DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to the application filed on 19 August 2024. Claims 1-18 are presently pending and are presented for examination.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE102023122014.4, filed on August 17, 2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 19, 2024 and December 20, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation discloses sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation discloses function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is:
“computing unit” in Claims 8 and 14. A review of the specification shows that it may be configured to process any one, any combination, or all of the following into a plurality of output signals 41 and includes at least one processor 86 and at least one memory 87 in [0043].
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it from being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it from being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8-11 and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wilken et al., US 20170049045 A1 (Hereinafter, “Wilken”) in view of Vandike US 20230161347 A1 in further view of Debilde US 20170006261 A1.
Regarding Claim 1, Wilken discloses an agricultural work machine comprising: a driver assistance system comprising at least one process model, See [0030], “The harvesting machine 1 further comprises a driver assistance system 4 for controlling the header 2. This driver assistance system 4 comprises a memory 5 for storing data, i.e., a memory in the sense of information technology, and a computing unit 6 for processing the data stored in the memory 5. The driver assistance system 4 is designed to support a driver 7 of the harvesting machine 1 during the operation of the harvesting machine. The driver assistance system 4 comprising the memory 5 and the computing unit 6 is schematically shown in FIG. 2.” And [0045], “The functional system model 5b is aligned with the current harvesting-process state by the computing unit 6.”
wherein the driver assistance system is configured as a See [0066], “implementation instruction 5d includes at least one premise, on the basis of which the computing unit 6 (driver assistance system), in the autonomous determination of the at least one header parameter, implements a prioritization between selected harvesting-process strategies 5a and/or between sub-strategies of a selected harvesting-process strategy 5a and/or between harvesting-process parameters to be set or optimized, and/or between header parameters to be specified. And [0067], “According to the explanations of the families of characteristics A, B presented above, specifically speaking, for the harvesting-process strategy for setting or optimizing the harvesting-process parameter “separation losses” (quality parameter), an implementation instruction 5d consists in the computing unit 6 specifying, in response to an increase in the harvesting-process parameter “separation losses” and on the basis of on the system model 5b, an increase in the header parameter “extension angle of the intake auger fingers” and/or an increase in the header parameter “cutter bar table length” (manipulated variables).”
wherein at least the manipulated variables in the static process model component run through an optimization step and, in the dynamic process model component, through a control loop structure.
See [0016], “at least one initial model is stored in the memory of the driver assistance system, which model can function as a starting value, in particular, for the aforementioned, continuous alignment of the functional system model. Given a suitable selection of the initial model, the functional system model can be brought into good conformance with the actual conditions in only a few alignment cycles.” And [0047], “In the sense of short reaction times of the harvesting machine 1 to changing harvesting-process states, it is preferably provided that the computing unit 6 determines the header parameters cyclically (i.e. control loop), in the sense described above.“
Wilken discloses a driver assistance system with process parameter optimization, but does not explicitly disclose performance map controls or dynamic process models. However, Vandike teaches a driver assistance system including map controls and the following:
wherein the at least one process model comprises one or more performance maps and a dynamic See [0027], “The present discussion, thus, proceeds with respect to systems that receive a prior information map of a field or map generated during a prior operation and also use an in-situ sensor to detect a variable indicative of one or more of an agricultural characteristic during a harvesting operation. The systems generate a model that models a relationship between the values on the prior information map and the output values from the in-situ sensor.” And [0055], “predictive map 264 can be provided to the control zone generator 213. Control zone generator 213 groups adjacent portions of an area into one or more control zones based on data values of predictive map 264 that are associated with those adjacent portions. A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting of controllable subsystems 216 may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map 264. In that case, control zone generator 213 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems 216.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the dynamic process mapping limitations disclosed in Vandike with reasonable expectation of success. The motivation for doing so would have been to improve the control algorithm, see Vandike [0148].
Wilken discloses a driver assistance system with process parameter optimization in a control loop, but does not explicitly disclose limit values. However, Debilde teaches a driver assistance system with full autonomy and non-linear process models including: wherein the dynamic non-linear process model comprises a static process model component and a dynamic process model component; Fig.3 and [0047], “One or more of the examples disclosed herein can relate to an autonomous vehicle perception system, which is not bound by static algorithms that are sensor-dependent. Moreover, the vehicle environment can be represented as semantic objects, which allow for further processing by high-level reasoning tasks, for example to determine whether or not the vehicle should stop, depending on the type of the detected object.“ And [0052], “the controller implements a model that encodes the conditional distribution of the pixel labels via an energy-based model. This model is optimized using a max-margin learning approach that tunes all model parameters simultaneously, using an ontological loss function. After training, the model allows for effective and efficient inference using graph cuts inference by means of α-expansion, obtaining accurate semantic labels for each pixel. Also [0054], “This functionality can allow higher-level processing of the detected objects in the vehicle's visual field. This can be used for a more intelligent autonomous feedback loop, for example, to intelligently steer the vehicle around objects or to steer it towards uncut crops/grass. A map of the field could be created on-the-go by combining camera output and GPS signal. The controller could be used as part of a driver assistance system to highlight objects in the machine's path, thereby increasing machine safety. Fast features extraction algorithms can be used, which can be an advantage for real-time implementation.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the fully autonomous limitations disclosed in Debilde with reasonable expectation of success. The
motivation for doing so would have been to enable fully autonomous harvesting behavior for
agricultural vehicles, see Debilde [0054].
Regarding Claim 2, Wilken discloses the following limitation dependent on Claim 1:
wherein the See [0016], [0047], and [0066].
further comprising a self-adjusting controller assigned to the control loop structure and configured to monitor the one or more quality parameters of a process. See [0006], [0031], and [0045].
Wilken discloses a driver assistance system with process parameter optimization in a control loop, but does not explicitly disclose limit values. However, Vandike teaches a driver assistance system with limit variables in [0072-0073].
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the variable threshold limitations disclosed in Vandike with reasonable expectation of success. The motivation for doing so would have been to improve the control algorithm, see Vandike [0148].
Regarding Claim 3, Wilken discloses the following limitation dependent on Claim 2:
wherein the optimization step and the control loop structure form an optimization method which is configured to adapt the at least one process model in a model adaptation step so that the dynamic non-linear process model component is configured to determine the static process model component of a process from the monitoring of the one or more quality parameters and using a parameter estimation method; See [0062-0069], “an implementation instruction 5d for implementing the mutually conflicting strategies preferably includes a multi-objective optimization, which can be a Pareto optimization, for example. Such a multi-objective optimization can be implemented particularly easily on the basis of the system model 5b, preferably on the basis of the aforementioned families of characteristics A, B and, further preferably, using the aforementioned characteristic control … In particular, in the implementation of the mutually conflicting strategies, it is preferably provided that an implementation instruction 5d includes at least one premise, on the basis of which the computing unit 6, in the autonomous determination of the at least one header parameter, implements a prioritization between selected harvesting-process strategies 5a and/or between sub-strategies of a selected harvesting-process strategy 5a and/or between harvesting-process parameters to be set or optimized, and/or between header parameters to be specified.”
wherein the one or more manipulated variables comprising the optimized work parameters and the one or more quality parameters dependent on a respective manipulated variable form input variables to the optimization method. See [0057-0062], “For the depiction of the functional relationships, at least one family of characteristics A, B is assigned to a harvesting-process parameter, wherein, in this case, this harvesting-process parameter is defined as an output variable of the at least one family of characteristics A, B. The input variable for the at least one family of characteristics A, B is preferably a header parameter, in particular the header parameters “cutterbar table length” and “extension angle of the intake auger fingers.”
Regarding Claim 4, Wilken discloses the following limitation dependent on Claim 3:
wherein, in the optimization step, optimized manipulated variables are determined from the manipulated variables derived from the at least one process model and specified quality parameters. See [0006], “quality of the function of the header can be evaluated. The first objective is to minimize the losses at the header itself … for example, “pick-up losses,” “cut crop losses,” “bouncing grain losses” or the like … downstream processes, i.e., threshing, separating, and cleaning, in particular, in the case of the combine harvester. Due simply to the number of header parameters, it becomes clear that setting the header parameters in an optimal manner is a highly complex task. “ And [0013], “the computing unit aligns the functional system model with the particular current harvesting-process state … refers to all state variables that are related to the harvesting process in any manner. These include field information and/or harvesting-process parameters and/or header parameters and/or environmental information.” And [0021], “A further implementation instruction that is preferably used consists of utilizing a multi-objective optimization for the simultaneous implementation of mutually conflicting harvesting-process strategies … in particular, the definition of premises makes it possible to effectively determine header parameters.” Also [0055] and [0065-0069].
Regarding Claim 5, Wilken discloses the following limitation dependent on Claim 4:
wherein the control loop structure comprises a self-adjusting controller and has as input the manipulated variables optimized in the optimization step, See [0061], “The above-described alignment of the system model 6b with the current harvesting-process state is preferably carried out, in the case of the system model 5b having at least one family of characteristics A, B, in that the computing unit 6 aligns the at least one family of characteristics A, B with the harvesting-process state during the on-going harvesting operation, in particular cyclically. On the basis of the initial model 5c, at least one initial family of characteristics is stored in the memory 5 as a starting value, wherein, in the first determination of the at least one header parameter, the computing unit 6 therefore carries out the determination of the at least one header parameter on the basis of the initial family of characteristics 5c. A series of real sensor measured values is plotted for the particular harvesting-process state in each of the FIGS. 3 to 4.”
the specified quality parameters and the quality parameters determined depending on the optimized manipulated variables; and wherein the self-adjusting controller is configured to derive optimized manipulated variables from a comparison of one or more specified quality parameters with one or more quality parameters that are determined, and transfer the optimized manipulated variables to the process and model adaptation step of a dynamic model adaptation. See [0067-0069], “ In addition, it is preferable that, for the harvesting-process strategy for setting or optimizing the harvesting-process parameter “cleaning losses”, an implementation instruction 5d consists in the computing unit 6 specifying, in response to an increase in the harvesting-process parameter “cleaning losses” and on the basis of the system model 5b, a decrease in the header parameter “extension angle of the intake auger fingers” and/or a reduction in the header parameter “cutterbar table length”.
Regarding Claim 6, Wilken discloses the following limitation dependent on Claim 5:
wherein the one or more quality parameters that are determined are transferred to the model adaptation step as an input variable. See [0057-0063].
Regarding Claim 8, Wilken discloses the following limitation dependent on Claim 3:
wherein the optimization method is integrated into the driver assistance system assigned to the agricultural work machine; wherein the driver assistance system includes a particular process model for a control and regulation device accommodating a computing unit; wherein the computing unit is configured to operate the optimization method using the particular process model; and
See [0010-0013], “a harvesting machine wherein the header, together with the driver assistance system, with a memory for storing data and with a computing unit, is designed to autonomously determine individual machine parameters of the header and to assign the individual machine parameters to the header … harvesting process strategies that are stored in the memory of the driver assistance system.” And [0012], “the computing unit aligns the functional system model with the current harvesting-process state during the on-going harvesting operation. The consideration here is that of aligning the functional system model forming the basis for the autonomous determination of the header parameters with the actual conditions.” [0013], “the computing unit aligns the functional system model with the particular current harvesting-process state using a recursive method, so that the functional system model incrementally approaches the actual conditions. The harvesting process state refers to all state variables that are related to the harvesting process in any manner. These include field information and/or harvesting-process parameters and/or header parameters and/or environmental information.” Also [0030-0031].
wherein the driver assistance system is further configured: (a) to determine the one or more optimized work parameters as the one or more manipulated variables using one or more sensor systems and to transfer the one or more optimized work parameters as the one or more manipulated variables to the optimization method; See [0032] and [0047-0051].
(b) the optimization method is configured to adapt the non-linear dynamic process model in a model adaptation step and then to derive the static process model component; (c) the static process model component is configured to determine the one or more optimized work parameters using the one or more quality parameters specifiable by an operator of the agricultural work machine; (d) the control loop structure is configured to receive the one or more optimized work parameters that are determined and the one or more quality parameters, and to determine the one or more optimized work parameters on a basis of the one or more optimized work parameters and the one or more quality parameters; (e) the optimized work parameters are then configured to be set in one or more working units of the agricultural work machine, and the one or more quality parameters are determined; See [0055-0063].
(f) a test step is configured to compare the one or more quality parameters that are determined with one or more predefined quality parameters, wherein, the control loop structure is configured to terminate; and (g) responsive to the control loop structure being activated, the control loop structure is configured to determine the one or more optimized work parameters and specify the one or more optimized work parameters to a particular working unit. See [0025], [0030-0037] and [0061].
Wilken discloses a driver assistance system with process parameter optimization in a control loop, but does not explicitly disclose limit values. However, Vandike teaches a driver assistance system with limit variables including: responsive to the comparison exceeding a limit value, the control loop structure is configured to activate, and wherein, responsive to the comparison no longer exceeding the limit value. See [0072-0073].
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the variable threshold limitations disclosed in Vandike with reasonable expectation of success. The motivation for doing so would have been to improve the control algorithm, see Vandike [0148].
Regarding Claim 9, Wilken discloses the following limitation dependent on Claim 1:
further comprising one or more working units; and wherein the driver assistance system is configured to automatically set the one or more manipulated variables that are optimized in the working units of the agricultural work machine. See [0032], “The determination of the header parameters is an autonomous determination to the extent that, in principle, the harvesting-process strategy 5a is implemented by the computing unit 6 without the need for intervention by the driver 7 … harvesting-process strategies 5a differ in terms of the objective of setting or optimizing harvesting-process parameters, which will be explained further below. “ And [0033], “It should be noted that the driver assistance system 4 can be centrally designed. The driver assistance system is used to control not only the header 2, but also downstream working units. It also is conceivable, however, that the driver assistance system 4 is decentrally structured and is composed of a number of individual control systems. It can then be provided, for example, that at least a portion of the working units of the harvesting machine 1 each have an assigned, decentralized control system.”
Regarding Claim 10, Wilken discloses the following limitation dependent on Claim 1:
wherein the control loop structure is configured to effect a dynamic adaptation of a process of generating the one or more manipulated variables and the determining of the one or more quality parameters before the at least one process model that has been saved has been adjusted to the process. See at least [0053-0063], “the system model 5b is aligned, preferably cyclically, with the actual harvesting-process state… at least one harvesting-process strategy(quality parameter) 5a is directed to the objective of setting or optimizing at least one harvesting-process parameter such as “cut crop losses,” … “cleaning losses,” or “fuel consumption,” or the like … this harvesting-process parameter is defined as an output variable of the at least one family of characteristics A, B. The input variable for the at least one family of characteristics A, B is preferably a header parameter.”… FIG. 4 shows the family of characteristics B for the functional relationship between the output variable “cleaning losses” and the input variables “cutterbar table length” and “extension angle of the intake auger fingers.” In [0060], “computing unit 6 always uses one and the same family of characteristics A, B, possibly with a modification based on the aforementioned alignment, as the basis for the determination of the at least one harvesting-process parameter.”
Regarding Claim 11, Wilken discloses the following limitation dependent on Claim 1:
wherein the non-linear dynamic process model describes a relationship between work parameters of a respective working unit and the quality parameters. See Fig.3 and [0057-0063].
Regarding Claim 13, Wilken discloses the following limitation dependent on Claim 1:
wherein the control loop structure is configured to remain active until the optimized work parameters derived from the non-linear dynamic process model meet one or more quality criteria. See Fig.3 and [0057-0063].
Regarding Claim 14, Wilken discloses the following limitation dependent on Claim 1:
further comprising a plurality of working units of the agricultural work machine; wherein each of the plurality of working units have an associated respective automated process unit; wherein the at least one process model comprises respective process models describing respective automated process units of the plurality of working units; and wherein at least one computing unit is configured to use process models in order to optimize the one or more optimized work parameters of the plurality of working units and to specify the one or more optimized work parameters for respective working units. See at least [0033-0035], “The driver assistance system is used to control not only the header 2, but also downstream working units. It also is conceivable, however, that the driver assistance system 4 is decentrally structured and is composed of a number of individual control systems. It can then be provided, for example, that at least a portion of the working units of the harvesting machine 1 each have an assigned, decentralized control system.” And [0042-0047].
Regarding Claim 15, Wilken discloses the following limitation dependent on Claim 14:
wherein the plurality of working units comprise one or more of a threshing unit, a separating unit, or a cleaning unit; wherein one or more automated process units comprise one or more of: an automatic threshing unit comprising the threshing unit and an associated threshing unit process model; an automatic separating unit comprising the separating unit and an associated separating unit process model; or an automatic cleaning unit comprising the cleaning unit and an associated cleaning unit process model. See at least [0005], “The optimal setting of the header parameters is highly significant not only for cutting and picking up, but also for all the downstream processes. In the case of a combine harvester, these downstream processes are, inter alia, threshing, separating, and cleaning.” Also [0034-0035, 0042, and 0048]
Regarding Claim 16, Wilken discloses the following limitation dependent on Claim 15:
wherein a common process model is assigned to at least the automatic threshing unit and the automatic separating unit. See [0046-0048].
Regarding Claim 17, Wilken discloses the following limitation dependent on Claim 15:
wherein a common process model is assigned to the automatic threshing unit, the automatic separating unit, and the cleaning unit. See [0046-0048].
Regarding Claim 18, Wilken discloses the following limitation dependent on Claim 14:
wherein the plurality of working units comprise a threshing unit, a separating unit, or a cleaning unit; wherein one or more automated process units comprise: an automatic threshing unit comprising the threshing unit and an associated threshing unit process model; an automatic separating unit comprising the separating unit and an associated separating unit process model; and an automatic cleaning unit comprising the cleaning unit and an associated cleaning unit process model. See [0042], “The aforementioned header parameters influence not only the function of the header 2 in the narrower sense, but also the function of the downstream working units, i.e., in this case, the function of the threshing unit 9a, the separation system 9b, and the cleaning system 9c. A spreader system 19 for spreading the material other than grain on the field may also need to be taken into consideration, which system can also be influenced by the header parameters of the header 2. Exemplary relationships are explained further below. And [0043], “a functional system model 5b for at least one part of the harvesting machine 1 is stored in the memory 5 of the driver assistance system 4, wherein the computing unit 6 carries out the aforementioned, autonomous determination of the at least one header parameter 2a-f on the basis of the system model 5b.”
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wilken in view of Vandike, in further view of Debilde, in further view of Illg et al., NPL “Scheduling Variables for Dynamic Local Model Networks with Local Regularized FIR Models”(Hereinafter “Illg”).
Regarding Claim 7, Wilken discloses the following dependent on Claim 3:
wherein the dynamic non-linear process model is configured as a See [0060-0063].
Wilken discloses a driver assistance system for a work machine, but does not explicitly disclose learning process models. However, Vandike teaches self-learning in [0077], “If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block 326, then one or more of the predictive model generator 210, predictive map generator 212, control zone generator 213, and control system 214 performs machine learning to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively, based upon the learning trigger criteria.“
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the variable threshold limitations disclosed in Vandike with reasonable expectation of success. The motivation for doing so would have been to improve the control algorithm, see Vandike [0148].
Wilken and Vandike teach a driver assistance system for a work machine with dynamic process model, but do not explicitly teach non-linear process models. However, Debilde teaches a driver assistance system with non-linear process modeling in [0052].
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the fully autonomous limitations disclosed in Debilde with reasonable expectation of success. The
motivation for doing so would have been to enable fully autonomous harvesting behavior for
agricultural vehicles, see Debilde [0054].
The combination would still however not teach a parameter estimation formed by a LMN-FIT model.
Illg teaches such a parameter estimation “wherein the parameter estimation method is formed by a LMN-FIR (local model networks with local finite impulse response) model.” Illg NPL, See Abstract—The objective of system identification is to derive models from input/output data. To extend advancements in regularization techniques for linear finite impulse response (FIR) models to the nonlinear domain, we employ local model networks (LMNs) with locally regularized FIR models to identify nonlinear processes. The training of the LMN is performed using the local linear model tree (LOLIMOT) algorithm, resulting in both the partitioning of the model space and the estimation of the corresponding linear local models for each region.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine ’s Wilken and Vandike’s device with the LMN-FIR limitations disclosed in Illg with reasonable expectation of success. The motivation for doing so would have been to incorporate linear methods into nonlinear system identification is the local model network thereby improving calculation times and subsequent responsiveness of the system, see Illg [Introduction].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wilken in view of Vandike, in further view of Debilde, in further view of Bosenberg et al., US 20230354738 A1 (Hereinafter “Rosenberg”), In further view of Baumgartner et al., US 20140019018 A1 (Hereinafter “Baumgartner”).
Regarding Claim 12, Wilken discloses the following limitation dependent on Claim 1:
wherein the agricultural work machine is further configured to: determine, See [0006], “criteria exists on the basis of which the quality of the function of the header can be evaluated. The first objective is to minimize the losses at the header itself.” Also [0031] and [0055-0062], “at least one harvesting-process strategy 5a is directed to the objective of setting or optimizing at least one harvesting-process parameter such as … “cleaning losses,” or “fuel consumption,” or the like. The implementation of the harvesting-process strategy 5a is intended to be accomplished, in this case, by a corresponding specification of header parameters, i.e., in this case and preferably, header parameters
such as “cutterbar table length” and “extension angle of the intake auger fingers.”
Wilken discloses a driver assistance system with process parameter optimization in a control loop, but does not explicitly disclose test steps or activating/terminating the model. However, Bosenberg teaches a work machine process model test steps in [0030] and [0045], “the output process model is validated before the transmission to the first working machine in a validation module and/or on a further real or virtual working machine. The process model or its individual modules can be checked for this purpose against defined test cases. Thus, for example, on the basis of predetermined test scenarios and thus predetermined sensor data, operating parameter data, and further items of information, the process model output can be checked with respect to permissible values. If the working machine is virtualized, i.e., a computer model of the working machine is generated, which can be subjected to virtually changing harvesting conditions and which then models the crop flows to be processed and therefore process states resulting therefrom, this can also be made the subject matter of the validation module.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the test scenario limitations disclosed in Bosenberg with reasonable expectation of success. The motivation for doing so would have been to validate the output process model before the transmission to the first working machine, see Bosenberg [0045].
Wilken and Bosenberg teach a driver assistance system with process parameter optimization with test scenarios, but does not explicitly disclose activating/terminating the model. However, Baumgartner teaches a work machine process model activation in [0043] Each of the available automatic settings 45 can be activated and deactivated independently of one another automatically or triggered by the operator 24, thereby making it possible to select any number of the simultaneously operating automatic settings 45. Preferably, all the automatic settings 45 are always activated in order to optimize the mode of operation of the agricultural working machine 1 … it is possible for the operator 24 to be explicitly notified via the display unit 22 when automatic settings 45 are deactivated. Also [0044], “The control/regulating unit 23 always visualizes the change of the quality parameters 40 independently of whether automatic settings 45 are activated or not.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wilken’s device with the process model activation limitations disclosed in Baumgartner with reasonable expectation of success. The motivation for doing so would have been to adjust and monitor working parameters, quality parameters or both, of the agricultural working machine in an automatable manner based on use of a family of characteristics stored in the control/regulating unit, see Baumgartner [Abstract].
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
US Publication US 20230309437 A1 by Palla et al.
US Publication US 20210235622 A1 by Baumgartner et al.
Conclusion
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/B.K.P./Examiner, Art Unit 3669 /KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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