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 .
Election/Restrictions
Applicant’s election without traverse of claims 11 and 12 in the reply filed on 05/08/2026 is acknowledged.
Claim Rejections - 35 USC § 102
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 11, 12 are rejected under 35 U.S.C. 102(a1) as being anticipated by Webb et al (Webb) (US 2023/0252791 A1).
Regarding claim 11, Webb discloses a computer-implemented method for use in identifying rogue plants in an agricultural field (e.g., a computer-implemented method of sensor input processing includes performing, using a processor onboard a vehicle, a machine learning (ML) processing on sensor inputs from sensors onboard the vehicle, identifying, according to a rule, a subset of data resulting from the ML processing; and generating the subset of data for modifying the ML processing for a subsequent use. The subsequent use may be either in a same agricultural environment that the vehicle is operating in or in a different agricultural environment, paragraph 7), the method comprising:
accessing, by a computing device, data specific to an agricultural field, the data including an image of the field captured, by at least one capture device, during a particular growth stage of a crop in the field (e.g., FIG. 2 shows another example method 1200 of processing agricultural images according to various embodiments described herein. The method 1200 starts at 1220. In various embodiments, the method may be implemented by an agricultural vehicle such as a tractor, a drone, a manually operated vehicle or a self-driving vehicle and in particular one or more modules disposed on the vehicle for surveying a farm or for providing a treatment. The vehicle may be called “biff” as an abbreviation. At 1230, images or sensor readings are received at the vehicle. At 1240, received images are analyzed either by a human or by a machine (e.g., using a machine learning ML or a computer vision algorithm) to label certain regions of the image as including agricultural objects of interest, paragraph 48);
identifying at least one plant included in the image (e.g., A platform may be installed on a vehicle 1304 and may include one or more cameras that capture images (e.g., image 1306) of a field environment. The image 1306 may include a row of a crop, paragraph 58, figure 3);
applying, by the computing device, a trained classifier model to the image and classifying, using the trained classifier model, the at least one plant included in the image as a rogue plant or as not a rogue plant (e.g., The image 1306 may include a row of a crop with possible weeds interspersed in between the crop in a real world agricultural environment 1316, paragraph 58, figure 3); and
in response to the at least one plant included in the image being classified as a rogue plant, striking the at least one plant from the agricultural field at about the same time the at least one plant is classified as a rogue plant and/or the image of the field is captured by the at least one capture device (e.g., a region of this image 1306 is shown in greater detail and having multiple vegetation objects that may be a desired vegetation 1314 (e.g., carrots or another crop) and undesired vegetation such as different types of weeds 1310, 1312. The onsite computer platform 1302, disposed on a treatment system 1303 (which may include modular components) may include a compute unit 1308 which may perform image classification/segmentation using various image processing algorithms 1318 which may include one or more ML models, paragraph 58).
Regarding claim 12, Webb discloses further comprising generating an output map identifying a location of the at least one plant in the agricultural field and identifying the at least one plant as a rogue plant (e.g., 1310, 1312, image 1306, figure 3).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUANG N VO whose telephone number is (571)270-1121. The examiner can normally be reached Monday-Friday, 7AM-4PM, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad K Ghayour can be reached at 571-272-3021. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUANG N VO/Primary Examiner, Art Unit 2683
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