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 .
DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for domestic benefit based on provisional application 63/342,034 filed on May 13, 2022.
DETAILED ACTION
Claims 1 – 8, 10 – 23, and 25 - 39 and are pending in the application.
Claims 1 and 16 are independent.
Claims 9 and 24 are cancelled.
This action is a first action based on a Request for Continued Examination application.
Given the amended claims 1 and 16, the 35 USC § 112(b) rejection is rescinded.
Claim Rejections - 35 USC § 103
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 – 8, 10 – 14, 16 – 23, 25 – 29, 31, 33, and 35 - 39 are rejected under are rejected under 35 U.S.C. 103 as being unpatentable over Sibley et al. (US PG Pub. No. 20220118555), herein “Sibley” in view of Harmon (PG Pub. No. 20210289693), herein “Harmon.”
Regarding claim 1,
Sibley teaches a system for automatically quantifying liquid coverage on plant surfaces, the system comprising: (Par. 0080: “Various examples and embodiments described below relate generally to robotics, autonomous driving systems, and autonomous agricultural application systems, such as an autonomous agricultural observation and treatment system, utilizing computer software and systems, computer vision and automation to autonomously identify an agricultural object including any and all unique growth stages of agricultural objects identified, including crops or other plants or portions of a plant, characteristics and objects of a scene or geographic boundary, environment characteristics, or a combination thereof.”)
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: (Par. 0080, line 1: “Some embodiments are implemented by a computer system. A computer system may include a processor, a memory, and a non-transitory computer-readable medium. The memory and non-transitory medium may store instructions for performing methods and steps described herein.”)
receive an image comprising a region of interest corresponding to one or more plant surfaces; (Par. 0090: “The system 100 can also include an image processing module 130, either on board a vehicle supporting the system 100, part of the system 100, embedded in the system 100, or supported by one or more servers or computing devices remote from the vehicle supporting the system 100. The image processing module 130 can be configured to process any and all images or other sensor data captured by the system 100 including feature extraction, object identification, detection, and classification, image matching, comparing, and corresponding with other images received simultaneously or previously of the same location, labelling unique features in each of the images, as well as point clouds from various other sensors such as that of lidars, or a combination thereof.” Par. 0263: “ FIG. 18C. illustrates a diagram 1803 to determine spray accuracy and spray health, spray health being whether external factors outside or correctly detecting target object and lining the treatment head onto the target object and tracking it as the target object moves away from the treatment unit, since the treatment unit is on a moving vehicle, a prior or predicted spray path 1876 can be generated. For example, a sensor, disposed on a moving vehicle, can receive an image frame 1862 having a plurality of crop objects and target objects, including detected target object 1872.”)
automatically identify one or more portions of the region of interest corresponding to liquid; (Par. 0262: “…the difference in pixels profiles detected from a first frame to a subsequent frame, accounting for homography estimation due to changes in translation of the image sensor, can generate a projectile segmentation. Similar techniques can be used to detect the splat or spot detection of the spray outcome onto the surface of the target and ground, for example, seeing the color of the ground and target plant change from unsprayed to sprayed. For example, a liquid projectile hitting a target plant will morph from a projectile having a small cross-sectional diameter to a flat area covering a portion of the dirt or leaf. In this example a liquid projectile may change the color of the dirt surrounding a plant, due to dry dirt turning wet from the liquid projectile hitting the dirt. In this case, the image sensors can detect a color change in the ground and determine that a splat is detected and that a detect target object for treatment has been treated, and logged or indexed by the treatment system. In one example, a stereo pair of cameras can detect sprays in each camera and associated with each other to fit a 3D line such that the system can detect and index a spray in the real world with 3D coordinates.” Par. 0124 – Automation and Par. 0188 – “automatically sample the images” Examiner’s Note – See also Par. 0141, 0194, 0197, 0208.)
and automatically determine a liquid coverage value for the region of interest in the image, wherein the liquid coverage value quantifies an area of the plant surfaces depicted in the region of interest that is covered by liquid. (0261: “FIG. 18B and FIG. 18C illustrate an example of spray detection, beam detection, or spray projectile detection. In these diagrams 1802 and 1803, one or more image sensors is scanning a local scene comprising a plurality of plants 1872 including target plants for treatment and crop plants for observation and indexing. As the sensor scans the scene while a vehicle supporting the sensor is moving in a lateral direction, the sensor will capture one or more image frames in sequence from one to another illustrated in image frames 1862, 1864, and 1866 where image frame 1864 and 1866 are frames captured by a sensor that captured image frame 1862 subsequently, but not necessarily the immediate next frame captured by the image sensor. During the capturing of images, if component treatment system having sensors and treatment units sends instructions to the treatment unit to perform a spray action, such as emit a fluid projectile, the image sensors would capture the spray action as it comes into the frame and then eventually disappears as the projectile is fully splashed onto the surface of the intended target or ground. In such example, the spray projectile, such as projectile 1875, can be detected and indexed by the image sensors and the treatment system, as well as the splat area 1877 after the spray has completed. The system can detect the splat size and location.”) See also Par. 0179, 0262, 0272, 0353, and 0368).
Sibley does not teach that adjusting spray parameters to achieve a saturation (or desired coverage level.) However, Harmon does teach following spraying of the agrochemical solution on the one or more plant surfaces of the crop (Par. 0016: “Each nozzle may, in tum, be configured to dispense an agricultural fluid (e.g., a pesticide or a nutrient) onto one or more underlying plants as the agricultural sprayer travels across a field to perform a spraying operation.” Par. 0051 and 0052.)
using the automatically determined (Par. 0034: “…to allow the operation of such components to be electronically or automatically controlled by the computing system 140.”) in real time (Par. 0016: “…dispense an agricultural fluid (e.g., a pesticide or a nutrient) onto one or more underlying plants as the agricultural sprayer travels across a field to perform a spraying operation. In this respect, a computing system may be configured to receive image data depicting one or more droplets that have been deposited on the underlying plant(s).” Par. 0029: “…as the sprayer 10 travels across the field. As will be described below, a computing system may be configured to analyze the captured image data to determine the size and/or shape of the imaged droplets for use in monitoring the operation of the sprayer 10.” Par. 0039 – images capture during a spraying operation. Par. 0051 and 0052.) liquid coverage value, determine an adjustment (Par. 0054: “In addition, as shown in FIG. 5, at (210), the method 200 may include initiating, with the computing system, a control action when the determined at least one of the size or the shape falls outside of the predetermined range. For instance, as described above, when the determined size and/or the shape of the agricultural fluid droplets falls outside of the associated predetermined range, the computing system 108 may be configured to initiate one or more control actions.” Par. 0045. Examiner’s Note – See other 29 instances of adjust[-ing, or -ment]. ) of one or more sprayer system parameters to achieve a saturation level (desired spray quality in size and shape, Par. 0018.) of spray coverage. (Par. 0041: “…the computing system 108 may be configured to identify and, subsequently, determine size and/or shape values of several droplets present on each plant present within the field(s) of view 104 of the imaging device(s) 102. For example, in one embodiment, the computing system 108 may be configured to further analyze each determined droplet size/shape value (e.g., compare to an associated range) individually. In another embodiment, the computing system 108 may be configured to determine an average or median droplet/size shape value for each imaged plant based on the individual determined size/shape values. Alternatively, the computing system 108 may be configured to identify a single droplet on each imaged plant or a portion of each imaged plant (e.g., each leaf) and subsequently, determine the size and/or shape values of such droplets.” See also Magidow, cited below, Par. 0027 that teaches a image analysis method that analyzes a maximum per-area leaf coverage which is a saturation. Par. 0034.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method of determining coverage of spraying on a chemical on a plant as in Sibley with a system and method that uses image analysis to determine droplet parameters such as size and shape of the droplet (coverage) on the plants within a field in real time and adjusts, while the spraying operation is being performed, the parameters of the spraying operation as in Harmon in order to improve agricultural outcomes. (Par. 0018)
Regarding claim 2,
The previously cited references teach the limitations of claim 1 which claim 2 depends. Sibley also teaches that the liquid on the plant surfaces comprises a sprayed- on solution comprising one or more members of 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. (Par. 0096: “The action can be that of a chemical fluid projectile emitted from a device as part of the treatment 311 directly onto a portion of a surface of the agricultural object 302. The fluid can be a single liquid projectile similar to that of a shape of a water droplet emitted from a water sprayer, a mist or aerosol, a volumetric spray across a period of time, or many other types of fluid that can be emitted from a device discussed later in this disclosure.” Par. 0352: “ In one embodiment, the agricultural treatment system may be configured to monitor the health of the spraying head and determine whether the spraying head is accurately emitting a fluid at a target object. In some instances, the spraying tip may build up residue or other particulate. For example, the spraying head may disperse a fluid containing a solution of salts or of other compounds.” See also Par. 0142.)
Regarding claim 3,
The previously cited references teach the limitations of claim 1 which claim 3 depends. Sibley also teaches automatically identifying one or more portions of the region of interest corresponding to liquid comprises applying an image analysis technique selected from the group consisting of color thresholding, edge detection, filtering, deep learning, neural networks, convolutions, depth estimation, active learning, and transfer learning. (Par. 0177: “. At step 750, the agricultural observation and treatment system can identify one or more salient points or salient regions of at least a portion of a subsequent frame. At step 760, the agricultural observation and treatment system can determine a change in position of the treatment system based on comparing the first and subsequent frame. At step 770, the agricultural observation and treatment system can verify or improve the determined change in position with the position determined by the location-based sensors, motions sensors, or both. At step 780, the agricultural observation and treatment system can determine a pose estimation. And at step 790, the agricultural observation and treatment system can send instructions to activate actuators. The points of interest to track for motion estimation and SLAM can be that of real-world objects or patterns detected, or salient cluster of points in an image or point cloud that can be tracked from frame to frame or point cloud to point cloud as a vehicle with image or point cloud sensors move in time. These can be detected by computer vision methods of detecting edges, corners, blobs, lines, etc., or by a machine learning algorithm configured to detect real world objects, such as agricultural objects, for example leaves for sensing systems pointed down at row crops, rocks, dirt, beds, troughs, crops, weeds, etc. For example, if a landmark such as a small rock in the dirt, or a leaf of a crop, in a frame captured by an image sensor, the compute unit can determine that a cluster of pixels of the frame…” See also Par. 0235.)
Regarding claim 4,
The previously cited references teach the limitations of claim 1 which claim 4 depends. Sibley also teaches the liquid coverage value is a quantity selected from the group consisting of an absolute surface area of the plant surfaces in the region of interest covered by liquid, a relative surface area of the plant surfaces in the region of interest covered by liquid, a number of droplets in the region of interest, a total liquid volume in the region of interest, a number of droplets per unit area of the region of interest, and a total liquid volume per unit area of the region of interest. (Par. 0261: “FIG. 18B and FIG. 18C illustrate an example of spray detection, beam detection, or spray projectile detection. In these diagrams 1802 and 1803, one or more image sensors is scanning a local scene comprising a plurality of plants 1872 including target plants for treatment and crop plants for observation and indexing. As the sensor scans the scene while a vehicle supporting the sensor is moving in a lateral direction, the sensor will capture one or more image frames in sequence from one to another illustrated in image frames 1862, 1864, and 1866 where image frame 1864 and 1866 are frames captured by a sensor that captured image frame 1862 subsequently, but not necessarily the immediate next frame captured by the image sensor. During the capturing of images, if component treatment system having sensors and treatment units sends instructions to the treatment unit to perform a spray action, such as emit a fluid projectile, the image sensors would capture the spray action as it comes into the frame and then eventually disappears as the projectile is fully splashed onto the surface of the intended target or ground. In such example, the spray projectile, such as projectile 1875, can be detected and indexed by the image sensors and the treatment system, as well as the splat area 1877 after the spray has completed. The system can detect the splat size and location.” See also Par. 0105, 0255, 0368. See also Varanasi cited below and Par. 0093)
Regarding claim 5,
The previously cited references teach the limitations of claim 1 which claim 5 depends. Sibley also teaches 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, and a short-wave infrared (SWIR) camera. (Par. 0249: “…the agricultural observation and treatment system can be configured to detect objects in real time as image or lidar sensors are receiving image capture data. The treatment system can, in real time, detect objects in a given image, determine the real-world location of the object, instruct the treatment unit to perform an action, detect the action (discussed below), and index the action as well as the detection of the object into a database. Additionally, the treatment system, at a server or edge computing device offline, can detect objects in a given image, spray projectiles, spray action, spot of splat detections, and index the object detections and spray action detections.” See also Par. 0277 and 0177.)
Regarding claim 6,
The previously cited references teach the limitations of claim 5 which claim 6 depends. Sibley also teaches the one or more imaging devices and/or sensors comprises a first imaging device and/or sensor that collects data (e.g., images) before a sprayed-on solution has been applied to the plant surfaces and a second imaging device and/or sensor that collects data (e.g., images) the sprayed-on solution has been applied to the plant surfaces. (Par. 0188: “The modular treatment module 900 may include a camera enclosure, or camera bank 904 that includes one or more cameras or other image sensing devices. In one example, the illumination units 910, treatment units 1100, supported by treatment unit frame 903, can all be operably mounted and connected to the camera bank 904 having a camera enclosure. The inner two cameras may be identification cameras to obtain digital imagery of agricultural objects, and the outer two cameras may be cameras used to obtain imagery of agricultural objects being treated including the treatment projectile, treatment profile, splat detection, treatment health and accuracy.” See paragraphs 0259 – 0261 that teach images taken before and after a treatment system applies the spray.)
Regarding claim 7,
The previously cited references teach the limitations of claim 1 which claim 7 depends. Sibley also teaches wherein the one or more imaging devices and/or sensors comprises a short-wave infrared (SWIR) camera, and wherein sufficient detectable contrast is achieved for accurate liquid coverage value determination without the need for any dyes to be added to the sprayed- on solution. (Par. 0124: “The systems, robots, computer software and systems, applications using computer vision and automation, or a combination thereof, can be implemented using data science and data analysis, including machine learning, deep learning including convolutional neural nets (“CNNs”), deep neural nets (“DNNs”), and other disciplines of computer-based artificial intelligence, as well as computer-vision techniques used to compare and correspond features or portions of one or more images, including 2D and 3D images, to facilitate detection, identification, classification, and treatment of individual agricultural objects, perform and implement visualization, mapping, pose of an agricultural object or of the robotic system, and/or navigation applications using simultaneous localization and mapping (SLAM) systems and algorithms, visual odometry systems and algorithms, including stereo visual odometry, or a combination thereof, receive and fuse sensor data with sensing technologies to provide perception, navigation, mapping, visualization, mobility, tracking, targeting, with sensing devices including cameras, depth sensing cameras or other depth sensors, black and white cameras, color cameras including RGB cameras, RGB-D cameras, infrared cameras, multispectral sensors, line scan cameras, area scan cameras, rolling shutter and global shutter cameras, optoelectric sensors, photooptic sensors, light detection and ranging sensors (LiDAR) including spinning Lidar, flash LiDAR, static Lidar, etc., lasers, radar sensors, sonar sensors, radio sensors, ultrasonic sensors and rangefinders, other range sensors, photoelectric sensors…” Examiner’s Note – Other paragraphs teach using infrared cameras such as paragraphs 0129, 0131, 0163, 0179, 0342, and 0343 and given the wide range of cameras used I Sibley it would have been obvious to one with ordinary skill in the art to use a short-wave infrared camera.)
Regarding claim 8,
The previously cited references teach the limitations of claim 1 which claim 7 depends. Sibley also teaches 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. (Par. 0107: “…the system can associate the different identifications of the same object, based on the objects state changes, or stage of growth or phenological changes, and display, via a series of views across time, the state change in sequence in the user interface 350. Identifying, storing and indexing, and associating portions of images and patches and other sensor readings of objects of the same type with near or the same locations of the same objects identified throughout time from different trials and identifying with different states of the same object in the geographic scene can be performed using various techniques including machine learning feature extraction, detection, and or classification to detect and identify objects in a given image frame as well as generating keyframes based on the objects and landmarks detected.” See also Par. 0187, 0188, 0194, and 0197 and other paragraphs that teach multiple images are taken depicting the resulting spray.)
Regarding claim 10,
The previously cited references teach the limitations of claim 1 which claim 10 depends. Sibley also teaches the system further comprising: a display comprising a display screen and a graphical user interface (GUI) (e.g., said GUI presented via a mobile device application, e.g., a smart phone app); and a remote communications module (e.g., said remote communications module comprising one or more members selected from the group consisting of a wireless internet connection, a universal serial bus (USB) connection, and a Bluetooth connection). (Par. 0192: “…a user can select in an application the indexed agricultural object, and a user interface of the agricultural treatment object can display information related to the agricultural object including images taken of the agricultural object, including multiple images taken at different locations, and with orientations of the image capture device, for capturing different views of the same agricultural object, as well as multiple images taken at different points in time as the agricultural treatment system conducts multiple trials and captures images of the same or near the same location as previously captured images.” Par. 0128: “For example, the communications module 426 can communicate signals, through a network 520 such as a wired network, wireless network, Bluetooth network, wireless network under 5G wireless standards technology, radio, cellular, etc.to edge and cloud computing devices including a mobile device 540, a device for remote computing of data including remote computing 530, databases storing image and other sensor data of crops such as crop plot repository 570, or other databases storing information related to agricultural objects, scenes, environments, images and videos related to agricultural objects and terrain, training data for machine learning algorithms, raw data captured by image capture devices or other sensing devices, processed data such as a repository of indexed images of agricultural objects.” See also Par. 0087 and 0127.)
Regarding claim 11,
The previously cited references teach the limitations of claim 10 which claim 11 depends. Sibley also teaches the instructions cause the processor to (i) graphically render the liquid coverage value for viewing by a person via the display and/or the remote communications module and/or (ii) communicate the liquid coverage value to a remote computing device (e.g., a device running farm management software, e.g., process control software) using the remote communications module. (Par. 0102: “In another example, each object 320 in the virtually constructed scene can be represented by the real-world object virtual 3d model associated with each of the objects 320. For example, each of the thousands or millions of objects, landmarks, or patterns can be visually represented as 2d or 3d models of each of the specific objects in the real world. Thus, a map, either a 2d or a 3d map can be generated and accessed, visually, illustrating each object, landmark, pattern or region of interest, in the real world such that each object's and/or landmark's visualization, structure, location, treatment and prediction details can be represented and displayed in the map.” Par. 0271: “As illustrated in FIGS. 18E and 18F, each spray projectile and splat detections can be indexed and visually displayed in a user interface. The 2D or 3D models 1880a , 1880b , and 1880c of each target object 1872, spray projectile 1875, and splash 1877 onto a surface of the ground and target object. Additionally, the 3D models can be superimposed on each other to reconstruct the spray action from the targeting of the target object, to the spraying of the target object, to the splash made and splat detected as illustrated in model 1880d of diagram 1806 of superimposed model 1882.”)
Regarding claim 12,
The previously cited references teach the limitations of claim 9 which claim 12 depends. Sibley also teaches 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 droplet coverage value or values to automatically determine the adjustment (e.g., a recommended adjustment) of the one or more sprayer system parameters, wherein the one or more environmental sensors comprise 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, a soil sensor, and a sprayer speed sensor. (Par. 0196: “In another example, in one mode of operation, in a first pass along a path along an agricultural environment, the agricultural treatment system obtains a first set of multiple images while the system moves along the path. For example, the agricultural treatment system uses onboard cameras and obtains multiple digital images of agricultural objects (e.g., plants, trees, crops, etc.). While obtaining the multiple images of the agricultural objects, the agricultural treatment system records positional and sensor information and associates this information for each of the obtained images. Some of this information may include geo-spatial location data (e.g., GPS coordinates), temperature data, time of day, humidity data, etc. The agricultural treatment system or an external system (such as a cloud-based service) may further process the obtained images to identify and classify objects found in the images. The processed images may then be stored on a local data storage device of the agricultural treatment system.”)
Regarding claim 13,
The previously cited references teach the limitations of claim 9 which claim 13 depends. Sibley also teaches a control system for controlling the one or more sprayer system parameters in accordance with the automatically determined adjustment. (Par. 0141: “The compute unit 420 can calculate a direction, orientation, and pressurization of the treatment unit 470 such that when the treatment unit 470 activates and opens a valve for the pressurized liquid to pass from the chemical selection module 480 to the treatment unit 470, a fluid projectile of a desired direction, orientation, and magnitude, from the pressure, will be emitted from the treatment unit 470 at the treatment head 472. The pump will keep the liquid stream from the chemical tank 482 to the treatment unit 470 at a constant pressure, whether or not there is flow. The chemical regulator 484 in the series of components will adjust and step down the pressure to a desired pressure controlled manually before a trial, controlled by the compute unit 420 before the trial, or controlled and changed in real time during a trial by the compute unit 420 either from remote commands from a user or automatically calculated by the compute module 424. The accumulator 487 will keep the liquid stream in series pressurized to the desired pressure adjusted and controlled by the chemical regulator 484, even after the treatment unit 470 releases and emits pressurized fluid so that the stream of fluid from the pump to the treatment unit 470 is always kept at a desired pressure without pressure drops from the release of pressurized fluid.” See full paragraph 0141.)
Regarding claim 14,
The previously cited references teach the limitations of claim 13 which claim 14 depends. Sibley also teaches 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 droplet coverage, wherein the instructions further cause the processor to automatically implement the determined adjustment(s) in real time via the 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, wherein the one or more sprayer system parameters comprises one or more members selected from the group consisting of sprayer speed, nozzle type, nozzle positioning and/or orientation, spray pressure, adjuvant / additive rate, and overall flow rate. (Par. 0263: “If the detection is not good enough, such as the line cannot be fitted, the system can determine that the spray did not happen, or happened but not at the intended target. Alternatively, the system can perform spray segmentation on the spray that was detected, whether within the predicted spray path 1876 or not, and determine whether the end of the spray or the splat detected lines up with the intended target. Thus, seeing where a target object should have been sprayed, and/or should have had a splat detected, and where the actual spray profile was detected, including 3D location, and where the spray splat was detected, can be used to evaluate the specific spray health of that particular spray, and whether intrinsic or extrinsic adjustments needs to be made. The adjustments can be accounting for wind that may have moved the spray, the speed of the vehicle not being accounted for properly as the system tracks an object from frame to frame, or mechanical defects such that the intended target and the line of sight after sending the correct instructions to orient the treatment head of the treatment unit are misaligned. Upon detecting an inaccurate or incorrect spray projectile, one or more of the discussed defects can be accounted for in real time and a second projectile can be reapplied on to the target object and tracked again for trajectory evaluation and its spray health and accuracy.” Par. 0272. See also Varanasi, cited in the conclusion section, paragraphs 0128, 0139 – 0148 and 0167 that teach that the size of the droplet can be changed depending on the changing conditions.)
Regarding claims 16 – 23 and 25 - 29, they are directed to a method of steps to implement the system or apparatuses set forth in claims 1 – 14, respectively. Sibley and Harmon teach the claimed system or apparatuses in claims 1 - 14. Therefore, Sibley and Harmon the method of steps in claims 1 – 14.
Regarding claim 31,
The previously cited references teach the limitations of claim 1 which claim 31 depends. Harmon also teaches wherein implementation of the one or more adjusted spray parameters results in an increased spray coverage percentage than would be achieved without the adjustment at the same level of agrochemical used per acre. (Par. 0047: “The control signals may, in turn, instruct the pump 52 to adjust its operation to increase or decrease the pressure of the agricultural fluid supplied to the nozzles 38 as desired.” See also Magidow, cited below, Par. 0047: The analyzed 110 spray particulate data may include a range of droplet sizes within the spray distribution. In addition, information identifying the analyzed sprayed mixture and additional variables that affect how the mixture is sprayed may be provided. This information may include: spray identification information, such as composition parameters, of the mixture including active ingredients and adjuvants; and additional spray parameters beyond the spray composition, such as delivery parameters, including active ingredient rates, adjuvants rates, spray pressure, rate of spray per acre ( e.g., spray volume per acre), spray pressure ( e.g., 20 psi, 40 psi), and nozzle type ( e.g., XRI 1002, XRl I 003 , and AIXR11002), as well as environmental parameters affecting spray, such as boom height and wind speed.” See also Par. 0027. Examiner’s Note – Although Magidow does not disclose the exact language of claim 31, the cited paragraphs do teach a designated per acre coverage and the adjustment of spray parameters to achieve those values.)
Regarding claim 33,
The previously cited references teach the limitations of claim 1 which claim 33 depends. Harmon also teaches wherein the one or more sprayer system parameters comprises an operational pressure of a spray system. (Par. 0047. See also Magidow, cited below, Par. 0020: “The analyzed 110 spray particulate data may include a range of droplet sizes within the spray distribution. In addition, information identifying the analyzed sprayed mixture and additional variables that affect how the mixture is sprayed may be provided. This information may include: spray identification information, such as composition parameters, of the mixture including active ingredients and adjuvants; and additional spray parameters beyond the spray composition, such as delivery parameters, including active ingredient rates, adjuvants rates, spray pressure, rate of spray per acre (e.g., spray volume per acre), spray pressure (e.g., 20 psi, 40 psi), and nozzle type (e.g., XR11002, XR11003, and AIXR11002), as well as environmental parameters affecting spray, such as boom height and wind speed.” Par. 0021: “With respect to the aforementioned delivery parameters affecting spray, when the spray is analyzed using a fluid delivery system, including closed systems such as wind tunnels, these delivery parameters may be controlled and/or monitored during testing. For example, spray pressure may be monitored using the fluid delivery system and variations in pressure may be recorded to confirm that spray analysis is recorded while the spray is delivered at a selected pressure, which may ensure accurate spray behavior analysis information is documented. In another example, for mixtures sprayed through a nozzle, the spray produced from the mixture may be affected by the nozzle type as well as the composition in the mixture, e.g., pesticides and adjuvants, and these variables may be recorded during analysis. In some cases, the analyzed fluid may be water, such as when water is used as a baseline for comparison with agricultural sprays formed of active ingredients.”)
Regarding claim 35,
The previously cited references teach the limitations of claim 1 which claim 35 depends. Sibley also teaches wherein the one or more sprayer system parameters comprises a nozzle position relative to a target plant surface and other nozzles. (Par. 0183: “…one or more treatment units comprised of one or more nozzles…” Par. 0280: “The vehicle 2110, illustrated in FIG. 23 can move with at least 6 degrees of freedom. Additionally, the treatment unit 2113 of the treatment system 2112 can also have coordinates associated with rotational movement including that of roll about an X axis, pitch about a Y axis, and yaw about a Z axis, as well as translational coordinates associated with lateral movement including an X, Y, and Z position in a geographic boundary. This can include rotating and moving a gimbal assembly of the treatment unit 1653 to a desired pitch angle 2002 and desired yaw angle 2004 when the treatment unit is configuring and orienting itself to position a nozzle or head of the treatment unit 1653 at a target or aligning a line of sight towards a target for emitting a projectile.”)
Regarding claim 36,
The previously cited references teach the limitations of claim 1 which claim 36 depends. Harmon also teaches wherein the one or more sprayer system parameters comprises a chemistry of the agrochemical solution. (Par. 0021: “Furthermore, the frame 12 may support an operator's cab 20 and a tank 22 configured to store or hold an agricultural fluid, such as a pesticide ( e.g., a herbicide, an insecticide, a rodenticide, and/or the like), a fertilizer, or a nutrient. However, in alternative embodiments, the sprayer 10 may include any other suitable configuration.” See Par. 0016. See also Magidow Par. 0020: “The analyzed 110 spray particulate data may include a range of droplet sizes within the spray distribution. In addition, information identifying the analyzed sprayed mixture and additional variables that affect how the mixture is sprayed may be provided. This information may include: spray identification information, such as composition parameters, of the mixture including active ingredients and adjuvants; and additional spray parameters beyond the spray composition, such as delivery parameters, including active ingredient rates, adjuvants rates, spray pressure, rate of spray per acre ( e.g., spray volume per acre), spray pressure ( e.g., 20 psi, 40 psi), and nozzle type ( e.g., XRI 1002, XRl I 003 , and AIXR11002), as well as environmental parameters affecting spray, such as boom height and wind speed.” See other paragraphs that teach selecting a mix or mixture.)
Regarding claim 37,
The previously cited references teach the limitations of claim 1 which claim 37 depends. Harmon also teaches when executed by the processor, cause the processor to use one or more environment conditions along with the determined liquid coverage value to determine the adjustment of the one or more sprayer system parameters, wherein the one or more environment conditions comprise one or more environment conditions selected from the group consisting of wind, temperature, humidity, canopy, leaf, tree density variability, and plant surface hydrophobicity. (Par. 0043: “When the determined size and/or shape values associated with the droplets fall outside of the associated range, the droplets deposited on the plants may be too small or large and/or of an undesirable shape, thereby resulting in poor spray quality and/or an undesirable application rate of the agricultural fluid. For example, the size and/or shape values of the droplets may fall outside of the associated range when the airspeed relative the sprayer 10 is too high ( e.g., due to the wind speed and/or the ground speed of the sprayer 10), the pressure of the agricultural fluid is too high or low, and/or the boom assembly 24 is too close to or far away from the canopy of the underlying plants. In such instances, the computing system 108 may be configured to initiate one or more control actions associated with improving the quality issues caused by the airspeed, the agricultural fluid pressure, and/or the boom assembly height.” See also Par. 0040 (temperature) and 0046. See also Magidow Par. 0005: “In some aspects, a computer-implemented method for depicting agricultural spray behavior as a spray distribution involves using a computer processor, which receives selections of an agricultural spray and parameters at which the agricultural spray is to be sprayed. The processor retrieves analyzed spray particulate data based on the selections, which includes a distribution of relative amounts of agricultural spray droplets within droplet size classes, where each class corresponds to a range of droplet sizes.” Par. 0017: “The disclosed approaches are useful in delivering spray analysis data in a user-friendly, visual format, which may educate users about predicted spray patterns according to spray parameters of interest and may allow users to refine spray parameters of interest based thereon. These implementations may additionally include information related to spray drift (e.g., off-target movement) due to wind speed and leaf coverage due to boom height. This may enable users to assess whether sprays will be effective for treating crops in certain environmental conditions.” Par. 0053.)
Regarding claim 38,
The previously cited references teach the limitations of claim 16 which claim 38 depends. Harmon also teaches that the method comprises spraying the
agrochemical solution on the one or more plant surfaces of the crop. (Par. 0039, “Additionally, the computing system 108 may be configured to determine the sizes and/or shapes of the agricultural fluid droplets deposited on the plants during the
spraying operation.”)
Regarding claim 39,
The previously cited references teach the limitations of claim 16 which claim 39 depends. Harmon also teaches that the method comprises spraying the
agrochemical solution on the crop using the one or more adjusted sprayer system parameters. (Par. 0047: “…the computing system 108 may be configured to control the operation of the pump 52 to execute the desired adjustment to the pressure of the agricultural fluid supplied to the nozzles 38. Specifically, the computing system 108 may be configured to transmit control signals to the pump 52 (e.g., via the communicative link 110). The control signals may, in turn, instruct the pump 52 to adjust its operation to increase or decrease the pressure of the agricultural fluid supplied to the nozzles 38 as desired.” See also Magidow Par. 0032: “Method 100 continues by receiving 140 selections identifying the sprayed fluids associated with the spray particulate data. The selection may be a user selection of one or more variables affecting spray such as spray composition parameters, including active ingredients and adjuvants; and spray parameters including delivery parameters, such as spray pressure, carrier volume rate (e.g., gallons per acre ("GPA" such as 10 GPA), product use rate (e.g., pesticide use rate, adjuvant use rate, or combinations), nozzle type, and environmental parameters, such as boom height and wind speed. For example, the selection may be one or more of an active ingredient and an adjuvant along with a selection of one or more of a rate of spray (e.g., carrier volume), a nozzle type and a spray pressure. In a further example, the selection may include one or more of a wind speed or boom height at which the spray is delivered. In some implementations, the received selection may be a user selection obtained from one or more user interfaces, such as from the user interfaces illustrated in FIGS. 2-6 of the present disclosure.”)
Claim 34 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over Sibley et al. (US PG Pub. No. 20220118555), herein “Sibley” in view of Harmon (PG Pub. No. 20210289693), herein “Harmon,” in further view of Magidow (PG Pub. No. 20140195948), herein “Magidow.”
Regarding claim 34,
The previously cited references teach the limitations of claim 1 which claim 34 depends. They do not teach a nozzle design. However, Magidow teaches wherein the one or more sprayer system parameters comprises a nozzle design. (Par. 0020: “The analyzed 110 spray particulate data may include a range of droplet sizes within the spray distribution. In addition, information identifying the analyzed sprayed mixture and additional variables that affect how the mixture is sprayed may be provided. This information may include: spray identification information, such as composition parameters, of the mixture including active ingredients and adjuvants; and additional spray parameters beyond the spray composition, such as delivery parameters, including active ingredient rates, adjuvants rates, spray pressure, rate of spray per acre (e.g., spray volume per acre), spray pressure (e.g., 20 psi, 40 psi), and nozzle type (e.g., XR11002, XR11003, and AIXR11002), as well as environmental parameters affecting spray, such as boom height and wind speed.” Par. 0032: “For example, the selection may be one or more of an active ingredient and an adjuvant along with a selection of one or more of a rate of spray ( e.g., carrier volume), a nozzle type and a spray pressure. In a further example, the selection may include one or more of a wind speed or boom height at which the spray is delivered. In some implementations, the received selection may be a user selection obtained from one or more user interfaces, such as from the user interfaces illustrated in FIGS. 2-6 of the present disclosure.” See also Par. 0021 and 0027 (nozzle type).)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method of determining coverage of spraying on a chemical on a plant as in Sibley with a system and method that uses image analysis to determine droplet parameters such as size and shape of the droplet (coverage) on the plants within a field in real time and adjusts, while the spraying operation is being performed, the parameters of the spraying operation as in Harmon wherein spray information includes the type of nozzle as in Magidow in order to have a better understanding of what affects spray patterns wherein spray patterns can be tested and customized. (Par. 0002)
Claims 15 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Sibley in view of Harmon in further view of Saccomanno (PG Pub. No. 20220413166), herein “Saccomanno.”
Regarding claim 15,
The previously cited references teach the limitations of claim 1 which claim 15 depends. Harmon does teach that a dye can be added. Saccomanno also teaches a sprayer retrofitted with an electronic injection system that is capable of infusing a crop compatible dye into liquid to be sprayed. (Abstract: “ A field of scattering particles (e.g., bubbles in water, aerosols such as dry fog, powders, etc.) is constructed spatially/temporally in the vicinity of the target and in the path of propagating wave energy to improve the fluence coverage and thereby enhance the overall effectiveness of the kinetic process. The scatterers can be added to an existing irradiation system (retrofit application) or added to the design of a new system (forward fit). Novel dosimeters and methods of dosimetry are also disclosed to more accurately characterize the fluence received over complex surfaces.” Par. 0102. Par. 0407: “In one embodiment, a dosimetric avatar is constructed such that a first surface portion creates a shadow on a second surface portion when irradiated from a source of wave energy external to the dosimetric avatar (e.g., a UVC, Visible/NIR, e-beam, etc.), the shadow geometry modeled after a shadow geometry on the second object (e.g., the achenes on a strawberry), and the shadowed surface portion constructed of a material that changes its properties when irradiated such that the changed properties correlates to a dose (e.g., using dyes that are used in dosimeter cards). The correlation of the changed properties is constructed in the form of one or more of algorithms, mathematical formulae, and lookup tables. For example, dosimetry cards react to fluences by a change in color/tone..” See also Par. 0397, 0398, and 0435.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method of determining coverage of spraying on a chemical on a plant as in Sibley with a system and method that uses image analysis to determine droplet parameters such as size and shape of the droplet (coverage) on the plants within a field in real time and adjusts, while the spraying operation is being performed, the parameters of the spraying operation as in Harmon with a system and method that improves coverage of target surfaces such as plants and using this spray system as a retrofit application which can also be used for colorants or dyes as in Saccomanno in order to implement the system into an already constructed environment. (Par. 0102).
Regarding claim 30, it is directed to a method of steps to implement the system or apparatuses set forth in claim 15. Sibley, Harmon, and Saccomanno teach the claimed system or apparatuses in claim 15. Therefore, Sibley, Harmon and Saccomanno teach the method of steps in claim 15.
Claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over Sibley in view of Harmon in further view of Feldhaus et al. (PG Pub. No. 20160368011), herein “Feldhaus.”
Regarding claim 32,
The previously cited references teach the limitations of claim 1 which claim 32 depends. They do not teach vehicle speed to achieve a certain spray coverage. However, Feldhaus does teach a one or more spray parameters to achieve the saturation level of spray coverage comprises a speed at which a sprayer moves through a field. (Abstract: “A system and method for dispersing fluids from an agricultural vehicle includes a sprayer that dispenses the fluids and a controller cooperative with a plurality of sensors to sense vehicle travel speed, vehicle travel direction, wind speed, wind direction, and the heights of first and second nozzles from the ground surface. The controller includes a memory storing a look-up table having fan angles of the first and second nozzles, and a processor that computes first and second spray pattern on the ground surface based on the fluid dispensed through the respective first and second nozzles. The processor determines an overlap region between the first and second spray patterns, compares the determined overlap region with a pre-determined overlap, and takes corrective action automatically by changing travel speed of the vehicle or changing a duration of time the fluids are dispensed from the first and second nozzles.” Par. 0006, 0044, (as a function of coverage), and 0047. Examiner’s Note – Feldhaus also teaches in many instances of spray overlap as interpreted as saturation of the spray.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method of determining coverage of spraying on a chemical on a plant as in Sibley with a system and method that uses image analysis to determine droplet parameters such as size and shape of the droplet (coverage) on the plants within a field in real time and adjusts, while the spraying operation is being performed, the parameters of the spraying operation as in Harmon with adjusting travel speed of vehicles to avoid excess coverage as in Feldhaus in order to avoid overlap and avoid spray patterns that drown the plants. (Par. 0047)
Response to Arguments
Careful consideration has been given to the Affidavit traversing the previous rejections and Applicant’s Arguments/Remarks filed on 03/31/2026. Although a response cannot be given for every point of each arguments, the main points of the arguments is concerning Magidow. The arguments concerning Magidow that did not teach a real time environment setting wherein the image was taken during or right after a spraying session. The added element of a determine in real time a liquid coverage area is taught by Harmon as rejected above. Harmon teaches in paragraphs 0039, 0051, and 0052 and others, analyzing received images during a spraying operation. Harmon does not explicitly teach the exact words of “saturation;” however, this can be inferred by the disclosed features of size and shape of the droplets on the plants, which Harmon teaches in many paragraphs. Magidow also teaches the element of saturation in paragraph 0027, wherein an image is taken and maximum per-area leaf coverage is determined. This action is a first office action in the filed Request for Continued Examination application.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure:
Varanasi et al. (US PG Pub. No. 2021/0169073) teaches a system for automatically quantifying liquid coverage on plant surfaces, the system comprising: (Abstract: “Systems and methods related to the formation of a reaction product on a surface are generally provided. The systems and methods described herein may allow for collection of the retention of a fluid by a surface in a relatively high amount. Such systems and methods may be useful in various applications including, for example, agriculture. In some embodiments, the systems and methods enhance water retention on hydrophobic surfaces of plants.” Par. 0201: “Retention of agricultural sprays on plant surfaces is an important challenge. Bouncing of sprayed pesticide droplets from leaves is a source of soil and groundwater pollution and pesticide overuse.” Par. 0127: “It thus may be advantageous to increase the efficiency of the pesticide spraying process by increasing the tendency of the pesticide droplets to stick to the plant and to increase the coverage of the surface of the leaves. Several parameters influence the outcome of droplet impacts on surfaces such as the liquid's surface tension and viscosity and the size of the droplets.”) Varanasi does not explicitly teach a processor of a computer device. But does teach image processing of liquid on a surface of a plant: Par. 0200: “Imaging
under UV light was performed after each spray, and image processing using Image was performed to determine the fraction of the surface covered by the liquid.” Varanasi may also teach the element of automatically identify one or more portions of the region of interest corresponding to liquid; (Par. 0101: “As described above, certain embodiments relate to surfaces and surfaces which are at least partially covered by reaction products. References to surfaces herein should be understood to encompass surfaces that are uncoated by reaction products, surfaces that are at least partially coated by a reaction product (where surface is understood to comprise the base surface but not the reaction product), and surfaces that comprise a reaction product coating at least a portion of their area (where the surface is understood to comprise the base surface and the reaction product). References to substrates herein should be understood to correspond to materials that comprise a surface which is exposed to the first species and the second species. References to the base surface herein should be understood to correspond to portions of the surface that are either coated or uncoated by reaction products (i.e., portions of the surface that are not reaction products).”)
Faers et al. (US PG Pub. No. 2022/0212796) teaches an automated system (Par. 0034) or autonomous (Par. 125) that automatically quantifies liquid sprayed/coverage onto crop/plant surfaces (Par. 0059 and 0273 – 0275) and uses image of the environment acquired by a camera. (Par. 0059).
Magidow (PG Pub. No. 20140195948), herein “Magidow,” cited in the previous office action and for claim 34, teaches capturing image data, analyzing the image data, and determine the size and shape of a droplet. (Par. 0004). However, Magidow may not analyze the data in real-time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST..
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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