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.
Status of the Claims
The pending claims in the present application are claims 1-9 and 11-20 of the Response dated 21 January 2026.
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 1-8, 11, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. App. Pub. no. 2010/0024296 A1 to Lazarus et al. (hereinafter referred to as “Lazarus”), in view of J.D. Hanson, et al. “RZWQM: Simulating the effects of management on water quality and crop production.” Agricultural Systems, Volume 57, Issue 2, 1998, Pages 161-195 (hereinafter referred to as “Hanson”), and further in view of U.S. Pat. App. Pub. No. 2020/0045898 A1 to Arriaza et al. (hereinafter referred to as “Arriaza”).
Regarding claim 1, Lazarus discloses the following limitations:
“A computer-implemented method for providing a migration risk index for controlling application of a product to an agricultural field, the method comprising: ...” - Lazarus discloses, “The invention relates to methods, using a computer and software system, for providing real time running forecasts during an agronomic period of a capacity for absorbing nutrients by a plurality of fields according to an agronomic standard, and/or for providing a running real time forecast for a plurality of fields of capacity for absorbing nutrients by current status versus agronomic limit of a targeted substance” (Abstract), “Benefits of the invention are anticipated to include the prevention of pollution of groundwater, such as from leaching, and prevention of pollution of surface water, such as by run off” (para. [0026]), “run off into surface water or leach down into ground water aquifers” (para. [0028]), “regulated chemicals, as a result, often leach into and through the soil and underlying geologic materials and into ground water aquifers, causing ground water pollution” (para. [0031]), “One goal is to insure that regulated chemicals do not run off into surface waters or leach into ground water and aquifers” (para. [0032]), and “NAFS would output nutrient application forecasting reports on a continuous or regularly scheduled basis. These reports would be guiding tools for the producer/farmer to help in scheduling applications per crop needs while minimizing the potential for groundwater contamination and unpermitted surface water runoff. The following are preferred components of NAFS output reports” (para. [0181]) including “8. Risk planning and forecasting (Potential for nutrient runoff forecasting)” (para. [0182]). Using the computer and the software system to output reports to help schedule applications while minimizing potential for groundwater contamination due to chemicals leaching down through the soil, in Lazarus, reads on the recited limitation. While Lazarus does not actually use the phrase “migration risk index” to describe its output reports, the fact that output reports relate to minimizing groundwater contamination due to leaching through soil, suggests the output reports (and/or perhaps the data contained therein) are a migration risk index or migration risk indices, as they have influence over the potential for groundwater contamination via leaching through soil. And the application of materials to soil (e.g., greenwater, freshwater, and solids), crop uptake of materials from the soil, the soil status, and extent of runoff, would logically necessarily affect absorption of materials by the soil, and thus, predictions or forecasts about them would be indicative of the probability (risk) of absorption. Additionally or alternatively, forecasting the capacity of fields for absorbing nutrients necessarily also forecasts the capacity of fields to not absorb nutrients (and migrate), as each of these outputs is merely the inverse of the other output.
“... receiving, by a receiving interface of a computing device, soil data for a target location, wherein the soil data comprises at least one of a soil type, a soil texture, a soil organic matter content, groundwater table depth, or a soil permeability at the target location; ...” - Lazarus discloses, “The NAFS system takes input data such as described above, QA/QC's the input data, processes and analyzes the data by performing calculations, running inbuilt models, and generates reports such as nutrient application forecasting reports” (para. [0087]), “Nutrient Application Forecasting System” with “A. Inputs” and “In a most preferred embodiment the following data, related to each input component presented in FIG. 1, would be cured, collected, and input into NAFS, and updated as available” (paras. [0104] and [0105]), including “7. Soils” and “Soil chemical and physical analysis data,” “P Index,” “Soil texture,” “Soil structure,” “Map unit,” “Soil limitations for crop irrigation,” and “Leaching Index” (paras. [0165]-[0172]), “providing periodic forecasts within an agronomic period of the status of and/or the limits for nutrient application, preferably liquid manure application, to a plurality of fields” (para. [0183]), and “inputting: directly or indirectly, into a computer, parameters” (claim 2). The NAFS receiving as input the soil data for fields, including soil chemical and physical data, soil texture, soil structure, and the like, in Lazarus, reads on the recited limitation.
“... receiving, by the receiving interface, weather data for the target location, wherein the weather data comprises at least one of historical precipitation data and/or forecasted precipitation data for the target location; ...” - See the aspects of Lazarus that have been mentioned above. Lazarus also discloses, as further “A. Inputs” of “Nutrient Application Forecasting System (NAFS)” (para. [0104]), the “8. Climate” (para. [0172]) that covers “Precipitation” (para. [0174]) and “Historic precipitation data” (para. [0178]). The NAFS receiving as input the precipitation data, including the historic precipitation data, for fields in Lazarus, reads on the recited limitation.
“... calculating, by a processor of the computing device, migration data of the product at the target location based on the soil data and the weather data, ...” - See the aspects of Lazarus that have been mentioned above. Lazarus also discloses, “The NAFS system takes input data such as described above, QA/QC's the input data, processes and analyzes the data by performing calculations, running inbuilt models, and generates reports” (para. [0087], “The following are preferred components of NAFS output reports. 1. Greenwater application planning and/or forecasting 2. Freshwater Application planning and/or forecasting 3. Manure solids Application planning and/or forecasting 4. Water rights utilization planning and forecasting 5. Crop uptake planning and forecasting 6. Soils nutrient status Planning and forecasting 7. Groundwater Quality Planning and Forecasting” (para. [0181] and “8. Risk planning and forecasting (Potential for nutrient runoff forecasting” (para. [0182]). The NAFS computer calculating values for the multiple components of the NAFS output reports, with the components being viewed alone or in various combinations, based on processing (running inbuilt models) performed by the system on NAFS inputs including data about “7. Soils” (para. [0165]) and “Precipitation” (paras. [0129] and [0140]), in Lazarus, reads on the recited limitation. As explained above, the outputs of the NAFS relate to the potential for groundwater contamination through leaching of chemicals through soil in fields, and thus, each of the outputs is “migration data of the product at the target location.”
“... determining, by the processor, the migration risk index for the target location based on the migration data, the migration risk index indicating a level of risk that the product will migrate beyond a desired retention zone into a lower soil layer or reach a groundwater zone; ...” - See the aspects of Lazarus that have been mentioned above. Determining forecasts for the NAFS output reports of the fields, including the multiple forms of planning and forecasting related to the potential for groundwater contamination via leaching through soil into underlying geologic materials, in Lazarus, reads on the recited limitation. The combination of forecasts from the two or more of the NAFS output reports acts as a “migration risk index.”
“... generating, by the processor, control data based on the migration risk index, the control data ... for controlling an application mechanism to vary an application rate of the product at the target location; and ...” - See the aspects of Lazarus that have been mentioned above. The NAFS computers generating report data based on the forecasts, wherein the report data is used to schedule applications of substances on fields, in Lazarus, reads on the recited limitation. For example, scheduling applications on some days and not on others, is a form of varying an application rate.
“... the control data being generated by comparing the migration risk index to a predefined risk threshold and being configured to reduce or prevent an application of the product when the migration risk index exceeds the threshold, and to allow the application when the risk index is within an acceptable range, thereby reducing a level of risk of groundwater contamination while enabling precise location-specific treatment.” - See the aspects of Lazarus that have been mentioned above. Lazarus also discloses, “The method includes outputting, as a function of the input, periodic indicia of the status of each field with respect to the targeted substance applied, as a function of anticipated and/or actual uptake, and/or outputting an indicia of an agronomic limit for future manure application to each field in an agronomic period based on the targeted substance and an agronomic standard” (para. [0183]). The reports being generated by the NAFS in connection with the forecasts and the agronomic limits, to ensure that any applications of substances stays within the agronomic limits, to reduce groundwater contamination while allowing treatment of fields with substances, in Lazarus, reads on the recited limitation.
The combination of Lazarus and Hanson (hereinafter referred to as “Lazarus/Hanson”) teaches limitations below of claim 1 that do not appear to be disclosed by Lazarus in their entirety:
The claimed “calculating” involves “using a soil mobility model, the soil mobility model being configured to model a transport rate of the product through the soil at the target location and to predict a product migration depth and likelihood of groundwater exposure, the modeling being based on a combination of the product’s degradation characteristics and its interaction with the specific soil type, including adsorption or desorption behavior and leaching potential” - Hanson discloses, “The Root Zone Water Quality Model (RZWQM) is a comprehensive simulation model designed to predict the hydrologic response, including potential for groundwater contamination, of alternative crop-management systems. The model is one-dimensional (vertical into the soil profile) and integrates physical, biological and chemical processes. It simulates crop development and the movement of water, nutrients and pesticides over and through the root zone for a representative unit area of an agricultural field over multiple years” and is for “manure and pesticide applications” (Abstract, p. 161), “The hydrologic processes include water infiltration from rain or irrigation, redistribution of water throughout the soil profile, plant water uptake and evaporation. In addition, this component ultimately controls the rate of chemical transport through the soil matrix, over the soil surface and into the plant” (p. 164), “The Green-Ampt equation is used to calculate infiltration rates into the soil,” “Zwf is the depth of the wetting front” (p. 165), “Average infiltration rate, V, is calculated” (p. 165), and “The saturated wetting front during infiltration may reach a shallow water table, if present” (p. 165). Use of the RZWQM to simulate and predict hydrologic response, including infiltration rates and the rates of transport through soil in agricultural fields, including depth of wetting front and if infiltration may reach a shallow water table, in Hanson, reads on the recited limitation.
Hanson discloses “use of experimentation and modeling is an efficient way to device and test new agricultural management systems” (Abstract), similar to the claimed invention and to Lazarus. 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 NAFS processes, of Lazarus, to include elements of the RZWQM, of Hanson, for how comprehensive RZWQM is, as taught by Hanson (see Abstract).
The combination of Lazarus, Hanson, and Arriaza (hereinafter referred to as “Lazarus/Hanson/Arriaza”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Lazarus/Hanson:
The claimed “control data” being of a kind “comprising one or more machine-readable instructions” - Lazarus discloses, “Automated controls could be added such that the program controlled an automated irrigation and valving control system” (para. [0006]). Although the examiner contends that automated controls necessarily include machine-readable instructions, Arriaza is cited for additional clarity. Arriaza discloses, “The system receives agronomic field data for a particular agronomic field, the agronomic field data comprising one or more input parameters for each of a plurality of locations on the agronomic field, nutrient application values for each of the plurality of locations, and measured yield values for each of the plurality of locations. The system computes, for each location of the plurality of locations, a required nutrient value indicating a required amount of nutrient to produce the measured yield values” (Abstract), “similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs” (para. [0072]), and “the agricultural intelligence computing system uses the nutrient recommendation to generate a script for an agricultural implement. The script may comprise one or more sets of instructions which, when executed by the agricultural implement, cause the agricultural implement to release a nutrient onto the agronomic field as per the nutrient recommendation. For example, the agricultural intelligence computing system may use nutrient recommendation to create application parameters for a nutrient release valve that describe an amount of a nutrient to release on one or more fields. The agricultural intelligence computing system may send the application parameters to an application controller which implements the application parameters using an agricultural implement, such as by cause the nutrient release valve to release the recommended amount of nutrient onto the agronomic field” (para. [0146]). The agricultural intelligence computing system providing application parameters for the application controller, in Arriaza, reads on the recited limitation.
“... providing the control data, including an application rate limit determined from the migration risk index, to agricultural equipment so that the agricultural equipment adjusts the application rate at the target location as the agricultural equipment operates in the field, ..” - See the aspects of Arriaza that have been referenced above. Providing the application parameters to the application controller that implements the parameters by causing the nutrient release value to release the recommended amount of nutrient onto the agronomic field, in Arriaza, when applied in the context of the automated controls for valving control systems, in Lazarus, reads on the recited limitation.
Arriaza discloses “an agricultural intelligence computing system” (Abstract), similar to the claimed invention and to Lazarus/Hanson. 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 automated controls, of Lazarus/Hanson, to include the application parameters, controller, and implement operations, of Arriaza, for “improving the performance of farming implements,” per Arriaza (para. [0148]).
Regarding claim 2, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, wherein the migration data comprises at least soil mobility data of the product.” - Lazarus discloses, “Leaching Index“ (para. [0172]). The calculated output reports data that includes or is otherwise based on the leaching index as an input, in Lazarus, reads on the recited limitation. Additionally or alternatively, virtually any data about soil characteristics reads on “soil mobility data,” including, for example, the “6. Soils nutrient status” of Lazarus (para. [0181]).
Regarding claim 3, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, wherein the soil mobility model is based on physical-chemical interaction of the product with the soil and/or degradation characteristics of the product.” - Lazarus discloses, “The NAFS system takes input data such as described above, QA/QC's the input data, processes and analyzes the data by performing calculations, running inbuilt models, and generates reports such as nutrient application forecasting reports “ (para. [0087]), “A. Inputs” and “the following data, related to each input component presented in FIG. 1, would be cured, collected, and input into NAFS, and updated as available“ (paras. [0104] and [0105]), “5. Chemical Fertilizers” and “Chemical Composition” (paras. [0149] and [0152]), and “7. Soils” and “Soil chemical and physical analysis data,” “P Index,” “Soil texture,” “Soil structure,” “Map unit,” “Soil limitations for crop irrigation,” and “Leaching Index” (paras. [0165]-[0172]). The inbuilt models utilizing composition and other physical data about soil and chemicals, in Lazarus, reads on the recited limitation.
Regarding claim 4, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, wherein the weather data further comprises future precipitation for the location.” - Lazarus discloses, “The embodiments of the invention provide AFOs/CAFOs with the ability to forecast the results of their application of AFO/CAFO generated manures and other chemical fertilizers and irrigation water to government regulated land application areas, in particular so as not to exceed regulatory limits and to follow basic agronomic standards. In preferred embodiments NAFS may utilize statistical database tools, software programs such as Excel, and databases such as SQL, Oracle or Access, as well as a variety of input data” and “rainfall predictions” (para. [0041]). The data about rainfall predictions, in Lazarus, reads on the recited limitation.
Regarding claim 5, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, wherein the location is part of the field and wherein the field comprises more than one location and/or the location is defined by geographic data.” - Lazarus discloses, “running real time forecast for a plurality of fields” (abstract). Various fields and combinations thereof, or parts of the fields, in Lazarus, read on the recited limitation.
Regarding claim 6, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, wherein the migration risk index comprises at least two stages.” - See the aspects of Lazarus that have been mentioned above. Instances where values in output reports of the NAFS promote minimizing the potential for groundwater contamination via leaching through soil, and other instances where the values, and other instances where they promote less or work against the minimizing, in Lazarus, reads on the recited limitation.
Regarding claim 7, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, further comprising: determining at the application rate of the product for the location based on the migration risk index.” - See the aspects of Arriaza that have been referenced above. Arriaza also discloses, “pesticide application rate controllers, such as metering drive controllers, orifice size or position controllers” (para. [0085]). Setting the application rates, in Arriaza, when applied in the context of the automated control, in Lazarus/Hanson, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 7.
Regarding claim 8, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 1, wherein the product comprises agrochemicals.” - Lazarus discloses, “inputting a parameter relating to the addition of the at least one targeted substance to a field by means of nutrient sources including at least one of manure solids, compost and commercial chemical fertilizers, and nutrients” (claim 11). The substances, in Lazarus, read on the recited limitation.
Regarding claim 11, while the claim is of different scope relative to claim 1, the claim recites limitations similar to those of claim 1. As such, the rationales applied in the rejection of claim 1 also apply for purposes of rejecting claim 11. Claim 11 is, therefore, also rejected under 35 USC 103 as obvious in view of Lazarus/Hanson/Arriaza.
Regarding claim 17, the Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 5, wherein the geographic data comprises size, shape, and geographic coordinates.” - Lazarus discloses, “In the example each field is given a name, a number, its acreage” (para. [0204]). Arriaza discloses, “Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries” (para. [0040]). Fields being given acreages, boundary identifiers, and geographic identifiers and coordinates, in Lazarus and Arriaza, reads on the recited limitation. Such a combination would have been obvious for purposes of providing accurate field data, per Arriaza (para. [0040]).
Regarding claim 18, 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 used to reject claim 6 also apply for purposes of rejecting claim 18. Claim 18 is, therefore, also rejected under 35 USC 103 as obvious in view Lazarus/Hanson/Arriaza.
Regarding claim 19, Lazarus/Hanson/Arriaza teaches the following limitations:
“The method of claim 8, wherein the agrochemicals are pesticides, fungicides, and/or fertilizers.” - See the aspects of Lazarus that have been mentioned above. Substances like the manure solids, compost and commercial chemical fertilizers, and nutrients, in Lazarus, read on the recited limitation.
Claims 9, 12-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus, in view of Hanson, further in view of Arriaza, and further in view of U.S. Pat. App. Pub. No. 2019/0347836 A1 to Sangireddy et al. (hereinafter referred to as “Sangireddy”).
Regarding claim 9, the combination of Lazarus, Hanson, Arriaza, and Sangireddy (hereinafter referred to as “Lazarus/Hanson/Arriaza/Sangireddy”) teaches limitations below that do not appear to be taught in their entirety by Lazarus/Hanson/Arriaza:
“The method according to claim 1, further comprising: generating a migration risk map of a field based on the determined migration risk indexes.” - See the aspects of Lazarus that have been referenced above. Lazarus does not appear to disclose or suggest the use of maps, outside of mentioning a “Map unit” (para. [0170]). Sangireddy teaches, “Display of graphical maps of agricultural fields, coded with color or other indicators of values of data pertaining to agronomy at high resolution, and updated on a daily basis or on demand by recalculating agronomy models with the high-resolution data” (abstract). Displaying the output reports values generated, in Lazarus, on annotated graphical maps, as in Sangireddy, reads on the recited limitation.
Sangireddy discloses “agronomy models” (abstract), similar to the claimed invention and to Lazarus/Hanson/Arriaza. 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 reporting of outputs of models, in Lazarus/Hanson/Arriaza, to include the use of annotated maps, as in Sangireddy, as a way to visually communicate values using color and other visual imagery, as taught by Sangireddy (para. [0047]).
Regarding claim 12, while the claim is of different scope relative to claims 1 and 9, the claim recites limitations similar to those recited by claims 1 and 9. As such, the rationales used to reject claims 1 and 9 also apply for purposes of rejecting claim 12. Claim 12 is, therefore, also rejected under 35 USC 103 as obvious in view of Lazarus/Hanson/Arriaza/Sangireddy.
Regarding claim 13, Lazarus/Hanson/Arriaza/Sangireddy teaches the following limitations:
“The method of claim 12, further comprising: determining the application rate of the product for the locations of the field; and ...” - See the aspects of Arriaza that have been referenced above. Setting the application rate of the nutrients for locations of the agronomic field, in Arriaza, reads on the recited limitation.
“... generating an application rate map of the product for the field based on the determined application rates.” - See the aspects of Arriaza that have been referenced above. Arriaza also discloses, “computing, for each of the subset of the plurality of locations, a residual value comprising a difference between the required nutrient value and the nutrient application value; generating a residual map comprising the residual values at the subset of the plurality of locations; generating, from the residual map and the one or more input parameters for each of the plurality of locations, particular model correction data for the particular agronomic field; storing the particular model correction data with an identifier of the particular agronomic field” (para. [0036]). Generating the residual maps for the nutrients for the locations of the agronomic field based on the required nutrients to be applied by the application rate controller, in Arriaza, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 13.
Regarding claim 14, Lazarus/Hanson/Arriaza/Sangireddy teaches the following limitations:
“The method of claim 12, further comprising: controlling agricultural equipment by the control data generated based on the migration risk map.” - See the aspects of Lazarus, Arriaza, and Sangireddy that have been referenced above. The application controller setting application parameters for controlling metering valves, based on the maps, of Arriaza and Sangireddy, when applied in the context of the automated valve control, in Lazarus, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 14.
Regarding claim 15, Lazarus/Hanson/Arriaza/Sangireddy teaches the following limitations:
“The method of claim 12, further comprising: executing the method, by the processor, based on instructions encoded on a non-transitory computer-readable medium.” - See the aspects of Lazarus and Sangireddy that have been referenced above. Lazarus also discloses, “The invention includes a method, utilizing a computer and software” (para. [0183]). Sangireddy discloses, “Computer system 400 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 400 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in main memory 406. Such instructions may be read into main memory 406 from another storage medium, such as storage device 410. Execution of the sequences of instructions contained in main memory 406 causes processor 404 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions” (para. [0111]). Utilizing the computer and software in Lazarus, including memory, instructions, and processors, in Sangireddy, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply for purposes of rejecting claim 15.
Regarding claim 16, while the claim is of different scope relative to claims 1 and 9, the claim recites limitations similar to those recited by claims 1 and 9. As such, the rationales applied in the rejections of claims 1 and 9 also apply for purposes of rejecting claim 16. Claim 16 is, therefore, also rejected under 35 USC 103 as obvious in view of Lazarus/Hanson/Arriaza/Sangireddy.
Regarding claim 20, Lazarus/Hanson/Arriaza/Sangireddy teaches the following limitations:
“The method of claim 16, wherein the agricultural equipment is a sprayer vehicle.”- See the aspects of Lazarus and Arriaza that have been referenced above. Sangireddy discloses, “a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130” (para. [0051]), and “examples of controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position” (para. [0093]). Using the information outputs to programmatically control the irrigation and valving control system, in Lazarus, by generating scripts based thereon for controlling operating parameters of agricultural machinery, including sprayers and their valve controllers and position actuators, in Sangireddy, reads on the recited limitation. Equipment including the sprayers, in Sangireddy, read on the recited limitation. 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 use of outputs, of Lazarus, to include control of agricultural machinery parameters, as in Arriaza, including sprayers, as in Sangireddy, for automated and accurate completion of tasks, per Sangireddy (see para. [0051]).
Response to Arguments
In view of the amendments to the claims, and associated remarks, in the Response, the prior rejection of the claims under 35 USC 101 has been reconsidered and withdrawn.
On pp. 10-13 of the Response, the applicant requests reconsideration and withdrawal of the prior rejections under 35 USC 102 and 103. 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
WIPO Int’l Pub. No. 2012/047834 A1 to Klavins discloses, “A method for establishing an agricultural pedigree for agricultural products comprises the steps of: (a) Providing an open communication network accessible information storage device adapted to receive input of data relating to agricultural product production and distribution from multiple sources; (b) Inputting said data into said information storage device; (c) Storing and said data; and (d) Providing access to said data via the open communication network, wherein the information storage device is configured to be used as at least: (i) A tool for traceability of the agricultural products, (ii) A real time decision making tool, and (iii) A predictive modeling tool.” (Abstract.)
Luck, J. D., et al. "Potential for pesticide and nutrient savings via map-based automatic boom section control of spray nozzles." Computers and Electronics in Agriculture 70.1 (2010): 19-26.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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