DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 02/24/2026, Applicant, on 06/18/2026, amended claims; no claims or added. Claims 1-20 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 06/18/2026 have been fully considered, but they are not fully persuasive. The 35 USC § 101 has been overcome. However, the updated 35 USC § 103 rejection of claims 1-20 are applied in light of Applicant's amendments.
Applicant’s arguments with respect to the rejection to the claim of 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
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 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, 8, 10-13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 10614562 (hereinafter “Mannar”) et al., in view of U.S. Patent 12419220 to (hereinafter “Vandike”) et al., in further view of U.S. Patent 10520482 (hereinafter “McPeek”) et al., and in further view of U.S. PGPub 20150022656 (hereinafter “Carr”) et al.
As per claim 1, Mannar teaches a computer-implemented method for managing risks for agricultural systems, the method comprising:
determining, by the one or more processors, a need for a risk mitigating technique based on the analysis and/or the assessment; Mannar 017: “For the system and method disclosed herein, technologies, such as vehicles, signal processing, computer vision, machine learning, actuarial loss models, and advanced analytics may be used to ingest images as they are captured, for example, by UAVs, and to further analyze the images to identify key characteristics of the aforementioned areas, and spatial temporal trends for growth and damage related, for example, to trees, crops, shrubs, plants, cultivations, farm produce, and other such objects generally. In this regard, although examples disclosed herein may be described in the context of trees, forests, plantations, etc., the system and method disclosed herein may be applicable to any of the aforementioned types of areas and objects generally. The spatial temporal trend may be combined with simulation techniques to predict risk of losses that may impact yield. The system and method disclosed herein may provide for quicker estimation of inventory of trees (e.g., number of trees), their growth, identify areas which have low growth, send alerts for areas with no trees or low tree density, and identify possible pest issues for faster intervention.” 035: “The inventory, growth, and risk prediction using image processing system and the method for inventory, growth, and risk prediction using image processing disclosed herein provide a technical solution to technical problems related, for example, to inventory, growth, and risk prediction using image processing for forestry and plantations. The system and method disclosed herein provide the technical solution of an image pre-processor that is executed by at least one hardware processor to receive a plurality of images captured by a vehicle (e.g., a UAV) during movement of the vehicle along a vehicle path, where the plurality of images include a plurality of objects (e.g., trees, crop, etc.), and pre-process the plurality of images for feature extraction from the plurality of images. A feature extractor that is executed by the at least one hardware processor may extract a plurality of features (e.g., tree centers, tree edges, tree crowns, etc.) of the plurality of objects from the plurality of pre-processed images by using a combination of computer vision techniques. An object level parameter generator that is executed by the at least one hardware processor may determine at least one parameter (e.g., tree crown size, tree location, etc.) related to the plurality of objects from the plurality of extracted features. A partition level output generator that is executed by the at least one hardware processor may generate, based on the at least one determined parameter and the plurality of extracted features, a spatial density model to provide a visual indication of density of distribution of the plurality of objects related to a portion (e.g., a particular area) of at least one of the plurality of images, and/or an alert corresponding to the plurality of objects related to the portion of the at least one of the plurality of images. According to examples, the at least one parameter related to the plurality of objects may include at least one location related to the plurality of objects, and a model corrector that is executed by the at least one hardware processor may to utilize information related to a previous image to increase an accuracy of the at least one location related to the plurality of objects.”
transmitting, by the one or more processors, the ASU selection to a drone system, wherein information in the drone system is provided by one or more drones that survey the ASU;032: “The mission controller may receive a work order related to a vehicle mission. According to an example, work orders may be received from various enterprises and cover a variety of applications of vehicles. The mission controller may translate the work order into a mission request. A mission request may identify, for example, an operation for a vehicle, a type of a vehicle to complete the operation, at least one type of sensor to be mounted on the vehicle, a vehicle operation crew, a movement plan, and/or an objective for the mission. For example, a mission request may indicate that a fixed wing vehicle or quadcopter (i.e., types of vehicles) may be equipped with a photo camera to take images of a plantation. After launching the mission, the vehicle may follow the movement plan autonomously (e.g., for an unmanned vehicle), or with varying degrees of remote operator guidance from the mission controller. Sensors mounted onto the vehicle may transmit data in real-time to the mission controller, which may transmit relevant data to the system disclosed herein for further analysis. The transmission of the relevant data may occur either after vehicle landing (e.g., for aerial vehicles), mission completion, or in real-time based on bandwidth availability.”
receiving, by the one or more processors, stress monitorinq data from the drone system, the stress monitorinq data includinq one or more of: hyperspectral sensor data, thermal sensor data, or RGB sensor data;037-049: “The images may be received directly from the vehicle 106, and/or from an image repository 108 that includes previously received images 104… For UAVs, the image repository 108 may be designated as a UAV image repository. The image repository 108 may include a plurality of the images 104, which for UAVs may be designated as UAV images. The training dataset may be generated from true Red-Green-Blue (RGB) color images, NIR (Near Infra-Red) images, from the image repository 108, or from multi-spectral cameras with more channels and layers…074: The partition level output generator 128 may use color segmentation and machine learning to identify changes in chlorophyll levels indicative of possible tree distress for pest issues. In this regard, early identification of such issues may lead to faster intervention and lower loss.”
based on the determined need for the risk mitigating technique, automatically generating or updating, by the one or more processors, one or more monitoring plans associated with the drone system; 030: “According to examples, the system and method disclosed herein may utilize a mission controller to assign and manage a mission upon receipt of a work order. The mission controller may maintain knowledge of a fleet of vehicles, sensors, and crew, as well as information regarding work order status, and mission status. The mission controller may translate the work order into a mission request by assigning vehicles, sensors, and crew to the mission request, identifying a movement plan of the vehicle, and an objective for the mission. Once the mission is launched, the system and method disclosed herein may analyze communication data (e.g., telemetry data) received during the mission, and may generate alarms and/or other information based on the detection of risks. The mission controller may also be used to plan a mission based on alerts identified from previous missions, for example, if a UAV mission image indicates a potential pest issue and/or blank spot. Additional missions may be automatically (e.g., without human intervention) planned to visit the same region over time to confirm if an issue is growing or stabilizing based on corrective actions taken (e.g., pesticide spray, etc.)”
and transmitting, by the one or more processors, the one or more monitorinq plans to the drone system to signal to the one or more drones to survey the ASU selection032: “After launching the mission, the vehicle may follow the movement plan autonomously (e.g., for an unmanned vehicle), or with varying degrees of remote operator guidance from the mission controller. Sensors mounted onto the vehicle may transmit data in real-time to the mission controller, which may transmit relevant data to the system disclosed herein for further analysis. The transmission of the relevant data may occur either after vehicle landing (e.g., for aerial vehicles), mission completion, or in real-time based on bandwidth availability.”
Mannar may not explicitly teach the following. However, Vandike teaches:
receiving, by one or more processors, an agricultural system unit (ASU) selection for an ASU associated with an agroforestry agricultural system from a user interface;Vandike 038: “Where a touch sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 using touch gestures…053: Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218. The operator interface controller 231 is also operable to present the predictive map 264 or predictive control zone map 265 or other information derived from or based on the predictive map 264, predictive control zone map 265, or both to operator 260…0127-143: WMA selector 486 selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Control zone generation system 488 then generates the control zones for the selected WMA or set of WMAs… At block 536, WMA selector 486 selects a WMA or a set of WMAs for which control zones are to be generated on the map under analysis… At block 612, control system 214 receives a sensor signal from geographic position sensor 204. The sensor signal from geographic position sensor 204 can include data that indicates the geographic location 614 of agricultural harvester 100, the speed 616 of agricultural harvester 100, the heading 618 or agricultural harvester 100, or other information 620. At block 622, zone controller 247 selects a regime zone, and, at block 624, zone controller 247 selects a control zone on the map based on the geographic position sensor signal.”
accessing, by the one or more processors, an analysis for a plant corresponding to the ASU selection based on a selection type of the ASU selection;Vandike 0127-143: WMA selector 486 selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Control zone generation system 488 then generates the control zones for the selected WMA or set of WMAs… At block 536, WMA selector 486 selects a WMA or a set of WMAs for which control zones are to be generated on the map under analysis… At block 612, control system 214 receives a sensor signal from geographic position sensor 204. The sensor signal from geographic position sensor 204 can include data that indicates the geographic location 614 of agricultural harvester 100, the speed 616 of agricultural harvester 100, the heading 618 or agricultural harvester 100, or other information 620. At block 622, zone controller 247 selects a regime zone, and, at block 624, zone controller 247 selects a control zone on the map based on the geographic position sensor signal.”
accessing, by the one or more processors, an assessment associated with the plant corresponding to the ASU selection based on the analysis and the selection type;Vandike 0127-143: “WMA selector 486 selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Control zone generation system 488 then generates the control zones for the selected WMA or set of WMAs. For each WMA or set of WMAs, different criteria may be used in identifying control zones… Block 548 indicates an example in which the control zone definition criteria are or include machine performance metrics. Block 550 indicates an example in which the control zone definition criteria are or includes operator preferences. Block 552 indicates an example in which the control zone definition criteria are or include other items as well. Block 549 indicates an example in which the control zone definition criteria are time based, meaning that agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has elapsed since agricultural harvester 100 entered a particular control zone. In some instances, the selected amount of time may be a minimum amount of time… At block 622, zone controller 247 selects a regime zone, and, at block 624, zone controller 247 selects a control zone on the map based on the geographic position sensor signal. At block 626, zone controller 247 selects a WMA or a set of WMAs to be controlled. At block 628, zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings that are obtained for the selected WMA or set of WMAs may come from a variety of different sources. For instance, block 630 shows an example in which one or more of the target settings for the selected WMA or set of WMAs is based on an input from the control zones on the map of the worksite.”
causing, by the one or more processors, results of processing the ASU selection to be indicated in a graphical user interface (GUI) based on the selection type; Vandike 038-053: “operator interface mechanisms 218 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 using touch gestures… controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 for the operator 260. Controller 231 may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. “
Mannar and Vandike are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar with the aforementioned teachings from Vandike with a reasonable expectation of success, by adding steps that allow the software to analyze and update data with the motivation to more efficiently and accurately organize and analyze information [Vandike 0127].
Mannar and Vandike may not explicitly teach the following. However, McPeek teaches:
wherein plants of the agroforestry agricultural system include cocoa trees; McPeek 022: “The present invention is not limited to a particular fruit tree or vine. Plants that can be analyzed by using the systems and methods described herein can include without limitation permanent crop plants, crop trees, forestry and fruit bearing plants. Examples of fruit bearing plants include but are not limited to abiu, acerola, almond, amla (indian gooseberry), apple, apricot, aprium, avocados, bael, bananas, ber (indian plum), blackberries, blood orange, blueberries, breadfruit, calamondin, cantaloupe melon, carambola (starfruit), cashew, the fruit, cherries, chestnut, chocolate, chokecherry, citron, cocoa, coconuts, coffee, corn plant, crabapple, cumquat, currant, custard-apple…”
generating, by the one or more processors, a heat map visualization based on the integrated drone data and the assessment associated with the plant, the heat map visualization configured to reflect a magnitude of a presence or absence of diseased plants within the ASU;
087-090: “In one aspect of the invention, the spectral data may differentiate between trees with diseases such as Citrus Greening, trees in decline, trees that are deficient in nutrients, and trees that are healthy based on the measured reflectance of the tree across certain spectral bands… For example, maps of plants affected by a particular disease, such as for example, Citrus Blight, may be generated as a function of time. The maps may then be viewed together to show how the condition has spread over time across particular regions of a grower's orchard or plot…0107: After determining each tree's health score, a map may be rendered showing each tree in a grove having a color that correlates to the tree's health. FIG. 6a illustrates a two-dimensional bird's eye view of the visualizations, however, in other embodiments of the invention, the color-coded scales may be overlaid onto the three-dimensional point cloud data for a three-dimensional visualization. For example, an interactive map showing several trees colored as red indicates that the trees in the grove are unhealthy. The scale thus provides a way for users to quickly recognize trees that require attention without detailed analysis.”
Mannar, Vandike, and McPeek are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar and Vandike with the aforementioned teachings from McPeek with a reasonable expectation of success, by adding steps that allow the software to utilize cocoa data with the motivation to more efficiently and accurately organize and analyze information [McPeek 022].
Mannar, Vandike, and McPeek may not explicitly teach the following. However, Carr teaches:
generating, by the one or more processors and via an integration service, integrated drone data by integratinq drone data associated with the one or more drones with the stress monitorinq data, the drone data includinq a drone flight protocol;Carr 0017: “ Another object of the present invention is to provide a cloud-based Software as a Service (SaaS) system that allows UAV or manned aircraft pilots to upload and manage the processing of their imagery over the internet…0037-0047: The payload requires a GPS receiver, but otherwise no inertial measurement capabilities or IMU, and is well suited for flight in a manned general aviation aircraft or a UAV… The pilot, after landing the plane or UAV, subjects the collect including photos and flight log (with recorded shutter times and GPS positions of the aircraft during flight) to a multi-step process that begins with local pre-processing and upload to a cloud-based network, followed by a one or two-pass processing in the cloud-based network…0050-0054: at step 220, the flight log file (with recorded shutter times and GPS positions of the aircraft during flight) is input at step 230, to extract the GPS locations for the aircraft 2 and to construct a local reference frame for the aircraft at its location for each photo in which to orient the camera(s) 110, 112. The local reference frame is defined by the aircraft position and velocity vectors, which are respectively approximately parallel to the aircraft yaw and roll axes… upload all of the foregoing TIFF format photos, flight log, public source data and calibration data to the cloud computing network 50. Once uploaded, the collective data is subjected to a one or two-pass image registration process described below.”
Mannar, Vandike, McPeek, and Carr are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar, Vandike, and McPeek with the aforementioned teachings from Carr with a reasonable expectation of success, by adding steps that allow the software to utilize cocoa data with the motivation to more efficiently and accurately organize and analyze information [Carr 0047].
As per claim 2, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition, Mannar teaches:
configuring, by the one or more processors, results of accessing the analysis and the assessment according to a specified format based on at least one of a location of the agroforestry agricultural system or a reporting standard; Mannar 097: “According to another example, at 710, the analysis output generator 132 may generate other KPIs 136 such as a blank spot alert and/or a stocking alarm based on a density analysis. For example, for any blank spots greater than a predetermined blank spot area threshold (e.g., 300 m.sup.2), the analysis output generator 132 may generate a blank spot alert, and provide corresponding visual indicators as shown in FIG. 7 and/or reports related to the blank spot alert. The blank spot alert may be correlated to a time period threshold for such blank spots (e.g., 1 month, where a blank spot alert is generated for a blank spot that is present for greater than the time period threshold). Further, for any areas that include a number of trees that are less than a predetermined tree number threshold (e.g., 10 trees per 100 m.sup.2), the analysis output generator 132 may generate a stocking alarm. Additional alarms may include, for example, alarms illustrated at 712 that include trees per area (e.g., if a number of trees per Hectare are less than or greater than a predetermined threshold that may be used for tree harvesting purposes), wood volume (e.g., if a wood volume for an area is less than or greater than a predetermined threshold that may be used for tree harvesting purposes), yield prior to harvest, and annual growth at different age levels projection (e.g., six months and eighteen months).”
As per claim 3, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition, Mannar teaches:
wherein accessing the assessment includes determining a number of plants within the ASU exhibiting a disease; Mannar 071-076: “The model corrector 124 may improve accuracy of the image analysis, for example, in cases of poor image quality, varying terrain, and difficulty in separating mature tree … the current image may include additional information such as trees lost due to diseases, flooding, etc. …The partition level output generator 128 may leverage the model outputs to estimate the yield from a partition (e.g., inventory for mature trees) and inventory, growth, and risk prediction (e.g., for younger trees risk of falling due to wind, pest risk, etc.) using data related to the number of trees, estimated tree diameter based on crown size, other sensor data including water table level, historical and forecasted weather data (e.g., wind speed, rainfall, etc.). The risk prediction model may learn the relationship between the multitude of variables to risk of each tree falling down (e.g., increased density of trees (higher stocking) may result in thinner trees which are more sensitive to falling down under certain wind conditions. In this regard, the simulation model 142 may take the past yield at harvest, estimated wood volumes from a number of trees at different ages/growth, and the number of fallen trees as a function of f(x1, x2, x3 . . . ), where the ‘x’ variables may represent wind, water table, rainfall, pest and disease, densities, etc.).”
As per claim 8, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition, McPeek teaches:
capturing, by the one or more processors, an image of at least a portion of the ASU of the ASU selection; andreceiving, by the one or more processors, an image analysis for the image, the image analysis including a number of plant parts that are damaged within the portion of the ASU.; McPeek 0073-0078: “he software component may then use this library as a classified data set for identifying correlations between various factors. For example, as described in more detail below, the library may be used to detect the presence of various conditions (e.g., diseases) in a plant given its hyperspectral or multispectral signature. That is, after the software component determines the hyperspectral or multispectral signature of a new or unknown plant, it may compare it against the library of hyperspectral or multispectral signatures. If the new plant's signature matches the signatures of diseased plants for example, then the software component may determine that the new plant has a specific type of disease… In the case of diseases such as “citrus greening”, “blight” or “citrus canker,” specific conditions are manifested in the leaf, trunk and/or fruit that change their spectral signature making them identifiable through machine vision techniques. Some diseases my also be observed by the changes they produce in the morphological characteristics of a plant. Citrus greening, for example, may lead to a less dense canopy compared to healthy trees. This reduction in canopy density may then be observed in the morphological features extracted from the data. Additional details are described, for example, in Lan et al., Applied Engineering in Agriculture Vol. 25 (4): 607-615 and Kumar, Arun, et al. “Citrus greening disease detection using airborne multispectral and hyperspectral imaging.” International Conference on Precision Agriculture. 2010. Each of these references are herein incorporated by reference in their entirety.”
Mannar, Vandike, and McPeek are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar and Vandike with the aforementioned teachings from McPeek with a reasonable expectation of success, by adding steps that allow the software to utilize imaging data with the motivation to more efficiently and accurately organize and analyze information [McPeek 0078].
As per claim 10, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition Mannar teaches:
accessing, by the one or more processors, historical weather information from one or more servers, the historical weather information being for an area including the agroforestry agricultural system; determining, by the one or more processors, the risk mitigating technique includes changing a schedule for supplying of water to at least a portion of the agroforestry agricultural system based on the historical weather information; Mannar 043-076: “the system and method disclosed herein may include optimization capabilities that leverage the simulated yield for an entire partition to identify an optimum harvesting schedule based on a combination of expected increment in yield due to growth, losses, and demand for the wood and/or pulp. The system and method disclosed herein may also leverage external data such as weather history to simulate the effect on losses such as fallen trees etc…076: The partition level output generator 128 may leverage the model outputs to estimate the yield from a partition (e.g., inventory for mature trees) and inventory, growth, and risk prediction (e.g., for younger trees risk of falling due to wind, pest risk, etc.) using data related to the number of trees, estimated tree diameter based on crown size, other sensor data including water table level, historical and forecasted weather data (e.g., wind speed, rainfall, etc.). The risk prediction model may learn the relationship between the multitude of variables to risk of each tree falling down (e.g., increased density of trees (higher stocking) may result in thinner trees which are more sensitive to falling down under certain wind conditions. In this regard, the simulation model 142 may take the past yield at harvest, estimated wood volumes from a number of trees at different ages/growth, and the number of fallen trees as a function of f(x1, x2, x3 . . . ), where the ‘x’ variables may represent wind, water table, rainfall, pest and disease, densities, etc.).”
As per claim 11, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition Mannar teaches:
wherein the results of processing the ASU selection include a prediction for an incidence of at least one disease in at least one group of plants in the agroforestry agricultural system over a defined future period of time, the prediction having been made using a prediction model for the agroforestry agricultural system; Mannar 025-043: “According to examples, the system and method disclosed herein may predict expected future yield over long term (e.g., 6 years) as trees mature, and risk of losses due to various factors such as wind damage, pest, disease, etc… A partition level output generator 128 that is executed by the at least one hardware processor may generate spatial density models included in the models 120, where such spatial density models may be unique to each tree species and age of the trees. The spatial density models may identify areas with low tree density and low tree growth (e.g., based on crown size). The spatial density models may be stored in a SQL database. Further, tree growth from spatial densities (e.g., historical data 140 at different ages) may be combined with external data 138 (e.g., rainfall, wind, water table, pest, and disease) for generating risk prediction models. The risk prediction model may aim to simulate the effects of the external data 138 and the historical data 140 on yield and wood volumes.”
As per claim 12, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition McPeek teaches:
wherein causing the results of processing the ASU selection to be indicated in the GUI includes causing the GUI to include a summary for the ASU, wherein the summary includes a number of plants associated with the ASU that are diseased for a single date or over a range of dates; McPeek 103-111: “ the present invention provides computer implemented systems and methods for performing fruit tree analysis and displaying the results to a user. In some embodiments, computer implemented systems and methods generate a report of the results of the analysis methods that provide information (e.g., fruit yield, tree quality, harvest date predictions, sprayer coordinates) to a user. In some embodiments, the report is provided over the Internet (e.g., on a smart phone, tablet or other wireless communication device) or on a computer monitor. …In one aspect of the invention, the dashboard may integrate the spatial information about a plot to allow users to generate precise counting and statistical reports about the number of trees or vines and their health. The dashboard may further include one or more filters 605-612 for filtering the plants visualized in the user interface or provided in the report. For example, the dashboard may include a search box 605 that allows a user to query the precise number of trees that are healthy in a particular block, column, row, or geographic region. The dashboard may further allow a user to use his or her mouse to select an area (e.g., a rectangular region) on the map, and determine the number of healthy trees inside the region. The dashboard may include a health filter 606 comprising the color-coded key 603 that correlates plant health score to color. Under each health score, the dashboard may show the number of trees or vines with that particular score, and a selectable button for viewing which trees have that particular score. For example, when the user selects the “View” button 607 shown under the Health Score labeled as “5” and colored as green, the map is re-rendered to only show the trees that have a health score of 5, which FIG. 6a indicates is a total of 824 trees. The dashboard may also allow a user to submit a query to determine the health status for a particular tree by clicking on a tree in the rendered map, or submitting the tree's block, column, and row number.”
Claims 14-15 are directed to the system for performing the method of claims 2-3 above. Since Mannar, Vandike, McPeek, and Carr teach the system, the same art and rationale apply.
Claims 4-5, 9, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 10614562 (hereinafter “Mannar”) et al., in view of U.S. Patent 12419220 to (hereinafter “Vandike”) et al., in further view of U.S. Patent 10520482 (hereinafter “McPeek”) et al., and in further view of U.S. PGPub 20150022656 (hereinafter “Carr”) et al. , and in even further view of U.S. PGPub 20210180081 (hereinafter “Van”) et al.
As per claim 4, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 3.
Mannar, Vandike, McPeek, and Carr may not explicitly teach the following. However, Van teaches:
wherein the disease is cocoa swollen shoot disease; Van 071-076: “TABLE-US-00010 TABLE 10 Viral Plant Pathogens Disease Causative Agent Alfamoviruses: Alfalfa mosaic alfamovirus Bromoviridae Alphacryptoviruses: Alfalfa 1 alphacryptovirus, Beet 1 alphacryptovirus, Beet 2 Partitiviridae alphacryptovirus, Beet 3 alphacryptovirus, Carnation 1 alphacryptovirus, Carrot temperate 1 alphacryptovirus, Carrot temperate 3 alphacryptovirus, Carrot temperate 4 alphacryptovirus, Cocksfoot alphacryptovirus, Hop trefoil 1 alphacryptovirus, Hop trefoil 3 alphacryptovirus, Radish yellow edge alphacryptovirus, Ryegrass alphacryptovirus, Spinach temperate alphacryptovirus, Vicia alphacryptovirus, White clover 1 alphacryptovirus, White clover 3 alphacryptovirus Badnaviruses Banana streak badnavirus, Cacao swollen shoot badnavirus…”
Mannar, Vandike, McPeek, Carr and Van are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar, Vandike, McPeek, and Carr with the aforementioned teachings from Van with a reasonable expectation of success, by adding steps that allow the software to utilize cocoa data with the motivation to more efficiently and accurately organize and analyze information [Van, Table 1].
As per claim 5, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
Mannar, Vandike, McPeek, and Carr may not explicitly teach the following. However, Van teaches:
wherein accessing the assessment includes receiving an indication of a test result from a test kit; Van 071-076: “TABLE-US-00010 TABLE 10 Viral Plant Pathogens Disease Causative Agent Alfamoviruses: Alfalfa mosaic alfamovirus Bromoviridae Alphacryptoviruses: Alfalfa 1 alphacryptovirus, Beet 1 alphacryptovirus, Beet 2 Partitiviridae alphacryptovirus, Beet 3 alphacryptovirus, Carnation 1 alphacryptovirus, Carrot temperate 1 alphacryptovirus, Carrot temperate 3 alphacryptovirus, Carrot temperate 4 alphacryptovirus, Cocksfoot alphacryptovirus, Hop trefoil 1 alphacryptovirus, Hop trefoil 3 alphacryptovirus, Radish yellow edge alphacryptovirus, Ryegrass alphacryptovirus, Spinach temperate alphacryptovirus, Vicia alphacryptovirus, White clover 1 alphacryptovirus, White clover 3 alphacryptovirus Badnaviruses Banana streak badnavirus, Cacao swollen shoot badnavirus…”
Mannar, Vandike, McPeek, Carr and Van are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar, Vandike, McPeek, and Carr with the aforementioned teachings from Van with a reasonable expectation of success, by adding steps that allow the software to utilize cocoa data with the motivation to more efficiently and accurately organize and analyze information [Van, Table 1].
As per claim 9, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
In addition Mannar teaches:
determining, by the one or more processors, the risk mitigating technique includes changes to a schedule for analyzing at least one group of plants included in the agroforestry agricultural system; and transmitting, by the one or more processors, a notification to at least one computing device…Mannar 043-076: “he spatial density models may identify areas with low tree density and low tree growth (e.g., based on crown size). The spatial density models may be stored in a SQL database. Further, tree growth from spatial densities (e.g., historical data 140 at different ages) may be combined with external data 138 (e.g., rainfall, wind, water table, pest, and disease) for generating risk prediction models. The risk prediction model may aim to simulate the effects of the external data 138 and the historical data 140 on yield and wood volumes…The partition level output generator 128 may leverage the model outputs to estimate the yield from a partition (e.g., inventory for mature trees) and inventory, growth, and risk prediction (e.g., for younger trees risk of falling due to wind, pest risk, etc.) using data related to the number of trees, estimated tree diameter based on crown size, other sensor data including water table level, historical and forecasted weather data (e.g., wind speed, rainfall, etc.). The risk prediction model may learn the relationship between the multitude of variables to risk of each tree falling down (e.g., increased density of trees (higher stocking) may result in thinner trees which are more sensitive to falling down under certain wind conditions. In this regard, the simulation model 142 may take the past yield at harvest, estimated wood volumes from a number of trees at different ages/growth, and the number of fallen trees as a function of f(x1, x2, x3 . . . ), where the ‘x’ variables may represent wind, water table, rainfall, pest and disease, densities, etc.)…”
Mannar, Vandike, McPeek, and Carr may not explicitly teach the following. However, Van teaches:
wherein the notification includes the schedule with the changes; Van 0690: “The PMP composition can be formulated for administration or administered by any suitable method, including, for example, intravenously, intramuscularly, subcutaneously, intradermally, percutaneously, intraarterially, intraperitoneally, intralesionally, intracranially, intraarticularly, intraprostatically, intrapleurally, intratracheally, intrathecally, intranasally, intravaginally, intrarectally, topically, intratumorally, peritoneally, subconjunctivally, intravesicularly, mucosally, intrapericardially, intraumbilically, intraocularly, intraorbitally, orally, topically, transdermally, intravitreally (e.g., by intravitreal injection), by eye drop, by inhalation, by injection, by implantation, by infusion, by continuous infusion, by localized perfusion bathing target cells directly, by catheter, by lavage, in cremes, or in lipid compositions. The compositions utilized in the methods described herein can also be administered systemically or locally. The method of administration can vary depending on various factors (e.g., the compound or composition being administered and the severity of the condition, disease, or disorder being treated). In some instances, PMP composition is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intraventricularly, or intranasally. Dosing can be by any suitable route, e.g., by injections, such as intravenous or subcutaneous injections, depending in part on whether the administration is brief or chronic. Various dosing schedules including but not limited to single or multiple administrations over various time-points, bolus administration, and pulse infusion are contemplated herein.”
Mannar, Vandike, McPeek, Carr and Van are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar, Vandike, McPeek, and Carr with the aforementioned teachings from Van with a reasonable expectation of success, by adding steps that allow the software to utilize scheduling data with the motivation to more efficiently and accurately organize and analyze information [Van 0690].
Claims 16 and 19 are directed to the system and CRM for performing the method of claim 4 above. Since Mannar, Vandike, McPeek, Carr, and Van teach the system and CRM, the same art and rationale apply.
Claims 6-7, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 10614562 (hereinafter “Mannar”) et al., in view of U.S. Patent 12419220 to (hereinafter “Vandike”) et al., in further view of U.S. Patent 10520482 (hereinafter “McPeek”) et al., and in further view of U.S. PGPub 20150022656 (hereinafter “Carr”) et al., and in even further view of U.S. PGPub 20180121726 (hereinafter “Redden”) et al.
As per claim 6, Mannar, Vandike, McPeek, and Carr teach all the limitations of claim 1.
Mannar, Vandike, McPeek, and Carr may not explicitly teach the following. However, Redden teaches:
whereinvisualization includes generating Redden 0014-0042: “The aggregated plant presence values 106 can be presented as a heat map of plant presence values showing the relative plant density of the field. Accordingly, gap identification 108 is performed to identify candidate gaps between plots by identifying areas of the field associated with low to zero plant presence (e.g., below a plant presence value threshold) in the aggregated plant presence data… Plant presence heat map 500 represents plant density along seed line 302a and the different shades of plant presence heat map 500 represent different plant presence values. Plant presence values 502 may correspond to individual plants, multiple plants, a predefined length (e.g., 2 feet, 1 meter, etc.) along a seed line, and so forth. In this example, dark plant presence values correspond to areas of relatively high plant presence or areas of robust plant growth and light values correspond to areas of low plant presence. At least some of the areas of low plant presence are gaps.”
Mannar, Vandike, McPeek, Carr, and Redden are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar, Vandike, McPeek, and Carr with the aforementioned teachings from Redden with a reasonable expectation of success, by adding steps that allow the software to utilize map data with the motivation to more efficiently and accurately organize and analyze information [Redden 0042].
As per claim 7, Mannar, Vandike, McPeek, Carr, and Redden teach all the limitations of claim 6.
Mannar, Vandike, McPeek, and Carr may not explicitly teach the following. However, Redden teaches:
wherein the density indicator is configured to indicate a number of plants included in the ASU; Redden 0032-0055: “ FIGS. 4A-4C show example instances of field data 102 collected along a seed line 302, in one embodiment. In this example, sensor 210 is a camera and each of the instances of field data 102 is an image (400a, 400b, 400c). Accordingly, data processing module 232 analyzes each image (400a, 400b, 400c) to determine the plant presence value 104 for each image (400a, 400b, 400c). In one embodiment, analyzing each instance of field data 102 includes analyzing pixels of images (400a, 400b, 400c) to determine the plant presence value 104 for each of these instances of field data. In one example, the plant presence value 104 for each of these instances of field data 102 is based on the number of green pixels in each of images (400a, 400b, 400c). Additionally, the number of pixels can be normalized (e.g., based on the average, median, max, or other measurement taken across the field) for a particular field (to enable the relative comparison among the different plots)... total number of seed lines and/or plots, stage of growth of plants in respective plots (since some plots may have been planted before others), type of plant in each plot (since different plants have different growth rates and sizes), how many plots are planted at a particular time (similar to stage of growth), average plant separation, and other field parameters and plant characteristics. Field characteristic data 110 can also determined heuristically using various statistical tools and/or methods. For example, Hough transform can used to determine the orientation of the seed lines, which is used to determine the orientation of field, among other statistical methods.”
Mannar, Vandike, McPeek, Carr, and Redden are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mannar, Vandike, McPeek, and Carr with the aforementioned teachings from Redden with a reasonable expectation of success, by adding steps that allow the software to utilize map data with the motivation to more efficiently and accurately organize and analyze information [Redden 0052].
Claims 17 and 20 are directed to the system and CRM for performing the method of claim 6 above. Since Mannar, Vandike, McPeek, Carr, and Redden teach the system and CRM, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Darnell; Lorne. SYSTEM AND METHOD FOR MONITORING LOGISTICAL LOCATIONS AND TRANSIT ENTITIES USING A CANONICAL MODEL, .U.S. PGPub 20200202473 The subject matter described herein relates, in general, to systems and methods for monitoring and/or managing a supply chain.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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