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. This office action is in response to application 18/395,037 filed on December 22, 2023. Claims 1-20 are pending. Information Disclosure Statement As required by M.P.E.P. 609(C), the applicant’s submission of the Information Disclosure Statement dated December 22, 2023 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609, a copy of the PTOL-1449 initialed and dated by the examiner is attached to the office action. 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. Claims 1, 3-4, 7-11, 13-14, and 17- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sibley et al. (US Publication US20210185886A1 ) in further view of Wu et al. ( US20190150357A1 ) . Regarding claim 1, Sibley teaches a system mounted in a vehicle, the system comprising: a boom arrangement that comprises a predefined number of electronically controllable sprayer nozzles and a plurality of image-capture devices configured to capture a plurality of field-of-views (FOVs) of a plurality of defined areas of an agricultural field ( FIG. 1A is a diagram depicting an example of an agricultural treatment delivery system ... Agricultural treatment delivery system 111 a may include one or more emitters ... configured emit a treatment 112 b ... FIG. 5 is a diagram depicting another example of an agricultural treatment delivery system ... delivery system 581 may include one or more image capture devices, such as a camera 504 ... objects within image frame 509 including agricultural objects 522 to 529 may traverse agricultural environment image 520 ... Configuration 1002 includes a boom 1060 configured to support an agricultural projectile delivery)([0047], [0107], [0110], and [0131] ; a configuration of an agricultural treatment delivery system includes a boom with nozzles (i.e., emitters) and capture devices (e.g., cameras ) , an exemplary captured area view of a field is shown in Figure 5 ) ; and one or more hardware processors ( one or more processors and one or more applications including executable instructions)([0052]) configured to: obtain a sequence of images corresponding to the plurality of FOVs from the plurality of image-capture devices ( FIG. 3 is an example of a flow diagram to control agricultural treatment delivery system autonomously ... At 304, data representing a subset of agricultural objects may be received ... which may include one or more processors configured to analyze sensor data (e.g., image data) captured from one or more sensors … As an agricultural treatment delivery system traverses )([0087] , [0088] , and [0089]; camera data is obtained as a vehicle traverses the field ) ; distinguish crop plants from weeds using a trained artificial intelligence (Al) model when the vehicle is motion on the agricultural field ( Analyzer logic 203 may be configured to implement computer vision algorithms and machine learning algorithms (or any other artificial intelligence-related techniques) … to construct and maintain a spatial semantic model as well as a time-series model of physiology and/or physical characteristics of a crop (or any other agricultural object, such as a limb or branch) relative to a stage-of-growth ... At 308, an agricultural object may be detected ... the imaged agricultural object may be correlated to data representing an indexed agricultural object in a subset of agricultural objects ... object identifier 2384 may detect and classify object 2322 a as a blossom 2322 b ... Object identifier 2384 may detect and classify object 2324 a as a weed 2324 b)([0085], [0091], and [0166] ; a model is used to classify object s (e.g., weeds, crops, etc.) as data is obtained ) , wherein the distinguishing of the crop plants from weeds using the trained Al model comprises: detecting a first set of crop plants … ( Examples of classifications with which to classify an agricultural object includes ... a class of fruit)([0102] ; classifications includes a first set (e.g., fruit) crops ) ; detecting a second set of crop plants manifesting an elastic change in physical characteristics of the second set of crop plants ( Examples of classifications with which to classify an agricultural object includes ... a class of bud (e.g., including leaf buds and fruit buds), a class of blossom )([0102]; classifications includes an elastic change in physical characteristic (e.g., blooming) crops) ; and detecting a third set of remaining crop plants different from the first set of crop plants and the second set of crop plants ( Examples of classifications with which to classify an agricultural object includes ... a class of pest (e.g., insects, rodents, birds, etc.), a class of disease (e.g., a fungus) )([0102]; classifications includes unhealthy (e.g., crops with pest or disease)) , and cause a specific set of electronically controllable sprayer nozzles from amongst the predefined number of electronically controllable sprayer nozzles to operate based on the distinguishing of the crop plants from the weeds, wherein the distinguished crop plants comprise the first set of crop plants … the second set of crop plants manifesting the elastic change in physical characteristics, and the third set of remaining crop plants ( At 310, an emitter from a subset of one or more emitters may be selected to perform an action ... At 312, an agricultural treatment may be emitted as a function of a policy ... a policy may cause agricultural projectile delivery system 2611 to apply a growth hormone ... to apply a treatment ... to germinate the blossom ... to apply a treatment (e.g., a caustic chemical) to lateral blossoms 3029 to terminate growth)([0092], [0093], [0186], [0187], and [0188] ; based on classification and policy, treatment is applied to sets of crops (e.g., applying growth, germinating blossom, treating unhealthy , etc. ) ) . Sibley differs from the claim in that Sibley fails to teach detecting drooping of (i.e., ragged) leaves using the AI model . However, detecting ragged leaves for treatment using an AI model is taught by Wu ( if the leaf size is too small or if the leaves are ragged in shape, fertilizers may be applied (spread or spot sprayed) to the particular plant or area of plants … artificial intelligent method using the neural network architecture ... Training sample patterns include ... wilting leaves)([0108] , [0190], and [0191] ) . The examiner notes Sibley and Wu teach treating crops. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sibley to include the detecting of Wu such that drooping leaves are detected using an AI model . One would be motivated to make such a combination to provide the advantage of improving detection of crop health by using leaves a s a health indicator. Regarding claim 3, Sibley -Wu teach t he system according to claim 1, wherein the one or more hardware processors are configured to receive geospatial location correction data from an external device placed at a fixed location in the agricultural field and geospatial location coordinates associated with the boom arrangement mounted on the vehicle (Sibley - Sensor platform 113 may also include one or more location or position sensors, such as one or more global positioning system (“GPS”) sensor ... to determine locations of an agricultural object ... For example, position data for one or more optical markers ... Marker 531 may be detected and analyzed to determine positioning information, to facilitate in-situ positioning or calibration, or to perform any other function)([0076] and [0109] ; the examiner notes Merriam Webster defines a “device” as a piece of equipment or a mechanism designed to serve a special purpose , a marker is a piece of equipment that indicates location information ) . Regarding claim 4, Sibley -Wu teach t he system according to claim 1, wherein the one or more hardware processors are configured to execute mapping of pixel data of the weeds or the crop plants in an image to distance information from a reference position of the boom arrangement when the vehicle is in motion, wherein the specific set of electronically controllable sprayer nozzles are operated further based on the executed mapping of pixel data (Sibley - At 630, an emitter is activated to apply an action ... at which a pixel associated with an optical sight is aligned with an optical ray that extends from the pixel to at least a portion of a targeted agricultural object ... Diagram 1200 also includes a trajectory processor 1283, which may be configured to track positions of target image 1222 b and to determine a distance 1227 ... at which an image of the target ... aligns with optical sight 1226 a)([0123] and [0135]) . Regarding claim 7, Sibley -Wu teach t he system according to claim 1, wherein the one or more hardware processors are further configured to determine an upcoming time slot to spray a chemical based on the distinguishing of the crop plants from the weeds (Sibley - Trajectory processor 583 may also be configured to predict an emission parameter (e.g., emission time) at which an agricultural object aligns with an optical sight. At a detected emission time, trajectory processor 583 may generate a control signal to ... activate an emitter to propel agricultural projectile 512 to intercept object 522 at a calculated time)([0112]) . Regarding claim 8, Sibley -Wu teach t he system according to claim 7, wherein the determining of the upcoming time slot to spray the chemical is further based on a size of the crop plant occupied in a two-dimensional space in x and y coordinate direction (although Sibley does not disclose determining time to spray based on a size of a plant, said determining is taught by Wu ( plants and crops have an expected shape and size that can be stored in memory ... If the shape or height does not match the expected shape, this is often an indication that something is wrong ... Upon such determination, a fungicide or fertilizer or herbicide, or some combination may be applied to the plants)([0123]) ). The examiner notes Sibley and Wu teach treating crops. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sibley to include the determining of Wu such that a time to spray is determined based on a size of a plant. One would be motivated to make such a combination to provide the advantage of improving detection of crop health by using plant size a s a health indicator ) . Regarding claim 9, Sibley -Wu teach t he system t he system according to claim 1, wherein the one or more hardware processors are further configured to determine one or more regions in the agricultural field where to spray a chemical based on the distinguishing of the crop plants from the weeds (Sibley - the sensor data being configured to identify an image of blossom 133 as vehicle traverses path portion 119 a. When sensor platform 113 detects blossom 133, agricultural treatment delivery system 111 a may be configured to trigger emission of treatment 112 b autonomously)([0060] ; regions with a target (e.g., blossom) are treated ) . Regarding claim 10, Sibley -Wu teach t he system t he system according to claim 1, wherein the one or more hardware processors are further configured to communicate control signals to operate a plurality of different sets of electronically controlled sprayer nozzles at different time instants during a spray session based on the distinguishing of the crop plants from the weeds (Sibley - FIG. 24 is an example of a flow diagram to implement one or more subsets of emitters to deliver multiple treatments to multiple subsets of agricultural objects)([0 172]; Figure 24 – operating different sets of emitters at different time instances (i.e., at respective detection of targets) is shown ) . Regarding method claims 11, 13 , 14, and 17-20, the claims generally correspond to system claims 1, 3, 4, and 7-10, respectively, and recites similar features in method form; therefore, the claims are rejected under similar rationale. Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sibley , Wu, and in further view of McCann (US20240074428A1) . Regarding claim 5, Sibley -Wu teach t he system applied above, wherein a confidence threshold indicative of detection sensitivity is used to detect plants (Wu - Using the captured images, the elements of the image first pass a calibrated color threshold (e.g. green) ... e.g. to detect weeds or crops)([0106] and [0107]) . Sibley -Wu differs from the claim in that Sibley -Wu fails to teach the threshold is used to include or exclude a category of plants . However, utilizing a threshold to include or exclude a category of plants is taught by McCann (a n image containing one or more plants 1704 may be passed through the AI framework 292 that has been previously trained to identify canola plants. The output from the AI framework 292 may correspond to one or more probability or confidence that each respective crop plant 1704 is a canola plant ... The spray operations may be customized based on thresholds defined within the priority matrices)([0145] and [0150]) . The examiner notes Sibley , Wu, and McCann teach treating crops. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sibley -Wu to include the utilizing of McCann such that a threshold is used to include or exclude a category of plants. One would be motivated to make such a combination to provide the advantage of avoiding wasteful application of treatment. Regarding method claim 15, the claims generally correspond to system claim 5 and recites similar features in method form; therefore, the claim is rejected under similar rationale. Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sibley , Wu, McCann , and in further view of Ding et al. (US20130034263A1) . Regarding claim 6, Sibley -Wu- McCann teach the system as applied above, wherein the confidence threshold is adjustable (McCann - the probability score threshold is adjustable )([0032]) . Sibley -Wu- McCann differs from the claim in that Sibley -Wu- McCann fails to teach adjusting the threshold based on a quality parameter of captured image. However, adjusting a threshold based on a quality parameter of captured image is taught by Ding ( using a single threshold value regardless of the input image can be problematic. Detection is among the most challenging vision tasks due in part to ... variability in image quality ... an adaptive thresholding detector to detect an object in an image comprises obtaining a set of candidate patches from the image and computing a normalized histogram ... This normalized histogram is input into the adaptive thresholding model to obtain a threshold value. The outputted threshold value is used to classifying the candidate patches as containing or not containing the object)([0006] and [0011]) . The examiner notes Sibley , Wu, McCann , and Ding teach detecting objects. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sibley -Wu- McCann to include the adjusting of McCann such that a threshold is adjusted based on image quality. One would be motivated to make such a combination to provide the advantage of improving image recognition. Regarding method claim 16, the claims generally correspond to system claim 6 and recites similar features in method form; therefore, the claim is rejected under similar rationale. Allowable Subject Matter Claim s 2 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider the reference fully when responding to this action. The document cited therein and enumerated below teaches a method and apparatus for d etecting objects in a n agricultural field for treatment . US20140001276A1 US20140180549A1 US20180330166A1 US20200193589A1 US20210153500A1 US20210235641A1 US20220211026A1 US20240007822A1 US20240196879A1 US20250248387A1 US10721859B2 US11310954B2 CN114818909A WO2022128189A1 Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture A digital sensor to measure real-time leaf movements and detect abiotic stress in plants crop and weed detection data with bounding boxes Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT Yongjia Pan whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1177 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday, 9:00 AM - 5:00 PM EST . 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, FILLIN "SPE Name?" \* MERGEFORMAT Scott Baderman can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-3644 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /YONGJIA PAN/ Primary Examiner, Art Unit 2118