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 information disclosure statement (IDS) submitted on 01/05/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
3. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al (U.S. 2023/0165234) and further in view of Anderson (U.S. 2024/0206451).
As per claims 1, 8 and 15 Anderson23 disclosed a method determining a locomotion mechanism state prescription for an autonomous farming machine (abstract, ([0001]-[0007], [0019]), fig. 1a-1c), the method comprising:
accessing a path of a farming machine through an operating environment comprising a field, the path comprising a plurality of interaction points ([0007], [0044], [0045]), and one or more of the interaction points corresponding to farming actions the farming machine performs at the interaction point to accomplish a farming objective ([0022]-[0023]), and wherein the farming machine is configurable between a plurality of locomotion mechanism states between the interaction points ([0061]);
applying a state prescription model to the path to generate the locomotion mechanism state prescription for the farming machine ([0022]-[0063],([0081]), the state prescription model: for each interaction point of the plurality of interaction points along the path, determining, for each of the plurality of locomotion mechanism states, an objective score for the interaction point based on farming actions performed at the point and the locomotion mechanism state; and identifying the locomotion mechanism state having a highest objective score at the point as a prescribed locomotion mechanism state for the interaction point ([0084-(0085]);
generating, for the autonomous or semi-autonomous farming machine, a locomotion mechanism, state prescription for the path, the locomotion mechanism state prescription comprising the prescribed locomotion mechanism state for each interaction point of the plurality of interaction points on the path ([0089]-[0090]); and
actuating the locomotion mechanism of the farming machine to implement the locomotion mechanism state prescription as the farming machine traverses the path in the field. ([0091])
However, Anderson23 did not disclose in detail, “an objective score for the interaction point based on farming actions performed at the point and the locomotion mechanism state”.
In the same field of endeavor Anderson24 disclosed the datastore 232 may include different types of data utilized or generated by the control system 230. For example, the datastore 232 may store image data 234, weed data 236, analysis data 237, score data 238, treatment plan data 240, and regime zone data 242. The image analysis module 244 may include one or more machine-learned models 245. The treatment planning module 246 may also include one or more machine-learned models 247. In different embodiments, the control system 230 may include fewer or additional components. The control system 230 may also include different components (Paragraph. 0041).
It would have been obvious to one having ordinary skill in the art before the effective filing date was to have incorporated the datastore 232 may include different types of data utilized or generated by the control system 230. For example, the datastore 232 may store image data 234, weed data 236, analysis data 237, score data 238, treatment plan data 240, and regime zone data 242. The image analysis module 244 may include one or more machine-learned models 245. The treatment planning module 246 may also include one or more machine-learned models 247. In different embodiments, the control system 230 may include fewer or additional components. The control system 230 may also include different components as taught by Anderson24 in the method and system of Anderson23 to autonomously customize the farming treatments.
4. As per claims 2,9, 16 Anderson23-Anderson24 disclosed wherein the plurality of locomotion mechanism states comprises, at least: an off state in which the locomotion mechanism is turned off, a stop state in which a locomotion mechanism of the farming machine is disengaged, an idle state in which the locomotion mechanism of the farming machine is idled, and a drive state in which the locomotion mechanism of the farming machine is engaged (Anderson23, Paragraph. 0045).
5. As per claims 3,10 Anderson23-Anderson24 disclosed wherein the drive state comprises a plurality of sub-states, each of the plurality of sub-states defining one or more of a velocity, a direction, and an acceleration implemented by the locomotion mechanism (Anderson23, Paragraph. 0045).
6. As per claims 4,11,17 Anderson23-Anderson24 disclosed wherein calculating the objective score comprises: for each interaction point of the plurality of interaction points on the path: determining, for each locomotion mechanism state of the plurality, a probability that performing a farming action at the interaction point when the locomotion mechanism is in a locomotion mechanism state advances the farming objective (Anderson23, Paragraph. 0084-0085).
7. As per claims 5,12,18 Anderson23-Anderson24 disclosed wherein the farming objective comprises optimizing a performance of farming actions in the field for a time efficiency (Anderson24, Paragraph. 0050). Claims 5,12 and 18 have the same motivation as to claim 1.
8. As per claims 6,13,19 Anderson23-Anderson24 disclosed wherein the farming objective comprises optimizing a performance of farming actions at interaction points in the field for a cost efficiency (Anderson24, Paragraph. 0050). Claims 6,13, and 19 have the same motivation as to claim 1.
9. As per claims 7,14,20 Anderson23-Anderson24 disclosed wherein the farming objective comprises optimizing a performance of farming actions at interaction points in the environment outside the field (Anderson24, Paragraph. 0050). Claims 7,14 and 20 has the same motivation as to claim 1.
Response to Arguments
10. Applicant's arguments filed 06/29/2026 have been fully considered but they are not persuasive. Response to applicant’s argument as follows.
A. Applicant argued that prior art did not disclose,” determining, for each of the plurality of locomotion mechanism states, an objective score for the interaction point based on farming actions performed at the point and the locomotion mechanism state”.
As to applicant’s argument Anderson23 disclosed, “In these embodiments, the tank management model may predict an expected weed density for a portion of the field and a required amount of treatment of a portion of the field concurrently, consecutively, or a combination thereof. For example, the tank management model may first predict an expected weed density for a portion of a field and then predict an amount of treatment required for the portion of the field based, in part, on the expected weed density.” (Paragraph. 0084). The score is interpreted as, “amount”. Anderson23 further more disclosed, “In some embodiments, the treatment module 255 determines an amount of material required for treatment of a portion of a field based, in part, on the location of weeds within the field and/or buffers around the weeds within the field. As discussed above, the amount of material required to treat an area may be based on the weed density, a number of weeds in an area of land. For example, an acre of a field covered in 2% weeds may require less herbicide than an acre of a field covered in 10% weeds. Alternatively, or additionally, the amount of material required to treat an area may be based on the location of the weeds within the field and/or a buffer around each weed. A buffer is an area around a weed in which treatment is applied. The size and/or shape of a buffer may vary based on weed species, user preferences, jurisdictional regulations, buffer recommendations determined by the farming machine 100, or the like. Depending on the size of the buffer and/or the location of the weeds, weeds may be located within the buffers of adjacent weeds. When weeds are within the buffers of adjacent weeds, less material may be required overall because weeds will receive residual spray applied to neighboring weeds. When the weeds are not within the buffers of adjacent weeds, more material may be required because weeds will not receive residual spray applied to neighboring weeds. As an example, two fields of similar size may each by covered in 5% weeds. However, a majority of the weeds in the first of the two fields may be located within one portion of the field and the majority of weeds in the second field may be distributed across all portions of the second field. Thus, weeds in the first field are more likely to be located in the buffers of adjacent weeds than weeds in the second field. Accordingly, more herbicide may be required to treat the weeds in the second field than the first field” (Paragraph. 0088). Regarding the “locomotion mechanism states” Anderson23 further disclosed, “The farming machine 100 includes a detection mechanism 110, a treatment mechanism 120, and a control system 130. The farming machine 100 can additionally include a mounting mechanism 140, a verification mechanism 150, a power source, digital memory, communication apparatus, or any other suitable component that enables the farming machine 100 to implement farming actions in a treatment plan. Moreover, the described components and functions of the farming machine 100 are just examples, and a farming machine 100 can have different or additional components and functions other than those described below (Paragraph. 0032).
B. Applicant argued that there is no motivation to combine the reference. Both references are patentably distinct.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Anderson24 taught in the method and system of Anderson23 to autonomously customize the farming treatments.
Conclusion
11. 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.
12. Any inquiry concerning this communication or earlier communication from the
examiner should be directed to Adnan Mirza whose telephone number is (571)-272-3885.
13. The examiner can normally be reached on Monday to Friday during normal
business hours. If attempts to reach the examiner by telephone are unsuccessful, the
examiner’s supervisor, Faris Almatrahi can be reached on (313)-446-4821.
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/ADNAN M MIRZA/Primary Examiner, Art Unit 3667
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