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 . 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 (i.e., changing from AIA to pre-AIA ) 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the “AMENDMENT & RESPONSE UNDER 37 C.F.R. § 1.116” of 06 February 2026 (hereinafter referred to as the “Amendment/Response”), has been entered.
Status of the Claims
The pending claims in the present application are claims 1-3, 5, 6, 8-10, 12, 13, 15-17, 19, and 20, as presented in the Amendment/Response.
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, 6, 8-10, 12, 13, 15-17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “method” of claims 1-3, 5, and 6 constitutes a process under 35 USC 101, the “system” of claims 8-10, 12, and 13 constitutes a machine under the statute, and the “non-transitory machine-readable information storage mediums” of claims 15-17, 19, and 20 constitutes a manufacture under the statute. Accordingly, claims 1-3, 5, 6, 8-10, 12, 13, 15-17, 19, and 20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“A ... method, comprising: receiving ... a plurality of parameters of a plurality of farms, wherein the plurality of parameters are captured ... at a plurality of time periods, ... wherein the plurality of parameters comprising information associated with the plurality of farms as (i) data monitored ... including ... images, data on soil nutrients (N, P, K), soil organic carbon (SOC), a moisture, a temperature, pH, EC, CO2 ..., and (ii) one or more intermediate states; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... pre-processing ... the plurality of parameters to obtain a set of pre-processed data comprising a plurality of versions of the plurality of farms, wherein each version from the plurality of versions comprises (i) an associated state of each farm amongst the plurality of farms, and (ii) one or more associated events, wherein the set of pre-processed data comprising cause-effect relationships between each farm’s version details, along with one or more causes, referred as one or more factors leading to change in each farm’s version, wherein semantic inferences and relationships are stored as states, farm configurations, and metadata to distinctly identify set of activities and are clubbed together forming each farm version; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determining ... a subset of parameters amongst the plurality of parameters based on the set of pre-processed data comprising the plurality of versions of the plurality of farms, wherein the subset of parameters comprise a temporal soil health, an improvement of a crop yield over period of time or seasons, greenhouse gas (GHG) emissions over the seasons, a storage of soil carbon over the seasons, a reduction in a carbon footprint, and a farm’s resilience to calamities; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... computing ... a farm portability score for each of the plurality of farms based on at least the subset of parameters; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... identifying ... one or more target farms amongst the plurality of farms, wherein the one or more target farms are decided for portability to transform each target farm in the current state to the desired optimal state representing a region as yield, soil health, and carbon sequestration based on the farm portability score, and measures performance of each farm; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... analyzing ... spatial and temporal similarity of one or more source farms and the one or more target farms to obtain a farm portability similarity index for each of the one or more target farms; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determining ... the optimal state for each of the one or more target farms based on the farm portability similarity index, wherein the optimal state is attained by each of the one or more target farms when performance affecting parameters reach an associated predefined threshold, wherein the performance affecting parameters are soil health and fertility, the crop yield, a quality, a return on investment (ROI), a water intake threshold, the greenhouse gas (GHS) emissions, and a sequestered carbon, and identifying an optimal traversal route for each of the one or more target farms to transform each target farm from the current state to the optimal state, comprises: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determining presence of one or more deviations by comparing one or more intermediary states of each of the one or more target farms with one or more reference intermediary states, upon monitoring the current state, and the one or more intermediary states of the one or more target farms; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“...in response to determining the presence of the one or more deviations, generating ... an updated optimal traversal route for each of the one or more target farms.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as concepts performed in the human mind, including observation (e.g., the recited “receiving” step), and evaluation, judgment, and/or opinion (e.g., the recited “pre-processing,” “determining,” “computing,” “identifying,” “analyzing,” and “generating” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “method” is “processor implemented” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “receiving” is performed “via one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “captured” involves “using one or more sensing devices ... wherein the one or more sensing devices are sensors deployed in proximity to the plurality of farms, drones, satellites, and image capturing devices” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “data monitored” involves “using on-field sensors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “images” include “satellite images” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “pre-processing” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “determining” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “computing” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “identifying” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “analyzing” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “determining” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating” is performed “via the one or more hardware processors” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, and mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2, 3, 5, and 6, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein the optimal traversal route comprises a first set of instructions to be performed by each of the one or more target farms” of claim 2, the “wherein the current state, and the one or more intermediary states of the one or more target farms are monitored based on the first set of instructions being performed” of claim 3, the “wherein the generated updated optimal traversal route comprises a second set of instructions to be performed by each of the one or more target farms, wherein the second set of instructions comprises instructions that are (i) different from the first set of instructions, or (ii) at least a subset of the first set of instructions” of claim 5, and the “wherein the first set of instructions and the second set of instructions are generated using at least one of one or more semantic ontological ..., and one or more knowledge graphs” of claim 6). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “processor implemented” of claims 2, 3, 5, and 6, and the “database” of claim 6). Accordingly, claims 2, 3, 5, and 6 also are rejected as ineligible under 35 USC 101.
Regarding pending claims 8-10, 12, and 13, while the claims are of different scope relative to claims 1-3, 5, and 6, the claims recite limitations similar to the limitations of claims 1-3, 5, and 6. As such, the rejection rationales applied to reject claims 1-3, 5, and 6 also apply for purposes of rejecting claims 8-10, 12, and 13. Limitations recited by claims 8-10, 12, and 13 that do not have a counterpart in claims 1-3, 5, and 6, such as the recited “system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to” limitations of claim 8, fail to warrant a finding of eligibility, because such limitations amount to additional elements that fail to meet the criteria of Step 2A, Prong Two and Step 2B, for the same reasons as the additional elements of claims 1-3, 5, and 6. Claims 8-10, 12, and 13 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding pending claims 15-17, 19, and 20, while the claims are of different scope relative to claims 1-3, 5, and 6 and to claims 8-10, 12, and 13, the claims recite limitations similar to the limitations of claims 1-3, 5, 6, 8-10, 12, and 13. As such, the rejection rationales applied to reject claims 1-3, 5, 6, 8-10, 12, and 13 also apply for purposes of rejecting claims 15-17, 19, and 20. Claims 15-17, 19, and 20 are, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5, 8-10, 12, 15-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. App. Pub. No. 2018/0132423 A1 to Rowan et al. (hereinafter referred to as “Rowan”), in view of U.S. Pat. App. Pub. No. 2013/0247655 A1 to Preiner et al. (hereinafter referred to as “Preiner”), further in view of U.S. Pat. App. Pub. No. 2023/0186201 A1 to Cella et al. (hereinafter referred to as “Cella”), further in view of U.S. Pat. App. Pub. No. 2022/0164736 A1 to Johnson (hereinafter referred to as “Johnson”), and further in view of WIPO Int’l Pub. No. 2016/183182 A1 to Basso (hereinafter referred to as “Basso”).
Regarding claim 1, Rowan discloses the following limitations:
“A processor implemented method, comprising: ...” - Rowan discloses, “According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination” (para. [0149]). The processors performing techniques pursuant to program instructions, in Rowan, reads on the recited limitation.
The combination of Rowan, Preiner, and Cella (hereinafter referred to as “Rowan/Preiner/Cella”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Rowan:
“... receiving, via one or more hardware processors, a plurality of parameters of a plurality of farms, wherein the plurality of parameters are captured using one or more sensing devices at a plurality of time periods, wherein the one or more sensing devices are sensors deployed in proximity to the plurality of farms, drones, satellites, and image capturing devices, wherein the plurality of parameters comprising information associated with the plurality of farms as (i) data monitored using on-field sensors including satellite images, data on soil nutrients (N, P, K), soil organic carbon (SOC), a moisture, a temperature, pH, EC, CO2 sensors, and (ii) one or more intermediate states; ...” - See the aspects of Rowan that have been cited above. Rowan also discloses, “Examples of field data 106 include ... (c) soil data (for example, type, composition, pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) fertilizer data (for example, nutrient type (Nitrogen, Phosphorous, Potassium), application type, application date, amount, source, method), (f) pesticide data (for example, pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant, application date, amount, source, method), (g) irrigation data (for example, application date, amount, source, method), (h) weather data (for example, precipitation, rainfall rate, predicted rainfall, water runoff rate region, temperature, wind, forecast, pressure, visibility, clouds, heat index, dew point, humidity, snow depth, air quality, sunrise, sunset), (i) imagery data (for example, imagery and light spectrum information from an agricultural apparatus sensor, camera, computer, smartphone, tablet, unmanned aerial vehicle, planes, or satellite” (para. [0088]), “An agricultural apparatus 111 may have one or more remote sensors 112 fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural intelligence computer system 130 and are programmed or configured to send sensor data to agricultural intelligence computer system 130” and “Sensor data may consist of the same type of information as field data 106” (para. [0090]), “Sensor data may consist of the same type of information as field data 106. In some embodiments, remote sensors 112 may not be fixed to an agricultural apparatus 111 but may be remotely located in the field and may communicate with network 109” (para. [0090]), “sensors 112 and controllers 114 may be affixed to soil sampling and measurement apparatus that is configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other tests pertaining to soil. For example, the apparatus disclosed in U.S. Pat. No. 8,767,194 and U.S. Pat. No. 8,712,148 may be used, and the present disclosure assumes knowledge of those patent disclosures” (para. [0137]), and “Block 702 represents program instructions for receiving data. In block 702, data is received; for example, system 130 (FIG. 1) receives yield data and permanent characteristics data as part of the field data 106. The data may include historical yield maps at the field level or sub field level, and maps representing persistent characteristics of the soil. The maps represent spatial-temporal patterns for the sub-fields and are used to classify a field into regions with distinctive or different productivity potentials” (para. [0200]). Receiving, by the hardware processors, the characteristics data of the field data, wherein the data is captured by sensors of various types and have spatial-temporal aspects, the sensors involving or being involved with use of unmanned aerial vehicles, satellites, and cameras at the farms, wherein the data including farm data such as soil sampling and measurement sensors associated with satellite imagery and light spectrum information, fertilizer data for nutrient types Nitrogen, Phosphorus, and Potassium, soil moisture, temperature, and pH, at various times, in Rowan, reads on the recited “receiving, via one or more hardware processors, a plurality of parameters of a plurality of farms, wherein the plurality of parameters are captured using one or more sensing devices at a plurality of time periods, wherein the one or more sensing devices are sensors deployed in proximity to the plurality of farms, drones, satellites, and image capturing devices, wherein the plurality of parameters comprising information associated with the plurality of farms as (i) data monitored using on-field sensors including satellite images, data on soil nutrients (N, P, K), ..., a moisture, a temperature, pH, ... and (ii) one or more intermediate states” limitation. Preiner discloses, “A soil analysis device” (Abstract), “Peaks in the attenuation spectrum allow identification of components of the soil. For example, attenuation peaks at wavelengths of approximately 200 nanometers and 300 nanometers indicate nitrate-nitrogen in the soil. Similarly, attenuation peaks at wavelengths of approximately 210 nanometers, 230 nanometers and wavelengths from 250-300 nanometers may be used to identify nitrite-nitrogen, bisulfide and organic carbon” (para. [0042]), and “In addition to measurements performed by the measurement cell 170, the soil analysis device 100 may also include additional measurement devices 120 in mixing chamber 110 for performing further measurements of the soil sample. Examples of measurement devices 120 include a conductivity probe” (para. [0051]). The identifying of organic carbon in the soil, and use of the electrical conductivity probe, in Preiner, reads on the recited, “monitored using on-field sensors including ... soil organic carbon (SOC), ... EC” limitation. Cella discloses, “a sensor kit 30100 may include any suitable combination of light sensors 30102, weight sensors 30104, temperature sensors 30106, CO2 sensors 30108, humidity sensors 30110” and “the sensor data collected by the edge device 28704 may include CO2 measurements indicating ambient levels of CO2 in the vicinity of a CO2 sensor 30108” (para. [1845]). Use of the CO2 sensors, in Cella, reads on the recited “monitoring using on-field sensors including ... CO2 sensors” limitation.
Preiner discloses a “soil analysis device” (Abstract), similar to the claimed invention and to Rowan. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have used, as or in one of the sensors in Rowan, the soil analysis device, or elements thereof, of Preiner, as doing so is explicitly contemplated by Rowan (para. [0137]). Preiner is the equivalent of one of U.S. Pat. No. 8,767,194, which was incorporated by reference in Rowan.
Cella discloses a “sensor kit” (para. [1845]), similar to the claimed invention and to Rowan. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have used, as or in one of the sensors in Rowan, the CO2 sensors, or elements thereof, of Cella, for the additional environmental data it provides.
Rowan/Preiner/Cella teaches the following limitations of claim 1:
“... pre-processing, via the one or more hardware processors, the plurality of parameters to obtain a set of pre-processed data comprising a plurality of versions of the plurality of farms, wherein each version from the plurality of versions comprises (i) an associated state of each farm amongst the plurality of farms, and (ii) one or more associated events, wherein the set of pre-processed data comprising cause-effect relationships between each farm’s version details, along with one or more causes, referred as one or more factors leading to change in each farm’s version, wherein semantic inferences and relationships are stored as states, farm configurations, and metadata to distinctly identify set of activities and are clubbed together forming each farm version; ...” - See the aspects of Rowan that have been cited above. Rowan also discloses, “At block 305, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise and distorting effects within the agronomic data including measured outliers that would bias received field data values” (para. [0143]), “At block 310, the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the preprocessed field data in order to identify datasets useful for initial agronomic model generation” (para. [0144]), “Satellite image data may be provided at different spatial, spectral and temporal resolutions. The satellite maps may provide information about agricultural crop assessment, crop health, change detection, environmental analysis, irrigated landscape mapping, yield determination and soils analysis. The images may be acquired at different times of the year and multiple times within a year” (para. [0190]), and “metadata about the created management zones is generated and stored” (para. [0232]). The preprocessing, via the hardware processors, of field data to obtained preprocessed field data including information about fields acquired at different time of the year and multiple times within the year, wherein the data includes information about crops, irrigation, and yields, in Rowan, reads on the recited “pre-processing, via the one or more hardware processors, the plurality of parameters to obtain a set of pre-processed data comprising a plurality of versions of the plurality of farms, wherein each version from the plurality of versions comprises (i) an associated state of each farm amongst the plurality of farms, and (ii) one or more associated events” limitation. The field data including data about outcomes following implementation of changes performed on the fields, as a result of the implementing of the changes in combination with other environmental factors, wherein the field data is stored for the fields, involving use of metadata that also is generated and stored, in Rowan, reads on the recited “wherein the set of pre-processed data comprising cause-effect relationships between each farm’s version details, along with one or more causes, referred as one or more factors leading to change in each farm’s version, wherein semantic inferences and relationships are stored as states, farm configurations, and metadata to distinctly identify set of activities and are clubbed together forming each farm version” limitation.
The combination of Rowan, Preiner, Cella, and Johnson (hereinafter referred to as “Rowan/Preiner/Cella/Johnson”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Rowan/Preiner/Cella:
“... determining, via the one or more hardware processors, subset of parameters amongst the plurality of parameters based on the set of pre-processed data comprising the plurality of versions of the plurality of farms, wherein the subset of parameters comprise a temporal soil health, an improvement of a crop yield over period of time or seasons, greenhouse gas (GHG) emissions over the seasons, a storage of soil carbon over the seasons, a reduction in a carbon footprint, and a farm’s resilience to calamities; ...” - See the aspects of Rowan that have been cited above. Rowan also discloses, “Examples of field data 106 include ... (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (c) soil data (for example, type, composition, pH, organic matter (OM), cation exchange capacity (CEC))” (para. [0088]), and “The process may be repeated using different criteria, different parameters, or different parameter values” (para. [0227]). Using, by the hardware processors, different parameter values associated with the preprocessed field data pertaining to the fields at different times of the year and multiple times within the year, wherein the parameter values and field data are indicative of harvest data and soil data, in Rowan, reads on the recited “determining, via the one or more hardware processors, subset of parameters amongst the plurality of parameters based on the set of pre-processed data comprising the plurality of versions of the plurality of farms, wherein the subset of parameters comprise a temporal soil health, an improvement of a crop yield over period of time or seasons, ... and a farm’s resilience to calamities” limitation. Johnson discloses, “Attributes have metrics that describe the attributes qualitative and/or quantitative metrics of measurement. Attributes may be physical, chemical, biological, and based on nutrition, sustainability, processing, quality, financial or another outcome. Attributes may impact: nutrition, product label, consumer purchasing behavior and therefore the price of a product and sales success; operational efficiencies including manufacturing, procurement and supply chain management; risks such as supply and quality; sustainability, for example GHG (green-house gases) emissions, soils health, chemical use, water use, and carbon sequestration; and yield and profitability” (para. [0032]), and “carbon prevented” (para. [0192]). The attributes of GHG emissions, carbon sequestration, and carbon prevented, in Johnson, when applied in conjunction with other parameters measured multiple times per year, in Rowan, reads on the recited “subset of parameters” also includes “greenhouse gas (GHG) emissions over the seasons, a storage of soil carbon over the seasons, a reduction in a carbon footprint”” limitation.
“... computing, via the one or more hardware processors, a farm portability score for each of the plurality of farms based on at least the subset of parameters; ...” - See the aspects of Rowan that have been cited above. Rowan also discloses, “the management zone delineation process is performed for different values of a management class count. A management class refers to one or more areas in a field that have relatively homogeneous yield limiting factors” (para. [0234]). Determining, via the hardware processors, homogeneity of yield limitation factors for zones of fields based on the parameter values associated with the preprocessed field data, in Rowan, reads on the recited limitation.
“... identifying, via the one or more hardware processors, one or more target farms amongst the plurality of farms, wherein the one or more target farms are decided for portability to transform each target farm in the current state to the desired optimal state representing a region as yield, soil health, and carbon sequestration based on the farm portability score, and measures performance of each farm; ...” - See the aspects of Rowan that have been cited above. Rowan also discloses, “Recommendations that are customized to the needs of individual zones to improve yield and profitability of the field may include prescriptions for seeding. The prescription, also referred to as recommendations, may specify certain seed hybrids, seed population and nitrogen fertilizer for different sub regions in a field. The recommendations may be determined based on characteristics of regions within a zone” (para. [0166]), “Data for soil characteristics of a field may be obtained based on soil samples collected from the field” (para. [0180]), and “a management zone delineation application is configured to allow growers to create manual scripts that contain settings and parameters to specify details for delineating management zones. The application may also provide a set of predefined script scenarios and made the set available to the grower. The scenarios may include a scenario that provides information about for example, predicted yield that may be obtained if the grower does not change their current agronomic practice. Another scenario may provide recommendations for achieving the best economic results. Other scenario may provide recommendations for achieving maximum yield from the field. These example scenarios may allow a grower to compare different agronomic practices in reference to the field, compare yield results if different practices are applied, and ultimately choose the recommendations or scenario that matches the grower's goals the best” (para. [0265]). Identifying, by the hardware processors, zones having relatively homogeneous yield limiting factors, from among fields, in Rowan, reads on the recited “identifying, via the one or more hardware processors, one or more target farms amongst the plurality of farms” limitation. The delineating of the management zones based on scenarios of current agronomic practices versus recommended agronomic practices, in terms of yield and other factors, based on characteristics and values of the management zones and their yield results, in Rowan, reads on the recited “wherein the one or more target farms are decided for portability to transform each target farm in the current state to the desired optimal state representing a region as yield, soil health, ... based on the farm portability score, and measures performance of each farm” limitation. The consideration of carbon sequestration, in Johnson, reads on the recited “carbon sequestration” limitation.
“... analyzing, via the one or more hardware processors, spatial and temporal similarity of one or more source farms and the one or more target farms to obtain a farm portability similarity index for each of the one or more target farms; ...” - See the aspects of Rowan that have been cited above. Rowan also discloses, “FIG. 8 depicts an example method for creating management zones for an agricultural field. In step 810, a first count value for a management class count of a plurality of management classes is determined. Selecting a first count value for the management classes may include selecting a number of management classes that has been shown in the past to be an optimal number of classes for creating the zones. A count of management classes is also referred to as a tuning parameter” (para. [0235]), and “Once a first count value is determined for a count of a plurality of classes, a first set of management zones is generated in step 820. The first set of management zones may be generated, for example, using a management zone delineation process that is performed using either clustering approaches or region merging approaches” (para. [0238]). Analyzing, by the hardware processors, homogeneity of yield limitation factors associated with spatial and temporal field data, to obtain sets of management zones for fields, in Rowan, reads on the recited limitation.
Johnson discloses “optimal ... management practices to grow plants” (para. [0005]), similar to the claimed invention and to Rowan/Preiner/Cella. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the parameters, in Rowan/Preiner/Cella, to include additional attributes or parameters, like the GHG emissions, carbon sequestration, and carbon prevented, in Johnson, to have “a positive impact on sustainability,” per Johnson (para. [0031]).
The combination of Rowan, Preiner, Cella, Johnson, and Basso (hereinafter referred to as Rowan/Preiner/Cella/Johnson/Basso”) teaches the following limitations of claim 1 that do not appear to be taught in their entirety by Rowan/Preiner/Cella/Johnson:
“... determining, via the one or more hardware processors, an optimal state for each of the one or more target farms based on the farm portability similarity index wherein the optimal state is attained by each of the one or more target farms when performance affecting parameters reach an associated predefined threshold, wherein the performance affecting parameters are soil health and fertility, the crop yield, a quality, a return on investment (ROI), a water intake threshold, the greenhouse gas (GHS) emissions, and a sequestered carbon, and ...” - See the aspects of Rowan and Johnson that have been cited above. Rowan also discloses, “Examples of field data 106 include ... (g) irrigation data (for example, application date, amount, source, method)” (para. [0088]), “The management zone delineation approach described herein may be widely implemented in a variety of agricultural applications. For example, the approach may be integrated with computer-based tools that a grower may use to optimize his agronomic practices. The approach may be implemented in an application that generates a graphical user interface for a user, and displays recommendations and strategy options to the grower” (para. [0262]), and “The scenarios may include a scenario that provides information about for example, predicted yield that may be obtained if the grower does not change their current agronomic practice. Another scenario may provide recommendations for achieving the best economic results. Other scenario may provide recommendations for achieving maximum yield from the field. These example scenarios may allow a grower to compare different agronomic practices in reference to the field, compare yield results if different practices are applied, and ultimately choose the recommendations or scenario that matches the grower's goals the best” (para. [0265]). Determining, by the hardware processors, scenarios that achieve best results or best match goals, for the zones of the fields, based on the sets of management zones, and identifying agronomic practices, recommendations, and strategies to carry out on the zones for achieving said optimality, with consideration of parameters including soil data, harvest data, and irrigation data, in Rowan, reads on the recited “determining, via the one or more hardware processors, an optimal state for each of the one or more target farms based on the farm portability similarity index wherein the optimal state is attained by each of the one or more target farms ..., wherein the performance affecting parameters are soil health and fertility, the crop yield, a quality, ... a water intake threshold” limitation. Additionally, Johnson discloses, “a return on investment is calculated” (claim 6). The inclusion of attributes of Johnson, including return on investment, green-house gas emissions, and carbon sequestration, as parameters in Rowan, reads on the recited “a return on investment (ROI), ... the greenhouse gas (GHS) emissions, and a sequestered carbon” limitation. Basso discloses, “Optimization of a property can include, for example, maximizing a desired property, meeting or exceeding a minimum threshold for a desired property, minimizing an undesired property, being at or below a maximum threshold for an undesired property, maintaining any property in a range, and/or increasing or decreasing a desired or undesired property relative to a baseline or target value” (para. [0054]). Consideration of thresholds for optimization, in Basso, when applied in the context of optimization in Rowan/Johnson, reads on the recited “wherein the optimal state is attained ... when performance affecting parameters reach an associated predefined threshold” and “threshold” limitations.
“... identifying an optimal traversal route for each of the one or more target farms to transform each target farm from a current state to the optimal state, comprises: determining presence of one or more deviations by comparing one or more intermediary states of each of the one or more target farms with one or more reference intermediary states, upon monitoring the current state, and the one or more intermediary states of the one or more target farms; and ...” - See the aspects of Rowan, Johnson, and Basso that have been cited above. Additionally, Rowan discloses, “the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare” (para. [0113]), and “comparing predicted agronomic property values created by the agronomic model against historical agronomic property values collected and analyzed” (para. [0145]). The identifying of agronomic practices, recommendations, and strategies to carry out on zones for achieving optimality, involving comparing predicted agronomic property values of fields with historical agronomic property values of fields, in a continuous cycle of sensing parameters, modifying parameters for optimality, and repeating, in Rowan, reads on the recited limitation. Note, for example, the discussion of “historical yield maps that represent spatial and temporal yield patterns for the sub-fields. Yield data may include information about yields of crops harvested from an agricultural field within one year or within several years” (para. [0175]), which is indicative of current and intermediary states, based on the exemplary ranges of time.
“... in response to determining the presence of the one or more deviations, generating, via the one or more hardware processors, an updated optimal traversal route for each of the one or more target farms.” - See the aspects of Rowan that have been cited above. Operation of the system and methodology in Rowan, whereby parameters are sensed, plans are made to optimize them and their associated fields, the plans are carried out on the fields, and the process is repeated continuously, in Rowan, reads on the recited limitation.
Basso discloses “precision crop modeling and management” (Abstract), similar to the claimed invention and to Rowan/Preiner/Cella/Johnson. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization processes, of Rowan/Preiner/Cella/Johnson, to include the consideration of thresholds, as in Basso, for versatility, as taught by Basso (para. [0054]).
Regarding claim 2, Rowan/Preiner/Cella/Johnson/Basso teaches the following limitations:
“The processor implemented method of claim 1, wherein the optimal traversal route comprises a first set of instructions to be performed by each of the one or more target farms.” - See the aspects of Rowan that have been cited above. The practices, recommendations, and strategies to be performed at each zone, for achieving optimality therein, in Rowan, reads on the recited limitation.
Regarding claim 3, Rowan/Preiner/Cella/Johnson/Basso teaches the following limitations:
“The processor implemented method of claim 1, wherein the current state, and the one or more intermediary states of the one or more target farms are monitored based on the first set of instructions being performed.” - See the aspects of Rowan that have been cited above. Rowan also discloses, “external data server computer 108 stores external data 110, including soil data representing soil composition for the one or more fields and weather data representing temperature and precipitation on the one or more fields. The weather data may include past and present weather data as well as forecasts for future weather data” (para. [0124]), and operation of various remote sensors that produce observations by monitoring conditions (see paras. [0088] and [0125]-[0138]). Continual operation of the sensors produces current data that becomes intermediate or past data. Operation of said sensors during performance of the optimizing practices, recommendations, and strategies in the zones, in Rowan, reads on the recited limitation.
Regarding claim 5, Rowan/Preiner/Cella/Johnson/Basso teaches the following limitations:
“The processor implemented method of claim 1, wherein the generated updated optimal traversal route comprises a second set of instructions to be performed by each of the one or more target farms, wherein the second set of instructions comprises instructions that are (i) different from the first set of instructions, or (ii) at least a subset of the first set of instructions.” - See the aspects of Rowan that have been cited above. Generating optimizing practices, recommendations, and strategies to be performed in the zones after cross validation of data and models, wherein the optimizing practices, recommendations, and strategies differ from those from prior to the cross validation, in Rowan, reads on the recited limitation.
Regarding claims 8-10 and 12, while the claims are of different scope relative to claims 1-3 and 5, the claims recite limitations similar to those recited by claims 1-3 and 5. As such, the rationales applied in the rejection of claims 1-3 and 5 also apply for purposes of rejecting claims 8-10 and 12. Limitations recited by claims 8-10 and 12 that do not appear to have a counterpart in claims 1-3 and 5, such as the recited “system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to” limitations, are taught by Rowan (see paras. [0150] and [0151]). Claims 8-10 and 12 are, therefore, also rejected under 35 USC 103 as obvious in view of Rowan/Preiner/Cella/Johnson/Basso.
Regarding claims 15-17 and 19, while the claims are of different scope relative to claims 1-3 and 5 and to claims 8-10 and 12, the claims recite limitations similar to those recited by claims 1-3, 5, 8-10, and 12. As such, the rationales applied in the rejection of claims 1-3, 5, 8-10, and 12 also apply for purposes of rejecting claims 15-17 and 19. Claims 15-17 and 19 are, therefore, also rejected under 35 USC 103 as obvious in view of Rowan/Preiner/Cella/Johnson/Basso.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rowan, in view of Preiner, further in view of Cella, further in view of Johnson, further in view of Basso, and further in view of WIPO Int’l Pub. No. 2017/137914 A1 to Chandrasenan et al. (hereinafter referred to as “Chandrasenan”).
Regarding claim 6, the combination of Rowan, Preiner, Cella, Johnson, Basso, and Chandrasenan (hereinafter referred to as “Rowan/Preiner/Cella/Johnson/Basso/Chandrasenan”) teaches limitations below that do not appear to be taught in their entirety by Rowan/Preiner/Cella/Johnson/Basso:
“The processor implemented method of claim 5, wherein the first set of instructions and the second set of instructions are generated using at least one of one or more semantic ontological database, and one or more knowledge graphs.” - See the aspects of Rowan that have been cited above. Generating optimizing practices, recommendations, and strategies as a result of on-going monitoring of zones and fields, in Rowan, reads on the recited “wherein the first set of instructions and the second set of instructions are generated” limitation. Chandrasenan discloses, “the received parameters and the knowledge base module 108d may be encoded in semantic knowledge representation formats such as ontologies that need to be parsed to derive parameters and the associated attributes. This would particularly be useful in interpreting information where information may not be clearly available and may require reasoning over knowledge graphs to conclude on the parameters and the associated attributes” (para. [042]). Using the semantic knowledge representation formats, such as ontologies, of Chandrasenan, reads on the recited “generated using at least one of one or more semantic ontological database, and one or more knowledge graphs” limitation.
Chandrasenan discloses “automated identification of agro-climatic zones” (Abstract), similar to the claimed invention and to Rowan/Preiner/Cella/Johnson/Basso. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the process of delineating zones, in Rowan/Preiner/Cella/Johnson/Basso, to include use of semantic knowledge representation formats, such as ontologies, and knowledge graphs, as in Chandrasenan, as “This would be particularly useful in interpreting information where information may not be clearly available,” as taught by Chandrasenan (para. [042]).
Regarding claim 13, while the claim is of different scope relative to claim 6, the claim recites limitations similar to those recited by claim 6. As such, the rationales applied in the rejection of claim 6 also apply for purposes of rejecting claim 13. Claim 13 is, therefore, also rejected under 35 USC 103 as obvious in view of Rowan/Preiner/Cella/Johnson/Basso /Chandrasenan.
Regarding claim 20, while the claim is of different scope relative to claims 6 and 13, the claim recites limitations similar to those recited by claims 6 and 13. As such, the rationales applied in the rejection of claims 6 and 13 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected under 35 USC 103 as obvious in view of Rowan/Preiner/Cella/Johnson/Basso /Chandrasenan.
Response to Arguments
On pp. 10-19 of the Amendment/Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. With respect to Step 2A of the eligibility analysis, the applicant argues that the claims are eligible because any abstract idea is implemented by a particular machine, per the meaning intended in MPEP 2106.05(b). (Amendment/Response, p. 10.) The applicant emphasizes the recited sensors and data monitored thereby. (Amendment/Response, pp. 10-12.) The applicant also argues that the claims integrate any abstract idea into a practical application by improving computer functionality. (Amendment/Response, p. 12.) The applicant emphasizes the recited cause-effect relationships and sets of activities to support the argument. (Amendment/Response, p. 12.) According to the applicant, the improvement to computer functionality also is evident with respect to multivariate versioning of farms (Amendment/Response, p. 12), and transforming farms (Amendment/Response, p. 13). The examiner finds the arguments unpersuasive. The recitation of various types of sensors and sensed data provides no details about the structure or operation of the sensors. As such, the recitations lack particularity (MPEP 21206.05(b)(I)). Further, the sensors merely gather data, and “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” (MPEP 2106.05(b)(III)). Also, the examiner views the cause-effect relationships, sets of activities, multivariate versioning, and transforming as pertaining to farms, and, at best, improving farms or farm planning. None of that would improve a computer or computer functionality. Those are just forms of data handled by generic, conventional computers. As such, an eligibility-warranting improvement under MPEP 2106.05(a) has not been established.
With respect to Step 2B of the eligibility analysis, the applicant sets forth arguments involving recitations (Amendment/Response, pp. 14 and 15) already covered in the discussion of Step 2A (Prong Two) above, and thus, the examiner’s rebuttals are similar. The applicant also argues that the eligibility rationales from the XY, LLC Federal Circuit decision should be applied to the applicant’s claims, such that they should be deemed eligible. (Amendment/Response, pp. 15-18.) The examiner finds the arguments unpersuasive. It is the examiner’s understanding that the Federal Circuit decision in the XY case is based on the fact that no abstract idea was recited by the claim(s) at issue in the case. The applicant’s claims, on the other hand, recite abstract ideas, as explained in detail in the 35 USC 101 section above. Thus, the rationales from the decision in the XY case are inapplicable to the applicant’s claims.
On pp. 19-26 of the Amendment/Response, the applicant requests reconsideration and withdrawal of the claim rejections under 35 USC 102. The applicant argues that Rowan fails to disclose, teach, or suggest the recited sensor limitations. (Amendment/Response, pp. 19-21.) The applicant’s arguments have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The applicant also argues that Rowan fails to disclose, teach, or suggest the recited creating cause-effect relationships, storing of semantic inferences and relationships as states, and metadata identifying sets of activities being clubbed together to form farm versions. (Amendment/Response, pp. 22 and 23.) The examiner finds the arguments unpersuasive. Rowan discloses monitoring of fields that would pick up differences in fields over time, including differences caused by implementing recommendation. Such differences establish cause and effect relationships. The characteristics of the fields are stored data, and can include text describing aspects of the fields at various points in time, thus reading on storing semantic inferences and relationships as states. Rowan also discloses using metadata with the other data, wherein combinations of the data (characteristics) define versions of fields (e.g., by defining the characteristics the fields have at specific moments in time). See the passages of Rowan that have been cited in the 35 USC 103 section above for a more detailed explanation.
The applicant also argues that Rowan fails to disclose, teach, or suggest the recited transform and performance measurement steps. (Amendment/Response, pp. 23-26.) The examiner finds the arguments unpersuasive. Implementing any recommendations to improve the fields, in Rowan, reads on the recited transform step. The fact that the states of the fields are related to sensed yield, soil health, and other performance measures, in Rowan, when combined with the carbon sequestration performance measure, in Johnson, reads on the recited limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. No. 11,704,576 B1 to McEntire et al. discloses, “A system and method for identifying ground types from one or more interpolated covariates. The method proceeds by accessing soil composition information for plots of land, in which the soil composition information includes measured soil sample results, environmental results, soil conductivity results or any combination thereof. The method continues by identifying covariates from the soil composition information. Subsequently, the method interpolates covariates associated with different locations with an interpolation training model. Voxels are generated that are each associated with interpolated covariates having a corresponding geographical location. The method trains a random forest training model with the interpolated covariates. The voxels traverse the trained random forest model to identify clusters of voxels that are co-associated. The method identifies a ground type by combining the co-associated clusters. Each ground type is associated with a crop zone, a soil fertility, or a farm management recommendation.” (Abstract.)
U.S. Pat. App. Pub. No. 2016/0078375 A1 to Ethington et al. discloses, “A computer-implemented method for recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory. The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.” (Abstract.)
U.S. Pat. App. Pub. No. 2018/0322590 A1 to Sundararajan et al. discloses, “Apparatuses, methods and storage media associated with agricultural testing and optimization are disclosed herein. In embodiments, an apparatus for performing agricultural testing and optimization may comprise a cavity to receive a container of soil nutrient solution sample of a location in an agricultural region; one or more sensors to collect sensor data from the soil nutrient solution sample; and one or more agricultural testing and optimization applications to perform agricultural testing and optimization for the location, based at least in part on the sensor data collected from the soil nutrient solution sample. Other embodiments may be disclosed or claimed.” (Abstract.)
U.S. Pat. App. Pub. No. 2020/0272971 A1 to Ruff et al. discloses, “A system for implementing a trial in one or more fields is provided. In an embodiment, an agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device. When the agricultural intelligence computing system receives application data for the one or more target agricultural fields, the agricultural intelligence computing system determines whether the one or more target agricultural fields are in compliance with the trial. The agricultural intelligence computing system then receives result data for the trial and, based on the result data, computes a benefit value for the trial.” (Abstract.)
U.S. Pat. App. Pub. No. 2021/0166019 A1 to White et al. discloses, “a system and method for agricultural management-zone delineation to be done over broad geographic extents without overly-localized field-specific data. The instant innovation guides precision agricultural sampling and management by delineating enhanced management zones based upon remote sensing and artificial intelligence and combining the two with data derived from an existing countrywide soil survey database. In an embodiment, the instant innovation uses artificial intelligence from multiple sources to provide granular zone detail. Output of the present innovation can be aggregated to produce management zone sizes that have a level of uncertainty compatible with the needs of the customer-farmer and implementable given the capabilities of available equipment.” (Abstract.)
U.S. Pat. App. Pub. No. 2023/0091677 A1 to Brown et al. discloses, “monitoring and intelligence generation for one or more farm fields by: using remote sensing to monitor progress of a developing crop; comparing a user's field to another field in the area; providing cropped area extent estimates for a current season; using one or more disease risk models to determine disease risk (or disease pressure) with respect to a field; or some combination thereof.” (Abstract.)
WIPO Int’l Pub. No. 2019/211853 A1 to Ben-ner et al. discloses, “a method comprising: computing state parameter(s) indicative of a state of a target crop at the target field based on output of crop physiological sensor(s), and classifying by a classifier(s), the state parameter(s) and the agricultural practice(s) into instructions for administration of the agricultural practice(s) to the target field, wherein yield and/or quality of the target crop at a future target event is predicted to be increased when the instructions are implemented relative to the yield and/or quality of the target crop that is predicted at the future target event when an alternative administration of the agricultural practice(s) is implemented, wherein the classifier(s) computes the instructions based on previously obtained instructions associated with respective reference fields associated with respective state parameter(s), and yield and/or quality of respective reference crops at respective reference fields at historical reference events corresponding to the future target event.” (Abstract.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor, can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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