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 .
Status of Claims
This office action is in response to application number 18/093,179 filed on 2/10/2026, in which
Claims 1-16 are presented for examination. Applicant amends Claims 1-8 and 10-16.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP22150134.9, filed on 1/4/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/4/2023 and the information disclosure statement (IDS) submitted on 2/11/2026 were received and considered by the examiner.
Response to Arguments
Applicant’s amendments and arguments, see pgs. , filed 2/10/2026, with respect to the objection to the drawings have been fully considered and are persuasive. The objection to the drawing of has been withdrawn.
Applicant’s amendments and arguments, see pgs. , filed 2/10/2026, with respect to the objection to Claim 12 have been fully considered and are persuasive. The objection to of has been withdrawn. However, in light of the amendments, new claim objections are introduced. Further details are provided below.
Applicant’s amendments and arguments, see pgs. 4-9 and 11-12, filed 2/10/2026, with respect to the claim interpretation of Claim 16 under 35 U.S.C. 112(f) has been fully considered but is not persuasive. Therefore, the claim interpretation of Claim 16 under 35 U.S.C. 112(f) set forth in the office action of 9/10/2025 is maintained and, in light of the amendments, an updated interpretation of Claim 16 is made. Further details are provided below.
Applicant’s amendments and arguments, see pgs. , filed 2/10/2026, with respect to the rejection of Claims 4-10 and 16 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejection of Claims 4-10 and 16 under 35 U.S.C. 112(b) set forth in the office action of has been withdrawn. However, in light of the amendments, new claim rejections under 35 U.S.C. 112(b) are introduced. Further details are provided below.
Applicant’s amendments and arguments, see pgs. , filed 2/10/2026, with respect to the rejection of Claims 1-3, 11-13, and 16 under 35 U.S.C. 102 and Claims 4-10 and 14-15 under 35 U.S.C. 103 have been fully considered and are moot because they are directed towards the amendments of Claims 1 and 16. Applicant states that Vandike does not discuss a local or a total difference computation and a control signal that automatically executes a targeted control to minimize a local or a total difference computation, as recited in the amendments of Claims 1 and 16. Examiner agrees that Vandike does not discuss a local or a total difference computation and therefore, in light of the amendments, the rejection of Claims 1-3, 11-13, and 16 under 35 U.S.C. 102 of has been withdrawn. However, in light of the amendments, an updated rejection of Claims 1-16 under 35 U.S.C. 103 is made. Further details are provided below. Although the arguments are moot, Examiner briefly addresses some of the arguments below.
Applicant further argues that Vandike does not discuss the amended Claim 1 features, with respect to a control signal to at least one actuator that automatically executes a targeted control on a left or right side of the header that minimizes the local or total difference computation. Applicant states that Vandike, para 0039, discusses control settings provided by a human operator with an actuator for maintaining header height settings, and independent implementation of height, roll, and tilt settings for the whole header. As stated above, Examiner agrees that Vandike does not discuss a local or total difference computation, however, Examiner respectfully disagrees that Vandike does not discuss and automated control of a left or side of the header. Although Vandike, para 0039, does discuss a human operator, it does define that an “operator of agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system.” Additionally, as cited in the original rejection, Vandike, para 0038, does discuss left and right variants of subsystems, including, para 0081, left and right controls for the draper belts to manage a biomass difference entering one side of the header as compared to the other side of the header (see also para 0046 which further discusses sensing biomass through the feeder and material distribution internal to the harvester), where, para 0069, the draper functionality is controlled using machine and header actuators. Further details are provided in the updated rejection below.
Claim Objections
Claims 1 and 16 are objected to because of the following informalities:
Claim 1 (line 12) and Claim 16 (line 11): “located in middle of” should be “located in a middle of” and
Claim 1 (lines 17-20) and Claim 16 (lines 16-19): “executes a targeted control […] based on a result of the processing that minimizes at least one of the local difference computation or the total difference computation” should be more clearly written, for example, “executes a targeted control […] based on a result of the processing, wherein the targeted control minimizes at least one of the local difference computation or the total difference computation”.
Appropriate correction is required.
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 recites 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 recites 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:
"control that performs" in Claim 16 (line 3).
Corresponding structure is found in the specification. Specification [pg. 7, para 0026], describes the central control unit as connected to other harvester hardware, “A central control unit (not shown) may be provided, usually near the operator cab 104, which is configured to be connected to all sensors, actuators, systems and operator interfaces such that all functions of the combine harvester may be controlled from the operator and/or automatically.” The specification [pg. 4, para 0013] further describes a control unit as containing a memory for storing crop parameters, “[…] several factors like the type of crop, the temperature, the humidity and other parameters and may be stored in a respective memory of the control unit or, alternatively, input by an operator into the control unit” and specification [pg. 5, para 0016], using inputs with the algorithm of the control unit, “[…] the additional estimated information provides a further input for the algorithm in the control unit […].” Therefore, for examination purposes, the “central control” will be interpreted as a “central control unit,” a hardware or a computer with memory and instructions, which can be connected to other harvester hardware components.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) 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 it/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 it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 (lines 9-14) and Claim 16 (lines 8-13) recite a “local difference computation” and a “total difference computation.” The “local difference computation” references only “a measured distribution function” and “a variable” representing a horizontal distance from the center of feeder housing, while the “total difference computation” references only an integration of “an absolute difference value.” The claim language describing the local and total difference computations does not clearly state or describe what the difference is nor does the language provide enough details to understand what mathematical models are being claimed. Therefore, there is insufficient explanation of what Claim 1 (lines 9-14) and Claim 16 (lines 8-13) refer to and instead should be more explicitly stated or more clearly described. For examination purposes, Claim 1 (lines 9-14) and Claim 16 (lines 8-13) will be read as follows (NOTE: this is an interpretation not an Examiner’s amendment).
[…] by performing a local difference computation based on a first mathematical model comprising
a measured distribution function and a target distribution function,
with a variable representing a horizontal distance of the feeder housing with origin coordinates located in middle of an opening of the feeder housing, or
performing a total difference computation
an integration of an absolute difference value, using
the measured distribution function and the target distribution function, over a width of the feeder; […]
Claim 11 (lines 1-2) recites “wherein executing comprises: […]” and Claim 15 (line 2) recites “monitoring the executing of the at least one control signal.” Claim 1 (lines 17-18) recites “directing the at least one control signal to [[in]] the at least one actuator that automatically executes a targeted control of the harvesting parameter […].” Therefore, for clarity and proper antecedent basis, Claim 11 (lines 1-2) should recite “wherein the executing the targeted control comprises: […]” and Claim 15 (line 2) should recite “monitoring the directing of the at least one control signal.”
Claims 2-10 and 13-14 are rejected by dependency on Claim 1 and Claim 12 is rejected by dependency on Claims 1 and 11.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over by Vandike et al., PG Pub US-2022/0110256-A1 (herein "Vandike") in view of Wilken et al., PG Pub US-2017/0049045-A1 (herein "Wilken").
Regarding Claim 1, Vandike discloses: (Currently Amended) A method [[for]] of automatically controlling a harvesting parameter on a header connected to a combine harvester, wherein the combine harvester comprises the header, the feeder, and a downstream processing device; and wherein crop is cut by the header, transferred to the feeder, and then transported to the processing device. See [Vandike, FIG. 1 and pg. 1, para 0037], which describes the harvester, including the header, the feeder, and the thresher, "[…] Agricultural harvester 100 includes front-end equipment, such as a header 102, and a cutter generally indicated at 104. Agricultural harvester 100 also includes a feeder house 106, a feed accelerator 108, and a thresher generally indicated at 110. The feeder house 106 and the feed accelerator 108 form part of a material handling subsystem 125. Header 102 is pivotally coupled to a frame 103 of agricultural harvester 100 along pivot axis 105. One or more actuators 107 drive movement of header 102 about axis 105 in the direction generally indicated by arrow 109.[…]. While not shown in FIG. 1, agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a roll angle, or both to the header 102 or portions of header 102", and [Vandike, FIGs. 3, 5, 7, 9, 12 and pg. 32, paras 0256-0276], which show the flow diagrams and describe examples of the methods for controlling the harvester.
Vandike further discloses: […] the method comprising steps of: detecting at least one crop property in [[the]] a feeder with a feeder housing by at least one sensor and outputting at least one corresponding crop property signal. See [Vandike, pgs. 4-5, para 0046], which explains that the harvester includes sensors for measuring various harvester and crop properties, "Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses a height of header 102 above ground 111; […]; a material other than grain (MOG) moisture sensor that senses a moisture level of the MOG passing through agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; a machine orientation sensor that senses the orientation of agricultural harvester 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. […] The crop property sensors may also sense the feed rate of biomass through feeder house 106, through the separator 116 or elsewhere in agricultural harvester 100. The crop property sensors may also sense the feed rate as a mass flow rate of grain through elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables. Crop property sensors can include one or more crop moisture sensors that sense moisture of crops being harvested by agricultural harvester. An internal material distribution sensor may sense material distribution internal to agricultural harvester 100." See also [Vandike, pg. 16, paras 0118-0120], which further describe the in-situ sensors which output the characteristics as signals, "[0118] Also, in the example shown in FIG. 6A, in-situ sensor 208 can include one or more of an agricultural characteristic sensor 402, operator input sensor 404, and a processing system 406. In-situ sensors 208 can include other sensors 408 as well. [0119] Agricultural characteristic sensor 402 senses values indicative of agricultural characteristics. […]. [0120] Processing system 406 may receive the sensor signals from one or more of agricultural characteristic sensor 402, and operator input sensor 404 and generate an output indicative of the sensed characteristic. For instance, processing system 406 may receive a sensor input from agricultural characteristic sensor 402 and generate an output indicative of an agricultural characteristic." Finally see [Vandike, pg. 18, para 0132], which further describes the material property sensors for detecting crop characteristics, "Harvested material property sensors 484 may sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. The crop properties may include such things as crop type, crop moisture, grain quality (such as broken grain), MOG levels, grain constituents such as starches and protein, MOG moisture, and other crop material properties. Other sensors could sense straw "toughness", adhesion of com to ears, and other characteristics that might be beneficially used to control processing for better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, etc."
Vandike further discloses: receiving the at least one corresponding crop property signal by a central control [[unit]]; processing the at least one corresponding crop property signal in the central control [[unit]]. See [Vandike, pg. 5, para 0049], which describes crop characteristics generated by the in-situ sensors to create a predictive map used to control the harvester, "Prior to describing how agricultural harvester 100 generates a functional predictive crop moisture map and uses the functional predictive crop moisture map for presentation or control, a brief description of some of the items on agricultural harvester 100, and their operation, will first be described. The description of FIGS. 2 and 3 describe receiving a general type of prior information map and combining information from the prior information map with a georeferenced sensor signal generated by an in-situ sensor, where the sensor signal is indicative of a characteristic in the field, such as characteristics of crop or weeds present in the field. […]; characteristics of crop properties such as crop height, crop moisture, crop density, crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight; and characteristics of machine performance […]. A relationship between the characteristic values obtained from in-situ sensor signals and the prior information map values is identified, and that relationship is used to generate a new functional predictive map. A functional predictive map predicts values at different geographic locations in a field, and one or more of those values may be used for controlling a machine, such as one or more subsystems of an agricultural harvester. […]. In some instances, a functional predictive map can be used for one or more of controlling an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user." See also [Vandike, pgs. 5-6, para 0051], which describes the harvester and in-situ sensors, which collect the characteristics and feed the characteristics to the predictive model, which the control system, and related controllers, use to control the harvester components, "FIG. 2 is a block diagram showing some portions of an example agricultural harvester 100. FIG. 2 shows that agricultural harvester 100 illustratively includes one or more processors or servers 201, data store 202, geographic position sensor 204, communication system 206, and one or more in-situ sensors 208 that sense one or more agricultural characteristics of a field concurrent with a harvesting operation. […]. The in-situ sensors 208 generate values corresponding to the sensed characteristics. The agricultural harvester 100 also includes a predictive model or relationship generator (collectively referred to hereinafter as "predictive model generator 210"), predictive map generator 212, control zone generator 213, control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. […]. Predictive model generator 210 illustratively includes a prior information variable-to-in-situ variable model generator 228, and predictive model generator 210 can include other items 230. Control system 214 includes communication system controller 229, operator interface controller 231, a settings controller 232, path planning controller 234, feed rate controller 236, header and reel controller 238, draper belt controller 240, deck plate position controller 242, residue system controller 244, machine cleaning controller 245, zone controller 247, and control system 214 can include other items 246. Controllable subsystems 216 include machine and header actuators 248, propulsion subsystem 250, steering subsystem 252, residue subsystem 138, machine cleaning subsystem 254, and controllable subsystems 216 can include a wide variety of other subsystems 256. For instance, control system 214 can generate one or more control signals to control material handling subsystem 125 to control or compensate for the internal material distribution within agricultural harvester 100 based on the received functional predictive map (with or without control zones)."
Vandike further discloses: transmitting at least one control signal by the central control [[unit]] to at least one actuator on the combine harvester; and directing the at least one control signal to [[in]] the at least one actuator that automatically executes a targeted control of the harvesting parameter on a left side or a right side of the header. See [Vandike, pg. 3, paras 0037-0038], which describes some details of the harvester structure, including the actuators used to control the components, and various subsystems of the harvester, including left and right variants, "[0037] […]. One or more actuators 107 drive movement of header 102 about axis 105 in the direction generally indicated by arrow 109. Thus, a vertical position of header 102 (the header height) above ground 111 over which the header 102 travels is controllable by actuating actuator 107. While not shown in FIG. 1, agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a roll angle, or both to the header 102 or portions of header 102. Tilt refers to an angle at which the cutter 104 engages the crop. The tilt angle is increased, for example, by controlling header 102 to point a distal edge 113 of cutter 104 more toward the ground. The tilt angle is decreased by controlling header 102 to point the distal edge 113 of cutter 104 more away from the ground. The roll angle refers to the orientation of header 102 about the front-to-back longitudinal axis of agricultural harvester 100. [0038] Thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, agricultural harvester 100 also includes a separator 116. Agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe […]. The material handling subsystem 125 also includes discharge beater 126, tailings elevator 128, clean grain elevator 130, as well as unloading auger 134 and spout 136. […]. Agricultural harvester 100 also includes a residue subsystem 138 that can include chopper 140 and spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, agricultural harvester 100 may have left and right cleaning subsystems, separators, etc., which are not shown in FIG. 1," and further including [Vandike, pg. 9, para 0069], actuators for the header functionality, speed, and draper functionality, for example, “For instance, settings controller 232 can generate control signals to control machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control, and other actuators 248 that affect the other functions of the agricultural harvester 100.” Also see [Vandike, pg. 11, para 0081], which provides another example of left and right control of the header using the draper belts, "By way of example, a generated predictive map 264 in the form of a predictive crop moisture map can be used to control one or more controllable subsystems 216. For example, the functional predictive crop moisture map can include predictive values of crop moisture georeferenced to locations within the field being harvested. […]. Similarly, the header height can be controlled to take in more or less material and thus the header height can also be controlled to control feed rate of material through the agricultural harvester 100. In other examples, if the predictive map 264 maps a predictive value of crop moisture forward of the machine being higher on one portion of the header than another portion of the header, resulting in a different biomass entering one side of the header than the other side, control of the header may be implemented. For example, a draper speed on one side of the header may be increased or decreased relative to the draper speed other side of the header to account for the additional biomass. Thus, the header and reel controller 238 can be controlled using georeferenced predictive values present in the predictive crop moisture map to control draper speeds of the draper belts on the header. The preceding example involving feed rate and header control using a functional predictive crop moisture map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using predictive values obtained from a predictive crop moisture map or other type of functional predictive map 263 to control one or more of the controllable subsystems 216." Also see again [Vandike, pgs. 5-6, para 0051], which describes the harvester and in-situ sensors, which collect the characteristics and feed the characteristics to the predictive model, which the control system, and related controllers, use to control the harvester components and subsystems, including the actuators. See also [Vandike, pgs. 15-16, paras 0114-0116], which explain that the control system generates the signals to control the header and other component actuators, the material handling subsystem, the feed rate, etc., using the functional predictive map, "[0114] Control system 214 can generate control signals to control header or other machine actuator(s) 248, such as to control a position or spacing of the deck plates. […]. Control system 214 can generate control signals to control material handling subsystem 125. […]. [0115] In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, header/reel controller 238 controls header or other machine actuators 248 to control a height, tilt, or roll of header 102. In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, feed rate controller 236 controls propulsion subsystem 250 to control a travel speed of agricultural harvester 100. […]. […], the settings controller 232 or another controller 246 controls material handling subsystem 125. […]. […], the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100. […], the draper belt controller 240 controls machine/header actuators 248 to control a draper belt on agricultural harvester 100. […]. [0116] It can thus be seen that the present system takes a prior information map that maps a characteristic such as a vegetative index value, historical crop moisture value, topographic characteristic value, or soil property value to different locations in a field. The present system also uses one or more in-situ sensors that sense in-situ sensor data that is indicative of a characteristic, such as crop moisture, and generates a model that models a relationship between the crop moisture sensed in-situ using the in-situ sensor and the characteristic mapped in the prior information map. Thus, the present system generates a functional predictive map using a model and a prior information map and may configure the generated functional predictive map for consumption by a control system or for presentation to a local or remote operator or other user. For example, the control system may use the map to control one or more systems of a combine harvester."
Vandike does not explicitly disclose: [… processing …] by performing a local difference computation based on a first mathematical model comprising a measured distribution function with a variable representing a horizontal distance of the feeder housing with origin coordinates located in middle of an opening of the feeder housing, or performing a total difference computation over a width of the feeder based on a second mathematical model comprising an integration of an absolute difference value; […] directing the at least one control signal […] that automatically executes a targeted control of the harvesting parameter […] of the header based on a result of the processing that minimizes at least one of the local difference computation or the total difference computation. However, see [Vandike, pgs. 11-13, paras 0083-0087, 0089-0090, 0093], which does broadly describe a method of using a predictive model, prior data, and learning criteria to estimate control or targets, but does not explicitly describe using the density and thickness, “[0083] In some examples, at block 316, agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other triggered learning. [0084] […]. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors 208. […]. [0085] In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map 258) are within a selected range or is less than a defined amount or is below a threshold value, then a new predictive model is not generated by the predictive model generator 210. […]. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 210 generates a new predictive model […]. […]. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways. [0086] Other learning trigger criteria can also be used. For instance, if predictive model generator 210 switches to a different prior information map […] the different prior information map may trigger relearning by predictive model generator […], or other items. In another example, transitioning of agricultural harvester 100 to a different topography or to a different control zone may be used as learning trigger criteria as well. [0087] […]. [0089] In other examples, relearning may be performed periodically or intermittently […]. [0090] […]. [0093] […]. Particularly, FIG. 4 shows, among other things, examples of the predictive model generator 210 and the predictive map generator 212 in more detail. […]. As shown, the predictive model generator 210 receives one or more of a vegetative index map 332, a historical crop moisture map 333, a topographic map 341, or a soil property map 343, or a prior operation map 400 as a prior information map. Historical crop moisture map 333 includes historical crop moisture values 335 indicative of crop moisture values across the field during a past harvest. Historical crop moisture map 333 also includes contextual data 337 that is indicative of the context or conditions that may have influenced the crop moisture value for the past year(s).”
However, Wilken teaches: [… processing …] by performing a local difference computation based on a first mathematical model comprising a measured distribution function with a variable representing a horizontal distance of the feeder housing with origin coordinates located in middle of an opening of the feeder housing, or performing a total difference computation over a width of the feeder based on a second mathematical model comprising an integration of an absolute difference value; […] directing the at least one control signal […] that automatically executes a targeted control of the harvesting parameter […] of the header based on a result of the processing that minimizes at least one of the local difference computation or the total difference computation. See [Wilken, pgs. 1-2, paras 0011-0012], which explain that a functional model is stored for the harvester that maps a functional relationship between harvesting parameters and a header parameter to enable autonomous determination of the header parameter, “[0011] In an embodiment, a functional system model for at least one part of the harvesting machine is stored in the memory of the driver assistance system, which system model forms the basis for the autonomous determination of the at least one header parameter. The term “functional system model” means that at least a portion of the functional relationships within the harvesting machine are depicted by the system model. Examples of this are provided further below. [0012] In another embodiment, the computing unit aligns the functional system model with the current harvesting-process state during the on-going harvesting operation. The consideration here is that of aligning the functional system model forming the basis for the autonomous determination of the header parameters with the actual conditions. Preferably, the header parameters are determined cyclically during the harvesting operation.” See also [Wilken, pg. 4, paras 0042-0045], which explains that the system uses a recursive method of comparing conditions, or harvesting processing parameters, with a functional system model to determine the function or parameter of the header and downstream components,"[0042] The aforementioned header parameters influence not only the function of the header 2 in the narrower sense, but also the function of the downstream working units, i.e., in this case, the function of the threshing unit 9a, the separation system 9b, and the cleaning system 9c. A spreader system 19 for spreading the material other than grain on the field may also need to be taken into consideration, which system can also be influenced by the header parameters of the header 2. Exemplary relationships are explained further below. [0043] In this case and preferably, a functional system model Sb for at least one part of the harvesting machine 1 is stored in the memory 5 of the driver assistance system 4, wherein the computing unit 6 carries out the aforementioned, autonomous determination of the at least one header parameter 2aj" on the basis of the system model Sb. [0044] The functional system model Sb is a computational model for depicting functional relationships within the harvesting machine 1. Examples of such functional relationships are explained further below. [0045] The functional system model Sb is aligned with the current harvesting-process state by the computing unit 6, preferably during the on-going harvesting operation. This means that the computing unit 6 checks to determine whether the functional relationships depicted in the functional system model Sb match the actual harvesting-process state. If this check reveals deviations, the computing unit 6 implements an appropriate change in the functional system model Sb. In a particularly preferred embodiment, this alignment takes place cyclically, wherein reference is made to the general part of the description with respect to the broad interpretation of the term "cyclically".” See also [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height, "[0048] As explained further above, the term "harvesting process state" includes all information related to the harvesting process. This includes field information such as "crop density," "crop height," "crop moisture," "stalk length" and "laid portion." This further includes […], the harvesting-process parameter "uniformity of transverse distribution of crop stream" as a measure of the uniform distribution of the crop across the width of the feeder 3, the harvesting-process parameter "temporal variation of crop stream" as a measure of the uniform distribution, with respect to time, of the crop along the conveyance direction of the feeder 3, […]. Finally, this also includes header parameters such as "knife height," "cutting angle," "cutter bar table length," "extension angle of the intake auger fingers," "reel position (horizontal)," "reel position (vertical)," "intake auger speed," "reel speed" and "cutting frequency," as well as environmental information such as "ambient temperature" and "ambient humidity." All this information to be incorporated into the computation of the harvesting-process state can be determined in different ways. [0049] In principle, an aforementioned harvesting-process parameter also can be the harvesting-process parameter "material feed height" as a measured of the throughput. With regard to the term "material feed height", it should be noted that this term should be broadly interpreted and includes both the material feed height of the crop stream picked up via the feeder 3, in the narrower sense, as well as the throughput of the crop stream conveyed via the feeder 3. In particular, the term "material feed height" can be replaced by the term "throughput" in the present case." See also [Wilken, pg. 5, paras 0058-0059], which further describes various families of harvest-processing parameters and the relationship between the parameters and the variation of the crop stream (uniformity across the width the feeder). Further, it explains that the system improves the crop stream so that the crop is more uniformly the distributed across the width of the feeder, which improves the other parameters, such as loss, "[0058] FIG. 3 shows the family of characteristics A for the functional relationship between the output variable "separation losses" and the input variables "cutterbar table length" and "extension angle of the intake auger fingers." As indicated, the separation losses are that much lower, at least in the first approximation, the greater the cutterbar table length is. It also is clear from the family of characteristics A that the separation losses are that much lower, the greater the extension angle of the intake auger fingers 17 is, i.e., the later the intake auger fingers are extended. These relationships make it apparent that the separation losses are that much lower in practical application, the smaller the temporal variation of the crop stream is. This temporal variation can be reduced by increasing the cutterbar table length and by increasing the extension angle for the intake auger fingers 17. [0059] FIG. 4 shows the family of characteristics B for the functional relationship between the output variable "cleaning losses" and the input variables "cutterbar table length" and "extension angle of the intake auger fingers." As indicated, the cleaning losses are that much lower, at least in the first approximation, the smaller the cutterbar table length is and the smaller the extension angle of the intake auger fingers 17 is. This is due to the fact that, in practical application, the cleaning losses are that much lower, the more uniformly the crop is distributed across the width of the feeder 3. Such a uniform distribution is achieved by a great cutterbar table length and a small extension angle of the intake auger fingers 17, i.e., an earlier extension of the intake auger fingers 17.” Finally see [Wilken, pg. 5, paras 0060-0062], which explains that the computing unit of the system uses the families of characteristics to determine the harvesting-process parameters. Further, since the system model is based in a functional relationship, it selects the family based on the current harvesting-process state, which estimates the parameters to control the header, "[0060] In principle, it can be provided that the computing unit 6 always uses one and the same family of characteristics A, B, possibly with a modification based on the aforementioned alignment, as the basis for the determination of the at least one harvesting-process parameter. Preferably, the computing unit 6 selects at least one family of characteristics A, B depending on the current harvesting-process state and uses this as the basis for the determination of the at least one header parameter. It is therefore possible to react to a change in harvesting-process states, for example, to a change in crop moisture, or the like, for example, by way of a suitable selection of the family of characteristics A, B."
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to automatically control a harvester header or feeder parameter using a local or total difference computation based on a model. Doing so allows using a functional relationship, which permits an operator, or system, to make one selection that impacts the processing strategy and header parameters and further simplifies the complexity of modifying a functional relationship between properties and parameters [Wilken, pg. 1, para 0011]. Additionally, doing so ensures a functional relationship of the on-going operations [Wilken, pg. 4, paras 0042-0045] and provides an opportunity to autonomously determine the parameters, which comprehensively regulates all of the relevant parameters in one system without driver intervention or requiring operator expertise [Wilken, pg. 3, para 0031]. Further, there is a direct relationship between the material feed height, throughput, and the crop stream and the distribution uniformity [Wilken, pg. 4, paras 0049-0050], which contributes to maintaining the relationship of the harvesting parameters and crop losses [Wilken, pg. 5, paras 0058-0062].
Examiner’s Note: The “uniformity of transverse distribution of crop stream as a measure of the uniform distribution of the crop across the width of the feeder” is being read as the “total difference computation,” [Wilken, pg. 4, paras 0042-0045 and 0048-0050], where the system model includes a computation or function that determines a measure of uniformity, such as, a comparison, or difference, between a measured distribution of a crop stream and a target distribution of a crop stream (i.e. a uniform distribution), and [Wilken, pg. 5, para 0055], adjusts the heading parameters to achieve optimization of parameter, including “uniformity of transverse distribution of crop stream.”
Regarding Claim 2, Vandike discloses the limitations of Claim 1.
Vandike further discloses: (Currently Amended) […] wherein the processing [[step]]comprises comparing, in the central control [[unit]], the at least one corresponding crop property signal with an upper threshold or a lower threshold for the at least one crop property. See [Vandike, pg. 12, para 0085], which explains that the measurements from the in-situ sensors are compared to previous values to identify if they are within a specified range or less than threshold value, "In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map 258) are within a selected range or is less than a defined amount or is below a threshold value, then a new predictive model is not generated by the predictive model generator 210. […]. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 210 generates a new predictive model using all or a portion of the newly received in-situ sensor data that the predictive map generator 212 uses to generate a new predictive map 264. […]. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways."
Regarding Claim 3, Vandike discloses the limitations of Claim 1.
Vandike further discloses: (Currently Amended) […] further comprising displaying, by the central control [[unit]], an indication of the at least one property to an operator. See [Vandike, pg. 9, para 0069], which explains the control system and operator interface controller can display the map and information, such as moisture, to the operator, "Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218. The operator interface controller 231 is also operable to present the predictive map 264 or predictive control zone map 265 or other information derived from or based on the predictive map 264, predictive control zone map 265, or both to operator 260. […]. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 for the operator 260. Controller 231 may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a crop moisture value displayed on the map based on the operator's observation."
Regarding Claim 4, Vandike discloses the limitations of Claim 1.
Vandike further discloses: (Currently Amended) […] wherein the at least one crop property is selected from at least one of: […] density or density distribution of the crop layer across the cross-section of the feeder, [[and]] or humidity of the crop. See again [Vandike, pgs. 4-5, para 0046], which explains the crop properties include crop or grain moisture, biomass and feed rate, and material distribution, "[0046] Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: […]; a material other than grain (MOG) moisture sensor that senses a moisture level of the MOG passing through agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; […]; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. […]. Characteristics of grain may also be sensed. Characteristics of grain may include, without limitations, grain moisture, grain size, and grain test weight. […]. The crop property sensors may also sense the feed rate of biomass through feeder house 106, through the separator 116 or elsewhere in agricultural harvester 100. The crop property sensors may also sense the feed rate as a mass flow rate of grain through elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables. Crop property sensors can include one or more crop moisture sensors that sense moisture of crops being harvested by agricultural harvester. An internal material distribution sensor may sense material distribution internal to agricultural harvester 100." See also [Vandike, pg. 5, para 0049], which describes controlling the harvester using the georeferenced signals and signals generated by the in-situ sensors for characteristics including crop or grain moisture and crop density, "The description of FIGS. 2 and 3 describe receiving a general type of prior information map and combining information from the prior information map with a georeferenced sensor signal generated by an in-situ sensor, where the sensor signal is indicative of a characteristic in the field, such as characteristics of crop or weeds present in the field. Characteristics of the field may include, but are not limited to, characteristics of a field […]; characteristics of crop properties such as crop height, crop moisture, crop density, crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight; […]. A relationship between the characteristic values obtained from in-situ sensor signals and the prior information map values is identified, and that relationship is used to generate a new functional predictive map. A functional predictive map predicts values at different geographic locations in a field, and one or more of those values may be used for controlling a machine, such as one or more subsystems of an agricultural harvester."
Vandike does not disclose thickness or thickness distribution of a crop layer across a cross-section of the feeder.
However, Wilken teaches: thickness or thickness distribution of a crop layer across a cross-section of the feeder. See [Wilken, pg. 3, para 0032], which explains that the header parameters are controlled using a harvesting-process strategy, "The determination of the header parameters is an autonomous determination to the extent that, in principle, the harvesting-process strategy Sa is implemented by the computing unit 6 without the need for intervention by the driver 7 or for a query to the driver during the determination of the header parameters in the narrower sense. […]. In this case, the stored harvesting-process strategies Sa differ in terms of the objective of setting or optimizing harvesting-process parameters, which will be explained further below.” Also see again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height.
As stated in MPEP § 2143(I)(A), it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to incorporate the thickness, or thickness distribution, measurement of Wilken with the density measurements of Vandike. Doing so would be technically feasible, with no inventive effort, because thickness, or thickness distribution is another measure of the crop stream intake and throughput [Wilken, pg. 4, para 0049]. Furthermore, the resulting combination of measurements would yield predictable results, where the measurements of Wilken and the measurements of Vandike would be expected to work as intended, with each measurement in the combined strategy performing the same.
Regarding Claim 5, Vandike as modified discloses the limitations of Claim 4.
Vandike further discloses: (Currently Amended) […] wherein the processing further comprises See again [Vandike, pgs. 4-5, para 0046], which explains the crop properties include crop or grain moisture, biomass and feed rate, and material distribution and [Vandike, pg. 5, para 0049], which describes controlling the harvester using the georeferenced signals and signals generated by the in-situ sensors for characteristics including crop or grain moisture, crop density. See also [Vandike, pg. 11, para 0078], which explains that control zones are used on the map by correlating geographic locations and control parameters, "[…] the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on predictive map 264" and [Vandike, pg. 12, para 0085], which explains that the predictive maps are update by comparison of the in-situ sensor data and the prior information, "In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map 258) are within a selected range or is less than a defined amount or is below a threshold value, then a new predictive model is not generated by the predictive model generator 210. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 210 generates a new predictive model using all or a portion of the newly received in-situ sensor data […]. […]. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways. Finally see [Vandike, pg. 20, paras 0148-0150], which explains that the actuators can be controlled using target settings for each control zone or regime zones, which are generated based on historical values, detected values, or combinations based on comparison of historic crop properties and detected crop properties, "[0148] Target setting identifier component 498 sets a value of the target setting that will be used to control the WMA or set of WMAs in different control zones. […]. [0149] In some examples, where agricultural harvester 100 is to be controlled based on a current or future location of the agricultural harvester 100, multiple target settings may be possible for a WMA at a given position. In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA. […]. For instance, different target settings for controlling the speed of the agricultural harvester be generated based upon, for example, a detected or predicted crop moisture value, a historic crop moisture value, a detected or predicted agricultural characteristic value, a detected or predicted vegetative index value, […], a detected or predicted feed rate value, […], or a combination of these. It will be noted that these are merely example and target settings for various WMAs can be based on various other values or combinations of values. […]. [0150] Therefore, in some examples, regime zone generation system 490 generates regime zones to resolve multiple different competing target settings. Regime zone criteria identification component 522 identifies the criteria that are used to establish regime zones for the selected WMA or set of WMAs on the functional predictive map under analysis. Some criteria that can be used to identify or define regime zones include, for example, crop moisture, agricultural characteristics, topographic characteristics, vegetative index characteristics, […], crop type or crop variety […], […], or crop state […]. These are merely some examples of the criteria that can be used to identify or define regime zones. Just as each WMA or set of WMAs may have a corresponding control zone, different WMAs or sets of WMAs may have a corresponding regime zone."
Vandike does not disclose: comparing the detected thickness […] with a target thickness […], or the detected thickness distribution with a target thickness distribution […].
However, Wilken teaches: comparing the detected thickness […] with a target thickness […], or the detected thickness distribution with a target thickness distribution […]. See again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height. Also see [Wilken, pg. 1, para 0010], which explains that the system has the capability to store parameters in memory, "In an embodiment, the invention provides a harvesting machine wherein the header, together with the driver assistance system, forms an automated header. This means that the driver assistance system, with a memory for storing data and with a computing unit, is designed to autonomously determine individual machine parameters of the header and to assign the individual machine parameters to the header. Such machine parameters are referred to herein as "header parameters." A basis for the determination of the header parameters is a selection, made by the user, of harvesting process strategies that are stored in the memory of the driver assistance system." See [Wilken, pg. 4, paras 0042-0045], which explains the system uses a recursive method of comparing conditions, or harvesting processing parameters, with a functional system model.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to use a comparison of harvesting parameters, such as a detected and a target thickness or thickness distribution. Doing so ensures a functional relationship of the on-going operations [Wilken, pg. 4, paras 0042-0045] and provides an opportunity to autonomously determine the parameters, which comprehensively regulates all of the relevant parameters in one system without driver intervention or requiring operator expertise [Wilken, pg. 3, para 0031]. Further, there is a direct relationship between the material feed height, throughput, and the crop stream and the distribution uniformity [Wilken, pg. 4, paras 0049-0050], which contributes to maintaining the relationship of the harvesting parameters and crop losses [Wilken, pg. 5, paras 0058-0062].
Regarding Claim 6, Vandike as modified discloses the limitations of Claim 5.
Vandike does not disclose: (Currently Amended) […] wherein the target thickness distribution or the target density distribution is constant over [[a]] the width of the feeder and proportional to a crop intake.
However, Wilken teaches: wherein the target thickness distribution or the target density distribution is constant over [[a]] the width of the feeder and proportional to a crop intake. See [Wilken, pg. 1, para 0007], which explains similar driver assistance systems that measure and control the crop stream to be constant over the header, "In a known agricultural harvesting machine (DE 10 2008 032 191 Al), a driver assistance system is provided for controlling, inter alia, the header. The driver assistance system ensures that the crop stream is steady by determining different header parameters on the basis of the data from a crop stream sensor. This optimization is therefore directed only to the relationships prevailing at the header itself. A similarly fixedly configured optimization is disclosed in WO 2014/093814 Al, which relates to a forage harvester." See again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to have a constant distribution over the width of the feeder, proportional to intake. Doing so, through monitoring and controlling parameters for uniformity of the crop stream [Wilken, pg. 4, paras 0048-0049] to keep the crop stream consistent, optimizes the control of the harvester [Wilken, pg. 1, para 0007]. Further, harvesting parameters, or inputs, can be used to optimize and prioritize multiple objectives, or parameters, for controlling the harvester [Wilken, pg. 5, para 0055] which is important for downstream processing of the crop and minimizing losses [Wilken, pg. 1, paras 0005-0006].
Regarding Claim 7, Vandike as modified discloses the limitations of Claim 4.
Vandike does not disclose: (Currently Amended) […] further comprising: the width of the feeder based on the detected thickness distribution or the detected density distribution over a width of the header.
However, Wilken teaches: (Currently Amended) […] further comprising: the width of the feeder based on the detected thickness distribution or the detected density distribution over a width of the header. See again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height. Also see again [Wilken, pg. 5, paras 0058-0059], which further describes various families of harvest-processing parameters and the relationship between the parameters and the variation of the crop stream (uniformity across the width the feeder). Further, it explains that the system improves the crop stream so that the crop is more uniformly the distributed across the width of the feeder, which improves the other parameters, such as loss. Finally see again [Wilken, pg. 5, para 0060], which explains that the computing unit of the system uses the families of characteristics to determine the harvesting-process parameters. Further, since the system model is based in a functional relationship, it selects the family based on the current harvesting-process state, which estimates the parameters to control the header.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to estimate a target state, or distribution, over the width of the feeder using the current distribution. Doing so ensures a functional relationship of the on-going operations [Wilken, pg. 4, paras 0042-0045] and provides an opportunity to autonomously determine the parameters, which comprehensively regulates all of the relevant parameters in one system without driver intervention or requiring operator expertise [Wilken, pg. 3, para 0031]. Further, there is a direct relationship between the material feed height, throughput, and the crop stream and the distribution uniformity [Wilken, pg. 4, paras 0049-0050], which contributes to maintaining the relationship of the harvesting parameters and crop losses [Wilken, pg. 5, paras 0058-0062].
Regarding Claim 8, Vandike as modified discloses the limitations of Claim 7.
Vandike does not explicitly disclose: (Currently Amended) […] wherein the target distribution of the thickness or the target distribution of the density of the crop layer over the width of the feeder is estimated using [[a]] the first mathematical model, the second mathematical model, or an experimental model of a flow of the crop in the header to the feeder. However, see [Vandike, pgs. 11-13, paras 0083-0087, 0089-0090, 0093], which does broadly describe a method of using a predictive model, prior data, and learning criteria to estimate control or targets, but does not explicitly describe using the density and thickness.
However, Wilken teaches: (Currently Amended) […] wherein the target distribution of the thickness or the target distribution of the density of the crop layer over the width of the feeder is estimated using [[a]] the first mathematical model, the second mathematical model, or an experimental model of a flow of the crop in the header to the feeder. See again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height. See [Wilken, pg. 5, paras 0058-0059], which further describes various families of harvest-processing parameters and the relationship between the parameters and the variation of the crop stream (uniformity across the width the feeder). Further, it explains that the system improves the crop stream so that the crop is more uniformly the distributed across the width of the feeder, which improves the other parameters, such as loss. Also see [Wilken, pg. 5, paras 0060-0062], which explains that the computing unit of the system uses the families of characteristics to determine the harvesting-process parameters. Further, since the system model is based in a functional relationship, it selects the family based on the current harvesting-process state, which estimates the parameters to control the header.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to use a model of the flow in the header to estimate target parameters, specifically density and thickness distributions. Doing so allows using a functional relationship, which permits an operator, or system, to make one selection that impacts the processing strategy and header parameters and further simplifies the complexity of modifying a functional relationship between properties and parameters [Wilken, pg. 1, para 0011].
Regarding Claim 9, Vandike as modified discloses the limitations of Claim 7.
Vandike does not explicitly disclose: (Original) […] wherein the target distribution of the thickness or the target distribution of the density of the crop layer over the width of the feeder is estimated based on data of previously measured distributions of thickness or density of the crop layer over the width of the feeder. However, see [Vandike, pgs. 11-13, paras 0083-0087, 0089-0090, 0093], which does broadly describe a method of using a predictive model, prior data, and learning criteria to estimate control or targets, but does not explicitly describe using the density and thickness.
However, Wilken teaches: (Original) […] wherein the target distribution of the thickness or the target distribution of the density of the crop layer over the width of the feeder is estimated based on data of previously measured distributions of thickness or density of the crop layer over the width of the feeder. See [Wilken, pg. 5, paras 0061-0062], which explains that the current harvesting-process state allows the computing unit to select an initial model of parameters from the family characteristics stored in memory. Further the system can adapt the family characteristics, "[0061] The above-described alignment of the system model 6b with the current harvesting-process state is preferably carried out, in the case of the system model Sb having at least one family of characteristics A, B, in that the computing unit 6 aligns the at least one family of characteristics A, B with the harvesting-process state during the on-going harvesting operation, in particular cyclically. On the basis of the initial model Sc, at least one initial family of characteristics is stored in the memory 5 as a starting value, wherein, in the first determination of the at least one header parameter, the computing unit 6 therefore carries out the determination of the at least one header parameter on the basis of the initial family of characteristics Sc. A series of real sensor measured values is plotted for the particular harvesting-process state in each of the FIGS. 3 to 4. [0062] In the aformentioned alignment, the computing unit 6 implements a change in the particular family of characteristics a, b in order to move the family of characteristics A, B closer to the real sensor measured values. For example, the entire family of characteristics A, B can be shifted in the direction of the particular output variable, which is upward or downward in FIGS. 3 to 4. It is particularly advantageous, however, when the shift of the family of characteristics A, B is achieved in such a way that it also induces a change in the curves of the particular characteristics."
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to use a prior data to estimate target parameters, specifically density and thickness distributions. Doing so allows the system to compare the real sensor data and converge the stored data with the potential real-time processing data [Wilken, pg. 5, para 0062].
Regarding Claim 10, Vandike as modified discloses the limitations of Claim 4.
Vandike does not explicitly disclose: (Currently Amended) […] wherein the further comprises comparing the detected thickness distribution or the detected density distribution of the crop layer across [[a]] the width of the feeder with a target distribution. However, see [Vandike, pgs. 11-13, paras 0083-0087, 0089-0090, 0093], which does broadly describe a method of using a predictive model, prior data, and learning criteria to estimate control or targets, but does not explicitly describe using the density and thickness.
However, Wilken teaches: (Currently Amended) […] wherein the further comprises comparing the detected thickness distribution or the detected density distribution of the crop layer across [[a]] the width of the feeder with a target distribution. See again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height and [Wilken, pg. 1, para 0010], which explains that the system has the capability to store parameters in memory. See [Wilken, pg. 4, paras 0042-0045], which explains the system uses a recursive method of comparing conditions, or harvesting processing parameters, with a functional system model.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to use a comparison of harvesting parameters, such as a detected and a target thickness or thickness distribution, over the width of the feeder. Doing so ensures a functional relationship of the on-going operations [Wilken, pg. 4, paras 0042-0045] and provides an opportunity to autonomously determine the parameters, which comprehensively regulates all of the relevant parameters in one system without driver intervention or requiring operator expertise [Wilken, pg. 3, para 0031]. Further, there is a direct relationship between the material feed height, throughput, and the crop stream and the distribution uniformity [Wilken, pg. 4, paras 0049-0050], which contributes to maintaining the relationship of the harvesting parameters and crop losses [Wilken, pg. 5, paras 0058-0062].
Regarding Claim 11, Vandike discloses the limitations of Claim 1.
Vandike further discloses: (Currently Amended) […] wherein the comprises: adjusting an auger speed or a belt speed relating to movement of the crop on the header transverse to a driving direction of the combine harvester; adjusting a clearance of a stripper plate; adjusting a position of a crop guiding plate; adjusting a feeder opening; or adjusting a belt speed relating to a longitudinal movement of the crop from the header into the feeder. See again [Vandike, pgs. 15-16, paras 0114-0115], which describe the various control signals and subsystems including the deck plates, the header and reel, and the draper belts, "[0114] Control system 214 can generate control signals to control header or other machine actuator(s) 248, such as to control a position or spacing of the deck plates. […]. [0115] In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, header/reel controller 238 controls header or other machine actuators 248 to control a height, tilt, or roll of header 102. In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, feed rate controller 236 controls propulsion subsystem 250 to control a travel speed of agricultural harvester 100. […]. […], the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100. […], the draper belt controller 240 controls machine/header actuators 248 to control a draper belt on agricultural harvester 100. […]." See again [Vandike, pg. 9, para 0069], which further explains adjusting the deck plates, the header and reel, and the draper belts, “[…]. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, […], header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control, and other actuators 248 that affect the other functions of the agricultural harvester 100. […]. Feed rate controller 236 can control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control a feed rate based upon the predictive map 264 or predictive control zone map 265 or both. For instance, as agricultural harvester 100 approaches an area having a crop moisture above a selected threshold, feed rate controller 236 may reduce the speed of agricultural harvester 100 to maintain constant feed rate of grain or biomass through the machine. Header and reel controller 238 can generate control signals to control a header or a reel or other header functionality. Draper belt controller 240 can generate control signals to control a draper belt or other draper functionality based upon the predictive map 264, predictive control zone map 265, or both. Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header based on predictive map 264 or predictive control zone map 265 or both, […]. […]. Other controllers included on the agricultural harvester 100 can control other subsystems based on the predictive map 264 or predictive control zone map 265 or both as well.”
Regarding Claim 12, Vandike discloses the limitations of Claim 11.
Vandike further discloses: (Currently Amended) […] wherein any of the the right side or on [[a]] the left side of the header. See again [Vandike, pg. 3, para 0038], which describes the various subsystems of the harvester, including left and right variants and [Vandike, pg. 11, para 0081], which provides another example of left and right control of the header using the draper belts.
Regarding Claim 13, Vandike discloses the limitations of Claim 1.
Vandike further discloses: (Currently Amended) […] further comprising before the central control [[unit]], a crop intake distribution over a width of the header based on: data collected by a forward looking sensor, a position of the combine harvester and satellite info, or geographic information about a current harvested area and a non-harvested area. See [Vandike, pg. 19, para 0142], which explains that the control system uses the predictive maps and the geographic position sensor to generate control signals, "At block 478, control system 214 then generates control signals to control the controllable subsystems based upon the one or more functional predictive maps 427 and 440 (or the functional predictive maps 427 and 440 having control zones) as well as an input from the geographic position sensor 204." Also see [Vandike, pg. 4, para 0043-0044], which explains that the harvester collects data from a forward looking camera, the ground speed sensor, and the positioning system, "[0043] FIG. 1 also shows that, in one example, agricultural harvester 100 includes ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward looking image capture mechanism 151, which may be in the form of a stereo or mono camera, and one or more loss sensors 152 provided in the cleaning subsystem 118. [0044] Ground speed sensor 146 senses the travel speed of agricultural harvester 100 over the ground. Ground speed sensor 146 may sense the travel speed of the agricultural harvester 100 by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axel, or other components. In some instances, the travel speed may be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of travel speed,” and [Vandike, pg. 6, para 0054], which further describes the geographic position sensor which is used to sense position using satellite info, "Geographic position sensor 204 illustratively senses or detects the geographic position or location of agricultural harvester 100. Geographic position sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensor 204 can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors." Also see [Vandike, pg. 10, para 0073],which explains that the sensor data collected for the crop and field characteristics is georeferenced, "Upon commencement of a harvesting operation, in-situ sensors 208 generate sensor signals indicative of one or more in-situ data values indicative of a plant characteristic, such as crop moisture, as indicated by block 288. […]. In some examples, data from on-board sensors is georeferenced using position heading or speed data from geographic position sensor 204.” Finally see [Vandike, pgs. 12-13, paras 0093-0096], which explains that the data used for the predictive model generator includes data from the vegetative index map, topographic map, prior information maps, and geographic data, for example georeferenced crop moisture levels as measured across the width of the header,"[0093] […]. As shown, the predictive model generator 210 receives one or more of a vegetative index map 332, a historical crop moisture map 333, a topographic map 341, or a soil property map 343, or a prior operation map 400 as a prior information map. […]. Historical crop moisture map 333 also includes contextual data 337 that is indicative of the context or conditions that may have influenced the crop moisture value for the past year(s). For example, contextual data 337 can include soil properties, such as soil type, soil moisture, soil cover, or soil structure, topographic characteristics, such as elevation or slope, plant date, harvest date, fertilizer application, seed type (hybrids, etc.), […]. […]. [0094] Besides receiving one or more of a vegetative index map 332, a historical crop moisture map 333, a topographic map 341, or a soil property map 343, or a prior operation map 400 as a prior information map, predictive model generator 210 also receives a geographic location 334, or an indication of a geographic location, from geographic position sensor 204. […]. [0095] […]. Processing system 338 processes one or more sensor signals generated by the crop moisture sensor 336 to generate processed sensor data identifying one or more crop moisture values. Processing system 338 can also geolocate the values received from the in-situ sensor 208. […]. [0096] Processing system 338 allocates or apportions an aggregate crop moisture detected by a crop moisture sensor during each time or measurement interval back to earlier geo-referenced regions based upon the travel times of the crop from different portions of the agricultural harvester, such as different lateral locations along a width of a header of the agricultural harvester and the ground speed of the harvester. For example, processing system 338 allocates a measured aggregate crop moisture from a measurement interval or time back to geo-referenced regions that were traversed by a header of the agricultural harvester during different measurement intervals or times. The processing system 338 apportions or allocates the aggregate crop moisture from a particular measurement interval or time to previously traversed geo-referenced regions which are part of the chevron shape area,” and [Vandike, pg. 14, para 0102], which further explains use the topographic data, crop moisture, and geographic location to influence the predictive map for control of the harvester, "Topographic characteristic-to-crop moisture model generator 346 identifies a relationship between in-situ crop moisture data 340 at a geographic location corresponding to where in-situ crop moisture data 340 was geolocated and topographic characteristic values from the topographic map 341 corresponding to the same location in the field where in-situ crop moisture data 340 was geolocated. Based on this relationship established by topographic characteristic-to crop moisture model generator 346, topographic characteristic-to-crop moisture model generator 346 generates a predictive crop moisture model. The predictive crop moisture model is used by predictive map generator 212 to predict a crop moisture at different locations in the field based upon the georeferenced topographic characteristic value contained in the topographic map 341 at the same locations in the field."
Regarding Claim 16, Vandike discloses: (Currently Amended) A combine harvester comprising a headercentral control wherein the combine harvester comprises a feeder, a processing device downstream of the feeder, at least one sensor that detects at least one crop property, and the at least one actuator. See again [Vandike, FIG. 1 and pg. 1, para 0037], which describes the harvester, including the header, the feeder, the thresher, and actuators for moving the header and [Vandike, FIGs. 3, 5, 7, 9, 12 and pg. 32, paras 0256-0276], which show the flow diagrams and describe examples of the methods for controller the harvester. See again [Vandike, pgs. 4-5, para 0046], which explains that the harvester includes sensors for measuring various harvester and crop properties. Finally see [Vandike, pg. 6, para 0051], which describes the control system and various controllers for controlling the actuators, “Control system 214 includes communication system controller 229, […], feed rate controller 236, header and reel controller 238, draper belt controller 240, deck plate position controller 242, […], zone controller 247, and control system 214 can include other items 246. Controllable subsystems 216 include machine and header actuators 248, propulsion subsystem 250, […], and controllable subsystems 216 can include a wide variety of other subsystems 256. For instance, control system 214 can generate one or more control signals to control material handling subsystem 125 to control or compensate for the internal material distribution within agricultural harvester 100 based on the received functional predictive map (with or without control zones).”
Vandike further discloses: [a central control that performs detecting at least one crop property in [[the]] a feeder with a feeder housing by [[the]] at least one sensor and outputting at least one corresponding crop property signal. See again [Vandike, pgs. 4-5, para 0046], which explains that the harvester includes sensors for measuring various harvester and crop properties. See also [Vandike, pg. 16, paras 0118-0120], which further describe the in-situ sensors which output the characteristics as signals. Finally see [Vandike, pg. 18, para 0132], which further describes the material property sensors for detecting crop characteristics.
Vandike further discloses: receiving the at least one corresponding crop property signal by the central control [[unit]]; processing the at least one corresponding crop property signal in the central control [[unit]]. See again [Vandike, pg. 5, para 0049], which describes crop characteristics generated by the in-situ sensors to create a predictive map used to control the harvester. Also see again [Vandike, pgs. 5-6, para 0051], which describes the harvester and in-situ sensors, which collect the characteristics and feed the characteristics to the predictive model, which the control system, and related controllers, use to control the harvester components.
Vandike further discloses transmitting at least one control signal by the central control [[unit]] to [[the]] at least one actuator on the combine harvester; and directing the at least one control signal to [[in]] the at least one actuator that automatically executes a targeted control of the harvesting parameter on a left side or a right side of the header. See again [Vandike, pg. 3, paras 0037-0038], which describes some details of the harvester structure, including the actuators used to control the components, and various subsystems of the harvester, including left and right variants, and further including [Vandike, pg. 9, para 0069], actuators for the header functionality, speed, and draper functionality, for example. Also see again [Vandike, pg. 11, para 0081], which provides another example of left and right control of the header using the draper belts. Also see again [Vandike, pgs. 5-6, para 0051], which describes the harvester and in-situ sensors, which collect the characteristics and feed the characteristics to the predictive model, which the control system, and related controllers, use to control the harvester components and subsystems, including the actuators. Finally see again [Vandike, pgs. 15-16, paras 0114-0116], which explain that the control system generates the signals to control the header and other component actuators, the material handling subsystem, the feed rate, etc., using the functional predictive map.
Vandike does not disclose: [… processing …] by performing a local difference computation based on a first mathematical model comprising a measured distribution function with a variable representing a horizontal distance of the feeder housing with origin coordinates located in middle of an opening of the feeder housing, or performing a total difference computation over a width of the feeder based on a second mathematical model comprising an integration of an absolute difference value; directing the at least one control signal to [[in]] the at least one actuator that automatically executes a targeted control of the harvesting parameter […] of the header based on a result of the processing that minimizes at least one of the local difference computation or the total difference computation. However, see [Vandike, pgs. 11-13, paras 0083-0087, 0089-0090, 0093], which does broadly describe a method of using a predictive model, prior data, and learning criteria to estimate control or targets, but does not explicitly describe using the density and thickness.
However, Wilken teaches: [… processing …] by performing a local difference computation based on a first mathematical model comprising a measured distribution function with a variable representing a horizontal distance of the feeder housing with origin coordinates located in middle of an opening of the feeder housing, or performing a total difference computation over a width of the feeder based on a second mathematical model comprising an integration of an absolute difference value; directing the at least one control signal to [[in]] the at least one actuator that automatically executes a targeted control of the harvesting parameter […] of the header based on a result of the processing that minimizes at least one of the local difference computation or the total difference computation. See again [Wilken, pgs. 1-2, paras 0011-0012], which explain that a functional model is stored for the harvester that maps a functional relationship between harvesting parameters and a header parameter to enable autonomous determination of the header parameter. Also see again [Wilken, pg. 4, paras 0042-0045], which explains that the system uses a recursive method of comparing conditions, or harvesting processing parameters, with a functional system model to determine the function or parameter of the header and downstream components. Also see again [Wilken, pg. 4, paras 0048-0049], which further explains that harvest processing state, and harvest processing parameters, include crop density, height, a measure of the uniformity of transverse distribution of crop stream across with width of the feeder, temporal variation of crop stream (uniformity across the width the feeder), and material feed height. Also see again [Wilken, pg. 5, paras 0058-0059], which further describes various families of harvest-processing parameters and the relationship between the parameters and the variation of the crop stream (uniformity across the width the feeder). Further, it explains that the system improves the crop stream so that the crop is more uniformly the distributed across the width of the feeder, which improves the other parameters, such as loss. Finally see again [Wilken, pg. 5, paras 0060-0062], which explains that the computing unit of the system uses the families of characteristics to determine the harvesting-process parameters. Further, since the system model is based in a functional relationship, it selects the family based on the current harvesting-process state, which estimates the parameters to control the header.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Wilken to automatically control a harvester header or feeder parameter using a local or total difference computation based on a model. Doing so allows using a functional relationship, which permits an operator, or system, to make one selection that impacts the processing strategy and header parameters and further simplifies the complexity of modifying a functional relationship between properties and parameters [Wilken, pg. 1, para 0011]. Additionally, doing so ensures a functional relationship of the on-going operations [Wilken, pg. 4, paras 0042-0045] and provides an opportunity to autonomously determine the parameters, which comprehensively regulates all of the relevant parameters in one system without driver intervention or requiring operator expertise [Wilken, pg. 3, para 0031]. Further, there is a direct relationship between the material feed height, throughput, and the crop stream and the distribution uniformity [Wilken, pg. 4, paras 0049-0050], which contributes to maintaining the relationship of the harvesting parameters and crop losses [Wilken, pg. 5, paras 0058-0062].
Examiner’s Note: The “uniformity of transverse distribution of crop stream as a measure of the uniform distribution of the crop across the width of the feeder” is being read as the “total difference computation,” [Wilken, pg. 4, paras 0042-0045 and 0048-0050], where the system model includes a computation or function that determines a measure of uniformity, such as, a comparison, or difference, between a measured distribution of a crop stream and a target distribution of a crop stream (i.e. a uniform distribution), and [Wilken, pg. 5, para 0055], adjusts the heading parameters to achieve optimization of parameter, including “uniformity of transverse distribution of crop stream.”
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Vandike in view of Wilken, further in view of Dima et al., PG Pub US-2019/0307070-A1 (herein "Dima").
Regarding Claim 14, Vandike discloses the limitations of Claim 13.
Vandike further discloses: (Currently Amended) […] wherein the forward-looking sensor [[is]] comprises a camera. See [Vandike, pg. 4, para 0043], which explains that the harvester collects data from a forward looking camera, "[0043] FIG. 1 also shows that, in one example, agricultural harvester 100 includes ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward looking image capture mechanism 151, which may be in the form of a stereo or mono camera, and one or more loss sensors 152 provided in the cleaning subsystem 118."
Vandike does not disclose: […] wherein the forward-looking sensor [[is]] comprises] a radar sensor, or a lidar sensor.
However, Dima teaches: […] wherein the forward-looking sensor [[is]] comprises] a radar sensor, or a lidar sensor. See [Dima, pg. 1, para 0006], which explains that other header controlling systems utilize a RADAR or LIDAR sensor, "A number of automations of work parameters of platforms, which had to be controlled in the past manually by the harvesting machine operator, have been proposed. For example, the position of the header over ground and thus the cutting height can be controlled automatically based on sensors in a ground- or crop-contour following manner (U.S. Pat. No. 6,615,570 B2, DE 44 11 646 Al). A further work parameter of a cutting platform of a combine to be controlled is the reel position. It was proposed to sense the position of the top of the crop with an ultrasonic sensor (GB 2 173 309 A) or with a camera (EP 2 681 984 Al) or with a combined RADAR and LIDAR sensor (EP 2 517 549 Al) and to adjust the vertical reel position ( and in EP 2 517 549 Al, also the reel speed and horizontal position) accordingly." Also see [Dima, pg. 6, para 0080], which suggests a laser or radar sensor in place of a camera, "Instead of a monocular or stereo camera, the camera 48 can be any sort of 3D sensor such as a laser, radar or a time-of-flight camera, as mentioned above."
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Dima to use a RADAR or LIDAR sensor. Doing so is a method known to the art [Dima, pg. 1, para 0006] that provides three dimensional capability, in high resolution, and allows comparing the alignment of the component, such as the reel, to a known three dimensional model without needing a marker mounted on the reel, such as with a camera [Dima, pg. 6, paras 0070-0076].
Regarding Claim 15, Vandike discloses the limitations of Claim 1.
Vandike does not disclose: (Currently Amended) […], further comprising: of the at least one control signal in the at least one actuator by detecting at least one actuator property by an actuator sensor and outputting at least one actuator property signal to the central control [[unit]].
However, Dima teaches: (Currently Amended) […], further comprising: of the at least one control signal in the at least one actuator by detecting at least one actuator property by an actuator sensor and outputting at least one actuator property signal to the central control [[unit]]. See [Dima, pg. 1, para 0007], which explains that header controlling systems use a second sensor on the actuator to generate actuator position, which gets fed back to the control system, "The automation mentioned in the preceding paragraph can be classified as an open loop system, in which a nominal value of a work parameter of the header is determined by a first sensor and a control unit determines a nominal parameter for an actuator adapted to influence the work parameter and sends a control signal to the actuator. The control signal is determined based on the difference between the nominal parameter and a feedback value from a second sensor which directly or indirectly provides a signal for the actual work parameter. The control signal is determined in a manner to minimize the difference between the nominal parameter and the feedback value. Such systems (cf. U.S. Pat. No. 6,615,570 B2) require a feedback sensor on board of the header, for example to detect the height of the header over ground or the position of the reel with respect to the header and/or a feedback sensor on board of the harvesting machine, in order to detect the position of the part of the harvesting machine (in case of a combine, the feederhouse) holding the header with respect to the harvesting machine."
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Vandike with Dima to place a second sensor and receive feedback on the actuator position. Doing so allows the system to compare to the nominal value of the work parameter to adjust the control signal to the actuator appropriately [Dima, pg. 1, para 0007] and accurately [Dima, pg. 4, para 0041]. Further, if the actuator sensor is mounted with the other sensors, both sensing systems can work in a common reference system, simplifying the conversion comparison of the nominal and current positions [Dima, pg. 2, para 0014].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN MARIE HARTMANN whose telephone number is (571)272-5309. The examiner can normally be reached M-F 7-5.
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, Kito Robinson can be reached at (571) 270-3921. 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.
/E.M.H./Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
Prosecution Insights