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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/21/2024 is in compliance with the provisions of 37 CFR 1.97 and have been entered into the record. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-12), computer program product (claims 18-20), and system (claims 13-17) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Mental process and Certain methods of organizing human activity), as set forth in exemplary claim 1, are: receiving… an agricultural system unit (ASU) selection for an ASU associated with an agroforestry agricultural system; accessing…an analysis for a plant corresponding to the ASU selection based on a selection type of the ASU selection; accessing… an assessment associated with the plant corresponding to the ASU selection based on the analysis and the selection type; determining… a need for a risk mitigating technique based on the analysis and/or the assessment;… wherein plants of the agroforestry agricultural system include cocoa trees. Independent claims 13 and 18 recite the CRM and system for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two of the 2019 PEG, the judicial exception is not integrated into a practical application. The additional elements are directed to by one or more processors … and 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; A computer system for managing risks for agricultural systems, the computer system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations comprising…; A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations for managing risks for agricultural systems… (as recited in claims 1, 13, and 18). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: by one or more processors … and 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; A computer system for managing risks for agricultural systems, the computer system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations comprising…; A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations for managing risks for agricultural systems… (as recited in claims 1, 13, and 18) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0047]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-12, 14-17, and 19-20) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-12 “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; wherein accessing the assessment includes determining a number of plants within the ASU exhibiting a disease; wherein the disease is cocoa swollen shoot disease; wherein accessing the assessment includes receiving an indication of a test result from a test kit; wherein accessing the assessment includes generating a heat map including a density indicator; wherein the density indicator is configured to indicate a number of plants included in the ASU; capturing, by the one or more processors, an image of at least a portion of the ASU of the ASU selection; and receiving, 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; 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, wherein the notification includes the schedule with the changes; 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; 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; 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”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (14-17, and 19-20) recite the CRM and system for performing the method of claims 2-13. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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.
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.”
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;Vandike 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…”
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].
As per claim 2, Mannar, Vandike, and McPeek 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, and McPeek 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, and McPeek 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, and McPeek 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, and McPeek 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, and McPeek 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, and McPeek 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 20210180081 (hereinafter “Van”) et al.
As per claim 4, Mannar, Vandike, and McPeek teach all the limitations of claim 3.
Mannar, Vandike, and McPeek 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, 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, and McPeek 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, and McPeek teach all the limitations of claim 1.
Mannar, Vandike, and McPeek 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, 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, and McPeek 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, and McPeek 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, and McPeek 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, 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, and McPeek 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, 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 20180121726 (hereinafter “Redden”) et al.
As per claim 6, Mannar, Vandike, and McPeek teach all the limitations of claim 1.
Mannar, Vandike, and McPeek may not explicitly teach the following. However, Redden teaches:
wherein accessing the assessment includes generating a heat map including a density indicator; 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, 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, and McPeek 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, and Redden teach all the limitations of claim 6.
Mannar, Vandike, and McPeek 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, 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, and McPeek 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, 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.
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