DETAILED ACTION
Claims 1-20 are considered in this office action. Claims 1-20 are pending examination.
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 .
Claim Rejections - 35 USC § 102
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 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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yanke et al. (US12022772) and herein after will be referred as Yanke.
Regarding Claim 1, Yanke teaches a detection and control system for an agricultural harvester, the detection and control system comprising: a controller comprising at least one memory and at least one processor (Col.5 Line 46-47: FIG. 2 is a schematic of an example harvester control system 112. In some implementations, the harvester control system 112 is in the form of a computer system, such as the computer system 1700, described in more detail below. Additional details of the harvester control system 112, such as processor 202 and memory 204, are included below in the context of computer system 1700.”),
wherein the controller is configured to: receive a sensor signal indicative of an image of harvested crop material within a feederhouse of the agricultural harvester (Col.5 Line 24-28: “For example, in some instances, one or more of the sensors 114 captures images of crop material moving through the row units 110, within a trough of a cross-auger of the corn header 108, or at one or more locations contained within the confines of the corn header 108.”);
analyze the image to detect at least one ear of corn with kernel loss (Col.5 Line 28-32: “The sensed crop material information is used to determine behaviors of the sensed crop material, such as whether the sensed crop material has escaped or is likely to escape collection, resulting in material loss such as material loss onto the ground”);
analyze the image to identify one or more parameters of the at least one ear of corn with kernel loss (Col.7 Line 33-40: “The processor 202 executes programs, such as an image analyzer program 224. The controller 200 utilizes data to determine or predict crop material behaviors (e.g., crop losses) on or near the header during harvesting. For example, the controller 200 utilizes the image data 218, the measured distribution data 220, and the target distribution data 222 to determine whether grain losses exceed a selected level of grain loss.”);
and determine an appropriate adjustment to one or more components of row units based on the one or more parameters of the at least one ear of corn with kernel loss (Col.7 Line 49-57: “For example, if the grain loss is above a selected level, the controller 200 generates one or more control signals that are used to actuate one or more actuators of the header. Actuation of one or more header actuators alters header parameters, which, in turn, alters, e.g., reduces, the grain loss level from the header. In some instances, if the determined grain loss is below a selected level, then the current header parameters are maintained.”).
Similarly Claim 13 is rejected on the similar rational.
Regarding Claim 2, Yanke teaches the detection and control system of claim 1.
Yanke also teaches wherein the controller is configured to cause display of the image to an operator of the agricultural harvester (Col.6 Line 54-58: “The harvester control system 112 also includes or is coupled to a display 212. The display 212 is operable to display information to a user, such as one or more images received from the region sensors 206.”).
Similarly Claim 15 is rejected on the similar rational.
Regarding Claim 3, Yanke teaches the detection and control system of claim 2.
Yanke also teaches wherein the image comprises a video feed, a still image, or both (Col.5 Line 33-36; “The image data collected by the sensors 114 can be presented in numerous ways. For example, in some implementations, the image data forms a single or a series of images having 2D coordinate systems.”).
Regarding Claim 4, Yanke teaches the detection and control system of claim 1.
Yanke also teaches detection and control system of claim 1, wherein the controller is configured to provide control signals to one or more actuators to automatically implement the appropriate adjustment to the one or more components of the row units (Col.22 Line 24-27: “In this example, when the distribution parameter criterion is satisfied, the mitigation action is to reduce a speed of a stalk roll of the associated row unit and adjust an amount of separation of deck plates.”).
Regarding Claim 5, Yanke teaches the detection and control system of claim 4.
Yanke also teaches wherein the controller is configured to provide the control signals to one or more actuators to adjust respective gaps between respective deck plates of the row units to automatically implement the appropriate adjustment to the one or more components of the row units (Col.22 Line 24-27: “In this example, when the distribution parameter criterion is satisfied, the mitigation action is to reduce a speed of a stalk roll of the associated row unit and adjust an amount of separation of deck plates.”).
Similarly Claim 16 is rejected on the similar rational.
Regarding Claim 6, Yanke teaches the detection and control system of claim 4.
Yanke also teaches the controller is configured to provide the control signals to one or more actuators to adjust a rotational rate of respective stalk rollers of the row units to automatically implement the appropriate adjustment to the one or more components of the row units (Col.22 Line 24-27: “In this example, when the distribution parameter criterion is satisfied, the mitigation action is to reduce a speed of a stalk roll of the associated row unit and adjust an amount of separation of deck plates.”).
Similarly Claim 17 is rejected on the similar rational.
Regarding Claim 7, Yanke teaches the detection and control system of claim 4.
Yanke also teaches the controller is configured to provide the control signals to one or more actuators to adjust an angle of the row units relative to the agricultural harvester to automatically implement the appropriate adjustment to the one or more components of the row units (Col.23 Line 53-58: “ In some instances, a mitigation action includes, for example, raising the header, changing the angle of the header, changing the speed of the harvester, or changing attributes of the reel of the header. Other mitigating actions may be performed to reduce harvest or capture of plant material associated with a plant type not being harvested.”).
Regarding Claim 8, Yanke teaches the detection and control system of claim 1.
Yanke also teaches the controller is configured to provide control signals to adjust a ground speed of the agricultural harvester based on the one or more parameters of the at least one ear of corn with kernel loss (Col.23 Line 53-58: “In some instances, a mitigation action includes, for example, raising the header, changing the angle of the header, changing the speed of the harvester, or changing attributes of the reel of the header. Other mitigating actions may be performed to reduce harvest or capture of plant material associated with a plant type not being harvested.”).
Regarding Claim 9, Yanke teaches the detection and control system of claim 1.
Yanke also teaches the controller is configured to utilize machine learning to detect the at least one ear of corn with kernel loss, to identify the one or more parameters of the at least one ear of corn with kernel loss, or any combination thereof (Col.20 Line 54-62: “In some instances, the measured distribution data includes data from a plurality of rows of a header and over a selected period of time. In some implementations, the analysis of these data are performed using numerical analysis, rules, one or more neural networks, machine learning algorithms, either alone or in combination with each other. In some instances, other types of data are also used to determine cause and mitigation information.”).
Regarding Claim 10, Yanke teaches the detection and control system of claim 1.
Yanke also teaches the controller is configured to utilize machine learning to determine the appropriate adjustment to the one or more components of row units based on the one or more parameters of the at least one ear of corn with kernel loss (Col.20 Line 54-62: “In some instances, the measured distribution data includes data from a plurality of rows of a header and over a selected period of time. In some implementations, the analysis of these data are performed using numerical analysis, rules, one or more neural networks, machine learning algorithms, either alone or in combination with each other. In some instances, other types of data are also used to determine cause and mitigation information.”).
Similarly Claim 18 is rejected on the similar rational of claims 9 or 10.
Regarding Claim 11, Yanke teaches the detection and control system of claim 1.
Yanke also teaches comprising an internal imaging system comprising at least one camera configured to be located at or along the feederhouse between the row units and a processing system of the agricultural harvester, wherein the at least one camera is configured to provide the sensor signal indicative of the image (Col.5 Line 24-32: “For example, in some instances, one or more of the sensors 114 captures images of crop material moving through the row units 110, within a trough of a cross-auger of the corn header 108, or at one or more locations contained within the confines of the corn header 108. The sensed crop material information is used to determine behaviors of the sensed crop material, such as whether the sensed crop material has escaped or is likely to escape collection, resulting in material loss such as material loss onto the ground”).
Similarly Claim 14 is rejected on the similar rational.
Regarding Claim 12, Yanke teaches the detection and control system of claim 11.
Yanke also teaches the at least one camera is mounted underneath a lower plate that defines a passageway for the harvested crop material to flow through the feederhouse (Col.5 Line 24-32: “For example, in some instances, one or more of the sensors 114 captures images of crop material moving through the row units 110, within a trough of a cross-auger of the corn header 108, or at one or more locations contained within the confines of the corn header 108. The sensed crop material information is used to determine behaviors of the sensed crop material, such as whether the sensed crop material has escaped or is likely to escape collection, resulting in material loss such as material loss onto the ground”).
Regarding Claim 19, Yanke teaches an agricultural harvester, comprising: a header comprising a plurality of row units distributed across a width of the header (“FIG. 1 is a perspective view of an example combine harvester 100 moving through a field 102 and harvesting crops 104.”);
and a controller comprising at least one memory and at least one processor (“FIG. 2 is a schematic of an example harvester control system 112. In some implementations, the harvester control system 112 is in the form of a computer system, such as the computer system 1700, described in more detail below. Additional details of the harvester control system 112, such as processor 202 and memory 204, are included below in the context of computer system 1700.”), wherein the controller is configured to:
receive a sensor signal indicative of an image of harvested crop material (Col.5 Line 24-32: “For example, in some instances, one or more of the sensors 114 captures images of crop material moving through the row units 110, within a trough of a cross-auger of the corn header 108, or at one or more locations contained within the confines of the corn header 108. The sensed crop material information is used to determine behaviors of the sensed crop material, such as whether the sensed crop material has escaped or is likely to escape collection, resulting in material loss such as material loss onto the ground”)
and determine, via machine learning, an undesirable level of kernel loss in the harvested crop material due to one or more parameters of one or more components of the plurality of row units based on the image (Col.20 Line 54-62: “In some instances, the measured distribution data includes data from a plurality of rows of a header and over a selected period of time. In some implementations, the analysis of these data are performed using numerical analysis, rules, one or more neural networks, machine learning algorithms, either alone or in combination with each other. In some instances, other types of data are also used to determine cause and mitigation information.”).
Regarding Claim 20, Yanke teaches agricultural harvester of claim 19. Yanke also teaches an internal imaging system comprising at least one camera located within a feederhouse, wherein the internal imaging system is communicatively coupled to the controller to provide the sensor signal indicative of the image of the harvested crop material within the feederhouse (Col.5 Line 24-32).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ganssle (US2024/0130280A1) discloses various embodiments for using artificial intelligence to monitor harvest losses of combine harvesters. Images can be periodically captured from a ground-facing camera mounted to a combine harvester. An amount of gleanings can be counted in the image. An estimated amount of harvest loss is then calculated based at least in part on the amount of gleanings. The estimated amount of the harvest loss can then be displayed to a user or can be used as the basis for automatically adjusting the operation of the combine harvester.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDHESH K JHA whose telephone number is (571)272-6218. The examiner can normally be reached M-F:0800-1700.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDHESH K JHA/Primary Examiner, Art Unit 3668
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