. Furthering the idea of contours [0038]-[0039] teach the boundary area can have an irregular shape, while [0069] and [0076] teach the system knowing the location of various static obstacles to avoid. The irregular shape and obstacle avoidance would be analogous to various contours of the working area. Additionally, Fig. 19 and [0085]-[0088] teach the system able to dynamically adapt the route in the event of any changes necessary to the route. This allows the system to add stops and new routes in an optimal way. The system can dynamically change the way the vehicle is operating based on a series of operating conditions in view of the total cost, time, or other applicable restriction.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar and Morimoto with Tahiliani; and have a reasonable expectation of success. All relate to the control of work vehicles in a specific work environment. As Tahiliani teaches in [0004] that there is a need for a system that can precisely and optimally make a track for the vehicle to follow. The system would maximize efficiency for the vehicle, preventing unnecessary turns, passes, and/or other movements. [0032] furthers this teaching by ensuring that a work area coverage is maximized for the lowest distance travelled. This would result in both fuel and cost savings and provide a more efficient way moving across the work area. Additionally, As Tahiliani teaches in [0087] there is a need to dynamically adapt a route for a work vehicle. By being able to constrain it based on the operational parameters of the vehicle it prevents cost overruns and excessive time being dedicated to the vehicle making long or unnecessary movements to adjusted route parameters.
The combination of Kakkar, Morimoto, and Tahiliani does not teach upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.
However, Senneff teaches “upon the work vehicle crossing the first passable boundary into a headland region proximate to the first non-passable exterior boundary, automatically triggering.” (Figs. 2, 4, and 5; and [0031]-[0032] and [0035]-[0048], teach the vehicle can determine the location of the vehicle and based on this location determination it can affect the control of the vehicle. When the vehicle is determined to have crossed the boundary the system takes some automatic actions. These include monitoring the vehicle position, and motion as well as the implement of the vehicle. The system can then, based on the monitoring, adjust the turning of the vehicle in order to better match an “ideal path.” This is triggered by the vehicle crossing a traversable boundary. The vehicle is automatically controlled by the determination of crossing the boundary and in relation to this the vehicle can turn around in the headland region.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, and Tahiliani with Senneff; and have a reasonable expectation of success. All relate to the control of work vehicles in a specific work environment. As established in [0002]-[0003] the end turn portion of agriculture vehicle travel is very important, and may be difficult to ensure that the end turn matches an ideal end turn. The automatic monitoring and adjustment of this turning ensures the most ideal pathing is followed for the vehicle.
Regarding claim 11, Kakkar teaches the system of claim 10, wherein the stored one or more vehicle motion characteristics comprise an available minimum turn radius ([0070] teaches the vehicle having a minimum turning radius used to classify its motion) and/or a wheel angle rate determined with respect to the work vehicle. ([0044] teaches the wheel angle of the vehicle used to classify its motion)
Regarding claim 19, Kakkar teaches a self-propelled work vehicle comprising the system of claim 10. ([0065] teaches the use of a self-propelled vehicle)
Regarding claim 20, Kakkar teaches the work vehicle of claim 19, wherein the at least one computing device comprises a vehicle controller. ([0065] teaches the use of a vehicle ECU to control the operations)
Claim(s) 7-9 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakkar, Morimoto, Tahiliani, and Senneff in view of Bast (US PG Pub 2022/0167543).
Regarding claim 7, Kakkar teaches the method of claim 1, wherein the one or more output signals are provided to an onboard display unit associated with the work vehicle ([0067] teaches the vehicle having a display/output unit to show vehicle information
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach to generate a display highlighting one or more aspects of the revised vehicle path.
However, Bast teaches “to generate a display highlighting one or more aspects of the revised vehicle path” ([0032] teaches the system displaying altered paths/relevant pathing information)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Bast; and have a reasonable expectation of success. All relate to the control of work vehicles as they approach the end of a row. The highlighting of a revised route would be obvious to try. GPS devices nowadays very often display a highlighting route and displays often highlight things to bring a user’s attention to a modified route. This allows for a user to operate a vehicle as best possible.
Regarding claim 8, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the method of claim 1.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach analyzing one or more of a plurality of predetermined vehicle turn types with respect to the transition between the current pass and the subsequent pass, based on the at least first non-passable boundary and the at least first passable boundary, based at least in part on a detected position and detected motion of the work vehicle, further accounting for at least one of the one or more vehicle motion characteristics, and selectively generating the revised vehicle path as corresponding to at least one of the one or more predetermined vehicle turn types.
However, Bast teaches “analyzing one or more of a plurality of predetermined vehicle turn types with respect to the transition between the current pass and the subsequent pass, based on the at least first non-passable boundary and the at least first passable boundary,” ([0036]-[0037] teaches the analysis of turn types based on the end of a row for movement, Fig 3a items 112, 122, and 126 teach a series of passes that the vehicle transitions between) “based at least in part on a detected position and detected motion of the work vehicle, further accounting for at least one of the one or more vehicle motion characteristics,” ([0038] teaches taking into account vehicle movement characteristic and position) and “selectively generating the revised vehicle path as corresponding to at least one of the one or more predetermined vehicle turn types.” ([0038] teaches generating a revised path of a vehicle based on characteristics relative to a vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Bast; and have a reasonable expectation of success. All relate to the control of work vehicles as they approach the end of a row. [0006]-[0007] of Bast teaches the use of end of row turning. These turn types are based on a vehicle speed/motion profile. These turn types can be executed best based on the analysis and it would be obvious to ensure that the types of turns used maximize the available end of row space. It would be obvious to try incorporating numerous turn types as vehicles can’t always make the exact turn that was planned as fields are often different shapes/sizes and require many different kinds of movement.
Regarding claim 9, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the method of claim 8.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach determining that none of the plurality of predetermined vehicle turn types are available for generating the revised vehicle path, based at least in part on the detected position and the detected motion of the work vehicle, further accounting for the at least one of the one or more vehicle motion characteristics, generating a new turn type with respect to the portion of the at least one boundary.
However, Bast teaches “determining that none of the plurality of predetermined vehicle turn types are available for generating the revised vehicle path, based at least in part on the detected position and the detected motion of the work vehicle, further accounting for the at least one of the one or more vehicle motion characteristics, generating a new turn type with respect to the portion of the at least one boundary.” ([0036]-[0038] teaches the system as generating a turn type that fits within the end of the boundary and executing said turn)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Bast; and have a reasonable expectation of success. All relate to the control of work vehicles as they approach the end of a row. [0006]-[0007] of Bast teaches the use of end of row turning. These turn types are based on a vehicle speed/motion profile. These turn types can be executed best based on the analysis and it would be obvious to ensure that the types of turns used maximize the available end of row space. It would be obvious to try incorporating numerous turn types as vehicles can’t always make the exact turn that was planned as fields are often different shapes/sizes and require many different kinds of movement. The generation of a new turning type would be obvious as well because this would allow for the vehicle to move most efficiently.
Regarding claim 16, Kakkar teaches the system of claim 10, wherein the one or more output signals are provided to an onboard display unit associated with the work vehicle ([0067] teaches the vehicle having a display/output unit to show vehicle information
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach to generate a display highlighting one or more aspects of the revised vehicle path.
However, Bast teaches “to generate a display highlighting one or more aspects of the revised vehicle path” ([0032] teaches the system displaying altered paths/relevant pathing information)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, and Tahiliani with Bast; and have a reasonable expectation of success. All relate to the control of work vehicles as they approach the end of a row. The highlighting of a revised route would be obvious to try. GPS devices nowadays very often display a highlighting route and displays often highlight things to bring a user’s attention to a modified route. This allows for a user to operate a vehicle as best possible.
Regarding claim 17, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the system of claim 10.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach analyze one or more of a plurality of predetermined vehicle turn types, wherein the predetermined vehicle turn types are stored in the data storage, based at least in part on a detected position and detected motion of the work vehicle, further accounting for at least one of the one or more vehicle motion characteristics; and selectively generate the revised vehicle path as corresponding to at least one of the one or more predetermined vehicle turn types.
However, Bast teaches “analyze one or more of a plurality of predetermined vehicle turn types,” ([0036]-[0037] teaches the analysis of turn types based on the end of a row for movement) “based at least in part on a detected position and detected motion of the work vehicle, further accounting for at least one of the one or more vehicle motion characteristics” ([0038] teaches taking into account vehicle movement characteristic and position) and “selectively generate the revised vehicle path as corresponding to at least one of the one or more predetermined vehicle turn types.” ([0038] teaches generating a revised path of a vehicle based on characteristics relative to a vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Bast; and have a reasonable expectation of success. All relate to the control of work vehicles as they approach the end of a row. [0006]-[0007] of Bast teaches the use of end of row turning. These turn types are based on a vehicle speed/motion profile. These turn types can be executed best based on the analysis and it would be obvious to ensure that the types of turns used maximize the available end of row space. It would be obvious to try incorporating numerous turn types as vehicles can’t always make the exact turn that was planned as fields are often different shapes/sizes and require many different kinds of movement.
Regarding claim 18, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the system of claim 17.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach upon determining that none of the plurality of predetermined vehicle turn types are available for generating the revised vehicle path, based at least in part on the detected position and the detected motion of the work vehicle, further accounting for the at least one of the one or more vehicle motion characteristics, generate a new turn type with respect to the portion of the at least one boundary.
However, Bast teaches “upon determining that none of the plurality of predetermined vehicle turn types are available for generating the revised vehicle path, based at least in part on the detected position and the detected motion of the work vehicle, further accounting for the at least one of the one or more vehicle motion characteristics, generate a new turn type with respect to the portion of the at least one boundary.” ([0036]-[0038] teaches the system as generating a turn type that fits within the end of the boundary and executing said turn)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Bast; and have a reasonable expectation of success. All relate to the control of work vehicles as they approach the end of a row. [0006]-[0007] of Bast teaches the use of end of row turning. These turn types are based on a vehicle speed/motion profile. These turn types can be executed best based on the analysis and it would be obvious to ensure that the types of turns used maximize the available end of row space. It would be obvious to try incorporating numerous turn types as vehicles can’t always make the exact turn that was planned as fields are often different shapes/sizes and require many different kinds of movement. The generation of a new turning type would be obvious as well because this would allow for the vehicle to move most efficiently.
Claim(s) 22-23 and 25-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakkar, Morimoto, Tahiliani, and Senneff in view of Liu (US PG Pub 2019/0353483).
Regarding claim 22 the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the method of claim 1.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach generating and storing input data sets corresponding to monitored work coverage and transitions performed by the work vehicle and/or an implement associated therewith as it travels through the work area; and developing the optimization routine over time based on correlation of the stored input data sets for each of various transitions with respect to different output parameters comprising an area traversed by the work vehicle on a per-pass basis and work area coverage for a plurality of passes defining a work plan.
However, Liu teaches “generating and storing input data sets corresponding to monitored work coverage and transitions performed by the work vehicle and/or an implement associated therewith as it travels through the work area” ([0023], [0026], and [0033] teach the system generating and storing vehicle track paths as the vehicle traverses over the work area. These track paths are saved with instructions to the master coverage map in order to determine the full output of a vehicle over a time period) and “developing the optimization routine over time based on correlation of the stored input data sets for each of various transitions with respect to different output parameters comprising an area traversed by the work vehicle on a per-pass basis and work area coverage for a plurality of passes defining a work plan.” ([0023], [0030], and p0033] teach the system using a historic path tracker to monitor how vehicles moved in a work coverage area over time. The saved data can be used determine optimal tracks for a current vehicle to use in the same work coverage area)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Liu; and have a reasonable expectation of success. All relate to the control of work vehicles in relation to a work area they are attempting to work in. As Liu teaches in [0033] the use of the historic path tracking and monitoring of previous work paths allows for a current vehicle to be more efficient. This would result in an optimized work flow. [0002]-[0003] teach that the use of historic data being saved allows for the vehicles to operate in tandem as well, this would improve total time used in a work area and present a net benefit for the operator of the equipment.
Regarding claim 23, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the method of claim 22.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach generating and storing the input data sets corresponding to monitored work coverage and transitions performed by a plurality of work vehicles and/or implements associated therewith as they travel through the work area.
However, Liu teaches “generating and storing the input data sets corresponding to monitored work coverage and transitions performed by a plurality of work vehicles and/or implements associated therewith as they travel through the work area.” ([0023], [0033], and Claim 1; teach the system as monitoring the work area of a series of vehicles. In particular the system can monitor the coverage of a first and a second vehicle and save the data as to create new tracks that do not intersect)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Liu; and have a reasonable expectation of success. All relate to the control of work vehicles in relation to a work area they are attempting to work in. As Liu teaches in [0002]-[0003] teach that the use of historic data being saved allows for the vehicles to operate in tandem as well, this would improve total time used in a work area and present a net benefit for the operator of the equipment.
Regarding claim 25, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the system of claim 10.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach generate and store input data sets corresponding to monitored work coverage and transitions performed by the work vehicle and/or an implement associated therewith as it travels through the work area; and develop the optimization routine over time based on correlation of the stored input data sets for each of various transitions with respect to different output parameters comprising an area traversed by the work vehicle on a per-pass basis and work area coverage for a plurality of passes defining a work plan.
However, Liu teaches “generate and store input data sets corresponding to monitored work coverage and transitions performed by the work vehicle and/or an implement associated therewith as it travels through the work area;” ([0023], [0026], and [0033] teach the system generating and storing vehicle track paths as the vehicle traverses over the work area. These track paths are saved with instructions to the master coverage map in order to determine the full output of a vehicle over a time period) and “develop the optimization routine over time based on correlation of the stored input data sets for each of various transitions with respect to different output parameters comprising an area traversed by the work vehicle on a per-pass basis and work area coverage for a plurality of passes defining a work plan.” ([0023], [0030], and p0033] teach the system using a historic path tracker to monitor how vehicles moved in a work coverage area over time. The saved data can be used determine optimal tracks for a current vehicle to use in the same work coverage area)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Liu; and have a reasonable expectation of success. All relate to the control of work vehicles in relation to a work area they are attempting to work in. As Liu teaches in [0033] the use of the historic path tracking and monitoring of previous work paths allows for a current vehicle to be more efficient. This would result in an optimized work flow. [0002]-[0003] teach that the use of historic data being saved allows for the vehicles to operate in tandem as well, this would improve total time used in a work area and present a net benefit for the operator of the equipment.
Regarding claim 26, the combination of Kakkar, Morimoto, Tahiliani, and Senneff teaches the system of claim 25.
The combination of Kakkar, Morimoto, Tahiliani, and Senneff does not teach generate and store the input data sets corresponding to monitored work coverage and transitions performed by a plurality of work vehicles and/or implements associated therewith as they travel through the work area.
However, Liu teaches “generate and store the input data sets corresponding to monitored work coverage and transitions performed by a plurality of work vehicles and/or implements associated therewith as they travel through the work area.” ([0023], [0033], and Claim 1; teach the system as monitoring the work area of a series of vehicles. In particular the system can monitor the coverage of a first and a second vehicle and save the data as to create new tracks that do not intersect)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kakkar, Morimoto, Tahiliani, and Senneff with Liu; and have a reasonable expectation of success. All relate to the control of work vehicles in relation to a work area they are attempting to work in. As Liu teaches in [0002]-[0003] teach that the use of historic data being saved allows for the vehicles to operate in tandem as well, this would improve total time used in a work area and present a net benefit for the operator of the equipment.
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.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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