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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 20, 2026 has been entered.
Status of Claims
This Office Action is in response to the Applicants’ filing on April 13, 2026. Claims 1-15 were previously pending, of which claim 1 has been amended, no claims have been cancelled, and no claims have been newly added. Accordingly, claims 1-15 are currently pending and are being examined below.
Response to Arguments
With respect to Applicant's remarks, see pages 6-12 filed April 13, 2026; Applicant’s “Amendment and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the 35 U.S.C. § 112(f) Interpretations, applicant’s amendments to the claims have not addressed the condition of using a means plus function to support the limitations that are defined by “devices” or “modules” so the interpretation is hereby maintained by the examiner.
With respect to the 35 U.S.C. § 112(b) Rejection , applicant’s amendment has canceled claim 4 recited in the rejection, foregoing the condition. Therefore, the objections to the claims are withdrawn.
With respect to the 35 U.S.C. § 101 Rejection, the arguments and amendments have been reviewed by the examiner, and they are persuasive. Therefore, the rejection is withdrawn.
Applicant's arguments regarding 35 U.S.C. § 103 Rejections have been fully considered but they are not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “applying a machine learning model to the image of the receiving vehicle to identify, using only image data from the camera, a type of receiving vehicle from among a plurality of possible different receiving vehicles, wherein the machine learning model identifies the type of receiving vehicle by comparing the image data to a trained model developed from analyzing images of multiple different receiving vehicle types" and "retrieving a real dimension value for the receiving vehicle from a stored lookup table correlating receiving vehicle types to known physical dimensions based on the type of receiving vehicle identified within the image data") are clearly defined in the prior art and maintained for the rejection below in view of the amended claims. Although the claims are interpreted in light of the specification, the new limitations from the amended claims are not persuasive . Therefore, the rejections under 35 U.S.C. § 103 are maintained, as presented in the Office Action below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation discloses sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation discloses function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“computing devices” in claims 1 and 2. A review of the specification shows that it may be associated with a portable electronic device, such as a laptop computer, a tablet computer or a smartphone, may include computing devices embedded in another agricultural machine, such as a receiving vehicle, or both in [0049].
“electronic device” in claims 1, 4 and 10. A review of the specification shows it is a user interface console of the receiving vehicle in [0012] such as a table computer or a smartphone in [0065].
“electromagnetic detecting and ranging module” in claims 4-9. A review of the specification shows that it is a light detecting and ranging (LiDAR) module, in other embodiments of the invention it is a radio detecting and ranging (RADAR) module in [0012].
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-12 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Suleman et al., US 2020/0133262 A1 (Hereinafter, “Suleman”) in view of Bonefas et al., US 2017/0042090 A1 (Hereinafter “Bonefas”) in further view of Faust et al., US 2022/0095539 A1 (Hereinafter “Faust”) in further view of Liu et al., US 2019/0103026 A1 (Hereinafter “Liu”).
Regarding Claim 1, Suleman discloses a system comprising: an agricultural harvester
(10) including –a crop processor for separating grain from material other than grain, See [0024], “the harvesting units 12 engage unharvested plants 20 and extract various agricultural products (e.g., com, wheat, cotton, etc.) from the plants. These agricultural products are transferred to the internal storage compartment 16, either directly or via a processing device configured to remove undesirable portions of the agricultural products.”
an unload conveyor (24) for transferring grain out of the agricultural harvester, See [0025], “Accordingly, the harvester 10 includes a conveyor 24 configured to transfer the agricultural
product to a mobile storage compartment while the harvester is in motion. The conveyor 24 may include an auger, a conveyor belt, or other suitable device configured to transfer the agricultural product from the internal storage compartment 16 to an outlet 26.”
a camera (93) for capturing images of an area adjacent to the agricultural harvester and generating image data, and a receiving vehicle including a grain bin for receiving transferred grain, one or more computing devices for –receiving the image data from the camera during a harvest operation, the image data including an image of the receiving vehicle; See [0053], “In the illustrated embodiment, the harvester control system 79 includes an optical sensor 93 and/or a measuring device 95 ( e.g., a three-dimensional measuring device), each communicatively coupled to the controller 82. In certain embodiments, the optical sensor 93 ( e.g., camera, infrared sensor, etc.) and/or the measuring device 95 are coupled to the conveyor (e.g., at the outlet) and configured to be directed toward the storage compartment.”
the location data including a distance of the receiving vehicle from the agricultural harvester and a fore-aft position of the receiving vehicle relative to the agricultural harvester, and
See [0056], “haul vehicle control system 43 may utilize the position and the dimensions of the selected zone, in addition to the position and velocity of the harvester, to determine the target (fore-aft) position and/or the target velocity of the haul vehicle. For example, the haul vehicle control system 43 may determine a target position that substantially aligns (fore-aft position and target distance) the selected zone with the conveyor outlet of the harvester. “
an electronic device including a graphical user interface and an automated guidance system, See [0041], “controller 56 is configured to enable automatic control of the haul vehicle … controller 56 is configured to instruct a user interface to present an indication to an operator that automatic control is enabled. “
the electronic device configured to receive the location data from the one or more computing devices, use the location data to generate a graphical representation illustrating the relative positions of the unload conveyor and the grain bin, present the graphical representation on the graphical user interface, and See [0042], “the controller 56 is an electronic controller having electrical circuitry configured to process data from the transceiver 44, the spatial locating device 48, and/or other components of the control system 43. “ Also [0054], “The display 94 of the user interface 92, in turn, may present the views to the operator, thereby assisting the operator in identifying alignment of the conveyor outlet with the first and second points on the storage compartment.”
automatically align, using the automated guidance system and the location data, the receiving vehicle with the unload conveyor. See [0056], “haul vehicle control system 43 may utilize the position and the dimensions of the selected zone, in addition to the position and velocity of the harvester, to determine the target position and/or the target velocity of the haul vehicle … vehicle control system 43 may determine a target position that substantially aligns the selected zone with the conveyor outlet of the harvester. “
Suleman discloses selecting a haul vehicle from a plurality of haul vehicles, but does not explicitly disclose the use of machine learning to identify a receiving vehicle. However, Bonefas teaches the controller further configured to identify the receiving vehicle from the image data using a machine learning algorithm including wherein the machine learning model identifies the type of receiving vehicle , retrieving a real dimension value for the receiving vehicle based on the type of receiving vehicle identified within the image data, and generating, using only the real dimension value and the image data, a location data of the receiving vehicle relative to the agricultural harvester, See [0107-0114], “First, the target detection module 401 receives image data from the image capture device 136 or the image processing system 138… the target detection module 401 identifies a region in the image data that likely corresponds to the target, which can be the receiving vehicle 12 or container 18 … the target discrimination module 406 uses a simple linear Support Vector Machine (SVM) classifier … more complex state-of the-art models can be used, depending of the performance and computation requirements of the control arrangement … In the example embodiment described above, a multi-staged system (candidate generation module 405 and target detection module 406) is used due to its simplicity and reduced computational resources. However, combined algorithms based on deep models can be used in alternative embodiments, and identify a grain bin of the receiving vehicle using a machine learning algorithm .” And [0115-0117], “Once a target is detected, a target tracking module 402 determines the pose trajectory of the target, such as the receiving vehicle 12 or the container 18. In one embodiment, the tracking module computes trajectory with four degrees of freedom (3D position plus heading), together with the dimensions of the target (length, width and height), using the input data from the image capture device 136 … The tracking module 402 comprises a target modeling module 408, a candidate pose generator 409, and an error function evaluator 410. The target modeling module 408 builds a model of the tracked vehicle (i.e. receiving vehicle 12 or container 18). The first iteration of the model is initialized to contain the point cloud enclosed by the target detection computed by the target detection module 401. The model is populated with all points within a certain depth and lateral distance of the initialization point, from the perspective of the target's frame.”
It would have been obvious to one of ordinary skill in the art prior to the effective filing
date of the application to have combined the methods to identify the receiving vehicle from the
image data using a machine learning algorithm, and identify a grain bin of the receiving vehicle
using a machine learning algorithm as taught by Bonefas with the systems and methods for monitoring, assisting and improving the process of periodically transferring harvested agricultural product to a mobile storage compartment (grain cart) while the harvester is in motion as taught by Suleman for the benefit of improving the accuracy of identifying and determining the position of the receiving vehicle and its associated grain bin with respect to the location and orientation of the harvester.
Although in paragraph [0110] Bonefas discloses that a “lazy evaluation” technique is commonly used to optimize machine learning pipelines and avoids the computation of extra features when an input windows is clearly classified as part of the background after only evaluating the first feature channel, Bonefas does not explicitly disclose applying a machine learning model to identify, using only image data from the camera, a receiving vehicle from among a plurality of possible different receiving vehicles; and determining, using the location and the size of the receiving vehicle in the image data, a location of the receiving vehicle relative to the agricultural harvester by comparing known dimensions of the receiving vehicle with the size of the receiving vehicle in the image data.
However, Faust teaches applying a machine learning model to the image of the receiving vehicle to identify, using only image data from the camera, a type of receiving vehicle from among a plurality of possible different receiving vehicles; (In paragraphs [0027-0028], Faust teaches that a camera 106 can be positioned to have a field of view that captures an image of the side portion 118 of trailer 116, and thus, the visual or optical features of the side portion of trailer 116 can be used to uniquely identify trailer 116, or at least to identify the type of the trailer 116, wherein the visual features can be detected using a computer vision analysis system, using a deep neural network, or using other image processing techniques and mechanisms for identifying visual features or characteristics in an image, a set of images, or a video).
Faust is considered to be analogous to the claimed invention in that they both pertain to the use of a machine learning model to identify a particular receiving vehicle for the receipt of material being transferred from a harvester. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Faust with the system as disclosed by Bonefas, where the use of machine-learning for image recognition is well understood in the art, and may be implemented without undue experimentation, and with a reasonable expectation of success and with predictable results. Doing so may be advantageous in that “Based on the unique trailer identifier or the type identifier, the settings values for the automatic cart filling control system can be obtained so that the cart is filled in a cart-specific way or in a cart type-specific way, depending upon whether the cart is uniquely identified or the cart type is identified” as suggested by Faust in paragraph [0027], thereby increasing the contextual sensitivity and accuracy of operation by the system, for example.
The combination of Bonefas and Faust does not explicitly disclose determining, using the location and the size of the receiving vehicle in the image data, a location of the receiving vehicle relative to the agricultural harvester by comparing known dimensions of the receiving vehicle with the size of the receiving vehicle in the image data.
However, Liu teaches determining, using the location and the size of the receiving vehicle in the image data, a location of the receiving vehicle relative to the agricultural harvester by comparing known dimensions of the receiving vehicle with the size of the receiving vehicle in the image data (In paragraph [0043], Liu teaches depth estimation according to an embodiment, where the tracker 330 uses a pinhole camera model to estimate the depth “Z” (distance) from a vehicle at point C to another object, where “f” represents the focal length of an image sensor of a client device 110 with the vehicle 140 and the other object may be another vehicle having a width “Y” (e.g., approximately 1.8 meters), and in some embodiments, the tracker 330 may use a lookup table to determine the expected width “Y,” height, or aspect ratio of a vehicle based on a type of the vehicle, and based on trigonometry, the tracker 330 may output a bounding box at point “p” having a width y=f*Y/Z, and thus, the tracker 330 determines the depth to be Z=f*Y/y).
Liu is considered to be analogous to the claimed invention in that they both pertain to utilizing the scale of a vehicle in a captured image with its known dimensions in order to determine its relative location. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Liu with the system as disclosed by the combination of Suleman, Bonefas and Faust, where the examiner understands that the taught image recognition technique is well understood in the art, and may be implemented without undue experimentation, and with a reasonable expectation of success and with predictable results. Doing so may be advantageous in that the detection of the vehicle and subsequent determination of location may be performed utilizing already known information such as its expected width, increasing efficiency of the calculation, for example.
Regarding Claim 2, Suleman discloses the following limitation dependent on claim 1:
the one or more computing devices being configured to –determine a location and a size of the receiving vehicle in the image data, and use the location and the size in determining the location of the receiving vehicle relative to the agricultural harvester. See [0055], “In certain embodiments, the controller 82 is configured to adjust the first and second points and the corresponding first and second positions of the storage compartment relative to the agricultural harvester based on input from the measuring device 95 and/or the optical sensor 93.”
Regarding Claim 3, Suleman discloses an automated harvester, but does not disclose machine learning. However, Bonefas teaches the following limitation dependent on claim 1:
the machine learning model including a deep learning model. In [0111], “In the example embodiment described above, a multi-staged system (candidate generation module 405 and target detection module 406) is used due to its simplicity and reduced computational resources. However, combined algorithms based on deep models can be used in alternative embodiments.”
It would have been obvious to one of ordinary skill in the art prior to the effective filing
date of the application to have combined the methods to identify the receiving vehicle from the
image data using a machine learning algorithm, and identify a grain bin of the receiving vehicle
using a machine learning algorithm as taught by Bonefas with the systems and methods for monitoring, assisting and improving the process of periodically transferring harvested agricultural product to a mobile storage compartment (grain cart) while the harvester is in motion as taught by Suleman for the benefit of improving the accuracy of identifying and determining the position of the receiving vehicle and its associated grain bin with respect to the location and orientation of the harvester.
Regarding Claim 4, Suleman discloses the following limitation dependent on claim 1:
the agricultural harvester further comprising an electromagnetic detecting and ranging module for generating fill and distribution data indicating a fill level and a grain distribution of grain in the receiving vehicle, the electronic device further configured to –receive the fill and distribution data, and use the location data and the fill and distribution data to generate the graphical representation illustrating the relative positions of the unload conveyor and the grain bin and illustrating the fill level of the grain bin. See [0057], “As previously discussed, the controller 82 is configured to select a zone within the bounding rectangle ( e.g., based on input from the user interface 92, the optical sensor 93, the measuring device 95, the orientation sensor 49, or a combination thereof). For example, an operator of the harvester may periodically select different zones ( e.g., based on an image provided by the optical sensor 93) during the unloading process, thereby establishing a substantially even distribution of agricultural product within the storage compartment. In addition, the controller 82 may automatically select the zone based on input from the optical sensor 93 and/or the measuring device 95. For example, if the controller 82 receives a signal from the optical sensor 93 and/or the measuring device 95 indicative of a level of agricultural product within the selected zone exceeding a threshold level (e.g., approaching the top of the storage compartment)…”
Regarding Claim 5 Suleman discloses the following limitation dependent on claim 4:
the electromagnetic detecting and ranging module performing a two-dimensional scan with radio detecting and ranging (RADAR) module. See [0053], “In the illustrated embodiment, the harvester control system 79 includes an optical sensor 93 and/or a measuring device 95 ( e.g., a three-dimensional measuring device), each communicatively coupled to the controller 82 ... The measuring device 95 may include a light detection and ranging (LIDAR) system, a radio detection and ranging (RADAR) system, an ultrasonic measuring system, any other suitable system configured to determine a position and/or an orientation of at least one element of the storage compartment relative to the measuring device, or a combination thereof.”
Regarding Claim 6 Suleman discloses the following limitation dependent on claim 4:
the electromagnetic detecting and ranging module performing a three-dimensional scan with light detecting and ranging (LiDAR) module. See [0053], “In the illustrated embodiment, the harvester control system 79 includes an optical sensor 93 and/or a measuring device 95 ( e.g., a three-dimensional measuring device), each communicatively coupled to the controller 82. In certain embodiments, the optical sensor 93 ( e.g., camera, infrared sensor, etc.) and/or the measuring device 95 are coupled to the conveyor (e.g., at the outlet) and configured to be directed toward the storage compartment. The measuring device 95 may include a light detection and ranging (LIDAR) system, a radio detection and ranging (RADAR) system, an ultrasonic measuring system, any other suitable system configured to determine a position and/or an orientation of at least one element of the storage compartment relative to the measuring device, or a combination thereof.”
Regarding Claim 7 Suleman discloses the following limitation dependent on claim 4:
the electromagnetic detecting and ranging module performing a two-dimensional scan with radio detecting and ranging (RADAR) module. See [0053], “In the illustrated embodiment, the harvester control system 79 includes an optical sensor 93 and/or a measuring device 95 ( e.g., a three-dimensional measuring device), each communicatively coupled to the controller 82 ... The measuring device 95 may include a light detection and ranging (LIDAR) system, a radio detection and ranging (RADAR) system, an ultrasonic measuring system, any other suitable system configured to determine a position and/or an orientation of at least one element of the storage compartment relative to the measuring device, or a combination thereof.”
Regarding Claim 8 Suleman discloses the following limitation dependent on claim 4:
the electromagnetic detecting and ranging module performing a three-dimensional scan with light detecting and ranging (LiDAR) module. See [0053], “In the illustrated embodiment, the harvester control system 79 includes an optical sensor 93 and/or a measuring device 95 ( e.g., a three-dimensional measuring device), each communicatively coupled to the controller 82. In certain embodiments, the optical sensor 93 ( e.g., camera, infrared sensor, etc.) and/or the measuring device 95 are coupled to the conveyor (e.g., at the outlet) and configured to be directed toward the storage compartment. The measuring device 95 may include a light detection and ranging (LIDAR) system, a radio detection and ranging (RADAR) system, an ultrasonic measuring system, any other suitable system configured to determine a position and/or an orientation of at least one element of the storage compartment relative to the measuring device, or a combination thereof.”
Regarding Claim 9 Suleman discloses the following limitation dependent on claim 4:
the electromagnetic detecting and ranging module being placed at an end of the unloading conveyor distal a body of the agricultural harvester. See [0053], “the optical sensor 93 ( e.g., camera, infrared sensor, etc.) and/or the measuring device 95 are coupled to the conveyor (e.g., at the outlet) and configured to be directed toward the storage compartment.”
Regarding Claim 10, Suleman discloses the following limitation dependent on claim 1:
the agricultural harvester further including a first wireless transceiver for sending and receiving wireless communications, the grain transfer system further comprising a receiving vehicle, the receiving vehicle including –a grain bin for receiving transferred grain, a second wireless transceiver for sending and receiving wireless communications, and a graphical user interface, wherein the electronic device is a user interface console of the receiving vehicle, the agricultural harvester communicates the location data to the receiving vehicle via the first wireless transceiver, and the receiving vehicle receives the location data via the second wireless transceiver. See Fig.2 and [0028], “By way of example, when the haul vehicle 30 enters an area of communication 36, communication is automatically established between a first transceiver on the haul vehicle 30 and a second transceiver on the harvester 10. That is, the controller of the haul vehicle detects the harvester upon receiving a signal from the harvester transceiver, and the controller of the harvester detects the haul vehicle upon receiving a signal from the haul vehicle transceiver.” Further, in [0029] “To initiate the docking process, an operator of the haul vehicle provides input to a user interface, thereby instructing the controller to enable automatic control of the haul vehicle… Once the haul vehicle substantially reaches the target position, the controller controls the steering control system and the speed control system to substantially maintain the target position and the target velocity, thereby facilitating transfer of agricultural product from the harvester to the storage compartment.”
Regarding Claim 11, Suleman discloses an alignment of the conveyor outlet to the storage container in [0067-0069], but does not explicitly disclose the use of image data to do so. However, Bonefas teaches the following limitation dependent on claim 1:
the unload conveyor including a spout, wherein the spout is positioned at a distal end of the unload conveyor and discharges the grain, and wherein an image of the unload conveyor and an image of the spout are visible in the image data. See [0030], “The crops discharged from the discharge accelerator 38 exit the harvesting machine 10 to the container 18 via an adjustable transfer device 40 in the form of a discharge spout 45.” And [0062], “Display an image of the container 18 together with a symbol 800 on the display unit representing the alignment of spout 45 with front edge 19A of container 18, which is coincident with the calculated expected point of incidence of the crop flow on the container.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use image data to display a conveyor spout disclosed in Bonefas, with reasonable expectation of success. The motivation for doing so would have been allow an operator to determine if the automated system is performing correctly or if any adjustments are necessary, see Bonefas [0007].
Regarding Claim 12, Suleman discloses an alignment of the conveyor outlet to the storage container in [0067-0069], but does not explicitly disclose the use of image data to do so. However, Bonefas teaches the following limitation dependent on claim 1:
the unload conveyor include a spout, the spout being configured to discharge grain and being positioned at a distal end of the unload conveyor, and the graphical representation on the graphical user interface further illustrating the position of the spout relative to the grain bin. See [0071], “Image showing a visual alignment indicator 800 (e.g., cross hairs, cross or target as shown in FIGS. 8A and 8B) to be aligned with front edge 19A of the container. The visual alignment indicator is created in the electronic control unit 112. The visual alignment indicator 800 is overlaid on top of the image from the camera 136 and streamed to the display unit 142 that the operator sees. Ideally, the operator would maneuver the spout 45 of the harvester 10 such that the visual alignment indicator 800 is pointed to the front edge 19A of the contain 18 and press a button to engage the system. The system would then identify the front edge 19A of the container 18 and track its position.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use a graphical user interface to display a conveyor spout as disclosed in Bonefas, with reasonable expectation of success. The motivation for doing so would have been allow an operator to determine if the automated system is performing correctly or if any adjustments are necessary, see Bonefas [0007].
Regarding Claim 14, Suleman discloses an alignment method for harvesters, but does not explicitly disclose the use of image data to do so. However, Bonefas teaches the following limitation:
determine the distance from the receiving vehicle to the harvester by using a ratio of the size information in the image data with the real dimension of the receiving vehicle and scaling the ratio with the focal length of the camera. See [0046], "Further, since the optical image capture device 136 is a stereo camera, its signals allow to estimate a distance between the harvesting machine 10 and the container 18." And [0048], "If the optical image capturing device 136 were a monocular camera, the size (pixels) of the near edge of the container 18 in the image could be used as an estimate for the mentioned distance."
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use image data in Bonefas to align a harvester conveyor disclosed in Suleman, with reasonable expectation of success. The motivation for doing so would have been to develop an improved system and method for controlling the position of the transfer device when unloading material into a container positioned at the rear of the harvester, see Bonefas [0007].
Suleman discloses an alignment method for harvesters, but does not explicitly disclose the use of image data to do so. However, Faust teaches the following limitation dependent on claim 1:
the camera has a focal length, and See [0022], ”camera 107 having different characteristics (e.g., different field of view, different focal length and/or zoom capabilities, etc.) can be utilized.”
the one or more computing devices being configured to - determine size information of the receiving vehicle in the image data, and See [0018], “A stereo camera on the spout of the harvester captures an image of the receiving vehicle … An image processing system determines dimensions of the receiving vehicle, and the distribution of the crop deposited inside it.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use image data in Faust to align a harvester conveyor disclosed in Suleman, with reasonable expectation of success. The motivation for doing so would have been to determine dimensions of the receiving vehicle, and the distribution of the crop deposited inside it, see Faust [0018].
Regarding Claim 15, Suleman discloses the following limitation dependent on claim 1:
the automated guidance system being configured to automatically align the receiving vehicle with the unload conveyor by - receiving the location data, See [0025], “mobile storage compartment may be automatically aligned with the conveyor outlet 26, thereby enhancing the efficiency of the harvester unloading process.” And [0027], “the target position corresponds to a position that substantially aligns the conveyor outlet 26 with an unloading point on the storage compartment 32. Accordingly, with the haul vehicle located at the target position, the agricultural product may be transferred from the harvester 10 to the storage compartment 32 while the vehicles are in motion. Because the controller automatically maintains the position of the storage compartment relative to the conveyor outlet during the unloading process,”
determining, using the location data, a geographic location of a machine, generating, using the location data, a wayline communicating a target travel path, comparing the geographic location of the machine to the location of the wayline, and steering the machine to travel along the wayline, wherein the machine is at least one of the agricultural harvester and the receiving vehicle. See [0038], “The controller 56 is also configured to determine a route(wayline) to the target position based at least in part on the target position, the second determined position of the haul vehicle, and the second determined velocity of the haul vehicle. Once the route is determined, the controller is configured to control the steering control system and the speed control system to direct the haul vehicle toward the target position along the route. Upon substantially reaching the target position, the controller is configured to control the steering control system and the speed control system to substantially maintain the target position and the target velocity. And [0044], “the steering control system may include other and/or additional systems to facilitate directing the haul vehicle along a target route.”
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Suleman in view of Bonefas, in further view of Faust, in further view of Liu, in further view of Weltman et al., WO-19955019576-A1 (Hereinafter “Weltman”).
Regarding Claim 13, Suleman discloses an alignment of the conveyor outlet to the storage container in [0067-0069], but does not explicitly disclose the use of concentric target lines to do so. However, Weltman teaches the following limitation dependent on claim 12:
wherein the graphical representation on the graphical user interface further illustrates concentric target lines around the spout. See at least pg. ln.22-25, “At this stage the display changes again, to the form shown in Figure 5 comprising a bullseye display 18 showing the target position 16 with concentric circles 19-22 around it, along with the indicated position 17.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use target lines in Weltman to align a harvester conveyor disclosed in Suleman, with reasonable expectation of success. The motivation for doing so would have been to further improve the accuracy of the alignment calibration process, see Suleman [0053].
Conclusion
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/B.K.P./Examiner, Art Unit 3669 /KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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