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 .
Response to Amendment
This office action is in response to the amendments filed X. Claims Y are amended. Claims Z are cancelled. Claims A are introduced. Claims B-C are pending and addressed below.
Response to Arguments
Applicants’ amendments to the abstract of the specification have overcome the objection to the specification. The specification objection is withdrawn.
Applicant’s amendments to the specification ([0044] and [0055] of the specification) have overcome the objection to the specification. The specification objections are withdrawn.
Applicant’s amendments to claims 1, 12, and 17-18 have overcome the objection to the claims. The claim objections are withdrawn.
Applicant’s amendments to claims 1 and 12 have overcome the rejection under 35 USC 112(b). The rejection under 35 USC 112(b) is withdrawn.
Applicant’s amendments to claims 1 and 12 have introduced a new rejection under 35 USC 112(b). See the relevant section below.
Applicant’s arguments against the prior art rejections of claims 1 and 12 have been considered but are moot because the new ground of rejection does not rely on any combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant’s arguments are only directed to the claim amendments, which add new limitations to the claims and are addressed below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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.
Claim 1 claims “generating a predicted input based, at least in part, on the predicted output and a cost function evaluated over a period of time ahead of a current position of the header or agricultural vehicle, the predicted input comprising at least one of a model header height and a model fluid pressure;”. However, examiner notes that it is unclear on what “ahead” means in the relationship between the “cost function evaluated over a period of time” and “a current position of the header or agricultural vehicle” as the claim appears to either compare time to a physical position or time to a temporal position. Examiner suggests amending the claim to better clarify what is meant by “a period of time ahead of a current position of the header or agricultural vehicle”. For prior art examination, examiner will interpret the claim as “generate a predicted input based, at least in part, on the predicted output and a cost function evaluated over a period of time from a current position of the header or agricultural vehicle, the predicted input comprising at least one of a model header height and a model fluid pressure;”.
Regarding claim 12, claim 12 is given the same rejection as claim 1 above as they recite similar matter.
Dependent claims 2-11 and 13-20 are also rejected as they depend on the indefinite matter of claim 1.
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 1-6, 8-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vandike (US20210029877A1) in view of Karst (US20220061218A1).
Regarding claims 1 and 12, Vandike discloses a system and method for adjusting an actual height of a header of an agricultural vehicle, the system and method comprising:
at least one sensor configured to provide a sensor output indicative of at least one of the actual height of the header and an actual fluid pressure of a fluid used in the adjustment of the actual height of the header;
[0031] of Vandike discloses, “Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses a height of header 102 above ground 111;” In Vandike, step 308 in Fig. 3A generates the control signal and step 310 applies said control signal to controllable subsystems such as machine/header actuator(s) 248 in Fig. 2.
a communication unit adapted to receive a terrain map;
See [0038] of Vandike, where Vandike discloses that the “agricultural harvester 100 can receive prior information map 258”, where the said map includes a “topographic map sensed by either a prior operation, plane, satellite, ground vehicle, GPS, etc.”, where the topographic map is defined as “elevations of the ground across different geographic locations in a field of interest” (paragraph 19). Paragraph 39 further discloses that the prior map information 258 may be downloaded and stored into data store 202 (shown in Fig. 2).
at least one actuator configured to adjust the actual height of the header;
See Fig. 2 of Vandike. Under controllable subsystem(s) 216 is machine/header actuator(s) 248. Said actuator 248 is disclosed to “control, for example, one or more of the sieve and chaffer settings, thresher clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality.” (Vandike, [0055]).
at least one processor; and
See Fig. 2 of Vandike. Processor(s)/server(s) 201 is disclosed as a component within the agricultural vehicle.
a memory device coupled to the at least one processor, the memory device including instructions that when executed by the at least one processor cause the at least one processor to:
See Fig. 2 of Vandike. Data store 202 is disclosed within the agricultural vehicle with the capability of storing information such as a prior information map 258 ([0039]).
identify, from the terrain map, one or more variations in a terrain of a field;
[0019] of Vandike discloses, “As an agricultural harvester travels across the terrain in known directions, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., areas of changing elevation). Topographic characteristics, when referred to below, can include, but are not limited to, the elevation, slope (e.g., including the machine orientation relative to the slope), and ground profile (e.g., roughness).”
generate, using a header model and based at least in part on the one or more variations in the terrain of the field and the sensor output, a predicted output comprising at least one of (1) a predicted header height, when the sensor output is indicative of the actual height of the header, to attain a preselected header height, and (2) a predicted fluid pressure, when the sensor output is indicative of the fluid pressure of the fluid, to attain a preselected fluid pressure for the header when the header or agricultural vehicle will be at a location in the field that corresponds to the one or more variations in the terrain of the field;
Vandike discloses that the agricultural harvester can sense variations in terrain of the field (see rationale of “identify, from the terrain map, one or more variations in a terrain of a field” above) and height of the header ([0024], “The control system responds to header error (e.g., the difference between the height setting and measured height of header 104 above ground 111 and, in some examples, tilt angle and roll angle errors)”).
Regarding a preselected header height, Vandike discloses in [0024], “The operator of agricultural harvester 100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting for header 102. For example, the operator inputs a setting or settings to a control system, described in more detail below, that controls actuator 107.” The operator’s inputted height setting being a preselected header height and the automatically formed control signal disclosed as step 308 in Fig. 3A of Vandike being the predicted height.
Regarding a predicted output comprising a location on the field, the variations of the terrain, and a header model, Vandike discloses the use of a topographic map and sensor to detect conditions and characteristics of the harvest operation and surroundings around an agricultural vehicle (Vandike, [0020]). Specifically, Vandike discloses that “The systems generate a model that models a relationship between characteristics derived from the topographic map and the output values from the in-situ sensors. The model is used to generate a functional predictive machine map that predicts, for example, power usage at different locations in the field. The functional predictive machine map, generated during the harvesting operation, can be used in automatically controlling a harvest during the harvesting operation.” As the citation above shows that characteristics of the map can be used to functionally control the agricultural vehicle based off of its location. Vandike further discloses in the same paragraph that the above are “only examples of machine characteristics that can be predicted based on the topographic characteristics and other machine characteristics can be predicted and used to control the machine as well.” [0083] of Vandike discloses that “the predictive model generator 210 may include other items 349 as well, which may include other types of predictive model generators to generate other types of machine characteristic models.” Models can be created for machine characteristics such as the header and controlled by the predictive map (Fig. 2, functional predictive map 263 communicates signals to control system 214 to control controllable subsystem(s) 216).
generate a predicted input based, at least in part, on the predicted output and a cost function evaluated over a period of time from a current position of the header or agricultural vehicle, the predicted input comprising at least one of a model header height and a model fluid pressure; and
While Vandike does not disclose generating a predicted input based, at least in part on the predicted output (Vandike, [0020, 0083]) and a cost function, from a similar field of endeavor, Karst discloses a system of automatic header control for farming equipment. Specifically, Karst discloses generating a predicted input based, at least in part, on the predicted output and a cost function (Karst, [0043], “goal-based optimization algorithms”, where optimization algorithms are based on cost functions) evaluated over a period of time ahead of a current position of the header or agricultural vehicle (Karst, [0043], “… Specifically, the controller 58 monitors the GPS location of the windrower 10 and implements the optimized operational settings based on the real time GPS location (e.g., to account for obstacles or other changes in the terrain based on the field map information).”, where a real time operation optimization indicates a continuous evaluation of cost), the predicted input comprising at least one of a model header height and a model fluid pressure (Karst, [0049], “By lifting the header 14 (e.g., via the lift cylinder 44) when traversing obstacles mapped in the field map 84, an impact force between the header 14 and the obstacle may be reduced or eliminated to prevent damage to the header 14.”, where the lifting of the header 14 is to a model header height).
One of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, to combine the algorithms of Karst to the system of Vandike as both systems disclose operational considerations with respect to the current environment and terrain, providing a direct functional upgrade to the predictive terrain generation of Vandike by further allowing optimized header height prediction.
generate, based on the predicted input and before the header or the agricultural vehicle reaches the identified variation in the terrain, one or more control signals for the at least one actuator to adjust the actual height of the header.
See the rationale above regarding “generate a predicted input based, …” containing the citation to a [0049] of Karst.
Regarding claims 2 and 13, with all of the limitations of claims 1 and 12, the system and method further comprise:
wherein the cost function is at least a function of a difference between at least one of, when the sensor output is indicative of the actual height of the header, the actual header height and the predicted header height, and, when the sensor output is indicative of the fluid pressure of the fluid, the actual fluid pressure and the predicted fluid pressure.
See Fig. 13 and [0050] of Karst, where Karst discloses the raising and lowering of a header height (the actual header height) in the “Header Lift Protection” optimization algorithm in response to an impact upcoming. One of ordinary skill in the art would find it obvious that the cost function is at least a function of a difference between at least one of (1) the actual header height and the predicted header height, and (2) the actual fluid pressure and the predicted fluid pressure as step 620 and 622 in Fig. 13 of Karst identify a before and after event adaptation of the header height, where the steps are to compensate for a predicted header height based on the impact.
Regarding claims 3 and 14, with all of the limitations of claims 1 and 12, the system and method further comprise:
wherein the memory device further includes instructions that when executed by the at least one processor cause the at least one processor to proactively generate the one or more control signals for the at least one actuator to adjust the actual height of the header before the header or the agricultural vehicle reaches the location in the field that corresponds to the one or more variations in the terrain.
See [0035] of Vandike. In light of the rationale of the limitation “generate, using a header model and based at least in part on the one or more variations in the terrain of the field and the sensor output…” from claim 1, where Vandike discloses the use of a functional predictive map that can control machine parameters (such as the machine/header actuator(s) 248 of the controllable subsystem(s) 216) in response to sensed data, one of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, that the system capable of detecting crop properties ([0035], “crop height, crop moisture, crop density, crop state”), machine performance ([0035], “loss levels, job quality, fuel consumption, and power utilization”), and header height would be able to control the header height to maximize machine performance metrics with respect to the crop location and terrain variations.
Regarding claims 4 and 15, with all of the limitations of claims 1 and 12, the system and method further comprise:
wherein the memory device further includes instructions that when executed by the at least one processor cause the at least one processor to communicate at least a portion of the predicted input to the header model as a feedback signal.
See Fig. 3A and 3B of Vandike. The control signal generated at step 308 moves forward to a logical check 312 that checks for a completed operation in Fig. 3B. In the case that the operation is not complete, the control signal can be used for feedback to tune one of the listed systems in 328.
Regarding claims 5 and 16, with all of the limitations of claims 4 and 15, the system and method further comprise:
wherein the memory device further includes instructions that when executed by the at least one processor cause the at least one processor to adjust the header model in response to the feedback signal.
See the rationale of claim 4. Step 328 of Fig. 3B in Vandike discloses the adjustment of models by checking the learning trigger criteria examples listed.
Regarding claim 6, with all of the limitations of claim 1, the system further comprises:
wherein the predicted input is further based, at least in part, on a constraint, and wherein the memory device further includes instructions that when executed by the at least one processor cause the at least one processor to receive a signal indicative of an operator defining the constraint.
See [0055] of Vandike. While Vandike does not explicitly disclose that the predicted input is based, at least in part, on a constraint, Vandike does disclose that an operator 260 is able to interact with operator interface mechanisms 218 by use of operator interface controller 231. Said controller is also able to control various other controllers such as settings controller 232 and path planning controller 234. Fixing the disclosed agricultural machine to a path configured by a path planning controller 234 would act as a constraint to the system by constraining components such as propulsion subsystem 250 and steering subsystem 252. One of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, that the agricultural vehicle would have the predicted input affected by the constrained choice of pathing as the input is based off of the functional predictive map. One would also find it further obvious that the interface controller 231 would have feedback to indicate that the constraint has been defined to the operator as a lack of the said feedback would indicate poor design leading to wasteful repetitive motions with constant trial and errors.
Regarding claim 8, with all of the limitations of claim 1, the system further comprises:
wherein the predicted input is further based, at least in part, on one or more constraints, and wherein the one or more constraints comprises at least one of an energy constraint that limits an energy consumption of the at least one actuator and a speed constraint that limits a speed or an acceleration for a displacement of the header.
See [0020] of Vandike The functional predictive machine map disclosed can control both the energy consumption (“In some cases, the functional predictive machine map is used to generate a mission or path planning for the agricultural harvester operating in the field, for example, to improve power utilization”) and speed of a header (“Of course, internal material distribution, power characteristics, ground speed, grain loss, tailings and grain quality are only examples of machine characteristics that can be predicted based on the topographic characteristics and other machine characteristics can be predicted and used to control the machine as well.”, where the ground speed of the agricultural harvester would also be the speed of the header)
Regarding claims 9 and 19, with all of the limitations of claims 1 and 12, the system and method further comprise:
wherein the header model comprises one or more models of at least one of a mechanical system for when the predicted output is the predicted header height and a hydraulic system of the header for when the predicted output is the predicted fluid pressure.
In light of the rationale of the limitation “generate, using a header model and based at least in part on the one or more variations in the terrain of the field and the sensor output, …” of claim 1, Vandike discloses that “the predictive model generator 210 may include other items 349 as well, which may include other types of predictive model generators to generate other types of machine characteristic models.” ([0083]). Vandike further discloses examples of control of the machine/header actuator(s) 248 ([0055], “In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, thresher clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control and other actuators 248 that affect the other functions of the agricultural harvester 100.”). As the system is capable of generating control signals that both control the machine/header actuator(s) 248 of controllable subsystem(s) 216 (Fig. 3A) and update the learned model at step 328 based off of learning criteria (Fig. 3B), one of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, that the agricultural harvester has a model of the header that further comprises additional mechanical systems.
Regarding claims 10 and 20, with all of the limitations of claim 9 and 19, the system and method further comprise:
wherein the header model further comprises one or more parameters obtained from one or more datasheets or one or more lookup tables.
While Vandike does not disclose the use of one or more datasheets, Karst discloses from [0031] of their disclosure, “The memory can store look-up tables, graphical representations of various functions, and other data or information for carrying out or executing the software, logic, algorithms, programs, set of instructions, etc.”
One of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, to combine the use of lookup tables from Karst to the system of Vandike as storing parameters for future use would allow for better referencing original values stored as the system modifies parameters depending on its operation in a field.
Regarding claim 17, with all of the limitations of claim 16, the method further comprises:
wherein generating the predicted input further comprises generating the predicted input based, at least in part, on a constraint, and further comprising receiving a signal indicative of an operator defining the constraint.
Vandike discloses in [0073], “In some instances, it may also be that operator 260 observes that automated control of a controllable subsystem, is not what the operator desires. In such instances, the operator 260 may provide a manual adjustment to the controllable subsystem reflecting that the operator 260 desires the controllable subsystem to operate in a different way than is being commanded by control system 214. Thus, manual alteration of a setting by the operator 260 can cause predictive model generator 210 to relearn a model, predictive map generator 212 to regenerate map 264, control zone generator 213 to regenerate the control zones on predictive control zone map 265 and control system 214 to relearn its control algorithm or to perform machine learning on one of the controller components 232-246 in control system 214 based upon the adjustment by the operator 260, as shown in block 322.” As shown, an operator 260 can adjust the system (define a constraint) that will cause the predictive model generator 210 to relearn its appropriate models (by generating a predicted input) that adjusts the feature related to the adjustment set by the operator.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vandike (US20210029877A1) in view of Karst (US20220061218A1) and in further view of Palla (US20220232763A1).
Regarding claim 7, with all of the limitations of claim 1, the system further comprises:
wherein the predicted input is further based, at least in part, on a plurality of constraints, and wherein at least some of the plurality of constraints are in a hierarchical arrangement with other constraints of the plurality of constraints.
While Vandike in view of Karst does not disclose the use of a hierarchical arrangement within a plurality of constraints, Palla discloses an agricultural harvester capable of using regime zones identified based on the geography of the harvester. Specifically disclosed from [0134] of Palla, the regime zones are areas with their own specified rules (constraints) but may also have overlapping zones that are contained within a precedence hierarchy to resolve which zone/rules take precedence by the agricultural harvester.
One of ordinary skill in the art would find it obvious, prior to the applicant’s effective, to apply the hierarchical system of Palla et al to the system of Vandike as the combination of hierarchies would resolve any possible undefined behaviors between overlapping regions while also softening the requirement of ensuring there are no overlapped regions.
Regarding claim 18, with all of the limitations of claim 12, the method further comprises:
wherein generating the predicted input further comprises
generating the predicted input based, at least in part, on a plurality of constraints, and further comprising evaluating an outcome for each of the plurality of constraints based on a hierarchical arrangement of the plurality of constraints.
Vandike discloses in [0068], “In some examples, at block 316, agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other triggered learning” and discloses the capability of setting trigger criterias to perform updates when the agricultural harvester detects a criteria such as blocks 318, 320, 321, 322, and 324 in Fig. 3B, where the above list from paragraph 68 show the affected models. However, Vandike does not disclose the use of a hierarchical arrangement of the plurality of constraints.
In light of the rationale of claim 7, Palla discloses the hierarchical arrangement of constraints in the form of regime zones. Palla further discloses the capability of identifying a priority of work machine actuators (WMA) (paragraph 132, “In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA.”). [0136] of Palla discloses an example that can resolve the priority, “In another example, the settings resolver may include a neural network or other artificial intelligence or machine learning system. In such instances, the settings resolvers may resolve the competing target settings based upon a predicted or historic quality metric corresponding to each of the different target settings.”, where the machine learning system evaluates each of the settings.
Claims 11 are rejected under 35 U.S.C. 103 as being unpatentable over Vandike (US20210029877A1) in view of Karst (US20220061218A1) and in further view of Seiders (US20190335661A1).
Regarding claim 11, with all of the limitations of claim 10, the system further comprises:
wherein the one or more parameters includes an estimated parameter obtained from one or more measurements taken from the header.
Examiner interprets that “one or more measurements taken from the header” to mean that there is a sensor reading from a sensor on the header itself. While Vandike does disclose the use of sensors to estimate header height, Vandike does not explicitly disclose the location of these sensors. From a similar field of endeavor, Seiders discloses automatically controlling the height of an implement of an agricultural work vehicle.
See Fig. 2 of Seiders. The figure shows the embodiment of hydraulic system 100 that comprises a header 32 of the agricultural work vehicle. [0030] of Seiders discloses that “a first height sensor 116 may be provided at or adjacent to the first lateral end 106 of the header 32, and a second height sensor 118 may be provided at or adjacent to the second lateral end 108 of the header 32. In some embodiments, a third height sensor 119 may be provided at or adjacent the center 110 of the header 32.”, where the additional sensors here being used to take a “suitable mathematical combination of local heights 120 measured by one or more of the height sensors 116, 118, 119.”
One of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, to utilize the placement and system of the height sensors disclosed by Seiders in Vandike’s system as the sensors would at least suffice as a substitute to the height sensors within Vandike, but would also provide a way of finding a more accurate height measurements from multiple reference points giving the capability of calculating a true value.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAEWOOK JUNG whose telephone number is (571)272-5470. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached on (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.J./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656