Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-12 and 14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Vandike (US 2022/0110238 A1), in view of Sauder (US 2014/0331631 A1), and in view of Bomleny (US 2022/0132722 A1)
Regarding Claim 1,
Disclosure by Vandike
Vandike discloses:
An agricultural harvesting system
“FIG. 1 is a partial pictorial, partial schematic illustration of a self-propelled agricultural harvester 100. In the illustrated example, agricultural harvester 100 is a combine harvester.” ([0033]); “FIG. 2 is a block diagram of agricultural harvester 100.” ([0038])
Rationale: Vandike discloses an agricultural harvesting system through “agricultural harvester 100,” which is expressly a “combine harvester,” and further discloses the system architecture in FIG. 2.
one or more processors; and
“FIG. 2 is a block diagram of agricultural harvester 100, showing that agricultural harvester 100 includes… processor(s)/server 201…” ([0038]); “It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components…” ([0206])
Rationale: Vandike expressly discloses one or more processors; and through “processor(s)/server 201” and through the statement that the logic may be implemented by “processors.”
memory storing instructions,
“It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components…” ([0206]); “In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently executed by a processor…” ([0206])
Rationale: Vandike expressly discloses memory storing instructions, because the software is “loaded into a memory” and then executed by a processor.
that, when executed by the one or more processors,
“software that is loaded into a memory and is subsequently executed by a processor… perform the functions associated with those systems, components, logic, or interactions.” ([0206])
Rationale: Vandike expressly discloses that, when executed by the one or more processors, by stating that software loaded into memory is “executed by a processor” to perform the disclosed functions.
configure the one or more processors to:
“It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components… [or] software that is loaded into a memory and is subsequently executed by a processor… to perform the functions associated with those systems, components, logic, or interactions.” ([0206])
Rationale: Vandike expressly discloses configure the one or more processors to: because the processor-executed software performs the control-system functions described throughout the reference.
the predictive stalk diameter data indicating predictive diameter values
“The predictive map 264 may then be a predictive vegetation height map that maps predicted vegetation height values to different geographic locations in the field. The predictive map 264 can also be a predictive vegetation density map that maps predicted vegetation density values to different geographic locations in the field.” ([0091])
Rationale: Vandike expressly discloses predictive data “indicating predictive… values” mapped to field locations. While the reference uses vegetation height and vegetation density rather than stalk diameter, it discloses the predictive-map framework for predicted agricultural values. A PHOSITA would have understood the same predictive-map architecture to be applicable to diameter values when supplied with stalk-diameter input data.
and generated prior to the operation at the worksite;
“The prior information map 258 can be selected from a plurality of different possible prior information maps… The prior information map 258 may be based on data collected prior to a current harvesting operation…” ([0069]); “collected data prior to a current harvesting operation” (FIG. 3A)
Rationale: Vandike expressly discloses predictive-map inputs that are generated prior to the operation at the worksite; because the prior information map is based on data collected before the current harvesting operation.
generate a control signal
“generate control signal(s) to control controllable subsystem(s) based on the predictive map…” (FIG. 3A, block 308); “generate control signal(s) to control controllable subsystem(s) based on the predictive biomass map” (FIG. 5, block 396); “Settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based upon predictive map 264, the predictive control zone map 265, or both.” ([0136])
Rationale: Vandike expressly discloses generate a control signal through multiple passages stating that the control system and settings controller generate control signals based on the predictive map.
to control a position of a deck plate
“FIG. 2 is a block diagram of agricultural harvester 100… Deck plate position controller 242…” ([0038]); “Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header…” ([0066])
Rationale: Vandike expressly discloses to control a position of a deck plate through “Deck plate position controller 242” and the accompanying description that it generates control signals to control deck plate position.
of a mobile agricultural harvesting machine
“FIG. 1 is a partial pictorial, partial schematic illustration of a self-propelled agricultural harvester 100.” ([0033]); “Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header…” ([0066])
Rationale: Vandike expressly discloses of a mobile agricultural harvesting machine because the deck plate is part of the header of the self-propelled agricultural harvester 100.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
an in-situ sensor configured to detect, during an operation at a worksite, a value of a stalk diameter at the worksite and generate stalk diameter sensor data indicative of the detected value of the stalk diameter;
determine a confidence level of predictive stalk diameter data,
determine a confidence level of the stalk diameter sensor data; and
select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data;
based on the selected control data.
Disclosure by Sauder
Sauder discloses:
an in-situ sensor
“The stalk measurement system 100 is shown installed on a combine 10 having four row units 90 in FIG. 18. Each stalk sensor 300 is preferably mounted to a row unit 90.” ([0067]-[0068])
Rationale: Sauder expressly discloses an in-situ sensor through “Each stalk sensor 300 is preferably mounted to a row unit 90” on the combine, i.e., a sensor located on the harvesting machine in the field during operation.
configured to detect, during an operation at a worksite, a value of a stalk diameter at the worksite
“At step 2105, the monitor board 250 monitors the positions of each feeler 315 of the stalk sensors 300a,b…” ([0069]); “At step 2135, the monitor board 250 preferably calculates the diameter of the stalk 25.” ([0069])
Rationale: Sauder expressly discloses configured to detect, during an operation at a worksite, a value of a stalk diameter at the worksite because the stalk sensors on the combine operate while harvesting and the monitor board “calculates the diameter of the stalk 25.”
and generate stalk diameter sensor data indicative of the detected value of the stalk diameter;
“At step 2140, the monitor board preferably associates the measured stalk diameter with a position in the field…” ([0070]); “the harvest monitor 200 preferably determines the standard deviation σ of stalk diameters…” ([0086]); “the harvest monitor 200 calculates the standard deviation σ… of stalk diameters and displays the value…” ([0100])
Rationale: Sauder expressly discloses and generate stalk diameter sensor data indicative of the detected value of the stalk diameter; because the measured stalk diameter is associated with field position and further processed statistically, which constitutes sensor data indicative of the detected stalk-diameter value.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate the stalk-diameter sensing of Sauder into the predictive-map-based harvesting control system of Vandike. Vandike already teaches using in-situ sensed agricultural characteristics together with predictive maps to control harvesting settings, including deck plate position, while Sauder teaches obtaining actual stalk-diameter measurements on a combine during harvesting. A PHOSITA would have recognized stalk diameter as a directly relevant physical parameter for deck-plate setting and would have expected predictable benefits in using actual stalk-diameter measurements as an additional control input for deck-plate adjustment, including improved matching of deck-plate position to crop conditions and reduced harvesting loss.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
determine a confidence level of predictive stalk diameter data,
determine a confidence level of the stalk diameter sensor data; and
select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data;
based on the selected control data.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
determine a confidence level of predictive stalk diameter data,
“A mobile agricultural machine receives a topographic map indicative of topographic characteristics of a worksite… A topographic confidence output is generated which is indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data.” (Abstract); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0195]-[0198]); “determine topographic confidence level 532” (FIG. 5)
Rationale: Bomleny expressly discloses determining a confidence level for map-based predictive data. Although the reference is directed to topographic map data rather than stalk-diameter map data, it discloses the confidence-analysis architecture for predictive map data generally. A PHOSITA would have found it obvious to apply the same confidence analysis to predictive stalk diameter data in the combined system because both are predictive georeferenced worksite datasets used for machine control.
determine a confidence level of the stalk diameter sensor data; and
“receive supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time” ([0196]); “based on the topographic map and the supplemental data” ([0197]); “obtain supplemental data for worksite 510… determine topographic confidence level 532” (FIG. 5)
Rationale: Bomleny discloses using later-collected supplemental data in a confidence determination. While it does not use the exact words “confidence level of the stalk diameter sensor data,” it teaches evaluating the reliability of current supplemental sensed data relative to previously obtained predictive data. In the combined system, the stalk-diameter sensor data of Sauder is the supplemental sensed data. A PHOSITA would have found it obvious to assess the reliability of that sensed data as part of the same confidence-based control framework so that the controller can rationally decide which data source to trust for control.
select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data;
“generate a topographic confidence output… based on the topographic map and the supplemental data” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198]); “topographic confidence system 330 can generate an action signal to automatically control a machine… to operate based on the topographic characteristics indicated by prior topographic map 600.” ([0129]); “the machine has entered a different zone” and operation can change “upon exit from one zone… and entrance into another zone…” ([0130])
Rationale: Bomleny discloses confidence-based control in which machine action depends on confidence output derived from map data and later-collected supplemental data. Although the reference does not literally recite the exact phrase “select one of the predictive stalk diameter data or the stalk diameter sensor data,” the disclosed confidence-based control architecture would have rendered such source selection obvious to a PHOSITA. When one data source has higher confidence than the other, using that higher-confidence source as the selected control data is a predictable implementation of the expressly disclosed confidence-output-based control scheme, not an impermissible reconstruction from the claim.
based on the selected control data.
“A topographic confidence output is generated, which is indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data. In some examples , an action signal is generated to control an action based on the topographic confidence output.”(Abstract); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198])
Rationale: Bomleny discloses control being generated based on the confidence-evaluated data output. In the combined system, once the controller selects the higher-confidence stalk-diameter data source, deck-plate control would predictably be performed based on the selected control data.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to incorporate the confidence-analysis and confidence-output-based control logic of Bomleny into the predictive-map and in-situ-stalk-diameter control framework established by Vandike and Sauder. Vandike teaches predictive-map-driven harvester control, including deck-plate control; Sauder teaches obtaining actual stalk-diameter measurements during harvesting; and Bomleny teaches determining a confidence level for predictive map data relative to later-collected supplemental data and generating control actions based on that confidence output. A PHOSITA would have had a clear technical reason to combine these teachings because predictive field data may be stale or less representative of current crop conditions, while real-time sensor data may vary in quality or coverage. Applying a confidence-based selection framework would have predictably improved control robustness by allowing the harvester to rely on the more trustworthy stalk-diameter information source at a given time for deck-plate positioning.
Regarding Claim 2,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 1, which is the basis for Claim 2.
Disclosure by Vandike
Vandike discloses:
wherein the predictive stalk diameter data comprises a predictive stalk diameter map
“the variable sensed by the in-situ sensors 208 may be stalk size. The predictive map 264 may then be a predictive stalk size map” ([0061])
Rationale: wherein the predictive stalk diameter data comprises a predictive stalk diameter map is disclosed because Vandike expressly teaches a predictive map for stalk-size data, which is the same class of plant-dimension data as stalk diameter, and a PHOSITA would have understood stalk diameter to be an obvious dimensional implementation of the disclosed stalk-size predictive map framework.
that maps predictive stalk diameter values
“The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values” ([0061])
Rationale: that maps predictive stalk diameter values is disclosed because the reference expressly teaches a predictive stalk-size map that “maps predicted stalk size values,” and stalk diameter is an obvious species of stalk size for a PHOSITA in this harvesting-control context.
to different geographic location in the worksite
“to different geographic locations in the field.” ([0061]); “A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field” (Abstract)
Rationale: to different geographic location in the worksite is disclosed because Vandike expressly teaches predictive map values tied “to different geographic locations in the field,” which corresponds to different geographic locations in the worksite.
and wherein the instructions, when executed by the one or more processors, configure the one or more processors to
“PROCESSOR(S)/SERVER 201” (FIG. 2); “software that is loaded into a memory and is subsequently executed by a processor” ([0206])
Rationale: and wherein the instructions, when executed by the one or more processors, configure the one or more processors to is disclosed because the reference expressly teaches processor-based execution of stored software to perform the predictive-map control functions.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
determine the confidence level of the predictive stalk diameter map
based on predictive data confidence criteria.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
determine the confidence level of the predictive stalk diameter map
“A topographic confidence output is generated which is indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data.” (Abstract); “generating a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0171]); “DETERMINE TOPOGRAPHIC CONFIDENCE LEVEL 532” (FIG. 5)
Rationale: determine the confidence level of the predictive stalk diameter map is taught because Bomleny expressly teaches determining a confidence level for map-based predictive worksite data. Although the disclosed map is a topographic map, the confidence-analysis mechanism is directed to predictive georeferenced worksite map data generally. A PHOSITA would have found it obvious to apply the same confidence-level determination to the predictive stalk-diameter map of Vandike.
based on predictive data confidence criteria
“based on the topographic map and the supplemental data” (Abstract); “receiving supplemental data indicative of characteristics relative to the worksite , the supplemental data collected after the first time . A topographic confidence output is generated which is indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data ” ([0006]); “topographic confidence system 330 can use any number of models in determining the topographic confidence level” ([0121])
Rationale: based on predictive data confidence criteria is taught because Bomleny expressly bases the confidence determination on identified inputs and evaluative considerations, including “the topographic map,” “the supplemental data,” and “models in determining the topographic confidence level.” Those disclosed bases constitute confidence criteria for assessing predictive map reliability. Applied to the predictive stalk-diameter map of Vandike, a PHOSITA would have used analogous predictive-data confidence criteria to determine the map’s confidence level.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to incorporate the confidence-level determination of Bomleny into the predictive stalk-size/stalk-diameter map control framework of Vandike, as informed by the stalk-diameter sensing teachings of Sauder. Vandike already teaches predictive maps for stalk-related values at different geographic locations in a field, and Sauder reinforces that stalk diameter is a practical in-field plant dimension for combine operation. Bomleny teaches determining a confidence level for predictive map data using identified evaluation inputs. A PHOSITA would have recognized that predictive stalk-diameter map data, like topographic map data, may vary in reliability depending on the age, basis, and corroboration of the map data, and thus would have predictably benefited from confidence-based assessment before using that map in harvesting control.
Regarding Claim 3,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 2, which is the basis for Claim 3.
Disclosure by Vandike
Vandike discloses:
wherein the predictive data confidence criteria includes one or more of:
“The learning trigger criteria can include any of a wide variety of different criteria.” ([0082])
Rationale: wherein the predictive data confidence criteria includes one or more of: is disclosed because Vandike expressly teaches that the system uses multiple identified “criteria” in deciding whether predictive-map-related relearning and updating should occur. In the context of predictive map reliability and use, a PHOSITA would have understood such criteria to be predictive-data confidence criteria.
a number of sensor readings taken by the in-situ sensor;
“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. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors 208 that exceeds a threshold triggers or causes the predictive model generator 210 to generate a new predictive model…” ([0082]); “THRESHOLD AMOUNT OF IN SITU DATA DETECTED” (FIG. 3B, block 318)
Rationale: a number of sensor readings taken by the in-situ sensor; is disclosed because Vandike expressly uses the “amount of in-situ sensor data” from “in-situ sensors 208” as a criterion. A PHOSITA would have understood the disclosed “amount” of sensor data to correspond to a number of sensor readings taken by the in-situ sensor.
a type of data used as a basis for the predictive stalk diameter map;
“The characteristics or data types represented by the mapped values in the prior information map 258 and the in-situ values sensed by the in-situ sensors 208 may be the same characteristics or data type or different characteristics or data types.” ([0072]); “the variable sensed by the in-situ sensors 208 may be stalk size. The predictive map 264 may then be a predictive stalk size map…” ([0061])
Rationale: a type of data used as a basis for the predictive stalk diameter map; is disclosed because Vandike expressly teaches that different “data types” may underlie the predictive map generation, and also expressly teaches a predictive stalk-size map. A PHOSITA would have understood stalk-diameter map generation to be based on the selected type of stalk-related data used as the map basis.
a freshness of data used as a basis for the predictive stalk diameter map.
“The prior information map 258 may be based on data collected prior to a current harvesting operation. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current growing season…” ([0069])
Rationale: a freshness of data used as a basis for the predictive stalk diameter map. is disclosed because Vandike expressly distinguishes prior data collected during “a previous year” from data collected “earlier in the current growing season,” which directly corresponds to the freshness of data used as the basis for the predictive map.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
a freshness of data used as a basis for the predictive stalk diameter map.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
a freshness of data used as a basis for the predictive stalk diameter map.
“The control system further obtains supplemental data relative to the field that is gathered in the time between the data for the baseline topographic map was collected and the operation to be performed on the field…” ([0022]); “the topographic map… may not show a new ridge of soil that was created on the field… in a time after the data for the topographic map was collected.” ([0021]); “receive a topographic map of a worksite… wherein the topographic characteristics are based on data collected at a first time; receive supplemental data… collected after the first time” ([0195]-[0197])
Rationale: a freshness of data used as a basis for the predictive stalk diameter map. is taught because Bomleny expressly bases confidence on whether map data were collected at an earlier time and whether later supplemental data indicate that the earlier map may no longer accurately reflect current conditions. A PHOSITA would have recognized this as using the freshness of the predictive-map data as a confidence criterion and would have applied the same principle to predictive stalk-diameter map data in the combined harvesting system.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to use the identified criteria of data quantity, data type, and data freshness in determining confidence for predictive stalk-diameter map data. Vandike teaches predictive-map generation and updating based on threshold amounts of in-situ data, different data types, and prior data collected at different times, while Bomleny teaches that predictive map confidence is determined through a confidence analysis using later-obtained data and evaluative processes directed to whether earlier map data still reliably represent current field conditions. A PHOSITA would have found it technically sensible and predictably beneficial to use these same kinds of criteria when assessing confidence in a predictive stalk-diameter map because the reliability of such a map would naturally depend on how much sensor data supported it, what type of data formed its basis, and how current that underlying data remained relative to present harvesting conditions.
Regarding Claim 4,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 1, which is the basis for Claim 4.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
determine the confidence level of the stalk diameter sensor data
based on sensor data confidence criteria.
Disclosure by Sauder
Sauder discloses:
based on sensor data confidence criteria.
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “Using a statistical function as is known in the art, the harvest monitor preferably determines the standard deviation σ of stalk diameters for the yield block 1812 about the mean μ of the histogram. If the standard deviation σ of stalk diameters in a given yield block exceeds a certain threshold … then the data point 4105 corresponding to the stalk block is preferably filtered out…” ([0086]); “the harvest monitor 200 calculates the standard deviation σ … of stalk diameters and displays the value of σ as the stalk variation” ([0100])
Rationale: based on sensor data confidence criteria is disclosed because Sauder expressly evaluates stalk-diameter sensor-derived data using a “statistical criterion,” “standard deviation σ,” and a “threshold.” A PHOSITA would have understood those disclosed statistical checks to be sensor-data confidence criteria for determining whether the stalk-diameter data are sufficiently reliable to be used or instead filtered out.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate the sensor-data-quality evaluation of Sauder into the predictive harvesting control system of Vandike. Vandike teaches using in-situ agricultural data in machine-control logic, while Sauder teaches evaluating stalk-diameter sensor-derived data using statistical criteria and thresholding. A PHOSITA would have recognized that deck-plate control based on stalk-diameter information would be more robust if poor-quality or highly variable stalk-diameter sensor data were identified using the disclosed statistical criteria before being relied upon in control.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
determine the confidence level of the stalk diameter sensor data
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
determine the confidence level of the stalk diameter sensor data
“A topographic confidence output is generated which is indicative of a confidence level…” (Abstract); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0197]); “a topographic confidence analyzer that determines the topographic confidence level based on the likelihood that the topographic characteristics of the worksite, as indicated by the topographic map, have changed based on the supplemental data” ([0200]-[0201]); “DETERMINE TOPOGRAPHIC CONFIDENCE LEVEL 532” (FIG. 5)
Rationale: determine the confidence level of the stalk diameter sensor data is taught because Bomleny expressly teaches a processor-based confidence-analysis framework in which later-collected sensed data are evaluated and a “confidence level” is determined. Although the reference discusses topographic characteristics rather than stalk diameter, it discloses the confidence-determination architecture itself. When applied to the stalk-diameter sensing of Sauder in the harvesting-control system of Vandike, a PHOSITA would have found it obvious to determine a confidence level for the stalk-diameter sensor data using the disclosed confidence-analysis approach and the statistical criteria taught by Sauder.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to incorporate the confidence-level determination framework of Bomleny into the stalk-diameter sensor-data evaluation of Sauder within the predictive harvesting control system of Vandike. Vandike teaches processor-executed harvesting control based on agricultural data, Sauder teaches evaluating stalk-diameter sensor-derived data using statistical criteria and thresholds, and Bomleny teaches determining a confidence level from sensed data through a confidence analyzer. A PHOSITA would have found it technically sensible and predictably beneficial to express the quality of stalk-diameter sensor data as a confidence level derived from sensor-data confidence criteria so that the controller could more reliably decide how much weight to assign to that sensor data in machine control.
Regarding Claim 5,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 4, which is the basis for Claim 5.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
wherein the sensor data confidence criteria includes one or more of:
a harvest state of the worksite along a travel path of the in-situ sensor;
a variance of stalk diameter across a width of a header of the mobile agricultural harvesting machine;
a sensor error state;
one or more characteristics of weeds of the worksite along the travel path of the in-situ sensor; and
a proximity of the in-situ sensor to a boundary of the worksite.
Disclosure by Sauder
Sauder discloses:
wherein the sensor data confidence criteria includes one or more of:
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “The thresholds Xu and Xo may comprise any of the following: multiples or fractions of the standard deviation σ added to or subtracted from the mean μ, multiples or fractions of the mean L, or constant numerical yield minimums...” ([0093])
Rationale: wherein the sensor data confidence criteria includes one or more of: is disclosed because Sauder expressly uses a “statistical criterion” and multiple threshold-based factors for deciding whether stalk-diameter-derived data should be used or filtered, which a PHOSITA would have understood to be sensor-data confidence criteria.
a harvest state of the worksite along a travel path of the in-situ sensor;
“The harvest monitor 200 preferably determines the ‘actual population by counting the stalks 25 sensed by the stalk sensor 300 of the active row over a predetermined travel distance (e.g., 30 feet)... The stalk count is preferably associated with the predetermined travel distance prior to the current location of the combine. The stalk count is also preferably associated with a region in the field being harvested. It should be appreciated that where the stalk count is used to determine the actual population, the actual population comprises a harvest metric...” ([0089]); “The row details screen 1200 preferably includes an emergence window 1215 that displays the percentage of seeds planted that emerged into harvestable stalks.” ([0090])
Rationale: a harvest state of the worksite along a travel path of the in-situ sensor; is disclosed because Sauder expressly ties stalk-sensor-derived information to a “predetermined travel distance” and to “a region in the field being harvested,” and further characterizes that information as “actual population” and “emergence,” which are harvest-state indicators of the worksite along the travel path of the in-situ sensor.
a variance of stalk diameter across a width of a header of the mobile agricultural harvesting machine;
““The row details screen preferably displays a stalk variation window 1235 that displays the variation in stalk width... the harvest monitor 200 calculates the standard deviation σ ... of stalk diameters and displays the value of σ as the stalk variation...” ([0100]); “for a given row unit 90-m in a combine having N rows, the harvest monitor 200 preferably first averages the stalk yield Ys from the last group of stalks ... for each row unit 90-m of the combine head...” ([0101])
Rationale: a variance of stalk diameter across a width of a header of the mobile agricultural harvesting machine; is disclosed because Sauder expressly teaches multiple stalk sensors on different row units of the combine head and expressly teaches “variation in stalk width” based on stalk diameters. A PHOSITA would have understood that obtaining stalk-diameter measurements from different row units across the combine head/header provides the basis for assessing variance across the width of the header.
a proximity of the in-situ sensor to a boundary of the worksite.
“To determine the field stalk width average, the stalk measurement system associates stalks to the current field (e.g., by comparing the stalks to a field boundary provided by the user in a setup phase)...” ([0092]); “The field boundary selection bar 1914 enables the user to select a field boundary file corresponding to a field to be harvested.” ([0076]); “the harvest monitor 200 in determining the location of each stalk sensor 300.” ([0076])
Rationale: a proximity of the in-situ sensor to a boundary of the worksite. is disclosed because Sauder expressly teaches use of a “field boundary” together with determination of the location of each stalk sensor 300. A PHOSITA would have understood that once the stalk-sensor location and field boundary are both known, proximity of the in-situ sensor to the worksite boundary is a directly derivable and obvious confidence criterion for deciding whether sensor data should be trusted near the field edge.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate the stalk-sensor-data quality and field-context factors of Sauder into the predictive harvesting control framework of Vandike. Vandike teaches processor-based harvester control using predictive and sensed agricultural information, while Sauder teaches evaluating stalk-sensor-derived data using statistical criteria, associating such data with harvested regions and travel distance, using multiple row-unit sensors across the header, and using field-boundary information. A PHOSITA would have recognized that these factors are technically relevant to confidence in stalk-diameter sensor data because stalk measurements can vary with local harvest state, row-to-row variation, and edge-of-field conditions.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
a sensor error state;
one or more characteristics of weeds of the worksite along the travel path of the in-situ sensor; and
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
a sensor error state;
“the machine settings and other operating parameters commanded by the operator (or the control system) can lead to error or other deviation in the performance of the agricultural machines.” ([0018]); “these sensors often have a limited field of view and thus they may not capture and feed information back ... quickly enough to adjust the machine settings...” ([0019]); “these systems may not observe the changes that can occur to the field in a timely or reliable way... vegetation growth on the field may obscure the view of such systems.” ([0020])
Rationale: a sensor error state; is taught because Bomleny expressly teaches conditions in which sensed information is limited, untimely, obscured, or otherwise unreliable, and further ties those conditions to “error or other deviation in the performance.” A PHOSITA would have understood such disclosed unreliable-sensing conditions to define a sensor error state or, at minimum, an obvious sensor-data-confidence criterion indicating degraded sensor reliability.
one or more characteristics of weeds of the worksite along the travel path of the in-situ sensor; and
“vegetation data (e.g., images of the vegetation, crop type, weed type, density, height, vegetation index, vegetation state data, etc.)...” ([0078]); “topographic confidence system 330 can determine a confidence in the topographic characteristics of the worksite or of particular geographic locations within the worksite based on any number of indications provided by supplemental data...” ([0079]); “the route can also be commanded based upon characteristics of the environment in which mobile machine 100 is operating that are sensed or otherwise detected by sensors 310...” ([0068])
Rationale: one or more characteristics of weeds of the worksite along the travel path of the in-situ sensor; and is taught because Bomleny expressly identifies “weed type, density, height, vegetation index, vegetation state data” as worksite data and further teaches that confidence and control can be based on such characteristics at geographic locations of the worksite. A PHOSITA would have found it obvious to use weed characteristics along the travel path of the in-situ sensor as sensor-data confidence criteria because weeds can interfere with or distort sensed crop/stalk measurements during harvesting operations.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to further include sensor-reliability and weed-related environmental criteria in the sensor-data confidence analysis of the combined harvesting system. Vandike teaches predictive harvesting control using sensed agricultural characteristics, Sauder teaches stalk-diameter sensing and statistical filtering of stalk measurements in relation to travel distance, field region, row-unit position, and field boundary, and Bomleny teaches that confidence analysis should account for sensing limitations, environmental characteristics, and vegetation data including weed-related data. A PHOSITA would have had a clear technical reason to combine these teachings because stalk-diameter sensor confidence is predictably affected by local harvest conditions, row-to-row variability, sensor reliability limitations, weed interference, and proximity to field edges, and using those criteria would have improved the robustness of confidence-based deck-plate control.
Regarding Claim 6,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 1, which is the basis for Claim 6.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data
based on a comparison of the confidence level of the predictive stalk diameter data to the confidence level of the stalk diameter sensor data.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data
based on a comparison of the confidence level of the predictive stalk diameter data to the confidence level of the stalk diameter sensor data.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data
“generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data; and” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0197]-[0198]); “based on the topographic confidence output, control system 304 can generate an action signal to control an action of one or more of the various components of computing architecture 300” ([0075]); “In this way, the control of machine 100 as it operates across worksite 602 can also vary depending on which confidence zone 614 it is operating within.” ([0122])
Rationale: select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data is taught because Bomleny expressly discloses a confidence-based control architecture in which machine action is driven by confidence output derived from predictive map data and supplemental data. Although Bomleny does not use the exact words “selected control data,” a PHOSITA would have understood that, in a system having both predictive data and current sensed data, confidence-driven control necessarily entails using the data source associated with the operative confidence determination as the data relied upon for control. In the combined system of Vandike and Sauder, that would have rendered it obvious to select either the predictive stalk diameter data or the stalk diameter sensor data as the selected control data.
based on a comparison of the confidence level of the predictive stalk diameter data to the confidence level of the stalk diameter sensor data.
“receive a topographic map of a worksite that indicates topographic characteristics of the worksite, wherein the topographic characteristics are based on data collected at a first time;” ([0195]); “receive supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time;” ([0196]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0197]); “the topographic confidence level can vary across worksite 602” and “control of machine 100 as it operates across worksite 602 can also vary depending on which confidence zone 614 it is operating within.” ([0122])
Rationale: based on a comparison of the confidence level of the predictive stalk diameter data to the confidence level of the stalk diameter sensor data is taught because Bomleny expressly evaluates confidence in predictive map data using later-collected supplemental data and uses the resulting confidence output to govern control behavior. While the reference does not literally recite two separately named confidence levels and a direct “comparison,” the disclosed framework inherently requires assessing the relative trustworthiness of prior predictive map data against current supplemental sensed data. A PHOSITA would have found it obvious, in implementing this confidence-based decision logic in the combined stalk-diameter control system, to compare the confidence level associated with the predictive stalk diameter data against the confidence level associated with the stalk diameter sensor data and then use the higher-confidence source for control.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to implement a comparison-based confidence selection between predictive stalk diameter data and stalk diameter sensor data in the harvesting control system. Vandike teaches predictive-map-based agricultural machine control, including predictive stalk-size map control concepts and control-signal generation for harvesting subsystems. Sauder teaches obtaining real-time stalk diameter measurements from stalk sensors on the combine during harvesting. Bomleny teaches generating a confidence output from prior map data and later supplemental data, and then controlling the machine based on that confidence output. A PHOSITA would have recognized that when both predictive stalk-diameter information and current stalk-diameter sensor information are available, a predictable and technically sensible implementation of the disclosed confidence-based control framework is to compare the respective confidence levels of those two sources and select the one having greater reliability for deck-plate-related control. This would have improved robustness against stale predictive data and noisy in-situ sensor data without changing the basic operation of the combined system.
Regarding Claim 7,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 1, which is the basis for Claim 7.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
compare both the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data to a confidence level threshold\
and selects one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the comparison.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
compare both the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data to a confidence level threshold
and selects one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the comparison.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
compare both the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data to a confidence level threshold
“the term likely means, in one example, a threshold likelihood or probability that a current topography characteristic deviates by a threshold amount from characteristics indicated by the prior topographic map.” ([0079]); “topographic confidence system 330 can include communication system 306, one or more processors, controllers, or servers 312, topographic confidence analyzer 400… action signal generator 406, threshold logic 408…” ([0081]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0197])
Rationale: Bomleny teaches a confidence-analysis framework that expressly includes a “confidence level,” a “threshold likelihood or probability,” a “threshold amount,” and “threshold logic 408.” Thus, the reference teaches comparing confidence-related information to a threshold. Although the reference is directed to topographic confidence rather than stalk diameter confidence, a PHOSITA would have found it obvious to apply the same threshold-based confidence analysis to both the predictive stalk diameter data and the stalk diameter sensor data in the combined system, because both are competing data sources used for agricultural machine control and both require threshold-based reliability assessment before control reliance.
and selects one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the comparison.
“an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198]); “wherein an operation of the mobile agricultural machine is based on a presence of the mobile agricultural machine in one of the plurality of confidence zones.” ([0181]); “In this way, the control of machine 100 as it operates across worksite 602 can also vary depending on which confidence zone 614 it is operating within.” ([0122]); “topographic confidence system 330 can generate action signals to control the operation of various components…” ([0082])
Rationale: Bomleny teaches that control action is selected and varied based on the outcome of the confidence analysis. While the reference does not recite the exact words “selected control data,” it expressly teaches confidence-output-based control variation and threshold-based confidence evaluation. In the combined system, once the predictive stalk diameter data and the stalk diameter sensor data are each evaluated against a confidence level threshold, a PHOSITA would have found it obvious to select the qualifying or higher-confidence data source as the data source used for control, i.e., to select one of the predictive stalk diameter data or the stalk diameter sensor data as selected control data based on the comparison.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to implement threshold-based comparison of the confidence level of predictive stalk diameter data and the confidence level of stalk diameter sensor data, and to select one of those data sources for control based on that comparison. Vandike teaches predictive-map-based agricultural machine control, including stalk-related predictive maps and control-signal generation for harvester subsystems. Sauder teaches obtaining and statistically filtering real-time stalk diameter data using threshold-based criteria. Bomleny teaches generating a confidence level, applying threshold logic, and varying control actions based on confidence output. A PHOSITA would have recognized that, when two candidate stalk-diameter data sources are available, a predictable and technically sound implementation of these combined teachings is to compare each source’s confidence against a confidence threshold and then use the source that satisfies the threshold or better satisfies the threshold for deck-plate-related control. This would have improved control robustness against stale predictive data and unreliable in-situ measurements while using known threshold-based decision logic in the same technical field.
Regarding Claim 8,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 7, which is the basis for Claim 8.
Disclosure by Vandike
Vandike discloses:
select one of the predictive stalk diameter data or the stalk diameter sensor data based on a preselected preference
“Block 550 indicates an example in which the control zone definition criteria are or include operator preferences.”; “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.” ([0084])
Rationale: select one of the predictive stalk diameter data or the stalk diameter sensor data based on a preselected preference is taught because Vandike expressly discloses that control criteria “include operator preferences,” and further discloses that operative threshold-related settings may be “set by an operator.” A PHOSITA would have understood such operator-established control criteria as a preselected preference for choosing between otherwise acceptable control alternatives in the harvesting control system.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
when both the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data satisfy the confidence level threshold.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
when both the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data satisfy the confidence level threshold.
“threshold logic 408 is configured to compare the various topographic confidence values to a variety of thresholds.” ([0105]); “the threshold may be used in the assignment of representations of the confidence value. For instance, in the example of ‘high, medium, and low’ as representations of the topographic confidence level, a threshold may indicate a range of topographic confidence levels to assign to each representation.” ([0105]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0197])
Rationale: when both the confidence level of the predictive stalk diameter data and the confidence level of the stalk diameter sensor data satisfy the confidence level threshold. is taught because Bomleny expressly discloses comparing confidence values to thresholds and using threshold ranges for confidence categorization. Although the reference is directed to topographic confidence values, a PHOSITA would have found it obvious to apply the same threshold-satisfaction framework to both candidate stalk-diameter data sources in the combined system, such that both the predictive stalk diameter data and the stalk diameter sensor data can each satisfy the confidence level threshold before final source selection is made.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to select one of the predictive stalk diameter data or the stalk diameter sensor data based on a preselected preference when both respective confidence levels satisfy the confidence level threshold. Bomleny teaches threshold-based evaluation of confidence values for control decisions, while Vandike teaches that harvesting-control criteria can include operator preferences. In the combined harvesting system, once both candidate stalk-diameter data sources satisfy the applicable confidence threshold, a PHOSITA would have recognized that a preselected operator preference is a predictable and technically sensible tie-breaking rule for choosing which acceptable source to use for control.
Regarding Claim 9,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 1, which is the basis for Claim 9.
Disclosure by Vandike
Vandike discloses:
obtain an information map
“obtaining a map” ([0254]);
Rationale: obtain an information map is disclosed because Vandike expressly recites “obtaining a map,” which is an information map used by the control methodology.
that includes values of a characteristic
“obtaining a map that includes values of a biomass characteristic” ([0254])
Rationale: that includes values of a characteristic is disclosed because Vandike expressly states that the obtained map “includes values of a biomass characteristic.”
corresponding to different geographic locations in a field;
“obtaining a map that includes values of a biomass characteristic corresponding to different geographic locations in a field” ([0254])
Rationale: corresponding to different geographic locations in a field; is expressly disclosed by the quoted passage reciting values “corresponding to different geographic locations in a field.”
generate, as the predictive stalk diameter data,
“the variable sensed by the in-situ sensors 208 may be stalk size. The predictive map 264 may then be a predictive stalk size map” ([0061])
Rationale: generate, as the predictive stalk diameter data, is disclosed because Vandike expressly teaches generating predictive stalk-related data, namely “a predictive stalk size map.” A PHOSITA would have understood stalk diameter to be an obvious stalk-size characteristic within this disclosed predictive stalk-data framework.
a functional predictive stalk diameter map of the field
“The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field.” ([0061]); “Thus, a functional predictive map 263, as described herein, may or may not include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263.” ([0064])
Rationale: a functional predictive stalk diameter map of the field is disclosed because Vandike expressly teaches a “predictive stalk size map” for the field and further expressly teaches that predictive map 264 is a “functional predictive map 263.” A PHOSITA would have understood a functional predictive stalk-diameter map to be an obvious species of the disclosed functional predictive stalk-size map.
that maps predictive stalk diameter values to the different geographic locations in the worksite,
“The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field.” ([0061])
Rationale: that maps predictive stalk diameter values to the different geographic locations in the worksite, is disclosed because Vandike expressly teaches a predictive stalk-size map that “maps predicted stalk size values to different geographic locations in the field.” A PHOSITA would have understood the claimed stalk diameter values to be an obvious stalk-size implementation, and the claimed worksite corresponds to the disclosed field.
based on the value of the characteristic in the information map at a geographic location
“a predictive agricultural model that models a relationship between the biomass characteristic and the agricultural characteristic based on a value of the biomass characteristic in the map at the geographic location” ([0273]); “generating a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the biomass characteristic in the map” ([0257])
Rationale: based on the value of the characteristic in the information map at a geographic location is disclosed because Vandike expressly teaches use of “a value of the biomass characteristic in the map at the geographic location” and generation of the functional predictive map “based on the values of the biomass characteristic in the map.”
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
and a stalk diameter value detected by the in-situ sensor corresponding to the geographic location.
Disclosure by Sauder
Sauder discloses:
and a stalk diameter value detected by the in-situ sensor corresponding to the geographic location.
“At step 2135, the monitor board 250 preferably calculates the diameter of the stalk 25.” ([0069]); “The stalk count is also preferably associated with a region in the field being harvested.” ([0089]); “the harvest monitor 200 in determining the location of each stalk sensor 300.” ([0076])
Rationale: and a stalk diameter value detected by the in-situ sensor corresponding to the geographic location. is disclosed because Sauder expressly teaches that the monitor board “calculates the diameter of the stalk 25,” and further teaches associating sensed stalk data with “a region in the field being harvested,” while also determining the location of each stalk sensor 300. A PHOSITA would have understood these teachings together as providing a stalk diameter value detected by the in-situ sensor corresponding to the geographic location.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to use the stalk-diameter sensing of Sauder as the specific in-situ agricultural characteristic input in the functional predictive map generation framework of Vandike. Vandike teaches obtaining an information map with characteristic values at different geographic locations and generating a functional predictive map based on mapped values and in-situ sensed agricultural characteristics at corresponding locations. Sauder teaches calculating stalk diameter with a stalk sensor and associating sensed stalk information with field region/location. A PHOSITA would have found it technically compatible and predictably beneficial to use stalk diameter as the sensed agricultural characteristic in Vandike so that the resulting predictive map specifically predicts stalk-diameter-related values for harvester control.
Regarding Claim 10,
The combination of Vandike, Sauder, and Bomleny establishes the agricultural harvesting system of Claim 1, which is the basis for Claim 10.
Disclosure by Vandike
Vandike discloses:
wherein, the instructions, when executed by the one or more processors to: configure the one or more processors to:
“PROCESSOR (S)/SERVER 201”; “DECK PLATE POSITION CONTROLLER 242”; “PREDICTIVE MAP GENERATOR 212” (FIG. 2); “One or more information maps are obtained by an agricultural work machine… A predictive map generator generates a predictive map… The predictive map can be output and used in automated machine control.” (Abstract)
Rationale: wherein, the instructions, when executed by the one or more processors to: configure the one or more processors to: is disclosed because Vandike expressly shows processor-based control architecture in FIG. 2, including “PROCESSOR (S)/SERVER 201,” “PREDICTIVE MAP GENERATOR 212,” and “DECK PLATE POSITION CONTROLLER 242,” and further explains that the predictive map is used in automated machine control.
select, as the selected control data, the predictive stalk diameter data;
“The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field.” ([0061]); “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 …” ([0067])
Rationale: select, as the selected control data, the predictive stalk diameter data; is disclosed because Vandike expressly teaches a “predictive stalk size map” and expressly teaches deck plate control “based on predictive map 264.” A PHOSITA would have understood that, when the deck plate position controller uses the predictive map as the basis for control, the predictive stalk-related data are selected as the control data. Stalk diameter is an obvious stalk-size implementation in this harvesting context.
determine a target deck plate spacing
“Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header …” ([0067]); “TARGET SETTING IDENTIFIER COMPONENT 498”; “SETTINGS RESOLVER IDENTIFIER COMPONENT 526” (FIG. 9)
Rationale: determine a target deck plate spacing is disclosed because Vandike expressly teaches a deck plate position controller that controls deck plate position, and further discloses target-setting and settings-resolver components. A PHOSITA would have understood determining a deck plate “position” for control to include determining a target deck plate spacing for the header deck plates.
based on a predictive stalk diameter value,
“The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field.” ([0061])
Rationale: based on a predictive stalk diameter value, is disclosed because Vandike expressly teaches a predictive stalk-size map containing “predicted stalk size values.” A PHOSITA would have understood stalk diameter to be an obvious dimensional stalk-size value for use in determining deck plate spacing.
in the predictive stalk diameter data,
“The predictive map 264 may then be a predictive stalk size map …” ([0061]); “FUNCTIONAL PREDICTIVE MAP 263”; “PREDICTIVE MAP 264” (FIG. 2)
Rationale: in the predictive stalk diameter data, is disclosed because Vandike expressly places the predicted stalk-size values in “predictive map 264,” which is part of the disclosed predictive-map data architecture. A PHOSITA would have understood the claimed predictive stalk diameter data to reside in that disclosed predictive stalk-related map data.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
across a width of a header of the mobile agricultural harvesting machine.
Disclosure by Sauder
Sauder discloses:
across a width of a header of the mobile agricultural harvesting machine.
“FIG. 3A” and “FIG. 3B” illustrate row units “90” on the header of the combine.; “for a given row unit 90-m in a combine having N rows, the harvest monitor 200 preferably first averages the stalk yield … for each row unit 90-m of the combine head …” ([0101]); “the overview windows also preferably display … data from all rows rather than a single row.” ([0108])
Rationale: across a width of a header of the mobile agricultural harvesting machine. is disclosed because Sauder expressly teaches multiple row units “90” on the combine header, teaches calculations “for each row unit 90-m of the combine head,” and teaches use of data from “all rows rather than a single row.” A PHOSITA would have understood these disclosures to correspond to values assessed across the transverse width of the header of the mobile agricultural harvesting machine.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to determine a target deck plate spacing from predictive stalk-related values across a width of a header of the mobile agricultural harvesting machine. Vandike teaches processor-executed predictive-map control of deck plate position using predictive stalk-related map data. Sauder teaches stalk-diameter sensing and row-by-row stalk-related evaluation across multiple row units of the combine head, thereby supporting use of stalk-related values across the header width. Bomleny, as incorporated through Claim 1, teaches the confidence-based framework for selecting predictive data for control. A PHOSITA would have recognized that, once predictive stalk-diameter data are selected for control within the combined Claim 1 system, it would have been technically sensible and predictably beneficial to use those predictive stalk-related values across the width of the header to determine a target deck plate spacing for improved harvesting performance.
Regarding Claim 11,
Disclosure by Vandike
Vandike teaches
A computer implemented method
“One or more information maps are obtained by an agricultural work machine.”“An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field.” (Abstract); “A predictive map generator generates a predictive map…” (Abstract)
Rationale: A computer implemented method is taught because Vandike expressly describes processor-based map generation, sensing, and control operations carried out by an agricultural work machine.
of controlling a mobile agricultural harvesting machine
“AGRICULTURAL HARVESTER 100” (Fig. 1); “CONTROL SYSTEM 214”“DECK PLATE POSITION CONTROLLER 242” (FIG. 2)
Rationale: of controlling a mobile agricultural harvesting machine is taught because Vandike expressly discloses an agricultural harvester with a control system and deck plate position controller for controlling the machine.
comprising:
“One or more information maps are obtained…”; ([0005]); “An in-situ sensor…” ([0005]); “A predictive map generator generates…” ([0005])
Rationale: comprising: is taught because Vandike discloses a method including multiple recited processing operations.
receiving stalk diameter sensor data
“RECEIVE SENSOR DATA FROM IN-SITU SENSOR 364” (FIG. 5); “REAL TIME (IN-SITU) SENSOR(S) 208” (FIG. 2)
Rationale: receiving stalk diameter sensor data is taught because Vandike expressly teaches receiving sensor data from in-situ sensors. While the primary reference uses stalk size rather than the exact words stalk diameter, stalk diameter is an obvious stalk-size parameter to a PHOSITA in this harvesting context.
generated by an in-situ sensor
“An in-situ sensor on the agricultural work machine senses an agricultural characteristic…” ([0005]); “REAL TIME (IN-SITU) SENSOR(S) 208” (FIG. 2)
Rationale: generated by an in-situ sensor is taught because Vandike expressly teaches sensing by an in-situ sensor and receiving sensor data from that in-situ sensor.
during an operation at a worksite;
“An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field.” ([0005]); “COLLECTED DATA PRIOR TO A CURRENT HARVESTING OPERATION 284” (FIG. 3A)
Rationale: during an operation at a worksite; is taught because Vandike expressly teaches sensing as the machine moves through the field, i.e., during harvesting operation at the field/worksite.
receiving predictive stalk diameter data
“PREDICTIVE MAP 264” (FIG. 2); “FUNCTIONAL PREDICTIVE MAP 263” (FIG. 2); “The predictive map can be output and used in automated machine control.” ([0005])
Rationale: receiving predictive stalk diameter data is taught because Vandike expressly teaches predictive map data used in automated machine control. Although the primary reference describes stalk size rather than stalk diameter, stalk diameter is an obvious stalk-size implementation.
that indicates predictive stalk diameter values
“the variable sensed by the in-situ sensors 208 may be stalk size.” ([0061]); “The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values…” ([0061])
Rationale: that indicates predictive stalk diameter values is taught because Vandike expressly teaches a predictive stalk size map with predicted stalk size values. A PHOSITA would have understood stalk diameter to be an obvious species of stalk size.
at different geographic locations at the worksite,
“The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field.” (Abstract); “CONTROL THE PREDICTIVE MAP GENERATOR TO GENERATE A PREDICTIVE MAP THAT PREDICTS A VALUE … AT DIFFERENT GEOGRAPHIC LOCATIONS ON THE FIELD BEING HARVESTED” (FIG. 3A)
Rationale: at different geographic locations at the worksite, is taught because Vandike expressly teaches predictive values at different geographic locations on the field being harvested, which is the claimed worksite.
the predictive stalk diameter values being generated prior to the operation at the worksite;
“COLLECTED DATA PRIOR TO A CURRENT HARVESTING OPERATION 284” (FIG. 3A); “the prior information map 258 is from a prior pass through the field during a prior operation” ([0061])
Rationale: the predictive stalk diameter values being generated prior to the operation at the worksite; is taught because Vandike expressly teaches use of prior information collected before the current harvesting operation to generate the predictive map.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly teach the following claim limitations:
determining a confidence level of the stalk diameter sensor data;
determining a confidence level of the predictive stalk diameter data;
selecting one of the stalk diameter sensor data or the predictive stalk diameter data as selected control data
based on the confidence level of the stalk diameter sensor data and the confidence level of the predictive stalk diameter data;
controlling a deck plate of the mobile agricultural harvesting machine
based on the selected control data.
Disclosure by Sauder
Sauder teaches:
determining a confidence level of the stalk diameter sensor data;
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “If the standard deviation σ of stalk diameters in a given yield block exceeds a certain threshold … then the data point 4105 corresponding to the stalk block is preferably filtered out…” ([0086])
Rationale: determining a confidence level of the stalk diameter sensor data; is taught because Sauder expressly evaluates stalk-diameter data using a statistical criterion and threshold-based filtering. A PHOSITA would have understood this to be a reliability or confidence determination for the stalk diameter sensor data.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate the statistical reliability assessment of stalk-diameter sensor data taught by Sauder into the predictive harvesting-control method of Vandike. Vandike teaches receiving in-situ stalk-related sensor data and predictive stalk-related map data for machine control, while Sauder teaches evaluating stalk-diameter data using statistical criteria and thresholds. A PHOSITA would have found it technically sensible and predictably beneficial to assess the reliability of the stalk-diameter sensor data before relying on it in control of the harvesting machine.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
determining a confidence level of the predictive stalk diameter data;
selecting one of the stalk diameter sensor data or the predictive stalk diameter data as selected control data
based on the confidence level of the stalk diameter sensor data and the confidence level of the predictive stalk diameter data;
controlling a deck plate of the mobile agricultural harvesting machine
based on the selected control data.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
determining a confidence level of the predictive stalk diameter data;
“topographic confidence system 330 determines a confidence level in the topographic characteristics relative to a worksite as indicated by a prior topographic map…” ([0082]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0197])
Rationale: determining a confidence level of the predictive stalk diameter data; is taught because Bomleny expressly teaches determining a confidence level for prior map-based worksite data. Although Bomleny is directed to topographic map data, it teaches the confidence-analysis architecture for predictive map data generally. A PHOSITA would have found it obvious to apply the same confidence analysis to predictive stalk-diameter data in the combined method.
selecting one of the stalk diameter sensor data or the predictive stalk diameter data as selected control data
“generate a topographic confidence output … based on the topographic map and the supplemental data” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198]); “the control of machine 100 as it operates across worksite 602 can also vary depending on which confidence zone 614 it is operating within.” ([0122])
Rationale: selecting one of the stalk diameter sensor data or the predictive stalk diameter data as selected control data is taught because Bomleny expressly teaches confidence-based control using prior map data and supplemental data. While it does not literally recite “selected control data,” a PHOSITA would have understood that confidence-based control between competing data sources requires choosing the data source to be used for control.
based on the confidence level of the stalk diameter sensor data and the confidence level of the predictive stalk diameter data;
“based on available supplemental data relative to the worksite 602 and/or the environment of the worksite 602” ([0122]); “generate a topographic confidence output indicative of a confidence level … based on the topographic map and the supplemental data” ([0197])
Rationale: based on the confidence level of the stalk diameter sensor data and the confidence level of the predictive stalk diameter data; is taught because Bomleny expressly bases confidence-driven control on both prior map data and supplemental data. A PHOSITA would have found it obvious to use the respective confidence levels of the predictive stalk diameter data and the stalk diameter sensor data as the basis for selecting the control data source in the combined method.
controlling a deck plate of the mobile agricultural harvesting machine
“DECK PLATE POSITION CONTROLLER 242” (FIG. 2); “…can generate control signals to control various other components of computing architecture 300 , as well as various other machines…” ([0129])
Rationale: controlling a deck plate of the mobile agricultural harvesting machine is taught because Vandike expressly discloses a deck plate position controller on the agricultural harvester.
based on the selected control data.
“generate action signals to control the operation of various components…” ([0129]); “GENERATE CONTROL SIGNAL(S) TO CONTROL CONTROLLABLE SUBSYSTEM(S) BASED ON THE PREDICTIVE MAP…” (FIG. 3A)
Rationale: based on the selected control data. is taught because Bomleny teaches confidence-based action signals and Vandike teaches control signals generated based on predictive map data. A PHOSITA would have found it obvious to use whichever stalk-diameter data source was selected through the confidence analysis as the basis for deck plate control.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to receive real-time stalk-diameter-related sensor data and predictive stalk-diameter-related map data, determine confidence for each data source, select between those data sources based on the respective confidence levels, and control the deck plate of the mobile agricultural harvesting machine using the selected data source. Vandike teaches receiving in-situ sensor data, generating and using predictive map data, and controlling harvester subsystems including the deck plate controller. Sauder teaches statistical evaluation and filtering of stalk-diameter data, thereby supplying sensor-data confidence analysis. Bomleny teaches confidence-based evaluation of prior map data using supplemental data and control action based on the resulting confidence output. A PHOSITA would have recognized that predictive agricultural data may become stale while real-time stalk-diameter sensor data may vary in reliability, and thus it would have been technically sensible and predictably beneficial to compare the reliability of the two data sources and use the more trustworthy source to control deck plate position during harvesting.
Regarding Claim 12,
The combination of Vandike, Sauder, and Bomleny establishes the computer implemented method of Claim 11, which is the basis for Claim 12.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly teach the following claim limitations:
determining the confidence level of the stalk diameter sensor data based on sensor data confidence criteria
and wherein determining the confidence level of the predictive stalk diameter data comprises determining the confidence level of the predictive stalk diameter data based on predictive data confidence criteria.
Disclosure by Sauder
Sauder teaches:
determining the confidence level of the stalk diameter sensor data based on sensor data confidence criteria
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “Using a statistical function as is known in the art, the harvest monitor preferably determines the standard deviation σ of stalk diameters for the yield block 1812 about the mean μ of the histogram.” ([0086]); “If the standard deviation σ of stalk diameters in a given yield block exceeds a certain threshold (e.g., 0.25μ, or 0.5 cm) then the data point 4105 corresponding to the stalk block is preferably filtered out…” ([0086])
Rationale: determining the confidence level of the stalk diameter sensor data based on sensor data confidence criteria is taught because Sauder expressly evaluates stalk-diameter-derived data using a “statistical criterion,” determines “the standard deviation σ of stalk diameters,” and compares that result to “a certain threshold.” A PHOSITA would have understood those disclosed statistical and threshold-based factors to be sensor data confidence criteria used to assess the reliability, and thus the confidence level, of the stalk diameter sensor data.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to determine the confidence level of the stalk diameter sensor data based on sensor data confidence criteria in the predictive harvesting-control method. Vandike teaches using in-situ stalk-related sensing in controlling a harvesting machine, while Sauder teaches evaluating stalk-diameter data using statistical criteria and threshold-based filtering. A PHOSITA would have found it technically sensible and predictably beneficial to use those disclosed statistical criteria as sensor data confidence criteria before relying on stalk-diameter sensor data in harvesting control.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
and wherein determining the confidence level of the predictive stalk diameter data comprises determining the confidence level of the predictive stalk diameter data based on predictive data confidence criteria.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
and wherein determining the confidence level of the predictive stalk diameter data comprises determining the confidence level of the predictive stalk diameter data based on predictive data confidence criteria.
“A topographic confidence output is generated which is indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data.” (Abstract); “The control system performs a confidence analysis on the baseline topographic map, based on the supplemental data as well as various algorithmic processes, and generates a topographic confidence output…” ([0022]); “topographic confidence system 330 can use any number of models in determining the topographic confidence level…” ([0121])
Rationale: and wherein determining the confidence level of the predictive stalk diameter data comprises determining the confidence level of the predictive stalk diameter data based on predictive data confidence criteria. is taught because Bomleny expressly teaches generating a confidence output for prior map-based predictive data “based on the topographic map and the supplemental data,” further teaches a “confidence analysis” based on “supplemental data” and “various algorithmic processes,” and also teaches use of “models in determining the topographic confidence level.” A PHOSITA would have understood those disclosed map-based inputs, supplemental-data checks, algorithmic processes, and models to be predictive data confidence criteria for determining the confidence level of predictive data. Applying that same confidence-analysis framework to predictive stalk diameter data in the combined method would have been obvious.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to determine the confidence level of the stalk diameter sensor data based on sensor data confidence criteria and to determine the confidence level of the predictive stalk diameter data based on predictive data confidence criteria. Vandike teaches the underlying predictive harvesting-control method that uses in-situ-sensed stalk-related data and predictive map data. Sauder teaches evaluating stalk-diameter data using statistical criteria and thresholds that provide confidence criteria for sensor data. Bomleny teaches confidence analysis for prior predictive map data using supplemental data, algorithmic processes, and models, which provide predictive data confidence criteria. A PHOSITA would have recognized that both real-time sensor data and prior predictive map data can vary in reliability, and thus it would have been technically sensible and predictably beneficial to determine the confidence of each data source using source-appropriate confidence criteria before selecting data for deck-plate control.
Regarding Claim 14,
The combination of Vandike, Sauder, and Bomleny establishes the computer implemented method of Claim 11, which is the basis for Claim 14.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly teach the following claim limitations:
comparing the confidence level of the predictive stalk diameter sensor data to the confidence level of the stalk diameter data
and selecting one of the stalk diameter sensor data or the predictive stalk diameter data as the selected control data based on the comparison.
Disclosure by Bomleny
Bomleny teaches:
comparing the confidence level of the predictive stalk diameter sensor data to the confidence level of the stalk diameter data
“topographic confidence system 330 can include communication system 306, one or more processors, controllers, or servers 312, topographic confidence analyzer 400, map generator(s) 402, data capture logic 404, action signal generator 406, threshold logic 408…” ([0081]); “topographic confidence system 330 determines a confidence level in the topographic characteristics relative to a worksite as indicated by a prior topographic map of the worksite, based on available supplemental data relative to the worksite or the environment of the worksite.” ([0082]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data” ([0197]); “a topographic confidence analyzer that determines the topographic confidence level based on the likelihood that the topographic characteristics of the worksite, as indicated by the topographic map, have changed based on the supplemental data.” ([0201])
Rationale: comparing the confidence level of the predictive stalk diameter sensor data to the confidence level of the stalk diameter data is taught because Bomleny expressly teaches a processor-based confidence-analysis framework that evaluates prior map data using supplemental data, includes “threshold logic 408,” and determines a “confidence level” for the prior map based on the supplemental data. Although Bomleny does not use the exact words of the claim, a PHOSITA would have understood this disclosed architecture to require an assessment of relative reliability between the prior predictive data source and the current sensed data source, which renders it obvious to compare the confidence level associated with the predictive stalk diameter data to the confidence level associated with the stalk diameter sensor data in the combined Claim 11 method.
and selecting one of the stalk diameter sensor data or the predictive stalk diameter data as the selected control data based on the comparison.
“topographic confidence system 330 can generate action signals to control the operation of various components of computing architecture 300…” ([0082]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data; and” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198]); “determining a plurality of confidence zones, each one of the confidence zones corresponding to a respective one of the plurality of confidence levels, wherein an operation of the mobile agricultural machine is based on a presence of the mobile agricultural machine in one of the plurality of confidence zones.” ([0181])
Rationale: and selecting one of the stalk diameter sensor data or the predictive stalk diameter data as the selected control data based on the comparison. is taught because Bomleny expressly teaches that machine control is driven by the confidence analysis results and that an action signal is generated “based on the topographic confidence output.” A PHOSITA would have found it obvious that, once the respective confidence levels of the predictive data and the sensed data are compared, one of those two data sources is selected as the operative control input, i.e., as the selected control data, based on that comparison.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to compare the confidence level of the predictive stalk diameter data to the confidence level of the stalk diameter sensor data and to select one of those data sources as the selected control data based on that comparison. Vandike teaches the underlying harvesting-control method using predictive stalk-related map data and deck-plate control. Sauder teaches statistical evaluation of stalk-diameter sensor data, thereby supporting confidence analysis of the sensed stalk-diameter source. Bomleny teaches determining a confidence level for prior map-based data using supplemental data and using the resulting confidence output to drive machine control decisions. A PHOSITA would have recognized that, when both predictive stalk-diameter data and current stalk-diameter sensor data are available in the Claim 11 method, a predictable and technically sound implementation is to compare their respective confidence levels and select the more reliable source for control of the harvesting machine.
Regarding Claim 15,
The combination of Vandike, Sauder, and Bomleny establishes the computer implemented method of Claim 11, which is the basis for Claim 15.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly teach the following claim limitations:
comparing the confidence level of the stalk diameter sensor data to a confidence level threshold
and comparing the confidence level of the predictive stalk diameter data to the confidence level threshold.
Disclosure by Sauder
Sauder teaches:
comparing the confidence level of the stalk diameter sensor data to a confidence level threshold
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “Using a statistical function as is known in the art, the harvest monitor preferably determines the standard deviation σ of stalk diameters for the yield block 1812 about the mean μ of the histogram.” ([0086]); “If the standard deviation σ of stalk diameters in a given yield block exceeds a certain threshold (e.g., 0.25μ, or 0.5 cm) then the data point 4105 corresponding to the stalk block is preferably filtered out…” ([0086])
Rationale: comparing the confidence level of the stalk diameter sensor data to a confidence level threshold is taught because Sauder expressly evaluates stalk-diameter-derived data using a statistical criterion, determines a statistical quality value for the stalk-diameter data, and compares that value to “a certain threshold.” A PHOSITA would have understood that threshold-based reliability assessment of the stalk-diameter sensor data to correspond to comparing the confidence level of the stalk diameter sensor data to a confidence level threshold.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate threshold-based evaluation of stalk-diameter sensor data into the predictive harvesting-control method. Vandike teaches the underlying method using in-situ stalk-related sensing and predictive map data for harvesting control, while Sauder teaches determining a statistical quality value for stalk-diameter data and comparing that value to a threshold. A PHOSITA would have found it technically sensible and predictably beneficial to use a confidence threshold when deciding whether the stalk-diameter sensor data are sufficiently reliable for use in control.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
and comparing the confidence level of the predictive stalk diameter data to the confidence level threshold.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
and comparing the confidence level of the predictive stalk diameter data to the confidence level threshold.
“Additionally, threshold logic 408 is configured to compare the various topographic confidence values to a variety of thresholds.” ([0105]); “The thresholds can be used to determine how much the topographic characteristics of the worksite (as indicated by supplemental data and the corresponding topographic confidence level) can deviate from the topographic characteristics indicated by the preexisting topographic map before a control of the machine(s) is adjusted…” ([0105]); “For instance, an operator or a user can input a threshold of 95% topographic confidence level, such that, only when the topographic confidence level is below 95% is some action signal generated.” ([0105])
Rationale: and comparing the confidence level of the predictive stalk diameter data to the confidence level threshold. is taught because Bomleny expressly teaches that “threshold logic 408” compares “topographic confidence values” to “thresholds,” including a threshold of “95% topographic confidence level.” Although Bomleny is directed to prior topographic map data rather than predictive stalk diameter data, it expressly teaches threshold-based comparison of confidence values for prior predictive map-based data. A PHOSITA would have found it obvious to apply that same threshold-based confidence comparison to the predictive stalk diameter data in the combined Claim 11 method.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to compare the confidence level of the stalk diameter sensor data to a confidence level threshold and to compare the confidence level of the predictive stalk diameter data to the confidence level threshold. Vandike teaches the underlying harvesting-control method using in-situ stalk-related sensor data, predictive stalk-related map data, and deck-plate control. Sauder teaches threshold-based statistical evaluation of stalk-diameter data, thereby supporting threshold comparison for the sensed stalk-diameter source. Bomleny teaches explicit comparison of confidence values for prior predictive map-based data to thresholds and adjustment of machine control based on that thresholded confidence analysis. A PHOSITA would have recognized that both real-time stalk-diameter sensor data and predictive stalk-diameter map data can vary in reliability, and thus it would have been technically sensible and predictably beneficial to apply confidence thresholds to each source before using either source in deck-plate-related harvesting control.
Regarding Claim 16,
The combination of Vandike, Sauder, and Bomleny establishes the computer implemented method of Claim 11, which is the basis for Claim 16.
Disclosure by Vandike
Vandike teaches:
determining, as a target deck plate position,
“At block 640, zone controller 247 accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target settings into a resolved target setting.” ([0162]); “At block 642, with zone controller 247 having identified the resolved target setting, zone controller 247 provides the resolved target setting to other controllers in control system 214, which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting.” ([0163])
Rationale: determining, as a target deck plate position, is taught because Vandike expressly teaches resolving “competing target settings into a resolved target setting” and then using that resolved target setting for machine control. A PHOSITA would have understood a resolved target setting for deck-plate control to correspond to a target deck plate position.
and wherein controlling the deck plate comprises controlling the deck plate
“DECK PLATE POSITION CONTROLLER 242” (FIG. 2); “Settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based upon predictive map 264, the predictive control zone map 265, or both. For instance, settings controller 232 can generate control signals to control machine and header actuators 248.” ([0136])
Rationale: and wherein controlling the deck plate comprises controlling the deck plate is taught because Vandike expressly discloses “DECK PLATE POSITION CONTROLLER 242” and further teaches that the settings controller generates control signals to control machine and header actuators.
based on the determined target deck plate position.
“At block 642, with zone controller 247 having identified the resolved target setting, zone controller 247 provides the resolved target setting to other controllers in control system 214, which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting.” ([0163])
Rationale: based on the determined target deck plate position. is taught because Vandike expressly states that control signals are generated and applied “based upon the resolved target setting.” A PHOSITA would have understood that once the target deck plate position is determined as the resolved target setting, deck-plate control is performed based on that determined target deck plate position.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly teach the following claim limitations:
either a deck plate position that accommodates a stalk having the largest predictive stalk diameter value across a width of a header of the mobile agricultural harvesting machine
or a deck plate position that accommodates the greatest number of plants across the width of the header,
Disclosure by Sauder
Sauder teaches:
either a deck plate position that accommodates a stalk having the largest predictive stalk diameter value
“The harvest monitor 200 preferably determines the full-ear stalk diameter by consulting a yield-diameter relationship...” ([0094]); “The mean stalk width L is preferably divided by the ‘full-ear’ stalk width...” ([0094])
Rationale: either a deck plate position that accommodates a stalk having the largest predictive stalk diameter value is taught because Sauder expressly teaches determining a “full-ear stalk diameter” and expressly uses stalk-width information as a control-relevant plant-dimension metric. A PHOSITA would have understood the disclosed full-ear stalk diameter as a larger stalk-diameter value that a deck plate position must accommodate during harvesting.
across a width of a header of the mobile agricultural harvesting machine
“The illustrated corn head includes four row units 90 disposed between five row dividers 88.” ([0057]); “To calculate a row yield contribution percentage Ycn for a given row unit 90-m in a combine having N rows, the harvest monitor 200 preferably first averages the stalk yield Ys from the last group of stalks (e.g., 50) for each row unit 90-m of the combine head...” ([0101])
Rationale: across a width of a header of the mobile agricultural harvesting machine is taught because Sauder expressly teaches a corn head having multiple row units and expressly teaches calculations performed “for each row unit 90-m of the combine head.” A PHOSITA would have understood these row-unit-based measurements to extend across the transverse width of the header.
or a deck plate position that accommodates the greatest number of plants across the width of the header,
“The row details screen 1200 preferably displays an ear count window 1212 in which the total number of ears per acre Et is displayed...” ([0095]); “To calculate a row yield contribution percentage Ycn for a given row unit 90-m in a combine having N rows, the harvest monitor 200 preferably first averages the stalk yield Ys from the last group of stalks (e.g., 50) for each row unit 90-m of the combine head...” ([0101]); “It should be appreciated that the yield contribution percentage Ycn comprises harvest data (or a ‘harvest metric’) based on the stalk diameters measured by the stalk measurement system 100.” ([0102])
Rationale: or a deck plate position that accommodates the greatest number of plants across the width of the header, is taught because Sauder expressly teaches an “ear count” and expressly teaches row-by-row harvest metrics for “each row unit 90-m of the combine head.” A PHOSITA would have understood those disclosed row-by-row plant-count and harvest-metric teachings to support determining a setting that accommodates the greatest number of plants across the width of the header.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to determine, as a target deck plate position, either a deck plate position that accommodates a stalk having the largest predictive stalk diameter value across a width of a header of the mobile agricultural harvesting machine or a deck plate position that accommodates the greatest number of plants across the width of the header, and to control the deck plate based on that determined target deck plate position. Vandike teaches predictive-map-based harvesting control, including resolving competing target settings into a resolved target setting and applying control signals based on that resolved target setting, as well as controlling header settings and deck-plate-related functionality. Sauder teaches stalk-diameter-based plant metrics, including determining full-ear stalk diameter, ear count, and row-by-row harvest information across multiple row units of the combine head. Bomleny, as incorporated through Claim 11, teaches confidence-based selection of the predictive versus sensed data used for control of harvesting-machine position-related components. A PHOSITA would have recognized that once Claim 11’s selected control data source is available, a predictable and technically sound implementation is to convert those selected stalk-diameter-related data into a resolved target deck plate position that either accommodates the largest relevant stalk across the header width or accommodates the greatest number of plants across the header width, thereby improving harvesting performance while using known row-based header information and known resolved-target-setting control logic.
Regarding Claim 17,
Disclosure by Vandike
Vandike discloses:
A mobile agricultural harvesting machine comprising:
“FIG. 1 is a partial pictorial, partial schematic illustration of a self-propelled agricultural harvester 100. In the illustrated example, agricultural harvester 100 is a combine harvester.” ([0033])
Rationale: A mobile agricultural harvesting machine comprising: is disclosed because Vandike expressly identifies “a self-propelled agricultural harvester 100” and states that it “is a combine harvester,” which is a mobile agricultural harvesting machine.
one or more actuators configured to adjust a spacing of each set of deck plates of the plurality of deck plates;
“FIG. 2 is a block diagram of agricultural harvester 100, showing that agricultural harvester 100 includes… Deck plate position controller 242…” ([0038]); “In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100.” ([0136])
Rationale: one or more actuators configured to adjust a spacing of each set of deck plates of the plurality of deck plates; is disclosed because Vandike expressly discloses “Deck plate position controller 242” and expressly states that it “controls machine/header actuators 248 to control a deck plate.” A PHOSITA would have understood controlling deck plate position by actuators to include adjusting deck-plate spacing.
a control system configured to:
“FIG. 2 is a block diagram of agricultural harvester 100, showing that agricultural harvester 100 includes… control system 214…” ([0038])
Rationale: a control system configured to: is disclosed because Vandike expressly discloses “control system 214” as part of the agricultural harvester architecture.
predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite;
“In some examples, the prior information map 258 is from a prior pass through the field during a prior operation… the variable sensed by the in-situ sensors 208 may be stalk size. The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field.” ([0061])
Rationale: predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite; is disclosed because Vandike expressly teaches “a predictive stalk size map” that “maps predicted stalk size values to different geographic locations in the field.” A PHOSITA would have understood stalk diameter to be an obvious stalk-size implementation, and the claimed worksite corresponds to the disclosed field.
the predictive stalk diameter values being generated prior to an operation at the worksite;
“In some examples, the prior information map 258 is from a prior pass through the field during a prior operation…” ([0061])
Rationale: the predictive stalk diameter values being generated prior to an operation at the worksite; is disclosed because Vandike expressly states that the predictive stalk-related map is based on a prior information map “from a prior pass through the field during a prior operation.”
generate a control signal to control a deck plate subsystem of the mobile agricultural harvesting machine based on the selected control data.
“In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100.” ([0136])
Rationale: This is disclosed because Vandike expressly teaches that the control system uses functional predictive map data to drive the deck plate position controller and machine/header actuators to control a deck plate on the agricultural harvester.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
a header comprising:
a plurality of row units; and
a plurality of sets of deck plates, each set of deck plates, of the plurality of sets of deck plates, corresponding to a respective row unit of the plurality of sets of row units;
an in-situ stalk diameter sensor corresponding to one of the row units, of the plurality of row units, the in-situ stalk diameter sensor configured to:
detect stalk diameters of plants in a crop row in a travel path of the row unit to which the in-situ stalk diameter sensor corresponds; and
generate stalk diameter sensor data indicative of the detected stalk diameters; and
select, as control data, one of the stalk diameter sensor data or predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite;
Disclosure by Sauder
Sauder discloses:
a header comprising:
“The stalk measurement system 100 is shown installed on a combine 10 having four row units 90 in FIG. 18.” ([0068])
Rationale: a header comprising: is disclosed because Sauder expressly describes the stalk measurement system installed on a combine having row units in the header arrangement shown in FIG. 18.
a plurality of row units;
“The stalk measurement system 100 is shown installed on a combine 10 having four row units 90 in FIG. 18.” ([0068])
Rationale: a plurality of row units is disclosed because Sauder expressly states that the combine has “four row units 90.”
and a plurality of sets of deck plates, each set of deck plates, of the plurality of sets of deck plates, corresponding to a respective row unit of the plurality of sets of row units;
“As illustrated in FIG. 12, the brackets 330 are configured such that the sensors 300a and 300b are disposed with their respective feelers 315 overlapping in the transverse direction.” ([0064]); “The illustrated displacement would correspond to the maximum displacement imposed by a stalk having a diameter equal to the transverse distance between the stripper plates 93a and 93b.” ([0064]); “The stalk measurement system 100 is shown installed on a combine 10 having four row units 90 in FIG. 18.” ([0067])
Rationale: a plurality of sets of deck plates, each set of deck plates, of the plurality of sets of deck plates, corresponding to a respective row unit of the plurality of sets of row unit; is disclosed because Sauder expressly teaches row units 90 and expressly identifies stripper plates “93a and 93b” associated with the row-unit stalk-sensing arrangement. A PHOSITA would have understood each row unit in the multi-row header to have its corresponding set of deck/stripper plates.
an in-situ stalk diameter sensor corresponding to one of the row units, of the plurality of row units, the in-situ stalk diameter sensor configured to:
“Each stalk sensor 300 is preferably mounted to a row unit 90.” ([0068])
Rationale: an in-situ stalk diameter sensor corresponding to one of the row units, of the plurality of row units, the in-situ stalk diameter sensor configured to: is disclosed because Sauder expressly states that “Each stalk sensor 300 is preferably mounted to a row unit 90,” i.e., corresponding to one of the row units.
detect stalk diameters of plants in a crop row in a travel path of the row unit to which the in-situ stalk diameter sensor corresponds;
“At step 2105, the monitor board 250 monitors the positions of each feeler 315 of the stalk sensors 300a,b…” ([0069])d; “At step 2135, the monitor board 250 preferably calculates the diameter of the stalk 25.” ([0069])
Rationale: detect stalk diameters of plants in a crop row in a travel path of the row unit to which the in-situ stalk diameter sensor corresponds is disclosed because Sauder expressly teaches stalk sensors mounted on row units, monitoring stalk-sensor feeler positions as the combine traverses the field, and calculating “the diameter of the stalk 25.” A PHOSITA would have understood these stalks to be plants in the crop row in the travel path of the corresponding row unit.
and generate stalk diameter sensor data indicative of the detected stalk diameters;
“At step 2140, the monitor board preferably associates the measured stalk diameter with a position in the field…” ([0070])
Rationale: generate stalk diameter sensor data indicative of the detected stalk diameters is disclosed because Sauder expressly teaches that the measured stalk diameter is associated with a position in the field, which constitutes generated stalk diameter sensor data indicative of the detected stalk diameters.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate the row-unit-based stalk-diameter sensing and corresponding stripper/deck-plate structure of Sauder into the predictive-map-based agricultural harvester control architecture of Vandike. Vandike teaches a mobile agricultural harvesting machine having a control system, machine/header actuators, deck-plate control, and predictive stalk-related map data, while Sauder teaches a header with multiple row units, row-unit-mounted stalk sensors, row-associated stripper/deck plates, and stalk-diameter measurements generated along the row unit's travel path. A PHOSITA would have found these teachings technically compatible and predictably beneficial because stalk diameter is directly relevant to deck-plate adjustment in a row-unit harvesting header.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
select, as control data, one of the stalk diameter sensor data or predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite;
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
select, as control data, one of the stalk diameter sensor data or predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite;
“receive a topographic map of a worksite that indicates topographic characteristics of the worksite, wherein the topographic characteristics are based on data collected at a first time;” ([0195]); “receive supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time;” ([0196]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data; and” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198])
Rationale: select, as control data, one of the stalk diameter sensor data or predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite is taught because Bomleny expressly teaches a control architecture that receives prior predictive map data and later-collected supplemental data, evaluates confidence based on both, and generates an action signal from that evaluation. Although Bomleny does not literally use the exact selection phrase, a PHOSITA would have understood that where both predictive map data and current sensed data are available for machine control, one of those two sources is selected as the operative control data based on the confidence-driven control framework.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to configure the mobile agricultural harvesting machine so that its control system selects, as control data, one of the stalk diameter sensor data or predictive stalk diameter data that provides predictive stalk diameter values at different geographic locations in a worksite, and then generates a control signal to control a deck plate subsystem based on the selected control data. Vandike teaches the predictive-map-based harvesting machine architecture, including control system 214, machine/header actuators 248, deck plate position controller 242, and predictive stalk-size map data generated prior to harvesting. Sauder teaches a header with multiple row units, corresponding stripper/deck plates, row-unit-mounted stalk sensors, and generation of stalk diameter sensor data for stalks encountered in the travel path of a row unit. Bomleny teaches a confidence-based framework using prior predictive map data and later supplemental sensed data to drive machine-control action signals. A PHOSITA would have recognized that predictive stalk-diameter information may be stale while current stalk-diameter sensor data may be more representative of present crop conditions, and thus it would have been technically sensible and predictably beneficial to select between those two available data sources as control data before commanding the deck plate subsystem.
Regarding Claim 18,
The combination of Vandike, Sauder, and Bomleny establishes the mobile agricultural harvesting machine of Claim 17, which is the basis for Claim 18.
Disclosure by Vandike
Vandike discloses:
wherein, when the control system selects, as the control data, the predictive stalk diameter data,
“In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100.” ([0136])
Rationale: wherein, when the control system selects, as the control data, the predictive stalk diameter data, is disclosed because Vandike expressly teaches a configuration in which “control system 214 receives the functional predictive map” and then uses that predictive map to drive deck-plate control. A PHOSITA would have understood that, in this operating mode, the predictive stalk-related data are the control data selected for control.
the control system is further configured to determine, as a target deck plate spacing,
“At block 640, zone controller 247 accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target settings into a resolved target setting.” ([0162]); “At block 642, with zone controller 247 having identified the resolved target setting, zone controller 247 provides the resolved target setting to other controllers in control system 214, which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting.” ([0163])
Rationale: the control system is further configured to determine, as a target deck plate spacing, is disclosed because Vandike expressly teaches determining a “resolved target setting” and then using that resolved target setting in control system 214. A PHOSITA would have understood a resolved target setting for the deck-plate subsystem to correspond to a target deck plate spacing.
and is configured to generate the control signal to control the one or more actuators to adjust the spacing of each set of deck plates of the plurality of sets of deck plates based on the target deck plate spacing.
“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…” ([0068]); “In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100.” ([0136])
Rationale: and is configured to generate the control signal to control the one or more actuators to adjust the spacing of each set of deck plates of the plurality of sets of deck plates based on the target deck plate spacing. is disclosed because Vandike expressly teaches that deck plate position controller 242 “generate[s] control signals” and “controls machine/header actuators 248 to control a deck plate.” A PHOSITA would have understood controlling deck plate position through machine/header actuators to include adjusting the spacing of the deck-plate sets based on the target deck plate spacing.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
a deck plate spacing that accommodates a largest predictive stalk diameter value, in the predictive stalk diameter data, across a width of the header
or that accommodates a greatest number of predictive stalk diameter values, in the predictive stalk diameter data, across the width of the header
Disclosure by Sauder
Sauder discloses:
a deck plate spacing that accommodates a largest predictive stalk diameter value, in the predictive stalk diameter data, across a width of the header
“The harvest monitor 200 preferably determines the full-ear stalk diameter by consulting a yield-diameter relationship…” ([0094]); “The mean stalk width L is preferably divided by the ‘full-ear’ stalk width…” ([0094]); “To calculate a row yield contribution percentage Ycn for a given row unit 90-m in a combine having N rows, the harvest monitor 200 preferably first averages the stalk yield Ys from the last group of stalks (e.g., 50) for each row unit 90-m of the combine head…” ([0101])
Rationale: a deck plate spacing that accommodates a largest predictive stalk diameter value, in the predictive stalk diameter data, across a width of the header is disclosed because Sauder expressly teaches determining “the full-ear stalk diameter” and expressly teaches row-by-row evaluation “for each row unit 90-m of the combine head.” A PHOSITA would have understood that accommodating the largest relevant stalk diameter across the width of the header is an obvious control objective when row-specific stalk-diameter values are available across the combine head.
or that accommodates a greatest number of predictive stalk diameter values, in the predictive stalk diameter data, across the width of the header
“The row details screen 1200 preferably displays an ear count window 1212 in which the total number of ears per acre Et is displayed…” ([0095]); “To calculate a row yield contribution percentage Ycn for a given row unit 90-m in a combine having N rows, the harvest monitor 200 preferably first averages the stalk yield Ys from the last group of stalks (e.g., 50) for each row unit 90-m of the combine head…” ([0101]); “It should be appreciated that the yield contribution percentage Ycn comprises harvest data (or a ‘harvest metric’) based on the stalk diameters measured by the stalk measurement system 100.” ([0102])
Rationale: or that accommodates a greatest number of predictive stalk diameter values, in the predictive stalk diameter data, across the width of the header is disclosed because Sauder expressly teaches an “ear count,” row-by-row calculations “for each row unit 90-m of the combine head,” and harvest metrics “based on the stalk diameters.” A PHOSITA would have understood these teachings to support choosing a deck-plate spacing that accommodates the greatest number of plants/stalk-diameter values represented across the width of the header.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to configure the control system, when predictive stalk diameter data are selected as the control data, to determine a target deck plate spacing that accommodates either the largest predictive stalk diameter value across the width of the header or the greatest number of predictive stalk diameter values across the width of the header, and to generate a control signal to control the one or more actuators to adjust the spacing of each set of deck plates based on that target deck plate spacing. Vandike teaches predictive-map-based deck-plate control, resolved target settings, and machine/header actuators for controlling deck plates. Sauder teaches row-unit-based stalk-diameter information across the combine head, including full-ear stalk diameter, ear count, and row-by-row stalk-diameter-based harvest metrics. Bomleny, as incorporated through Claim 17, teaches the confidence-based framework under which predictive data can be selected for machine control. A PHOSITA would have recognized that, once predictive stalk-diameter data are selected for control, a predictable and technically sound implementation is to convert those predictive values across the width of the header into a target deck plate spacing that either clears the largest expected stalks or accommodates the greatest number of plants represented by the predictive data, thereby improving harvesting performance while using known header-actuator control.
Regarding Claim 19,
The combination of Vandike, Sauder, and Bomleny establishes the mobile agricultural harvesting machine of Claim 17, which is the basis for Claim 19.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
determine a confidence level of the stalk diameter sensor data based on sensor data confidence criteria;
determine a confidence level of the predictive stalk diameter data based on predictive data confidence criteria; and
select, as the control data, one of the stalk diameter sensor data or the predictive stalk diameter data based on the confidence level of the stalk diameter sensor data confidence level and the confidence level of the predictive stalk diameter data.
Disclosure by Sauder
Sauder discloses/teaches:
determine a confidence level of the stalk diameter sensor data based on sensor data confidence criteria;
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “Using a statistical function as is known in the art, the harvest monitor preferably determines the standard deviation O of stalk diameters for the yield block 1812 about the meanu of the histogram.” ([0086]); “If the standard deviation O of stalk diameters in a given yield block exceeds a certain threshold (e.g., 0.25u, or 0.5 cm) then the data point 4105 corresponding to the stalk block is preferably filtered out...” ([0086])
Rationale: determine a confidence level of the stalk diameter sensor data based on sensor data confidence criteria; is disclosed because Sauder expressly evaluates stalk-diameter-derived data using a “statistical criterion,” determines “the standard deviation” of the stalk diameters, and compares that result to “a certain threshold.” A PHOSITA would have understood those statistical and threshold-based factors to be sensor data confidence criteria used to determine the reliability, and thus the confidence level, of the stalk diameter sensor data.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to determine a confidence level of the stalk diameter sensor data based on sensor data confidence criteria in the control system of the mobile agricultural harvesting machine. Vandike teaches the predictive-map-based harvesting-machine architecture with deck-plate control and in-situ stalk-related sensing, while Sauder teaches evaluating stalk-diameter data using statistical criteria and threshold-based filtering. A PHOSITA would have found it technically sensible and predictably beneficial to use those disclosed statistical criteria as sensor data confidence criteria before relying on stalk-diameter sensor data in machine control.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
determine a confidence level of the predictive stalk diameter data based on predictive data confidence criteria; and
select, as the control data, one of the stalk diameter sensor data or the predictive stalk diameter data based on the confidence level of the stalk diameter sensor data confidence level and the confidence level of the predictive stalk diameter data.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
determine a confidence level of the predictive stalk diameter data based on predictive data confidence criteria;
“topographic confidence system 330 determines a confidence level in the topographic characteristics relative to a worksite as indicated by a prior topographic map of the worksite, based on available supplemental data relative to the worksite or the environment of the worksite.” ([0082]); “Additionally, threshold logic 408 is configured to compare the various topographic confidence values to a variety of thresholds.” ([0105]); “The thresholds can be used to determine how much the topographic characteristics of the worksite (as indicated by supplemental data and the corresponding topographic confidence level) can deviate from the topographic characteristics indicated by the preexisting topographic map before a control of the machine(s) is adjusted...” ([0105])
Rationale: determine a confidence level of the predictive stalk diameter data based on predictive data confidence criteria is disclosed because Bomleny expressly teaches determining a “confidence level” for prior predictive map-based data and further teaches that this confidence determination is based on available supplemental data and threshold-based evaluation criteria. Although Bomleny is directed to topographic map data rather than predictive stalk-diameter data, it teaches the confidence-analysis architecture for predictive map data generally. A PHOSITA would have found it obvious to apply that same predictive-data confidence analysis to predictive stalk diameter data in the combined harvesting machine.
and select, as the control data, one of the stalk diameter sensor data or the predictive stalk diameter data based on the confidence level of the stalk diameter sensor data confidence level and the confidence level of the predictive stalk diameter data.
“topographic confidence system 330 determines a confidence level in the topographic characteristics relative to a worksite as indicated by a prior topographic map of the worksite, based on available supplemental data relative to the worksite or the environment of the worksite.” ([0082]); “topographic confidence system 330 can generate action signals to control the operation of various components of computing architecture 300...” ([0082]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data; and” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198])
Rationale: select, as the control data, one of the stalk diameter sensor data or the predictive stalk diameter data based on the confidence level of the stalk diameter sensor data confidence level and the confidence level of the predictive stalk diameter data. is disclosed because Bomleny expressly teaches a confidence-driven control architecture that evaluates prior predictive data using supplemental sensed data and then generates action signals based on the resulting confidence output. While Bomleny does not literally recite the exact selection phrase, a PHOSITA would have understood that, when both predictive and current-sensed data are available, the control system selects the operative control data source based on the relative confidence associated with those two sources.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to configure the control system of the mobile agricultural harvesting machine to determine a confidence level of the stalk diameter sensor data based on sensor data confidence criteria, determine a confidence level of the predictive stalk diameter data based on predictive data confidence criteria, and select, as the control data, one of the stalk diameter sensor data or the predictive stalk diameter data based on the confidence level of the stalk diameter sensor data confidence level and the confidence level of the predictive stalk diameter data. Vandike teaches the underlying harvesting-machine architecture using predictive stalk-related map data and deck-plate control. Sauder teaches statistical evaluation and threshold-based filtering of stalk-diameter data, thereby supplying sensor-data confidence criteria. Bomleny teaches determining a confidence level for prior predictive map-based data using supplemental data and threshold logic and using the resulting confidence output to drive machine control. A PHOSITA would have recognized that predictive stalk-diameter data may become stale while real-time stalk-diameter sensor data may vary in reliability, and thus it would have been technically sensible and predictably beneficial to determine the confidence of each source using source-appropriate criteria and then select the more trustworthy source as the control data for deck-plate-related harvesting control.
Regarding Claim 20,
The combination of Vandike, Sauder, and Bomleny establishes the mobile agricultural harvesting machine of Claim 19, which is the basis for Claim 20.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
compare the confidence level of the stalk diameter sensor data to the confidence level of the predictive stalk diameter data
and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has the higher confidence level.
Disclosure by Bomleny
Bomleny discloses:
compare the confidence level of the stalk diameter sensor data to the confidence level of the predictive stalk diameter data
“topographic confidence system 330 determines a confidence level in the topographic characteristics relative to a worksite as indicated by a prior topographic map of the worksite, based on available supplemental data relative to the worksite or the environment of the worksite.” ([0082]); “Additionally, threshold logic 408 is configured to compare the various topographic confidence values to a variety of thresholds.” ([0105]); “For instance, an operator or a user can input a threshold of 95% topographic confidence level, such that, only when the topographic confidence level is below 95% is some action signal generated.” ([0105])
Rationale: compare the confidence level of the stalk diameter sensor data to the confidence level of the predictive stalk diameter data is disclosed because Bomleny expressly teaches determining a “confidence level” for prior predictive map-based data using supplemental data and further teaches comparison logic for confidence values. Although Bomleny does not literally state the exact claimed comparison between two stalk-diameter data sources, a PHOSITA would have understood that where a prior predictive data source and a current sensed data source are both available in the confidence-analysis framework, their relative confidence must be assessed against one another in order to decide which source should govern control. Thus, comparing the confidence level of the stalk diameter sensor data to the confidence level of the predictive stalk diameter data would have been obvious in view of the disclosed confidence-evaluation architecture.
and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has the higher confidence level.
“topographic confidence system 330 can generate action signals to control the operation of various components of computing architecture 300…” ([0082]); “generate a topographic confidence output indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data; and” ([0197]); “an action signal generator configured to generate an action signal based on the topographic confidence output.” ([0198]); “In this way, the control of machine 100 as it operates across worksite 602 can also vary depending on which confidence zone 614 it is operating within.” ([0122])
Rationale: and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has the higher confidence level. is disclosed because Bomleny expressly teaches that control action is driven by the result of the confidence analysis and varies depending on the resulting confidence condition. While Bomleny does not literally recite selecting “whichever … has the higher confidence level,” a PHOSITA would have found it obvious that, when two candidate data sources are available for control, the data source with the higher confidence level is the one selected as the operative control data. Selecting the higher-confidence source is the straightforward and predictable implementation of the disclosed confidence-based control framework.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to configure the control system to compare the confidence level of the stalk diameter sensor data to the confidence level of the predictive stalk diameter data and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has the higher confidence level. Vandike teaches a predictive-map-based harvesting-machine architecture, including predictive stalk-related data and deck-plate-related control. Sauder teaches statistical evaluation and threshold-based filtering of stalk-diameter sensor data, thereby supplying a basis for determining confidence in the sensed stalk-diameter source. Bomleny teaches determining confidence for prior predictive map-based data using supplemental data and using the resulting confidence output to drive machine-control action. A PHOSITA would have recognized that predictive stalk-diameter data may be stale while current stalk-diameter sensor data may be noisy or incomplete, and thus it would have been technically sensible and predictably beneficial to compare the confidence of the two available data sources and select the source having the higher confidence level as the control data for deck-plate-related harvesting control.
Regarding Claim 21,
The combination of Vandike, Sauder, and Bomleny establishes the mobile agricultural harvesting machine of Claim 19, which is the basis for Claim 21.
Claim limitations Not Explicitly Disclosed by Vandike
Vandike does not explicitly disclose the following claim limitations:
compare the confidence level of the stalk diameter sensor data to a confidence level threshold
and to compare the confidence level of the predictive stalk diameter data to the confidence level threshold,
and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has a confidence level that satisfies the confidence level threshold.
Disclosure by Sauder
Sauder discloses:
compare the confidence level of the stalk diameter sensor data to a confidence level threshold
“At step 2045, the harvest monitor 200 preferably filters data points 4105 using a statistical criterion.” ([0086]); “Using a statistical function as is known in the art, the harvest monitor preferably determines the standard deviation O of stalk diameters for the yield block 1812 about the meanu of the histogram.” ([0086]); “If the standard deviation O of stalk diameters in a given yield block exceeds a certain threshold (e.g., 0.25u, or 0.5 cm) then the data point 4105 corresponding to the stalk block is preferably filtered out...” ([0086])
Rationale: compare the confidence level of the stalk diameter sensor data to a confidence level threshold is disclosed because Sauder expressly teaches determining a statistical quality value for stalk-diameter data and comparing that value to “a certain threshold.” A PHOSITA would have understood this threshold-based reliability assessment to correspond to comparing the confidence level of the stalk diameter sensor data to a confidence level threshold.
Motivation to Combine Vandike and Sauder
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike and Sauder before them, to incorporate threshold-based evaluation of stalk-diameter sensor data into the control system of the mobile agricultural harvesting machine. Vandike teaches the predictive-map-based harvesting-machine architecture with deck-plate-related control, while Sauder teaches statistical evaluation of stalk-diameter data and comparison of that evaluation to a threshold. A PHOSITA would have found it technically sensible and predictably beneficial to use a confidence threshold in deciding whether stalk-diameter sensor data are sufficiently reliable for control.
Claim limitations Not Explicitly Disclosed by the Combination of Vandike and Sauder
After combining the teachings of Vandike and Sauder, the following claim limitations are not explicitly disclosed:
and to compare the confidence level of the predictive stalk diameter data to the confidence level threshold,
and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has a confidence level that satisfies the confidence level threshold.
Disclosure by Bomleny
Bomleny provides teachings for the following remaining missing elements:
and to compare the confidence level of the predictive stalk diameter data to the confidence level threshold,
“In operation, topographic confidence system 330 determines a confidence level in the topographic characteristics relative to a worksite as indicated by a prior topographic map of the worksite, based on available supplemental data relative to the worksite or the environment of the worksite.” ([0082]); “Additionally, threshold logic 408 is configured to compare the various topographic confidence values to a variety of thresholds.” ([0105]); “For instance, an operator or a user can input a threshold of 95% topographic confidence level, such that, only when the topographic confidence level is below 95% is some action signal generated.” ([0105])
Rationale: and to compare the confidence level of the predictive stalk diameter data to the confidence level threshold, is disclosed because Bomleny expressly teaches determining a confidence level for prior predictive map-based data and expressly teaches that “threshold logic 408” compares confidence values to thresholds. Although Bomleny is directed to topographic map data rather than predictive stalk-diameter data, it teaches the threshold-based confidence-analysis architecture for predictive data generally. A PHOSITA would have found it obvious to apply that same threshold comparison to the confidence level of the predictive stalk diameter data.
and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has a confidence level that satisfies the confidence level threshold.
“Topographic confidence system 330 can generate action signals to control the operation of various components of computing architecture 300...” ([0082]); “The thresholds can be used to determine how much the topographic characteristics of the worksite (as indicated by supplemental data and the corresponding topographic confidence level) can deviate from the topographic characteristics indicated by the preexisting topographic map before a control of the machine(s) is adjusted...” ([0105]); “For instance, an operator or a user can input a threshold of 95% topographic confidence level, such that, only when the topographic confidence level is below 95% is some action signal generated.” ([0105])
Rationale: to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has a confidence level that satisfies the confidence level threshold. is disclosed because Bomleny expressly teaches that control action depends on whether a confidence level satisfies a threshold criterion. While Bomleny does not literally recite a selection between stalk-diameter sensor data and predictive stalk-diameter data, a PHOSITA would have understood that, when two candidate data sources are available, and threshold-based confidence gating is used, the source whose confidence satisfies the threshold is selected as the operative control data. This is the straightforward and predictable implementation of the disclosed threshold-based confidence-control framework.
Motivation to Combine Vandike, Sauder, and Bomleny
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Vandike, Sauder, and Bomleny before them, to configure the control system to compare the confidence level of the stalk diameter sensor data to a confidence level threshold, to compare the confidence level of the predictive stalk diameter data to the confidence level threshold, and to select, as the control data, whichever of the stalk diameter sensor data and the predictive stalk diameter data has a confidence level that satisfies the confidence level threshold. Vandike teaches the harvesting-machine architecture using predictive stalk-related data and deck-plate-related control. ([0032], [0136]) Sauder teaches threshold-based statistical evaluation of stalk-diameter sensor data. ([0086]) Bomleny teaches determining confidence for prior predictive map-based data, comparing confidence values to thresholds, and using thresholded confidence outcomes to govern machine-control action. A PHOSITA would have recognized that both real-time stalk-diameter sensor data and predictive stalk-diameter data can vary in reliability, and thus it would have been technically sensible and predictably beneficial to apply a confidence threshold to each source and then use the source that satisfies the threshold as the control data for deck-plate-related harvesting control.
Response to Arguments
Applicant’s arguments submitted on 12/08/2025 have been fully considered but are not persuasive as to the presently pending grounds of rejection.
First, Applicant’s request for withdrawal of the prior interpretation under 35 U.S.C. 112(f) is acknowledged. In view of the amendments and upon reconsideration of the claim language as presently presented, the prior 112(f) interpretation is withdrawn.
Second, Applicant’s arguments directed to the prior written description rejection under 35 U.S.C. 112(a) have been considered. In view of the amendments and the current claim language, the rejection under 112(a) is withdrawn.
Third, Applicant’s arguments directed to the prior indefiniteness rejection under 35 U.S.C. 112(b) have been considered. In view of the amendments and the current claim language, the rejection under 112(b) is withdrawn.
With respect to obviousness, Applicant’s arguments are directed to the Non-Final Office Action rejections based on Sauder, Han, Dickson, Vandike, and Bengochea. Those arguments are not responsive to, and therefore do not overcome, the presently maintained rejection(s) under 35 U.S.C. 103 in this Final Office Action. The Final Office Action relies on a different combination of references necessitated by Applicant’s amendments, namely Vandike, Sauder, and Bomleny, and sets forth new findings and new rationales based on the amended claim language.
Applicant argues, for example, that Han allegedly does not teach or suggest “determine a confidence level of predictive stalk diameter data,” and that Dickson allegedly does not teach or suggest selecting between predictive stalk diameter data and stalk diameter sensor data, or controlling deck plate position based on the selected data. Applicant further argues that Dickson is directed to navigation rather than stalk diameter or deck plate control. Those arguments were made against the earlier Non-Final rejection and the earlier references then applied. Because the present Final rejection no longer relies on Han, Dickson, Sauder, Vandike, or Bengochea for the maintained 103 rejection, those arguments are moot. They do not address the teachings, findings, or articulated reasons to combine set forth in the Final Office Action based on the newly applied references. See, e.g., the Final rejection’s reliance on Vandike for predictive-map-based harvester control and deck-plate control, Sauder for in-situ stalk-diameter sensing and stalk-diameter-derived data, and US20220132722A1 for confidence analysis and confidence-based control selection.
Stated differently, Applicant’s substantive 103 remarks do not traverse the rejection actually before the Office in the Final Action. The question at this stage is not whether Han teaches confidence in predictive stalk diameter data, nor whether Dickson teaches deck plate control. Rather, the issue is whether the presently cited references—Vandike, Sauder, and Bomleny—teach or render obvious the amended claims for the reasons set forth in the Final Office Action. Applicant has not directed substantive argument to those references or to the specific factual findings and motivations to combine set forth in the Final rejection.
Accordingly, Applicant’s arguments directed to the non-final 103 rejection are unavailing because they do not correspond to the presently maintained grounds of rejection. To the extent Applicant relies on those arguments for patentability of amended claims 1, 11, and 17, and their respective dependent claims, such arguments are not persuasive.
Therefore:
the prior interpretation under 35 U.S.C. 112(f) is withdrawn;
the prior rejection under 35 U.S.C. 112(a) is withdrawn;
the prior rejection under 35 U.S.C. 112(b) is withdrawn; and
the rejection(s) under 35 U.S.C. 103 set forth in the Final Office Action are maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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 OLUWABUSAYO ADEBANJO AWORUNSE whose telephone number is (571)272-4311. The examiner can normally be reached M - F (8:30AM - 5PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571) 270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUWABUSAYO ADEBANJO AWORUNSE/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662