DETAILED ACTION
This Office Action is in response to Applicants application filing on May 16, 2025. Claim(s) 1-28 is/are currently pending in the instant application. The application is a Continuation of U.S. application 16/813,209 filed on March 9, 2020, now abandoned, which is a Continuation of U.S. application 14/925,797, filed on October 28, 2015, now U.S. Patent 10,586,158.
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 Examiner acknowledges the Applicants filing of IDS references on December 7, 2022, June 7, 2023, June 29, 2023, and January 26, 2024. The references have been considered at this time. A copy of the annotated IDS sheet is included in this correspondence.
Specification
The abstract of the disclosure is objected to because it far exceeds the 150 work limit. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Drawings
The drawings are objected to because Figures 10 and 11 are not clear enough to be understood. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-28 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system or “apparatus”, method or “process”, or product or “article of manufacture” and is a method or product for recommending harvest times which is a process (Step 1: YES).
The Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent product Claim 11. Claim 1 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
receiving data for an identification graphical user interface (GUI) over a digital data communication network from an agricultural intelligence computer system;
displaying the identification GUI on a user device, the identification GUI providing an interface region for a user to input identification data for the one or more agricultural fields;
after receiving input identification data from the user, transmitting the user inputted identification data to the agricultural intelligence computer system;
receiving data for a data manager GUI over the digital data communication network from the agricultural intelligence computer system;
displaying the data manager GUI on the user device, the data manager GUI providing one or more regions for a user to input field data comprising a plurality of values representing crop seed data for the one or more agricultural fields identified;
after receiving input field data from the user, transmitting the inputted field data to the agricultural intelligence computer system;
receiving data for a recommendation GUI over the digital data communication network from the agricultural intelligence computer system; and
displaying the recommendation GUI, the recommendation GUI displaying a harvest time recommendation for harvesting crop grown from a specific hybrid seed planted in the one or more agricultural fields, based, at least in part, on the inputted field data and the inputted identification data.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. Receiving and displaying input data for one or more agricultural field, receiving and displaying input crop seed data for the one or more fields, receiving and displaying recommended harvest time for a crop grown on one or more agricultural fields recites a fundamental economic practice and/or managing personal behaviors. Accordingly, the claim recites an abstract idea. The non-transitory storage media storing instructions executed by a computing device in Claim 11 appears to be just software. Claims 11 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Processes”. Receiving and displaying input data for one or more agricultural field, receiving and displaying input crop seed data for the one or more fields, receiving and displaying recommended harvest time for a crop grown on one or more agricultural fields recites a concept performed in the human mind. But for the “GUI with identification, data manager, and recommendation”, “a digital communication network”, and “ an agricultural intelligence computer system”, the claim encompasses a user collecting data regarding a planted field of corn with a specific type of hybrid seed and tracking input data to determine an estimated harvest time using his/her mind and/or pen and paper. The mere nominal recitation of GUI’s and data transmitted between a user device and a agriculture computer system does not take the claim out of the mental processes grouping. Accordingly, the claim recites an abstract idea. The non-transitory storage media storing instructions executed by a computing device in Claim 11 appears to be just software. Claims 11 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mathematical Concepts”. Ingesting data regarding a specific field planted with a specific type of corn while also tracking weather data to estimate and recommend optimal harvest time recites a mathematical formulas, equations, and calculations. While not specifically disclosing an equation in the claim, the disclosure includes equations [0123-00125] related to calculating moisture content and [0135] related to hybrid seed moisture at R6. [0140] includes an equation for dry down rate. [0146] further defines an equation for dry down rate. The claim relates to a calculation based on input parameters for weather and temperature to optimize the harvest recommendation based on drying a specific type of corm. Accordingly, the claim recites an abstract idea. The non-transitory storage media storing instructions executed by a computing device in Claim 11 appears to be just software. Claims 11 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite GUI’s, a user device, a digital data communication network and an agricultural intelligence system (Claims 1 and 11). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 11 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0053] about implementation using general purpose or special purpose computing devices [user 102 interacts with agricultural intelligence computer system 130 using field manager computing device 104 configured with an operating system and one or
more application programs or apps; the field manager computing device 104 also may interoperate with the agricultural intelligence computer system independently and automatically under program control or logical control and direct user interaction is not always required. Field manager computing device 104 broadly represents one or more of a smart phone, PDA, tablet computing device, laptop computer, desktop computer, workstation, or any other computing device capable of transmitting and receiving information and performing the functions described herein. Field manager computing device 104 may communicate via a network using a mobile application stored on field manager computing device 104, and in some embodiments, the device may be coupled using a cable 113 or connector to the sensor 112 and/or controller 114.] and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1 and 11 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-10, and 12-28 further define the abstract idea that is present in their respective independent claims 1 and 11 and thus correspond to Certain Methods of Organizing Human Activity, Mental Processes, and or Mathematical Concepts and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims include steps or processes which are similar to that disclosed in MPEP 2106.05(d), (f), (g), and/or (h) which include activities and functions the courts have determined to be well-understood, routine, and conventional when claimed in a generic manner, or as insignificant extra solution activity, or as merely indicating a field of use or technological environment in which to apply the judicial exception.
Claim 2 is covered by MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and MPEP 2106.05(d) II. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claims 3 and 10 are covered by MPEP 2106.05(d) II. iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
Claim 4 is a known calculation is therefore merely MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Claims 5, 7, and 9 are covered by MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Claim 6 is a further calculation based on historical data MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Claim 8 is covered by MPEP 2106.05(f)(2) v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Claims 21 and 22 are directed to MPEP 2106.05(g)(3) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
Claim 23 is equivalent to MPEP 2106.05(d) II. iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
Claim 24 is covered by MPEP 2106.05(d) II. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claims 12-20 and 15-28 are identical to dependent claims 2-10 and 21-24.
Therefore, the claims 2-10 and 12-28 are directed to an abstract idea. Thus, the claims 1-28 are not patent-eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mewes et al. U.S. Publication 2016/0217230 A1 (hereafter Mewes).
Regarding claim 1, receiving data for an identification graphical user interface (GUI) over a digital data communication network from an agricultural intelligence computer system (see at least [0023] harvest advisory model 100, input data 102 and communication network [0050] These data are made available via an API-based system for obtaining weather data for any given location and timeframe. Likewise, global, national, and regional databases of soil and land-cover characteristics are also made available via an API system, making these potentially predictive parameters readily available for association with any harvest-related observations that might become available. These data, along with user-provided crop metadata, can be used to diagnose and predict the growth and maturation of a crop to be harvested, with or without the aforementioned systems and methods including whether or not the processes are performed on a mobile device. Using a field data collection device, such as a smartphone or tablet, observations of conditions impacting harvest operations can be provided in near real-time. These observations may include grain or plant moisture samples (whether from a trial harvest operation or manual sampling), crop harvestability metrics, field accessibility metrics, or any number of other characteristics of the crop or field that may impact the crop's behaviors or its harvest operations. Additionally, through combined application of locational information available from the device, and crop or field metadata relating to the aforementioned observations that might be collected through an application accessible on the device, data on additional predictive factors can be collected and associated with each observation. When combined with automatically-collected weather, soil, and other environmental data, these data can be used as the basis for automatically building artificial intelligence models that either simulate the future expected states of the harvest-impacting conditions directly, or that act to provide corrective adjustments to the outputs of one or more physical models for simulating the underlying processes.);
displaying the identification GUI on a user device, the identification GUI providing an interface region for a user to input identification data for the one or more agricultural fields (see at least [0024] he input data 102 may also include field and soil specifications 140, which may further include data such as for example the soil type 141 in which a crop was planted, and attributes such as soil temperature 142, soil moisture 143, current and/or forecasted soil conditions 144, overall soil profile 145, levels of vegetative debris 146 existing at the time of planting, and nutrient levels 147 present.);
after receiving input identification data from the user, transmitting the user inputted identification data to the agricultural intelligence computer system (see at least [0022] FIG. 1 is a system diagram of a harvest advisory model 100 for evaluating, diagnosing, and predicting various agronomic conditions that have an impact on farm and harvest operations, according to one embodiment of the present invention. The harvest advisory model 100 is performed within one or more systems and/or methods that includes several components, each of which define distinct activities required to apply real-time, field-level data representative of assessments of localized weather conditions, together with real-time and location-tagged communication of data of various types and content, and long-range climatological and/or meteorological forecasting to analyze crops, plants, soils, and agricultural products, to generate a plurality of harvest condition advisory outputs, and enable a harvest advisory tool 200 configured to provide diagnostic support to farm and harvest operations.));
receiving data for a data manager GUI over the digital data communication network from the agricultural intelligence computer system (see at least Fig. 1, model 100);
displaying the data manager GUI on the user device, the data manager GUI providing one or more regions for a user to input field data comprising a plurality of values representing crop seed data for the one or more agricultural fields identified (see at least [0023] The input data 102 may further include crop and plant specifications 120 such as for example the type, variety and relative maturity of the crop 121, and other planting, chemical application, and harvest data 122, such as for example the date that a field was planted with seed, the population of planted seeds per specified area, when a field was sprayed for example with a pesticide, desiccant, other chemical, the type and amount of chemical applied, and anticipated temporal harvest data such as an expected harvest date or harvest window. The crop and plant specifications 120 may also include the desired crop temperature 123, crop moisture 124, seed moisture 125, facility storage dimensions and specifications 126, and airflow characteristics for a stored crop 127. Additional information may include planting information such as plant depth 128 and row width 129.);
after receiving input field data from the user, transmitting the inputted field data to the agricultural intelligence computer system (see at least Fig. 1, model 100, input 120);
receiving data for a recommendation GUI over the digital data communication network from the agricultural intelligence computer system (see at least Fig. 1, model 100, outputs 192-198); and
displaying the recommendation GUI, the recommendation GUI displaying a harvest time recommendation for harvesting crop grown from a specific hybrid seed planted in the one or more agricultural fields, based, at least in part, on the inputted field data and the inputted identification data (see at least [0031] The harvest condition output profile 182, as noted above, is output data 180 of the harvest advisory model 100. Once the harvest advisory model 100 has generated the harvest condition output profile 182, the present invention applies the harvest condition output profile 182 to develop the harvest advisory tool 200 for analyzing many types of information in harvest operations. The harvest condition output profile 182 provides estimates, for example, of standing crop dry-down rates, anticipated harvest dates and suitability, fuel consumption optimizers for forced-air drying, indicators of plant ‘toughness’ for anticipating harvest windows, and possible loss of field workability due to the formation of frost in the soils prior to post-harvest tillage.).
Claim 11 is substantially similar to claim 1 and therefore rejected under the same rationale.
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.
Claim(s) 2-10, and 12-22, 24-26, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mewes et al. U.S. Publication 2016/0217230 A1 (hereafter Mewes) in view of Espinoza et al. Corn Production Handbook – University of Arkansas Division of Agriculture (hereafter Espinoza).
Regarding claim 2, Mewes discloses that of claim 1 including crop seed data input but fails to discuss R6 data for a specific seed.
Espinoza discloses, in the same field of invention, discloses corn growth and development including significance of each stage, planting, seeding rate, growing degree days, drainage and irrigation, fertilizer needs, and pest protections including hybrid seeds therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the harvest model system as disclosed by Mewes with the corn farming data as provided by Espinoza for growing, drying and harvesting particular corn seeds as combining known prior art elements yields predictable results. (KSR A)
Regarding claim 3, the combination of Mewes and Espinoza discloses wherein the data manager GUI further includes a weather input region for a user to input weather data and the harvest time recommendation is based, at least in part, on the inputted weather data, and wherein the weather data comprises digital data representing values for historical average, maximum, and minimum daily temperature, historical daily dewpoint temperatures, historical average relative humidity, and historical saturated vapor pressure for a given temperature for the one or more fields (see at least Mewes [0043] the model 100 is initialized 210 by receiving various input data 102, such as weather and location specifications 110. Crop and plant specifications 120 may also be applied at this phase. The harvest advisory model 100 uses this information to develop predictions of expected weather conditions 230 using one or more weather modeling paradigms. [0044] The expected weather conditions 230, and additional input data 102, are then applied in step 240 to one or more precision agriculture models 160. Output from such models 160 may also be further applied to develop 242 artificial intelligence models for further analysis of the harvest condition 270).
Regarding claim 4, the combination of Mewes and Espinoza discloses wherein the harvest time recommendation is based on a grain dry down time series determined by the agricultural intelligence computer system that represents moisture levels of the specific hybrid seed, and the grain dry down time series is based, at least, in part, on creation of an equilibrium moisture content time series by the agricultural intelligence computer system which comprises: deriving an average daily dry-basis equilibrium moisture content fraction value at a specific time using computer execution of a digital representation of a Chung-Pfost equation; compiling the equilibrium moisture content time series using derived average daily dry-basis equilibrium moisture content fraction values over a series of time data points (see at least Espinoza section 9, page 73-75. Chung-Pfost is the established relationship between equilibrium moisture content (EMC) and equivalent relative humidity (ERH. The original Chung-Pfost equation was developed in the late 20th century. The modified equation for specific grain types and conditions in the early 2000’s).
Regarding claim 5, the combination of Mewes and Espinoza discloses wherein the specific hybrid seed is a type of hybrid seed and the grain dry down time series is based, at least in part, on an R6 moisture content calculated by the agricultural intelligence computer system, and wherein calculation of the R6 moisture content comprises: deriving a dry down start date drying coefficient based, at least in part, on R6 data for the specific hybrid seed; deriving an R6 adjustment factor based, at least in part, on relative maturity of the specific hybrid seed; calculating an R6 moisture content for the specific hybrid seed using the dry down start date drying coefficient, the R6 adjustment factor, the relative maturity of the specific hybrid seed, and a baseline relative maturity of the type of hybrid seed (see at least Espinoza pages 1-30).
Regarding claim 6, the combination of Mewes and Espinoza discloses wherein the dry down start date drying coefficient is calculated as a median of a posterior distribution of R6 dates for the specific hybrid seed, where the posterior distribution of R6 dates is a compilation of historical R6 dates for the specific hybrid seed measured across one or more fields (see at least Mewes [0026] The physical, empirical, or observed agricultural data 130 may further include descriptive metadata reflecting actual performance, realization, or implementation of one or more of crops, plants, fields, soils, and facilities. This metadata may include crop and seed metadata 133 such as the relative maturity and dry-down characteristics of the variety, pest and disease susceptibility or resistance, whether a crop is irrigated or non-irrigated, and type and origin of seeds (for example, genetically modified or non-genetically modified, and company source). Such metadata may also include soils metadata 134 such as information relating to previous crop grown on or in the soil, tillage practice, soil composition, presence of surface and/or subsurface drainage, nutrient composition, rate of degradation, rate of water movement in both a lateral and vertical direction, presence of materials such as salts in soil strata, and facility metadata 135 such as metadata relating to type characteristics, for example whether the facility is a forced-air drying facility or a fuel-based mechanical grain drying facility or an open-air-based holding facility or a silo, and their associated resources and costs.).
Regarding claim 7, the combination of Mewes and Espinoza discloses wherein the R6 adjustment factor is calculated as a median of a posterior distribution of variation between observed maturity dates and estimated R6 dates for the specific hybrid seed measured across one or more fields (see at least Espinoza, Table on pg. 6, R6 at 105-102 days. Hybrid seeds will have different time to reach one or more growth stages R1-R6).
Regarding claim 8, the combination of Mewes and Espinoza discloses wherein the baseline maturity duration timeframe is configured based upon the type of the hybrid seed (see at least Espinoza, pg. 11 Growing Degree Days).
Regarding claim 9, the combination of Mewes and Espinoza discloses wherein creation of the grain dry down time series comprises: calculating a rate of change in moisture value for the specific hybrid seed at a specific time, where the rate of change in moisture equals a difference between the moisture content within the specific hybrid seed and the equilibrium moisture content at a specific time, multiplied by a drying coefficient;
determining the moisture content within the specific hybrid seed based on the R6 moisture content for the specific hybrid seed;
deriving the equilibrium moisture content from the equilibrium moisture content time series at the specific time calculated (see at least Mewes [0030] Other models contemplated within the scope of the present invention include crop-specific, site-specific, and attribute-specific physical models, such as models for simulating temperature, moisture, wetness, in-field crop dry-down, plant-atmosphere vapor diffusion, plant rewetting associated with precipitation, and deterministic and stochastic grain or plant drying models that parameterize the unique drying characteristics of a specific crop to be dried based on characteristics of the drying process and statistical models, and models for analyzing storage characteristics of crops, seeds, soils, and storage facilities. It is contemplated that the input data 102 may be applied to existing precision agriculture models, as well as customized models for specific harvest conditions 270, for example simulation of expected dry-down of grain or plant in the particular field, grain and plant moisture levels, and temperature and moisture content of a root-based crop over time, and other simulations, predictions, and forecasts as noted herein.);
determining the drying coefficient based upon a function of relative maturity expressed in days; compiling the calculated rate of change in moisture values to create the grain dry down time series (see at least Espinoza, section 8 Corn Harvesting, pages 67-71. Corn drying rates page 66).
Regarding claim 10, the combination of Mewes and Espinoza discloses wherein the harvest recommendation is a date from the grain dry down time series where the grain moisture equals a target moisture value (see at least Mewes [0004] many commodities require that the harvested product be at or below a product-dependent moisture threshold before they can be stably stored at ambient temperatures (at least without taking specific steps to keep the product stable, such as the maintenance of a constant airflow through the product). On the other extreme, delaying harvest for too long can result in the crop becoming overly dry, potentially exposing seeds to damage during the threshing process, or removing permissible water weight from the product. Such an occurrence of delayed harvest may result in lower crop revenue, since payments are often based on mass. Similarly, crop temperature thresholds may be a major consideration for long-term storage of some crops [0010] It is another such objective of the present invention to provide a system and method of predicting the time-varying unit costs, per percent moisture per unit of mass or volume, associated with fuel-based or forced-air mechanical drying of crops resulting from changing weather conditions and the characteristics of the drying facility. Yet another objective of the present invention is to provide a system and method of predicting the time-varying unit costs, per unit of mass or volume, associated with the combined impacts of time-varying grain or plant moisture levels of a crop to be harvested and the time-varying unit costs per percent moisture per unit of mass or volume of forced-air or fuel-based mechanical drying. [0025] The input data 102 may also include physical, empirical, or observed agricultural data 130, such as for example sampled crop moisture content 131. This may include data such as samples and/or observations of actual grain or plant moisture taken from a planted field at one or more times, and the associated dry-down of the grain or plant in a particular field over time.).
Claim 12 is substantially similar to claim 2 and therefore rejected under the same rationale.
Claim 13 is substantially similar to claim 3 and therefore rejected under the same rationale.
Claim 14 is substantially similar to claim 4 and therefore rejected under the same rationale.
Claim 15 is substantially similar to claim 5 and therefore rejected under the same rationale.
Claim 16 is substantially similar to claim 6 and therefore rejected under the same rationale.
Claim 17 is substantially similar to claim 7 and therefore rejected under the same rationale.
Claim 18 is substantially similar to claim 8 and therefore rejected under the same rationale.
Claim 19 is substantially similar to claim 9 and therefore rejected under the same rationale.
Claim 20 is substantially similar to claim 10 and therefore rejected under the same rationale.
Regarding claim 21, the combination of Mewes and Espinoza discloses wherein displaying the harvest time recommendation includes displaying a moisture content dry down graph for the one or more fields (see at least Espinoza Fig. 8-1, page 66).
Regarding claim 22, the combination of Mewes and Espinoza discloses wherein the interface region of the identification GUI includes a map and inputting identification data for the one or more fields includes the user selecting specific regions graphically displayed on the map (see at least Mewes [0058] Regardless of the source, the field-level remotely-sensed raw or image data may be used by the harvest advisory model 100 to map the crop field and generate a time-series profile of harvest activity. It is contemplated that remotely-sensed satellite imagery data 117 and remotely-captured drone imagery data 118 may be analyzed prior to or in concert with application to the one or more modeling paradigms discussed herein. The remotely-sensed satellite imagery data 117 and remotely-captured drone imagery data 118 may be analyzed using a normalized difference vegetative index (NDVI) that provides the user with an evaluation of plant health, biomass, nutrient content and moisture or wetness content.).
Regarding claim 24, the combination of Mewes and Espinoza discloses wherein the interface region of the identification GUI includes selectable field information data from an external database and inputting identification data for the one or more fields includes the user selecting field information data corresponding to the one or more fields (see at least Mewes [0016] Artificial intelligence is also incorporated to this more comprehensive dataset to draw automatic associations between available external data and the harvest-related condition to yield further models for simulating harvest conditions. Artificial intelligence in the present invention is also retrained as more and more data are accumulated, and the results may be tested against independent data in an effort to find the most reliable model.).
Claim 25 is substantially similar to claim 21 and therefore rejected under the same rationale.
Claim 26 is substantially similar to claim 22 and therefore rejected under the same rationale.
Claim 28 is substantially similar to claim 24 and therefore rejected under the same rationale.
Claim(s) 23 and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mewes et al. U.S. Publication 2016/0217230 A1 (hereafter Mewes) in view of Hantschel et al. U.S. Publication 2012/0316847 A1 (hereafter Hantschel).
Regarding claim 23, Mewes discloses that of claim 1 but fails to disclose the user drawing boundaries on a map of the agricultural field.
Hantschel discloses, in a similar field of invention, the exploration of predicted economically viable hydrocarbons where the user has an interface to navigate geological maps, draw a polygon around areas of interest or otherwise select areas on the map and drag and drop elements onto the map via the user interface. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the crop harvesting model as taught by Mewes with the use interface mapping ability as disclosed by Hantschel for modifying a map of a resource area as applying known techniques to known devices for improvement yields predictable results. (KSR D)
Claim 27 is substantially similar to claim 23 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited prior art generally refers to crop harvesting prediction including optimal timeframes based on weather tracking and plant maturity including associated methods and systems.
U.S. Publication 2022/0039320 A1 - Methods for delayed harvesting of corn fields are provided herein. These methods provide an extended, flexible period of time to harvest corn. The methods allow growers to harvest their corn at the optimal time for drying down or accessing seed, without increasing the risk of losing yield to lodging.
U.S. Publication 2025/0013953 A1 - Disclosed herein are system, method, and computer program product embodiments for generating a recommended harvesting schedule. In embodiments, input data is obtained that includes a respective representation of a crop yield curve for each crop zone in a plurality of crop zones and a set of harvesting constraints including at least one harvesting resource constraint. Based on the input data, a local search heuristic iterates over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule and outputs the current best harvesting schedule as the recommended harvesting schedule. The iteration may include determining a solution score for each candidate harvesting schedule based at least upon a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule, and evaluating each candidate harvesting schedule based on the solution score determined therefor.
U.S. Publication 2017/0061299 A1 - A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyses.
U.S. Publication 20014/0067745 A1 - Methods, apparatuses and computer program products are provided for providing targeted recommendations of agricultural inputs based on a given localized usage context. Methods are provided that include receiving one or more indications of the localized usage context, determining one or more suggested agricultural inputs based on the usage context, and causing the one or more suggested agricultural inputs to be provided. In the context of a further method, a plurality of usage scenarios may be presented for selection, each of the usage scenarios being associated with one or more additional indications of the localized usage context. According to an additional method, probabilities of achieving target and minimum acceptable yields may be determined and presented along with the usage scenarios, thereby allowing a user to select one or more usage scenarios in order to receive the input recommendations based thereon.
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/DYLAN C WHITE/Primary Examiner, Art Unit 3625 August 21, 2026
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