DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-29 are pending.
Prior art:
D1: WO2019014810A1 Zhou et al
D2: US20220327815A1 Ruiz et al
D3: US20220211026A1 McCann et al
D4: US20220132829A1 Kwak et al
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1, 5-18, 20-22 and 26-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D2.
Regarding claims 1 and 21, D1 teaches a system for automatically quantifying liquid coverage on exposed plant surfaces, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
receive an image comprising a region of interest corresponding to one or more plant surfaces;
(D1, "Taking the region of interest ROI (region of interest) as the sampling region from the target circular region", [p5]; D2, "The test input includes an image showing plants belonging to different species", "the image may show crop plants of a particular crop species in an agricultural field and weed plants of one or more weed species among said crop plants (i.e., being spread between the crop plants)", [0025]; D1 teaches receiving an image and extracting a "region of interest ROI" (sampling region) from it, but D1's captured object is a test paper, not a plant surface; D2 teaches receiving a test input image whose region shows crop/weed plant surfaces)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate D2's plant-surface image content into D1's ROI-extraction technique in order to apply D1's image-sampling framework specifically to images of plant surfaces. The combination of D1 and D2 also teaches other enhanced capabilities.
The combination of D1 and D2 further teaches:
automatically identify one or more portions of the region of interest corresponding to liquid; and
(D1, "the sample fitting algorithm analyzes all the image objects in it, finds the image object that is considered to be the spray object", "The second is to analyze the sampling area, determine the spray image object, and split the image object corresponding to the spray that may be stuck to obtain a plurality of individual spray image objects.", [p5]; automatically analyzing the sampled region of interest to identify image objects/portions corresponding to spray (i.e., liquid droplets))
automatically determine a liquid coverage value for the region of interest in the image, wherein the liquid coverage value quantifies (i) an area of the plant surfaces depicted in the region of interest that is covered by liquid and/or (ii) a volume of liquid covering the plant surfaces,
(D1, "the amount of the spray image object in the sample image corresponding to the test paper is counted, the area of each spray image object is calculated, and the spray image object is calculated. The area ratio occupied in the sampled image gives the density and coverage of the spray.", [p7]; "the distribution parameter comprises any one or more of a density of the spray, a size of the spray, and an area ratio of the spray in the target environment", [p9]; automatically determining an area-ratio "coverage" value for the spray objects in the sampled region, which reads on the claimed area-based liquid coverage value)
wherein the instructions, when executed by the processor, cause the processor to automatically identify the liquid coverage value for the plant surfaces using output of a segmentation module.
(D1, "The processing module 1102 is configured to convert the sample image into a binary image, and fit each image object on the binary image to a polygon based on a preset polygon fitting algorithm", " The determining module 1103 is configured to determine a spray image object according to the fitted polygon, and determine a distribution parameter of the spray in the target environment based on the determined spray image object", [p9]; D2, "The semantic segmentation module is also referred to as “segmentation module” herein", [0011]; "The segmentation module of the dual task CNN performs a second task in segmenting the test input image to determine a class for each pixel of the test input image", [0016]; D1 teaches a processing/determining module chain that fits and classifies image objects to derive the coverage value, functionally a segmentation operation, but does not use the term "segmentation module"; D2 expressly names and defines a "segmentation module" performing pixel-level classification feeding a coverage-relevant output; Incorporating D2's expressly-named segmentation module into D1's image-object determination pipeline would derive the coverage value from a pixel-level classification output)
Regarding claims 5 and 26, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the instructions, when executed by the processor, cause the processor to automatically identify the liquid coverage value for the plant surfaces using one or more post-spray images corresponding to a field of view comprising the one or more plant surfaces after spraying with a liquid.
(D1, "the spray will adhere to the test paper by means of mist droplets or the like. Image capture is performed for each test strip.", [p5]; D2, "the image may show crop plants of a particular crop species in an agricultural field and weed plants of one or more weed species among said crop plants", [0025]; D1 teaches that image capture occurs only after spray has adhered to the test surface (i.e., a post-spray-only image, no pre-spray reference used); D2 discloses plant-surface content because D2 relies on capturing images of real crops and weeds ([0025]) and evaluating the physical surface structures of those plants (their leaves, stems, and fruits) to properly classify and segment them. Incorporating D2's plant-surface imagery into D1's single-capture-after-spray technique would transition D1's liquid-coverage detection from a test paper to actual plant surfaces found in an agricultural field)
Regarding claim 6, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 5, wherein the instructions, when executed by the processor, cause the processor to automatically identify the liquid coverage value using no pre-spray images from a pre-spray camera.
(D1 teaches image capture occurring exclusively after the liquid has adhered to the surface: "On each test strip, the spray will adhere to the test paper by means of mist droplets or the like. Image capture is performed for each test strip" [p5]. D1 further teaches performing this capture and analysis using a single camera apparatus: "the analysis instrument includes a front lens 201 and a rear lens 202... the camera-equipped device such as a smartphone photographs an image including the enlarged test strip area through the rear lens 202" [p5]. D1 explicitly outlines a workflow where a single camera (e.g., a smartphone) captures an image of the target surface only after the spray droplets have already adhered. Because D1's image processing algorithm determines the spray distribution/coverage entirely by analyzing the color and shape of the droplets in this single post-spray image, the system inherently achieves its goal without ever capturing or referencing a "before" picture. Therefore, D1 inherently teaches automatically identifying the liquid coverage value using no pre-spray images from a pre-spray camera)
Regarding claims 7 and 27, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 5, wherein the instructions, when executed by the processor, cause the processor to (i) segment multiple types of objects in the post-spray images using the segmentation module, and (ii) classify certain segmented objects as leaves from the post-spray images, after which the liquid coverage value for the region of interest is automatically determined.
(D1, "analyzes all the image objects in it, finds the image object that is considered to be the spray object", [p5]; D2, "The classes include the determined weed species (and also the crop species and soil species)", [0046]; D1 teaches segmenting the post-spray image into multiple image objects and finding the spray-object type; D2 teaches classifying segmented pixels/objects into plant (leaf-bearing) classes such as crop species; Incorporating D2's leaf/plant-class classification into D1's post-spray multi-object segmentation would identify which segmented objects are leaves before determining coverage)
Regarding claim 8, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the liquid on the plant surfaces comprises a sprayed-on solution comprising one or more members selected from the group consisting of water, an adjuvant, an additive, a crop-compatible dye, an agrochemical solution, a liquid solution of a pesticide, a liquid solution of a fertilizer, and a foliar fertilizer.
(D1, "the object refers to a test paper capable of collecting a spray in a target environment, and the spray may be, for example, spraying a pesticide. A droplet of water produced after a liquid substance such as water", [p7]; spray liquid is a pesticide or water)
Regarding claim 9, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, further comprising one or more imaging devices and/or sensors for obtaining the image, wherein the one or more imaging devices and/or sensors comprises at least one member of the group consisting of a camera, a digital camera, a camera phone, a thermal imaging device, a night vision camera, a Light Detection and Ranging (LiDAR) device, an electronic image sensor, a charge-coupled device (CCD), an active-pixel sensor (CMOS sensor), a smart image sensor, an intelligent image sensor, a red-green-blue (RGB) camera, and a short-wave infrared (SWIR) camera.
(D1, "the camera-equipped device such as a smartphone photographs an image including the enlarged test strip area through the rear lens 202.", [p5]; D2, "Suitable optical detection components 230 are multispectral cameras, stereo cameras, IR cameras, CCD cameras, hyperspectral cameras, ultrasonic or LIDAR (light detection and ranging system) cameras ", [0139]; D1 teaches a camera/smartphone imaging device; D2 lists CCD and LiDAR devices among detection components)
Regarding claim 10, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the instructions, when executed by the processor, cause the processor to automatically identify a background mask corresponding to non-plant-surface portions of the image, and to apply the background mask to the image, thereby eliminating non-plant surface portions from the second image, and to automatically identify the liquid coverage value for the plant surfaces depicted in the background-eliminated image.
(D2, "the system may provide for automated soil segmentation: A robust and simple color-based segmentation algorithm can be used automatically remove the presence of ground (soil) and automatically subtract it from the manual segmentation", [0070]; D1, "the area outside the closed circular area in the middle is the image of the magnifying glass related structure. The area needs to be removed during the detection", [p8]; D1 teaches general removal of an extraneous background area before analysis; D2 specifically teaches a background mask removing non-plant (soil) portions; Incorporating D2's soil-masking technique into D1's background-removal step would eliminate non-plant-surface portions before coverage analysis)
Regarding claim 11, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the instructions, when executed by the processor, cause the processor to automatically determine a series of liquid coverage values for regions in a sequence of images in real time, as the sequence of images is obtained.
(D2, "The detection system may be configured to detect in field conditions as the smart machinery moves through the field.", [0128]; "This allows for targeted treatment based on the real-time field condition as present at the time of treatment while the farming machinery traverses in the field", [0131]; D1 lacks any real-time/sequential-image teaching (its method analyzes discrete static test-paper images); D2 fills this gap by teaching continuous real-time field-condition sensing as a machine traverses; Incorporating D2's real-time sequential sensing into D1's per-image coverage determination would obtain a running series of values)
Regarding claim 12, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, the system further comprising:
a display comprising a display screen and a graphical user interface (GUI), wherein the instructions cause the processor to graphically render the liquid coverage value for viewing by a person via the display.
(D1, "specifically displaying a corresponding distribution parameter on a display interface, or by voice prompting", [p9]; D2, "computing device 950 may be used as a GUI frontend for a user to capture test input images and provide them to the computer device 900", [0107]; " a front end component (e.g., a client computer having a graphical user interface...)", [0124]; D1 teaches displaying the computed distribution parameter on a display interface; D2 expressly teaches a GUI frontend rendering computed results)
Regarding claim 13, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, the system further comprising:
a remote communications module, wherein the instructions cause the processor to communicate the liquid coverage value to a remote computing device using the remote communications module.
(D2, "The connectivity system 212 is configured to communicatively couple the smart farming machinery 210 to the distributed computing environment. It may be configured to provide data collected on the smart farming machinery 210 to one or more remote computing resources", [0135]; teaching an explicit connectivity/remote-communication system)
Regarding claim 14, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the instructions, when executed by the processor, cause the processor to use the determined liquid coverage value to automatically determine an adjustment of one or more sprayer system parameters to achieve a desired level of liquid coverage, wherein the one or more sprayer system parameters comprises at least one member selected from the group consisting of a sprayer speed, a nozzle type, a nozzle positioning and/or orientation, a number of nozzles used, a spray pressure, an adjuvant and/or additive rate, an overall flow rate, and a boom orientation and/or height.
(D2, " The control system 232 is configured to control and/or monitor the detection components, the tank valves or the nozzle valves following a control protocol", [0142]; "parameters are derived from such analysis to derive and/or output control signals for the tank and nozzle valves", [0144]; teaching derivation of nozzle/tank valve control signals (reading on "overall flow rate") from image-analysis output)
Regarding claim 15, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 14, wherein the system comprises one or more environmental sensors for capturing environmental data corresponding to one or more environmental conditions at a location and at a time the image(s) is/are obtained, and
wherein the instructions, when executed by the processor, cause the processor to use the environmental data along with the determined liquid coverage value or values to automatically determine the adjustment of the one or more sprayer system parameters, wherein the one or more environmental sensors comprises one or more sensors selected from the group consisting of a temperature sensor, a humidity sensor, a pressure sensor, a wind sensor, a light sensor, an air quality sensor, a gas sensor, a rainfall sensor, a radiation sensor, and a soil sensor.
(D2, "the detection components 230 may include sensors to measure humidity, light, temperature, wind or any other suitable field condition", [0139]; D2 expressly discloses humidity, light, temperature, and wind sensors)
Regarding claim 16, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the instructions, when executed by the processor, cause the processor to automatically determine a series of liquid coverage values for regions of interest in a sequence of images and use the automatically determined values to automatically determine the adjustment of the one or more sprayer system parameters to achieve the desired level of liquid coverage, wherein the instructions cause the processor to automatically implement the determined adjustment(s) in real time via a control system for controlling the one or more sprayer system parameters, thereby operating the sprayer system in real time to improve liquid coverage by accounting for one or more changing conditions.
(D2, "This allows for targeted treatment based on the real-time field condition as present at the time of treatment while the farming machinery traverses in the field", [0131]; "the control system is further configured to generate control signals to actuate the treatment mechanism once the position of the treatment mechanism reached the field position that was analyzed", [0133]; D2 teaches a control system implementing real-time adjustments to a sprayer/treatment mechanism as field conditions change while traversing)
Regarding claim 17, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, further comprising a first camera for obtaining the post-spray image.
(D1, "the camera captures the enlarged image", [p5], see comments on claim 6; D2, [0025], see comment on claim 5; D1 teaches a camera obtaining the (post-spray) image of the test surface; combined with D2's plant-surface content, D1 and D2 together teach a first camera obtaining the post-spray image of plant surfaces)
Regarding claim 18, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 17, wherein the first camera is mounted on a sprayer for spraying the liquid onto the plant surfaces, and
wherein the sprayer is mounted on a device or vehicle that moves the sprayer over the plant surfaces.
(D2, "The sprayer 220 may be releasably attached or directly mounted to the tractor", [0137]; "the smart sprayer system includes a detection system 228 with multiple detection components 230 arranged along the boom ... The detection component 230 may be an optical detection component 230 providing an image of the field. Suitable optical detection components 230 are multispectral cameras, stereo cameras, IR cameras, CCD cameras, hyperspectral cameras, ultrasonic or LIDAR (light detection and ranging system) cameras", [0139]; D2 expressly teaches an optical detection component (camera) mounted along the sprayer boom, with the sprayer itself mounted on a tractor that traverses the field)
Regarding claims 20 and 22, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the segmentation module comprises a leaf identification module and/or a multi-object segmentation module.
(D2, "Using a leaf segmentation algorithm (e.g. the leaf segmentation algorithm described earlier), automatically generated labelled masks for each image are obtained", [0085]; "a dual task convolutional neural network 120 (DTCNN) ... classes include the determined weed species (and also the crop species and soil species)", [0046]; D2 teaches both a leaf segmentation algorithm (leaf identification module) and a multi-class (multi-object) segmentation module classifying crop, several weed species, and soil)
Regarding claim 28, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 20, wherein the segmentation model comprises a multi-object segmentation module trained to:
(1) perform a first segmentation process that segments the received image into plant and non-plant surfaces;
(2) mask the non-plant surfaces; and
(3) perform a second segmentation process that further segments an unmasked portion of the received image into sprayed and non-sprayed segments.
(D2, "the system may provide for automated soil segmentation: A robust and simple color-based segmentation algorithm can be used automatically remove the presence of ground (soil) and automatically subtract it from the manual segmentation", [0070]; D1, "determine a spray image object according to the fitted polygon, and determine a distribution parameter of the spray", [p4, Fig. 11]; D2 teaches a first segmentation stage separating plant from non-plant (soil) and masking out the non-plant portion; D1 teaches a second-stage determination of spray vs. non-spray image objects on the (sampled) image; Incorporating D2's plant/non-plant masking step ahead of D1's spray-object determination would confine the spray/non-spray segmentation to plant-surface pixels only)
Regarding claim 29, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the system of claim 28, wherein the multi-object segmentation module comprises a convolutional neural network (CNN).
(D2, "The image processing for achieving a semantic segmentation of the text input image is performed by a dual task convolutional neural network 120 (DTCNN)”, [0044])
Claim(s) 2-4, 19 and 23-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D2 and further in view of D3 and D4.
Regarding claims 2 and 23, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination does not expressly disclose but D3 and D4 teach the system of claim 1, wherein the instructions, when executed by the processor, cause the processor to automatically identify the liquid coverage value for the plant surfaces using (i) one or more pre-spray images corresponding to a field of view comprising the one or more plant surfaces prior to spraying with a liquid, and (ii) one or more post-spray images corresponding to the field of view comprising the one or more plant surfaces after spraying with the liquid.
(D3, "the target verification may comprise image registration and/or geocoordinate registration whereby the previously captured sensor data of the target and/or geocoordinates may be saved and compared to the newly captured sensor data. For example, a matching two photos of the same target plant at different times, which might be slightly different (e.g. different angle, different position in photo, moved by wind, etc.)", [0144]; "the spray vector computation subsystem may generate commands that the targeting system 842 sends to the drone navigation system 608 and the drone mechanical system 650 to adjust the drone's pre-spray state to the desired spray state", [0171]; "The success determination subsystem may use post-spray target data gathered by the target data acquisition system to provide feedback to the targeting system 842 regarding the effectiveness of the spray", [0174]; D4, " a plurality of cameras 130 spaced along boom 110. Each camera 130 corresponds to one or more of nozzles 108 and includes a field of view (FOV) 132 configured to image a portion 134 of the field that is to be sprayed", [0044]; D3 expressly teaches capturing and comparing a previously-captured (pre-spray) image against a newly-captured (post-spray) image of the same target plant; D4 supplies the plant-surface field-of-view camera architecture; Incorporating D3's before/after image-registration technique into D4's plant-surface-imaging camera system would determine coverage from paired pre/post images)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate D3's before/after image-registration technique into D4's plant-surface-imaging camera system would determine coverage from paired pre/post images. It would have been also obvious to incorporate D3 and D4 into D1 and D2 in order to accurately evaluate the effectiveness of the spray application by capturing and comparing pre-spray and post-spray images of the plant surfaces within a targeted field of view, thereby improving the precision and reliability of the liquid coverage measurement. The combination of D1, D2, D3 and D4 also teaches other enhanced capabilities.
Regarding claims 3 and 24, the combination of D1, D2, D3 and D4 teaches its/their respective base claim(s).
The combination further teaches the system of claim 2, wherein the instructions, when executed by the processor, cause the processor to (i) identify leaf areas from a pre-spray image and a post-spray image using the segmentation module, (ii) match a pair of leaves as segmented in the pre-spray image and the post-spray image that correspond to the same leaf, and (iii) use the matched pair of leaves to spatially align the pre-spray image and the post-spray image, after which the liquid coverage value for the region of interest is automatically determined.
(D2, “well known leaf segmentation algorithms can be used to extract all pixels from an image of the original real-world single-species image and annotate them with the corresponding class information”, [0024]; "Using a leaf segmentation algorithm (e.g. the leaf segmentation algorithm described earlier), automatically generated labelled masks for each image are obtained", [0085]; " a dual task convolutional neural network 120 (DTCNN) ... classes include the determined weed species (and also the crop species and soil species)", [0046]; D3, "matching two photos of the same target plant at different times, which might be slightly different (e.g. different angle, different position in photo, moved by wind, etc.)", [0144]; "a contact area for a targeted weed may include a portion of a leaf, an entire leaf, a group of leaves, stem, root(s), or the entire plant", [0138]; D2 teaches identifying leaf areas via a leaf-segmentation algorithm; D3 teaches an image-registration process matching two photos of the same plant target captured at different times (accounting for angle/position differences), used to align/verify the target; Incorporating D3's leaf-level image-registration/matching technique into D2's leaf-segmented pre- and post-spray images would spatially align the two images via a matched leaf pair before computing coverage)
Regarding claims 4 and 25, the combination of D1, D2, D3 and D4 teaches its/their respective base claim(s).
The combination further teaches the system of claim 2, wherein the instructions, when executed by the processor, cause the processor to (i) segment multiple types of objects in pre-spray images of a pre-spray video stream and post-spray images of a post-spray video stream using the segmentation module, (ii) classify certain segmented objects as leaves from the pre-spray images and the post-spray images, (iii) use a matching module to find matching pairs of pre-spray images and post-spray images that contain the same leaves, (iv) and use a leaf matching module to match a pair of leaves as segmented in a pre-spray image and post-spray image that correspond to the same leaf, after which the liquid coverage value for the region of interest is automatically determined.
(D4, "An image segmentation component 276 is configured to perform image segmentation on a received image, to segment or divide the image into different portions for processing. A plant classifier 278 ... is configured to identify areas in the image that represent a target plant type", [0052]; "the image can include an image within a time series of still images acquired by camera 260, or an image from a video feed obtained from camera 130", [0066]; D3, matching two photos of the same target at different times, [0144] (see comments on claim 3); D4 teaches a segmentation component and plant classifier operating on a video feed/time-series of images from the cameras, satisfying multi-object segmentation and classification "from a video stream"; D3 teaches a matching technique between two images (photos) captured at different times of the same target; Incorporating D3's cross-time image-matching module into D4's video-stream-based segmentation/classification pipeline would find matching leaf pairs between pre-spray and post-spray video-derived images)
Regarding claim 19, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination of D1, D2, D3 and D4 further teaches the system of claim 17, further comprising a second camera for obtaining a pre-spray image.
(D3, "there may be one or more field scanning drones 902 and one or more field treatment drones 904. The field scanning drones 902 may be aerial drones ... instrumented with one or more flight cameras 906 ... In some aspects, the field scanning drone 902 may comprise one or more plant scanning cameras 912 separate from the flight cameras 906. The field scanning drone 902 may traverse the field gathering field data ...", [0196]; D4, "Block 404 identifies a particular one of cameras 260 (i.e., selecting a first camera) to be calibrated and used to acquire images", [0063]; "At block 470, other cameras 130 can be calibrated and utilized for image capture and control of corresponding nozzles 108.", [0078]; D3 teaches a separate scanning-drone camera system that gathers field data (pre-treatment) distinct from the treatment drone's camera used for post-spray verification; D4 teaches calibrating and using multiple distinct cameras in the system; Incorporating D3's separate pre-treatment scanning camera into D4's multi-camera architecture would add a second, dedicated camera for capturing the pre-spray image)
Conclusion
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 10/3/2026
