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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/12/2026 has been entered.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification (MPEP 608.01, ¶6.31).
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 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.
Claim(s) 1-9 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hurd (US20180024549A1) in view of Upadhyay (US12189383B2).
Regarding claim 1, Hurd teaches;
A computer implemented method of controlling an agricultural vehicle (taught as a system, element 100, implemented into agricultural vehicles, elements 1-2, using executive controllers, element 3, and used for automated tillage, paragraph 0048), the computer implemented method comprising:
detecting a sorting criterion [interpreted to be a sensor value, such as ones described in table 1 of the disclosure] on the agricultural vehicle (taught as sensors, including navigation related, detection and proximity sensors, paragraph 0021);
generating a request for settings information, the request being indicative of the sorting criterion (taught as requesting input from a user in an initial setup, paragraph 0019);
sending the request for settings information to a setting processing system (taught as requesting input from a user in an initial setup, paragraph 0019, and user selection of setting arrangements for task specific settings, paragraph 0031);
receiving a response to the request for settings information (taught as requesting input from a user in an initial setup, paragraph 0019, and the user provides a selection of setting arrangements for task specific settings, paragraph 0031), the response including the settings information based on the sorting criterion (taught as defining primary settings for any given operation, paragraph 0030, which are saved for the agricultural vehicle for use after a first initialization, paragraph 0029), the settings information being indicative of a plurality of different settings values of a plurality of different agricultural vehicles with a corresponding sorting criterion (taught as cloning settings defined from the agricultural vehicle(s)/equipment, paragraph 0038, wherein the settings include calibration settings that are saved [aggregated] for future use, paragraph 0049, for use with multiple/paired machine equipment, paragraph 0051, and include further settings that are stored in the cloud based database for work operations, paragraph 0053); and
controlling the agricultural vehicle based on the aggregated setting value (taught as, when selecting task-specific settings, defining parameters and behaviors of the machine such as turning radius, path planning, equipment etc. paragraph 0031, to control the vehicle, paragraph 0034).
However, Hurd does not explicitly teach; aggregating the plurality of different setting values of the plurality of different agricultural vehicles to obtain an aggregated setting value based on the plurality of different setting values.
Upadhyay teaches; aggregating the plurality of different setting values of the plurality of different agricultural vehicles to obtain an aggregated setting value based on the plurality of different setting values (taught as updating the class specific model based on the received local model data, column 13 lines 37-42; wherein the collection/aggregation of local model data from each vehicle in the plurality of vehicles is combined to obtain updated parameters of the class-specific model to vehicles included in the vehicle class, column 33 lines 34-44).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the local models for vehicles as taught by Upadhyay in the system taught by Hurd and modified by Hodson in order to improve vehicle reliability and efficiency. By modifying the logic to sort the fleet’s data into vehicle/machine types and aggregate those subsets into the individual vehicle classes, the system can ensure that when a vehicle requests settings, it receives optimized settings based on a local model pertaining to that vehicle type. As suggested by Upadhyay, such models would increase predictions of prognostics and adjust vehicle parameters as a result (column 2 lines 45-58). To reiterate, one would take the settings data of profile/machine information taught from Hurd and Hodson, and implement local vehicle models as taught by Upadhyay, in order to improve the vehicle reliability and efficiency by better addressing a vehicle class in defining local parameters.
Regarding claim 3, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 1 (see claim 1 rejection). Hurd further teaches; wherein controlling the agricultural vehicle comprises:
automatically setting a settings value for a controllable subsystem on the agricultural vehicle based on the aggregated setting value (taught as defining primary settings for any given operation, paragraph 0030, which are shared among agricultural vehicles [only 2 vehicles are explicitly taught, but extending it to more vehicles would be obvious] for use after a first initialization, paragraph 0029).
Regarding claim 4, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 1 (see claim 1 rejection). Hurd further teaches; wherein controlling the agricultural vehicle comprises:
controlling an operator interface system on the agricultural vehicle to generate an operator-observable output indicative of the aggregated setting value (taught as controlling the system through an interface, such as a computer or mobile device that allows a user to input and define settings, paragraph 0034; examples of indicating a settings output include visualizing a path from predefined settings, paragraph 0045).
Regarding claim 5, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 4 (see claim 4 rejection). Hurd further teaches; wherein controlling the operator interface system comprises:
pre-populating a settings input interface display with a pre-populated aggregated setting value based on the aggregated setting value (taught as using settings that are saved automatically and automatically loaded, paragraph 0029);
detecting an operator input interacting with the settings input interface display (taught as a user inputting or interacting with settings, such as to select task-specific settings, paragraph 0031); and
controlling the agricultural vehicle based on the operator input (taught as controlling machine behavior based on the settings configuration, paragraph 0031 and 0034)
Regarding claim 6, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 5 (see claim 5 rejection). Hurd further teaches; wherein detecting the operator input comprises:
detecting an operator input modifying the pre-populated aggregated setting value on the settings input interface display to obtain a modified setting value (taught as the primary/initialized settings being easily changed or adjustable, paragraph 0030, such as for task-specific settings, paragraph 0034) and wherein controlling the agricultural vehicle comprises controlling the agricultural vehicle based on the modified setting value (taught as controlling machine behavior based on the settings configuration, paragraph 0031 and 0034).
Regarding claim 7, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 5 (see claim 5 rejection). Hurd further teaches; wherein detecting the operator input comprises:
detecting an operator input selecting the pre-populated aggregated setting value on the settings input interface display to obtain a selected setting value (taught as different setting configurations for task-specific settings, paragraph 0034) and wherein controlling the agricultural vehicle comprises controlling the agricultural vehicle based on the selected setting value (taught as controlling machine behavior based on the settings configuration, paragraph 0031 and 0034).
Regarding claim 8, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 5 (see claim 5 rejection). Hurd further teaches; wherein pre-populating a settings input interface display with the aggregated setting value, comprises:
controlling an operator interface system to run a settings wizard to display the settings input interface display (taught as, upon initial connection to the system, generating requests for the user [indicating some settings application/wizard to input settings initially] to input various settings and calibrations, paragraph 0029) and pre-populating a settings input interface display in the wizard with the aggregated setting value (taught as automatically loading the saved settings after the initial setup onto the agricultural vehicle(s), paragraph 0029).
Regarding claim 9, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 2 (see claim 2 rejection). Hurd further teaches; and further comprising:
detecting a setting value for a plurality of different settings on the agricultural vehicle (taught as receiving inputs for settings or receiving saved settings from another vehicle in regard to both vehicles 1 and 2, paragraph 0029); and
sending the detected setting value for the plurality of different settings to the setting processing system (taught as automatically loading the saved settings after the initial setup onto the agricultural vehicle(s), paragraph 0029).
Regarding claims 14-16 and 18-20, it has been determined that no further limitations exist apart from those previously addressed in claims 1-2 and 4-8. Therefore, claims 14-16 and 18-20 are rejected under the same rationales as claims 1-2 and 4-8, wherein;
Claim 14 corresponds to claim 1
Claim 15 corresponds to claim 2
Claim 16 corresponds to claim 4
Claim 18 corresponds to claim 6
Claim 19 corresponds to claim 7
Claim 20 corresponds to claim 8
Claim(s) 10-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hurd (US20180024549A1) in view of Hodson (US20210105940A1) and Upadhyay (US12189383B2).
Regarding claim 10, Hurd teaches;
A computer implemented method, comprising:
Receiving settings data comprising a plurality of setting values from a plurality of different agricultural vehicles, the plurality of setting values comprising a setting value of a controllable subsystem from each vehicle of the plurality of different agricultural vehicles (taught as defining primary settings for any given operation, paragraph 0030, which are saved for the agricultural vehicle for use after a first initialization, paragraph 0029, which are cloned settings defined from the agricultural vehicle/equipment, paragraph 0038, wherein the settings include calibration settings that are saved [aggregated] for future use, paragraph 0049, for use with multiple/paired machine equipment, paragraph 0051);
Identifying at least one sorting criterion corresponding to the received settings data (taught as identifying sensor and input information for the work operation, including sorting conditions like position, equipment type etc. paragraph 0035);
Receiving a request for settings data (taught as a startup use for the equipment/vehicle initializing settings, paragraph 0029); and
Responding to the request with [[a selected aggregated setting value]] based on the request (taught as automatically implementing previously generated settings, paragraph 0029, including the use of task specific settings, paragraph 0031); and
Controlling the controllable subsystem of an agricultural vehicle based on the selected aggregated setting value (taught as, when selecting task-specific settings, defining parameters and behaviors of the machine such as turning radius, path planning, equipment etc. paragraph 0031, to control the vehicle, paragraph 0034).
However, Hurd does not explicitly teach; Sorting, based on the at least one sorting criterion, the received settings from the plurality of different agricultural vehicles to obtain a plurality of sets of sorted settings data, each respective set of sorted settings data comprising a respective subset of setting values having at least two setting values of the plurality of setting values, that corresponds to the respective set of sorted settings data;
Aggregating each respective subset of setting value of the plurality of sets of sorted settings data to obtain a respective aggregated setting value corresponding to each respective set of sorted settings data; the selected aggregated setting value comprising the respective aggregated setting value of one set of sorted settings data of the plurality of sets of sorted settings data,
Responding to the request with a selected aggregated setting value that is selected based on one or more sorting criterions corresponding to the request.
Hodson teaches Sorting, based on the at least one sorting criterion, the received settings from the plurality of different agricultural vehicles to obtain a plurality of sets of sorted settings data (taught as unique identifiers in data found in a combination record, element 190 [which identifies different machine, implement and operator combinations that have been synchronized, paragraph 0034], paragraph 0054, including unique identifiers [indexing] for searching, paragraph 0054), each set of sorted settings data comprising a respective subset of setting values of the plurality of setting values (taught as further using the combination record in part, such as taking subsets of data where, for example, implements are considered similar/identical to suggest settings for, paragraph 0057 [this would, in essence, take the aggregated data, divide it into different subsets of data associated with specific operator, machine, and implement combinations, and use overlap/perceived equivalencies to combine them together for suggested settings, e.g. combining settings from 100, 106, and 110, identifying based on equivalence/usability between 104 and 106, and create a suggested setting based on the combined settings of 100, 104, and 110]);
Aggregating each respective subset of setting value to obtain a respective aggregated setting value corresponding to each set of sorted settings data (taught as the combination record, which combines all setting configurations and provides setting data to control/configuration systems, paragraph 0038, including controllable subsystems for height, speed, and implement settings, paragraph 0039).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine multiple settings information as taught by Hodson in the system taught by Hurd in order to improve setting configurations. Having specific configurations for different conditions [operator, equipment, condition settings etc.] allows for more consistent operations for the preferences of an operator, as suggested by Hodson (paragraph 0038). To reiterate, one would implement a combination record as taught by Hodson to expand on the settings system taught by Hurd, using the sensor and unique identifiers to be able to search [sort/index] different configurations for different situations, operators and conditions.
However, Hodson does not explicitly teach; each respective set of sorted settings data comprising a respective subset of setting values having at least two setting values of the plurality of setting values that corresponds to the respective set of sorted settings data,
Aggregating each respective subset of setting value of the plurality of sets of sorted settings data to obtain a respective aggregated setting value corresponding to each respective set of sorted settings data;
the selected aggregated setting value comprising the respective aggregated setting value of one set of sorted settings data of the plurality of sets of sorted settings data,
Responding to the request with a selected aggregated setting value that is selected based on one or more sorting criterions corresponding to the request.
Upadhyay teaches; each respective set of sorted settings data comprising a respective subset of setting values having at least two setting values of the plurality of setting values that corresponds to the respective set of sorted settings data (taught as dividing a population of vehicles into a plurality of vehicle classes, column 10 lines 47-54, essentially, input data from a plurality of vehicles is then sorted/divided into classifications),
Aggregating each respective subset of setting value of the plurality of sets of sorted settings data to obtain a respective aggregated setting value corresponding to each respective set of sorted settings data (taught as updating the class specific model based on the received local model data, column 13 lines 37-42; wherein the collection/aggregation of local model data from each vehicle in the plurality of vehicles is combined to obtain updated parameters of the class-specific model to vehicles included in the vehicle class, column 33 lines 34-44);
the selected aggregated setting value comprising the respective aggregated setting value of one set of sorted settings data of the plurality of sets of sorted settings data (taught as the collection/aggregation of local model data from each vehicle in the plurality of vehicles is combined to obtain updated parameters of the class-specific model to vehicles included in the vehicle class, column 33 lines 34-44),
Responding to the request with a selected aggregated setting value that is selected based on one or more sorting criterions corresponding to the request (taught as sending instructions to retrain local models of the vehicles with updated parameters, column 33 lines 44-47).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the local models for vehicles as taught by Upadhyay in the system taught by Hurd and modified by Hodson in order to improve vehicle reliability and efficiency. By modifying the logic to sort the fleet’s data into vehicle/machine types and aggregate those subsets into the individual vehicle classes, the system can ensure that when a vehicle requests settings, it receives optimized settings based on a local model pertaining to that vehicle type. As suggested by Upadhyay, such models would increase predictions of prognostics and adjust vehicle parameters as a result (column 2 lines 45-58). To reiterate, one would take the settings data of profile/machine information taught from Hurd and Hodson, and implement local vehicle models as taught by Upadhyay, in order to improve the vehicle reliability and efficiency by better addressing a vehicle class in defining local parameters.
Regarding claim 11, Hurd as modified by Hodson and Upadhyay teaches;
The computer implemented method of claim 10 (see claim 10 rejection). However, Hurd does not explicitly teach; wherein responding to the request comprises:
parsing the request to identify sorting criteria corresponding to the request; and
selecting the selected aggregated setting value based on the sorting criteria corresponding to the request.
Hodson teaches; wherein responding to the request comprises:
parsing the request to identify sorting criteria corresponding to the request (taught as assigning unique identifiers to settings, paragraph 0054); and
selecting the selected aggregated setting value based on the sorting criteria corresponding to the request (taught as a matching ID request to implement requested combination records, paragraph 0054).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine multiple settings information as taught by Hodson in the system taught by Hurd in order to improve setting configurations. Having specific configurations for different conditions [operator, equipment, condition settings etc.] allows for more consistent operations for the preferences of an operator, as suggested by Hodson (paragraph 0038). To reiterate, one would implement a combination record as taught by Hodson to expand on the settings system taught by Hurd, using the sensor and unique identifiers to be able to search [sort/index] different configurations for different situations, operators and conditions.
Regarding claim 13, Hurd as modified by Hodson and Upadhyay teaches;
The computer implemented method of claim 10 (see claim 10 rejection). Hurd further teaches; wherein identifying sorting criteria comprises:
identifying, as the sorting criteria, an operating condition under which the agricultural vehicle from which the settings data was received is operating (taught as using known information data, in conjunction with settings to determine vehicle behavior, which indicates the use of machine type and condition and equipment, as a sorting mechanism in determining the operation, paragraph 0035, including the establishment of task specific settings, paragraph 0031),
identifying, as the sorting criteria, a geographic location of the agricultural vehicle from which the settings data was received (taught as using position data, in conjunction with settings to determine vehicle behavior, which indicates the use of geographical data as a sorting mechanism in determining the operation, paragraph 0035).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hurd (US20180024549A1) as modified by Hodson (US20210105940A1) and Upadhyay (US12189383B2) and further in view of Ricci (US20130144470A1).
Regarding claim 12, Hurd as modified by Hodson and Upadhyay teaches;
The computer implemented method of claim 10 (see claim 10 rejection). However, Hurd does not explicitly teach; wherein the respective aggregated setting value corresponding to each set of sorted settings data comprises a median value of the respective subset of setting values.
Ricci teaches; wherein the respective aggregated setting value corresponding to each set of sorted settings data comprises a median value of the respective subset of setting values (taught as controlling parameters/settings of multiple settings parameters based on a suitable mathematical algorithm, such as by a median of multiple values, paragraph 0067, and by use of other nearby/proximal vehicle settings, paragraph 0069).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to ch0ose from a number of settings/preferences by a median value as taught by Ricci in the system taught by Hurd in order to improve the selection of settings. Median selections are suitable for data sets to help filter out outliers/extremes in data sets, which helps attain the more appropriate settings that an average might miss based on outliers/extremes.
Claim(s) 22 is rejected under 35 U.S.C. 103 as being unpatentable over Hurd (US20180024549A1) as modified by Upadhyay (US12189383B2) and further in view of Ricci (US20130144470A1).
Regarding claim 22, Hurd as modified by Upadhyay teaches;
The computer implemented method of claim 1 (see claim 1 rejection). However, Hurd does not explicitly teach; wherein aggregating the plurality of different setting values to obtain an aggregated setting value comprises aggregating the plurality of different setting values to obtain, as the aggregated setting value, one of a mean, a median, a limit, a threshold value, or a function.
Ricci teaches; wherein aggregating the plurality of different setting values to obtain an aggregated setting value comprises aggregating the plurality of different setting values to obtain, as the aggregated setting value, one of a mean, a median, a limit, a threshold value, or a function (taught as controlling parameters/settings of multiple settings parameters based on a suitable mathematical algorithm, such as by a median of multiple values, paragraph 0067, and by use of other nearby/proximal vehicle settings, paragraph 0069).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to ch0ose from a number of settings/preferences by a median value as taught by Ricci in the system taught by Hurd in order to improve the selection of settings. Median selections are suitable for data sets to help filter out outliers/extremes in data sets, which helps attain the more appropriate settings that an average might miss based on outliers/extremes.
Response to Arguments
Applicant argues that on pages 9-11 that amended independent claims 1, 10, and 14 are not taught by the previously recited prior art.
The examiner agrees that the previously recited prior art is insufficient to address the amended material, and withdraws the previous rejections. However, new rejections in light of Upadhyay are presented above to rectify the deficiencies of Hurd and Hodson. In particular, Upadhyay address the aggregation of data from multiple vehicles, sorting/dividing them into specific vehicle class related data, and applying a specific local model to a vehicle based on the matching vehicle class data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For further import/download of settings from other vehicles onto a different vehicle; US20150185030A1
For further implementations of aggregating data for the settings of a vehicle, GB2561621A
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 9:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached on (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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