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
An information disclosure statement (IDS) was filed on September 8th, 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
In response to the Office Action was mailed on June 8, 2026, independent claims 102 and 116 are amended by Applicant. Claims 103 and 117 have been canceled by Applicant. No new matter was added. Accordingly, claims 102, 104-116, and 118-125 are currently pending.
Response to Arguments
Applicant's arguments filed September 8th, 2026, have been fully considered but they are not persuasive.
In the previous Office Action, independent claims 102 and 116 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Petro et al., US 20240032525 A1 (hereinafter referred to as “Petro”).
Applicant has amended claims 102 and 116 to include the following subject matter of now canceled claims 103 and 117.
modifying the one or more parameters based on the recommended change, wherein modifying the one or more parameters does not comprise retraining the pre-trained machine learning model.
In the remarks, Applicant argues (Remarks, page 7):
Petro fails to disclose or suggest… modifying one or more parameters, where such modification does not comprise retraining the pre-trained machine learning model. Rather, Petro describes that in block 512, "the computing system may be configured to quantize and compile the model to run on embedded hardware in the autonomous device" (Petro, para. [0057]) which in other words is merely to "convert [the] model to run on embedded hardware." (Petro, Figure 5). Additionally, Figure 6 of Petro describes use of a trained model, and paragraph [0056] of Petro, merely describe how "the computing system is configured to build a model based on sample data" and "the model may be used to make predictions or decisions about the content of a newly acquired image." (Petro, paragraph [0056]) The Office Action fails to point to any disclosure of Petro that discloses or suggests "wherein modifying the one or more parameters does not comprises retraining the pre-trained machine leaning model" as recited in claim 1.
The Examiner respectfully disagrees. As previously cited, Petro teaches modifying the one or more parameters based on the recommended change. Petro determines which classes (i.e., weed, grass., etc.) an image belongs based on threshold results. Fig. 6. Parameters may be modified based on the determined class. For example, at block 606, processing circuit may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. If processing circuit identifies a weed, the device may be configured to activate a dispenser or weed sprayer at block 610. If processing circuit 44 identifies a boundary, processing circuit 44 may be configured at block 612 to operate a turn-around algorithm to drive the device 10 in a different direction away from the boundary. Petro, Fig. 6.
The retraining the pre-trained machine learning model does not occur between block steps 606 and 610, 612 or 614, associated with modifying the parameters. The model training or converting process described in Petro, Fig. 5 steps, 510 and 512 are completed prior to modifying the parameters described in block steps 606 and 610, 612 or 614.
For at least these reasons, independent claims 102 and 116 stand rejected under 35 U.S.C. 102(a)(2) as being anticipated by Petro.
Accordingly dependent claims 104, 106-114, 118, 120-121 and, 123-125, are therefore unpatentable at least by virtue of their dependency on claims 102 or 116.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 102, 104, 106-114, 116, 118, 120-121 and, 123-125 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Petro et al., US 20240032525 A1 (hereinafter referred to as “Petro”).
Regarding claim 102 (Currently Amended), Petro discloses a method comprising: providing a sample image of a sample object to a user (Petro: Acquire image, block 500, at a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055], Fig. 5); receiving an indication from the user identifying the sample object (Petro: At a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof. ¶ [0055], Fig. 5); generating, via a tuning algorithm, a recommended change to one or more parameters based on the indication from the user (Petro: tune a set of weights used for classifying images based on labels.¶ [0049]), wherein a decision algorithm is configured to instruct an action associated with an object of interest in one or more images using (i) a pre-trained machine learning model that characterizes the object of interest in the one or more images, and (ii) the one or more parameters (Petro: The autonomous device may be configured to use the weights to make predictions about what classes (e.g., grass, weeds, boundary ) an image belongs to as the images are obtained from the camera. ¶ [0049]. Fig. 6); and modifying the one or more parameters based on the recommended change (Petro: tuned weights used to make predictions about what classes an image belongs to as the images are obtained from the camera. ¶ [0049]), wherein modifying the one or more parameters does not comprise retraining the pre-trained machine learning model (Petro: The retraining the pre-trained machine learning model does not occur between block steps 606 and 610, 612 or 614 associated with modifying the parameters. The model training or converting process described in Petro, Fig. 5 steps, 510 and 512 are completed prior to modifying the parameters described in block steps 606 and 610, 612 or 614).
Regarding claim 104 (New), Petro discloses the method of claim 102, wherein the tuning algorithm comprises a rule-based algorithm, a statistical model-based algorithm, or both (Petro: ¶ [0049]).
Regarding claim 106 (Previously Presented), Petro discloses the method of claim 102, wherein the tuning algorithm is configured to generate a recommended change to one or more parameters in order to optimize a predetermined metric of interest (Petro: Threshold Fig. 6).
Regarding claim 107 (Previously Presented), Petro The method of claim 102, wherein the pre-trained machine learning model is configured to predict one or more properties of the object of interest in one or more images (Petro: Figs. 5-6).
Regarding claim 108 (Previously Presented), Petro discloses the method of claim 107, further comprising storing the one or more predicted properties of the object of interest in an embedding associated with the object of interest (Petro: a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing. ¶ [0055], Fig. 5);
Regarding claim 109 (Previously Presented), Petro discloses the method of claim 107, wherein the one or more predicted properties comprises a first object score representing likelihood that the object of interest is a first object type (Petro: At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]).
Regarding claim 110 (Previously Presented), Petro discloses the method of claim 109, wherein generating a recommended change comprises generating a recommended change to a first threshold value, wherein the decision algorithm is configured to instruct a first action in response to the first object score satisfying the first threshold value (Petro: At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]).
Regarding claim 111 (Previously Presented), Petro discloses the method of claim 110, wherein the first object type is a crop and the first action comprises instructing an implement to not damage the object of interest or to damage the object of interest (Petro: At a block 606, 612, 614).
Regarding claim 112 (Previously Presented), Petro the method of claim 109, wherein the one or more predicted properties further comprises a second object score representing likelihood that the object of interest is a second object type (Petro: At block 606, 612, 614).
Regarding claim 113 (Previously Presented), Petro discloses the method of claim 112, wherein generating a recommended change comprises generating a recommended change to a second threshold value, wherein the decision algorithm is configured to instruct a second action in response to the second object score satisfying the second threshold value (Petro: At a block 606, 612, 614).
Regarding claim 114 (Previously Presented), Petro discloses the method of claim 113, wherein the second object type is a weed and the second action comprises instructing an implement to damage the object of interest (Petro: At a block 606, 610).
Regarding claim 116 (Currently Amended), claim 102 is substantially similar to claim 116. Therefore, claim 116 is rejected for the same reasons as claim 102.
Regarding claim 118 (Previously Presented), claim 104 is substantially similar to claim 118. Therefore, claim 118 is rejected for the same reasons as claim 104.
Regarding claim 120 (Previously Presented), claim 106 is substantially similar to claim 120. Therefore, claim 120 is rejected for the same reasons as claim 106.
Regarding claim 121 (Previously Presented), Petro discloses the system of claim 116, further comprising an implement configured to manipulate the object of interest (Petro: At a block 606, 612, 614).
Regarding claim 123 (Previously Presented), Petro discloses the system of claim 116, further comprising a camera configured to collect a plurality of sample images of a plurality of sample objects, wherein the sample objects are representative of objects of interest to be characterized by the pre-trained machine learning model (Petro: Fig. 5, Training Model with a plurality of images. ¶ [0055]).
Regarding claim 124 (Previously Presented), Petro discloses the system of claim 123, further comprising a display configured to display the plurality of sample images (Petro: Acquire image, block 500, at a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055]).
Regarding claim 125 (Previously Presented), Petro discloses the system of claim 116, wherein the object of interest is a plant (Petro: Acquire image, block 500, at a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 105 and 119 are rejected under 35 U.S.C. 103 as being unpatentable over Petro in view of Webb et al., US 20230252791 A1 (hereinafter referred to as “Webb”).
Regarding claim 105 (Previously Presented), Petro does not disclose the method of claim 102, wherein the tuning algorithm comprises a second pre-trained machine learning model, and wherein the second pre-trained machine learning model is separate from the pre-trained machine learning model configured to characterize the object of interest in an image.
However, Webb discloses wherein the tuning algorithm comprises a second pre-trained machine learning model, and wherein the second pre-trained machine learning model is separate from the pre-trained machine learning model configured to characterize the object of interest in an image. (Webb: ML model 2 to classify the object, ML model 1 detects crop ¶ [0179]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Petro wherein the tuning algorithm comprises a second pre-trained machine learning model, and wherein the second pre-trained machine learning model is separate from the pre-trained machine learning model configured to characterize the object of interest in an image, as taught by Webb, in order to improve on the accuracy and efficiency of the computing device for identifying and eradicating unwanted crop.
Regarding claim 119 (Previously Presented), claim 105 is substantially similar to claim 119. Therefore, claim 119 is rejected for the same reasons as claim 105.
Claim 115 is rejected under 35 U.S.C. 103 as being unpatentable over Petro in view of Sibley et al., US 11425852 B2 (hereinafter referred to as “Sibley”).
Regarding claim 115 (Previously Presented), Petro does not disclose the method of claim 107, wherein the one or more predicted properties comprises a number of images in which the object of interest is pictured, and wherein generating a recommended change comprises generating a recommended change to a minimum threshold quantity of images in which the object of interest is pictured, for instructing an action associated with the object of interest.
However, discloses wherein the one or more predicted properties comprises a number of images in which the object of interest is pictured, and wherein generating a recommended change comprises generating a recommended change to a minimum threshold quantity of images in which the object of interest is pictured, for instructing an action associated with the object of interest (Sibley: determine target in one or more images using the ML algorithm and activating a treatment mechanism. Figs. 45B and Fig. 44 The number N may be 3 to 4 frames, and may depend on extrinsic factors such as…a confidence level of the ML detection and the tracking algorithm).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Petro wherein the one or more predicted properties comprises a number of images in which the object of interest is pictured, and wherein generating a recommended change comprises generating a recommended change to a minimum threshold quantity of images in which the object of interest is pictured, for instructing an action associated with the object of interest, as taught by Sibley, in order to improve on the accuracy and efficiency of the computing device for identifying and eradicating unwanted crop.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Petro in view of Chrysanthakopoulos et al., US 20190200519 A1 (hereinafter referred to as Chrysanthakopoulos”).
Regarding claim 122 (Previously Presented), Petro does not disclose the system of claim 121, wherein the implement comprises a laser, and the system further comprises a control system configured to direct the laser at the object of interest.
However, Chrysanthakopoulos discloses wherein the implement comprises a laser, and the system further comprises a control system configured to direct the laser at the object of interest (Chrysanthakopoulos: micro wave or laser, for high insolation areas is focused sunlight where a large, simple, optical lens is placed in the center of the robot body that focuses sun rays on the non-crop subject 299, rapidly increasing its temperature and essentially burning/boiling the stem, as close to the ground as feasible. ¶ [0092], Fig. 9 energy beam).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Petro wherein the implement comprises a laser, and the system further comprises a control system configured to direct the laser at the object of interest, as taught by Chrysanthakopoulos, in order to eradicate unwanted crops.
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 DEIRDRE L BEASLEY whose telephone number is (571)270-0452. The examiner can normally be reached Monday-Friday 8 a.m. -5 p.m.
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, Chris Kelley can be reached at (571) 272-7331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DLB/Patent Examiner, Art Unit 2482
/CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482
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