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 .
Response to Arguments
Applicant’s remarks have been fully considered.
Regarding the rejection of claim 20 under 35 USC 112, the rejection is maintained because while the amendment filed does add the language “the given mobile machine,” it does not strike through “the one or more mobile machines” as described in the remarks. Examiner assumes this is an unintended oversight and will examine the claim assuming that this will be corrected in the next response.
Regarding the rejection under 35 USC 103, Applicant argues that the combination of Zhang, Johnson and Dix does not teach the amended limitations of claims 1, 16 and 20. Examiner agrees, however this is moot in light of the new grounds of rejection.
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 1-9 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 20210003416 A1) in view of Matthews (US 20170223888 A1) and Xiao et al. (US 20190291720 A1).
Regarding claim 1, Wilson teaches: A method, comprising:
…
using, by the computing system, the trained model to generate new routing information for a given field, (See Wilson [0096] for use of machine learning or AI technique or algorithm to provide user with suggested paths via path generation system)
wherein the new routing information comprises a set of new [waylines], and generating the set of new [waylines] comprises generating the new [waylines] to minimize the skipped areas between adjacent swaths of the given field and to minimize overlap between adjacent [waylines] while satisfying a minimum turn radius constraint of a mobile machine. (See Wilson [0095] for generation of paths to maximize field coverage and minimize skips or overlaps between planted rows. See [0135]-[0142] for consideration of minimum threshold turn radius defined by user. See [0072] for paths defined base on known characteristics of the vehicle and implement.)
Wilson does not explicitly teach:
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;
training, by the computing system, a deep learning model using the initial routing information; …
Wilson does not explicitly teach the use of waylines defined by waypoints along the desired path of the vehicle.
Wilson does describe the guidance path as being defined by A-B lines (See [0086]) and the improvement of the guidance system through machine learning (See [0132]).
Matthews teaches a method of agricultural vehicle guidance based on waylines comprised of a plurality of points (See Matthews Fig. 2, [0003]-[0004], [0010]-[0012], and throughout).
Xiao teaches a method of training a machine learning model for vehicle maneuvering based on historical data including historical paths (See Xiao [0004], [0027]) wherein the path may be defined by multiple points, similar to the waylines of the present application (See Xiao [0070]).
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 guidance system of Wilson to incorporate the waylines of Matthews as the means of defining the path data in order to provide a flexible path and data structure, and to use the training method of Xiao to perform the initial training of the machine learning algorithm used to provide the suggested paths, in order to optimize the performance of the learned model.
Regarding claim 2, modified Wilson 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 Wilson [0068], [0072] for path system receiving field characteristics and vehicle characteristics)
Regarding claim 3, modified Wilson teaches: The method of claim 1, wherein the new routing information comprises new waylines. (See Wilson [0086] for creating new guidance paths. See Matthews Fig. 2 for waylines, Xiao [0070] for paths defined by points)
Regarding claim 4, modified Wilson 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 Wilson [0072] for planning based on swath width, edges or center guidance. See [0079] for adjustment of swath spacing, [0087] for adjustment of skips and overlaps between swaths.)
Regarding claim 5, modified Wilson 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 Wilson [0007] for commanding automatic steering unit based on guidance paths.)
Regarding claim 6, modified Wilson 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 Xiao [0005], [0024], [0063]-[0069] for neural network trained to process vehicle dynamics and path information.)
Regarding claim 7, modified Wilson 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 Xiao [0063]-[0064] where vehicle dynamics information may include vehicle size, weight, turn radius, tire information, etc.)
Regarding claim 8, modified Wilson 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 Wilson [0056], [0068] guidance based on field characteristics, requiring that the model be trained on field characteristics.)
Regarding claim 9, modified Wilson 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 Wilson [0068] for field characteristics such as field map, region, or boundary.)
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 1, Wilson teaches: A method, comprising:
…
using, by the computing system, the trained model to generate new routing information for a given field, (See Wilson [0096] for use of machine learning or AI technique or algorithm to provide user with suggested paths via path generation system)
wherein the new routing information comprises a set of new [waylines], and generating the set of new [waylines] comprises generating the new [waylines] to minimize the skipped areas between adjacent swaths of the given field and to minimize overlap between adjacent [waylines] while satisfying a minimum turn radius constraint of a mobile machine. (See Wilson [0095] for generation of paths to maximize field coverage and minimize skips or overlaps between planted rows. See [0135]-[0142] for consideration of minimum threshold turn radius defined by user. See [0072] for paths defined base on known characteristics of the vehicle and implement.)
Wilson does not explicitly teach:
receiving, by a computing system, initial routing information, the initial routing information defining routes followed by or to be followed by a given mobile machine in one or more fields and comprising a set of initial waylines recorded by the given mobile machine while operating in the one or more fields;
training, by the computing system, a deep learning model using the initial routing information; …
Wilson does not explicitly teach the use of waylines defined by waypoints along the desired path of the vehicle.
Wilson does describe the guidance path as being defined by A-B lines (See [0086]) and the improvement of the guidance system through machine learning (See [0132]).
Matthews teaches a method of agricultural vehicle guidance based on waylines comprised of a plurality of points (See Matthews Fig. 2, [0003]-[0004], [0010]-[0012], and throughout).
Xiao teaches a method of training a machine learning model for vehicle maneuvering based on historical data including historical paths (See Xiao [0004], [0027]) wherein the path may be defined by multiple points, similar to the waylines of the present application (See Xiao [0070]).
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 guidance system of Wilson to incorporate the waylines of Matthews as the means of defining the path data in order to provide a flexible path and data structure, and to use the training method of Xiao to perform the initial training of the machine learning algorithm used to provide the suggested paths, in order to optimize the performance of the learned model.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 20210003416 A1) in view of Matthews (US 20170223888 A1), Xiao et al. (US 20190291720 A1) and Dix et al. (US 20170357262 A1).
Regarding claim 10, Wilson in view of Matthews and Xiao teaches: The method as set forth in claim 1,
Wilson in view of Matthews and Xiao does not explicitly teach: 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.
However, Dix teaches a system for planning a path for an agricultural vehicle (See Dix [0002], [0004] and throughout) including a determination of path cost based on fuel consumption or time required. (See Dix [0043] for determination of cost based on fuel consumption or time required)
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 system of Wilson to incorporate the cost function of Dix in order to further optimize the performance of the agricultural vehicle.
Regarding claim 11, Wilson in view of Matthews and Xiao teaches: The method as set forth in claim 1,
Wilson in view of Matthews and Xiao does not explicitly teach: 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.
However, Dix teaches a system for planning a path for an agricultural vehicle (See Dix [0002], [0004] and throughout) including a determination of path cost based on fuel consumption or time required. (See Dix [0043] for determination of cost based on fuel consumption or time required)
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 system of Wilson to incorporate the cost function of Dix in order to further optimize the performance of the agricultural vehicle.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 20210003416 A1) in view of Matthews (US 20170223888 A1), Xiao et al. (US 20190291720 A1) and Johnson (US 20230242095 A1).
Regarding claim 12, Wilson in view of Matthews and Xiao teaches: The method as set forth in claim 1,
Wilson in view of Matthews and Xiao does not explicitly teach: 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.
However, Johnson teaches the control of an agricultural vehicle based on soil damage. (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)
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 system of Wilson to incorporate the soil characteristic information of Johnson to further optimize the performance of the agricultural vehicle.
Regarding claim 13, Wilson in view of Matthews and Xiao teaches: The method according to claim 1, further comprising:
Wilson in view of Matthews and Xiao does not explicitly teach: receiving, by the computing system, weather data; and further training, by the computing system, the deep learning model according to the weather data.
However, Johnson teaches the control of an agricultural vehicle based on soil damage. (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.)
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 system of Wilson to incorporate the weather information of Johnson to further optimize the performance of the agricultural vehicle.
Conclusion
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/JACOB KENT BESTEMAN-STREET/
Examiner, Art Unit 3661
Prosecution Insights