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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of Claims
The following is an office action in response to the communication filed on 04/25.
Claims 7 and 9 have been cancelled.
Claims 1-2 are currently pending.
Claims 1-2 have been examined.
Claim Objections
Claim 21 is objected to because of the following informalities: The claim currently recites “. . . interrupts the the first . . .” which the examiner notes appears to contain a typographical error and recommends updating to “. . . interrupts the first . . .” to avoid article-noun agreement issues. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 10-19, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Anderson (EP 2177965 A2; hereinafter Anderson).
Regarding claim 1, Anderson discloses the subject matter below:
A computer-implemented method for generating a deployment plan and coordinating a vehicle fleet (see Anderson at least [0012] ". . . the illustrative embodiments
provide a computer implemented method . . ."), the method comprising:
generating, using a database-driven management system, the deployment plan for performing a plurality of agricultural work processes on a field (see Anderson at least [0014] ". . . agricultural vehicles 104, 106, and 108 may be any type of harvesting, threshing, crop cleaning, combine/harvester, or other suitable agricultural vehicle. In this example, agricultural vehicles 104, 106, and 108 operate on field 110, which may be any type of land used to cultivate crops for agricultural purposes. Agricultural vehicles 104, 106, and 108 operate in a coordinated manner . . ."; [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0118] "The process generates a point-to-point path plan starting from the current location . . ."), with the plurality of agricultural work processes including a plurality of work steps (see Anderson at least [0060] ". . . machine behaviors . . . may be coordination behaviors for tasks and sub-tasks or aspects of a task. Examples of tasks may include, without limitation, harvesting a field, performing a chemical spray of an outdoor area, driving a route, performing a collection routine, mowing a field or other ground area, traveling over terrain, detecting objects in an environment, detecting objects in sub-terrain, and the like."), the deployment plan comprising using the vehicle fleet that includes a plurality of agricultural work vehicles (see Anderson at least [0026] "In a path mapping mode, the different paths may be mapped by an operator . . . paths may be identical for each pass of a field, though in parallel for multiple vehicles, the operator may rely on the fact that agricultural vehicles 104, 106, and 108 will move along the same path each time.”), wherein the agricultural work vehicles have at least one work unit, being one or both of a component of a respective agricultural work vehicle or adapted to the respective agricultural work vehicle, for performing at least one of the plurality of agricultural work processes (see Anderson at least [0012] “With reference to the figures and in particular with reference to Figure 1, embodiments of the present invention may be used in a variety of vehicles, such as automobiles, trucks, harvesters, combines, agricultural equipment, construction equipment, tractors, mowers, armored vehicles, and utility vehicles.”; Figure 1- agricultural work vehicles have work units integrated in or attached to the work vehicle), wherein the vehicle fleet includes at least one autonomous agricultural work vehicle that has a respective work unit integrated therein or adapted thereto (see Anderson at least [0012] “With reference to the figures and in particular with reference to Figure 1, embodiments of the present invention may be used in a variety of vehicles, such as automobiles, trucks, harvesters, combines, agricultural equipment, construction equipment, tractors, mowers, armored vehicles, and utility vehicles.”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; Figure 1- vehicles shown have work units integrated therein or adapted thereto); and
coordinating, using the deployment plan, amongst the plurality of agricultural work vehicles to assign the plurality of work steps including assigning the at least one autonomous agricultural work vehicle at least one work step from the plurality of work steps to perform a respective agricultural work process (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field.’”), wherein coordinating comprises determining, by the management system based on the respective agricultural work process to be performed by the at least one autonomous agricultural work vehicle, one or more operating parameters for performing the at least one work step for the at least one autonomous agricultural work vehicle or the respective work unit integrated therein or adapted thereto (see Anderson at least [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . ."); and transmitting, by the management system, the one or more operating parameters to the at least one autonomous agricultural work vehicle so that the at least one autonomous agricultural work vehicle performs the at least one work step automatically (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0132] “The process may also be implemented by a machine controller that is located in a back office . . .”; [0136] “The process then assigns one or more machine behaviors to each vehicle in the group of vehicles . . .”).
Regarding claim 2, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the management system generates a travel route for the at least one autonomous agricultural work vehicle and the one or more operating parameters for performing the at least one work step automatically along the travel route (see Anderson at least [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . .");
wherein at least one work unit is part of the at least one autonomous agricultural work vehicle or the at least one work unit is connected to the at least one autonomous agricultural work vehicle (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; Figure 1- agricultural work vehicles have work units integrated in or attached to the work vehicle); and
wherein the one or more parameters comprise deployment parameters which, depending on the at least one work step to be performed by the at least one autonomous agricultural work vehicle, specify functional scope possessed by the at least one work unit (see Anderson at least [0065] “. . . roles [(i.e., deployment parameters)] may be assigned by a back office computer . . .”; Figure 11- mission receipt in step 1102 and role assignment in step 1104 impact downstream functions).
Regarding claim 3, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the management system automatically receives planning data which are from a respective agricultural work vehicle of the plurality of agricultural work vehicles (see Anderson at least [0092] “Learned knowledge base 606 may be a separate component of knowledge base 600 [(i.e., part of the management system)], or alternatively may be integrated with a priori knowledge base 602 . . . Learned knowledge base 606 contains knowledge learned as the vehicle spends more time in a specific work area, and may change temporarily or long-term depending upon interactions with on line knowledge base 604 and user input. For example, learned knowledge base 606 may detect the absence of a tree that was present the last time it received environmental data from the work area. Learned knowledge base 606 may temporarily change the environmental data associated with the work area to reflect the new absence of a tree, which may later be permanently changed upon user input confirming the tree was in fact cut down.”; Figure 6- shows the collective knowledge base 600 with components 602, 604, 606);
wherein the respective agricultural work vehicle automatically performs at least one of generating, recording, or determining the planning data (see Anderson at least [0067] “. . . vehicle 400 includes machine controller 402, steering system 404, braking system 406, propulsion system 408, sensor system 410, communication unit 412, behavior system 416, behavior library 418, and knowledge base 420.”; [0072] “Sensor system 410 is a high integrity perception system and may be a set of sensors used to collect information about the environment around a vehicle. In these examples, the information is sent to machine controller 402 to provide data in identifying how the vehicle should move in different modes of operation.”);
wherein the planning data supplied by the respective agricultural work vehicle correlate with one another in time or are independent of one another in time (see Anderson at least [0067] “. . . vehicle 400 includes machine controller 402, steering system 404, braking system 406, propulsion system 408, sensor system 410, communication unit 412, behavior system 416, behavior library 418, and knowledge base 420.”; [0072] “Sensor system 410 is a high integrity perception system and may be a set of sensors used to collect information about the environment around a vehicle. In these examples, the information is sent to machine controller 402 to provide data in identifying how the vehicle should move in different modes of operation.”; [0106] “. . . the timestamp of the current stored data is compared to the timestamp of newly arrived data. The current data is only overwritten if the time stamp of the newly arrived data is more recent than the time stamp of the currently stored data.”);
wherein the respective agricultural work vehicle automatically transmits the planning data to the management system (see Anderson at least [0092] “Learned knowledge base 606 may be a separate component of knowledge base 600 [(i.e., part of the management system)], or alternatively may be integrated with a priori knowledge base 602 . . . Learned knowledge base 606 contains knowledge learned as the vehicle spends more time in a specific work area, and may change temporarily or long-term depending upon interactions with on line knowledge base 604 and user input. For example, learned knowledge base 606 may detect the absence of a tree that was present the last time it received environmental data from the work area. Learned knowledge base 606 may temporarily change the environmental data associated with the work area to reflect the new absence of a tree, which may later be permanently changed upon user input confirming the tree was in fact cut down.”; Figure 6- shows the collective knowledge base 600 with components 602, 604, 606);
wherein the management system automatically determines, based on the planning data, the one or more operating parameters of the respective agricultural work vehicle (see Anderson at least [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . .");
wherein the management system automatically transmits, to the respective agricultural work vehicle, the one or more operating parameters (see Anderson at least [0018] “. . . a high integrity coordination system is present in which coordination instructions may be transmitted using a high integrity communication system located on each vehicle to each of agricultural vehicles 104, 106, and 108 from a remote location . . .”; [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . ."); and
wherein the respective agricultural work vehicle uses the one or more operating parameters for automatic control of the respective agricultural work vehicle (see Anderson at least [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0036] “. . . different types of vehicles may include . . . autonomous vehicles . . .”; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . . The process then follows the path plan according to the coordination behaviors and role assignment (step 1112) associated with the mission or task, with the process terminating thereafter.").
Regarding claim 4, Anderson discloses claim 3 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the planning data, received by the management system, comprises one or more of: crop data; ground data; yield data; area data; weather data; localization data; route data; obstacle mapping data; consumption data; or machine condition data (see Anderson at least [0091] “Online knowledge base 604 [(i.e., part of management system)] may . . . wirelessly access the Internet [(i.e. external data source)]. Online knowledge base 604 dynamically provides information to a machine control process which enables adjustment to sensor data processing, site-specific sensor accuracy calculations, and/or exclusion of sensor information. For example, online knowledge base 604 may include current weather conditions of the operating environment [(i.e., planning data)] from an on line source. In some examples, on line knowledge base 604 may be a remotely accessed knowledge base. This weather information [(i.e., planning data)] may be used by [the] machine controller . . . to determine which sensors to activate in order to acquire accurate environmental data for the operating environment . . . Other types of information that may be obtained include, without limitation, vegetation information, such as foliage deployment, leaf drop status, and lawn moisture stress, and construction activity, which may result in landmarks in certain regions being ignored.”).
Regarding claim 5, Anderson discloses claim 3 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein while creating the deployment plan based on the planning data, at least one location in a vicinity of the field to be worked by the at least one autonomous agricultural work vehicle is determined (see Anderson at least [0024] “In this manner, agricultural vehicles 104, 106 and 108 drive from start to finish [(i.e., location)] along the mapped path. Agricultural vehicles 104, 106 and 108 still may include some level of obstacle detection to keep agricultural vehicles 104, 106 and 108 from running over or hitting an obstacle, such as a field worker or another agricultural vehicle. These actions also may occur with the aid of a coordination component . . .”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”);
further comprising:
identifying an external event (see Anderson at least [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102 [(i.e., external events)], system or component failure in a vehicle, and the like. In response to a dynamic condition, the movement of a vehicle may be altered in various ways, including, without limitation, stopping the vehicle, accelerating propulsion of the vehicle, decelerating propulsion of the vehicle, and altering the direction of the vehicle, for example.”);
responsive to identifying the external event:
interrupting a respective agricultural work process being performed by the at least one autonomous agricultural work vehicle (see Anderson at least [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102, system or component failure in a vehicle, and the like. In response to a dynamic condition, the movement of a vehicle may be altered [(i.e., interrupted)] in various ways, including, without limitation, stopping the vehicle, accelerating propulsion of the vehicle, decelerating propulsion of the vehicle, and altering the direction of the vehicle . . .”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”); and
automatically moving the at least one autonomous agricultural work vehicle to the at least one location (see Anderson at least [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102, system or component failure in a vehicle, and the like. In response to a dynamic condition, the movement of a vehicle may be altered in various ways, including, without limitation, stopping the vehicle, accelerating propulsion of the vehicle, decelerating propulsion of the vehicle, and altering the direction of the vehicle [(i.e., moving to the identified location)] . . .”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”).
Regarding claim 6, Anderson discloses claim 3 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein, when generating the deployment plan using the planning data, processes for one or more of procurement, pickup or transfer of operating resources for one or both of the at least one autonomous agricultural work vehicle or between the plurality of agricultural work vehicles are determined (see Anderson at least [0012] “. . . the illustrative embodiments provide a computer implemented method, apparatus, and computer program product for coordinating multiple vehicles. Machine behaviors are assigned to multiple vehicles performing a task. The vehicles are coordinated to perform the task using the assigned behaviors and a number of signals received from other vehicles and the environment during performance of the task.”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0061] “. . . an example of sub-tasks or aspects for the task of harvesting a field may include, without limitation, specific behaviors such as . . . stopping at a pre-determined location to off-load the collected crop . . .”).
Regarding claim 10, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . further comprising the plurality of agricultural work vehicles of the vehicle fleet that are active on the field to be worked exchange data with each other so that the at least one autonomous agricultural work vehicle is informed about an occurrence of a situation that influences the at least one work step to be performed by the at least one autonomous agricultural work vehicle (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0066] “. . . if agricultural vehicle 104 is at the top of the field, agricultural vehicle 104 may assign itself the role of ‘leader’ and communicate that. . . to agricultural vehicles 106 and 108, each of which in turn will assign itself the role of ‘follower.’”; Figure 11- role assignment impacts operation).
Regarding claim 11, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the at least one autonomous agricultural work vehicle performs one or both of acquiring or determining operational data based on sensor data generated at least one sensor apparatus resident on the at least one autonomous agricultural work vehicle while the at least one autonomous agricultural work vehicle performs the at least one work step as part of a current deployment plan (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0057] “. . . a coordinated behavior of ‘harvest a field’ may result in sequences of primitive behaviors such as, without limitation, ‘move to the edge of the field,’ ‘travel in a north-south direction,’ and ‘stop at the end of the field.’ These sequences or steps may be executed by high integrity machine control system 302.”; [0093] “Sensor system 700 is an example of one implementation of sensor system 410 in Figure 4.”; [0094] “The sensors in sensor system 700 may be selected such that one of the sensors is always capable of sensing information needed to operate the vehicle in different operating environments.”; FIG. 4- Sensor system 410 is part of vehicle 400);
wherein the at least one autonomous agricultural work vehicle records the operational data (see Anderson at least [0073] “Sensor system 410 is a high integrity perception system and may be a set of sensors used to collect information about the environment around a vehicle . . . the information is sent to machine controller 402 to provide data in identifying how the vehicle should move in different modes of operation.”);
wherein the at least one autonomous agricultural work vehicle transmits the operational data to the management system during or after performing at least one work step (see Anderson at least [0092] “Learned knowledge base 606 [(i.e., part of management system)] contains knowledge learned as the vehicle spends more time in a specific work area . . .”);
wherein the management system, based on analysis of the operational data, modifies the current deployment plan in order to generate a modified deployment plan (see Anderson at least [0026] “Intervention or deviation from the mapped path may occur only when an obstacle is present. Also, in an illustrative embodiment, with the path mapping mode, way points may be set to allow agricultural vehicles 104, 106, and 108 to stop at grain collection points.”; [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102, system or component failure in a vehicle, and the like. In response to a dynamic condition, the movement of a vehicle may be altered in various ways, including, without limitation, stopping the vehicle, accelerating propulsion of the vehicle, 40 decelerating propulsion of the vehicle, and altering the direction of the vehicle, for example.”); and
wherein the management system modifies control of the at least one autonomous agricultural work vehicle from the current deployment plan to the modified deployment plan (see Anderson at least [0026] “Intervention or deviation from the mapped path may occur only when an obstacle is present. Also, in an illustrative embodiment, with the path mapping mode, way points may be set to allow agricultural vehicles 104, 106, and 108 to stop at grain collection points.”; [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102, system or component failure in a vehicle, and the like. In response to a dynamic condition, the movement of a vehicle may be altered in various ways, including, without limitation, stopping the vehicle, accelerating propulsion of the vehicle, 40 decelerating propulsion of the vehicle, and altering the direction of the vehicle, for example.”).
Regarding claim 12, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the vehicle fleet comprises at least one manned work vehicle and the at least one autonomous agricultural work vehicle (see Anderson at least [0012] “The illustrative embodiments recognize a need for a system and method where multiple combination manned/autonomous vehicles can accurately navigate and manage a work-site.”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses. In an illustrative example, different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”);
wherein both of the at least one manned work vehicle and the at least one autonomous agricultural work vehicle are taken into account when generating the deployment plan so that the at least one manned work vehicle works in cooperation with the at least one autonomous agricultural work vehicle (see Anderson at least [0026] "In a path mapping mode, the different paths may be mapped by an operator prior to reaching field 110. With the harvesting example, paths may be identical for each pass of a field, though in parallel for multiple vehicles, the operator may rely on the fact that agricultural vehicles 104, 106, and 108 will move along the same path each time.”; [0036] “. . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”);
wherein machine condition data indicative of failure of the at least one manned work vehicle is transmitted to the management system (see Anderson at least [0144] “The process begins by receiving an indication of a vehicle failure in a group of vehicles executing a task (step 1902). The indication of vehicle failure may be received through a communications system, such as high integrity communications system 312 in Figure 3. The process assesses current role assignment, including the role assignment of the failed vehicle (step 1904).”);
wherein, responsive to receiving the machine condition data indicative of failure of the at least one manned work vehicle, the management system:
determines whether the at least one autonomous agricultural work vehicle is suitable to perform at least one action previously assigned to the at least one manned work vehicle that is indicative of failure (see Anderson at least [0036] “. . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.” ; [0145] “Other vehicles performing the same task may need to assume the role of ‘follower’ or take on the aspect of the assigned task that the failed vehicle was to perform. In another illustrative embodiment, the failed vehicle may have been performing the task according to the role of ‘leader.’ In this example, another vehicle in the group of vehicles executing the task will need to assume the role of leader. The process then reassigns roles in order to complete the task without the failed vehicle (step 1906), with the process terminating thereafter.”); and
responsive to determining that the at least one autonomous agricultural work vehicle is suitable to perform at least one action previously assigned to the at least one manned work vehicle that is indicative of failure, causing the at least one autonomous agricultural work vehicle to perform the at least one action previously assigned to the at least one manned work vehicle (see Anderson at least [0145] “Other vehicles performing the same task may need to assume the role of ‘follower’ or take on the aspect of the assigned task that the failed vehicle was to perform. In another illustrative embodiment, the failed vehicle may have been performing the task according to the role of ‘leader.’ In this example, another vehicle in the group of vehicles executing the task will need to assume the role of leader. The process then reassigns roles in order to complete the task without the failed vehicle (step 1906), with the process terminating thereafter.”).
Regarding claim 13, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the fleet comprises a plurality of autonomous agricultural work vehicles (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses. In an illustrative example, different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”); and
wherein a communication system and a localization infrastructure are used for decentralized guidance and control of the plurality of autonomous agricultural work vehicles operating together in the field (see Anderson at least [0025] “In a teleoperation mode, for example, an operator, in a remote location from the vehicle, may operate or wirelessly control agricultural vehicle 104 across field 110 in a fashion similar to other remote controlled vehicles. With this type of mode of operation, the operator may control agricultural vehicle 104 through a wireless controller [(i.e., localization infrastructure)].”; [0034] “. . . heterogeneous sets of communication links and channels [(i.e., communication system)] are located on multiple vehicles in a worksite to provide high integrity communication with fault tolerance.”; [0065] “For example, high integrity coordination system 310 may be distributed between a number of vehicles and a remote location other than the number of vehicles. If high integrity coordination system 310 is located entirely or distributed between a remote location such as a back office computer and a number of vehicles, roles may be assigned by a back office computer [(i.e., localization infrastructure)] . . .”).
Regarding claim 14, Anderson discloses claim 1 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the fleet comprises a plurality of autonomous agricultural work vehicles (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses. In an illustrative example, different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”);
wherein the autonomous agricultural work vehicles in the vehicle fleet are managed by the management system as a pool of vehicles (see Anderson at least [0133] “. . . a process for coordinating and controlling multiple vehicles . . . may also be implemented by a machine controller that is located in a back office [(i.e., management system)] . . .”; [0134] “The process begins by identifying a task for a group of vehicles [(i.e., pool of vehicles)] . . .”);
wherein a user request is input to the management system indicating a request to release one or more available autonomous agricultural work vehicles and manned agricultural work vehicles (see Anderson at least [0013] “Back office 102 [(i.e., management system)] may supply . . . coordination components to different vehicles . . .”; [0027] “. . . the operator [(i.e., user)] may start and stop [(i.e., release)] agricultural vehicles 104, 106, and 108 as needed . . . Some or all of the different operations in these examples may be performed with the aid of a coordination component . . .”); and
wherein, responsive to the user request, use of the one or more available autonomous agricultural work vehicles and manned agricultural work vehicles are released (see Anderson at least [0013] “Back office 102 [(i.e., management system)] may supply . . . coordination components to different vehicles . . .”; [0027] “. . . the operator [(i.e., user)] may start and stop [(i.e., release)] agricultural vehicles 104, 106, and 108 as needed . . . Some or all of the different operations in these examples may be performed with the aid of a coordination component . . .”).
Regarding claim 15, Anderson discloses the subject matter below:
A management system (see Anderson at least [0001] “The present disclosure relates generally to systems and methods for vehicle navigation and more particularly systems and methods for high integrity coordination of multiple off-road vehicles. As an example, embodiments of this invention provide a method and system utilizing a versatile robotic control module for coordination and navigation of a vehicle.”) comprising:
a communication interface (see Anderson at least [0018] “. . . a high integrity coordination system is present in which coordination instructions may be transmitted using a high integrity communication system located on each vehicle to each of agricultural vehicles 104, 106, and 108 from a remote location, or may
be transmitted from a control vehicle on field 110 to the other vehicles operating on field 110.”);
a memory unit (see Anderson at least [0043] “Memory 206 . . . may be . . . a random access memory or any other suitable volatile or non-volatile storage device.”); and
a computing unit in communication with the communication interface and the memory unit (see Anderson at least [0013] “Embodiments of the present invention may also be used in a single computing system or a distributed computing system.”; Figure 2- processor unit 204, memory 206, and communications unit 210 are all in communication), the computing unit configured to:
generate a deployment plan for performing a plurality of agricultural work processes on a field, with the plurality of agricultural work processes including a plurality of work steps (see Anderson at least [0014] ". . . agricultural vehicles 104, 106, and 108 may be any type of harvesting, threshing, crop cleaning, combine/harvester, or other suitable agricultural vehicle. In this example, agricultural vehicles 104, 106, and 108 operate on field 110, which may be any type of land used to cultivate crops for agricultural purposes. Agricultural vehicles 104, 106, and 108 operate in a coordinated manner . . ."; [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0045] “The processes of the different embodiments may be performed by processor unit 204 using computer implemented instructions . . .”; [0118] "The process generates a point-to-point path plan starting from the current location . . ."), the deployment plan comprising using a vehicle fleet that includes a plurality of agricultural work vehicles (see Anderson at least [0026] "In a path mapping mode, the different paths may be mapped by an operator . . . paths may be identical for each pass of a field, though in parallel for multiple vehicles, the operator may rely on the fact that agricultural vehicles 104, 106, and 108 will move along the same path each time.”), wherein the agricultural work vehicles have at least one work unit, being one or both of a component of a respective agricultural work vehicle or adapted to the respective agricultural work vehicle, for performing at least one of the plurality of agricultural work processes (see Anderson at least Figure 1- agricultural work vehicles have work units integrated in or attached to the work vehicle), wherein the vehicle fleet includes at least one autonomous agricultural work vehicle that has a respective work unit integrated therein or adapted thereto (see Anderson at least [0012] “With reference to the figures and in particular with reference to Figure 1, embodiments of the present invention may be used in a variety of vehicles, such as automobiles, trucks, harvesters, combines, agricultural equipment, construction equipment, tractors, mowers, armored vehicles, and utility vehicles.”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; Figure 1- vehicles shown have work units integrated therein or adapted thereto); and
coordinate, using the deployment plan, amongst the plurality of agricultural work vehicles to assign the plurality of work steps including assigning the at least one autonomous agricultural work vehicle at least one work step from the plurality of work steps to perform a respective agricultural work process (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0045] “The processes of the different embodiments may be performed by processor unit 204 using computer implemented instructions . . .”; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field.’”), wherein coordinating comprises:
determining, by the management system based on the respective agricultural work process to be performed by the at least one autonomous agricultural work vehicle, one or more operating parameters for performing the at least one work step for the at least one autonomous agricultural work vehicle or the respective work unit integrated therein or adapted thereto (see Anderson at least [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . ."); and
transmitting, by the management system, the one or more operating parameters to the at least one autonomous agricultural work vehicle so that the at least one autonomous agricultural work vehicle performs the at least one work step automatically (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0132] “The process may also be implemented by a machine controller that is located in a back office . . .”; [0136] “The process then assigns one or more machine behaviors to each vehicle in the group of vehicles . . .”).
Regarding claim 16, Anderson discloses claim 15 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the management system is configured to generate a travel route for the at least one autonomous agricultural work vehicle and the one or more operating parameters for performing the at least one work step automatically along the travel route (see Anderson at least [0030] "Routes and patterns may be performed with the aid of a knowledge base in accordance with an illustrative embodiment."; [0031] "Paths may be stored and accessed with the aid of a knowledge base . . ."; [0036] “. . . different types of vehicles may include . . . autonomous vehicles . . .”; [0085] “. . . specific mission behaviors may be a top level behavior or task including a detailed plan on how the behavior or task is to be executed.”; [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”; [0118] "The process generates a point-to-point path plan starting from the current location . . . The process then follows the path plan according to the coordination behaviors and role assignment (step 1112) associated with the mission or task, with the process terminating thereafter.");
wherein the management system is further configured to receive planning data which are from one or both of a respective agricultural work vehicle of the plurality of agricultural work vehicles or from an external data source (see Anderson at least [0091] “Online knowledge base 604 [(i.e., part of management system)] may . . . wirelessly access the Internet [(i.e. external data source)]. Online knowledge base 604 dynamically provides information to a machine control process which enables adjustment to sensor data processing, site-specific sensor accuracy calculations, and/or exclusion of sensor information. For example, online knowledge base 604 may include current weather conditions of the operating environment [(i.e., planning data)] from an on line source. In some examples, on line knowledge base 604 may be a remotely accessed knowledge base. This weather information [(i.e., planning data)] may be used by [the] machine controller . . . to determine which sensors to activate in order to acquire accurate environmental data for the operating environment . . . Other types of information that may be obtained include, without limitation, vegetation information, such as foliage deployment, leaf drop status, and lawn moisture stress, and construction activity, which may result in landmarks in certain regions being ignored.”; [0092] “Learned knowledge base 606 may be a separate component of knowledge base 600 [(i.e., part of the management system)], or alternatively may be integrated with a priori knowledge base 602 . . . Learned knowledge base 606 contains knowledge learned as the vehicle spends more time in a specific work area, and may change temporarily or long-term depending upon interactions with on line knowledge base 604 and user input. For example, learned knowledge base 606 may detect the absence of a tree that was present the last time it received environmental data from the work area. Learned knowledge base 606 may temporarily change the environmental data associated with the work area to reflect the new absence of a tree, which may later be permanently changed upon user input confirming the tree was in fact cut down.”; Figure 6- shows the collective knowledge base 600 with components 602, 604, 606);
wherein management system is configured to receive the planning data from the respective agricultural work vehicle (see Anderson at least [0067] “. . . vehicle 400 includes machine controller 402, steering system 404, braking system 406, propulsion system 408, sensor system 410, communication unit 412, behavior system 416, behavior library 418, and knowledge base 420.”; [0072] “Sensor system 410 is a high integrity perception system and may be a set of sensors used to collect information about the environment around a vehicle. In these examples, the information is sent to machine controller 402 to provide data in identifying how the vehicle should move in different modes of operation.”); and
wherein the planning data supplied by one or both of the respective agricultural work vehicle or the external data source correlate with one another in time or are independent of one another in time (see Anderson at least [0067] “. . . vehicle 400 includes machine controller 402, steering system 404, braking system 406, propulsion system 408, sensor system 410, communication unit 412, behavior system 416, behavior library 418, and knowledge base 420.”; [0072] “Sensor system 410 is a high integrity perception system and may be a set of sensors used to collect information about the environment around a vehicle. In these examples, the information is sent to machine controller 402 to provide data in identifying how the vehicle should move in different modes of operation.”; [0106] “. . . the timestamp of the current stored data is compared to the timestamp of newly arrived data. The current data is only overwritten if the time stamp of the newly arrived data is more recent than the time stamp of the currently stored data.”).
Regarding claim 17, Anderson discloses claim 16 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the planning data, received by the management system, comprises one or more of: crop data; ground data; yield data; area data; weather data; localization data; route data; obstacle mapping data; consumption data; or machine condition data (see Anderson at least [0091] “Online knowledge base 604 [(i.e., part of management system)] may . . . wirelessly access the Internet [(i.e. external data source)]. Online knowledge base 604 dynamically provides information to a machine control process which enables adjustment to sensor data processing, site-specific sensor accuracy calculations, and/or exclusion of sensor information. For example, online knowledge base 604 may include current weather conditions of the operating environment [(i.e., planning data)] from an on line source. In some examples, on line knowledge base 604 may be a remotely accessed knowledge base. This weather information [(i.e., planning data)] may be used by [the] machine controller . . . to determine which sensors to activate in order to acquire accurate environmental data for the operating environment . . . Other types of information that may be obtained include, without limitation, vegetation information, such as foliage deployment, leaf drop status, and lawn moisture stress, and construction activity, which may result in landmarks in certain regions being ignored.”).
Regarding claim 18, Anderson discloses claim 17 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein, when the computing unit is generating the deployment plan using the planning data, the computing unit is further configured to determine processes for one or more of procurement, pickup or transfer of operating resources for one or both of the at least one autonomous agricultural work vehicle or between the plurality of agricultural work vehicles (see Anderson at least [0012] “. . . the illustrative embodiments provide a computer implemented method, apparatus, and computer program product for coordinating multiple vehicles. Machine behaviors are assigned to multiple vehicles performing a task. The vehicles are coordinated to perform the task using the assigned behaviors and a number of signals received from other vehicles and the environment during performance of the task.”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses . . . different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”; [0045] “The processes of the different embodiments may be performed by processor unit 204 using computer implemented instructions . . .”; [0061] “. . . an example of sub-tasks or aspects for the task of harvesting a field may include, without limitation, specific behaviors such as . . . stopping at a pre-determined location to off-load the collected crop . . .”).
Regarding claim 19, Anderson discloses claim 16 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter below:
. . . wherein the deployment plan is performed based on one or both of a predefined objective or an optimization strategy (see Anderson at least [0057] “There is at least one of a shared goal, shared future state, shared intention, shared plan, or a shared mission which may be shared a priori . . .”).
Regarding claim 21, Anderson discloses the subject matter of claim 5 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter indicated in bold below:
. . . wherein the at least one autonomous agricultural work vehicle comprises a first autonomous agricultural work vehicle and a second autonomous agricultural work vehicle (see Anderson at least [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses. In an illustrative example, different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”);
wherein the external event comprises change in weather or occurrence of damage to the first autonomous agricultural work vehicle (see Anderson at least [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102 [(i.e., external events)], system or component failure in a vehicle, and the like.”);
wherein, responsive to the management system detecting the external event, the management system:
interrupts the the first autonomous agricultural work vehicle from performing the respective agricultural work process (see Anderson at least [0029] “. . . dynamic conditions impact the movement of a vehicle. A dynamic condition is a change in the environment around a vehicle. For example, a dynamic condition may include, without limitation, movement of another vehicle in the environment to a new location, detection of an obstacle, detection of a new object or objects in the environment, change in soil or surface conditions, change in weather, change in sky obstruction, receiving user input to change the movement of the vehicle, receiving instructions from a back office, such as back office 102 [(i.e., external events)], system or component failure in a vehicle, and the like. In response to a dynamic condition, the movement of a vehicle may be altered in various ways, including, without limitation, stopping the vehicle . . .”); and
automatically controls moving the second autonomous agricultural work vehicle to the at least one location (see Anderson at least [0024] “In this manner, agricultural vehicles 104, 106 and 108 drive from start to finish [(i.e., location)] along the mapped path. Agricultural vehicles 104, 106 and 108 still may include some level of obstacle detection to keep agricultural vehicles 104, 106 and 108 from running over or hitting an obstacle, such as a field worker or another agricultural vehicle. These actions also may occur with the aid of a coordination component . . .”; see Anderson at least [0145] “Other vehicles performing the same task may need to assume the role of ‘follower’ or take on the aspect of the assigned task that the failed vehicle was to perform. In another illustrative embodiment, the failed vehicle may have been performing the task according to the role of ‘leader.’ In this example, another vehicle in the group of vehicles executing the task will need to assume the role of leader. The process then reassigns roles in order to complete the task without the failed vehicle (step 1906), with the process terminating thereafter.”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson in view of Jensen et al. (Jensen, M. A. F., Bochtis, D., Sørensen, C. G., Blas, M. R., & Lykkegaard, K. L. (2012). In-field and inter-field path planning for agricultural transport units. Computers & Industrial Engineering, 63(4), 1054-1061.; hereinafter Jensen).
Regarding claim 8, Anderson discloses claim 1 as recited in the claim and applied above.
Anderson is considered analogous material because it relates to coordinating agricultural work vehicles.
Anderson discloses the subject matter indicated in bold below:
. . . wherein the deployment plan is for . . . a first field to be worked (see Anderson at least [0014] “In this example, agricultural vehicles 104, 106, and 108 operate on field . . .”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses. In an illustrative example, different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”) and at least a second field to be worked; and
wherein travel routes between a farmyard and the first field to be worked . . . are determined (see Anderson at least [0057] “. . . a coordinated behavior of ‘harvest a field’ may result in sequences of primitive behaviors such as, without limitation, ‘move to the edge of the field,’ . . .”) . . .
wherein the at least one autonomous agricultural work vehicle is transferred according to the travel routes (see Anderson at least Figure 12- deployment plan with a navigational path is generated and followed in steps 1204 and 1206; Figure 13- step 1302).
While Anderson discloses autonomous agricultural work vehicles working in a field, planning travel to a field, and execution of a deployment plan including navigation, it does not appear to explicitly disclose a plurality of fields to be worked nor determining travel to and between fields while creating a deployment plan.
Jensen teaches the subject matter underlined below:
. . . wherein the deployment plan is for a plurality of fields to be worked including a first field to be worked and at least a second field to be worked (see Jensen at least pg. 1056, paragraph 2 “Let F = {1, 2, ...} denote the set of the fields where harvesting operation is carried out.”; pg. 1058, paragraph 3 “The path planning system was evaluated in a selected area comprising two fields . . .”); and
wherein travel routes between a farmyard and the first field to be worked and between first field and the second field to be worked are determined while creating the deployment plan (see Jensen at least Figure 8- path planning and associated route options between two fields are illustrated);
wherein at least one type of transport is determined depending on number of autonomous agricultural work vehicles are used in the deployment plan and their individual equipment with work units (see Jensen at least Figure 1- SU supporting units and PU primary units designate different types of agricultural work machines with different work units; Figure 8- determining the transportation of vehicles depends on their type, either SU or PU, and the number of vehicles); and . . .
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the autonomous agricultural work vehicles and deployment planning of Anderson with the multiple field and transport planning therebetween method as taught by Jensen to generate a deployment plan for a plurality of fields, plan travel routes to and between two fields while creating the deployment plan, and determine the type of transport based on the number of vehicles and their individual equipment with work units. Doing so would enable coordination of multiple worksites while improving downtime as recognized by Jensen (see Jensen at least pg. 1060, paragraph 3 "The optimal paths of the transport units reduce the time that a harvester is not working whilst waiting for a transport unit to unload."). Additionally, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the deployment plan execution of Anderson with the travel route determination as taught by Jensen to transfer an autonomous agricultural work according to the determined travel routes. Doing so would improve operation downtime as recognized by Jensen (see Jensen at least pg. 1060, paragraph 3 "The optimal paths of the transport units reduce the time that a harvester is not working whilst waiting for a transport unit to unload.").
Regarding claim 20, Anderson discloses claim 16 as recited in the claim and applied above.
Anderson discloses the subject matter indicated in bold below:
. . . wherein the computing unit is configured to generate the deployment plan for . . . a first field to be worked (see Anderson at least [0013] “Embodiments of the present invention may also be used in a single computing system or a distributed computing system.”; [0014] “In this example, agricultural vehicles 104, 106, and 108 operate on field . . .”; [0036] “. . . the different illustrative embodiments may be applied to other types of vehicles and other types of uses. In an illustrative example, different types of vehicles may include controllable vehicles, autonomous vehicles, semi-autonomous vehicles, or any combination thereof.”) . . .
determine travel routes between a farmyard and the first field (see Anderson at least [0013] “Embodiments of the present invention may also be used in a single computing system or a distributed computing system.”; [0057] “. . . a coordinated behavior of ‘harvest a field’ may result in sequences of primitive behaviors such as, without limitation, ‘move to the edge of the field,’ . . .”) . . .
While Anderson discloses autonomous agricultural work vehicles working in a field, planning travel to a field, and creation of a deployment plan including navigation via a computing unit, it does not appear to explicitly disclose a plurality of fields to be worked nor determining travel to and between fields while creating a deployment plan.
Jensen teaches the subject matter underlined below:
. . . to generate the deployment plan for a plurality of fields to be worked including a first field to be worked and at least a second field to be worked (see Jensen at least pg. 1056, paragraph 2 “Let F = {1, 2, ...} denote the set of the fields where harvesting operation is carried out.”; pg. 1058, paragraph 3 “The path planning system was evaluated in a selected area comprising two fields . . .”); and
. . . as part of the deployment plan, . . . determine travel routes between a farmyard and the first field to be worked and between first field and the second field to be worked while creating the deployment plan (see Jensen at least Figure 8- path planning and associated route options between two fields are illustrated).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the autonomous agricultural work vehicles and deployment planning of Anderson with the multiple field and transport planning therebetween method as taught by Jensen to generate a deployment plan for a plurality of fields and plan travel routes to and between two fields while creating the deployment plan. Doing so would enable coordination of multiple worksites while improving downtime as recognized by Jensen (see Jensen at least pg. 1060, paragraph 3 "The optimal paths of the transport units reduce the time that a harvester is not working whilst waiting for a transport unit to unload.").
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Anderson in view of Pichlimaier et al. (US 20170336787 A1; hereinafter Pichlimaier).
Regarding claim 22, Anderson discloses the subject matter of claim 11 as recited in the claim and applied above. Additionally, Anderson discloses the subject matter indicated in bold below:
. . . wherein the operational data is used as a basis for deployment planning . . .
wherein the current deployment plan includes at least one step to be performed by the at least one autonomous agricultural work vehicle; and . . .
While Anderson discloses using operational data as a basis for deployment planning and a current deployment plan that includes at least one step to be performed by the at least one autonomous agricultural work vehicle, it does not appear to explicitly disclose the operational data being indicative of energy consumption of the at least one autonomous agricultural work vehicle, the operational data being used as a basis for deployment planning in order to plan for at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle, nor the management system analyzing the operational data to determine whether to adjust the current deployment plan responsive to determining that the at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle influences execution of at least one step to be performed by the at least one autonomous agricultural work vehicle.
Pichlimaier teaches the subject matter underlined below:
. . . wherein the operational data is indicative of energy consumption of the at least one autonomous agricultural work vehicle (see Pichlimaier at least [0034] “Using data indicative of robot functions may help to sort out problems, at the same time preserving the simplicity approach: if the motor shows low performance but the vehicle should otherwise move fast (detected by the time and position data as above), the energy in the robot may be low, indicating a recharge may be required [(i.e., energy consumption as operational data)].”);
wherein the operational data is used as a basis for deployment planning in order to plan for at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle (see Pichlimaier at least [0013] “Where the agricultural operation is planting, the delivered resource from the AAM may comprises seed. Where it is spraying, the resource may be water, fertilizer, or pesticide or any other liquid, gaseous or solid matter. Alternately or additionally, where the or each autonomous agricultural vehicle is electrically powered, the resource may be energy in form of fuel or an electric charge [(i.e., energy)]. By enabling the AAM's to replenish necessary resources in the field, they need not carry for example a full days supply and thus can be made smaller and lighter, reducing the effects of soil compaction.”; [0038] “. . . the host vehicle CLU 10 comprises a reservoir 34 holding a resource required to enable the or each robot 12 to perform the agricultural operation. Each robot and the CLU 10 comprise mutually configured means 36, 38 for delivery of the resource from CLU to robot in the field. In FIG. 5A a robot 12 having low supply in an onboard reservoir 40 approaches the CLU. In FIG. 5B the mutually configured means 36, 38 cooperate to position the robot such that resource from the CLU reservoir 34 may be transferred to the robot reservoir 40 with minimal or zero spillage.”); . . .
wherein the management system analyzes the operational data to determine whether to adjust the current deployment plan responsive to determining that the at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle influences execution of at least one step to be performed by the at least one autonomous agricultural work vehicle (see Pichlimaier at least [0013] “Where the agricultural operation is planting, the delivered resource from the AAM may comprises seed. Where it is spraying, the resource may be water, fertilizer, or pesticide or any other liquid, gaseous or solid matter. Alternately or additionally, where the or each autonomous agricultural vehicle is electrically powered, the resource may be energy in form of fuel or an electric charge [(i.e., energy)]. By enabling the AAM's to replenish necessary resources in the field, they need not carry for example a full days supply and thus can be made smaller and lighter, reducing the effects of soil compaction.”; [0034] “Using data indicative of robot functions may help to sort out problems, at the same time preserving the simplicity approach: if the motor shows low performance but the vehicle should otherwise move fast (detected by the time and position data as above), the energy in the robot may be low, indicating a recharge may be required [(i.e., energy consumption as operational data)].”; [0038] “. . . the host vehicle CLU 10 comprises a reservoir 34 holding a resource required to enable the or each robot 12 to perform the agricultural operation. Each robot and the CLU 10 comprise mutually configured means 36, 38 for delivery of the resource from CLU to robot in the field. In FIG. 5A a robot 12 having low supply in an onboard reservoir 40 approaches the CLU [(i.e., transfer of operational resource impacts deployment)]. In FIG. 5B the mutually configured means 36, 38 cooperate to position the robot such that resource from the CLU reservoir 34 may be transferred to the robot reservoir 40 with minimal or zero spillage.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the using operational data as a basis for deployment planning and a current deployment plan that includes at least one step to be performed by the at least one autonomous agricultural work vehicle of Anderson with the operational data being indicative of energy consumption of the at least one autonomous agricultural work vehicle, the operational data being used as a basis for deployment planning in order to plan for at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle, and the management system analyzing the operational data to determine whether to adjust the current deployment plan responsive to determining that the at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle influences execution of at least one step to be performed by the at least one autonomous agricultural work vehicle as taught by Pichlimaier to have the operational data be indicative of energy consumption of the at least one autonomous agricultural work vehicle, wherein the operational data is used as a basis for deployment planning in order to plan for at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle, wherein the current deployment plan includes at least one step to be performed by the at least one autonomous agricultural work vehicle, and wherein the management system analyzes the operational data to determine whether to adjust the current deployment plan responsive to determining that the at least one of procurement, pickup or transfer of operational resources to operate the at least one autonomous agricultural work vehicle influences execution of at least one step to be performed by the at least one autonomous agricultural work vehicle. Doing so would reduce the required on-board fuel for the at least one autonomous agricultural work vehicle, thus reducing weight and, ultimately, soil compaction, as recognized by Pichlimaier (see Pichlimaier at least [0013] “Where the agricultural operation is planting, the delivered resource from the AAM may comprises seed. Where it is spraying, the resource may be water, fertilizer, or pesticide or any other liquid, gaseous or solid matter. Alternately or additionally, where the or each autonomous agricultural vehicle is electrically powered, the resource may be energy in form of fuel or an electric charge [(i.e., energy)]. By enabling the AAM's to replenish necessary resources in the field, they need not carry for example a full days supply and thus can be made smaller and lighter, reducing the effects of soil compaction.”).
Response to Arguments
Applicant's arguments filed 04/29/2025 have been fully considered but they are not persuasive.
(A) Applicant argues, “Claim 1:
“Claim 1 recites in part:
“coordinating, using the deployment plan, amongst the plurality of agricultural work vehicles to assign the plurality of work steps including assigning the at least one autonomous agricultural work vehicle at least one work step from the plurality of work steps to perform a respective agricultural work process, wherein coordinating comprises:
“determining, by the management system based on the respective agricultural work process to be performed by the at least one autonomous agricultural work vehicle, one or more operating parameters for the at least one autonomous agricultural work vehicle or the respective work unit integrated therein or adapted thereto; and transmitting, by the management system, the one or more operating parameters to the at least one autonomous agricultural work vehicle so that the at least one autonomous agricultural work vehicle performs the at least one work step automatically.
“Applicant respectfully contends that Anderson fails to teach or suggest claim 1 as amended. In particular, amended claim 1 recites as part of the deployment planning and coordination of a vehicle fleet with at least one autonomous work vehicle that, depending on the at least one autonomous work vehicle agricultural work process to be performed, operating parameters are generated by the management system for at least one autonomous agricultural work vehicle.
“Applicant respectfully contends that Anderson does not teach or suggest, in addition to the path information, operating parameters for an integrated or adapted working unit as part of the operational plan and then transmitted together with the operational plan. In other words, Anderson merely aims to provide a route plan for the autonomous vehicles, which is then driven by the vehicles. Control of working units by transmitting operating parameters are not disclosed or suggested in Anderson. Thus, while movements may be coordinated, operating parameters are not. As such, Applicant respectfully contends that amended claim 1 is patentable over the cited art.
“Claim 15:
“Though of different scope than claim 1, claim 15 is patentable at least based on the arguments presented above,” (from remarks pages 10-11).
As to Point (A), examiner respectfully disagrees. Applicant appears to argue that control of working units by transmitting operating parameters are not disclosed in the prior art reference Anderson. However, Anderson does disclose the operational parameters as claimed. Some examples of operating parameters used in controlling the working units of Anderson include the specification of directionality of movement or the types of passes to be made (Anderson at least [0086] “For example, the task may be ‘harvest a field’ and the sub-tasks or aspects of the task may include ‘power up the vehicle,’ ‘execute a follow the leader mode,’ ‘harvest in a parallel manner,’ ‘avoid obstacles while harvesting,’ ‘coordinate with combine vehicle to unload grain while harvesting,’ ‘make a headland turn,’ and ‘harvest the field in a north-to-south direction starting on the west end of the field with multiple vehicles making adjacent, parallel passes of the field [(i.e., operating parameters)].’”). Additional detail regarding how these operational parameters are used for control by Anderson is supplied in the mapping of claim 1 above. As such, neither claim 1 nor claim 15 as recited in the claims overcome the prior art of record.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TABITHA KRESS whose telephone number is (703) 756-1763. The examiner can normally be reached MTWR 06:30-16:30 CST.
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/TABITHA KRESS/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
6/27/25
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