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 Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-8, 10-15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by HANSEN, U.S. Patent Application Publication No. 2023/0101136 A1 (‘136).
As per claim 1, ‘136 discloses a method comprising:
using a mobile machine to perform work in a field using first machine settings (e.g., See ‘136; [0158], which discloses operating an agricultural machine in a field using selected machine settings);
recording performance information, the performance information indicating a performance of the mobile machine while performing the work in the field (e.g., See ‘136; [0159] – [0162], which discloses collecting operational data that indicates how the machine performed during field operation);
using a computing system of the mobile machine to input the recorded performance information into a trained deep learning model and to receive new machine settings information from the trained deep learning model (e.g., See ‘136; [0139] – [0141], [0153] and [0164], which discloses a trained machine learning model, including a deep learning system, with performance information to determine new machine settings); and
using the computing system to control the mobile machine according to the new machine settings information generated according to trained deep learning model (e.g., See ‘136; [0167] – [0168], which discloses applying the new machine settings to control subsequent machine operation).
As per claim 2, ‘136 further discloses:
recording environmental information while the mobile machine is performing the work in the field, the environmental information relating to the environment in which the mobile machine is operating (e.g., See ‘136; [0150], which discloses collecting environmental information while the machine is performing field work, including weather conditions and terrain conditions); and
using the computing system to input the environmental information into the trained deep learning model and to receive new machine settings information from the trained deep learning model (e.g., See ‘136; [0141], which discloses using field data as an input to a trained machine learning model that determines new machine settings).
As per claim 3, ‘136 further discloses that the environmental information comprises one or more of field crop information, wind direction or speed, ambient temperature, ambient humidity, soil characteristics, time of day, date, and geographic region (e.g., See ‘136; [0150], which discloses environmental information including weather conditions and terrain conditions, including ambient temperature, ambient humidity, and soil characteristics).
As per claim 4, ‘136 further discloses that the field crop information comprises one or more of crop height, crop color, crop moisture, crop lodging, and weed information (e.g., See ‘136; [0089], [0110] and [0149], which discloses field crop information including crop moisture, downed crop which corresponds to crop lodging under BRI, and weed information).
As per claim 5, ‘136 further discloses that the mobile machine is a grain harvester and the performance information comprises one or more of ground speed, fuel efficiency, crop throughput, crop quality, crop cleanliness, straw quality and crop yield (e.g., See ‘136; [0114] - [0115], which discloses harvester performance information including crop yield and fuel efficiency).
As per claim 7, ‘136 further discloses that the mobile machine includes a tillage implement and the performance information comprises one or more of soil granularity, stone size, stone density, soil uniformity, biomass treatment and soil turning quality (e.g., See ‘136; [0111] and [0128], which discloses a tilling machine that evaluates tilling performance based on image data including ground engagement of tilling tools, which corresponds to soil turning quality under BRI).
As per claim 8, ‘136 further discloses recording machine settings information while the mobile machine is performing the work in the field, and using the computing system to input the machine settings information into the trained deep learning model and to receive the new machine settings information from the trained deep learning model (e.g., See ‘136; [0141], which discloses using machine data, including machine settings information, as an input to the trained machine learning model that determines new machine settings).
As claims 10 and 20, these claims are rejected for at least the same reasons as claim 1 because claims 10 and 20 recite substantially the same features with only statutory category and minor wording differences.
As per claims 11, 12, 13, 14 and 15, these claims are rejected for at least the same reasons as corresponding claims 2, 3, 4, 5 and 8, respectively, because these claims recite substantially the same limitations in system form.
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over HANSEN, U.S. Patent Application Publication No. 2023/0101136 A1 (‘136), as applied to claim 1, from above, in view of Smith, U.S. Patent Application Publication No. 2016/0165803 A1 (‘803).
As per claim 6, ‘136 does not specifically disclose that the mobile machine is a baler and the performance information comprises one or more of bale density, bale weight, bale size, bale dryness, straw length and ash content.
‘803 discloses the missing feature (e.g. See ‘803; [0012] and [0042], which discloses a baler using bale weight and bale moisture information to adjust bale density).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of ‘803 into ‘136 for the purpose of using baler performance information to improve bale density control, thereby producing more consistent bales.
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over HANSEN, U.S. Patent Application Publication No. 2023/0101136 A1 (‘136), as applied to claim 1, from above, in view of Ferrari, U.S. Patent Application Publication No. 2019/0392269 A1 (‘269).
As per claim 9, ‘136 does not specifically disclose storing the trained deep learning model on the mobile machine.
‘269 discloses the missing feature (e.g., See ‘269; [0066], which discloses storing the trained learning model on the agricultural machine).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of ‘269 into ‘136 for the purpose of generating new machine settings, thereby allowing continued control during field work.
As per claim 16, this claim is rejected for at least the same reasons as corresponding claim 9 because this claim recites substantially the same limitations in system form.
References Considered but Not Relied Upon
The following references were considered but were not relied upon with respect to any prior art rejections:
(1) US 2021/0015045 A1, which discloses using machine learning from multiple harvesters to adjust harvester settings based on conditions and performance;
(2) US 2018/0271015 A1, which discloses using reinforcement learning to control combine harvester actions from machine and field information;
(3) US 2021/0120736 A1, which discloses using machine learning to detect harvester impurities and generate control signals for harvester component adjustment;
(4) US 2021/0120737 A1, which discloses using pre-harvest and post-harvest images with machine learning to estimate yield and output control signals;
(5) US 2021/0321657 A1, which discloses using machine learning to adjust harvesting delay models and generate machine control signals from yield data; and
(6) US 6,119,442 B2, which discloses uses machine vision to monitor crop material and automatically adjust combine settings.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD D HARTMAN JR whose telephone number is (571)272-3684. The examiner can normally be reached M-F 8:30 - 4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 June 6, 2026
/RDH/
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