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 .
Claim Status
Claims 1, 18, 20, and 22 have been amended. Claims 2-3 and 13-14 are cancelled.
Claims 1, 4-12, and 15-22 are pending.
Response to Arguments
Applicant’s arguments, see pages 8-11, filed 05/12/2026, with respect to the rejection(s) of claim(s) 1-22 under 35 USC 102(a)(1) have been fully considered and are persuasive due to newly amended limitations not addressed in the prior Office Action of record. Therefore, the 35 USC 102(a)(1) rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 103 Miller (20200012415; already of record) in view of Ren et al. (20170350721; hereinafter Ren).
In regards to Applicant’s arguments that Miller fails to teach modular assembly specific learning architecture, the Examiner respectfully disagrees. Miller discloses:
“The data handles may be set for real-time or near real-time rolling or batch-based updates from respective databases and/or local controllers of the system (see discussion below) and/or for updates on particular time schedules or upon user demand.” ¶ 26
“the tangible elements and data handles in the model are related to one another in many cases, manual changes to operational parameters/settings made by the user may automatically flow down to related operational settings, causing automated secondary commands to be issued to compensate ... a command to speed the combine up issued by the user manually may automatically trigger a secondary command to the trailing baling implement causing it to increase its speed to match the combine. Such automated secondary actions may also apply to relationships the user defines between model elements” ¶ 34
“FIG. 1 depicts an exemplary system 10 for reviewing and revising real-time or near real-time, farm-related data and/or altering equipment operational parameters/settings.” ¶ 36
“the mapping module 48 may utilize machine learning programs or techniques. The mapping module 48 may utilize information from the movements and changes in physical state of the model elements and/or changes in the data handles, and apply that information to one or more machine learning techniques to generate one or more operational correlations or other relational observations for more general application” ¶ 76
Wherein it can be seen that Miller does disclose of autonomously learning or optimizing the process model based on received real-time data and Miller does not solely rely on an initial manual process as recited in paragraph 34.
In regards to Applicant’s argument pertaining to a filtering module, the claimed limitation is newly amended and not addressed in the prior Office Action of record and will be addressed in the detailed rejection 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, 18, and 22 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.
Claims 1, 18, and 22 recite the following limitations (or limitations analogous to):
“generating, during operation, at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine by a first process model running in a central control unit of the at least one first agricultural working machine or distributed onto multiple control units on the at least one first agricultural working machine based on at least one first working data set, the first working data set being formed by first operating parameter data, first machine sensor data, first functional unit data, and/or first working data derived from these data, to influence an agricultural result, resulting in at least one subsequent working data set, wherein the subsequent working data set is at least partially formed by subsequent machine sensor data, subsequent functional unit data, subsequent operating parameter data, and/or subsequent working data derived from these data;”
Wherein it is unclear based upon the newly amended limitations, as to which parts of the limitations are intended to be differentiated by the bolded “or” statement.
For example, the limitation may be interpreted that the “at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine” is running in a central control unit of the at least one first agricultural working machine, or it may be distributed onto multiple control units of the at least one first agricultural working machine.
A second possible interpretation is that “a first process model” of the “at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine” is running in a central control unit of the at least one first agricultural working machine, or “a first process model” of the “at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine” may be distributed onto multiple control units of the at least one first agricultural working machine.
A third possible interpretation is that there may be “a first process model” of the “at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine” is running in a central control unit of the at least one first agricultural working machine, or the “at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine” (Note: without a first process model) may be distributed onto multiple control units of the at least one first agricultural working machine.
Additionally, it is unclear as to whether the limitation of “based on at least one first working data set, the first working data set being formed by first operating parameter data, first machine sensor data, first functional unit data, and/or first working data derived from these data,” is included only with the limitations regarding multiple control units, or if the limitations are to be included with a central control unit as well.
For the purposes of compact prosecution, the Examiner will interpret the claimed limitations similarly to the prior Office Action of record of “a first process model” of the “at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine” is running in a central control unit of the at least one first agricultural working machine and that the newly amended limitations regarding the at least one first working data set pertain only to the multiple control units. Furthermore, see the claim interpretation set forth below.
Claim Interpretation
The amended claimed limitations contain several “or” statements and for the purposes of compact prosecution and clarity, the addressed limitations will be bolded and will follow with its corresponding citation.
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.
Claim(s) 1, 4-12, and 15-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller (20200012415; already of record) in view of Ren et al. (20170350721; hereinafter Ren).
Regarding claim 1, Miller teaches a method for operating one or more of a plurality of working machines (Miller: Abstract), the plurality of working machines comprising;
at least one first agricultural working machine (Miller: “including a plurality of mobile agricultural devices and structures” ¶ 75), the at least one first agricultural working machine including a self-propelled root crop harvesting machine or multiple first combined agricultural working machines, the multiple first combined agricultural working machines comprising a tractor with a root crop harvesting machine towed thereby (Miller: “a tractor 212 (with associated data handles 214) towing a discing implement” ¶ 75, see also ¶ 71);
and at least one further working machine in combination with an electronic data processing (EDP) device, the EDP device arranged remotely from the at least one first agricultural working machine (Miller: “a system including a plurality of communication elements (e.g., transmitters) respectively freestanding or mounted to a plurality of farm implements, structures and devices, and a graphical user interface hosted by and/or in communication with a computing device” ¶ 17 (see also ¶67, ¶92),
wherein the method comprises:
generating, during operation, at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the at least one first agricultural working machine by a first process model running in a central control unit of the at least one first agricultural working machine (Miller: “a dynamic model within which farm equipment, natural characteristics, systems and structures may be spatially viewed, related and/or manipulated based on real-time or near real-time data” ¶ 35, “Each of the plurality of n local controllers 12 includes a communication element 24 (see FIG. 2) for transmitting data regarding present conditions surrounding the controller 12 and/or regarding equipment and/or structure(s) to which the controller 12 is mounted” ¶ 37, “the local controller(s) may issue commands to actuating mechanisms with which they are in communication to carry out the changes flowing from the user's editing of the model” ¶ 33) or distributed onto multiple control units on the at least one first agricultural working machine based on at least one first working data set, the first working data set being formed by first operating parameter data, first machine sensor data, first functional unit data, and/or first working data derived from these data, to influence an agricultural result (Miller: “a local controller mounted to the tractor may issue a speed change command” ¶ 33), resulting in at least one subsequent working data set (Miller: “The data handles may be set for real-time or near real-time rolling or batch-based updates from respective databases and/or local controllers of the system” ¶ 26, “data handles in the model are related to one another in many cases, manual changes to operational parameters/settings made by the user may automatically flow down to related operational settings, causing automated secondary commands” ¶ 34, see also ¶ 27, 31, 33), wherein the subsequent working data set is at least partially formed by subsequent machine sensor data, subsequent functional unit data, subsequent operating parameter data, and/or subsequent working data derived from these data (Miller: “FIG. 1 depicts an exemplary system 10 for reviewing and revising real-time or near real-time, farm-related data and/or altering equipment operational parameters/settings” ¶ 36);
transmitting, during or after operation, at least a part of a machine data set comprising at least a part of the first working data set, at least a part of the at least one subsequent working data set (Miller: “Each of the plurality of n local controllers 12 includes a communication element 24 (see FIG. 2) for transmitting data regarding present conditions surrounding the controller 12 and/or regarding equipment and/or structure(s) to which the controller 12 is mounted” ¶ 37), and the first process model output via at least one interface to the EDP device, which is arranged remotely from the at least one first agricultural working machine (Miller: “The automatically populated data handles may be manipulated, supplemented and/or deleted by a user via the graphical user interface. The data handles may be set for real-time or near real-time rolling or batch-based updates from respective databases and/or local controllers of the system” ¶ 26, “manual changes to operational parameters/settings made by the user may automatically flow down to related operational settings ... a command to speed the combine up issued by the user manually ...” ¶ 34),
automatically changing, without human input on the EDP device, in consideration of items of information of the first machine data set, of a further machine data set of one or multiple of the at least one further working machine (Miller: “The change in parameter(s) and/or setting(s) may be communicated via the computing device to local controller(s) mounted to such equipment and structure(s)” ¶ 33, “the tangible elements and data handles in the model are related to one another in many cases, manual changes to operational parameters/settings made by the user may automatically flow down to related operational settings, causing automated secondary commands to be issued to compensate ... a command to speed the combine up issued by the user manually may automatically trigger a secondary command to the trailing baling implement causing it to increase its speed to match the combine. Such automated secondary actions may also apply to relationships the user defines between model elements” ¶ 34, see also ¶ 76), of the first process model and/or a further process model of one or multiple of the at least one further working machine, a basic process model stored in the EDP device, the first process model, or the further process model (Miller: “Data handles may also be included and populated in the model automatically by the mapping module by reference to the library of data handle types ... The data handles may be set for real-time or near real-time rolling or batch-based updates from respective databases and/or local controllers of the system” ¶ 26)
...
transmitting at least a part of the automatically-changed process model as an output process model for an operation from the EDP device directly to at least one of the plurality of working machines (Miller: “the local controller(s) may issue commands to actuating mechanisms with which they are in communication to carry out the changes flowing from the user's editing of the model” ¶ 33), and
automatically further operating the at least one of the plurality of working machines in accordance with the transmitted at least a part of the automatically-changed process model as the output process model for the operation from the EDP device (Miller: “Such automated secondary actions may also apply to relationships the user defines between model elements. In the example discussed previously, the user's designation of a “low area” within which a tractor should not travel in wet conditions may automatically flow down to other mobile equipment as well” ¶ 34).
However, Miller fails to teach wherein the machine data sets for adapting the process model are filtered in a filter module, in particular on an assembly-specific basis, and wherein the process model is changed in individual assembly-specific modules and these are compiled to form a changed process model.
In a similar field of endeavor, Ren teaches wherein the machine data sets for adapting the process model are filtered in a filter module, in particular on an assembly-specific basis, and wherein the process model is changed in individual assembly-specific modules and these are compiled to form a changed process model (Ren: “The angle, the velocity, the position information and the course angle of the vehicle are data-fusion processed by the seven-dimensional EKF filtering model, and the motional attitude angle of the vehicle is updated in real time” ¶ 62, “The attitude data of the vehicle is calculated by the seven-dimensional EKF filtering model, through the quaternion attitude updating algorithm” ¶ 64, see also ¶ 66).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the modeling system of Miller so that it also includes the element of a filtering module, as taught by Ren, in order to improve dataset accuracy (Ren: ¶ 103).
Regarding claim 4, Miller in view of Ren teaches the method as claimed in claim 1, wherein the machine data set comprises at least one item of process feedback information (Miller: “the tractor transmits a new location and heading in a southerly direction” ¶ 79, see also ¶ 61, 70).
Regarding claim 5, Miller in view of Ren teaches the method as claimed in claim 1, wherein items of information to supplement the first machine data set are received via a working machine interface (Miller: “The user and/or the user interface 26 may communicate the identifications to the computing device 14 via the user interface 16” ¶ 68).
Regarding claim 6, Miller in view of Ren teaches the method as claimed in claim 1, wherein the process model is adapted based on of the machine data sets of at least two uncoupled working machines (Miller: “The plurality of local controllers 12 preferably include at least two controllers 12 mounted respectively to functionally and physically separate mobile farm implements, such as a combine and an independently-movable baling implement” ¶ 65, “The change in parameter(s) and/or setting(s) may be communicated via the computing device to local controller(s) mounted to such equipment and structure(s), and the local controller(s) may issue commands to actuating mechanisms with which they are in communication to carry out the changes flowing from the user's editing of the model” ¶ 33).
Regarding claim 7, Miller in view of Ren teaches the method as claimed in claim 1, wherein the machine data set of the at least one first agricultural working machine is supplemented via an observer interface of the at least one first agricultural working machine with items of adaptation information of an external observer (Miller: “The automatically populated data handles may be manipulated, supplemented and/or deleted by a user via the graphical user interface” ¶ 26, see also ¶ 27).
Regarding claim 8, Miller in view of Ren teaches the method as claimed in claim 1, wherein items of sensor information of the at least one first agricultural working machine are processed to generate sensor data in a signal processing module of the at least one first agricultural working machine (Miller: “the sensing element 30 may be integrated in a housing or body of the local controller 12, though it may also be physically separate from the remainder of the local controller 12 and in electronic communication” ¶ 47).
Regarding claim 9, Miller in view of Ren teaches the method as claimed in claim 1, wherein the machine data set is transmitted in dependence on a predetermined event (Miller: “a first burst of data transmissions and subsequently the tractor transmits a new location and heading in a southerly direction” ¶ 79, Miller: “The change in parameter(s) and/or setting(s) may be communicated” ¶ 33, Note: Wherein it can be seen that any change to the system can result in transmitting the machine data).
Regarding claim 10, Miller in view of Ren teaches the method as claimed in claim 1, wherein the process model runs on the at least one first agricultural working machine distributed in individual control devices (Miller: “The plurality of local controllers 12 preferably include at least two controllers ... several local controllers 12 may be mounted to a single device—such as a movable irrigation device—to monitor the location of and/or collect sensor data from multiple parts of the device” ¶ 65).
Regarding claim 11, Miller in view of Ren teaches the method as claimed in claim 1, wherein the output process model is used as a new basic process model (Miller: “the local controller(s) may issue commands to actuating mechanisms with which they are in communication to carry out the changes flowing from the user's editing of the model” ¶ 33, “the computing device 14 may receive equipment and structure identifications. Each identification may comprise an initial definition for a tangible model element and/or an instruction to incorporate a new tangible model element into the model ... where the local controllers 12 are configured to store (and, preferably, periodically update) in memory elements 32 identifying, parametric and status information regarding the equipment and structures to which they are mounted” ¶ 68).
Regarding claim 12, Miller in view of Ren teaches the method as claimed in claim 1, wherein respective machine data sets of the plurality of working machines are each allocated into individual groups for machine-spanning comparability (Miller: “the three-dimensional model 200 is illustrated including a plurality of mobile agricultural devices and structures ...” ¶ 75).
Regarding claim 15, Miller in view of Ren teaches the method as claimed in claim 1, wherein the process model is changed by at least one method of artificial intelligence (Miller: “changes in physical state of the model elements and/or changes in the data handles, and apply that information to one or more machine learning techniques to generate one or more operational correlations or other relational observations for more general application” ¶ 76).
Regarding claim 16, Miller in view of Ren teaches the method as claimed in claim 1, wherein the output process model is made available at least partially to the operator of one of the plurality of working machines on a mobile device for app-based generation of action instructions (Miller: “The display 20 may also or alternatively display a 3D visual model to the user, the model being based at least in part on data received from the local controllers 12 in real-time or near real-time” ¶ 52, see also ¶ 53).
Regarding claim 17, Miller in view of Ren teaches the method as claimed in claim 1, wherein the EDP device receives items of information to supplement the first or one of the further machine data sets via an auxiliary interface (Miller: “the computing device 14 may receive and implement one or more user inputted changes to one or more model elements” ¶ 81, see also ¶ 82).
Regarding claim 18, Miller teaches an arrangement comprising a plurality of agricultural working machines and an electronic data processing (EDP) device, the arrangement carrying out steps comprising (Miller: Miller: “The performance of certain of the operations may be distributed among the one or more processing elements, not only residing within a single machine, but deployed across a number of machines” ¶ 97, see also ¶ 65):
...
In regards to the remainder of claim 18, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
Regarding claim 19, Miller in view of Ren teaches the method as claimed in claim 1, wherein the at least one first agricultural working machine is one of:
a self-propelled harvesting machine; or
the at least one first or multiple first combined agricultural working machine is a combination of a tractor with a harvesting machine towed thereby (Miller: “The plurality of local controllers 12 preferably include at least two controllers 12 mounted respectively to functionally and physically separate mobile farm implements, such as a combine and an independently-movable baling implement” ¶ 65).
Regarding claim 20, Miller in view of Ren teaches the method as claimed in claim 1, wherein the at least one interface to the EDP device is arranged remotely from the at least one first agricultural working machine (Miller: “For instance, the user interface 16 may include a headset and hand controller” ¶ 52).
Regarding claim 21, Miller teaches the method as claimed in claim 1, wherein the at least a part of the changed process model that is transmitted as the output process model for an operation is for a subsequent operation from the EDP device to at least one of the working machines (Miller: “if both tractor and baling implement are traveling east within a field according to a first burst of data transmissions and subsequently the tractor transmits a new location and heading in a southerly direction, the virtual control program 42 may turn the baling implement within the model” ¶ 79).
Regarding claim 22, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Duke et al. (20190075727; hereinafter Duke) is in the similar field of endeavor of agricultural model generation as the claimed invention.
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.
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/C.P./ Examiner, Art Unit 3663
/TYLER J LEE/ Primary Examiner, Art Unit 3663
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