DETAILED ACTION
This non-final Office action is responsive to the application filed July 28th, 2025. Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/11/26 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 10 and 18 are objected to because of the following informalities: the claims ending limitation recites “the overall one or more target fields” which lacks antecedent basis and should recite “the one or more target fields”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7, 9, 12-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ethington (U.S 2016/0073573 A1) in view of Griffin (U.S 2014/0278731 A1) in view of Aldor-Noiman (U.S 2017/0228475 A1).
Claim 1
Regarding Claim 1, Ethington discloses the following:
A computer-implemented method for identifying hybrid seeds for planting in fields, the method comprising [see at least Paragraph 0006 for reference to a computer-implemented method for recommending agricultural activities; Paragraph 0023 for reference to methods for analyzing crop-related data and providing field condition data and strategic recommendations; Figure 5 and related text regarding the method for managing agricultural activities; Figure 6 and related text regarding a method for recommending agricultural activities in the agricultural environment]
receiving, by a processor, a first dataset of hybrid seeds for planting on one or more target fields, the first dataset including: historical yield data for the hybrid seeds, one or more properties of plants grown from the hybrid seeds, and environmental classification data for the hybrid seeds [see at least Paragraph 0006 for reference to the method including receiving a plurality of field definition data and retrieving a plurality of input data from data networks; Paragraph 0029 for reference to field-specific data provided to the agricultural intelligence computer system including (a) field data (e.g., field name, soil type, acreage, tilling status, irrigation status), (b) harvest data (e.g., 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, weather information (e.g., temperature, rainfall) to the extent maintained or accessible by the user, previous growing season information), (c) soil composition (e.g., pH, organic matter (OM), cation exchange capacity (CEC), (d) planting data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) nitrogen data (e.g., application date, amount, Source), (f) pesticide data (e.g., pesticide, herbicide, fungicide, other Substance or mixture of Substances intended for use as a plant regulator, defoliant, or desiccant), (g) irrigation data (e.g., application date, amount, Source), and (h) Scouting observations (photos, videos, free form notes, Voice recordings, Voice transcriptions, weather conditions (temperature, precipitation (current and over time), Soil moisture, crop growth stage, wind Velocity, relative humidity, dew point, black layer)); Paragraph 0059 for reference to the planting advisor module providing planting date recommendations specific to the location of the field; Paragraph 0060 for reference to the planting advisor module receiving data to make reservations based on seed characteristic data including seed hybrid data; Paragraph 0063 for reference to the planting advisor recommended seed characteristic being based on maximum average yield]
receiving, by the processor, location data for the one or more target fields [see at least Paragraph 0075 for reference to the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location; Paragraph 0088 for reference to grower 110 provides field definition data 160 descriptive of the location, layout, geography, and topography of fields 120 via user devices; Paragraph 0089 for reference to agricultural intelligence computer system 150 identifies a location for each of fields 122 and 124 based on field definition data 160 and identifies a field region for each of fields 122 and 124]
selecting, by the processor, a subset of the hybrid seeds based at least on the environmental classification data for the hybrid seeds, the location data for the one or more target fields, and the one or more properties of the plants grown from the hybrid seeds [see at least Paragraph 0165 for reference to the grower applying a filter to identify particular subgroups of fields for review based on characteristics including average yield; Figure 25 and related text regarding item 2510 ‘filter’ applied to particular subgroups of fields for review including current cultivated crop, acreage, average yield, tilling practices or methods, and residue levels]
generating, by the processor, a representative yield value for each hybrid seed in the subset of hybrid seeds based on one or more averages, per growth cycle, of the historical yield data [see at least Paragraph 0029 for reference to field specific data including expected yield and actual yield as a subset of harvest data based off the previous growing season; Paragraph 0033 for reference to the agricultural intelligence computer system using a client-server architecture configured for exchanging data over a network; Paragraph 0049 for reference to the agricultural intelligence computing system using data regarding the user's farming practices within the current season and for historical seasons, thereby facilitating historical analysis; Paragraph 0049 for reference to the agricultural intelligence system using historical practices of each particular grower on a subject field or to alternately use historical practices for the corresponding region to predict the planting date of a crop when the actual planting date is not provided by the grower; Paragraph 0061 for reference to the planting advisory module receiving and processing data points to simulate possible yield potentials; Paragraph 0063 for reference to recommended seed characteristic may be recommended based on any of the maximum yield at any planting date, the maximum average yield across a set of planting dates, or the earliest possible harvesting date (e.g., where a later harvesting date is not desired due to predicted weather, a relative maturity may be selected in order to enable a desired harvesting date); Paragraph 0074 for reference to the field health advisory module receiving and processing data to determine and identify a crop health index]
generating, by the processor, risk values for the subset of hybrid seeds based on yield variability of the hybrid seeds over time as indicated in the historical yield data [see at least Paragraph 0060 for reference to field specific data including an indication of risk preference; Paragraph 0060 for reference to the indication of risk preference relating to the user being willing to risk a specific number of bushels per acre to increase the chance of producing a specific larger number of bushels per acre; Paragraph 0061 for reference to risk tolerance being calculated based for a high profit/high risk scenario, a low risk scenario, a balanced risk/profit scenario, and a user defined scenario]
generating, by the processor, a second dataset of hybrid seeds for planting based on the risk values and the representative yield values, wherein the second dataset includes ones of the hybrid seeds from the subset of hybrid seeds that have representative yield values above a specific yield threshold and risk values below a specific risk target [see at least Paragraph 0022 for reference to growers must consider at least some of the following decision constraints: fuel and resource costs, historical and projected weather trends, soil conditions, projected risks posed by pests, disease and weather events, and projected market values of agricultural commodities (i.e., crops); Paragraph 0061 for reference to the planting advisor module generating such simulations for each planting date and displays a planting date recommendation for the user on the user device wherein the recommendation includes the recommended planting date, projected yield, relative maturity, and graphs the projected yield against planting date; Paragraph 0061 for reference to the planting advisor utilizing additional data to generate simulations including a risk tolerance; Paragraph 0061 for reference to the planting advisor module providing the option of modeling and displaying alternative yield scenarios for planting data and projected yield by modifying one or more data points associated with seed characteristic data, field-specific data, desired planting population and/or seed cost, expected yield, and/or indication of risk preference]
While Ethington discloses the limitations above, it does not disclose generating, by the processor, a representative yield value for each hybrid seed in the subset of hybrid seeds based on one or more averages, per growth cycle, of the historical yield data; where the specific yield threshold and specific risk target are defined by a relative curve; and controlling, by the processor, an agricultural machine to plant, in the one or more target fields, the hybrid seeds of the second dataset.
However, Griffin discloses the following:
generating, by the processor, a representative yield value for each hybrid seed in the subset of hybrid seeds based on one or more averages, per growth cycle, of the historical yield data [see at least Paragraph 0063 for reference to the method giving greater weight running portfolio analysis on data sets relating to crop seasons that had a “typical” or average yield rather than crop seasons for which yield was abnormally high or abnormally low; Paragraph 0084 for reference to historical and current data are therefore used to populate predictive models, to assist growers and others in the agricultural production industries; Figure 4 and related text regarding a graph based on the information that underlies the chart shown in FIG. 3, depicting the average output (means) and covariances between all bundle combinations of products]
where the specific yield threshold and specific risk target are defined by a relative curve [see at least Paragraph 0058 for reference to individual farms and products can be evaluated against the potential possibilities curve (the efficient frontier) to show how a more efficient bundle of products can increase expected output (means), lower realized risk, or both; Figure 4 and related text regarding a graph based on the information that underlies the chart shown in FIG. 3, depicting the average output (means) and covariances between all bundle combinations of products; Figure 5 and related text regarding the depicted Grower 2's selected soybean varieties and the percentage of total crop acreage planted, plotted against average yield versus risk]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the growth cycle and curve analysis of Griffin. Doing so would help farmers mitigate risks and improve profitability, as stated in Griffin (Paragraph 0073).
While the combination of Ethington and Griffin disclose the limitations above, they do not disclose controlling, by the processor, an agricultural machine to plant, in the one or more target fields, the hybrid seeds of the second dataset.
However, Aldor-Noiman discloses the following:
controlling, by the processor, an agricultural machine to plant, in the one or more target fields, the hybrid seeds of the second dataset [see at least Paragraph 0054 for reference to 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 to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system; Paragraph 0083 for reference to an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the control of agricultural machines of Aldor-Noiman. Removing or correcting some of the measurements helps reduce the impact on the yield measurements of factors such as weather, as stated by Aldor-Noiman (Paragraph 0048).
Claim 2
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 2, Ethington discloses the following:
wherein the environmental classification data for the hybrid seeds includes relative maturity of each of the hybrid seeds [see at least Paragraph 0049 for reference to agricultural intelligence computer system determines a relative maturity value of the crops based on expected heat units over the growing season in light of the planting date, the user's farming practices, and field-specific data; Paragraph 0060 for reference to seed characteristic data including see maturity level data including for example, a relative maturity level of a given seed (e.g., a comparative relative maturity (“CRM”) value or a silk comparative relative maturity (“silk CRM”)), growing degree units (“GDUs”) until a given stage such as silking, mid-pollination, black layer, or flowering, and a relative maturity level of a given seed at physiological maturity (“Phy. CRM”)]
Claim 3
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 3, Ethington discloses the following:
wherein the one or more properties of the plants grown from the hybrid seeds include a height of the plants grown from the hybrid seeds [see at least Paragraph 0070 for reference to suitable seed data may include, for example, data related to, grain drydown, stalk strength, root strength, stress emergence, staygreen, drought tolerance, ear flex, test eight, plant height, ear height, mid-season brittle stalk, plant vigor, fungicide response, growth regulators sensitivity, pigment inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing, kernel texture, emergence, harvest appearance, harvest population, seedling growth, cob color, and husk cover]
Claim 4
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 4, Ethington discloses the following:
wherein the hybrid seeds include corn hybrid seeds [see at least Paragraph 0048 for reference to the agricultural intelligence computer system provides field growth stage conditions (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages) for the crops being grown in each listed field; Paragraph 0125 for reference to as part of field condition data 180 provided, agricultural intelligence computer system 150 runs or executes field growth stage data module 413 (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages)]
Claim 5
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein a higher risk value for a hybrid seed of the risk values is associated with a higher year-to-year or field-to-field yield variability of the hybrid seeds.
Regarding Claim 5, Griffin discloses the following:
wherein a higher risk value for a hybrid seed of the risk values is associated with a higher year-to-year or field-to-field yield variability of the hybrid seeds [see at least Paragraph 0075 for reference to the data being analyzed to determine the means and covariances and the covariances are calculated for the entire system (or subset of data) and then used to determine the bundle of products that minimize covariance risk for a given level of output (e.g. yield, returns, profit, etc.); Paragraph 0073 for reference to the use of multiyear on-farm data being used to populate models; Paragraph 0074 for reference to the data including crop variety or hybrid]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the year-to-year variance calculation of Griffin. Providing the year-to-year variance calculations help farmers mitigate risks and improve profitability, as stated in Griffin (Paragraph 0073).
Claim 6
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 6, Ethington discloses the following:
wherein generating the second dataset comprises determining a relationship between the representative yield value for a specific hybrid seed and the risk value associated with the specific hybrid seed [see at least Paragraph 0061 for reference to the planting advisor module receives and processes the sets of data points to simulate possible yield potentials wherein possible yield potentials are calculated for various planting dates; Paragraph 0061 for reference to the planting advisor module additionally utilizes additional data to generate such simulation wherein the additional data may include simulated weather between the planting data and harvesting date, field workability, seasonal freeze risk, drought risk, heat risk, excess moisture risk, estimated soil temperature, and/or risk tolerance]
Claim 7
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 7, Ethington discloses the following:
wherein generating the second dataset comprises determining an expected yield return for a specified amount of risk [see at least Paragraph 0029 for reference to field specific data including expected yield and actual yield as a subset of harvest data based off the previous growing season; Paragraph 0033 for reference to the agricultural intelligence computer system using a client-server architecture configured for exchanging data over a network; Paragraph 0049 for reference to the agricultural intelligence computing system using data regarding the user's farming practices within the current season and for historical seasons, thereby facilitating historical analysis; Paragraph 0049 for reference to the agricultural intelligence system using historical practices of each particular grower on a subject field or to alternately use historical practices for the corresponding region to predict the planting date of a crop when the actual planting date is not provided by the grower; Paragraph 0061 for reference to the planting advisory module receiving and processing data points to simulate possible yield potentials; Paragraph 0074 for reference to the field health advisory module receiving and processing data to determine and identify a crop health index]
Claim 9
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein generating the second dataset comprises fitting a frontier curve from the representative yield values and risk values such that a specific hybrid seed to which a specific point on the frontier curve corresponds that has a higher yield is associated with a higher risk.
Regarding Claim 9, Griffin discloses the following:
wherein generating the second dataset comprises fitting a frontier curve from the representative yield values and risk values such that a specific hybrid seed to which a specific point on the frontier curve corresponds that has a higher yield is associated with a higher risk [see at least Paragraph 0058 for reference to the individual farms and products being evaluated against a potential probabilities curve (the efficient frontier) which provides the least risky combinations to achieve a particular yield rate; Figure 4 and related text regarding the efficient frontier curve in which each circle [item 2] represents the average yield and risk associated with obtaining a certain yield with that variety and growers can select which portfolio of variety against the curve (item 8)]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the optimal frontier curve usage of Griffin. Using the optimal frontier curve allows growers to make conscious decisions and manage their risk while maximizing potential return without undertaking undesired risk levels, as stated in Griffin (Paragraph 0058).
Claim 12
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein controlling the agricultural machine includes controlling the agricultural machine, via an executable script transmitted to the agricultural machine, to cause the agricultural machine to plant one or more target fields with the second dataset of hybrid seeds.
Regarding Claim 12, Aldor-Noiman discloses the following:
wherein controlling the agricultural machine includes controlling the agricultural machine, via an executable script transmitted to the agricultural machine, to cause the agricultural machine to plant one or more target fields with the second dataset of hybrid seeds [see at least Paragraph 0054 for reference to 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 to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system; Paragraph 0083 for reference to an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the control of agricultural machines of Aldor-Noiman. Removing or correcting some of the measurements helps reduce the impact on the yield measurements of factors such as weather, as stated by Aldor-Noiman (Paragraph 0048).
Claim 13
Regarding Claim 13, Ethington discloses the following:
An agricultural intelligence computer system for use in identifying hybrid seeds for planting in fields, the agricultural intelligence computer system comprising [see at least Paragraph 0007 for reference to a networked agricultural intelligence system for recommending agricultural activities is provided; Paragraph 0023 for reference to methods and systems for analyzing crop-related data, and providing field condition data and strategic recommendations for maximizing crop yield are desirable; Figure 1 and related text regarding example agricultural environment including a plurality of fields that are monitored and managed with an agricultural intelligence computer system that is used to manage and recommend agricultural activities]
one or more processors [see at least Paragraph 0099 for reference to user system 202 includes a processor 205 for executing instructions; Paragraph 0099 for reference processor 205 may include one or more processing units, for example, a multi-core configuration; Figure 2 and related text regarding item 205 ‘processor’; Figure 3 and related text regarding item 305 ‘processor’]
one or more non-transitory computer-readable storage media storing executable instructions which, when executed using the one or more processors, cause the one or more processors to [see at least Paragraph 0080 for reference to a computer program is provided, and the program is embodied on a computer readable medium; Paragraph 0080 for reference to one or more components may be in the form of computer-executable instructions embodied in a computer-readable medium; Paragraph 0099 for reference to memory area 210 is any device allowing information such as executable instructions and/or written works to be stored and retrieved wherein memory area 210 may include one or more computer readable media; Figure 2 and related text regarding item 210 ‘memory’]
receive a first dataset of hybrid seeds for planting on one or more target fields, the first dataset including: historical yield data for the hybrid seeds, one or more properties of plants grown from the hybrid seeds, and environmental classification data for the hybrid seeds [see at least Paragraph 0006 for reference to the method including receiving a plurality of field definition data and retrieving a plurality of input data from data networks; Paragraph 0029 for reference to field-specific data provided to the agricultural intelligence computer system including (a) field data (e.g., field name, soil type, acreage, tilling status, irrigation status), (b) harvest data (e.g., 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, weather information (e.g., temperature, rainfall) to the extent maintained or accessible by the user, previous growing season information), (c) soil composition (e.g., pH, organic matter (OM), cation exchange capacity (CEC), (d) planting data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) nitrogen data (e.g., application date, amount, Source), (f) pesticide data (e.g., pesticide, herbicide, fungicide, other Substance or mixture of Substances intended for use as a plant regulator, defoliant, or desiccant), (g) irrigation data (e.g., application date, amount, Source), and (h) Scouting observations (photos, videos, free form notes, Voice recordings, Voice transcriptions, weather conditions (temperature, precipitation (current and over time), Soil moisture, crop growth stage, wind Velocity, relative humidity, dew point, black layer)); Paragraph 0059 for reference to the planting advisor module providing planting date recommendations specific to the location of the field; Paragraph 0060 for reference to the planting advisor module receiving data to make reservations based on seed characteristic data including seed hybrid data; Paragraph 0063 for reference to the planting advisor recommended seed characteristic being based on maximum average yield]
receive location data for the one or more target fields [see at least Paragraph 0075 for reference to the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location; Paragraph 0088 for reference to grower 110 provides field definition data 160 descriptive of the location, layout, geography, and topography of fields 120 via user devices; Paragraph 0089 for reference to agricultural intelligence computer system 150 identifies a location for each of fields 122 and 124 based on field definition data 160 and identifies a field region for each of fields 122 and 124]
select a subset of the hybrid seeds based at least on the environmental classification data for the hybrid seeds, the location data for the one or more target fields, and the one or more properties of the plants grown from the hybrid seeds [see at least Paragraph 0165 for reference to the grower applying a filter to identify particular subgroups of fields for review based on characteristics including average yield; Figure 25 and related text regarding item 2510 ‘filter’ applied to particular subgroups of fields for review including current cultivated crop, acreage, average yield, tilling practices or methods, and residue levels]
generate a representative yield value for each hybrid seed in the subset of hybrid seeds based on one or more averages, per growth cycle, of the historical yield data [see at least Paragraph 0029 for reference to field specific data including expected yield and actual yield as a subset of harvest data based off the previous growing season; Paragraph 0033 for reference to the agricultural intelligence computer system using a client-server architecture configured for exchanging data over a network; Paragraph 0049 for reference to the agricultural intelligence computing system using data regarding the user's farming practices within the current season and for historical seasons, thereby facilitating historical analysis; Paragraph 0049 for reference to the agricultural intelligence system using historical practices of each particular grower on a subject field or to alternately use historical practices for the corresponding region to predict the planting date of a crop when the actual planting date is not provided by the grower; Paragraph 0061 for reference to the planting advisory module receiving and processing data points to simulate possible yield potentials; Paragraph 0063 for reference to recommended seed characteristic may be recommended based on any of the maximum yield at any planting date, the maximum average yield across a set of planting dates, or the earliest possible harvesting date (e.g., where a later harvesting date is not desired due to predicted weather, a relative maturity may be selected in order to enable a desired harvesting date); Paragraph 0074 for reference to the field health advisory module receiving and processing data to determine and identify a crop health index]
generate risk values for the subset of hybrid seeds based on yield variability of the hybrid seeds over time as indicated in the historical yield data [see at least Paragraph 0060 for reference to field specific data including an indication of risk preference; Paragraph 0060 for reference to the indication of risk preference relating to the user being willing to risk a specific number of bushels per acre to increase the chance of producing a specific larger number of bushels per acre; Paragraph 0061 for reference to risk tolerance being calculated based for a high profit/high risk scenario, a low risk scenario, a balanced risk/profit scenario, and a user defined scenario]
generate a second dataset of hybrid seeds for planting based on the risk values and the representative yield values, wherein the second dataset includes ones of the hybrid seeds from the subset of hybrid seeds that have representative yield values above a specific yield threshold and risk values below a specific risk target, where the specific yield threshold and specific risk target are defined by a relative curve [see at least Paragraph 0022 for reference to growers must consider at least some of the following decision constraints: fuel and resource costs, historical and projected weather trends, soil conditions, projected risks posed by pests, disease and weather events, and projected market values of agricultural commodities (i.e., crops); Paragraph 0061 for reference to the planting advisor module generating such simulations for each planting date and displays a planting date recommendation for the user on the user device wherein the recommendation includes the recommended planting date, projected yield, relative maturity, and graphs the projected yield against planting date; Paragraph 0061 for reference to the planting advisor utilizing additional data to generate simulations including a risk tolerance; Paragraph 0061 for reference to the planting advisor module providing the option of modeling and displaying alternative yield scenarios for planting data and projected yield by modifying one or more data points associated with seed characteristic data, field-specific data, desired planting population and/or seed cost, expected yield, and/or indication of risk preference]
While Ethington discloses the limitations above, it does not disclose generate a representative yield value for each hybrid seed in the subset of hybrid seeds based on one or more averages, per growth cycle, of the historical yield data; where the specific yield threshold and specific risk target are defined by a relative curve; and control an agricultural machine to plant, in the one or more target fields, the hybrid seeds of the second dataset.
However, Griffin discloses the following:
generate a representative yield value for each hybrid seed in the subset of hybrid seeds based on one or more averages, per growth cycle, of the historical yield data [see at least Paragraph 0063 for reference to the method giving greater weight running portfolio analysis on data sets relating to crop seasons that had a “typical” or average yield rather than crop seasons for which yield was abnormally high or abnormally low; Paragraph 0084 for reference to historical and current data are therefore used to populate predictive models, to assist growers and others in the agricultural production industries; Figure 4 and related text regarding a graph based on the information that underlies the chart shown in FIG. 3, depicting the average output (means) and covariances between all bundle combinations of products]
where the specific yield threshold and specific risk target are defined by a relative curve [see at least Paragraph 0058 for reference to individual farms and products can be evaluated against the potential possibilities curve (the efficient frontier) to show how a more efficient bundle of products can increase expected output (means), lower realized risk, or both; Figure 4 and related text regarding a graph based on the information that underlies the chart shown in FIG. 3, depicting the average output (means) and covariances between all bundle combinations of products; Figure 5 and related text regarding the depicted Grower 2's selected soybean varieties and the percentage of total crop acreage planted, plotted against average yield versus risk]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the growth cycle and curve analysis of Griffin. Doing so would help farmers mitigate risks and improve profitability, as stated in Griffin (Paragraph 0073).
While the combination of Ethington and Griffin disclose the limitations above, they do not disclose control an agricultural machine to plant, in the one or more target fields, the hybrid seeds of the second dataset.
However, Aldor-Noiman discloses the following:
control an agricultural machine to plant, in the one or more target fields, the hybrid seeds of the second dataset [see at least Paragraph 0054 for reference to 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 to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system; Paragraph 0083 for reference to an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the control of agricultural machines of Aldor-Noiman. Removing or correcting some of the measurements helps reduce the impact on the yield measurements of factors such as weather, as stated by Aldor-Noiman (Paragraph 0048).
Claim 14
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 14, Ethington discloses the following:
wherein the one or more properties of the plants grown from the hybrid seeds include a height of the plants grown from the hybrid seeds [see at least Paragraph 0070 for reference to suitable seed data may include, for example, data related to, grain drydown, stalk strength, root strength, stress emergence, staygreen, drought tolerance, ear flex, test eight, plant height, ear height, mid-season brittle stalk, plant vigor, fungicide response, growth regulators sensitivity, pigment inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing, kernel texture, emergence, harvest appearance, harvest population, seedling growth, cob color, and husk cover]
wherein the hybrid seeds include corn hybrid seeds [see at least Paragraph 0048 for reference to the agricultural intelligence computer system provides field growth stage conditions (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages) for the crops being grown in each listed field; Paragraph 0125 for reference to as part of field condition data 180 provided, agricultural intelligence computer system 150 runs or executes field growth stage data module 413 (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages)]
Claim 15
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 15, Ethington discloses the following:
wherein the executable instructions, when executed using the one or more processors to generate the second dataset, cause the one or more processors to determine a relationship between the representative yield value for a specific hybrid seed and the risk value associated with the specific hybrid seed [see at least Paragraph 0061 for reference to the planting advisor module receives and processes the sets of data points to simulate possible yield potentials wherein possible yield potentials are calculated for various planting dates; Paragraph 0061 for reference to the planting advisor module additionally utilizes additional data to generate such simulation wherein the additional data may include simulated weather between the planting data and harvesting date, field workability, seasonal freeze risk, drought risk, heat risk, excess moisture risk, estimated soil temperature, and/or risk tolerance]
Claim 16
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, regarding Claim 16, Ethington discloses the following:
wherein generating the second dataset comprises determining an expected yield return for a specified amount of risk [see at least Paragraph 0029 for reference to field specific data including expected yield and actual yield as a subset of harvest data based off the previous growing season; Paragraph 0033 for reference to the agricultural intelligence computer system using a client-server architecture configured for exchanging data over a network; Paragraph 0049 for reference to the agricultural intelligence computing system using data regarding the user's farming practices within the current season and for historical seasons, thereby facilitating historical analysis; Paragraph 0049 for reference to the agricultural intelligence system using historical practices of each particular grower on a subject field or to alternately use historical practices for the corresponding region to predict the planting date of a crop when the actual planting date is not provided by the grower; Paragraph 0061 for reference to the planting advisory module receiving and processing data points to simulate possible yield potentials; Paragraph 0074 for reference to the field health advisory module receiving and processing data to determine and identify a crop health index]
Claim 17
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein the executable instructions, when executed using the one or more processors to generate the second dataset, cause the one or more processors to fit a frontier curve from the representative yield values and risk values such that a specific hybrid seed to which a specific point on the frontier curve corresponds that has a higher yield is associated with a higher risk.
Regarding Claim 17, Griffin discloses the following:
wherein the executable instructions, when executed using the one or more processors to generate the second dataset, cause the one or more processors to fit a frontier curve from the representative yield values and risk values such that a specific hybrid seed to which a specific point on the frontier curve corresponds that has a higher yield is associated with a higher risk [see at least Paragraph 0058 for reference to the individual farms and products being evaluated against a potential probabilities curve (the efficient frontier) which provides the least risky combinations to achieve a particular yield rate; Figure 4 and related text regarding the efficient frontier curve in which each circle [item 2] represents the average yield and risk associated with obtaining a certain yield with that variety and growers can select which portfolio of variety against the curve (item 8)]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the optimal frontier curve usage of Griffin. Using the optimal frontier curve allows growers to make conscious decisions and manage their risk while maximizing potential return without undertaking undesired risk levels, as stated in Griffin (Paragraph 0058).
Claim 20
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein the executable instructions, when executed using the one or more processors to control the agricultural machine to plant the hybrid seeds of the second dataset, cause the one or more processors to control the agricultural machine, via an executable script transmitted to the agricultural machine, to cause the agricultural machine to plant the one or more target fields with the second dataset of hybrid seeds.
Regarding Claim 20, Aldor-Noiman discloses the following:
wherein the executable instructions, when executed using the one or more processors to control the agricultural machine to plant the hybrid seeds of the second dataset, cause the one or more processors to control the agricultural machine, via an executable script transmitted to the agricultural machine, to cause the agricultural machine to plant the one or more target fields with the second dataset of hybrid seeds [see at least Paragraph 0054 for reference to 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 to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system; Paragraph 0083 for reference to an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the control of agricultural machines of Aldor-Noiman. Removing or correcting some of the measurements helps reduce the impact on the yield measurements of factors such as weather, as stated by Aldor-Noiman (Paragraph 0048).
Claim(s) 8, 11, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ethington (U.S 2016/0073573 A1) in view of Griffin (U.S 2014/0278731 A1) in view of Aldor-Noiman (U.S 2017/0228475 A1), as applied in claims 1 and 13, in view of Xu (U.S 2017/0105335 A1).
Claim 8
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, they do not disclose wherein generating the second dataset comprises selecting a first hybrid seed of the subset of hybrid seeds with a first risk value above a first threshold and a second hybrid seed of the subset of hybrid seeds with a second risk value below a second threshold; and wherein the first hybrid seed and the second hybrid seed have corresponding yield values above a third threshold.
Regarding Claim 8, Xu discloses the following:
wherein generating the second dataset comprises selecting a first hybrid seed of the subset of hybrid seeds with a first risk value above a first threshold and a second hybrid seed of the subset of hybrid seeds with a second risk value below a second threshold; wherein the first hybrid seed and the second hybrid seed have corresponding yield values above a third threshold [see at least Paragraph 0087 for reference to a specific field dataset is evaluated by creating an agronomic model and using specific quality thresholds for the created agronomic model; Paragraph 0087 for reference to the agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not meet configured quality thresholds are used during future data subset selection steps; Figure 3 and related text regarding item 310 ‘Agronomic Data Subset Selection’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the hybrid seed system of Ethington to include the allocation and planting instruction of Xu. Doing so would assist in determining an optimal seeding rate that produces the desired return for the grower, as stated by Xu (Paragraph 0004).
Claim 11
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, they do not disclose determining an allocation quantity for each of the second dataset of hybrid seeds based on an amount and location of each target field of the one or more target fields.
Regarding Claim 11, Xu discloses the following:
determining an allocation quantity for each of the second dataset of hybrid seeds based on an amount and location of each target field of the one or more target fields [see at least Paragraph 0031 for reference to the system being programmed to receive digital data presenting planting parameters including hybrid seed type information and sowing row width; Paragraph 0122 for reference to the mixture model logic using the empirical mixture model to calculate an optimal seeding rate distribution for the target hybrid seeds and row width; Paragraph 0125 for reference to the optimal seeding rate recommendation logic determining point estimation and interval estimation of the optimal seeding rate for the given see and sowing width using the optimal seeding rate distribution; Figure 1 and related text regarding item 170 ‘Seeding Rate Recommendation Subsystem’ and item 175 ‘Optimal Seeding Rate Recommendation Logic’; Figure 5 and related text regarding the process for determining a recommended seeding rate for a specific hybrid seed and sowing width of corn planted at a specific geo-location; Examiner notes distribution of target hybrid seeds as ‘planting quantity’ and row width as ‘planting location’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the hybrid seed system of Ethington to include the allocation and planting instruction of Xu. Doing so would assist in determining an optimal seeding rate that produces the desired return for the grower, as stated by Xu (Paragraph 0004).
Claim 19
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, they do not disclose wherein the executable instructions, when executed by the one or more processors, further cause the one or more processors to determine an allocation quantity for each of the second dataset of hybrid seeds based on an amount and location of each target field of the one or more target fields.
Regarding Claim 19, Xu discloses the following:
wherein the executable instructions, when executed by the one or more processors, further cause the one or more processors to determine an allocation quantity for each of the second dataset of hybrid seeds based on an amount and location of each target field of the one or more target fields [see at least Paragraph 0031 for reference to the system being programmed to receive digital data presenting planting parameters including hybrid seed type information and sowing row width; Paragraph 0122 for reference to the mixture model logic using the empirical mixture model to calculate an optimal seeding rate distribution for the target hybrid seeds and row width; Paragraph 0125 for reference to the optimal seeding rate recommendation logic determining point estimation and interval estimation of the optimal seeding rate for the given see and sowing width using the optimal seeding rate distribution; Figure 1 and related text regarding item 170 ‘Seeding Rate Recommendation Subsystem’ and item 175 ‘Optimal Seeding Rate Recommendation Logic’; Figure 5 and related text regarding the process for determining a recommended seeding rate for a specific hybrid seed and sowing width of corn planted at a specific geo-location; Examiner notes distribution of target hybrid seeds as ‘planting quantity’ and row width as ‘planting location’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the hybrid seed system of Ethington to include the allocation and planting instruction of Xu. Doing so would assist in determining an optimal seeding rate that produces the desired return for the grower, as stated by Xu (Paragraph 0004).
Claim(s) 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ethington (U.S 2016/0073573 A1) in view of Griffin (U.S 2014/0278731 A1) in view of Aldor-Noiman (U.S 2017/0228475 A1), as applied in claims 1 and 13, in view of Starr (U.S 2018/0014452 A1).
Claim 10
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein the subset of hybrid seeds is associated with a seed portfolio of a particular grower; and wherein controlling the agricultural machine to plant the hybrid seeds of the second dataset include controlling the agricultural machine to plant multiple of the hybrid seeds in the one or more target fields, each at a different percentage of the overall one or more target fields.
Regarding Claim 10, Griffin discloses the following:
wherein the subset of hybrid seeds is associated with a seed portfolio of a particular grower [see at least Paragraph 0058 for reference to the boxes 4 a and 4 b represent the portfolio of varieties actually chosen by each of two growers, Grower 1 (depicted by box 4 a) and Grower 2 (depicted by box 4 b); Paragraph 0069 for reference to variation, such as in environmental characteristics and management techniques, means that a given crop variety may perform entirely differently in one grower's portfolio than it would in a different grower's; FIG. 2 shows an exemplary process for determining risk for a portfolio of agricultural products]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the seed portfolio analysis of Griffin. Given the right portfolio of products, as growing conditions in a given year lead to lower yield in one variety, strong performance from another variety might offset some of the realized risk, providing balance and predictability to overall yield and expectations, as stated in Griffin (Paragraph 0006).
While Griffin discloses the limitations above, it does not disclose wherein controlling the agricultural machine to plant the hybrid seeds of the second dataset include controlling the agricultural machine to plant multiple of the hybrid seeds in the one or more target fields, each at a different percentage of the overall one or more target fields.
However, Starr discloses the following:
wherein controlling the agricultural machine to plant the hybrid seeds of the second dataset include controlling the agricultural machine to plant multiple of the hybrid seeds in the one or more target fields, each at a different percentage of the overall one or more target fields [see at least Paragraph 0086 for reference to for in - season scenarios, for example, actual data occurring in real time may be input into the computing element 32, the computing element 32 analyzes the data, outputs data to be viewed by a user, and the user may take action based on the outputted data or the outputted data may be communicated to an agricultural device to control operation of the agricultural device; Paragraph 0173 for reference to a plurality of projections and/or other data may be provided by the system 20 and computing element 32 for a plurality of zones or a plurality of fields; Paragraph 0174 for reference to the system and the computing element may communicate the projections and/or other data to one or more agricultural devices to assist with controlling the one or more agricultural devices in accordance with the communicated data]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the control of agricultural equipment of Aldor-Noiman to include the different percentages of Starr. A user may then address, via the system and computing element, the underperforming zone(s)/ field(s), determine a cause for low or lower performance, and determine a remedy, as stated by Starr (Paragraph 0173).
Claim 18
While the combination of Ethington, Griffin, and Aldor-Noiman disclose the limitations above, Ethington does not disclose wherein the subset of hybrid seeds is associated with a seed portfolio of a particular grower; and wherein the executable instructions, when executed using the one or more processors to control the agricultural machine to plant the hybrid seeds of the second dataset, cause the one or more processors to control the agricultural machine to plant multiple of the hybrid seeds in the one or more target fields, each at a different percentage of the overall one or more target fields.
Regarding Claim 18, Griffin discloses the following:
wherein the subset of hybrid seeds is associated with a seed portfolio of a particular grower [see at least Paragraph 0058 for reference to the boxes 4 a and 4 b represent the portfolio of varieties actually chosen by each of two growers, Grower 1 (depicted by box 4 a) and Grower 2 (depicted by box 4 b); Paragraph 0069 for reference to variation, such as in environmental characteristics and management techniques, means that a given crop variety may perform entirely differently in one grower's portfolio than it would in a different grower's; FIG. 2 shows an exemplary process for determining risk for a portfolio of agricultural products]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the agricultural intelligence system of Ethington to include the seed portfolio analysis of Griffin. Given the right portfolio of products, as growing conditions in a given year lead to lower yield in one variety, strong performance from another variety might offset some of the realized risk, providing balance and predictability to overall yield and expectations, as stated in Griffin (Paragraph 0006).
While Griffin discloses the limitations above, it does not disclose wherein the executable instructions, when executed using the one or more processors to control the agricultural machine to plant the hybrid seeds of the second dataset, cause the one or more processors to control the agricultural machine to plant multiple of the hybrid seeds in the one or more target fields, each at a different percentage of the overall one or more target fields.
However, Starr discloses the following:
wherein the executable instructions, when executed using the one or more processors to control the agricultural machine to plant the hybrid seeds of the second dataset, cause the one or more processors to control the agricultural machine to plant multiple of the hybrid seeds in the one or more target fields, each at a different percentage of the overall one or more target fields [see at least Paragraph 0086 for reference to for in - season scenarios, for example, actual data occurring in real time may be input into the computing element 32, the computing element 32 analyzes the data, outputs data to be viewed by a user, and the user may take action based on the outputted data or the outputted data may be communicated to an agricultural device to control operation of the agricultural device; Paragraph 0173 for reference to a plurality of projections and/or other data may be provided by the system 20 and computing element 32 for a plurality of zones or a plurality of fields; Paragraph 0174 for reference to the system and the computing element may communicate the projections and/or other data to one or more agricultural devices to assist with controlling the one or more agricultural devices in accordance with the communicated data]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the control of agricultural equipment of Aldor-Noiman to include the different percentages of Starr. A user may then address, via the system and computing element, the underperforming zone(s)/ field(s), determine a cause for low or lower performance, and determine a remedy, as stated by Starr (Paragraph 0173).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wych, Robert D. "Production of hybrid seed corn." Corn and corn improvement 18 (1988): 565-607.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2011/0054921 A1
Lynds, Heather
SYSTEM FOR PLANNING THE PLANTING AND GROWING OF PLANTS
CN 106386118 A
Zhou et al.
Agricultural Planting Method
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
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