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 .
Information Disclosure Statement
The references listed on the IDS filed 10/2/2025 have been considered by the Examiner.
Response to Arguments
Applicant’s remarks have been fully considered.
In light of the terminal disclaimer filed, the double-patenting rejection has been withdrawn.
Applicant’s arguments regarding the rejection under 35 USC 103 focus on the failure of the cited art to teach the amended limitation of “… and comprising a set of initial waylines recorded by the one or more mobile machines while operating in the one or more fields,” added to claims 1, 16 and 20.
This new limitation is made of up part of claim 3, “initial routing information comprises initial waylines,” along with the whole of claim 14, “wherein the initial routing information is recorded by one or more mobile machines operating in the one or more fields.
Both claim 3 and claim 14 were rejected in the previous office action. Applicant does not appear to explain why they believe the cited art does not teach the amended limitations of claim 1, but merely asserts that they do not.
Examiner respectfully disagrees. Regarding the limitation that the initial routing information comprises initial waylines, Examiner refers to Figs. 15-16 of the application, wherein the waylines appear to simply be a geometric representation of the route followed by or to be followed by the vehicle. Examiner does not believe that this is distinct from the path described in Dix Figs. 3-5, [0034]-[0045]. [0035]-[0037] describe ways the path may be defined mathematically. Referring specifically to [0043], “the path may pass through points or curves,” and [0045], “the path may be a continuous line or a collection of discrete points.”
Regarding the limitation that the initial routing information is recorded by one or more machines operating in the one or more fields, Examiner asserts that Zhang teaches this limitation. Zhang [0020], for example, teaches the use of training data collected from a given site used to train a deep neural network in order to operate a vehicle at the given site. Zhang [0025]-[0026] also describe using target route data to train the model, and [0030] describes the target route data as including a specific route in the site collected by a vehicle and corresponding driving trajectory data point.
Therefore, Examiner asserts that Zhang in view of Johnson teaches the limitations of claim 14, and Zhang in view of Johnson and Dix teaches the limitations of claim 3, and the incorporation of these limitations into claim 1 does not render it non-obvious over Zhang in view of Johnson and Dix.
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.
Claim 20 is 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 20 recites the limitation "the one or more mobile machines" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20210223056 A1) in view of Johnson (US 20230242095 A1) and Dix et al. (US 20170357262 A1).
Regarding claim 1, Zhang teaches: A method, comprising:
receiving, by a computing system, initial routing information, the initial routing information defining routes followed by or to be followed by one or more mobile machines in one or more [fields] and comprising a set of initial [waylines] recorded by the one or more mobile machines while operating in the one or more [fields]; (See Zhang Fig. 1 and [0024]-[0026] for target route data and historic route data provided to computing device, [0030] for description of target route data as including route data collected by a vehicle for a specific route within the target site)
training, by the computing system, a deep learning model using the initial routing information; and (See Zhang Fig. 1 and [0025]-[0026] for training of machine learning model based on data including the target route data. See [0020] for deep neural network.)
using, by the computing system, the trained model to generate new routing information for a given [field]. (See Zhang Fig. 1 and [0024]-[0026] for computing device including machine learned model, see [0084] for generation of trajectory points and control of the vehicle.)
Zhang does not explicitly teach the use of waylines or the application of the route generating system to an agricultural field.
Per the specification of the present application, in particular Figs. 15-16 and the associated paragraphs, the waylines appear to be mathematical or geometric representations of the route data.
Dix teaches a system for tracking the swath of an agricultural vehicle (See Abstract), similar to that of the current application, which includes a mathematical representation of the vehicle route. (See Dix Figs. 3-5, [0034]-[0045]. [0035]-[0037] describe ways the path may be defined mathematically. Referring specifically to [0043], “the path may pass through points or curves,” and [0045], “the path may be a continuous line or a collection of discrete points.”)
Johnson teaches a method of agricultural vehicle path planning (See Johnson [0007]-[0016] for generation of a recommended travel path in a field) incorporating machine learning for analysis of the field and generation of the path (See Johnson [0106]-[0108] for analysis of the field data by machine learning and feedback to improve the algorithm).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application, to modify the path-planning method of Zhang to incorporate the mathematical representation of the vehicle route, as taught in Dix, and to apply the method to generating a path for an agricultural vehicle through a field, as taught in Johnson, to provide the benefits of machine learning-based planning to agricultural vehicles.
Regarding claim 2, modified Zhang teaches: The method of claim 1, comprising using, by the computing system, the trained model to generate the new routing information for the given field and a given mobile machine. (See Zhang Fig. 2 and [0027]-[0033] for generation of a route for a specific vehicle. See Johnson [0075] for input data specific to the vehicle used in the path generation.)
Regarding claim 3, modified Zhang teaches: The method of claim 1, wherein the new routing information comprises new waylines. (See Dix [0045] for updating the corrected path)
Regarding claim 4, modified Zhang teaches: The method of claim 3, further comprising: extracting, by the computing system, distances between the initial waylines; and further training, by the computing system, the deep learning model according to the initial waylines and the extracted distances. (See Dix Figs 1, 3, for planning based on a swath or width of the vehicle or vehicle attachment. See [0003]-[0006] for determination of path based on distance between intended path and current position of the vehicle. See [0020] for determination of relative proximity to one or more rows of swaths.)
Regarding claim 5, modified Zhang teaches: The method as set forth in claim 1, further comprising controlling a given mobile machine, by the computing system, to follow routes in a field according to the new routing information. (See Zhang [0020] and throughout for control of the vehicle based on the planned route. See Johnson [0018] and throughout for navigation of the vehicle over recommended travel path.)
Regarding claim 6, modified Zhang teaches: The method as set forth in claim 1, further comprising: receiving, by the computing system, initial mobile machine information; and further training, by the computing system, the deep learning model according to the initial mobile machine information. (See Johnson [0106] for feedback processing system 152 to provide further training to the algorithm, vehicle index sensors 132 providing input to feedback system)
Regarding claim 7, modified Zhang teaches: The method of claim 6, wherein the initial mobile machine information comprises one or more of machine model information, machine type information, machine size information, machine shape information, machine ground footprint information, machine turn radius information, and energy usage information. (See Johnson [0075] for vehicle index data/sensors used to calculate the force of the vehicle on the ground. See [0080]-[0084] for vehicle characteristics 158 including physical dimensions, weight, make and model. See Dix [0033] for minimum turn radius, [0043] for fuel consumption)
Regarding claim 8, modified Zhang teaches: The method as set forth in claim 1, further comprising:
receiving, by the computing system, initial field information; and
further training, by the computing system, the deep learning model according to the initial field information. (See Johnson Fig. 2 and [0080]-[0081], [0085] for soil measure identification system 148 including feedback processing system 152. See [0106] for feedback processing system 152 to provide further training to the algorithm.)
Regarding claim 9, modified Zhang teaches: The method of claim 8, wherein the initial field information comprises one or more of field size information, field shape information, field elevation information, field topology information, soil type information, soil condition information, crop type information, crop lodging information, soil compaction information, weed density information, and weed location information. (See Johnson Fig. 2 and [0080]-[0085] for soil measure identification system 148 including cone index signal processor 184, soil type identifier 194, soil moisture identifier 196. See [0083] in particular for terrain maps (including at least field shape and area information, and potentially elevation and topology) soil type maps, yield maps, soil measure and damage maps (soil condition/compaction information)
Regarding claim 10, modified Zhang teaches: The method as set forth in claim 1, wherein the trained model is configured to generate the new routing information to minimize fuel consumption of the mobile machine when performing a given field operation. (See Dix [0043] for determination of cost based on fuel consumption or time required)
Regarding claim 11, modified Zhang teaches: The method as set forth in claim 1, wherein the trained model is configured to generate the new routing information to minimize operation time of the mobile machine when performing a given field operation. (See Dix [0043] for determination of cost based on fuel consumption or time required)
Regarding claim 12, modified Zhang teaches: The method as set forth in claim 1, wherein the trained model is configured to generate the new routing information to minimize soil compaction caused by the mobile machine when performing a given field operation. (See Johnson [0003]-[0005] for avoiding undesired level of soil compaction. See [0007] and throughout for generation of a soil damage score based on soil and vehicle information and generation of route to reduce damage score)
Regarding claim 13, modified Zhang teaches: The method according to claim 1, further comprising: receiving, by the computing system, weather data; and further training, by the computing system, the deep learning model according to the weather data. (See Johnson Fig. 2 and [0080]-[0085] for soil measure identification system 148 including feedback processing system 152. See [0086] for soil moisture estimation based on weather information. See [0106] for feedback processing system 152 to provide further training to the algorithm.)
Regarding claims 16-19, the claims are directed to a system for performing the method of claims 1-4 and are rejected under the same rationale.
Regarding claim 20, Zhang teaches: A method, comprising:
receiving, by a computing system, initial routing information, the initial routing information defining routes followed by a given mobile machine in one or more [fields] and comprising a set of initial [waylines] recorded by the one or more mobile machines while operating in the one or more [fields]; (See Zhang Fig. 1 and [0024]-[0026] for target route data and historic route data provided to computing device, [0030] for description of target route data as including route data collected by a vehicle for a specific route within the target site)
training, by the computing system, a deep learning model using the initial routing information; and (See Zhang Fig. 1 and [0025]-[0026] for training of machine learning model based on data including the target route data. See [0020] for deep neural network.)
using, by the computing system, the trained model to generate new routing information for a given [field]. (See Zhang Fig. 1 and [0024]-[0026] for computing device including machine learned model, see [0084] for generation of trajectory points and control of the vehicle.)
Zhang does not explicitly teach the use of waylines or the application of the route generating system to an agricultural field.
Per the specification of the present application, in particular Figs. 15-16 and the associated paragraphs, the waylines appear to be mathematical or geometric representations of the route data.
Dix teaches a system for tracking the swath of an agricultural vehicle (See Abstract), similar to that of the current application, which includes a mathematical representation of the vehicle route. (See Dix Figs. 3-5, [0034]-[0045]. [0035]-[0037] describe ways the path may be defined mathematically. Referring specifically to [0043], “the path may pass through points or curves,” and [0045], “the path may be a continuous line or a collection of discrete points.”)
Johnson teaches a method of agricultural vehicle path planning (See Johnson [0007]-[0016] for generation of a recommended travel path in a field) incorporating machine learning for analysis of the field and generation of the path (See Johnson [0106]-[0108] for analysis of the field data by machine learning and feedback to improve the algorithm).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application, to modify the path-planning method of Zhang to incorporate the mathematical representation of the vehicle route, as taught in Dix, and to apply the method to generating a path for an agricultural vehicle through a field, as taught in Johnson, to provide the benefits of machine learning-based planning to agricultural vehicles.
Conclusion
THIS ACTION IS MADE FINAL. 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 JACOB KENT BESTEMAN-STREET whose telephone number is (571)272-2501. The examiner can normally be reached M-TH 8:00-5:00. 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, Peter Nolan can be reached on 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format.
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/JACOB KENT BESTEMAN-STREET/
Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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