DETAILED ACTION
This Office Action is in response to Applicants application filing received on January 17, 2025. Claim(s) 1-2 is/are currently pending in the instant application. The application claims priority to U.S. provisional application 63/622,802, which was filed on January 19, 2024.
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 .
Drawings
The drawings are objected to because the copies of Figures 1 and 2 are low quality and are illegible. The Applicant is required to filed replacement drawings. The Examiner suggests that the Applicant rotate the images and use a landscape style layout to allow for more of the page to be used in conveying the images. 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2 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 1-2 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 for accessing risk related to crops 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. Claim 1 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
a. Selecting a field in which a crop is to be planted;
b. Gathering data from the selected field affecting growth of the crop, the data comprising information on soil composition and moisture content in the selected field;
c. Planting the crop seed in the selected field and gathering data from the planting including characteristics of the crop seed, local weather conditions and moisture content of the soil composition;
d. Monitoring the data and changes in the weather conditions and moisture content as the crop grows;
e. Comparing the gathered data with known factors affecting mycotoxin growth in creating a report;
f. Using the report to forecast a risk of mycotoxin contamination of crops harvested from the selected field.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. Selecting a field, collecting data about the soil and moisture, planting crops and monitoring the weather, comparing the data to known mycotoxin growth, and creating a report to forecast contamination recites a recites a commercial interaction, and also a legal interaction. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Process”. Selecting a field, collecting data about the soil and moisture, monitoring the weather on planted crops, comparing the collected data to known mycotoxin growth, and forecasting contamination of crops recites a recites a concept performed in the human mind. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims does not recite any additional elements or computer hardware. The Planting of crops is the only piece considered an additional elements is does not integrate the judicial exception into a practical application as it’s not more than an activity the courts have found to be well-understood, routine and conventional when claimed in a merely generic manner or as insignificant extra solution activity. Therefore claim 1 is 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. [0007] about implementation using general purpose or special purpose computing devices (a method for gathering and
inputting granular real-time field data such as soil conditions, planting dates and crop characteristics to be utilized in a platform that can be delivered to harvesting equipment,
cell phones, and computers.) 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 claim 1 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claim 2 further define the abstract idea that is present in their respective independent claim 1 and thus correspond to Certain Methods of Organizing Human Activity and/or Mental Processes 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 generally covered under 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);
Therefore, the claim 2 is directed to an abstract idea. Thus, the claims 1-2 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Perry et al. U.S. Publication 2019/0050948 A1 (hereafter Perry).
Regarding claim 1, Perry discloses a. Selecting a field in which a crop is to be planted (see at least [0004] The crop prediction engine maps, for a particular type of crop, a combination of one or more characteristics of the plot of land and characteristics of farming operations performed for the planted crop to an expected corresponding crop productivity. In response to receiving a request from a grower to optimize crop productivity for a first type of crop and a first portion of land on which the first crop is to be planted, the request identifying a first set of farming operations to be performed by the grower, the system accesses field information describing characteristics of the first portion of land and applies the crop prediction engine to the accessed field information and the first set of farming operations to produce a first expected crop productivity.);
b. Gathering data from the selected field affecting growth of the crop, the data comprising information on soil composition and moisture content in the selected field (see at least [0008] The growth information can include information about one or more of corn, rice, cotton, and soybeans. In an embodiment, the growth information includes information about crop varieties, date ranges for planting crops, crop planting rate ranges, crop planting depth, soil temperatures for planting crops, atmospheric temperatures for planting crops, soil textures for planting crops, soil types for planting crops, weather conditions for planting crops, drainage conditions for planting crops, crop seedbed preparation methods, and crop planting locations. In an embodiment, the growth information includes information about one or more of: row spacing, a number of rows, a type of irrigation, a type of tillage, a type of seed treatment, a type of foliar treatment, a type of floral treatment, a type of soil treatment, a soil type, a soil pH, soil nutrient composition, previously planted crop types and varieties, effects of microbial composition or treatment, microbial composition application rate and date, effects of insecticide and insecticide application rate and date, effects of fungicide and fungicide application rate and date, and effects of fertilizer and fertilizer application rate and date. [0010] The field information can include one or more of: information describing historical characteristics of the first portion of land and information describing current characteristics of the first portion of land. In an embodiment, accessing field information comprises collecting the field information from one or more sensors located at the first portion of land. In an embodiment, accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground.);
c. Planting the crop seed in the selected field and gathering data from the planting including characteristics of the crop seed, local weather conditions and moisture content of the soil composition (see at least [0011] The crop prediction engine can map combinations of field information inputs and farming operation inputs to crop productivity probability distributions based on one or more machine-learned relationships between combinations of portions of the crop growth information and corresponding crop productivities. In this embodiment, applying the crop prediction engine to the accessed field information and the first set of farming operations comprises determining, based on the one or more machine-learned relationships, the first expected crop productivity for the first type of crop planted and grown at the first portion of land using the first set of farming operations.);
d. Monitoring the data and changes in the weather conditions and moisture content as the crop grows (see at least [0008] The growth information can include information about one or more of corn, rice, cotton, and soybeans. In an embodiment, the growth information includes information about crop varieties, date ranges for planting crops, crop planting rate ranges, crop planting depth, soil temperatures for planting crops, atmospheric temperatures for planting crops, soil textures for planting crops, soil types for planting crops, weather conditions for planting crops, drainage conditions for planting crops, crop seedbed preparation methods, and crop planting locations. In an embodiment, the growth information includes information about one or more of: row spacing, a number of rows, a type of irrigation, a type of tillage, a type of seed treatment, a type of foliar treatment, a type of floral treatment, a type of soil treatment, a soil type, a soil pH, soil nutrient composition, previously planted crop types and varieties, effects of microbial composition or treatment, microbial composition application rate and date, effects of insecticide and insecticide application rate and date, effects of fungicide and fungicide application rate and date, and effects of fertilizer and fertilizer application rate and date.);
e. Comparing the gathered data with known factors affecting mycotoxin growth in creating a report (see at least [0036] In some embodiments, crop quality refers to a physical or chemical attribute of a crop, for instance one or more of: a genetic trait, modification, or edit (or lack thereof); an epigenetic signature or lack thereof; a crop content (e.g., moisture, protein, carbohydrate, ash, fiber, fat, oil); a crop color, whiteness, weight, transparency, hardness, percent chalky grains, proportion of corneous endosperm, presence of foreign matter, absorption of water, milling degree, kernel size distribution or volume, average grain length or breadth, density, or length/breadth ratio; a number or percentage of broken kernels or kernels with stress cracks; a falling number; a farinograph; a number or percentage of immature grains; a measure of wet gluten; a sodium dodecyl sulfate sedimentation; toxin levels (for example, mycotoxin levels, including vomitoxin, fumonisin, ochratoxin, or aflatoxin levels); damage levels (for example, mold, insect, heat, cold, frost or other material damage); and the like. In some embodiments, crop quality refers to an attribute of a production method or environment, for instance one or more of: a soil type, chemistry, or structure; a climate type, weather type, or magnitude or frequency of weather events; a soil or air temperature or moisture; a number of degree days; a rain quantity; an irrigation type or lack thereof; a tillage frequency; a cover crop (past and present); a crop rotation; whether the crop is organic, shade grown, greenhouse grown, fair-wage grown, no-till, pollution-free, or carbon neutral; levels and types of fertilizer, chemical, herbicide, or pesticide use or lack thereof; a geography of production (for example, country of origin, American Viticultural Area, mountain grown)) ;
f. Using the report to forecast a risk of mycotoxin contamination of crops harvested from the selected field (see at least [0040] a user of the agronomist client device 108 (such as an agricultural specialist, an individual scouting or observing a planted crop, etc.) can review the crop prediction information, including a set of farming operations identified by the crop prediction system 125 to optimize a predicted crop production, and can modify the identified set of farming operations. For example, a user of the agronomist client device 108 may access prediction information and a corresponding set of farming operations identified by the crop prediction system 125 that includes the application of a particular type of fertilizer and a harvest date. The user of the agronomist client device 108 can change the type of fertilizer to be applied, for instance based on the type of fertilizer being unavailable to a particular grower, and can change the harvest date, for instance by moving the harvest date up based on expected inclement weather. In other words, a user of the agronomist client device 108 can modify farming operations identified by the crop prediction system 125 as optimal based on information available to the user but not available to the crop prediction system 125 at the time the predictions were made. Likewise, an agronomist can modify field information on which the crop prediction system 125 is applied (such as a field location, a crop type, an expected rainfall, and the like), and can request that the crop prediction information generated by the crop prediction system be re-generated in order to observe the effect of the modified field information on the crop prediction information. It should be noted that in some embodiments, the agronomist client device 108 and the broker client device 104 can be the same device, and the broker and agronomist can be the same entity.).
Regarding claim 2, Perry discloses in which data from the report is assessed to adjust crop harvesting methods to minimize presence of toxin contaminated grain harvested from the crop (see at least [0136] the request to generate an optimized crop production can come from a grower or broker immediately before planting, shortly after planting, or mid-season, for instance in response to a grower manually adjusting one or more field parameters or farming operations previously provided by the crop prediction module 425. By applying a crop prediction model at various points throughout a growing season, the set of farming operations performed by a grower can be iteratively optimized, accounting for mid-season changes or events related to growing, land, or market characteristics.).
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 growing practices and toxin accumulation during the growing season including associated methods and systems.
U.S. Publication 2016/0309646 A1 - an agricultural system is provided and includes an information gathering component and a computing element. The information gathering component is configured to gather information pertaining to at least one agronomic characteristic of a land area of interest and generate agricultural data associated with the gathered information. The agricultural data is configured to be transmitted over a network. The computing element includes a processor and a memory. The computing element is configured to receive at least one of the agricultural data from the information gathering component and agricultural data from a source. The computing element is configured to determine an agronomic ratio associated with two agronomic characteristics based on the received data, and the computing element is configured to generate agronomic ratio data associated with the agronomic ratio. The agronomic ratio data is configured to be transmitted over the network.
U.S. Publication 2020/0359550 A1 - An agricultural method includes selecting a seed with predetermined end product properties; selecting microbes to assist seed growth and providing the microbes to help the seed during early stage growth; planting the seed on a farm and periodically capturing growth data with one or more sensors; storing the growth data on a blockchain; viewing the growth data by an interested party; and controlling an irrigation system in response to the growth data.
U.S. Patent 10,180,998 B2 - 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN C WHITE whose telephone number is (571)272-1406. The examiner can normally be reached M-F 7:30-4:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DYLAN C WHITE/Primary Examiner, Art Unit 3625 February 20, 2026