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 .
Examiner’s Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants’ definition which is not specifically set forth in the claims.
Response to Amendment
The amendment filed 3/23/2026 has been entered. Claims 1, 3-10, 12, and 14-19 remain pending in the application. Claim 21-24 have been added Applicant’s amendments to the Claims have overcome the rejection of Claim 4 and 9 under U.S.C. 112(b) previously set forth in the Office Action mailed 11/21/2025.
Response to Arguments
Applicant's arguments filed 3/23/2026 have been fully considered but they are not persuasive.
Regarding the rejection under U.S.C. 112(b), the applicant states that “the strategy is indicative or one or more goals” and amends the claim to include such as well, which does not address the fundamental issue of the rejection that “There is no indication what the strategy comprises, or what precisely the strategy does, beyond general association with determining machine parameters for a machine. The “strategy” is an indefinite process, system, method, or the like which can mean anything, broad to a degree as to be functionally without meaning and indefinite.” Unless the intent is to patent goals, the strategy is not clearly defined by this.
The applicant cites paragraphs [0006], [0045], and [0048], however the argument that “strategy specifications” implement the “strategy”, yet paragraph [[0045] states that “The strategy 7 may comprise strategy specifications 8,” which is a circular definition to state that a “strategy specification” complements a “strategy, yet a strategy” is partially made of “strategy specification”- in fact using “may comprise” language which means this may not even be the case and still fall under what the application considers “strategy”
Similarly, the paragraphs cited to define “strategy parameter” both as that which parameterizes strategy, which fails to address the issue of the “strategy” as well as being machine parameters. Amending the claim to delineate that strategy parameters are machine parameters according to the strategy addresses what a “strategy parameter” is, but as stated above more is needed to resolve the issue of the “strategy”
Regarding the rejection under U.S.C. 103, applicant alleges that “neither Hunt nor Sidon teach that the "initial strategy parameters" are merely "initial" strategy parameters subject to further optimization”. Firstly, Sidon does disclose this as demonstrated in the rejection with paragraphs (0081] “If blocks 538 and 540 indicate that the correlation is not within a threshold and that a minimum variance has not been identified, operation proceeds to block 556. Illustratively, blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified that results in a minimum variance, or highest correlation, between the a priori data based estimated yield value and the sensed yield data generated based on in situ data during operation of machine 100.”. Secondly, and probably more pertinently to the arguments, the examiner disagrees that Hunt teaches away from using the parameters as initial parameters to improve on. Stating “best” parameters is not the Hunt reference stating that this is the ideal parameter which cannot or will not be improved upon, simply that it is the best that it can locate. Similarly, paragraphs [0056] discloses “Search engine 134 then searches target store 136 for the most closely matching performance targets 138.” which also is not saying that the initially-found parameters are not the best possible parameters but rather the ones that most closely match- it would not teach a person having ordinary skill in the art away from improving the parameters, just that they are starting with the best ones that could be found. Further, Hunt states that [0063] “However, if machine 102 is still being operated, then setting control system 276 illustratively determines whether the settings need to be adjusted in order for the calculated current performance metric values to more closely match the performance target metric values.” Which makes changes from the initial parameter explicitly taught in the reference. For this and the other reasons above, the examiner maintains the rejection and considers the previous rejection proper.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claims are directed to the nondescript “a strategy”, which is indefinite as one having ordinary skill in the art would not be clear as to what may infringe on the claim. There is no indication what the strategy comprises, or what precisely the strategy does, beyond general association with determining machine parameters for a machine. The “strategy” is an indefinite process, system, method, or the like which can mean anything, broad to a degree as to be functionally without meaning and indefinite.
The claims further recite “and wherein the strategy is parameterized by the one or more strategy parameters that were selected in order to adapt the strategy to the local context;” which is unclear as to whether the applicant is claiming a step by which the system operates to parameterize the strategy using the said parameters, describing the strategy as being parameterized by these certain parameters, claiming a step of generating the strategy from the parameters, or the like.
Both claims recite the limitations “wherein the strategy comprising strategy specifications, an instruction for use, and optimized machine parameters,” which appears to be a typo for a sentence such as “wherein the claims comprise” or “are comprising”
Claims 2-18 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending from, indefinite claims, and failing to render the strategy as patentably definite.
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, 3, 4, 14, 15, and 18, 19, 21, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt (US 20160086291), herein after referred to as Hunt, in view of Sidon (US 20210321567), herein after referred to as Sidon.
Regarding Claim 1, Hunt discloses:
determining whether at least one aspect of the local context for performing the agricultural tasks is identical or within a predefined amount to a same local context or to an other local context; (see at least [0054] “By way of example, if a performance target was stored for the same field in the previous year, and the wind speed and the moisture level were approximately the same as current conditions, then that performance target may have the best set of performance target metrics that system 128 can locate.”)
responsive to determining that the at least one aspect of the local context for performing the agricultural tasks is identical to the same local context or to the other local context, (see at least [0055] “For instance, system 128 can gather relevant information and automatically identify the top N performance targets in target store 126 based upon how they relate to the gathered information.”)
selecting one or more strategy parameters previously optimized by at least one agricultural machine in the same local context or in the other local context; (see at least [0028] “In the example shown in FIG. 2, each performance target 154-158 illustratively includes a set of metric values 164. Each performance target 154-158 can also include machine settings 166 that were in place at the time that the metric values 164 were obtained. ”)
wherein a strategy indicative of one or more optimization goals uses the one or more strategy parameters that were selected as ... strategy parameters in a strategy, (see at least [0021] “the operator can retrieve the previously-stored performance target and the corresponding target performance metrics can be used by the control system on the combine in an attempt to maintain performance metrics that are close to the target performance metrics.”)
wherein the strategy indicative of one or more optimization goals comprising strategy specifications, an instruction for use, and optimized machine parameters, (see at least [0028] “”In the example shown in FIG. 2, each performance target 154-158 illustratively includes a set of metric values 164. Each performance target 154-158 can also include machine settings 166 that were in place at the time that the metric values 164 were obtained. )
and wherein the strategy is parameterized by the one or more strategy parameters that were selected in order to adapt the strategy to the local context; (see at least [0045] “In one example, for instance, metric value comparison component 274 compares the current performance metric values 270 against the target performance metric values 272 and generates a difference signal indicative of the difference between those two sets of values. Expert system 278 then accesses control rules, functions, etc. 280 to identify various actions that can be taken in order to adjust the operation of machine”)
wherein the strategy specification is indicative of a respective optimization goal or weighting of a plurality of optimization goals; (see at least [0066] “some performance metric values may be more important than others. In that case, the expert system can weight those performance metric values more heavily in its calculation of whether and what machine settings need to be adjusted.”)
determining, using the strategy, one or more optimized machine parameters for the agricultural production machine in order to perform the agricultural task, whereby the strategy, with the one or more strategy parameters as the initial strategy parameters, uses the strategy specifications as input for the instruction for use in order to generate the one or more optimized machine parameters as outputs; (see at least [0059] “ Setting control system 276 then calculates machine settings based upon the obtained set of performance target metrics. This is indicated by block 355. For instance, setting control system 276 can calculate machine settings for all configurable controlled systems on machine 102. "”)
and automatically, using the one or more optimized machine parameters, controlling at least a part of the agricultural production machine. (see at least [0084] “the control system will automatically control the machine to obtain those performance target metric values. In this way, the user need not attempt to recreate the precise settings that were used to obtain the high level of performance, as the control system can do this automatically. ”)
wherein a control assembly has a database of stored strategy parameters correlated to data on local contexts; (see at least [0053] “ performance targets are stored in target store 136 and are indexed by field location or by farm.”)
wherein the stored strategy parameters of driver assistance systems of agricultural production machines have been successively optimized in optimization routines (see at least [0028] “It can be seen in FIG. 2 that target store 136 illustratively includes a set of performance targets 138. The individual performance targets in the set of performance targets 138 are illustratively represented by numbers 154, 156 and 158 in FIG. 2. FIG. 2 also shows that target store 136 can include performance targets from other fleets of machines, which may be located in a similar geographic region, or which may be operating under similar operating conditions, or which may be relevant to user 110 for other reasons.”)
during execution of agricultural tasks in a variety of local contexts (see at least [0029] “the index values 170 can also include a date and time 178 when the performance target was obtained, a location 180 (that can be provided by a geographic location generator, such as a GPS system), a field identifier 182 that identifies the particular field (and perhaps the farm) where the performance target was set, the crop type 184, the operator ID 186 and it can include a wide variety of other characteristics, such as whether the crop was wet, whether there was downed crop, whether there was a strong wind, the wind direction, etc.") (*Examiner interprets the variety of characteristics of the performance target as meaning the performance targets come from a variety of local contexts)
wherein the driver assistance system determines context data of the local context of the agricultural task using the sensor assembly; (see at least [0052] “component 128 uses crop type identifier system 118 to automatically identify the crop type, with which machine 102 is being used. This is indicated by block 322. This can be obtained using a sensor input 324.”)
wherein the driver assistance system transmits the context data to the control assembly; (see at least [Fig. 7] [0090] “target store 136, search engine 134 and remote systems 114 (or any other parts of the control architecture 100) can be located at a remote server location 502. Therefore, machine 102 accesses those systems through remote server location 502.”)
based on comparing the transmitted context data with the context data stored in the database; (see at least [0054] “Component 128 can use this to search for a most relevant performance target in target store 136.”)
and for further modification and optimization while performing the agricultural task in order to determine the one or more optimized machine parameters (see at least [0063] “However, if machine 102 is still being operated, then setting control system 276 illustratively determines whether the settings need to be adjusted in order for the calculated current performance metric values to more closely match the performance target metric values.
Hunt does not explicitly disclose:
wherein a strategy uses the one or more strategy parameters that were selected as initial strategy parameters in a strategy,
from initial strategy parameters for adaptation to the local context of the agricultural task;
wherein the control assembly determines the one or more strategy parameters as the initial strategy parameters
wherein the control assembly transmits the one or more strategy parameters as the initial strategy parameters to the driver assistance system of the agricultural production machine;
and wherein the driver assistance system uses the one or more strategy parameters as the initial strategy parameters to determine the one or more optimized machine parameters.
In the same field of endeavor, Sidon discloses:
wherein a strategy indicative of one or more optimization goals uses the one or more strategy parameters that were selected as initial strategy parameters in a strategy, (see at least [0079] “the map and/or model parameters can be output to another harvesting machine, to be used in the control of that machine (e.g., generation of yield map data during operation of the other harvesting machine)." [0081] "blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified that results in a minimum variance, or highest correlation, between the a priori data based estimated yield value and the sensed yield data generated based on in situ data”)
from initial strategy parameters for adaptation to the local context of the agricultural task; (see at least [0036] “A priori data collection system (or systems) 202 illustratively collects a priori data corresponding to a target or subject field, that can be used by machine 100 to generate a model (such as a yield map of the field) that can be used to control machine 100." [0051] "a yield map can be dynamically generated based upon a priori data (such as aerial imagery data) and in situ data, such as actual yield data sensed on the machine (e.g., using sensor(s) 236 during the harvesting operation”)
wherein the control assembly determines the one or more strategy parameters as the initial strategy parameters (see at least [0068] “In response to this, logic 414 can select the yield map which is output at block 432.”)
wherein the control assembly transmits the one or more strategy parameters as the initial strategy parameters to the driver assistance system of the agricultural production machine; (see at least [0079] “the map and/or model parameters can be output to another harvesting machine, to be used in the control of that machine (e.g., generation of yield map data during operation of the other harvesting machine).”)
and wherein the initial strategy parameters are used: for initial use in performing the agricultural task; (see at least [0081] “blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified that results in a minimum variance, or highest correlation, between the a priori data based estimated yield value and the sensed yield data generated based on in situ data”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to store, transmit, and receive a strategy parameter as an initial strategy parameter, and use the initial strategy parameter to determine an optimized parameter, as taught by Sidon to receive a strategy parameter, apply it for use, and then optimize the process from that initial parameter [0079]. The fact that the Sidon reference uses the initially received parameters and then optimizes them demonstrates that this is an initial strategy parameter, unlike Hunt which simply receives a strategy parameter and applies it.
Regarding Claim 3, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the agricultural production machine determines sensor data relating to achievement of the strategy specifications (see at least [0045] “ metric value comparison component 274 compares the current performance metric values 270 against the target performance metric values 272 and generates a difference signal indicative of the difference be”)
using sensor data generated by the sensor assembly and indicative of execution of the agricultural task; (see at least [0025] “ The sensor signals and control signals can also be provided to performance metric calculation system 120 which can calculate a wide variety of a different types of performance metrics that can characterize the performance of mobile machine 102, or external machine 104, or both.”)
Hunt does not explicitly disclose:
wherein the driver assistance system successively generates the one or more optimized machine parameters during the execution of the agricultural task including generating the initial strategy parameters at a beginning of the execution of the agricultural task and successively generating subsequent strategy parameters in order to successively optimize operation of the agricultural production machine.
In the same field of endeavor, Sidon discloses:
wherein the driver assistance system successively generates the one or more optimized machine parameters during the execution of the agricultural task including generating the initial strategy parameters at a beginning of the execution of the agricultural task and successively generating subsequent strategy parameters in order to successively optimize operation of the agricultural production machine. (see at least [0036] “A priori data collection system (or systems) 202 illustratively collects a priori data corresponding to a target or subject field, that can be used by machine 100 to generate a model (such as a yield map of the field) that can be used to control machine 100." [0081] "blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified that results in a minimum variance, or highest correlation, between the a priori data based estimated yield value and the sensed yield data generated based on in situ data during operation of machine 100.”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to successively generate the one or more optimized machine parameters during the execution of the agricultural task including generating the initial strategy parameters at a beginning of the execution of the agricultural task and successively generating subsequent strategy parameters in order to successively optimize operation of the agricultural production machine, as taught by Sidon to receive a strategy parameter, apply it for use [0081, and then iteratively optimize the process from that initial parameter [0081].
Regarding Claim 4, modified Hunt discloses the limitations of Claim 3, and Hunt further discloses:
wherein a user of the agricultural production machine adjusts at least one of the strategy parameters (see at least [0069] “ user 102 can make various setting adjustments or other operational adjustments to increase the performance of machine 102. At some point, it is assumed that user 110 observes that the operation of machine 102 is sufficient to represent a performance target.”)
to generate jointly optimized strategy parameter , which is based on both user input and an optimization routine, one or both of during or after executing an optimization routine; (see at least [0046] “The output signals 282 can include setting adjustment signals that are sent to a user interface display, or another user interface device, to indicate to user 110 which particular adjustments should be made. Those adjustments can then be made manually by user 110. This is indicated by block 284. The output signals from control system 126 can also include setting adjustment signals that are provided directly to the controlled systems 130 to automatically adjust the settings of the controlled systems." [0069] “For instance, user 110 can observe that machine 102 is performing at a very high level. In that case, user 110 illustratively provides an input through a suitable user input mechanism that indicates that performance target saving/selection component 128 is to store the current performance metric values as a performance target 136.”)
and wherein the driver assistance system transmits the jointly optimized strategy parameters and context data of the local context of the agricultural task to a control assembly. (see at least [0026] “Performance target saving/selection component 128 illustratively generates user interface displays 106 with user input mechanisms 108 that allow user 110 to save the calculated performance metrics as a performance target 138.”)
Regarding Claim 14, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the initial strategy parameters have been optimized by a same agricultural production machine (see at least [0021] “the performance metrics for the machine, when it is performing well, can be calculated and saved as a performance target.”)
as performing the agricultural task in the local context in a same or a similar local context. (see at least [0021] “When an operator is operating a similar machine under similar circumstances (such as for the same crop type, in a similar field, etc.) the operator can retrieve the previously-stored performance target and the corresponding target performance metrics can be used by the control system”)
Regarding Claim 15, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
determining based on location or distance of the local context to the other local context; (see at least [0027] “ target store 136 can include performance targets from other fleets of machines, which may be located in a similar geographic region, or which may be operating under similar operating conditions, or which may be relevant to user 110 for other reasons.”)
and wherein the initial strategy parameters were optimized by another agricultural production machine. (see at least [0021] “When an operator is operating a similar machine under similar circumstances (such as for the same crop type, in a similar field, etc.) the operator can retrieve the previously-stored performance target and the corresponding target performance metrics can be used by the control system”)
Regarding Claim 18, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the agricultural production machine is a combine harvester; (see at least [0022] “mobile machine 102 may be an agricultural machine, such as a combine”)
Hunt does not explicitly disclose, but does make obvious:
and wherein the optimized machine parameters are one or both of a threshing drum speed or a gap width of a threshing drum. (see at least [0019] “For instance, machine settings on a combine may be settings that set a particular fan speed, a rotor clearance, sieve settings, settings for the chaffer openings, engine speed, travel speed, etc.”)
The Hunt reference does not explicitly disclose the threshing drum speed or the gap width of a threshing drum, however this would be obvious to a person having ordinary skill in the art at the time of the applicant’s claimed invention. Hunt discloses a plurality of different harvester subsystems ranging from crop handling, overall machine control, internal system, external system, and the like while leaving the list open to other potential subsystems. A person having ordinary skill in the art would understand that other typical harvester subsystems and parameters may be controlled in the way described by Hunt, including the threshing drum speed or a gap width of a threshing drum.
Regarding Claim 19, Hunt discloses:
a sensor assembly; (see at least [0024] “mobile machine 102 illustratively (and by way of example only) includes ... sensors 122,”)
and a driver assistance system (see at least [0023] “ the user interface displays can be displays that allow a user to store performance metrics and to retrieve previously-store performance metrics for use in controlling the mobile machine 102”)
in communication with the sensor assembly, (see at least [0054] “System 128 can also identify other criteria, based on sensor inputs, that can be used to automatically select a set of target performance metrics”)
the driver assistance system configured to: determine whether at least one aspect of the local context for performing the agricultural tasks is identical or within a predefined amount to a same local context or to an other local context; (see at least [0054] “By way of example, if a performance target was stored for the same field in the previous year, and the wind speed and the moisture level were approximately the same as current conditions, then that performance target may have the best set of performance target metrics that system 128 can locate.”)
responsive to determining that the at least one aspect of the local context for performing the agricultural tasks is identical to the same local context or to the other local context, (see at least [0055] “For instance, system 128 can gather relevant information and automatically identify the top N performance targets in target store 126 based upon how they relate to the gathered information.”)
select one or more strategy parameters previously optimized by at least one agricultural machine in the same local context or in the other local context; (see at least [0028] “In the example shown in FIG. 2, each performance target 154-158 illustratively includes a set of metric values 164. Each performance target 154-158 can also include machine settings 166 that were in place at the time that the metric values 164 were obtained. ”)
wherein a strategy uses the one or more strategy parameters that were selected as ... strategy parameters in a strategy, (see at least [0021] “the operator can retrieve the previously-stored performance target and the corresponding target performance metrics can be used by the control system on the combine in an attempt to maintain performance metrics that are close to the target performance metrics.”)
wherein the strategy comprising strategy specifications, an instruction for use, and optimized machine parameters, (see at least [0028] “”In the example shown in FIG. 2, each performance target 154-158 illustratively includes a set of metric values 164. Each performance target 154-158 can also include machine settings 166 that were in place at the time that the metric values 164 were obtained. )
and wherein the strategy is parameterized by the one or more strategy parameters that were selected in order to adapt the strategy to the local context; (see at least [0045] “In one example, for instance, metric value comparison component 274 compares the current performance metric values 270 against the target performance metric values 272 and generates a difference signal indicative of the difference between those two sets of values. Expert system 278 then accesses control rules, functions, etc. 280 to identify various actions that can be taken in order to adjust the operation of machine”)
determine, using the strategy, one or more optimized machine parameters for the agricultural production machine in order to perform the agricultural task, whereby the strategy, with the one or more strategy parameters as the initial strategy parameters, uses the strategy specifications as input for the instruction for use in order to generate the one or more optimized machine parameters as outputs; (see at least [0059] “ Setting control system 276 then calculates machine settings based upon the obtained set of performance target metrics. This is indicated by block 355. For instance, setting control system 276 can calculate machine settings for all configurable controlled systems on machine 102. "”)
wherein the strategy is indicative of one or more optimization goals, (see at least [0065] “ For instance, the rules or functions 280 can act as a mapping between deviations from certain target performance metric values and the particular settings that need to be adjusted. ”)
wherein the strategy specification is indicative of a respective optimization goal or weighting of a plurality of optimization goals; (see at least [0066] “some performance metric values may be more important than others. In that case, the expert system can weight those performance metric values more heavily in its calculation of whether and what machine settings need to be adjusted.”)
wherein the one or more strategy parameters comprise operating parameters according to the strategy for operation of the agricultural production machine; (see at least [0045] “ Expert system 278 then accesses control rules, functions, etc. 280 to identify various actions that can be taken in order to adjust the operation of machine 102 (or its configuration settings, etc.) so that its performance (indicated by the current performance metric values 270) more closely matches the desired performance (indicated by the target performance metric values 272).”)
and automatically, using the one or more optimized machine parameters, control at least a part of the agricultural production machine. (see at least [0084] “the control system will automatically control the machine to obtain those performance target metric values. In this way, the user need not attempt to recreate the precise settings that were used to obtain the high level of performance, as the control system can do this automatically. ”)
wherein a control assembly has a database of stored strategy parameters correlated to data on local contexts; (see at least [0053] “ performance targets are stored in target store 136 and are indexed by field location or by farm.”)
wherein the stored strategy parameters of driver assistance systems of agricultural production machines have been successively optimized in optimization routines (see at least [0028] “It can be seen in FIG. 2 that target store 136 illustratively includes a set of performance targets 138. The individual performance targets in the set of performance targets 138 are illustratively represented by numbers 154, 156 and 158 in FIG. 2. FIG. 2 also shows that target store 136 can include performance targets from other fleets of machines, which may be located in a similar geographic region, or which may be operating under similar operating conditions, or which may be relevant to user 110 for other reasons.”)
during execution of agricultural tasks in a variety of local contexts (see at least [0029] “the index values 170 can also include a date and time 178 when the performance target was obtained, a location 180 (that can be provided by a geographic location generator, such as a GPS system), a field identifier 182 that identifies the particular field (and perhaps the farm) where the performance target was set, the crop type 184, the operator ID 186 and it can include a wide variety of other characteristics, such as whether the crop was wet, whether there was downed crop, whether there was a strong wind, the wind direction, etc.") (*Examiner interprets the variety of characteristics of the performance target as meaning the performance targets come from a variety of local contexts)
wherein the driver assistance system determines context data of the local context of the agricultural task using the sensor assembly; (see at least [0052] “component 128 uses crop type identifier system 118 to automatically identify the crop type, with which machine 102 is being used. This is indicated by block 322. This can be obtained using a sensor input 324.”)
wherein the driver assistance system transmits the context data to the control assembly; (see at least [Fig. 7] [0090] “target store 136, search engine 134 and remote systems 114 (or any other parts of the control architecture 100) can be located at a remote server location 502. Therefore, machine 102 accesses those systems through remote server location 502.”)
based on comparing the transmitted context data with the context data stored in the database; (see at least [0054] “Component 128 can use this to search for a most relevant performance target in target store 136.”)
and for further modification and optimization while performing the agricultural task in order to determine the one or more optimized machine parameters (see at least [0063] “However, if machine 102 is still being operated, then setting control system 276 illustratively determines whether the settings need to be adjusted in order for the calculated current performance metric values to more closely match the performance target metric values.
Hunt does not explicitly disclose:
wherein a strategy uses the one or more strategy parameters that were selected as initial strategy parameters in a strategy,
from initial strategy parameters for adaptation to the local context of the agricultural task;
wherein the control assembly determines the one or more strategy parameters as the initial strategy parameters
wherein the control assembly transmits the one or more strategy parameters as the initial strategy parameters to the driver assistance system of the agricultural production machine;
and wherein the driver assistance system uses the one or more strategy parameters as the initial strategy parameters to determine the one or more optimized machine parameters.
In the same field of endeavor, Sidon discloses:
wherein a strategy indicative of one or more optimization goals uses the one or more strategy parameters that were selected as initial strategy parameters in a strategy, (see at least [0079] “the map and/or model parameters can be output to another harvesting machine, to be used in the control of that machine (e.g., generation of yield map data during operation of the other harvesting machine)." [0081] "blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified that results in a minimum variance, or highest correlation, between the a priori data based estimated yield value and the sensed yield data generated based on in situ data”)
from initial strategy parameters for adaptation to the local context of the agricultural task; (see at least [0036] “A priori data collection system (or systems) 202 illustratively collects a priori data corresponding to a target or subject field, that can be used by machine 100 to generate a model (such as a yield map of the field) that can be used to control machine 100." [0051] "a yield map can be dynamically generated based upon a priori data (such as aerial imagery data) and in situ data, such as actual yield data sensed on the machine (e.g., using sensor(s) 236 during the harvesting operation”)
wherein the control assembly determines the one or more strategy parameters as the initial strategy parameters (see at least [0068] “In response to this, logic 414 can select the yield map which is output at block 432.”)
wherein the control assembly transmits the one or more strategy parameters as the initial strategy parameters to the driver assistance system of the agricultural production machine; (see at least [0079] “the map and/or model parameters can be output to another harvesting machine, to be used in the control of that machine (e.g., generation of yield map data during operation of the other harvesting machine).”)
and wherein the initial strategy parameters are used: for initial use in performing the agricultural task; (see at least [0081] “blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified that results in a minimum variance, or highest correlation, between the a priori data based estimated yield value and the sensed yield data generated based on in situ data”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to store, transmit, and receive a strategy parameter as an initial strategy parameter, and use the initial strategy parameter to determine an optimized parameter, as taught by Sidon to receive a strategy parameter, apply it for use, and then optimize the process from that initial parameter [0079]. The fact that the Sidon reference uses the initially received parameters and then optimizes them demonstrates that this is an initial strategy parameter, unlike Hunt which simply receives a strategy parameter and applies it.
Regarding Claim 21, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the control assembly uses at least one map in order to generate the one or more optimized machine parameters; (see at least [0065] “the rules or functions 280 can act as a mapping between deviations from certain target performance metric values and the particular settings that need to be adjusted.”)
and wherein the control assembly using the initial strategy parameters reduces a size of the at least one map. (see at least [0021] “the corresponding target performance metrics can be used by the control system on the combine in an attempt to maintain performance metrics that are close to the target performance metrics.”)
Regarding Claim 23, modified Hunt discloses the limitations of Claim 21, and Hunt further discloses:
wherein the initial strategy parameters were previously optimized using a plurality of agricultural production machines. (See at least [0021] “metrics for the machine, when it is performing well, can be calculated and saved as a performance target. When an operator is operating a similar machine under similar circumstances (such as for the same crop type, in a similar field, etc.) the operator can retrieve the previously-stored performance target" [0079] " Therefore, when those performance metrics are downloaded to the second machine (as a performance target), the control system automatically sets the machine settings, and makes adjustments to them, to obtain the performance that was previously obtained using the first machine.”)
Regarding Claim 24, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the at least one aspect of the local context for performing the agricultural tasks is determined not to be identical but within a predefined distance to the other local context; (see at least [0027] “The individual performance targets in the set of performance targets 138 are illustratively represented by numbers 154, 156 and 158 in FIG. 2. FIG. 2 also shows that target store 136 can include performance targets from other fleets of machines, which may be located in a similar geographic region,”)
responsive to determining that the at least one aspect of the local context for performing the agricultural tasks is not identical to the same local context but within the predefined distance to the other local context, selecting the one or more strategy parameters previously optimized by at least one agricultural machine in the other local context; (see at least [0027] “illustratively represented by numbers 154, 156 and 158 in FIG. 2. FIG. 2 also shows that target store 136 can include performance targets from other fleets of machines, which may be located in a similar geographic region, or which may be operating under similar operating conditions, or which may be relevant to user 110 for other reasons." (*Examiner interprets that the similar geographic region or similar conditions includes selecting a target from a similar geographic region but not similar condition)
and wherein the initial strategy parameters optimized for the other local context are used: for initial use in performing the agricultural task in the same local context; (see at least [0043] “Those target performance metric values are indicated by block 272, and they can be obtained from target store 136, for instance.”)
and for further modification and optimization while performing the agricultural task in order to determine the one or more optimized machine parameters for the same local context. (see at least [0063] “However, if machine 102 is still being operated, then setting control system 276 illustratively determines whether the settings need to be adjusted in order for the calculated current performance metric values to more closely match the performance target metric values.”)
Claims 5, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt (US 20160086291), herein after referred to as Hunt, in view of Sidon (US 20210321567), herein after referred to as Sidon, and Dasgupta (US 20200402184), herein after referred to as Dasgupta.
Regarding Claim 5, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the strategy comprises at least one map which depicts one or more relationships between the machine parameters and the strategy specifications; (see at least [0065] “the rules or functions 280 can act as a mapping between deviations from certain target performance metric values and the particular settings that need to be adjusted. ”)
wherein using the instruction for use, the driver assistance system determines the optimized machine parameters based on the strategy specifications from the at least one map; (see at least [0065] “the rules and functions illustratively accommodate for these off-setting adjustments to generate machine setting adjustments that will cause the current performance metric values to more closely conform to the performance target values, as a whole”)
Hunt des not explicitly disclose:
and wherein the map has a plurality of coefficients which parameterize mathematical functions.
In the same field of endeavor, Dasgupta discloses:
and wherein the map has a plurality of coefficients which parameterize mathematical functions. (see at least [0038] “The linear piece-wise function and relative parameters S1, S2, S3 may be established by any suitable method, such as those described below with regards to the coefficients" [0049] "In this equation, η.sub.3, η.sub.4, η.sub.5, and η.sub.6 are coefficient parameters for the different decision variables”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to include a map having a plurality of coefficients which parameterize mathematical functions as taught by Dasgupta to control a harvester using a combination of parameters [0040].
Regarding Claim 22, modified Hunt discloses the limitations of Claim 21, but Hunt does not explicitly disclose:
wherein the initial strategy parameters comprise initial coefficients of the at least one map.
In the same field of endeavor, Dasgupta discloses:
wherein the initial strategy parameters comprise initial coefficients of the at least one map. (See at least [0040] “The coefficients used herein (e.g., penalty coefficients) are used to weight the parameters (e.g., weight the importance of each parameter relative to the other parameters) and are established by any suitable method such as manual setting by a user, optimization methods”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to include a map having a plurality of coefficients which parameterize mathematical functions as taught by Dasgupta to control a harvester using a combination of parameters [0040].
Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt (US 20160086291), herein after referred to as Hunt, in view of Sidon (US 20210321567), herein after referred to as Sidon, Dasgupta (US 20200402184), herein after referred to as Dasgupta, and Muench (US 20180196441), herein after referred to as Muench.
Regarding Claim 6, modified Hunt discloses the limitations of Claim 5, but Hunt does not explicitly disclose:
wherein the strategy comprises a cost function;
wherein the cost function weights the strategy specifications in that using the instruction for use, the driver assistance system determines the one or more optimized machine parameters from the at least one map by using the cost function to minimize costs of the cost function.
In the same field of endeavor, Muench discloses:
wherein the strategy comprises a cost function; (see at least [0019] “The control device can, as noted above, implement a predictive, model-based control through optimization of the cost function. ”)
wherein the cost function weights the strategy specifications (see at least [0037] “The cost function associates desired operating states with lower costs [rather] than undesired operating states.”)
in that using the instruction for use, the driver assistance system determines the one or more optimized machine parameters from the at least one map by using the cost function to minimize costs of the cost function. (see at least [0037] “The quantities of the optimization problem and the associated cost function are as a whole indicated as 146 and sent to the solving device 100, which generates a sequence of command quantities 154, which solves the optimization problem and minimizes the associated cost function. The sequence of target quantities 154 is a timewise successive sequence of command quantities, which contain information concerning the speed of the harvesting machine.”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to use a cost function as the strategy, which weighs the strategy specification to determine optimized machine parameters by using the const function to minimize costs of the cost function, as taught by Muench to implement model-based control of a harvester [0019].
Regarding Claim 7, modified Hunt discloses the limitations of Claim 6, but Hunt does not explicitly disclose:
wherein the driver assistance system sets various machine parameters in an optimization routine, which form interpolation points of the at least one map;
and wherein the driver assistance system determines one or both of the plurality of coefficients or at least one shift of the at least one map from the interpolation points.
In the same field of endeavor, Muench discloses:
wherein the driver assistance system sets various machine parameters in an optimization routine, which form interpolation points of the at least one map; (see at least [0033] “The device 98 is additionally sent data 138, which enable a weighting of the parameters contained in the cost function. Said data 138 can at least in part be able to be entered via the operator interface 88, so that an operator can, for example, choose if comfort (i.e., a limitation of the maximum accelerations and decelerations of the harvesting machine 10 that occur in operation and/or a quantity derived therefrom, which, for example, can correspond to a time integral of the root of the squared accelerations) or an optimum work load of the harvesting machine 10 is more important to him.”) (*Examiner interprets the Hunt reference to read on the limitation regarding interpolation as under the broadest reasonable interpretation, the reference performs the same function as the claim limitation but using different terminology. As such, a person having ordinary skill in the art would understand the reference to read on the claim.”
and wherein the driver assistance system determines one or both of the plurality of coefficients or at least one shift of the at least one map from the interpolation points. (see at least [0033] “The data 138 for weighting the cost function are converted to weight matrices for the cost function by the device 98 (or another part of the control device 80)." [0037] " In operation the device 98 calculates the quantities for an optimization problem and an associated cost function”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt set various machine parameters in an optimization routine, which form interpolation points of the at least one map and determine one or both of the plurality of coefficients or at least one shift of the at least one map from the interpolation points, as taught by Muench to implement model-based control of a harvester [0019].
Regarding Claim 8, modified Hunt discloses the limitations of Claim 7, but Hunt does not explicitly disclose:
wherein one or both: the driver assistance system selects the interpolation points depending on the initial strategy parameters; or execution of the optimization routine is shortened responsive to using the initial strategy parameters based on the local context as compared to execution of the optimization routine based on non-context-dependent initial strategy parameters.
In the same field of endeavor, Muench discloses:
wherein one or both: the driver assistance system selects the interpolation points … ; or execution of the optimization routine is shortened responsive to using the initial strategy parameters based on the local context as compared to execution of the optimization routine based on non-context-dependent initial strategy parameters. (see at least [0033] “Alternatively or additionally, the operator can select which performance parameters of the harvesting machine 10, such as work load of the drive engine 46, throughput and/or losses are important to him. ”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to provide that the driver assistance system selects the interpolation points, as taught by Muench to implement model-based control of a harvester [0019].
In the same field of endeavor, Sidon discloses:
depending on the initial strategy parameters (see at least [0070] “At block 510, a harvesting system flow model is obtained. The harvesting system flow model is, in one example, pre-generated" [0071] "The harvesting system flow model includes a set of parameters that model material flow (e.g., lateral and transverse delays) through harvesting system 285”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to select vehicle control characteristics depending on the initial strategy parameters, as taught by Sidon to receive a strategy parameter, apply it for use, and then optimize the process from that initial parameter [0079].
Regarding Claim 9, modified Hunt discloses the limitations of Claim 7, and Hunt further discloses:
wherein the driver assistance system, ...sets the optimized machine parameters of the initial strategy parameters as optimized machine parameters in the agricultural production machine; (see at least [0058] “once a performance target has been identified (either manually or automatically), control system 126 obtains the selected set of performance target metrics 166 for the selected performance target from data store 136." [0059] "Setting control system 276 then calculates machine settings based upon the obtained set of performance target metrics”)
Hunt does not explicitly disclose:
executing the optimization routine,
wherein, during or after executing the optimization routine, the initial strategy parameters are modified to generate modified optimized machine parameters;
and wherein the driver assistance system implements the modified optimized machine parameters to set the machine parameters for the agricultural production machine during or after executing the optimization routine.
In the same field of endeavor, Sidon disclose:
executing the optimization routine, (see at least [0081] “blocks 538 and 540 result in the operation continuing, iteratively, until an optimal set of model parameters have been identified”)
wherein, during or after executing the optimization routine, the initial strategy parameters are modified to generate modified optimized machine parameters; (see at least [0082] “in adjusting parameters of the flow model at block 556 and applying of the flow model, with the adjusted parameters, to identify modified parameters that result in a threshold correlation between the detected yield map and the estimated yield map. ”)
and wherein the driver assistance system implements the modified optimized machine parameters to set the machine parameters for the agricultural production machine during or after executing the optimization routine. (see at least [0079] “this map is identified as an optimized map and can be output at block 432, as shown in FIG. 4. For example, machine 100 (or other systems or machines in architecture 200) is controlled based on the output map. This is represented at block 544.”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to perform an optimization routine, and set the parameters only during or after the optimization routine, as taught by Sidon to receive a strategy parameter, apply it for use, and then optimize the process from that initial parameter [0079].
Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt (US 20160086291), herein after referred to as Hunt, in view of Sidon (US 20210321567), herein after referred to as Sidon, , and Muench (US 20180196441), herein after referred to as Muench.
Regarding Claim 10, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein the initial strategy parameters comprise one or both of the strategy specifications or initial strategy parameters of the instruction for use; (see at least [0028] “In the example shown in FIG. 2, each performance target 154-158 illustratively includes a set of metric values 164. Each performance target 154-158 can also include machine settings 166 that were in place at the time that the metric values 164 were obtained. ”)
of at least one map depicting relationships between the machine parameters and the strategy specifications. (see at least [0065] “ the rules or functions 280 can act as a mapping between deviations from certain target performance metric values and the particular settings that need to be adjusted”)
Hunt does not explicitly disclose:
and wherein the initial strategy parameters of the instruction for use comprise one or both of weights of a cost function or coefficients
In the same field of endeavor, Muench discloses:
and wherein the initial strategy parameters of the instruction for use comprise one or both of weights of a cost function or coefficients (see at least [0033] “The device 98 is additionally sent data 138, which enable a weighting of the parameters contained in the cost function.”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to provide that initial strategy parameters of the instruction for use comprise one or both of weights of a cost function or coefficients, as taught by Muench to implement model-based control of a harvester [0019].
Regarding Claim 12, modified Hunt discloses the limitations of Claim 1, and Hunt further discloses:
wherein a same strategy specification leads to different optimized machine parameters depending on the strategy parameters; and wherein the same strategy specification leads to the different optimized machine parameters (see at least [0065] “increasing power utilization may improve the power utilization performance metric, but it may also decrease fuel efficiency, under certain circumstances. Therefore, the rules and functions illustratively accommodate for these off-setting adjustments to generate machine setting adjustments that will cause the current performance metric values to more closely conform to the performance target values, as a whole.”)
Hunt does not explicitly disclose:
depending on the initial strategy parameters including weights of a cost function.
In the same field of endeavor, Muench discloses:
depending on the initial strategy parameters including weights of a cost function. (see at least [0033] “The device 98 is additionally sent data 138, which enable a weighting of the parameters contained in the cost function.”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to provide that the optimized machine parameters depend on the initial strategy parameters including weights of a cost function., as taught by Muench to implement model-based control of a harvester [0019].
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt (US 20160086291), herein after referred to as Hunt, in view of Sidon (US 20210321567), herein after referred to as Sidon, and Thomas (US 20210080615), herein after referred to as Thomas.
Regarding Claim 16, modified Hunt discloses the limitations of Claim 1, but Hunt does not explicitly disclose:
wherein determining whether at least one aspect of the local context is identical or within the predefined amount to the other local context is determined via a degree of similarity by determining whether a climatic zone is the same for both the local context and the other local context.
In the same field of endeavor, Thomas discloses:
wherein determining whether at least one aspect of the local context is identical or within the predefined amount to the other local context is determined via a degree of similarity by determining whether a climatic zone is the same for both the local context and the other local context. (see at least [0034] “The Köppen climate classification system can be used as part of a geographic similarity metric.”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural information field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to determine whether at least one aspect of the local context is identical or within the predefined amount to the other local context is determined via a degree of similarity by determining whether a climatic zone is the same for both the local context and the other local context, as taught by Thomas to determine if two locations will have similar climates based on being in the same or similar climactic zone [0034].
Regarding Claim 17, modified Hunt discloses the limitations of Claim 16, but Hunt does not explicitly disclose:
wherein the climatic zones are divided into similar climatic zones according to Koppen-Geiger classification.
In the same field of endeavor, Thomas discloses:
wherein the climatic zones are divided into similar climatic zones according to Koppen-Geiger classification. (see at least [0034] “The Köppen climate classification system can be used as part of a geographic similarity metric.”)
The above pieces of prior art are considered analogous as they both represent inventions in the agricultural information field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hunt to device the climactic zones into similar climatic zones according to Koppen-Geiger classification, as taught by Thomas to determine if two locations will have similar climates based on being in the same or similar Koppen-Geiger zone [0034].
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 JACOB D UNDERBAKKE whose telephone number is (571)272-6657. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/JACOB DANIEL UNDERBAKKE/Examiner, Art Unit 3662
/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662
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