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
Claims 1, 4, 6-16, and 19-22 are pending in this application.
Claims 2-3, 5, 17-18 are cancelled.
Claims 1, 4, 7-12, 14, 16, and 19-22 are amended.
Claims 1, 4, 6-16, and 19-22 are presented for examination.
Response to Amendments
The amendments, filed on 27 March 2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-22 has been fully considered. The 112(b) rejection has been withdrawn.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 6-16, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US Publication 2019/0387658 A1) in view of Blank (US Publication 2019/0146426 A1).
Regarding claim 1, Henry teaches a method for adapting a process model for operating a first working machine of multiple working machines, which comprises one first agricultural working machine or multiple combined first agricultural working machine, and at least one further working machine, wherein the first agricultural working machine is a self-propelled harvesting machine, or the multiple combined first agricultural working machines are a combination of a tractor with a harvesting machine towed thereby, wherein the adaptation takes place on an EDP device arranged remotely from the first working machine, the method comprising: generating, to influence the agricultural work result, at least one first process model output comprising at least one control command for at least one controllable or regulatable functional unit of the first working machine (Henry: Para. 7, 74; intelligent control of one or both of an agricultural work vehicle or implement; site-specific control model that provides sets of output controls respectively for sets of input conditions; store the one or more site-specific control models), in operation, by the first process model running in a central control unit of the first working machine or distributed across one or more control units on the first working machine, based on at least one first working data set, wherein the first working data set is formed by first operating parameter data, first machine sensor data, first functional unit data, and/or first working data derived from these data (Henry: Para. 20; anterior sensor devices positioned to collect anterior sensor data in a forward direction relative to a path of travel of an agricultural work vehicle or implement through a field), by which at least one following working data set results (Henry: Para. 7; a site-specific control model that provides sets of output controls respectively for sets of input conditions), wherein the at least one following working data set is at least partially formed by following machine sensor data, following functional unit data, following operating parameter data, and/or following working data derived from these data (Henry: Para. 28; posterior sensor data that describes the portion of the field after the manipulation of the portion of the field); receiving, by the EDP device via at least one interface from the at least one first working machine, which is in particular arranged remotely (Henry: Para. 35; input devices for permitting an operator to control the operation of one or more components of the work vehicle 10 and/or one or more components of the implement), during or after the operation thereof, at least a part of a first machine data set comprising: at least a part of the first working data set and at least a part of one or more of the at least one following working data set (Henry: Para. 8; obtaining, by a computing system comprising one or more computing devices and from one or more anterior sensor devices positioned to collect anterior sensor data in a forward direction relative to a path of travel of the agricultural work vehicle or implement through a field, anterior sensor data that describes a portion of the field), and changing, on the EDP device, a basic process model stored in the EDP device, the first process model or a further process model in consideration of: items of information of the first machine data set, a further machine data set of one or multiple of the further working machines, of the first process model and/pr and/or a further process model of one or multiple of the further working machines (Henry: Para. 7; updating the site-specific control model based at least in part on the comparison of the posterior set of conditions to the target set of conditions for the portion of the field to form an updated version of the site-specific control model).
Henry doesn’t explicitly teach wherein the machine data sets of multiple working machines are each allocated into individual groups for machine-spanning comparability, subsequently providing at least a part of the changed process model is subsequently as the at least one first process model output for an operation by the EDP device.
However Blank, in the same field of endeavor, teaches wherein the machine data sets of multiple working machines are each allocated into individual groups for machine-spanning comparability (Blank: Para. 49, 53; compares the performance of various combines and their operators on a near real time basis), subsequently providing at least a part of the changed process model is subsequently as the at least one first process model output for an operation by the EDP device. (Blank: Para. 45, 53; generates a display, with various interactive display elements on control user interface; update an interface display that shows the settings; execute a change in machine settings that it identifies, if operator approves the change).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 4, Henry teaches the method as claimed in claim 1, wherein the at least one first process model output is used as the new basic process model (Henry: Para. 8; the site-specific control model based at least in part on the comparison of the posterior set of conditions to the target set of conditions for the portion of the field to form an updated version of the site-specific control model).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 6, Henry teaches the method as claimed in claim 1, wherein the machine data sets for adapting the process model are filtered in a filter module (Henry: Para. 30, 59; computing system can compare the posterior set of conditions to a target set of conditions for the portion of the field; crop-specific target conditions, time-of-year-specific target conditions).
Regarding claim 7, Henry teaches the method as claimed in claim 1, wherein the step of changing the basic process model, the first process model, or the further process model comprises changing the respective model in at least one assembly-specific module and wherein the assembly-specific module is combined or compiled with further process model modules to form the changed process model (Henry: Para. 30-31; the posterior sensor data can be analyzed to determine how successful use of the set of output controls was to achieve the target conditions; update the site-specific control model based at least in part on the comparison of the posterior set of conditions to the target set of conditions for the portion of the field to form an updated version of the site-specific control model).
Regarding claim 8, Henry teaches the method as claimed in claim 1, wherein items of adaptation information are recorded via an observer interface of the EDP device for adapting the basic process model, the first process model, or the further process model (Henry: Para. 35, 103; the target set of conditions can be user-supplied target conditions; input devices for permitting an operator to control the operation).
Regarding claim 9, Henry teaches the method as claimed in claim 1, wherein the basic process model, the first process model, or the further process model is changed by at least one method of artificial intelligence (Henry: Para. 95; the site-specific control model can be a machine-learned model, such as, for example, artificial neural networks).
Regarding claim 10, Henry teaches the method as claimed in claim 1, wherein the items of feedback, assessment, and/or opening input data are used for the changing of the basic process model, the first process model, or the further process model (Henry: Para. 35, 103; the target set of conditions can be user-supplied target conditions; input devices for permitting an operator to control the operation).
Regarding claim 11, Henry doesn’t explicitly teach wherein the at least one first process model output is validated before being provided for use by the first agricultural working machine or another of the multiple working machines in a validation module and/or on a further real or virtual working machine.
However Blank, in the same field of endeavor, teaches wherein the at least one first process model output is validated before being provided for use by the first agricultural working machine or another of the multiple working machines in a validation module and/or on a further real or virtual working machine (Blank: Para. 45; engine may operate at a higher level of automation, such that it will use control automation logic to control machine to execute a change in machine settings that it identifies, if operator approves the change).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 12, Henry doesn’t explicitly teach wherein the at least one first process model output is made available at least partially to the operator of one of the multiple working machines on a mobile device for app-based generation of action instructions.
However Blank, in the same field of endeavor, teaches wherein the at least one first process model output is made available at least partially to the operator of one of the multiple working machines on a mobile device for app-based generation of action instructions (Blank: Para. 45, 53; generates a display, with various interactive display elements on control user interface; update an interface display that shows the settings; execute a change in machine settings that it identifies, if operator approves the change).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 13, Henry doesn’t explicitly teach wherein the EDP device receives items of information to supplement the first or one of the further machine data sets via an auxiliary interface.
However Blank, in the same field of endeavor, teaches wherein the EDP device receives items of information to supplement the first or one of the further machine data sets via an auxiliary interface (Blank: Para. 84-85; remote analytics and control system; receives the various machine data from control recommendation and learning engine on harvester).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 14, Henry doesn’t explicitly teach wherein the machine data sets depicted in a database of the EDP device are supplemented with machine data sets of another of the multiple working machines depicted in a further database of a further EDP device.
However Blank, in the same field of endeavor, teaches wherein the machine data sets depicted in a database of the EDP device are supplemented with machine data sets of another of the multiple working machines depicted in a further database of a further EDP device (Blank: Para. 84-85; remote analytics and control system; receives the various machine data from control recommendation and learning engine on harvester).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 15, Henry teaches an EDP device having at least one computer program product, wherein the computer program product carries out the method as claimed in claim 1 (Henry: Para. 77; memory can also store computer-readable instructions that can be executed by the one or more processors).
Regarding claim 16, Henry teaches a harvesting machine comprising the process model; that is changed according to the method as claimed in claim 1 (Henry: Para. 7; system for intelligent control of one or both of an agricultural work vehicle or implement; site-specific control model).
Regarding claim 19, Henry doesn’t explicitly teach wherein the step of subsequently providing at least a part of the changed process model as the at least one first process model output for an operation by the EDP device is for transmission to the first working machine and/or another of the further multiple working machines, and/or transmitted in the direction thereof.
However Blank, in the same field of endeavor, teaches wherein the step of subsequently providing at least a part of the changed process model as the output process model for an operation by the EDP device is for transmission to the first working machine and/or one of the further working machines, and/or transmitted in the direction thereof (Blank: Para. 45, 53; generates a display, with various interactive display elements on control user interface; update an interface display that shows the settings; execute a change in machine settings that it identifies, if operator approves the change).
It would have been obvious to one having ordinary skill in the art to modify the data updated site-specific control model (Henry: Para. 7) with the remote control user interface display (Blank: Para. 45, 53) with a reasonable expectation of success because displaying the suggested changes for automatic machine controls based on real-time data analysis allows for informing the operator in case approval or veto is required (Blank: Para. 45, 53).
Regarding claim 20, Henry teaches the method as claimed in claim 1, wherein the first working data set furthermore contains associated feedback, assessment, and/or operating input data (Henry: Para. 23, 25; determine an anterior set of conditions for the portion of the field based at least in part on the anterior sensor data; site-specific control model to obtain a first set of output controls based at least in part on the anterior set of conditions for the portion of the field).
Regarding claim 21, Henry teaches the method as claimed in claim 1, wherein the at least one following working data set furthermore contains associated feedback, assessment, and/or operating input data (Henry: Para. 29; determine the posterior set of conditions based on the posterior sensor data; percent residue cover; soil roughness; weed population).
Regarding claim 22, Henry teaches the method as claimed in claim 6, wherein the machine data sets for adapting the process model are filtered by assembly in the filter module (Henry: Para. 30, 59; computing system can compare the posterior set of conditions to a target set of conditions for the portion of the field; crop-specific target conditions, time-of-year-specific target conditions).
Response to Arguments
Applicant’s arguments with respect to claims 1-22 have been fully considered, but are not persuasive.
The applicant’s attorney argues that the prior arts do not teach the working data sets including “functional unit data.”
In response to the applicant’s argument above, the applicant’s amendments states “the first working data set is formed by first operating parameter data, first machine sensor data, first functional unit data, and/or first working data derived from these data.” The broadest reasonable interpretation of this optional language is or, so the first working data set is first operating parameter data, first machine sensor data, first function unit data, or first working data. The prior arts don’t need to teach “functional unit data.”
The same is true for “one following working data set is at least partially formed by following machine sensor data, following functional unit data, following operating parameter data, and/or following working data derived from these data.” The examiner suggests claiming functional unit data in non-optional language.
The applicant next argues that the prior arts do not teach “machine-spanning comparability.”
In response to the argument above, the applicant’s specification includes “In a data set memory 23, in particular designed as a database, the machine data sets of all working machines are then collected. In an optional extraction module 24, the items of data set information of individual machines can be extracted in individual groups, allocated according to assemblies, for example, for the purpose of machine-spanning comparability.” (Specification: Para. 73)
Blank teaches a data store with data, operator information corresponding to the operators of each of the combines, machine details identifying the particular machines being used, the current machine settings for each machine that are updated by the machines, and historical data collected from the various machines (Blank: Para. 49). This teaches a database. Blank teaches learning analytics and a control system that receives information from the machine and other machines indicating the usage and efficiency of the control rules on those machines. The system can re-rank the rules, adjust the rules, and add new rules or delete existing rules based on the analysis (Blank: Para. 49). Blank teaches a remote user that can access comparison information that compares the performance of various combines and their operators on a near real time basis (Blank: Para. 53). The prior art does teach collecting a large dataset over various machines, that can be grouped, re-ranked, and adjusted so that comparison information across various combines and operators are available to the system for performance improvement purposes.
The applicant next argues that claim 1 is not specifically limited to harvesting machines for root crops.
In response to the applicant’s argument above, the applicant has amended “wherein the first agricultural working machine is a self-propelled harvesting machine, or the multiple combined first agricultural working machines are a combination of a tractor with a harvesting machine towed thereby” into the preamble. Applicant’s arguments rely on language solely recited in preamble recitations in claim 1. When reading the preamble in the context of the entire claim, the recitation of “self-propelled harvesting machine, or the multiple combined first agricultural working machines are a combination of a tractor with a harvesting machine towed” is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Henry’s data updated site-specific control model with Blank’s remote control user interface display reads on applicant’s method for adapting a process model for operating one of multiple work machines. The rejection is maintained.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571)272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on (571) 272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/ Supervisory Patent Examiner, Art Unit 3663
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