DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-31 are pending in Instant Application.
Priority
Examiner acknowledges Applicant’s claim to priority benefits of 63/386,307 filed 12/06/2022.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 04/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 16-18 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 16 states the agricultural implement includes a tillage implement however claim 15, which claim 16 depends on, already states that a tillage implement.
Claim 17 states the agricultural vehicle however claim 1, which claim 17 depends on, already states the agricultural vehicle.
Claim 18 states the processors that include sensor/function interface however claim 1, which claim 18 depends on, already states processors that include sensor/function interface. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7, 11-14, 17-26, and 29-31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Heitmann (USPGPub 2019/0261561). As per claim 1, Heitmann discloses an autonomous driver system for an agricultural vehicle assembly, the autonomous driver system includes: a sensor interface configured for coupling with one or more of vehicle sensors of an agricultural vehicle or implement sensors of an agricultural implement (see at least paragraph 0014; wherein controlling the actuator depending on an operating or harvesting process parameter detected by the sensors); a function interface configured for coupling with one or more of vehicle actuators of the agricultural vehicle or implement actuators of the agricultural implement (see at least paragraph 0016; wherein at least one actuator configured to adjust and/or actuate the at least one harvested material handling means); and one or more hardware processors for an autonomous driving controller in communication with the sensor and function interfaces, and at least one memory storing instructions that, when executed by the one or more hardware processors (see at least paragraph 0068; wherein the driver assistance system 25 comprises a computing device 37, a memory 38 as well as a graphical user interface 39. The computing device 37 is one example of a processor and is configured to process data saved in the memory 38. Moreover, the computing device 37 of the driver assistance system 25 receives and processes the sensor data 34a from the sensor system 34, as well as the provided external information 35), causes the one or more hardware processors to: autonomously implement a planned agricultural operation with the agricultural vehicle and the agricultural implement (see at least paragraph 0021; wherein the driver assistance system may be activated by the detection of a critical forage harvester mode of operation); and identify and remedy one or more operation disturbances outside of the planned agricultural operation (see at least paragraph 0024; wherein analyze the mode of operation of the forage harvester and identify a critical mode of operation), wherein identifying and remedying includes: identifying the one or more operation disturbances outside of the planned agricultural operation (see at least paragraph 0024; wherein analyze the mode of operation of the forage harvester and identify a critical mode of operation) with one or more of the vehicle sensors or the implement sensors (see at least paragraph 0014; wherein controlling the actuator depending on an operating or harvesting process parameter detected by the sensors); selecting one or more remedial actions (see at least paragraph 0019; wherein the driver assistance system may have selectable, working-unit-specific strategies saved in the memory for optimizing the mode of operation of the individual working units) for the one or more operation disturbances (see at least paragraph 0025; wherein process at least one set of rules saved in the computing device to overcome the critical mode of operation by proposing optimized operating parameters of one or more working units, taking into account interactions between the working units); and implementing the selected one or more remedial actions with one or more of the vehicle actuators or the implement actuators (see at least paragraph 0027; wherein adjust, independently, the operating parameters generated in step b) of one of the plurality of working units, and operate the forage harvester with the optimized operating parameters). As per claim 2, Heitmann discloses wherein identifying the one or more operation disturbances includes comparing measurements of one or more of the vehicle sensors or the implement sensors with an agronomy tree (see at least paragraph 0078; wherein when it is activated, the dialog module 40 offers various subdialogs that are always available depending on the operating situation of the forage harvester 1, or can be dependent on an existence of a specific operating situation. A.sub.n overview of possible subdialogs is shown in FIG. 5. “Optimize workshop default” 42, “Optimize harvesting” 43, “Optimize when forage harvester is stationary” 44, “Support use of additional assistance systems” 45 and “Dialog with process participants” 46 subdialogs are provided). As per claim 3, Heitmann discloses wherein selecting the one or more remedial actions including selecting the one or more remedial actions from the agronomy tree (see at least paragraph 0074; wherein the input/output device 23 of the driver assistance system 25 may comprise a touch-sensitive screen, thereby allowing the operator to activate the dialog module 40 by a specific selection of the individual pictograms 50). As per claim 4, Heitmann discloses wherein the agronomy tree includes a plurality of operation disturbance and remedy branches, and each operation disturbance and remedy branch includes at least: a disturbance designation for each operation disturbance of the one or more operation disturbances and disturbance characteristics associated with the disturbance designation (see at least paragraph 0078; wherein when it is activated, the dialog module 40 offers various subdialogs that are always available depending on the operating situation of the forage harvester 1, or can be dependent on an existence of a specific operating situation. A.sub.n overview of possible subdialogs is shown in FIG. 5. “Optimize workshop default” 42, “Optimize harvesting” 43, “Optimize when forage harvester is stationary” 44, “Support use of additional assistance systems” 45 and “Dialog with process participants” 46 subdialogs are provided), wherein one or more of the vehicle sensors or the implement sensors are configured to sense characteristics corresponding to the disturbance characteristics; and a remedy designation for each remedial action of the one or more remedial actions and actuator instructions associated with the remedy designation, wherein one or more of the vehicle actuators or the implement actuators are configured to implement the actuator instructions (see at least paragraph 0030; wherein the working units can be assigned a sensor system that is configured to detect signals to determine operating parameters as well as working-unit-specific parameters. The driver assistance system is configured to receive and evaluate the signals of the respective sensor system. “Operating parameters” may comprise information that can be set by the actuators of the respective working unit, such as the rotational speed, distance, etc. The term “work-unit-specific parameter” describes the respective work result of a working unit, such as the throughput, cutting length, and/or harvested material cracking. In one implementation, the driver assistance system is the device that centrally receives and evaluates signals from the respective sensor system, so that the driver assistance system functions as a supervisor). As per claim 5, Heitmann discloses wherein implementing the selected one or more remedial actions includes prioritizing implementing of the selected one or more remedial actions to override the autonomous implementing of the planned agricultural operation (see at least paragraph 0037; wherein adjustments for optimizing the flow of material and chopping quality may necessitate the coordinated adaptation of operating parameters of the attachment and feed device in order to avoid excessive lengths, combining the two adjusting machines, the feed machine attachment and feed machine, into a supply machine is more advantageous). As per claim 7, Heitmann discloses comprising an autonomous perception module in communication with the sensor interface (see at least paragraph 0014; wherein controlling the actuator depending on an operating or harvesting process parameter detected by the sensors). As per claim 11, Heitmann discloses wherein the one or more vehicle sensors include one or more of visual, video, laser, radar, LiDAR, ultrasound, torque, speed, acceleration, tachometer, dynamometer, position, load cell, radio- frequency identification (RFID), short range radio frequency, infrared, temperature, encoder, GPS or real-time kinematic (RTK) sensors (see at last paragraph 0060; wherein sensor 17 can be designed as a near infrared (NIR) sensor that is also configured to detect contents such as the raw ash or raw protein content of the harvested material flowing by). As per claim 12, Heitmann discloses wherein the one or more implement sensors include one or more of visual, video, laser, radar, LiDAR, ultrasound, pressure, flow meter, load cell, radio-frequency identification (RFID),short range radio frequency, infrared, temperature, encoder, moisture, hyperspectral, yield monitor, GPS, real-time kinematic (RTK) or position sensors (see at last paragraph 0060; wherein sensor 17 can be designed as a near infrared (NIR) sensor that is also configured to detect contents such as the raw ash or raw protein content of the harvested material flowing by). As per claim 13, Heitmann discloses wherein the one or more vehicle actuators include one or more of throttle, brake, transmission, steering, hydraulic pressure, hydraulic flow rate, hydraulic valve, hydraulic cylinder, control valve, centrifugal clutch, variable speed pulley actuators (see at least paragraph 0016; wherein at least one actuator configured to adjust and/or actuate the at least one harvested material handling means). As per claim 14, Heitmann discloses wherein the one or more implement actuators include one or more of gang angle, gang height, implement height, disk depth, knife depth, hydraulic pressure, hydraulic flow rate, hydraulic valve, hydraulic cylinder, agricultural product pump, control valve, modulating nozzle, row section, pneumatic actuators, centrifugal clutch, variable speed pulley actuators (see at least paragraph 0062; wherein actuators 32 (not shown in FIG. 1) for monitoring and adjusting and/or actuating the attachment 2). As per claim 17, Heitmann discloses comprising the agricultural vehicle (see at least abstract; wherein agricultural work machine). As per claim 18, Heitmann discloses wherein the one or more hardware processors include one or more of the sensor interface or the function interface (see at least paragraph 0068; wherein the driver assistance system 25 comprises a computing device 37, a memory 38 as well as a graphical user interface 39. The computing device 37 is one example of a processor and is configured to process data saved in the memory 38. Moreover, the computing device 37 of the driver assistance system 25 receives and processes the sensor data 34a from the sensor system 34, as well as the provided external information 35). As per claim 19, Heitmann discloses a method for generating an agronomy tree of an autonomous driver system, the method comprising: generating an operation disturbance branch for an operation disturbance (see at least paragraph 0073; wherein the dialog module 40 is either activated by the operator or, in automatic mode, automatically when a critical mode of operation of the forage harvester 1 or at least one working unit 30 exists), generating includes: collecting one or more disturbance characteristics (see at least paragraph 0020; wherein harvesting process parameters may comprise working results that can be determined qualitatively and/or quantitatively of individual working units up to the working result of the forage harvester in its entirety); and associating one or more disturbance thresholds with the one or more collected disturbance characteristics (see at least paragraph 0020; wherein harvesting process parameters may comprise working results that can be determined qualitatively and/or quantitatively of individual working units up to the working result of the forage harvester in its entirety); associating one or more remedial actions with the operation disturbance branch (see at least paragraph 0021; wherein perform an optimization of the one or more working units. conversely, a critical mode of operation detected in the context of automated process monitoring can quickly lead to an independent activation of the driver assistance system in order to counteract the critical mode of operation), the one or more remedial actions each include: instructions for autonomous conduct of a remedial action of the one or more remedial actions with an agricultural vehicle or an agricultural implement (see at least paragraph 0021; wherein the driver assistance system may be activated by the detection of a critical forage harvester mode of operation); and wherein sensing disturbance characteristics satisfying the disturbance thresholds are indicative of the operation disturbance (see at least paragraph 0014; wherein sensors monitoring operation of the working units), and implementing of the associated one or more remedial actions is configured to address the operation disturbance (see at least paragraph 0020; wherein selectable working-unit-specific strategies may each be directed toward a target for adjusting or optimizing at least one harvesting process parameter by specifying at least one operating parameter of at least one of the working units). As per claim 20, Heitmann discloses wherein generating the operation disturbance branch and associating one or more remedial actions with the operation disturbance branch are repeated for a plurality of different operation disturbances (see at least paragraph 0078; wherein the dialog module 40 offers various subdialogs that are always available depending on the operating situation of the forage harvester 1, or can be dependent on an existence of a specific operating situation. A.sub.n overview of possible subdialogs is shown in FIG. 5. “Optimize workshop default” 42, “Optimize harvesting” 43, “Optimize when forage harvester is stationary” 44, “Support use of additional assistance systems” 45 and “Dialog with process participants” 46 subdialogs are provided). As per claim 21, Heitmann discloses wherein generating the operation disturbance branch and associating one or more remedial actions with the operation disturbance branch include operator queries for one or more of the disturbance characteristics, the disturbance thresholds or the remedial actions (see at least paragraph 0019; wherein the driver assistance system may have selectable, working-unit-specific strategies saved in the memory for optimizing the mode of operation of the individual working units. Individual selectability of working-unit-specific strategies may improve or optimize the mode of operation). As per claim 22, Heitmann discloses wherein generating the operation disturbance branch and associating one or more remedial actions with the operation disturbance branch include receiving one or more of an agricultural vehicle characteristic bundle or agricultural implement characteristic bundle having one or more of the disturbance characteristics , the disturbance thresholds or the remedial actions for the operation disturbance (see at least paragraph 0019; wherein the driver assistance system may have selectable, working-unit-specific strategies saved in the memory for optimizing the mode of operation of the individual working units. Individual selectability of working-unit-specific strategies may improve or optimize the mode of operation). As per claim 23, Heitmann discloses wherein generating the operation disturbance branch and associating one or more remedial actions with the operation disturbance branch include receiving one or more of the disturbance characteristics, the disturbance thresholds or the remedial actions for the operation disturbance from an operation disturbance and remedy log (see at least paragraph 0019; wherein the driver assistance system may have selectable, working-unit-specific strategies saved in the memory for optimizing the mode of operation of the individual working units. Individual selectability of working-unit-specific strategies may improve or optimize the mode of operation). As per claim 24, Heitmann discloses wherein the one or more disturbance thresholds includes one or more of: specified characteristic values for the one or more collected disturbance characteristics; and recognized features for use with AI or machine learning modules (see at least paragraph 0030; wherein working units can be assigned a sensor system that is configured to detect signals to determine operating parameters as well as working-unit-specific parameters. The driver assistance system is configured to receive and evaluate the signals of the respective sensor system. “Operating parameters” may comprise information that can be set by the actuators of the respective working unit, such as the rotational speed, distance, etc. The term “work-unit-specific parameter” describes the respective work result of a working unit, such as the throughput, cutting length, and/or harvested material cracking. In one implementation, the driver assistance system is the device that centrally receives and evaluates signals from the respective sensor system, so that the driver assistance system functions as a supervisor). As per claim 25, Heitmann discloses comprising: sensing disturbance characteristics according to the collected one or more disturbance characteristics; identifying the operation disturbance according to satisfaction of the one or more disturbance thresholds (see at least paragraph 0030; wherein working units can be assigned a sensor system that is configured to detect signals to determine operating parameters as well as working-unit-specific parameters. The driver assistance system is configured to receive and evaluate the signals of the respective sensor system. “Operating parameters” may comprise information that can be set by the actuators of the respective working unit, such as the rotational speed, distance, etc. The term “work-unit-specific parameter” describes the respective work result of a working unit, such as the throughput, cutting length, and/or harvested material cracking. In one implementation, the driver assistance system is the device that centrally receives and evaluates signals from the respective sensor system, so that the driver assistance system functions as a supervisor); and autonomously implementing the associated one or more remedial actions to address the operation disturbance according to identification of the operation disturbance (see at least paragraph 0027; wherein adjust, independently, the operating parameters generated in step b) of one of the plurality of working units, and operate the forage harvester with the optimized operating parameters). As per claim 26, Heitmann discloses wherein autonomously implementing the associated one or more remedial actions includes operating one or more vehicle actuators of an agricultural vehicle or implement actuators of an agricultural implement (see at least paragraph 0027; wherein adjust, independently, the operating parameters generated in step b) of one of the plurality of working units, and operate the forage harvester with the optimized operating parameters). As per claim 29, Heitmann discloses wherein autonomously implementing the associated one or more remedial actions includes implementing the one or more remedial actions while conducting an autonomous agricultural operation (see at least paragraph 0027; wherein adjust, independently, the operating parameters generated in step b) of one of the plurality of working units, and operate the forage harvester with the optimized operating parameters). As per claim 30, Heitmann discloses wherein associating the one or more remedial actions with the operation disturbance branch includes associating a plurality of remedial actions with the operation disturbance branch, each of the remedial actions of the plurality of remedial actions having a priority for implementation relative to other remedial actions of the plurality of remedial actions (see at least paragraph 0037; wherein adjustments for optimizing the flow of material and chopping quality may necessitate the coordinated adaptation of operating parameters of the attachment and feed device in order to avoid excessive lengths, combining the two adjusting machines, the feed machine attachment and feed machine, into a supply machine is more advantageous). As per claim 31, Heitmann discloses wherein generating includes selecting one or more of vehicle or implement sensors configured for collection of the one or more disturbance characteristics (see at least paragraph 0030; wherein working units can be assigned a sensor system that is configured to detect signals to determine operating parameters as well as working-unit-specific parameters. The driver assistance system is configured to receive and evaluate the signals of the respective sensor system. “Operating parameters” may comprise information that can be set by the actuators of the respective working unit, such as the rotational speed, distance, etc. The term “work-unit-specific parameter” describes the respective work result of a working unit, such as the throughput, cutting length, and/or harvested material cracking. In one implementation, the driver assistance system is the device that centrally receives and evaluates signals from the respective sensor system, so that the driver assistance system functions as a supervisor); and associating the one or more remedial actions with the operation disturbance branch includes selecting one or more vehicle actuators configured for conducting the one or more remedial actions (see at least paragraph 0027; wherein adjust, independently, the operating parameters generated in step b) of one of the plurality of working units, and operate the forage harvester with the optimized operating parameters…see at least paragraph 0019; wherein the driver assistance system may have selectable, working-unit-specific strategies saved in the memory for optimizing the mode of operation of the individual working units).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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 6 and 28 are rejected under 35 U.S.C. 103(a) as being unpatentable over Heitmann (USPGPub 2019/0261561) in view of Mahler et al. (USPGPub 2023/0350409). As per claim 6, Heitmann does not explicitly mention wherein implementing the selected one or more remedial actions includes re-initiating the planned agricultural operation after implementing the selected one or more remedial actions. However Mahler does disclose: wherein implementing the selected one or more remedial actions includes re-initiating the planned agricultural operation after implementing the selected one or more remedial actions (see at least paragraph 0017; wherein the control device, executing the analysis routine by its processing device, processes the collected data to determine or identify a particular irregularity (e.g., the processing device selects a particular type of irregularity from a discrete set of potential irregularities). Depending on the identified irregularity, the control device (e.g., the processing unit) may generate an instruction and execute the instruction, which upon execution of the instruction enables the autonomous agricultural production machine to be put back into operation (e.g., normal operation). After being put back into operation, the autonomous agricultural production machine may continue or resume the work step previously being performed). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Mahler with the teachings as in Heitmann. The motivation for doing so would have been to provide safely and reliably executions by autonomous agricultural production machines, see Mahler paragraph 0019. As per claim 28, Heitmann does not explicitly mention wherein autonomously implementing the associated one or more remedial actions includes interrupting an autonomous agricultural operation, implementing the one or more remedial actions, and re-initiating the autonomous agricultural operation.
However Mahler does disclose: wherein autonomously implementing the associated one or more remedial actions includes interrupting an autonomous agricultural operation, implementing the one or more remedial actions, and re-initiating the autonomous agricultural operation (see at least paragraph 0017; wherein the control device, executing the analysis routine by its processing device, processes the collected data to determine or identify a particular irregularity (e.g., the processing device selects a particular type of irregularity from a discrete set of potential irregularities). Depending on the identified irregularity, the control device (e.g., the processing unit) may generate an instruction and execute the instruction, which upon execution of the instruction enables the autonomous agricultural production machine to be put back into operation (e.g., normal operation). After being put back into operation, the autonomous agricultural production machine may continue or resume the work step previously being performed). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Mahler with the teachings as in Heitmann. The motivation for doing so would have been to provide safely and reliably executions by autonomous agricultural production machines, see Mahler paragraph 0019.
Claims 8-10, 15-16, and 27 are rejected under 35 U.S.C. 103(a) as being unpatentable over Heitmann (USPGPub 2019/0261561) in view of Hurd et al. (USPGPub 2023/0350410). As per claim 8, Heitmann does not explicitly mention wherein the autonomous perception module includes one or more hardware processors having a machine learning application or artificial intelligence application for identifying operation disturbances with observations of one or more of the vehicle sensors or the implement sensors. However Hurd does disclose: wherein the autonomous perception module includes one or more hardware processors having a machine learning application or artificial intelligence application for identifying operation disturbances with observations of one or more of the vehicle sensors or the implement sensors (see at least paragraph 0042; wherein one or more machine learning and artificial intelligence subsystems configured to fuse data collected from multiple sensors together to provide the autonomously-operated machinery and vehicles 102 with situational awareness to avoid obstacles and other terrain characteristics during the performance of agricultural activities 104). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Hurd with the teachings as in Heitmann. The motivation for doing so would have been to improve efficiencies in conducting agricultural activities that further return cost savings, see Hurd paragraph 0007. As per claim 9, Hurd discloses wherein identifying the one or more operation disturbances includes identifying operation disturbances with the machine learning application or artificial intelligence application (see at least paragraph 0042; wherein one or more machine learning and artificial intelligence subsystems configured to fuse data collected from multiple sensors together to provide the autonomously-operated machinery and vehicles 102 with situational awareness to avoid obstacles and other terrain characteristics during the performance of agricultural activities 104). As per claim 10, Heitmann does not explicitly mention wherein the one or more operation disturbances include an implement blockage, implement fouling, forthcoming obstacle, engaged obstacle, fouled spray nozzle, tire deflation, tire slippage, or vehicle power draw. However Hurd dose disclose: wherein the one or more operation disturbances include an implement blockage, implement fouling, forthcoming obstacle, engaged obstacle, fouled spray nozzle, tire deflation, tire slippage, or vehicle power draw (see at least paragraph 0022; wherein analyzes one or more specific operational parameters of autonomous machine and vehicle activity, such as perception of terrain, identification of obstacles). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Hurd with the teachings as in Heitmann. The motivation for doing so would have been to improve efficiencies in conducting agricultural activities that further return cost savings, see Hurd paragraph 0007. As per claim 15, Heitmann does not explicitly mention wherein the agricultural implement includes a tillage implement.
However Hurd does disclose: wherein the agricultural implement includes a tillage implement (see at least paragraph 0047; wherein automating specific tillage equipment). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Hurd with the teachings as in Heitmann. The motivation for doing so would have been to improve efficiencies in conducting agricultural activities that further return cost savings, see Hurd paragraph 0007. As per claim 16, Hurd discloses comprising the tillage implement (Hurd see at least paragraph 0047; wherein automating specific tillage equipment). As per claim 27, Heitmann does not explicitly mention wherein the one or more disturbance thresholds includes one or more recognized features for use with one or more of an AI module or machine learning module, and identifying the operation disturbance according to satisfaction of the one or more disturbance thresholds includes analyzing sensed disturbance characteristics with one or more of the AI module or the machine learning module. However Hurd does disclose: wherein the one or more disturbance thresholds includes one or more recognized features for use with one or more of an AI module or machine learning module, and identifying the operation disturbance according to satisfaction of the one or more disturbance thresholds includes analyzing sensed disturbance characteristics with one or more of the AI module or the machine learning module (see at least paragraph 0042; wherein one or more machine learning and artificial intelligence subsystems configured to fuse data collected from multiple sensors together to provide the autonomously-operated machinery and vehicles 102 with situational awareness to avoid obstacles and other terrain characteristics during the performance of agricultural activities 104). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Hurd with the teachings as in Heitmann. The motivation for doing so would have been to improve efficiencies in conducting agricultural activities that further return cost savings, see Hurd paragraph 0007.
Relevant Art
The prior art made of record and not relied upon are considered pertinent to applicant’s disclosure: USPGPub 2025/0255207 – Provides a guidance assembly for an agricultural vehicle includes a pathing system configured to provide an array of swaths. The guidance assembly includes a composite guidance system in communication with an automated driving interface and the pathing system. USPGPub 2024/0103530 – Provides one or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD S ISMAIL whose telephone number is (571)272-1326. The examiner can normally be reached M - F: 8:00AM- 4:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662