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 .
This final action is in response to Applicant’s filing dated May 19, 2026. Claims 1-20 are currently pending and have been considered, as provided in more detail below.
*Examiner Note: Claim language is bolded. Cited References and Applicant’s arguments are italicized. Examiner interpretations are preceded with an asterisk *.
Response to Arguments
Applicant’s arguments filed 5/19/26 have been considered but are moot because the arguments are directed to a combination of references that is no longer relied upon and has necessitated a new ground of rejection as outlined below regarding independent claims 1 and 16. While the new ground of rejection may rely on some of the previous references applied in the prior rejection of record, a new additional reference has been added to some of the combinations and introduced for Applicant’s consideration given the amended independent claims as discussed in detail below. Applicant’s arguments regarding claim 11 have been fully considered, but they are not persuasive. Hansen discloses sensors 141 configured to detect pitch, roll and yaw of the agricultural work machine, as well as acceleration on multiple axes, wherein a surface roughness estimator determines or estimates a surface roughness of the worksite area based on the detected motion data, pitch data, roll data and/or other position or movement data. This is data indicative of machine bouncing, as newly recited in amended claim 11. Hansen further discloses that this component estimates the current or predicted acceleration of the machine which demonstrates that the disclosed machine bouncing data is not limited to the machine’s current location but is used predictively and consistent with the claimed data being indicative of a ride quality issue at an upcoming location. Therefore, the combination of Hansen and Frye continues to teach and/or suggest each limitation of amended claim 11 and the rejection is maintained as discussed in detail below.
Response to Amendment
Regarding the rejections under 35 USC 103, amendments made to the claims have necessitated new grounds of rejection as outlined below.
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 1-6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hansen (US 2023/0094319 A1) in view of Schoeny (US 20200037491 A1).
Regarding amended claim 1, Hansen discloses A system (see at least para. [0050] of Hansen
which discloses “a control system for an agricultural machine”) comprising: one or more processors (Fig. 1, 128 see at least para. [0083] of Hansen which discloses “one or more processors 128”); and memory storing instructions (see at least para. [0052] of Hansen which discloses a “memory storing instructions”), executable by the one or more processors (see at least para. [0050] –[0052] of Hansen which discloses “at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed”).
Hansen does disclose that, when executed by the one or more processors, configure the
one or more processors to: obtain data (see at least para. [0121] of Hansen which discloses “data collection component 402 is configured to collect or otherwise obtain data during the operation of work machine 102 on the worksite. This can include data from sensors 124 on machine 102. For example, worksite imaging sensors 150 can obtain images of the worksite in a path of work machine 102”) indicative of a ride quality issue (see at least para. [0127] of Hansen which discloses “detected roughness can be utilized to select a target machine speed based on a predefined or user selected ride quality or ride smoothness setting”); identify a ride quality issue at the worksite based on the data (see at least para. [0158] of Hansen which discloses “setting change selection component 416 can weight ride quality based on indications of terrain roughness from terrain roughness detector component”, *Hansen teaches identifying a ride quality issue by determining terrain roughness at locations along the machine’s path based on sensor data and/or terrain maps – see at least para. [0158] of Hansen. Because Hansen expressly links detected roughness to ride quality (see at least para. [0127] of Hansen), this determination constitutes identification of a ride quality issue for those locations); and control the work machine based, at least, on the ride quality issue (see at least para. [0044]-[0045] of Hansen which discloses “the control system is configured to: determine the target machine speed based on the ride quality parameter”) at the worksite.
Hansen may not explicitly disclose obtaining proximate data corresponding to a location at
a worksite different than an upcoming location at the worksite at which a work machine performs a current operation, the proximate data indicative of a ride quality issue at the upcoming location; identify a ride quality issue at the upcoming location; nor control the vehicle based, at least, on the ride quality issue at the upcoming location.
However, Schoeny discloses obtaining proximate data (see at least para. [0028] of Schoeny
which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) corresponding to a location at a worksite different (see at least para. [0028] of Schoeny which discloses “with each sensor 120 configured to detect one or more parameters indicative of one or more field conditions associated with the adjacent swath 72B being marked by the corresponding marker assembly 28A as the vehicle/implement 50/10 makes a pass along the current field swath 72C”, *Examiner interprets the work machine/vehicle/implement 50/10 is located at current swath 72C which is a location different than the upcoming location (adjacent swath 72B), at the time the proximate data is obtained) than an upcoming location (see at least para. [0008] of Schoeny which discloses “an adjacent second swath within the field. The method may also include monitoring, with the computing device, a field condition associated with the adjacent second swath as the agricultural machine makes the first pass across the field based on data received from a sensor provided in association with the support arm. In addition, the method may include adjusting, with the computing device, the operating parameter(s) of the agricultural machine as the agricultural machine makes a second pass across the field to perform the agricultural operation along the adjacent second swath based at least in part on the monitored field condition”, *Examiner interprets the “adjacent second swath within the field” to correspond to the claimed upcoming location) at the worksite at which a work machine (Fig. 1, 10 and see at least para. [0020] of Schoeny which discloses “the agricultural machine corresponds to an agricultural implement 10, namely a planter. However, in other embodiments, the agricultural machine may correspond to any other suitable agricultural implement, such as a tillage implement, seeder, fertilizer, sprayer and/or the like. In addition, it should be appreciated that, as used herein, the term “agricultural machine” may refer not only to implements configured to be towed or otherwise pulled across a field, but also to the agricultural vehicle (e.g., a tractor) configured to tow or pull such implement(s) across the field and/or the combination of a vehicle/implement. Thus, for example, an agricultural machine may correspond separately to an agricultural vehicle or implement or collectively to the combination of an agricultural vehicle/implement”) performs a current operation (see at least para. [0017] of Schoeny which discloses “an agricultural machine (e.g., an agricultural implement and/or vehicle) makes a pass across the field during the performance of an agricultural operation”), the proximate data indicative of a ride quality issue at the upcoming location; identify a ride quality issue at the upcoming location (see at least para. [0029] of Schoeny which discloses “the sensor(s) 120 may be configured to monitor a parameter indicative of the seedbed quality of the adjacent swath 72B, such as by configuring the sensor(s) 120 as an accelerometer or other suitable sensor capable of monitoring the soil roughness of the adjacent swath 72B (e.g., by detecting movement/shaking of the support arm 30 and/or the marker component 32). In such an embodiment, based on the roughness data captured for the adjacent swath 72B, one or more operating parameters associated with the operation of the vehicle 50 and/or implement 10 may be adjusted as the vehicle/implement 50/10 make a subsequent pass along the previously marked/mapped swath 72B to account for variations in the seedbed quality. For instance, the speed of the vehicle 50 may be adjusted based on the monitored seedbed quality, such as by increasing the speed of the vehicle 50 along a section(s) of the swath 72B determined to have a higher seedbed quality (e.g., due to low soil roughness and/or low surface profile variations) and/or decreasing the speed of the vehicle 50 along a section(s) of the swath 72B determined to have a lower seedbed quality (e.g., due to high soil roughness and/or high surface profile variations). As another example, one or more operating parameters of the implement 10 may be adjusted based on the monitored seedbed quality, such as by adjusting the down pressure/force applied to the row units 24 based the seedbed quality along all or a given section(s) of the swath 72B or by adjusting the down pressure/force applied to a row cleaner or closer provided in association with each row unit 24 along all or a given section(s) of the swath 72B” and see at least para. [0043] of Schoeny which discloses “the field condition data may be geo-located along the adjacent swath 72B such that localized variations in the monitored field condition(s) may be identified and mapped to a corresponding location within the field 70 … the field map 150 identifies variations in the seedbed quality along the adjacent swath 72B as monitored based on the data received from the associated sensor(s) 120. In such an embodiment, by identifying the location of variations in the seedbed quality, the swath 72B may, for example, be divided into separate sections or zones representing varying levels or degrees of seedbed quality”); control the vehicle based, at least, on the ride quality issue at the upcoming location (see at least para. [0043] of Schoeny which discloses “the operation of the vehicle 50 and/or the implement 10 may be actively adjusted as the vehicle/implement 50/10 may a subsequent pass across the mapped swath 72B to account for the localized variations in the monitored field condition, such as by increasing the ground speed of the vehicle/implement 50/10 as the implement 10 passes through the high seedbed quality zones 152 and decreasing the speed of the vehicle/implement 50/10 as the implement 10 passes through the low seedbed quality zone 156” and see at least para. [0055] of Schoeny which discloses “adjusting the operating parameter(s) of the agricultural machine as the machine makes a second pass across the field to perform the agricultural operation along the adjacent second swath based at least in part on the monitored field condition”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Hansen to include proximate data corresponding to a location at a worksite different than an upcoming location at the worksite at which a work machine performs a current operation, the proximate data indicative of a ride quality issue at the upcoming location; identify a ride quality issue at the upcoming location; and control the vehicle based, at least, on the ride quality issue at the upcoming location; as taught in Schoeny with a reasonable expectation of success in order to convert Hansen’s system from reactive to predictive/pre-emptive control, thereby improving operator comfort, reducing machine vibration and component wear, and enabling proactive adjustment before the machine reaches a rough terrain location. Additionally, Schoeny’s accelerometer based detection of soil roughness via movement/shaking of the support arm detects the same type of vibration/motion data that Hansen expressly correlates with ride quality and a person of ordinary skill in the thar art before the effectively filing date of the claimed invention would understand that Schoeny’s roughness data is one of the same character as the ride quality data disclosed in Hansen. See para. [0003] of Schoeny for motivation.
Regarding amended claim 2, Hansen, as modified by Schoeny, discloses wherein the
proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) comprises at least one of: prior operation data generated during a prior operation at the worksite (see at least para. [0082] of Hansen which discloses “System(s) 120 are configured to collect prior data that can be used by work machine in performing a work assignment on a worksite. Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc. The prior data can be used to generate a model, such as a predictive map, that can be used to control work machine 102. Examples of prior data include, but are not limited to, location conditions that identify various conditions that can affect operation of work machine 102”); or remote data (see at least para. [0121] of Hansen which discloses “component 402 can receive any data that indicates operation of various aspects of machine 102, either from on-board sensors or from remote sources such as remote imaging components, unmanned aerial vehicles (UAVs) or drones, other machines on the worksite, etc.” and see at least para. [0144] of Hansen which discloses “the target metric value can be selected based on input from remote computing system 118”) generated by a system remote from the worksite (see at least para. [0082] of Hansen which discloses “Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc.”, i.e., these sources are remote from the worksite, *Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., prior operation data generated during a prior operation at the worksite, etc.).
Regarding amended claim 3, Hansen, as modified by Schoeny, discloses wherein the work
machine (Fig. 1, 102 and see at least para. [0075] of Hansen which discloses “Mobile work machine 102 can be any type of work machine that moves and performs tasks on a worksite. Some mobile work machines perform aerial work operations, while other machines may perform nautical or under water work operations, and some machines perform ground-based work operations. Examples of work operations include agricultural, construction, and/or turf and forestry work operations”) comprises a first work machine having a first machine type and wherein the proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) comprises prior operation data generated during a prior operation (see at least para. [0082] of Hansen which discloses “System(s) 120 are configured to collect prior data that can be used by work machine in performing a work assignment on a worksite. Prior data can be generated from a wide variety different types or sources) conducted by a second work machine (see at least para. [0084] of Hansen which discloses “support machine 104, other machines 116 (such as other machines operating on a same worksite as work machine 102)”) having a second machine type, different than the first machine type (see at least para. [0077] of Hansen which discloses “While machine 102 is illustrated with a single box in FIG. 1 , machine 102 can include multiple machines (e.g., a towed implement towed by a support or towing machine 104). In this example, the elements of machine 102 illustrated in FIG. 1 can be distributed across a number of different machines“).
Regarding amended claim 4, Hansen, as modified by Schoeny, discloses wherein the
proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) indicates one or more of: (i) machine speed; (ii) machine bouncing; or (iii) a terrain characteristic (see at least para. [0121] of Hansen which discloses “work quality-based machine speed control system 150. System 150 includes an in situ data collection component 402, an application detection component 404, a work quality metric comparison component 406, an operator presence detection component 408, a lateral error detection component 410, a terrain roughness detection component 412” *Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., terrain characteristic etc.).
Regarding amended claim 5, Hansen, as modified by Schoeny, discloses wherein the
proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) is generated by one or more sensors of the work machine (see at least para. [0083] of Hansen which discloses “that work machine 102 includes in situ data collection system 126, one or more processors 128, a data store 130, and can include other items 132 as well. Sensors 124 can include any of a wide variety of sensors depending on the type of work machine 102. For instance, sensors 124 can include material sensors 134, position/route sensors 136, speed sensors 138, worksite imaging sensors 140, orientation and/or inertial sensors 141, and can include other sensors 142 as well”).
Regarding amended claim 6, Hansen, as modified by Schoeny, discloses wherein the
instructions, when executed by the one or more processors, further configure the one or more processors to: compare the proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) to a threshold (see at least para. [0124] of Hansen which discloses “Metric comparison component 406 is configured to compare a current metric value for a quality metric (representing a current performance characteristic of work machine 102) to a target or threshold value”); and identify the ride quality issue at the upcoming location at the worksite based on comparison of the data to the threshold (see at least para. [0049] of Schoeny which discloses “it may be desirable for the controller(s) 102, 104 to apply certain thresholds or control rules when determining how and when to make active adjustments. For instance, if the size of a given zone within the field map is below a predetermined size threshold, the controller(s) 102, 104 may be configured to ignore the zone and not make any active operational adjustments as the implement 10 passes across such zone. Similarly, the controller(s) 102, 104 may be configured to apply a variation threshold to determine when to make any active operational adjustments. For instance, if the difference between the monitored field condition(s) along adjacent sections of the field is below a predetermined variation threshold, the controller(s) 102, 104 may be configured to ignore the difference and apply the same operational setting(s) across the adjacent sections of the field. In such an embodiment, the various zones provided within the field map may, for example, be identified based on a set of predetermined variance thresholds such that the difference in the monitored field condition(s) between differing zones is significant enough to warrant adjusting the operation of the vehicle 50 and/or the implement 10 as the implement 10 transitions between such zones”).
Regarding claim 10, Hansen, as modified by Schoeny, discloses wherein the instructions
when executed by the one or more processors, further configure the one or more processors to control the work machine by controlling one or more of: (i) an interface mechanism (see at least para. [0078] of Hansen which discloses “interface mechanism(s) 112. Operator interface mechanism(s) 112 can include such things as a steering wheel, pedals, levers, joysticks, buttons, dials, linkages, etc. In addition, mechanism(s) 112 can include a display device that displays user actuatable elements, such as icons, links, buttons, etc. Where the device is a touch sensitive display, those user actuatable items can be actuated by touch gestures. Similarly, where mechanism(s) 112 includes speech processing mechanisms, then operator 110 can provide inputs and receive outputs through a microphone and speaker, respectively. Operator interface mechanism(s) 112 can include any of a wide variety of other audio, visual or haptic mechanisms”) of the work machine to generate a presentation; (ii) a propulsion subsystem (see at least para. [0090] of Hansen which discloses “Propulsion subsystem 156 includes an engine (or other power source) that drives a set of ground engaging traction elements, such as wheels or tracks”) of the work machine to change a speed of the work machine; (iii) an actuator of the work machine to adjust a position of an implement of the work machine (see at least para. [0091] of Hansen which discloses “actuators 164 that change the positioning of a header, the concave clearance, etc., based upon the predicted yield or biomass to be encountered by the machine. In the case of an agricultural tilling machine, settings control component 144 can control the positioning or down pressure on the tilling implement by controlling actuators 162”); or (iv) an actuator of the work machine to adjust a biasing force applied to an implement of the work machine (*Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., an actuator of the work machine to adjust a position of an implement of the work machine).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Hansen (US 2023/0094319 A1) in view of Schoeny (US 20200037491 A1) further in view of Vandike (US 2022/0110246A1).
Regarding claim 7, Hansen, as modified by Schoeny, discloses wherein the threshold (see at
least para. [0124] of Hansen which discloses “Metric comparison component 406 is configured to compare a current metric value for a quality metric (representing a current performance characteristic of work machine 102) to a target or threshold value”) is identified during the current operation.
Hansen, as modified by Schoeny, may not explicitly disclose the threshold is identified
based on operator control adjustment data corresponding to a location at the worksite different than the upcoming location, the operator control adjustment data indicating an operator adjustment to the work machine.
However, in the same field of endeavor, Vandike discloses based on operator control
adjustment data corresponding to a location at the worksite different than the upcoming location, the operator control adjustment data indicating an operator adjustment to the work machine (see at least para. [0065] of Vandike which discloses “Control zone generator 213 can divide the predictive map 264 into control zones based on the values on the predictive map 264. Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Hansen, as modified by Schoeny to include the threshold is identified based on operator control adjustment data corresponding to a location at the worksite different than the upcoming location, the operator control adjustment data indicating an operator adjustment to the work machine, as taught in Vandike with a reasonable expectation of success in order to adapt ride quality thresholds in real time to the operator’s demonstrated preferences and thereby improve consistency of machine behavior, operator comfort and work quality across different locations in the same worksite. See para. [0065] of Vandike for motivation.
Regarding amended claim 8, Hansen, as modified by Schoeny, discloses wherein the
proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) includes a first machine setting value (see at least para. [0091] of Hansen which discloses “Settings control component 144 can control one or more of subsystems 108 in order to change machine settings based upon the predicted and/or observed conditions or characteristics of the worksite” and see at least para. [0105] of Hansen which discloses “The sensors can include machine setting sensors that are configured to sense the various configurable settings on combine 200” and see at least para. [0117] of Hansen which discloses “Machine settings display generator 320 illustratively obtains the current machine settings for the combine 100 under analysis and generates display elements indicative of those machine settings”).
Hansen, as modified by Schoeny, may not explicitly disclose wherein the instructions when
executed by the one or more processors, further configure the one or more processors to: identify a second machine setting value based on the first machine setting value, the second machine setting value matching the first machine setting value; and control the work machine based, at least, on the second machine setting value.
However, in the same field of endeavor, Vandike discloses and wherein the instructions
when executed by the one or more processors, further configure the one or more processors to: identify a second machine setting value based on the first machine setting value (see at least para. [0052] of Vandike which discloses “the information map 258 may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the prior year can be used by predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by in-situ sensors 208 during the current harvesting operation. The predictive model is then used by predictive map generator 210 to generate a predictive yield map” and see at least para. [0047] of Vandike which discloses “predictive map generator 212 generates a predictive map 264 that predicts the target machine speed value at different locations across the field” and see at least para. [0049] of Vandike which discloses “The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field”, *Because speed is a configurable operational parameter of the agricultural harvester, these predicted speed values constitute machine setting values), the second machine setting value matching the first machine setting value; and control the work machine based, at least, on the second machine setting value (see at least para. [0054] of Vandike which discloses “A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant” and “control zone generator 213 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems 216”, *Vandike further teaches that the predictive speed map is generated and used during harvesting operations, thereby teaching identifying a second machine setting value based on a first machine setting value and controlling the work machine based on that machine setting value).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the data of Hansen, as modified by Schoeny, to include wherein the instructions when executed by the one or more processors, further configure the one or more processors to: identify a second machine setting value based on the first machine setting value, the second machine setting value matching the first machine setting value; and control the work machine based, at least, on the second machine setting value, as taught in Vandike with a reasonable expectation of success in order to facilitate consistent and predictable control of the work machine based on known or previously determined optimal settings for specific worksite conditions, thereby improving machine performance and operator comfort.
Regarding amended claim 9, Hansen, as modified by Schoeny, discloses wherein the
proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) includes a first machine setting value (see at least para. [0091] of Hansen which discloses “Settings control component 144 can control one or more of subsystems 108 in order to change machine settings based upon the predicted and/or observed conditions or characteristics of the worksite” and see at least para. [0105] of Hansen which discloses “The sensors can include machine setting sensors that are configured to sense the various configurable settings on combine 200” and see at least para. [0117] of Hansen which discloses “Machine settings display generator 320 illustratively obtains the current machine settings for the combine 100 under analysis and generates display elements indicative of those machine settings”) and wherein the instructions when executed by the one or more processors, further configure the one or more processors to: scale (see at least para. [0091] of Hansen which discloses “Settings control component 144 can control one or more of subsystems 108 in order to change machine settings based upon the predicted and/or observed conditions or characteristics of the worksite” and see at least para. [0158] of Hansen which discloses “ride quality can be weighted to increase or decrease the target machine speed, depending on the desired smoothness of the ride experience by the operator. For example, based on the ride quality weighting parameter, a threshold (e.g., maximum) attitude and/or acceleration of machine 102 can be determined”, * This corresponds to scaling since weighting means applying a scaling factor to an existing machine setting value) the first machine setting value.
Hansen, as modified by Schoeny, may not explicitly disclose the first machine setting value
to identify a second machine setting value; and control the work machine based, at least, on the second machine setting value.
However, in the same field of endeavor, Vandike discloses the first machine setting value
(see at least para. [0052] of Vandike which discloses “the information map 258 may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the prior year can be used by predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by in-situ sensors 208 during the current harvesting operation. The predictive model is then used by predictive map generator 210 to generate a predictive yield map”)
to identify a second machine setting value (see at least para. [0047] of Vandike which discloses “predictive map generator 212 generates a predictive map 264 that predicts the target machine speed value at different locations across the field” and see at least para. [0049] of Vandike which discloses “The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field”, *Because speed is a configurable operational parameter of the agricultural harvester, these predicted speed values constitute machine setting values); and control the work machine based, at least, on the second machine setting value (see at least para. [0054] of Vandike which discloses “A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant” and “control zone generator 213 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems 216”, *Vandike further teaches that the predictive speed map is generated and used during harvesting operations, thereby teaching identifying a second machine setting value based on a first machine setting value and controlling the work machine based on that machine setting value).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the data of Hansen, as modified by Schoeny, to include the first machine setting value to identify a second machine setting value; and control the work machine based, at least, on the second machine setting value, as taught in Vandike with a reasonable expectation of success in order to facilitate consistent and predictable control of the work machine based on known or previously determined optimal settings for specific worksite conditions, thereby improving machine performance and operator comfort.
Claims 11, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hansen (US 2023/0094319 A1) in view of Frye (US 2024/0294048 A1).
Regarding amended claim 11, Hansen discloses A computer-implemented method of
controlling (see at least para. [0004] of Hansen which discloses “A method of controlling a mobile agricultural machine”) an agricultural work machine (Fig. 1, 102 and see at least para. [0103] of Hansen which discloses “an agricultural work machine” and see at least para. [0103] of Hansen which discloses “an agricultural work machine” and see at least para. [0074] of Hansen which discloses “an autonomous or semi-autonomous agricultural machine” and see at least para. [0075] of Hansen which discloses “Mobile work machine 102 can be any type of work machine that moves and performs tasks on a worksite … some machines perform ground-based work operations. Examples of work operations include agricultural”) comprising: obtaining data (see at least para. [0121] of Hansen which discloses “data collection component 402 is configured to collect or otherwise obtain data during the operation of work machine 102 on the worksite. This can include data from sensors 124 on machine 102. For example, worksite imaging sensors 150 can obtain images of the worksite in a path of work machine 102”) indicative of a ride quality issue (see at least para. [0127] of Hansen which discloses “detected roughness can be utilized to select a target machine speed based on a predefined or user selected ride quality or ride smoothness setting”) at a worksite (see at least para. [0093] of Hansen which discloses “worksite operations while machine 102 traverses the field or other worksite. A field operation refers to any operation performed on a worksite or field” and see at least para. [0096] of Hansen which discloses “Prior data collection system 120 illustratively collects worksite data, such as prior data corresponding to a target field to be operated upon by machine 102. Briefly, by prior, it is meant that the data is formed or obtained beforehand, prior to the operation by machine 102”) at which
the agricultural work machine (see at least para. [0103] of Hansen which discloses “an agricultural work machine”) performs a current operation (see at least para. [0158] of Hansen which discloses “setting change selection component 416 can weight ride quality based on indications of terrain roughness from terrain roughness detector component 412. As noted above, component 412 can determine terrain roughness based on sensor signals from sensor(s) 124 (e.g., accelerometers, gyroscopes, imaging sensors, etc.). Alternatively, or in addition, component 412 can determine terrain roughness based on terrain maps. In any case, ride quality can be weighted to increase or decrease the target machine speed, depending on the desired smoothness of the ride experience by the operator. For example, based on the ride quality weighting parameter, a threshold (e.g., maximum) attitude and/or acceleration of machine 102 can be determined”, *Hansen explicitly obtains ride quality related data for locations along the worksite in advance of the machine reaching them), wherein the data indicates one or more of (i) machine speed (see at least para. [0136] of Hansen which discloses “Terrain roughness can be utilized to select a target machine speed based on a predefined or user selected ride quality or ride smoothness setting”) or (ii) machine bouncing (see at least para. [0136] of Hansen which discloses “sensors 141 can detect pitch, roll, and yaw of machine 102, as well as acceleration on multiple axes. Thus, pitch data during a sampling interval can be used to obtain pitch acceleration and roll data for the sampling interval can be used to obtain roll acceleration” and see at least para. [0138] of Hansen which discloses “component 412 can estimate the precise attitude (e.g., yaw data, roll data, or both) of machine 102 as well as the current or predicted acceleration (e.g., in meters per second squared (m/s2) on any of a number of axes”); identifying a ride quality issue at the worksite based on the data (see at least para. [0158] of Hansen which discloses “setting change selection component 416 can weight ride quality based on indications of terrain roughness from terrain roughness detector component”, *Hansen teaches identifying a ride quality issue by determining terrain roughness at locations along the machine’s path based on sensor data and/or terrain maps – see at least para. [0158] of Hansen. Because Hanse expressly links detected roughness to ride quality (see at least para. [0127] of Hansen), this determination constitutes identification of a ride quality issue for those locations); controlling the agricultural work machine based, at least, on the ride quality issue (see at least para. [0044]-[0045] of Hansen which discloses “the control system is configured to: determine the target machine speed based on the ride quality parameter”) at the worksite.
Hansen may not explicitly disclose a ride quality issue at an upcoming location; identifying
a ride quality issue corresponding to the upcoming location; nor controlling the vehicle based, at least, on the ride quality issue corresponding to the upcoming location.
However, Frye discloses identifying a ride quality issue at an upcoming location (see at least
para. [0041] of Frye which discloses “the suspension control system 100 may determine an extent to which an upcoming road portion is expected to be rough. For example, the system 100 may look-ahead a threshold distance (e.g., 2 kilometers, 3 kilometers, 5 kilometers, 7 kilometers) and determine a percentage of the threshold distance which is expected to be rough” and see at least para. [0052] of Frye which discloses “The suspension controller 220 may additionally obtain look-ahead information 234 to determine an extent to which upcoming road segments are expected to be rough based on the map 122” and see at least para. [0053] of Frye which discloses “the controller 220 may determine that a threshold percentage of an upcoming distance is expected to be rough”, *Examiner interprets the upcoming road segments of Frye to be the claimed upcoming location). Frye further discloses controlling the vehicle based, at least, on the ride quality issue corresponding to the upcoming location (see at least para. [0041] of Frye which discloses “The system 100 may determine whether this percentage exceeds a threshold, and if so, trigger an adjustment of the suspension (e.g., raise the ride height). For example, the percentage may be 20% and the threshold distance may be 4 kilometers. In this example, the system 100 may raise the ride height based on at least 800 meters of the 4 kilometers being indicated in the map 122 as being rough. The trigger may further require that the vehicle 102 is within a threshold distance of the start of a rough road segment (e.g., within 100 meters, 200 meters, and so on, which may optionally be adjusted according to vehicle speed”, *Examiner interprets the triggering of adjustment to suspension to be another example of controlling a vehicle based, at least, on the ride quality issue at the upcoming location).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the method of Hansen to include data indicating a ride quality issue at an upcoming location; identifying a ride quality issue corresponding to the upcoming location; and controlling the vehicle based, at least, on the ride quality issue corresponding to the upcoming location, as taught in Frye with a reasonable expectation of success in order to improve operator comfort, reduce machine vibration and component wear, and enable proactive rather than reactive control of the work machine when approaching a rough terrain at a worksite/location. See para. [0041] and [0053] of Frye for motivation.
Regarding amended claim 13, Hansen, as modified by Frye, discloses wherein obtaining the
data (see at least para. [0121] of Hansen which discloses “data collection component 402 is configured to collect or otherwise obtain data during the operation of work machine 102 on the worksite. This can include data from sensors 124 on machine 102. For example, worksite imaging sensors 150 can obtain images of the worksite in a path of work machine 102”) comprises obtaining one or more of: (i) prior operation data generated during a prior operation at the worksite (see at least para. [0082] of Hansen which discloses “System(s) 120 are configured to collect prior data that can be used by work machine in performing a work assignment on a worksite. Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc. The prior data can be used to generate a model, such as a predictive map, that can be used to control work machine 102. Examples of prior data include, but are not limited to, location conditions that identify various conditions that can affect operation of work machine 102”); or (iii) remote data (see at least para. [0121] of Hansen which discloses “component 402 can receive any data that indicates operation of various aspects of machine 102, either from on-board sensors or from remote sources such as remote imaging components, unmanned aerial vehicles (UAVs) or drones, other machines on the worksite, etc.” and see at least para. [0144] of Hansen which discloses “the target metric value can be selected based on input from remote computing system 118”) generated by a system remote from the worksite (see at least para. [0082] of Hansen which discloses “Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc.”, i.e., these sources are remote from the worksite, *Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., prior operation data generated during a prior operation at the worksite, etc.).
Regarding amended claim 15, Hansen, as modified by Frye, discloses wherein controlling
the agricultural work machine (Fig. 1, 102 and see at least para. [0103] of Hansen which discloses “an agricultural work machine”) comprises one or more of: (i) controlling an interface mechanism (see at least para. [0078] of Hansen which discloses “interface mechanism(s) 112. Operator interface mechanism(s) 112 can include such things as a steering wheel, pedals, levers, joysticks, buttons, dials, linkages, etc. In addition, mechanism(s) 112 can include a display device that displays user actuatable elements, such as icons, links, buttons, etc. Where the device is a touch sensitive display, those user actuatable items can be actuated by touch gestures. Similarly, where mechanism(s) 112 includes speech processing mechanisms, then operator 110 can provide inputs and receive outputs through a microphone and speaker, respectively. Operator interface mechanism(s) 112 can include any of a wide variety of other audio, visual or haptic mechanisms”) of the agricultural work machine to generate a presentation; (ii) controlling a propulsion subsystem (see at least para. [0090] of Hansen which discloses “Propulsion subsystem 156 includes an engine (or other power source) that drives a set of ground engaging traction elements, such as wheels or tracks”) of the agricultural work machine to change a speed of the work machine; (iii) controlling an actuator of the agricultural work machine to adjust a position of an implement of the agricultural work machine (see at least para. [0091] of Hansen which discloses “actuators 164 that change the positioning of a header, the concave clearance, etc., based upon the predicted yield or biomass to be encountered by the machine. In the case of an agricultural tilling machine, settings control component 144 can control the positioning or down pressure on the tilling implement by controlling actuators 162”); or (iv) controlling an actuator of the agricultural work machine to adjust a biasing force applied to an implement of the work machine (*Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., controlling an actuator of the agricultural work machine to adjust a position of an implement of the agricultural work machine).
Claims 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hansen (US 2023/0094319 A1) in view of Frye (US 2024/0294048 A1) and further in view of Schoeny (US 20200037491 A1).
Regarding amended claim 12, Hansen, as modified by Frye, discloses the data (see at least
para. [0121] of Hansen which discloses “data collection component 402 is configured to collect or otherwise obtain data during the operation of work machine 102 on the worksite. This can include data from sensors 124 on machine 102. For example, worksite imaging sensors 150 can obtain images of the worksite in a path of work machine 102”).
Hansen, as modified by Frye, may not explicitly disclose obtaining proximate data
corresponding to a location at the worksite different than the upcoming location.
However, Schoeny discloses obtaining proximate data (see at least para. [0028] of Schoeny
which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) corresponding to a location at the
worksite different (see at least para. [0028] of Schoeny which discloses “with each sensor 120 configured to detect one or more parameters indicative of one or more field conditions associated with the adjacent swath 72B being marked by the corresponding marker assembly 28A as the vehicle/implement 50/10 makes a pass along the current field swath 72C”, *Examiner interprets the work machine/vehicle/implement 50/10 is located at current swath 72C which is a location different than the upcoming location (adjacent swath 72B), at the time the proximate data is obtained) than the upcoming location (see at least para. [0008] of Schoeny which discloses “an adjacent second swath within the field. The method may also include monitoring, with the computing device, a field condition associated with the adjacent second swath as the agricultural machine makes the first pass across the field based on data received from a sensor provided in association with the support arm. In addition, the method may include adjusting, with the computing device, the operating parameter(s) of the agricultural machine as the agricultural machine makes a second pass across the field to perform the agricultural operation along the adjacent second swath based at least in part on the monitored field condition”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Hansen, as modified by Frye, to include obtaining proximate data corresponding to a location at the worksite different than the upcoming location; as taught in Schoeny with a reasonable expectation of success in order to convert Hansen’s system from reactive to predictive/pre-emptive control, thereby improving operator comfort, reducing machine vibration and component wear, and enabling proactive adjustment before the machine reaches a rough terrain location. Additionally, Schoeny’s accelerometer based detection of soil roughness via movement/shaking of the support arm detects the same type of vibration/motion data that Hansen expressly correlates with ride quality and a person of ordinary skill in the thar art before the effectively filing date of the claimed invention would understand that Schoeny’s roughness data is one of the same character as the ride quality data disclosed in Hansen. See para. [0003] of Schoeny for motivation.
Regarding amended claim 14, Hansen, as modified by Frye and Schoeny, discloses wherein
obtaining the proximate data (see at least para. [0028] of Schoeny which discloses “parameters indicative of one or more field conditions associated with the adjacent swath 72B”, *Examiner interprets the “parameters indicative of … conditions associated with the adjacent swath 72B” to correspond to the claimed proximate data) comprises obtaining sensor data corresponding to the location of the worksite different than the upcoming location (see at least para. [0068] of Frye which discloses “Thus, the system aggregates the information such that it can provide a cohesive road roughness map for different geographic areas”) and generated by one or more sensors of the agricultural work machine (see at least para. [0083] of Hansen which discloses “that work machine 102 includes in situ data collection system 126, one or more processors 128, a data store 130, and can include other items 132 as well. Sensors 124 can include any of a wide variety of sensors depending on the type of work machine 102. For instance, sensors 124 can include material sensors 134, position/route sensors 136, speed sensors 138, worksite imaging sensors 140, orientation and/or inertial sensors 141, and can include other sensors 142 as well”).
Claims 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hansen (US 2023/0094319 A1) in view of Frye (US 2024/0294048 A1) and further in view of Vandike (US 2022/0110246A1).
Regarding amended claim 16 Hansen discloses An agricultural work machine (Fig. 1, 102
and see at least para. [0074] of Hansen which discloses “an autonomous or semi-autonomous agricultural machine” and see at least para. [0075] of Hansen which discloses “Mobile work machine 102 can be any type of work machine that moves and performs tasks on a worksite … some machines perform ground-based work operations. Examples of work operations include agricultural”) comprising:
one or more processors (Fig. 1, 128 see at least para. [0083] of Hansen which discloses “one or more processors 128”); and memory storing instructions (see at least para. [0052] of Hansen which discloses a “memory storing instructions”), executable by the one or more processors (see at least para. [0050] –[0052] of Hansen which discloses “at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed”) , that, when executed by the one or more processors, configure the one or more processors to: obtain prior operation data (see at least para. [0082] of Hansen which discloses “System(s) 120 are configured to collect prior data that can be used by work machine in performing a work assignment on a worksite. Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc. The prior data can be used to generate a model, such as a predictive map, that can be used to control work machine 102. Examples of prior data include, but are not limited to, location conditions that identify various conditions that can affect operation of work machine 102”) indicative of a ride quality issue (see at least para. [0127] of Hansen which discloses “detected roughness can be utilized to select a target machine speed based on a predefined or user selected ride quality or ride smoothness setting”), a worksite at which the agricultural work machine performs a current operation (see at least para. [0158] of Hansen which discloses “setting change selection component 416 can weight ride quality based on indications of terrain roughness from terrain roughness detector component 412. As noted above, component 412 can determine terrain roughness based on sensor signals from sensor(s) 124 (e.g., accelerometers, gyroscopes, imaging sensors, etc.). Alternatively, or in addition, component 412 can determine terrain roughness based on terrain maps. In any case, ride quality can be weighted to increase or decrease the target machine speed, depending on the desired smoothness of the ride experience by the operator. For example, based on the ride quality weighting parameter, a threshold (e.g., maximum) attitude and/or acceleration of machine 102 can be determined”, *Hansen explicitly obtains ride quality related data for locations along the worksite in advance of the machine reaching them).
Hansen may not explicitly disclose a ride quality issue corresponding to an upcoming
location at a worksite wherein the prior operation data identifies a first machine setting value from a prior operation; identify a ride quality issue corresponding to the upcoming location at the worksite based on the data; identify a second machine setting value corresponding to the upcoming location based on the first machine setting value and the ride quality issue; and control the agricultural work machine based, at least, on the ride quality issue second machine setting value corresponding the upcoming location at the worksite.
However, Frye discloses identifying a ride quality issue corresponding to an upcoming
location (see at least para. [0041] of Frye which discloses “the suspension control system 100 may determine an extent to which an upcoming road portion is expected to be rough. For example, the system 100 may look-ahead a threshold distance (e.g., 2 kilometers, 3 kilometers, 5 kilometers, 7 kilometers) and determine a percentage of the threshold distance which is expected to be rough” and see at least para. [0052] of Frye which discloses “The suspension controller 220 may additionally obtain look-ahead information 234 to determine an extent to which upcoming road segments are expected to be rough based on the map 122” and see at least para. [0053] of Frye which discloses “the controller 220 may determine that a threshold percentage of an upcoming distance is expected to be rough”, *Examiner interprets the upcoming road segments of Frye to be the claimed upcoming location). Frye further discloses controlling the agricultural work machine based, at least, on the ride quality issue corresponding the upcoming location at the worksite (see at least para. [0041] of Frye which discloses “The system 100 may determine whether this percentage exceeds a threshold, and if so, trigger an adjustment of the suspension (e.g., raise the ride height). For example, the percentage may be 20% and the threshold distance may be 4 kilometers. In this example, the system 100 may raise the ride height based on at least 800 meters of the 4 kilometers being indicated in the map 122 as being rough. The trigger may further require that the vehicle 102 is within a threshold distance of the start of a rough road segment (e.g., within 100 meters, 200 meters, and so on, which may optionally be adjusted according to vehicle speed”, *Examiner interprets the triggering of adjustment to suspension to be another example of controlling a vehicle based, at least, on the ride quality issue at the upcoming location at the worksite).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the method of Hansen to include data indicating a ride quality issue at an upcoming location; identifying a ride quality issue corresponding to the upcoming location; controlling the agricultural work machine based, at least, on the ride quality issue corresponding the upcoming location at the worksite, as taught in Frye with a reasonable expectation of success in order to improve operator comfort, reduce machine vibration and component wear, and enable proactive rather than reactive control of the work machine when approaching a rough terrain at a worksite/location. See para. [0041] and [0053] of Frye for motivation.
Hansen, as modified by Frye may not explicitly disclose wherein the prior operation data
identifies a first machine setting value from a prior operation; identify a ride quality issue corresponding to the upcoming location at the worksite based on the data; identify a second machine setting value corresponding to the upcoming location based on the first machine setting value and the ride quality issue; and control the agricultural work machine based, at least, on the ride quality issue second machine setting value corresponding the upcoming location at the worksite.
However, Vandike discloses wherein the prior operation data identifies a first machine
setting value (see at least para. [0052] of Vandike which discloses “the information map 258 may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the prior year can be used by predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by in-situ sensors 208 during the current harvesting operation. The predictive model is then used by predictive map generator 210 to generate a predictive yield map”); identify a ride quality issue corresponding to the upcoming location at the worksite based on the data; identify a second machine setting value (see at least para. [0097] of Vandike which discloses “predictive model generator 210 generates one or more predictive models, such as predictive model 350, that model a relationship between a value in the one or more information map 258, and a machine speed being sensed by the in-situ sensor 208 … the predictive model 350 is provided to predictive map generator 212 which generates a functional predictive speed map 360 that maps a predicted, target machine speed based on the information map 258 and the predictive speed model 350”) corresponding to the upcoming location based on the first machine setting value and the ride quality issue (see at least para. [0037] of Vandike which discloses “where the sensor signal is indicative of a characteristic in the field, such as characteristics of the field itself, crop characteristics of crop or grain present in the field, or characteristics of the agricultural harvester. Characteristics of the field may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed type, soil moisture, surface quality”); and control the agricultural work machine based, at least, on the ride quality issue second machine setting value corresponding the upcoming location at the worksite (see at least para. [0099] of Vandike which discloses “control system 214 generates control signals to control the controllable subsystems 216 based upon the functional predictive speed map”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the agricultural work machine of Hanse, as modified by Frye to include wherein the prior operation data identifies a first machine setting value from a prior operation; identify a ride quality issue corresponding to the upcoming location at the worksite based on the data; identify a second machine setting value corresponding to the upcoming location based on the first machine setting value and the ride quality issue; and control the agricultural work machine based, at least, on the ride quality issue second machine setting value corresponding the upcoming location at the worksite, as taught in Vandike with a reasonable expectation of success in order to derive a machine setting value for the upcoming location that is calibrated to both the machine’s own historical performance at that location and the currently identified ride quality issue instead of relying only on a generic or default threshold-triggered response. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that incorporating a prior operation record of an actual machine setting value into Hansen’s ride quality responsive control system, as taught by Vandike, would allow the resulting control value for the upcoming location to reflect the specific setting that precisely tailored control output to improve operator comfort and machine performance, and reducing the likelihood of over or under correction that a threshold only approach could produce. See para. [0093] and [0099] of Vandike for motivation.
Regarding amended claim 17, Hansen, as modified by Frye and Vandike, discloses wherein
the data (see at least para. [0121] of Hansen which discloses “data collection component 402 is configured to collect or otherwise obtain data during the operation of work machine 102 on the worksite. This can include data from sensors 124 on machine 102. For example, worksite imaging sensors 150 can obtain images of the worksite in a path of work machine 102”) comprises one or more of: (i) proximate data corresponding to a location at the worksite different than the upcoming location; (ii) prior operation data generated during a prior operation at the worksite (see at least para. [0082] of Hansen which discloses “System(s) 120 are configured to collect prior data that can be used by work machine in performing a work assignment on a worksite. Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc. The prior data can be used to generate a model, such as a predictive map, that can be used to control work machine 102. Examples of prior data include, but are not limited to, location conditions that identify various conditions that can affect operation of work machine 102”); or (iii) remote data (see at least para. [0121] of Hansen which discloses “component 402 can receive any data that indicates operation of various aspects of machine 102, either from on-board sensors or from remote sources such as remote imaging components, unmanned aerial vehicles (UAVs) or drones, other machines on the worksite, etc.” and see at least para. [0144] of Hansen which discloses “the target metric value can be selected based on input from remote computing system 118”) generated by a system remote from the worksite (see at least para. [0082] of Hansen which discloses “Prior data can be generated from a wide variety different types or sources, such as from aerial or satellite images, thermal images, etc.”, i.e., these sources are remote from the worksite, *Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., prior operation data generated during a prior operation at the worksite, etc.).
Regarding amended claim 18, Hansen, as modified by Frye and Vandike, discloses wherein
the data is indicative of one or more of: (i) machine speed; (ii) machine bouncing; or (iii) a terrain characteristic (see at least para. [0121] of Hansen which discloses “work quality-based machine speed control system 150. System 150 includes an in situ data collection component 402, an application detection component 404, a work quality metric comparison component 406, an operator presence detection component 408, a lateral error detection component 410, a terrain roughness detection component 412” *Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., terrain characteristic etc.).
Regarding amended claim 19, Hansen, as modified by Frye and Vandike, discloses wherein
the data comprises sensor data corresponding to a location of the worksite different than the upcoming location (see at least para. [0068] of Frye which discloses “Thus, the system aggregates the information such that it can provide a cohesive road roughness map for different geographic areas”) and generated by one or more sensors of the agricultural work machine (see at least para. [0083] of Hansen which discloses “that work machine 102 includes in situ data collection system 126, one or more processors 128, a data store 130, and can include other items 132 as well. Sensors 124 can include any of a wide variety of sensors depending on the type of work machine 102. For instance, sensors 124 can include material sensors 134, position/route sensors 136, speed sensors 138, worksite imaging sensors 140, orientation and/or inertial sensors 141, and can include other sensors 142 as well”).
Regarding amended claim 20, Hansen, as modified by Frye and Vandike, discloses wherein
the instructions when executed by the one or more processors, further configure the one or more processors to control the agricultural work machine by controlling one or more of: (i) an interface mechanism (see at least para. [0078] of Hansen which discloses “interface mechanism(s) 112. Operator interface mechanism(s) 112 can include such things as a steering wheel, pedals, levers, joysticks, buttons, dials, linkages, etc. In addition, mechanism(s) 112 can include a display device that displays user actuatable elements, such as icons, links, buttons, etc. Where the device is a touch sensitive display, those user actuatable items can be actuated by touch gestures. Similarly, where mechanism(s) 112 includes speech processing mechanisms, then operator 110 can provide inputs and receive outputs through a microphone and speaker, respectively. Operator interface mechanism(s) 112 can include any of a wide variety of other audio, visual or haptic mechanisms”) of the agricultural work machine to generate a presentation; (ii) a propulsion subsystem (see at least para. [0090] of Hansen which discloses “Propulsion subsystem 156 includes an engine (or other power source) that drives a set of ground engaging traction elements, such as wheels or tracks”) of the agricultural work machine to change a speed of the agricultural work machine; (iii) an actuator of the agricultural work machine to adjust a position of an implement of the agricultural work machine (see at least para. [0091] of Hansen which discloses “actuators 164 that change the positioning of a header, the concave clearance, etc., based upon the predicted yield or biomass to be encountered by the machine. In the case of an agricultural tilling machine, settings control component 144 can control the positioning or down pressure on the tilling implement by controlling actuators 162”); or (iv) an actuator of the agricultural work machine to adjust a biasing force applied to an implement of the agricultural work machine (*Examiner interprets that since these limitations are cited in the alternative only 1 limitation is required, i.e., controlling an actuator of the agricultural work machine to adjust a position of an implement of the agricultural work machine).
Additional Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kelber (US 2019/0059222A1) discloses a crop processing system that may include sensors for detecting grain loss, broken grain, and MOG in the harvested grain, and use data from those sensors to adjust components of the processing system 202 to achieve the operating parameters indicated by the user interface element 210. By way of example, the control system 206 may continuously determine a percentage of grain loss, a percentage of broken grain and a percentage of MOG in the harvested grain using data from sensors in the processing system 202. It may then adjust operating settings in the processing system 202 to adjust each percentage to reflect changes in the user interface element 210. In response to the change illustrated in FIGS. 3 and 4, for example, the crop processing system may position the concave grates closer to the rotor, decrease the fan speed, or both. Hunt (US 2016/0086291A1) discloses sensors 122 that sense a variety of variables and provide sensor signals to sensor conditioning components 124. Sensor conditioning components 124 can perform compensation, linearization, filtering, image processing, or a wide variety of other calibration and conditioning operations on the sensor signals. Control system 126 illustratively receives the sensor signals, after they are conditioned, and generates control signals to control various aspects of mobile machine 102, or external machine 104, or both, based upon the sensed variables. The control signals are provided to various controlled systems 130 that are controlled based upon the sensor signals.
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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/DANA D IVEY/Examiner, Art Unit 3662
/D.D.I/August 3, 2026
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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