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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/09/23 and 09/19/24 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over VANDIKE et al (U.S. 2022/0110250) and further in view of Anderson (U.S. 2022/0132723).
1. As per claims 1,15 VANDIKE disclosed a system for automatically controlling work vehicle operation, the system comprising:
a work vehicle including a plurality of subsystems (Fig. 1: agricultural harvester 100; Fig. 2: controllable subsystems 216 are listed);
a remote computing system located remote from the work vehicle (system illustrated in Fig. 2, see para. 0121: “[…] elements of FIG. 2 […] may be disposed on wide variety of different devices, […] devices may include […] mobile devices, such as a palm top computer, a cell phone, a smart phone [..] etc.”; the wording mobile device implies that they can be remote from the harvester),
the remote computing system configured to:
receives a first input indicative of a plurality of conditions present at a plurality of locations within a field or a work site (see Fig. 3A: blocks 281-284 and para 0062: “[..] prior information map 258 maps values of a variable, corresponding to a first characteristic, to different locations in the field […]”, e.g., soil property map);
receives a second input indicative of a plurality of operational capability parameters and one or more transfer functions associated with the work vehicle (see Fig. 3A; implicit by para 0060: “Settings controller can generate control signals to control various settings on the agricultural harvester […] In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer setting, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position , draper functionality”; controlling these settings requires information about the operational capabilities as well as transfer functions that translate the received information into specific settings);
determines a plurality of settings for the work vehicle (see para 0060) and a plurality of expected vehicle parameters indicative of an expected operation of the work vehicle or one or more of the plurality of subsystems during an upcoming task at each of the plurality of locations based on the plurality of conditions present at the corresponding location of the plurality of locations [Upon commencement of a harvesting operation, in-situ sensors 208 generate sensor signals indicative of one or more in-situ data values indicative of an agricultural characteristic, for example, a non-machine characteristic, such as a characteristic of the field, or a machine characteristic, such as a machine setting, operating characteristic, or characteristic of machine performance, as indicated by block 288.] (Paragraph. 0064), the plurality of operational capability parameters, and the one or more transfer functions, each setting being associated with an operation of one or more of the plurality of subsystems (see Fig. 3A: block 294 and para. 0066: “The relationship or model generated by predictive model generator is provided to predictive may generator. Predictive map generator generates a predictive map that predicts a value of the characteristic sensed by the in-situ sensors at different geographic locations in a field being harvested, […] using the predictive model and the prior information map […]”; in-situ sensors are illustrated in Fig. 4B); and
generates a map identifying the plurality of settings and the plurality of expected vehicle parameters at each of the plurality of locations for each of the plurality of subsystems (Fig. 3A: block 296 and para. 0068: “Predictive map generator can provide the predictive map to the control system […] includes values that can be read by control system and used as the basis for generating control signals for one ore more of the different controllable subsystems of the agricultural harvester.”); and an onboard computing system located on the work vehicle (Fig. 2: control system 214) Examiner interpreted “agricultural harvester values” as vehicle parameters, the onboard computing system configured to:
control an operation of the work vehicle based on the generated map as the work vehicle traverses the field or the work site (Fig. 3A: block 308 and para 0071: “[…] control system generates control signals to control the controllable subsystems based on the predictive map […] and the input from the geographic position sensor [..]”);
determine a plurality of actual outcomes at each of the plurality of locations for each of the plurality of subsystems as the work vehicle traverses the field or the work site; (see para. 76: “[…] of variations within the in-situ sensor data are […] above the threshold value, for example, then the predictive model generator generates a new predictive model using all or portions fo the newly received in-situ sensor data that the predictive map generator uses to generate new predictive map. “; this implies that the control system then generates control signals to control the controllable subsystems based on the new predictive map; also see para. 0070-71 referring to Fig. 3A block 308:”[…] control system generates control signals to control the controllable subsystems based […] any other in-situ sensors”) Examiner interpreted the “agriculture harvester values” as vehicle parameters; and compare the plurality of actual outcomes and the plurality of expected vehicle parameters at each of the plurality of locations [the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on predictive map 264 or zones on predictive control zone map 265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on predictive map 264 or the zones on predictive control zone map 265 conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves through the field. by way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of agricultural harvester 100 may be unable to see the information corresponding to the predictive map 264 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the predictive map 264 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map 264 and also be able to change the predictive map 264. In some instances, the predictive map 264 accessible and changeable by a manager located remotely may be used in machine control] (Paragraph 0069).
However, VANDIKE did not disclose in detail, “apply one or more offsets to the plurality of settings when the plurality of actual outcomes differs from the plurality of expected vehicle parameters by more than a threshold amount”.
In the same field of endeavor Anderson disclosed, “These changes in the topography of the field may not be represented in a topographic map provided to the operator (or the control system) of the agricultural machine that is based on data collected prior to the occurrence of the anomalies and/or events. Thus, the machine settings and other operating parameters commanded by the operator (or the control system) based on these agricultural characteristic maps can lead to error or other deviation in the performance of the agricultural machines and furthermore threshold logic 1408 is configured to compare various characteristics of the worksite to a variety of thresholds. The thresholds can be automatically generated by system 1330 (such as by machine learning logic 1410), input by an operator or a user, or generated in various other ways. For example, thresholds may be used to determine a level of deviation from an expected value, or a level of deviation from the surrounding areas of the worksite to determine areas of the worksite that may have agricultural characteristic changes. For instance, if the growth of crops (as measured by vegetative index data) at a particular geographic location within the worksite deviates by a threshold amount from an expected level of crop growth or as compared to crops in the surrounding areas of the worksite, then agricultural characteristic confidence system 1330 can be controlled to generate an agricultural characteristic confidence value for the worksite or the particular geographic location within the worksite, indicating that an agricultural characteristic (e.g., topography, soil characteristics, such as soil moisture, nutrient levels, as well as various other agricultural characteristics) change may be likely or may have occurred (see para 0073 & para 0168).
It would have been obvious to one having ordinary skill in the art at the time of the filing was made to have incorporated These changes in the topography of the field may not be represented in a topographic map provided to the operator (or the control system) of the agricultural machine that is based on data collected prior to the occurrence of the anomalies and/or events. Thus, the machine settings and other operating parameters commanded by the operator (or the control system) based on these agricultural characteristic maps can lead to error or other deviation in the performance of the agricultural machines and furthermore threshold logic 1408 is configured to compare various characteristics of the worksite to a variety of thresholds. The thresholds can be automatically generated by system 1330 (such as by machine learning logic 1410), input by an operator or a user, or generated in various other ways. For example, thresholds may be used to determine a level of deviation from an expected value, or a level of deviation from the surrounding areas of the worksite to determine areas of the worksite that may have agricultural characteristic changes. For instance, if the growth of crops (as measured by vegetative index data) at a particular geographic location within the worksite deviates by a threshold amount from an expected level of crop growth or as compared to crops in the surrounding areas of the worksite, then agricultural characteristic confidence system 1330 can be controlled to generate an agricultural characteristic confidence value for the worksite or the particular geographic location within the worksite, indicating that an agricultural characteristic (e.g., topography, soil characteristics, such as soil moisture, nutrient levels, as well as various other agricultural characteristics) change may be likely or may have occurred as taught by Anderson in the method and system of VANDIKE to increase the efficiency of the automation.
2. As per claims 2,16 VANDIKE-Anderson disclosed wherein, when determining the plurality of actual outcomes, the onboard computing system is configured to: receive sensor data indicative of the plurality of actual outcomes; and determine the plurality of actual outcomes at each of the plurality of locations based on the received sensor data (VANDIKE, see Fig. 4B, showing examples of real-time sensors, e.g. harvested material property sensors 984 and machine sensors 982 that include loss sensors 152, clean grain camera 150, machines speed sensor 146, power utilization sensor 973, etc; para 0070: “[..] input from […] in-situ sensors 208 are received by the control system […]”).
3. As per claim 3 VANDIKE-Anderson disclosed wherein the remote computing system is configured to store the generated map (VANDIKE, see Fig. 2: data store 202; see para 0082: “The predictive control zone map […] may be stored locally on data store”).
4. As per claim 4 VANDIKE-Anderson disclosed wherein the remote computing system is configured to transmit the generated map to the onboard computing system when the remote computing system is communicatively coupled to the onboard computing system (VANDIKE, para. 0068:” Predictive map generator 212 can provide the predictive map 264 to the control system […]”).
5. As per claims 5,17 VANDIKE-Anderson disclosed wherein the first input comprises at least one of topographical data, soil type data, or weather data (VANDIKE, see Fig. 3A: block 281: soil property map; also see para 0062:” weather conditions”).
6. As per claims 6,18 VANDIKE-Anderson disclosed wherein the first input comprises at least one of planting data or harvesting data (both soil properties and whether conditions can be considered harvesting data; also see para. 0042:” [..] prior information map 258 may also encompass other types of data that were obtained prior to a harvesting operation […]”).
7. As per claims 7,19 VANDIKE-Anderson disclosed wherein the first input comprises image data depicting plants present within the field (VANDIKE, see para. 0086: “Processing system processes one or more sensor signals, such as images, obtained via the optical sensor to generate processed sensor data, such as processed image data, identifying one or more non-machine characteristic such as characteristic of the field”; also see Fig. 1: forward looking image capture mechanism 151, thus images will depict plants; image data can be considered a first input as it is indicative of a plurality of conditions present at a plurality of locations within a field or a work site).
8. As per claims 8,11,20 VANDIKE-Anderson disclosed wherein the plurality of expected vehicle parameters comprises an expected engine load on the work vehicle and the plurality of actual outcomes comprises an actual engine load on the work vehicle (VANDIKE, see Fig. 3B: block 318 and para. 0075).
9. As per claims 9,14 VANDIKE-Anderson disclosed wherein: the plurality of expected vehicle parameters comprises at least one of an expected grain loss, an expected grain returns amount, an expected grain cleanliness parameter, or an expected grain moisture content; and the plurality of actual outcomes comprises at least one of an actual grain loss, an actual grain returns amount, an actual grain cleanliness parameter, or an actual grain moisture content (VANDIKE, see para. 0066: “Predictive map generator generates a predictive map that predicts a value of the characteristic sensed by the in-situ sensors at different geographic locations in a field being harvested” and para. 0070:” […] input from […] in-situ sensors 208 are received by the control system”; determination of actual outcomes is thus implied; see Fig. 4B: In-situ sensors 208 include clean grain camera 150, loss sensors 150 etc.”).
10. As per claim 10 VANDIKE disclosed a system for automatically controlling work vehicle operation, the system comprising:
a work vehicle including a plurality of subsystems (Fig. 1: agricultural harvester 100; Fig. 2: controllable subsystems 216 are listed);
a remote computing system located remote from the work vehicle (system illustrated in Fig. 2, see para. 0121: “[…] elements of FIG. 2 […] may be disposed on wide variety of different devices, […] devices may include […] mobile devices, such as a palm top computer, a cell phone, a smart phone [..] etc.”; the wording mobile device implies that they can be remote from the harvester),
the remote computing system to:
receives a first input indicative of a plurality of conditions present at a plurality of locations within a field or a work site (see Fig. 3A: blocks 281-284 and para 0062: “[..] prior information map 258 maps values of a variable, corresponding to a first characteristic, to different locations in the field […]”, e.g., soil property map);
receives a second input indicative of a plurality of operational capability parameters and one or more transfer functions associated with the work vehicle (see Fig. 3A; implicit by para 0060: “Settings controller can generate control signals to control various settings on the agricultural harvester […] In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer setting, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position , draper functionality”; controlling these settings requires information about the operational capabilities as well as transfer functions that translate the received information into specific settings);
determines a plurality of settings for the work vehicle (see para 0060) and a plurality of expected vehicle parameters indicative of an expected operation of the work vehicle or one or more of the plurality of subsystems during an upcoming task at each of the plurality of locations based on the plurality of conditions present at the corresponding location of the plurality of locations [Upon commencement of a harvesting operation, in-situ sensors 208 generate sensor signals indicative of one or more in-situ data values indicative of an agricultural characteristic, for example, a non-machine characteristic, such as a characteristic of the field, or a machine characteristic, such as a machine setting, operating characteristic, or characteristic of machine performance, as indicated by block 288.] (Paragraph. 0064), the plurality of operational capability parameters, and the one or more transfer functions, each setting being associated with an operation of one or more of the plurality of subsystems (see Fig. 3A: block 294 and para. 0066: “The relationship or model generated by predictive model generator is provided to predictive may generator. Predictive map generator generates a predictive map that predicts a value of the characteristic sensed by the in-situ sensors at different geographic locations in a field being harvested, […] using the predictive model and the prior information map […]”; in-situ sensors are illustrated in Fig. 4B); and
generates a map identifying the plurality of settings and the plurality of expected vehicle parameters at each of the plurality of locations for each of the plurality of subsystems (Fig. 3A: block 296 and para. 0068: “Predictive map generator can provide the predictive map to the control system […] includes values that can be read by control system and used as the basis for generating control signals for one ore more of the different controllable subsystems of the agricultural harvester.”) Examiner interpreted “agricultural harvester values” as vehicle parameters; and an onboard computing system located on the work vehicle (Fig. 2: control system 214), the onboard computing system configured to:
control an operation of the work vehicle based on the generated map as the work vehicle traverses the field or the work site (Fig. 3A: block 308 and para 0071: “[…] control system generates control signals to control the controllable subsystems based on the predictive map […] and the input from the geographic position sensor [..]”);
determine a plurality of actual outcomes for each of the plurality of subsystems at each of the plurality of locations as the work vehicle traverses the field or the work site; compare the plurality of actual outcomes and the plurality of expected outcomes at each of the plurality of locations. compare the plurality of actual outcome and the plurality of expected vehicle parameters at each of the plurality of locations (see para. 76: “[…] of variations within the in-situ sensor data are […] above the threshold value, for example, then the predictive model generator generates a new predictive model using all or portions fo the newly received in-situ sensor data that the predictive map generator uses to generate new predictive map. “; this implies that the control system then generates control signals to control the controllable subsystems based on the new predictive map; also see para. 0071 referring to Fig. 3A block 308:”[…] control system generates control signals to control the controllable subsystems based […] any other in-situ sensors”) Examiner interpreted the “agriculture harvester values” as vehicle parameters; and compare the plurality of actual outcome and the plurality of expected vehicle parameters at each of the plurality of locations [the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on predictive map 264 or zones on predictive control zone map 265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on predictive map 264 or the zones on predictive control zone map 265 conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves through the field. by way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of agricultural harvester 100 may be unable to see the information corresponding to the predictive map 264 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the predictive map 264 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map 264 and also be able to change the predictive map 264. In some instances, the predictive map 264 accessible and changeable by a manager located remotely may be used in machine control] (Paragraph 0069).
However, VANDIKE did not explicitly disclose, “control a ground speed of the work vehicle based on the plurality of actual outcomes at each of the plurality of locations and apply one or more offsets to the plurality of actual outcome differs from the plurality of expected vehicle parameters by more than a threshold amount”.
In the same field of endeavor Anderson disclosed, “These changes in the topography of the field may not be represented in a topographic map provided to the operator (or the control system) of the agricultural machine that is based on data collected prior to the occurrence of the anomalies and/or events. Thus, the machine settings and other operating parameters commanded by the operator (or the control system) based on these agricultural characteristic maps can lead to error or other deviation in the performance of the agricultural machines and furthermore threshold logic 1408 is configured to compare various characteristics of the worksite to a variety of thresholds. The thresholds can be automatically generated by system 1330 (such as by machine learning logic 1410), input by an operator or a user, or generated in various other ways. For example, thresholds may be used to determine a level of deviation from an expected value, or a level of deviation from the surrounding areas of the worksite to determine areas of the worksite that may have agricultural characteristic changes. For instance, if the growth of crops (as measured by vegetative index data) at a particular geographic location within the worksite deviates by a threshold amount from an expected level of crop growth or as compared to crops in the surrounding areas of the worksite, then agricultural characteristic confidence system 1330 can be controlled to generate an agricultural characteristic confidence value for the worksite or the particular geographic location within the worksite, indicating that an agricultural characteristic (e.g., topography, soil characteristics, such as soil moisture, nutrient levels, as well as various other agricultural characteristics) change may be likely or may have occurred (see para 0073 & para 0168).
It would have been obvious to one having ordinary skill in the art at the time of the filing was made to have incorporated These changes in the topography of the field may not be represented in a topographic map provided to the operator (or the control system) of the agricultural machine that is based on data collected prior to the occurrence of the anomalies and/or events. Thus, the machine settings and other operating parameters commanded by the operator (or the control system) based on these agricultural characteristic maps can lead to error or other deviation in the performance of the agricultural machines and furthermore threshold logic 1408 is configured to compare various characteristics of the worksite to a variety of thresholds. The thresholds can be automatically generated by system 1330 (such as by machine learning logic 1410), input by an operator or a user, or generated in various other ways. For example, thresholds may be used to determine a level of deviation from an expected value, or a level of deviation from the surrounding areas of the worksite to determine areas of the worksite that may have agricultural characteristic changes. For instance, if the growth of crops (as measured by vegetative index data) at a particular geographic location within the worksite deviates by a threshold amount from an expected level of crop growth or as compared to crops in the surrounding areas of the worksite, then agricultural characteristic confidence system 1330 can be controlled to generate an agricultural characteristic confidence value for the worksite or the particular geographic location within the worksite, indicating that an agricultural characteristic (e.g., topography, soil characteristics, such as soil moisture, nutrient levels, as well as various other agricultural characteristics) change may be likely or may have occurred as taught by Anderson in the method and system of VANDIKE to increase the efficiency of the automation.
12. As per claim 12 VANDIKE-Anderson disclosed wherein, when controlling the ground speed, the onboard computing system is configured to determine the ground speed for the work vehicle at each of the plurality of locations such that the actual engine load at each location of the plurality of locations corresponds to a maximum threshold engine load (Anderson, Paragraph. 0233). The same motivation of claim 10 applies to claim 12.
13 As per claim 13 VANDIKE-Anderson disclosed wherein the maximum threshold engine capacity of the work vehicle is less than a maximum engine capacity of the work vehicle (VANDIKE, see Fig. 3B: block 318 and para. 0075).
Response to Arguments
14. Applicant's arguments filed 09/25/2025 have been fully considered but they are not persuasive. Response to applicant’s argument is as follows.
A. Applicant argued that prior art did not disclose, “comparing, with the onboard computing system, the plurality of actual outcomes and the plurality of vehicle parameters at each of the plurality of locations for each of the plurality of subsystems”.
As applicant’s argument VANDIKE disclosed, “the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on predictive map 264 or zones on predictive control zone map 265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on predictive map 264 or the zones on predictive control zone map 265 conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves through the field. by way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of agricultural harvester 100 may be unable to see the information corresponding to the predictive map 264 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the predictive map 264 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map 264 and also be able to change the predictive map 264. In some instances, the predictive map 264 accessible and changeable by a manager located remotely may be used in machine control” (Paragraph. 0069).
Conclusion
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/ADNAN M MIRZA/Primary Examiner, Art Unit 3667