DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 6/11/2025, Applicant, on 9/10/2025, amended claim 1. Claims 1, 3-5, 7-18, and 20 are pending in this application and have been rejected below.
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 .
Response to Amendment
Applicant’s amendments are acknowledged.
Revised 35 USC 101 rejections of claims 1, 3-5, 7-18, and 20 are applied in light of Applicant’s amendments and explanations.
New 35 USC § 103 rejections of claims 1, 3-5, 7-18, and 20 are applied in light of Applicant’s amendments and explanations.
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, 3-5, 7-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for assessing one or more agricultural trials in a field. Examiner formulates an abstract idea analysis, following the framework described in the MPEP as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 1-12) and "system" (claims 13-20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
identifying, … two regions in a field for assessment, at least one of the two regions associated with an agricultural trial in the field
generating … an aggregate fitness metric for the two regions, the aggregate fitness metric indicative of a similarity between the two regions in the field the two regions include a first control region of the field and a second test region of the field.
wherein generating the aggregate fitness metric includes: computing, … a fitness metric for each location of multiple locations in the first region, based on a physical distance between the location in the first region and each location in the second region;
aggregating… each of the computed fitness metrics
determining… whether the aggregate fitness metric satisfies a defined threshold
automatically discarding, by the agricultural computer system, the trial in response to the determination that the aggregate fitness metric fails to satisfy the defined threshold
Independent claims 13 and 17 recite substantially similar claim language.
Dependent claims 3-5, 7-12, 14-16, 18, and 20 recite the same or similar abstract idea(s) as independent claims 1, 13, and 17 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1, 3-5, 7-18, and 20 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- 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)" as the limitations identified above are directed to assessing one or more agricultural trials in a field and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation of one or more agricultural trials in a field, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-20 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" further comprising generating a plot of the fitness metric for the field per location of a first region of the two regions and/or a second regions of the two regions" (claim 11) " wherein the plot includes one or more visual distinctions indicative of the fitness metric, the visual distinction including one or more of color, shading, hatching, or shape" (claim 12), however the aforementioned elements directed to the receiving of user input/selection of data to view via a dashboard and displaying corresponding data via the dashboard merely amount to generic GUI elements of a general purpose computer used to "apply" the abstract idea (MPEP 2106.05(f)) and/or is merely an attempt at limiting the abstract idea of assessing one or more agricultural trials in a field to a particular field of use/technological environment of a GUI dashboard (MPEP 2106.05(h)) and therefore the GUI dashboard input and display of data fails to integrate the abstract idea into a practical application;
" A computer-implemented method for use… by an agricultural computer system,” and “A non-transitory computer-readable storage medium comprising executable instructions for assessing a trial in a field, which when executed by at least one processor, cause the at least one processor to,” (claims 1, 13, and 17) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of a computer-implemented method for use in assessing one or more agricultural trials in a field is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1, 3-5, 7-18, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of assessing one or more agricultural trials in a field, as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to assessing one or more agricultural trials in a field.
Claims 1, 3-5, 7-18, and 20 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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, 3-5, 7-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2019/0057461 to Ruff et al. (hereafter referred to as Ruff) in view of U.S. Patent Application Publication Number 2022/0312661 to Ascedo et al. (hereafter referred to as Ascedo) and in further view of U.S. Patent Application Publication Number 2018/0260504 to Hu (hereafter referred to as Hu).
As per claim 1, Ruff teaches:
A computer-implemented method for use in assessing one or more agricultural trials in a field, the method comprising (Paragraph Number [0046] teaches systems and methods for implementing trials in one or more fields are described herein. In an embodiment, an agricultural intelligence computer system is communicatively coupled to a plurality of field manager computing devices. The agricultural intelligence computer system receives field data for a plurality of agricultural fields and uses the field data to identify fields which would benefit from performing a particular trial. The agricultural intelligence computer system sends a trial participation request to a field manager computing device associated with an identified field which guarantees a particular benefit for participating in the trial. If the field manager computing device agrees to participate in the trial, the agricultural intelligence computer system identifies locations on the identified field for implementing the trial and sends the data to the field manager computing device. The agricultural intelligence computer system may track practices on the identified field to determine whether the identified field is in compliance with the trial. The agricultural intelligence computer system may additionally receive data identifying results of the trial and use the data to compute one or more benefits of the trial).
identifying, by an agricultural computer system, two regions in a field for assessment, at least one of the two regions associated with an agricultural trial in the field (Paragraph Number [0128] teaches a trial refers to performing one or more different agricultural activities in a portion of an agricultural field in order to identify a benefit or detriment of performing the one or more different agricultural activities. As an example, a subfield area may be selected in an agricultural field to implement a fungicide trial. Within the subfield area, the crops may receive an application of fungicide while the rest of the field and/or a different subfield area on the field does not receive an application of fungicide. Alternatively, the rest of the field may receive the application of fungicide while the crops within the subfield area do not. The subfield areas of the field where the one or more different agricultural activities are performed are referred to herein as test locations. In some embodiments, subfield areas that do not include the different agricultural activities can also be assigned and referred to as test locations).
generating, by the agricultural computer system, an aggregate fitness metric for the two regions, the aggregate fitness metric indicative of a similarity between the two regions in the field (Paragraph Number [0160] teaches the agricultural intelligence computing system models a benefit to a field of implementing an experimental trial. For example, the agricultural intelligence computing system may identify one or more fields for performing a fungicide application trial. The agricultural intelligence computing system may identify one or more fields which have been damaged by fungus in the past and/or are likely to be damaged by fungus in the future. The agricultural intelligence computing system may additionally determine that a yield of the field and/or total profit for the field would result or be benefited by application of a particular fungicide. The agricultural intelligence computing system may additionally determine that a yield and/or profit benefit for the field by application of a particular fungicide would likely be detectable based on the size of the yield and/or profit benefit, the variability of the yield and/or profit benefit across the field, and/or the size of the field and the size of the trial or test regions. Based on the determinations, the agricultural intelligence computing system may identify the one or more fields as good candidates for the fungicide application trial).
the two regions include a first control region of the field and a second test region of the field (Paragraph Number [0128] teaches a trial refers to performing one or more different agricultural activities in a portion of an agricultural field in order to identify a benefit or detriment of performing the one or more different agricultural activities. As an example, a subfield area may be selected in an agricultural field i to implement a fungicide trial. Within the subfield area, the crops may receive an application of fungicide while the rest of the field and/or a different subfield area on the field does not receive an application of fungicide. Alternatively, the rest of the field may receive the application of fungicide while the crops within the subfield area do not. The subfield areas of the field where the one or more different agricultural activities are performed are referred to herein as test locations. In some embodiments, subfield areas that do not include the different agricultural activities can also be assigned and referred to as test locations).
determining, by the agricultural computer system, whether the aggregate fitness metric satisfies a defined threshold (Paragraph Number [0138] teaches based on the application data, it is determined whether the one or more target agricultural fields are in compliance with the trial. For example, the agricultural intelligence computing system may determine whether a test location of an appropriate size has been implemented in an appropriate position and with the appropriate planting, product, and/or management rules. If the one or more target agricultural fields are not in compliance with the trial, the agricultural intelligence computing system may determine a manner of updating the trial to allow the field manager a chance to be in compliance with the trial. For example, if the field manager planted an incorrect population rate in a location selected for the trial, the agricultural intelligence computing system may identify a new location for implementing part or all of the trial and send data identifying the new location to the field manager computing device).
automatically discarding, by the agricultural computer system, the trial in response to the determination that the aggregate fitness metric fails to satisfy the defined threshold. (Paragraph Number [0138] teaches based on the application data, it is determined whether the one or more target agricultural fields are in compliance with the trial. For example, the agricultural intelligence computing system may determine whether a test location of an appropriate size has been implemented in an appropriate position and with the appropriate planting, product, and/or management rules. If the one or more target agricultural fields are not in compliance with the trial, the agricultural intelligence computing system may determine a manner of updating the trial to allow the field manager a chance to be in compliance with the trial. For example, if the field manager planted an incorrect population rate in a location selected for the trial, the agricultural intelligence computing system may identify a new location for implementing part or all of the trial and send data identifying the new location to the field manager computing device).
Ruff teaches assessing one or more agricultural trials in a field but does not explicitly teach a fitness metric for each location of multiple locations in the first region as described by the following citations from Ascedo:
wherein generating the aggregate fitness metric includes: computing, by the agricultural computer system, a fitness metric for each location of multiple locations in the first region (Paragraph Number [0168] teaches an impact parameter characteristic of resilience can be derived from the scaled dissimilarity (distance) between the network properties (e.g., 16S network properties, ITS network properties) of treated and control samples in a given location, as a measure of the effect of a given input or practice (e.g., treatment, management practice, product, etc.) on the bacterial and fungal network properties of the soil from one location. A linear regression model can be used to model the network properties, using location and timepoint only. The residuals of these models are then projected onto a 10-dimensional space using principal component analysis (PCoA), retaining 83% of variation in the residuals. In more detail, a method for determining impact parameters can include: modelling network properties from samples, using desired contextual parameters (e.g., location, time point), with collection of residuals; running a PCoA on these residuals and generate a multi-dimensional location for each sample; and calculating the distance between the treatment and control centroids. For each location, the impact parameter is the weighted distance between treated and control samples of that location. Impact parameter values are thus distances (i.e. non-negative), and an impact parameter value of zero means that the treatment had negligible effect on the network properties of the soil microbiome. Furthermore, the magnitude of the impact parameter correlates with the magnitude of the effect of the particular input/practice).
aggregating, by the agricultural computer system, each of the computed fitness metrics (Paragraph Number [0127] teaches the set of network properties can include properties derived from co-exclusion networks and co-occurrence networks, such as transitivity, assortativity, and/or other properties/parameters described in applications incorporated by reference. Network properties can be determined for different types of organism communities (e.g., bacterial communities, fungal communities, etc.) independently of each other or in an aggregated manner. Paragraph Number [0158] teaches transforming a first grouping of positive pairs of organisms and a second grouping of negative pairs of organisms (i.e., organisms represented in the sample dataset, related to co-inclusion and co-exclusion, respectively) into one or more aggregate matrices representing co-inclusion parameters (e.g., the whole number of potential associations between all the taxa in the pool, associations that are described as system relevant interdependencies including: biotic interactions, environmental affinities, dispersal restrictions, etc.) and co-exclusion parameters (e.g., for various taxonomic units associated with metacommunities or other communities represented in the set of samples)).
Both Ruff and Ascedo are directed to field monitoring and analysis. Ruff discloses assessing one or more agricultural trials in a field. Ascedo improves upon Ruff by disclosing a fitness metric for each location of multiple locations in the first region. One of ordinary skill in the art would be motivated to further include a fitness metric for each location of multiple locations in the first region, to efficiently determine how the various trials change based on soil variation that vary based on location. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of assessing one or more agricultural trials in a field in Ruff to further utilize a fitness metric for each location of multiple locations in the first region as disclosed in Ascedo, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Ruff teaches assessing one or more agricultural trials in a field but does not explicitly teach a fitness metric based on a distance between the location in the first region and each location in the second region as described by the following citations from Hu:
based on a distance between the physical location in the first region and each location in the second region (Paragraph Number [0124] teaches when more than one candidate sampling location minimizes the distance metric for a management zone, the server can then be programmed to apply additional criteria to choose one from the more than one candidate sampling location. Example additional criteria or constraints include having a minimum distance to the boundary of the management zone or having an agricultural characteristic value in a specific range. These additional criteria or constraints can also be applied earlier to filter candidate sampling locations upfront. The server can also be configured to reevaluate the distance metric with adjusted weights for the more than one candidate sampling location. Paragraph Number [0125] teaches the server is configured to transmit data regarding the selected sampling locations to a display device or a remote client device. For each selected sampling location, the data can include the geographic coordinate (e.g., longitude and latitude), index of the enclosing management zone, distance from the boundary of the enclosing management zone, the corresponding set of agricultural characteristic values, or the corresponding value of the distance metric. Paragraph Number [0135] teaches the server is programmed to then select one of the candidate sampling locations in the management zone based on the normalized and weighted values. The server is configured to first identify those candidate sampling locations that minimize a distance metric measuring the distance between the values for the agricultural characteristics at these candidate sampling locations and the model values for the agricultural characteristics. The distance metric can include a sum of weighted absolute differences or squared differences over all the agricultural characteristics. The distance metric can also comprise another distance function known to someone skilled in the art. Paragraph Number [0136] teaches when multiple candidate sampling locations minimize the distance metric, the server can be configured to report all these candidate sampling locations. Alternatively, the server is configured to then select one of the multiple sampling locations having the smallest distance to the boundary of the management zone. Other criteria or constraints can be used to narrow down the list of candidate sampling locations, such as having a smallest distance to one specific side of the management zone or having an agricultural characteristic value in a particular range).
Both the combination of Ruff and Ascedo and Hu are directed to field monitoring and analysis. The combination of Ruff and Ascedo discloses assessing one or more agricultural trials in a field. Hu improves upon the combination of Ruff and Ascedo by disclosing a fitness metric based on a distance between the location in the first region and each location in the second region. One of ordinary skill in the art would be motivated to further include a fitness metric based on a distance between the location in the first region and each location in the second region, to efficiently determine how the various trials change based on soil variation that vary based on distance from a fixed observation point. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of assessing one or more agricultural trials in a field in the combination of Ruff and Ascedo to further utilize a fitness metric based on a distance between the location in the first region and each location in the second region as disclosed in Hu, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 13, Ruff teaches:
A system for use in assessing trials in fields, the system comprising an agricultural computer system, which is configured, by executable instructions, to: (Paragraph Number [0046] teaches systems and methods for implementing trials in one or more fields are described herein. In an embodiment, an agricultural intelligence computer system is communicatively coupled to a plurality of field manager computing devices. The agricultural intelligence computer system receives field data for a plurality of agricultural fields and uses the field data to identify fields which would benefit from performing a particular trial. The agricultural intelligence computer system sends a trial participation request to a field manager computing device associated with an identified field which guarantees a particular benefit for participating in the trial. If the field manager computing device agrees to participate in the trial, the agricultural intelligence computer system identifies locations on the identified field for implementing the trial and sends the data to the field manager computing device. The agricultural intelligence computer system may track practices on the identified field to determine whether the identified field is in compliance with the trial. The agricultural intelligence computer system may additionally receive data identifying results of the trial and use the data to compute one or more benefits of the trial).
The remainder of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 17, Ruff teaches:
A non-transitory computer-readable storage medium comprising executable instructions for assessing a trial in a field, which when executed by at least one processor, cause the at least one processor to (Paragraph Number [0116] teaches computer system 400 also includes a main memory 406, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 402 for storing information and instructions to be executed by processor 404. Main memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400 into a special-purpose machine that is customized to perform the operations specified in the instructions.
The remainder of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 3, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claim 1.
In addition, Ruff teaches:
wherein the first and second regions are part of the agricultural trial. (Paragraph Number [0128] teaches a trial refers to performing one or more different agricultural activities in a portion of an agricultural field in order to identify a benefit or detriment of performing the one or more different agricultural activities. As an example, a subfield area may be selected in an agricultural field to implement a fungicide trial. Within the subfield area, the crops may receive an application of fungicide while the rest of the field and/or a different subfield area on the field does not receive an application of fungicide. Alternatively, the rest of the field may receive the application of fungicide while the crops within the subfield area do not. The subfield areas of the field where the one or more different agricultural activities are performed are referred to herein as test locations. In some embodiments, subfield areas that do not include the different agricultural activities can also be assigned and referred to as test locations).
As per claim 4, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claim 1.
In addition, Ruff teaches:
wherein the first region of the two regions includes at least one feature, and where the second region of the two regions excludes the at least one feature (Paragraph Number [0129] teaches trials may be performed for testing the efficacy of new products, different management practices, different crops, or any combination thereof. For example, if a field usually does not receive fungicide, a trial may be designed wherein crops within a selected portion of the field receive fungicide at one or more times during the development of the crop. As another example, if a field usually is conventionally tilled, a trial may be designed wherein a selected portion of the field is not tilled. Thus, trials may be implemented for determining whether to follow management practice recommendations instead of being constrained to testing the efficacy of a particular product. Additionally, or alternatively, trials may be designed to compare two different types of products, planting rates, equipment, and/or other management practices).
As per claim 5, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claim 1.
In addition, Ruff teaches:
wherein the at least one feature includes one or more of seed type, field treatment, irrigation application, and soil type (Paragraph Number [0129] teaches trials may be performed for testing the efficacy of new products, different management practices, different crops, or any combination thereof. For example, if a field usually does not receive fungicide, a trial may be designed wherein crops within a selected portion of the field receive fungicide at one or more times during the development of the crop. As another example, if a field usually is conventionally tilled, a trial may be designed wherein a selected portion of the field is not tilled. Thus, trials may be implemented for determining whether to follow management practice recommendations instead of being constrained to testing the efficacy of a particular product. Additionally, or alternatively, trials may be designed to compare two different types of products, planting rates, equipment, and/or other management practices).
As per claims 7 and 20, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claims 1, and 17 respectively.
In addition, Ruff teaches:
wherein computing the fitness metric for each location of the multiple locations in the first region includes determining the fitness metric consistent with:
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wherein F represents the fitness metric, S and U represent the first and second regions, p and q represent points within the first and second regions, w( d) represents a weighting function with respect to points p and q, and d represents a Euclidean distance between the points p and q. (Paragraph Number [0160] teaches the agricultural intelligence computing system models a benefit to a field of implementing an experimental trial. For example, the agricultural intelligence computing system may identify one or more fields for performing a fungicide application trial. The agricultural intelligence computing system may identify one or more fields which have been damaged by fungus in the past and/or are likely to be damaged by fungus in the future. The agricultural intelligence computing system may additionally determine that a yield of the field and/or total profit for the field would result or be benefited by application of a particular fungicide. The agricultural intelligence computing system may additionally determine that a yield and/or profit benefit for the field by application of a particular fungicide would likely be detectable based on the size of the yield and/or profit benefit, the variability of the yield and/or profit benefit across the field, and/or the size of the field and the size of the trial or test regions. Based on the determinations, the agricultural intelligence computing system may identify the one or more fields as good candidates for the fungicide application trial. (See also Paragraph Numbers [0220]-[0222] which further defines the relationship between regions and specific locations within the regions) Paragraph Number [0226] teaches the prioritizations based on minimizing the effect on yield or maximizing the benefits of performing the trials may be implemented along with other constraints. For example, the agricultural intelligence computing system may initially attempt to place at least two testing locations in each management zone. The agricultural intelligence computing system may then pseudo-randomly select additional testing locations while assigning higher weights to locations with low yields or high responsiveness. As another example, the agricultural intelligence computing system may attempt to place testing locations in a minimum of a high responsiveness and high yield location, a high responsiveness and low yield location, a low responsiveness and high yield location, and a low responsiveness and low yield location).
As per claim 8, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claims 1 and 7.
In addition, Ruff teaches:
further comprising storing, by the agricultural computer system, the aggregate fitness metric and/or the computed fitness metric per location in one or more data structures (Paragraph Number [0160] teaches the agricultural intelligence computing system models a benefit to a field of implementing an experimental trial. For example, the agricultural intelligence computing system may identify one or more fields for performing a fungicide application trial. The agricultural intelligence computing system may identify one or more fields which have been damaged by fungus in the past and/or are likely to be damaged by fungus in the future. The agricultural intelligence computing system may additionally determine that a yield of the field and/or total profit for the field would result or be benefited by application of a particular fungicide. The agricultural intelligence computing system may additionally determine that a yield and/or profit benefit for the field by application of a particular fungicide would likely be detectable based on the size of the yield and/or profit benefit, the variability of the yield and/or profit benefit across the field, and/or the size of the field and the size of the trial or test regions. Based on the determinations, the agricultural intelligence computing system may identify the one or more fields as good candidates for the fungicide application trial).
wherein the one or more data structures are included in the agricultural computer system or a separate computer system (Paragraph Number [0046] teaches systems and methods for implementing trials in one or more fields are described herein. In an embodiment, an agricultural intelligence computer system is communicatively coupled to a plurality of field manager computing devices. The agricultural intelligence computer system receives field data for a plurality of agricultural fields and uses the field data to identify fields which would benefit from performing a particular trial. The agricultural intelligence computer system sends a trial participation request to a field manager computing device associated with an identified field which guarantees a particular benefit for participating in the trial. If the field manager computing device agrees to participate in the trial, the agricultural intelligence computer system identifies locations on the identified field for implementing the trial and sends the data to the field manager computing device. The agricultural intelligence computer system may track practices on the identified field to determine whether the identified field is in compliance with the trial. The agricultural intelligence computer system may additionally receive data identifying results of the trial and use the data to compute one or more benefits of the trial).
As per claim 9, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claim 1.
In addition, Ruff teaches:
further comprising defining a weighting function indicative of one or more characteristics of the field (Paragraph Number [0168] teaches the agricultural intelligence computing system may be programmed or configured to consider these factors individually and/or in combination. For example, the agricultural intelligence computing system may be programmed to identify fields with a highest percentage of the field dedicated to a new product. Additionally, or alternatively, the agricultural intelligence computing system may be programmed or configured to select fields that include more than a threshold number of experiments and are associated with one or more other risky activities. These factors may be weighted such that certain factors are considered more heavily than others).
wherein the aggregate fitness metric is based on the weighting function (Paragraph Number [0226] teaches the prioritizations based on minimizing the effect on yield or maximizing the benefits of performing the trials may be implemented along with other constraints. For example, the agricultural intelligence computing system may initially attempt to place at least two testing locations in each management zone. The agricultural intelligence computing system may then pseudo-randomly select additional testing locations while assigning higher weights to locations with low yields or high responsiveness).
As per claim 10, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claims 1 and 9.
In addition, Ruff teaches:
wherein the weighting function is defined as: EQUATION and/or wherein the weighting function is defined based on elevation and/or soil composition/type disparity across the field (Paragraph Number [0226] teaches the prioritizations based on minimizing the effect on yield or maximizing the benefits of performing the trials may be implemented along with other constraints. For example, the agricultural intelligence computing system may initially attempt to place at least two testing locations in each management zone. The agricultural intelligence computing system may then pseudo-randomly select additional testing locations while assigning higher weights to locations with low yields or high responsiveness. As another example, the agricultural intelligence computing system may attempt to place testing locations in a minimum of a high responsiveness and high yield location, a high responsiveness and low yield location, a low responsiveness and high yield location, and a low responsiveness and low yield location. (Examiner asserts that this teaches at least the alternative of defining the weighting function based on type disparity across a field)).
As per claim 11, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claim 1.
In addition, Ruff teaches:
further comprising generating a plot of the fitness metric for the field per location of a first region of the two regions and/or a second regions of the two regions (Paragraph Number [0061] teaches when field data 106 is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system, the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information. In an example embodiment, the user may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, the user 102 may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system 130) and drawing boundaries of the field over the map. Such CLU selection or map drawings represent geographic identifiers. Paragraph Number [0062] teaches the agricultural intelligence computer system 130 is programmed to generate and cause displaying a graphical user interface comprising a data manager for data input. After one or more fields have been identified using the methods described above, the data manager may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices. The data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs).
As per claim 12, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claims 1 and 11.
In addition, Ruff teaches:
wherein the plot includes one or more visual distinctions indicative of the fitness metric, the visual distinction including one or more of color, shading, hatching, or shape (Paragraph Number [0061] teaches when field data 106 is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system, the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information. In an example embodiment, the user may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, the user 102 may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system 130) and drawing boundaries of the field over the map. Such CLU selection or map drawings represent geographic identifiers. Paragraph Number [0062] teaches the agricultural intelligence computer system 130 is programmed to generate and cause displaying a graphical user interface comprising a data manager for data input. After one or more fields have been identified using the methods described above, the data manager may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices. The data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs).
As per claim 14, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claim 13.
In addition, Ruff teaches:
wherein the agricultural computer system is further configured, by executable instructions, to: filter out ones of the fitness metrics below a defined threshold (Paragraph Number [0182] teaches the system 130 is programmed to manage the list of grower fields at a granular level. The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments).
dilate a boundary defined by remaining ones of the plurality of locations, by a distance d (Paragraph Number [0182] teaches the system 130 is programmed to manage the list of grower fields at a granular level. The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments).
erode the dilated boundary by a multiple of the distance d; and then, dilate the eroded boundary by the distance d (Paragraph Number [0220] teaches within the zones, the agricultural intelligence computing system may identify possible locations for testing locations. The size and shape of testing locations may be determined based on variability in a particular field or zone. The historic yield data is broken into uniform grids of potential testing locations different sizes; the total testing area required, including buffer areas around testing locations, is calculated for each testing location size given an acceptable statistical significance for the answer; and the optimal configuration is the one that minimizes the total testing area. The optimal size, shape, and number of testing locations can also be determined from modeled yield variability data from historic images, or modeled yield variability data based on predictors to a model trained on historic yield variability data).
and define the dilated boundary as a perimeter of the agricultural trial in the field (Paragraph Number [0182] teaches the system 130 is programmed to manage the list of grower fields at a granular level. The system 130 is therefore configured to identify certain boundaries or other problematic areas of the fields that will not participate in prescribed experiments, and further determine specific strips or squares, with buffer areas in between, that will participate in prescribed experiments).
As per claim 15, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claims 13 and 14.
In addition, Ruff teaches:
wherein the agricultural computer system is further configured, by executable instructions, to fit the fitness metrics to a defined scale, prior to filtering the ones of the fitness metrics (Paragraph Number [0110] teaches the agricultural intelligence computer system 130 is configured or programmed to implement field dataset evaluation. In an embodiment, a specific field dataset is evaluated by creating an agronomic model and using specific quality thresholds for the created agronomic model. Agronomic models may be compared and/or validated using one or more comparison techniques, such as, but not limited to, root mean square error with leave-one-out cross validation (RMSECV), mean absolute error, and mean percentage error. For example, RMSECV can cross validate agronomic models by comparing predicted agronomic property values created by the agronomic model against historical agronomic property values collected and analyzed. In an embodiment, the agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not meet configured quality thresholds are used during future data subset selection steps (block 310)).
As per claim 16, the combination of Ruff, Ascedo, and Hu teaches each of the limitations of claims 13-15.
In addition, Ruff teaches:
wherein the agricultural computer system is further configured, by executable instructions, to advance data associated with the trial along with a defined perimeter of the trial for further processing (Paragraph Number [0110] teaches the agricultural intelligence computer system 130 is configured or programmed to implement field dataset evaluation. In an embodiment, a specific field dataset is evaluated by creating an agronomic model and using specific quality thresholds for the created agronomic model. Agronomic models may be compared and/or validated using one or more comparison techniques, such as, but not limited to, root mean square error with leave-one-out cross validation (RMSECV), mean absolute error, and mean percentage error. For example, RMSECV can cross validate agronomic models by comparing predicted agronomic property values created by the agronomic model against historical agronomic property values collected and analyzed. In an embodiment, the agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not mee