DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to the applicant’s filing on May 2, 2025. Claims 1-20 are pending examined below.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because Fig 4. reference character “430” has been used to designate both “Receive second input(s) of feedback” and “Adjust guidance reinforcement learning model”. According specification [0120], “Receive second input(s) of feedback” should be referenced as --420--. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: specification [0109] line 3, the phrase “information 201” should be --information 301--. Appropriate correction is required.
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 1-11 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maeder et al., US 2022/0406104 A.
As to claim 1, Maeder teaches an agricultural system, comprising (Fig. 1):
an agricultural vehicle (Fig. 1); and
a guidance system configured to control one or more operations of the agricultural vehicle and comprising (Figs. 1-2):
at least one processor (¶ 36 and Figs. 1-2); and
at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system to (Figs. 1-2):
obtain a guidance reinforcement learning model configured to generate guidance information for guiding the agricultural vehicle through one or more agricultural processes in a predetermined agricultural region (¶ 81-84 and Fig. 5);
guide the agricultural vehicle through one or more agricultural processes in the predetermined agricultural region using the guidance reinforcement learning model (¶ 84-85 and Fig. 5);
receive one or more inputs of feedback grading one or more actions taken by the guidance system and/or the agricultural vehicle while the guidance system guides the agricultural system through the one or more agricultural processes (¶ 73-80, 84-86 and Figs. 3, 5); and
adjust the guidance reinforcement learning model responsive to the received one or more inputs of feedback (¶ 73-80, 84-86 and Fig. 3, 5).
As to claim 2, Maeder teaches wherein the instructions are configured to, when executed by the at least one processor, cause the guidance control system to guide the agricultural vehicle through the one or more agricultural processes in the predetermined agricultural region by performing a guidance process comprising: using the guidance reinforcement learning model to generate the guidance information for performing the one or more agricultural processes; and using the guidance information to guide the agricultural vehicle through the one or more agricultural processes in the predetermined agricultural region (Figs. 2-3, 5).
As to claim 3, Maeder teaches an output user interface, wherein the guidance process comprises controlling the output user interface to provide a user- perceptible output of the guidance information (¶ 36-37 and Figs. 2, 4-5).
As to claim 4, Maeder teaches the output user interface comprises an output display and the user-perceptible output comprises a visual representation of the guidance information (¶ 60 and Figs. 4).
As to claim 5, Maeder teaches the guidance process comprises controlling the one or more operations of the agricultural vehicle during the one or more agricultural processes (¶ 42-43).
As to claim 6, Maeder teaches the one or more operations comprises at least a steering operation of the agricultural vehicle (¶ 36, 42-43).
As to claim 7, Maeder teaches the guidance information indicates one or more recommended actions for the agricultural vehicle (¶ 14, 92).
As to claim 8, Maeder teaches the guidance reinforcement learning model is configured to process state information, representing a state of the agricultural vehicle and/or the predetermined agricultural region, to generate the guidance information (Figs. 2-6 and associated text).
As to claim 9, Maeder teaches the state information comprises environment parameters comprising one or more of field layout data, obstacle data, soil condition data, crop distribution data, terrain and/or topography data, start and end point data, weather condition data, or time restriction data (¶ 23, 78 and Fig. 4; e.g. soil condition).
As to claims 10-11, Maeder teaches the state information comprises vehicle information comprising one or more of: previous coverage data; machine characteristic data; position information; historic position information and/or a fuel level (¶ 33, 38, 92; e.g. position information).
As to claim 13, Maeder teaches the instructions are configured to, when executed by the at least one processor, cause the guidance control system to obtain the guidance reinforcement learning model by performing a training process comprising training the guidance reinforcement learning model via one or more simulated agricultural processes within one or more simulated agricultural regions (Figs. 2-3 and associated text; e.g. training models).
As to claim 14, Maeder teaches the training process comprises performing one or more iterations of: guiding a simulated agricultural vehicle through one or more simulated agricultural processes in one or more simulated agricultural regions using the guidance reinforcement learning model; receiving one or more second inputs of feedback grading one or more actions taken by the guidance system and/or the simulated agricultural vehicle while the guidance system guides the simulated agricultural system through the one or more simulated agricultural processes in the simulated region; and adjusting the guidance reinforcement learning model responsive to the received one or more second inputs of feedback (Figs. 2-3 and associated text; e.g. training models).
As to claim 15, Maeder teaches the one or more simulated agricultural regions comprises a simulated agricultural region modelled after the predetermined agricultural region (Figs. 2-3 and associated text).
As to claim 16, Maeder teaches the one or more simulated agricultural regions comprises only the simulated agricultural region modelled after the predetermined agricultural region (Figs. 2-3 and associated text).
As to claim 17, Maeder teaches the one or more inputs of feedback comprises one or more of: positive feedback for covering a new part of the agricultural region, negative feedback for overlapping previously covered areas of the agricultural region, negative feedback for missing areas of the agricultural region, negative or positive feedback for fuel consumption during an agricultural process, and/or negative or positive feedback for time taken to perform an agricultural process (¶ 10, 40, 43; e.g. missing areas).
As to claim 18, Maeder teaches one or more of: at least one input interface for receiving at least one input of feedback; at least one vehicle sensor for generating vehicle sensor data identifying one or more inputs of feedback responsive to a property of the agricultural vehicle; and/or at least one region sensor for generating region sensor data identifying one or more inputs of feedback responsive to a property of predetermined agricultural region (Figs. 2-3 and associated text).
As to claim 19, Maeder teaches the guidance information indicates a recommended route for the agricultural vehicle during the performance of the one or more agricultural processes within the predetermined agricultural region (¶ 42-43, 47; e.g. intended route vs. modified operating mode).
Claims 20 is rejected based on the same rationale as used in claim 1.
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.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Maeder et al., US 2022/0406104 A in view of Negi et al., US 2022/0129840 A1.
As to claim 12, Maeder teaches the guidance reinforcement learning model as discussed in claim 1 above. Maeder does not specifically teach the reinforcement learning model comprises at least one of a Q-learning model, a Deep Q Networks model, a Proximal Policy Optimization model and/or an Actor-Critic model. However, Negi teaches reinforcement learning model comprises a Q-learning model (¶ 25, 48). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Q-learning model in Maeder’s reinforcement learning model for efficiently developing better operating mode for the agricultural machine.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schleicher et al. (US 2022/0405498 A1) discloses implement management system for determining implement state on a vehicle.
Vesperman et al. (US 2022/0350991 A1) discloses vision guidance system using dynamic edge detection.
Rajakumar et al. (US 2023/0027496 A1) discloses obstacle detection for an agricultural vehicle.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mary Cheung whose telephone number is (571) 272-6705. The examiner can normally be reached on Monday, Tuesday and Thursday from 10:00 AM to 7:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Christian Chace, can be reached on (571) 272-4190.
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The fax phone numbers for the organization where this application or proceedings is assigned are as follows:
(571) 273-8300 (Official Communications; including After Final Communications labeled “BOX AF”)
(571) 273-6705 (Draft Communications)
/MARY CHEUNG/ Primary Examiner, Art Unit 3665 August 8, 2026
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