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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 5/14/2026, Applicant responded on 8/14/2026. Amended claims 1, 5, 8, 12, 14, 18.
Claims 1-19 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 5, 8, 12, 14, 18 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 5, 8, 12, 14, 18 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…The pending claims recite specific technical operations, configurations, and structures to enable optimization of seed rates in a manner that overcomes conventional validation drift (e.g., as caused by grower biases or other skewed logic, etc.). See, para. [0018] of the filed application. The technical solution involves segregating agricultural data based on harvest timing, for example, to train an ensemble model; generating a corresponding response curve; and then validating the response curve using a validation curve generated by a second model by calculating errors along the continuous curves. This non-generic, specialized architecture constitutes a tangible technical improvement in agricultural modeling, providing a concrete solution to the complex problem of modeling non-linear agronomic data (with the aim, in this example, of identifying optimum seeding rates for target fields)....As such, in connection with determining an optimal (recommended) seeding rate for a field, the pending claims uniquely look at both the modeled representation of yield and seeding rate in the form of the response curve and the observed representation of yield and seeding rate in the form of the validation curve. See, for example, paras. [0049]-[0053] and [0089] of the filed application. An error is then calculated between the curves, along the entirety of the curves, for example, to ensure that the ensemble of models are providing reasonable recommendations between and beyond observed data points of the validation set from which the validation curve is generated. See, para. [0049]…The claims recite a specific, technical process for identifying particular seeding rates for a field, for example, to lift crop yield, using a unique combination of computational models applied to specifically separated data (based on timing associated with harvest of crops of the multiple agricultural fields) to produce a pair of curves (i.e., a response curve and a validation curve), which are then compared to calculate an error along the curves and, ultimately, identify the target seeding rate for planting….as it requires implementing specific models on a computing device to process specially selected data in a manner that cannot practically be done mentally-e.g., generating specific curves via different models and calculating error between the curves along lengths of the curves. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (claims not abstract where they recite a specific, structured way of achieving a result, rather than a result itself). In connection therewith, the USPTO's October 2019 Update to the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) explicitly states that "claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." See, Oct. 2019 PEG Update at p. 7 (emphasis added). Importantly, this evaluation must consider what the human mind is equipped to do, within practical bounds. Here, the human mind is not equipped to perform the recited steps of Claim 1, as noted above, or any other recited claim… the pending claims require a highly technical workflow that includes segregating heterogeneous regional data based on actual physical crop harvest timing, passing a training set through a first specialized "ensemble of models" to train the models and map dynamic yield responses, executing a distinct "second model" over the harvest-timed validation set, and performing geometric or algebraic curve error tracking along a structural length of the resulting curves. A human using pen and paper cannot plot, align, and continuously reconcile independent multi-model response curves and separate validation curves along their lengths to dynamically output seed rate optimization parameters See also, PEG Example 39 (for claims related to training a neural network, "the claim does not recite a mental process because the steps are not practically performed in the human mind.")…The pending claims are directed to identifying optimal seeding rates for a field. The claims recite training an ensemble of models, generating a response curve, and generating a validation curve. The curves, then, are compared in order to calculate an error therebetween that can be used in identifying (or recommending) a particular seeding rate for a field. This is not organizing human activity. The Office appears to rely on the relation of the claims to agriculture as a per se characterization of the claims in this category. However, the mere identification of a field of use (e.g., agriculture) does not redefine what the claims are directed to. Here, it is clear that the claims are directed to a unique, novel technique for identifying desired seeding rates for a field to provide enhanced performance in the field that avoids data bias and validation drift in such agronomic modeling. That is not a commercial or legal interaction, nor is it related to managing personal behavior or relationships, nor is it related to interactions between people. The pending claims are simply not directed to a method of organizing human activity…the pending claims recite an unconventional, timing-based data partitioning or separation. To this point, the claims do not merely direct a practitioner to analyze standard datasets. They instead explicitly dictate a specific physical constraint for data management: "separating... based on timing associated with harvest of crops of the multiple agricultural fields." This additional element of the claims replaces standard arbitrary, random, or fixed- interval temporal data splitting with an agronomic temporal boundaries metric, ensuring that the training and validation subsets natively reflect the actual crop maturation and harvesting cycles of the region in which the fields are present…The pending claims also recite a specific dual-model and multi-curve architecture, as a further additional element. With regard to these features/elements, rather than broadly performing data analysis, the pending claims define a specific sequence of structural steps: generating a response curve via an ensemble of models, generating a validation curve via a separate second model, and calculating a dynamic error between the curves along lengths of the curves in order to enable identification of the optimal seeding rate for the target field. This represents a specialized, technical solution to data bias and validation drift in agronomic modeling, for example, as part of seeding rate selections for a field….the pending claims, as represented by Claim 1, also provide a practical, physical action resulting from the dynamic error calculation: "recommending a seeding rate for a target field in the region, based on the response curve and the calculated error." This structure provides a tangible predictive improvement to precision farming, which aligns directly with the Federal Circuit's reasoning in McRO, which held that claims utilizing a specific set of rules to achieve a technological result are patent-eligible because they do not preempt all approaches, but rather define a specific, parameterized technical implementation….the pending claims herein integrate any alleged idea(s) (cited in the Office Action) into a practical application under Step 2A, Prong Two, which provides a clear and unmistakable technical solution to a technical problem…as stated in Uniloc USA, INC. v. LG Electronics USA, INC., No. 19-1835 (Fed. Cir. 2020), a claim's compatibility with conventional computers does not render it abstract. The allegedly conventional computer may be used, as here, and as in Uniloc, to provide technical improvement through performance of the specific limitations. Here, the technical improvement includes the specific sequence of ordered operations which permit the models to generate the particular response curve and validation curve, which are then in turn used to determine the recommended seeding rate for the target field. Regardless of the particular hardware, the pending claims recite a clear improvement in technology…the pending claims utilize a unique multi-curve approach identifying the recommended seeding rate. These additional limitations, of the response curve and the validation curve, in combination, do not merely apply the alleged idea - they define a clear and precise meaningful limitation of the allege idea. As such, through the unique use of both the response curve and the validation curve, to uniquely look at both the modeled representation of yield and seeding rate in the form of the response curve and the observed representation of yield and seeding rate in the form of the validation curve, the claims integrate the alleged idea into a practical application and/or recite an inventive concept. At the least, this feature of the pending claims introduces an inventive concept that significantly exceeds the routine or conventional operation of standard computer systems. To this point, the prior art in precision agriculture typically utilizes singular predictive models or applies basic scalar error offsets (e.g., standard root-mean-square errors). Claim 1, for example, introduces an unconventional cooperative model layout where an ensemble of models and a separate second model generate distinct geometric representations (curves), which are then evaluated and matched along lengths of the curves to look at both modeled representations of data. This surely is not routine and conventional in the field of identifying target seeding rates for fields. What's more, the computing device in the pending claims is not acting as a generic database tool for simple storage and automation. It is specifically configured as a multi-stage modeling engine that corrects its own predictive models by evaluating the length-based variance between independent ensemble-driven response curves and specialized validation curves. This specific ordered combination produces a highly accurate agronomic optimization that cannot be dismissed as routine, satisfying the eligibility requirements under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018). Further, the pending claims should be designated as eligible. "Examiners are reminded that if it is a "close call" as to whether a claim is eligible, they should only make a rejection when it is more likely than not (i.e., more than 50%) that the claim is ineligible under 35 U.S.C. 101." See, USPTO Memo re Reminders on evaluating subject matter eligibility of claims, August. 4, 2025. At worst, the pending claims, especially as amended herein, are a close call whereby the rejection should be withdrawn. For all of the foregoing reasons, pending Claims 1-19 involve patent eligible subject matter. Reconsideration and withdrawal of the § 101 rejection of these claims is therefore respectfully requested….” The Examiner respectfully disagrees.
Unlike the Memos, Enfish, McRO, Uniloc USA, INC, Example 39, by Applicant’s own disclosure and admission, the claims indeed recite and direct to, …optimization of seed rates in a manner that overcomes conventional validation drift (e.g., as caused by grower biases or other skewed logic, etc.)…problem of modeling non-linear agronomic data (with the aim, in this example, of identifying optimum seeding rates for target fields)… determining an optimal (recommended) seeding rate for a field, the pending claims uniquely look at both the modeled representation of yield and seeding rate in the form of the response curve and the observed representation of yield and seeding rate in the form of the validation curve. See, for example, paras. [0049]-[0053] and [0089] of the filed application… An error is then calculated between the curves, along the entirety of the curves, for example, to ensure that the ensemble of models are providing reasonable recommendations between and beyond observed data points of the validation set from which the validation curve is generated. See, para. [0049]… process for identifying particular seeding rates for a field, for example, to lift crop yield, using a unique combination of computational models applied to specifically separated data (based on timing associated with harvest of crops of the multiple agricultural fields) to produce a pair of curves (i.e., a response curve and a validation curve), which are then compared to calculate an error along the curves and, ultimately, identify the target seeding rate for planting… generating specific curves via different models and calculating error between the curves along lengths of the curves…segregating heterogeneous regional data based on actual physical crop harvest timing, passing a training set through a first specialized "ensemble of models" to train the models and map dynamic yield responses, executing a distinct "second model" over the harvest-timed validation set, and performing geometric or algebraic curve error tracking along a structural length of the resulting curves… continuously reconcile independent multi-model response curves and separate validation curves along their lengths to dynamically output seed rate optimization parameters… identifying optimal seeding rates for a field. The claims recite training an ensemble of models, generating a response curve, and generating a validation curve… The curves, then, are compared in order to calculate an error therebetween that can be used in identifying (or recommending) a particular seeding rate for a field… to provide enhanced performance in the field that avoids data bias and validation drift in such agronomic modeling… timing-based data partitioning or separation… dictate a specific physical constraint for data management: "separating... based on timing associated with harvest of crops of the multiple agricultural fields." This additional element of the claims replaces standard arbitrary, random, or fixed- interval temporal data splitting with an agronomic temporal boundaries metric, ensuring that the training and validation subsets natively reflect the actual crop maturation and harvesting cycles of the region in which the fields are present… a specific dual-model and multi-curve architecture…define a specific sequence of structural steps: generating a response curve via an ensemble of models, generating a validation curve via a separate second model, and calculating a dynamic error between the curves along lengths of the curves in order to enable identification of the optimal seeding rate for the target field… solution to data bias and validation drift in agronomic modeling, for example, as part of seeding rate selections for a field…recommending a seeding rate for a target field in the region, based on the response curve and the calculated error." This structure provides a tangible predictive improvement to precision farming…specific sequence of ordered operations which permit the models to generate the particular response curve and validation curve, which are then in turn used to determine the recommended seeding rate for the target field… a unique multi-curve approach identifying the recommended seeding rate… through the unique use of both the response curve and the validation curve, to uniquely look at both the modeled representation of yield and seeding rate in the form of the response curve and the observed representation of yield and seeding rate in the form of the validation curve… through the unique use of both the response curve and the validation curve, to uniquely look at both the modeled representation of yield and seeding rate in the form of the response curve and the observed representation of yield and seeding rate in the form of the validation curve…a multi-stage modeling engine that corrects its own predictive models by evaluating the length-based variance between independent ensemble-driven response curves and specialized validation curves. This specific ordered combination produces a highly accurate agronomic optimization…, which is a problem directed to, organizing human activity (i.e. human observing and modeling agricultural behaviors and generating farming and seeding behaviors recommendations for human growers, recommending and directing human growers when to seed and how much to seed based on human calculations), and a mental process (i.e. human observing and modeling agricultural behaviors and generating farming and seeding behaviors recommendations for human growers, recommending and directing human growers when to seed and how much to seed based on human calculations), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components performing extra solution activities, gathering data and outputting data, and generally linked to a technical environment, i.e. computer. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and WURC).
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process and organizing human activities, applied with generic computing components and generally linked to a technical environment, i.e. computer. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes and certain methods of organizing human activities implemented using or applying generic computer components.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. See MPEP 2106.04(a)(2).
Further, the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
And, the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
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);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
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);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered. Examiner finds persuasive Applicant’s remarks on pg14-15.
The closest prior art are US Patent to US20200005401A1 to Bull et al., (hereinafter referred to as “Bull”) in view of US Patent Publication to US20240393262A1 to Hong et al., (hereinafter referred to as “Hong”)
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 8, 14,
accessing, by a computing device, data related to multiple agricultural fields in a region, the data including multiple observations, which are indicated by yield of the multiple agricultural fields over at least one season, seeding rate of the multiple agricultural fields over the at least one season, location, soil data representative of the multiple agricultural fields, and genetic data for seeds planted in the multiple agricultural fields over the at least one season;
separating, by the computing device, the accessed data into a training set and a validation set, based on timing associated with harvest of crops of the multiple agricultural fields;
training an ensemble of models, representative of seeding rate relative to yield, based on the training set;
generating, via the trained ensemble of models, a response curve, defining a yield response to seeding rate;
generating, via a second model, a validation curve, based on the validation set;
calculating an error between the generated response curve and the validation curve along a length of the generated response curve and/or alone a length of the validation curve; and
recommending a seeding rate for a target field in the region, based on the response curve and the calculated error.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1, 8, 14.
The prior art rejection is hereby withdrawn.
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 1-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 8, 14) recites, “A … method for use in recommending seeding rates for one or more agricultural fields, the method comprising:
accessing, by a …, data related to multiple agricultural fields in a region, the data including multiple observations, which are indicated by yield of the multiple agricultural fields over at least one season, seeding rate of the multiple agricultural fields over the at least one season, location, soil data representative of the multiple agricultural fields, and genetic data for seeds planted in the multiple agricultural fields over the at least one season;
separating, by the …, the accessed data into a training set and a validation set, based on timing associated with harvest of crops of the multiple agricultural fields;
training an ensemble of models, representative of seeding rate relative to yield, based on the training set;
generating, via the trained ensemble of models, a response curve, defining a yield response to seeding rate;
generating, via a second model, a validation curve, based on the validation set;
calculating an error between the generated response curve and the validation curve along a length of the generated response curve and/or alone a length of the validation curve; and
recommending a seeding rate for a target field in the region, based on the response curve and the calculated error.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …accessing, by a …, data related to multiple agricultural fields in a region, the data including multiple observations, which are indicated by yield of the multiple agricultural fields over at least one season, seeding rate of the multiple agricultural fields over the at least one season, location, soil data representative of the multiple agricultural fields, and genetic data for seeds planted in the multiple agricultural fields over the at least one season; separating, by the …, the accessed data into a training set and a validation set, based on timing associated with harvest of crops of the multiple agricultural fields; training an ensemble of models, representative of seeding rate relative to yield, based on the training set; generating, via the trained ensemble of models, a response curve, defining a yield response to seeding rate; generating, via a second model, a validation curve, based on the validation set; calculating an error between the generated response curve and the validation curve along a length of the generated response curve and/or alone a length of the validation curve; and recommending a seeding rate for a target field in the region, based on the response curve and the calculated error…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims recite a mental process.
Further, …accessing, by a …, data related to multiple agricultural fields in a region, the data including multiple observations, which are indicated by yield of the multiple agricultural fields over at least one season, seeding rate of the multiple agricultural fields over the at least one season, location, soil data representative of the multiple agricultural fields, and genetic data for seeds planted in the multiple agricultural fields over the at least one season; separating, by the …, the accessed data into a training set and a validation set, based on timing associated with harvest of crops of the multiple agricultural fields; training an ensemble of models, representative of seeding rate relative to yield, based on the training set; generating, via the trained ensemble of models, a response curve, defining a yield response to seeding rate; generating, via a second model, a validation curve, based on the validation set; calculating an error between the generated response curve and the validation curve along a length of the generated response curve and/or alone a length of the validation curve; and recommending a seeding rate for a target field in the region, based on the response curve and the calculated error…, are human observing and modeling agricultural behaviors and generating farming and seeding behaviors recommendations for human growers, recommending and directing human growers when to seed and how much to seed based on human calculations, which are, commercial or legal interactions, managing personal behavior or relationships or interactions between people, therefore the claims recite certain methods of organizing human activities.
Accordingly, the claims recite a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 8, 14: computer-implemented, computing device, system comprising at least one computing device, A non-transitory computer readable storage medium including executable instructions for, executed by at least one processor, cause the at least one processor to
Claim 13, 19: planting device
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…accessing…”, “…recommending…”, “…transmitting…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…accessing…”, data output – “…recommending…”, “…transmitting…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0020] FIG. 1 illustrates an example system 100 in which one or more aspect(s) of the present disclosure may be implemented. Although the system 100 is presented in one arrangement, other embodiments may include the parts of the system 100 (or other parts) arranged otherwise depending on, for example, relationships between users, farm equipment and fields; data flows; types of seeds; types and/or locations of fields; planting activities; privacy and/or data requirements; etc.
[0021] As shown, the system 100 generally includes a region 102, which is divisible into different fields 103. The fields 103 may be distributed throughout the region 102, whereby some fields 103 may be adjacent to one another, while other fields 103 are spaced apart from one another. In general, the fields 103 are owned, operated and/or managed by user 104. In this way, the user 104 may include a farmer, or a grower business or entity, which is responsible for planting, growing, and harvesting crops from the fields 103. As such, the user 104 is a person, or group of people, which are responsible for making decisions related to the fields 103 (e.g., a farmer, etc.). For example, the user 104 may decide the seeds to be planted in the fields 103 and the planting parameters associated with planting the seeds in the fields 103 (e.g., seeding rate, etc.), management practices to employ, and harvest timing, etc.
[0022] In addition to the fields 103 in FIG. 1, the system 100 also includes a number of agricultural equipment (e.g., equipment 106a-b, etc.), a data server 108 (or multiple data servers), and an agricultural computer system 116, each of which is coupled to (and is in communication with) one or more network(s). The network(s) is/are indicated generally by arrowed lines in FIG. 1, and may each include, without limitation, one or more of a local area network (LAN), a wide area network (WAN) (e.g., the Internet, etc.), a mobile/cellular network, a virtual network, and/or another suitable public and/or private network capable of supporting communication among parts of the system 100 illustrated in FIG. 1, or any combination thereof.
[0023] In this example embodiment, the agricultural equipment includes a planting device 106a and a planting device 106b, each disposed in one of the fields 103. It should be appreciated that different numbers and/or types of planting devices, which may be distributed differently among the fields 103, may be included in other system embodiments.
[0024] The planting devices 106a-b may include, for example, planters or other mechanisms for planting seeds in the fields 103 illustrated in FIG. 1. The planting devices 106a- b may be automated, or reliant, at least in part, on a human operator, etc. The planting devices 106a-b, in general, may be configured to disturb the soil in the fields 103, place a seed, and repeat at one or more planting speeds, etc. In connection therewith, the planting devices 106a-b are configured to perform the planting operations according to specific planting parameters. For example, the planting devices 106a-b are configured to plant particular seeds in a location at a specific seeding rate, where the seeding rate may change from location to location. In this manner, one of the fields 103 may include a consistent seeding rating, or multiple different seeding rates in different parts thereof. As the planting parameters are implemented, the planting devices 106a-b are configured to record data indicative of the planting parameters. That is, the planting devices 106a-b may be configured to confirm compliance with planting parameters, or actually measure the planting parameters as the planting progresses.
[0025] The fields 103 historically have been planted, by the planting devices 106a-b, and harvested, by other farm equipment (not shown). The fields 103 may then be again planted and harvested, season over season. In connection therewith, data is captured and/or collected from the fields 103. The data may be collected manually, or automatically, etc.
[0026] In this example embodiment, the fields 103 are included in a trial experiment, in which the same seed is planting in ones of the fields 103, or parts of the fields 103, at multiple different seeding rates. As such, the seeding rates and the locations (e.g., longitude and latitude, etc.) of the seeding rates in the fields 103 is part of the data collected for the fields 103, by the planting devices 106a-b. In addition to seeding rate, the planting devices 106a-b are each also configured to identify the seed being planted, for example, by identifier, brand, relative maturity, etc. Again, the planting devices 106a-b are configured to transmit the planting data to the data server 108 (via one or more networks), which, in turn, is configured to store the data.
[0034] Initially, the agricultural computer system 116 is configured to train a machine learning model to define a seeding rate (or density) by yield curve for the seeds planted in the fields 103. As part thereof, the agricultural computer system 116 is configured to define a training set of data and a validation set of data. The training set and the validation set generally include a division of the data above, where the seeding rate, yield, seed, soil data and genetic data for the seed are included in the data. The division may be based on years or dates, regions or locations, seeds, etc. In one example, the validation set is defined by n-fold or year cross validation, where n environments (e.g., field/year combinations, etc.) or years are withheld for use in validation, i.e., as the validation set.
[0035] Next, in this example embodiment, the agricultural computer system 116 is configured to generate additional data to be included in the training set of data. The generated data may be referred to herein as synthetic observations. The agricultural computer system 116 is configured to generate the synthetic observations by plotting, for each same seed and/or environment, the yield versus the seeding rate. The agricultural computer system 116 is configured to then fit a specific response curve/distribution to the plotted data points. The response curve may include a curve as defined, or described in W. G. Duncan, "The Relationship Between Corn Population and Yield", Agronomy Journal, Published February 1958, which is incorporated herein by reference. Other response curves, which define the relationship between yield and population of seeds (or seed density or seeding rate) may be employed in other embodiments.
[0036] When the curve or distribution is fit, the agricultural computer system 116 is configured to define the synthetic observations along the curve or distribution. The number of synthetic observations is limited to a percentage of the training set of data. For example, the number of synthetic observations may be less than about 70%, less than about 50%, less than about 30%, less than about 25%, less than about 5%, etc., of the data included the training set. In addition, the agricultural computer system 116 is configured, in this example, to include noise,and specifically, Gaussian noise, in the synthetic observations, as N(pu, Nis a normaldistribution with mean u and standard deviation o (e.g., N(0,a2), etc.). It should be understood that other mechanisms may be employed in other embodiments to inject noise into the synthetic observations.
[0037] Once the synthetic observations are defined, the agricultural computer system 116 is configured to add the synthetic observations to the training set of data. It should be appreciated that, in this embodiment, the synthetic observations are added to the training set of data, but not to the validation set of data. That said, the synthetic observations may be added to the validation set in other embodiments of the present disclosure.
[0038] In this example embodiment, with the training set of data defined, the agricultural computer system 116 is configured to train a model based on the training set of data. In addition, in this example embodiment, the model includes an ensemble of models. In particular, the model includes an ensemble of XGBRegressor models, which are regression- specific implementations of XGBoost (eXtreme Gradient Boosting). The model parameters of the models, in this example, are defined in three classes: general parameters, booster parameters, and task parameters. For example, a booster parameter is defined to select a particular booster to use, while a base score parameter is the initial prediction score of all instances, and global bias and max depth parameters are the maximum depth of the tree. Other parameters to be set prior to training and using the model, as understood by those skilled in the art, are provided in Table 1, below.
[0039] In connection with the specific ensemble of XGBRegressor models, for example, in this example embodiment, the objective is set to reg:squarederror for regression withsquared loss; is set to false; max depth is set to 3; treemethod is set to auto;and n_estimators is set to 1001. It should be appreciated that other values for these and other parameters are to be employed in various instances of the XGBRegressor models consistent with the description herein. That is, it should be understood that in this example embodiment, and others, parameters are selected and/or tuned (or left as default) using a cross validation approach on a subset of the data to enhance and/or optimize performance.
[0040] For the training, in view of the parameters and training set above, specific trees of the ensemble of XGBRegressor models are defined, which cooperate to predict yield based on the input seeding rate.
[0041] That is, each predicted output of seeding rate by yield is an average of the predictions from all members of the ensemble of models. In this way, the agricultural computer system 116 is configured to generate seeding rate (or density) by yield curves, or D x Y curves, through iterating over a configuration defined range of seeding rates.
[0042] In this example embodiment, the D x Y curves are variable based on the prediction of the ensemble of models. Optionally, the agricultural computer system 116 is configured to smooth the curve by application of a response curve, or by fitting a curve to the predicted outputs generated by the models, as explained herein. The smoothed D x Y curve then defines the model for prediction of yield based on seeding rate for the specific seed and field combination(s).
[0043] It should be appreciated that other models may be used in other system embodiments of the present disclosure. For instance, random forest and/or neural network models, and variants thereof, may be used in other embodiments of the present disclosure.
[0044] Next, the agricultural computer system 116 is configured to validate the trained model, based on the validation set of data. In this embodiment, to limit information leakage, validation is done by leaving or holding out entire environments and/or year combinations, as the validation set of data, as explained above. Specifically, for environments, field/year combinations are divided into separate training and validation sets of data based on specific environments. And, for year, the training set of data includes all years prior to and/or after a hold out year which is defined as the validation set of data.
[0045] In this example embodiment, the agricultural computer system 116 is configured to validate the trained model based on yield prediction, seeding rate and economic return on investment (ROI). Yield is provided based on two yield dimensions, which relate to the D x Y curve prediction and point prediction. That is, the yield is based on a seeding rate intercept of the D x Y curve. The prediction point relies on the root-mean-squared error or RMSE between yield prediction and observed point yield prediction for the data included in the validation set, i.e., the holdout observations. In this way, the validation is provided with limited or no assumptions about yield versus seed density relationship.
[0046] The agricultural computer system 116 is configured to fit a Malthus model curve based on the validation set of data. Hereinafter, a Malthus model curve refers to a curve that may be fit to the observations consistent with the description in, for example, W.G. Duncan, "The Relationship Between Corn Population and Yield," Agronomy Journal, Published February 1958.
[0047] In this example embodiment, the agricultural computer system 116 is configured to then compare the RMSE of the predicted yield curve or curve parameters to the observed fitted curve. For the validation set of data points, individually, the RMSE is calculatedaccording to Equation (1) below, where is the model yield, Yobs,i is the observed yield, and Nis the number of observed yields.
[0048] And, for the fitted curve based on the validation set of data, the RMSE iscalculated according to Equation (2) below, where is the model yield, Yobs,i is thecorresponding yield on the fitted curve, and N is the number of data points to be considered. In connection therewith, tens, hundreds, thousands, hundreds of thousands, etc. data points may be considered (e.g., upwards of 135,000 seeds/hectare in 1,000 seed increments to help ensure appropriate coverage and resolution of yield (e.g., to approximate density response across different environments, etc.), etc.). That said, it should be appreciated that the number of data points considered may be varied by region and may be somewhat empirical in nature.
RSMED x Y (2)
[0049] With reference to FIGS. 2A-2B, the model curve 200 (as generated by the model) is illustrated in a graph of yield versus seeding rate, as the Model D x Y curve. In another embodiment, the model may indirectly generate the model curve 200 by generating one or more curve parameters (e.g., one or more derivatives or slopes, etc.) of the curve 200. Also, in FIG. 2A, the data points 204 of the validation set of data are shown, along with the fitted curve 206 for the data points of the validation set. The fitted curve 206 is shown in both FIG. 2A and FIG. 2B. In FIG. 2A, it should be understood that the RMSE metric is calculated between the model D x Y curve 200 and the data points 204 of the validation set, consistent with Equation 1 above. Here, the number of data points 204 in the validation set, or the value of N, is six. Conversely, in FIG. 2B, the RMSE is calculated based on the fitted curve 206, consistent with Equation (2), rather than the individual data points of the validation set. In this way, the RMSE metric assumes that the yield density response follows the generated validation curve. Comparing accuracy along the entire curve ensures that the model is providing reasonable recommendations between and beyond observed data points of the validation set from which the observed fit curve is generated, which is associated with a relatively smooth and rational yield response.
[0063] As indicated above, the network(s) of the system 100 are generally illustrated in FIG. 1 by arrowed lines. In connection therewith, the network(s) broadly represent any combination of one or more data communication networks including local area networks, wide area networks, internetworks or internets, using any of wireline or wireless links, including terrestrial or satellite links. The network(s) may be implemented by any medium or mechanism that provides for the exchange of data between the various elements of FIG. 1. The various elements of FIG. 1 may also have direct (wired or wireless) communications links. For instance, the planting equipment 106a-b in the system 100, data server 108, agricultural computer system 116, and other elements of the system 100 may each comprise an interface compatible with the network(s) and programmed, or configured, to use standardized protocols for communication across the networks, such as TCP/IP, Bluetooth, CAN protocol and higher-layer protocols, such as HTTP, TLS, and the like.
[0064] Agricultural computer system 116 is programmed, or configured, to receive field data from field manager computing device 110, external data 112 from data server 114, and sensor data from one or more remote sensors in the system 100. Agricultural computer system 116 may be further configured to host, use or execute one or more computer programs, other software elements, digitally programmed logic, such as FPGAs or ASICs, or any combination thereof to perform translation and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation and sending of scripts, in the manner described further in other sections of this disclosure.
[0071] In an embodiment, model and field data is stored in model and field data repository layer 1060. Model data comprises data models created for one or more fields. For example, a crop model may include a digitally constructed model of the development of a crop on the one or more fields. "Model," in this context, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored or calculated output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things. Persons of skill in the field find it convenient to express models using mathematical equations, but that form of expression does not confine the models disclosed herein to abstract concepts; instead, each model herein has a practical application in a computer in the form of stored executable instructions and data that implement the model using the computer. The model may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields. Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.
[0074] For purposes of illustrating a clear example, FIG. 1 shows a limited number of instances of certain functional elements. However, in other embodiments, there may be any number of such elements. For example, embodiments may use thousands or millions of different mobile computing devices 110 associated with different users. Further, the system 116 and/or data server 108 may be implemented using two or more processors, cores, clusters, or instances of physical machines or virtual machines, configured in a discrete location or co-located with other elements in a datacenter, shared computing facility or cloud computing facility.
[0075] In an embodiment, the implementation of the functions described herein using one or more computer programs or other software elements that are loaded into and executed using one or more general-purpose computers will cause the general-purpose computers to be configured as a particular machine or as a computer that is specially adapted to perform the functions described herein. Further, each of the flow diagrams that are described herein may serve, alone or in combination with the descriptions of processes and functions in prose herein, as algorithms, plans or directions that may be used to program a computer or logic to implement the functions that are described. In other words, all the prose text herein, and all the drawing figures, together are intended to provide disclosure of algorithms, plans or directions that are sufficient to permit a skilled person to program a computer to perform the functions that are described herein, in combination with the skill and knowledge of such a person given the level of skill that is appropriate for disclosures of this type.
[0076] In an embodiment, user 104 interacts with agricultural computer system 116 using field manager computing device 110 configured with an operating system and one or more application programs or apps. The field manager computing device 110 also may interoperate with the agricultural computer system 116 independently and automatically under program control or logical control and direct user interaction is not always required. Field manager computing device 110 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 110 may communicate via a network using a mobile application stored on field manager computing device 110, and in some embodiments, the device may be coupled using a cable or connector to one or more sensors and/or other apparatus in the system 100. A particular user 104 may own, operate or possess and use, in connection with system 100, more than one field manager computing device 110 at a time.
[0079] A commercial example of the mobile application is CLIMATE FIELDVIEW, commercially available from Climate LLC, Saint Louis, Missouri. The CLIMATE FIELDVIEW application, or other applications, may be modified, extended, or adapted to include features, functions, and programming that have not been disclosed earlier than the filing date of this disclosure. In one embodiment, the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare. The combinations and comparisons may be performed in real time and are based upon scientific models that provide potential scenarios to permit the grower to make better, more informed decisions.
[0085] Next, as shown in FIG. 3, the agricultural computer system 116 generates, at 306, synthetic observations based on the training set. The synthetic observations include additional observations, which are consistent with the accessed data included in the training set. In particular, the agricultural computer system 116 plots the data points of the training set and further fits a curve to the data points of the training set. In this example embodiment, the curve is a Malthus fitted model with Gaussian noise. In connection therewith, the curve provides an estimate of the underlying distribution, while the noise then adds variance around this mean to more accurately represent real world data and to prevent/inhibit overfitting of the underlying model. The points along the fitted model then are identified, at different seeding rates, to provide the synthetic observations. The agricultural computer system 116 adds the synthetic observations to the training set. In one or more embodiments, the synthetic observations are limited to about 10%, 20%, 40% or 50%, or some other percentage (i.e., lower or higher, or discrete values in between), in order to avoid, for example, over-indexing on the synthetic observations, when training the model, as explained below.
[0086] At 308, the agricultural computer system 116 trains the model, which in this example embodiment, includes an XGBoost Regressor, and in particular, an ensemble of XGBoost Regressors. The model is trained, by the agricultural computer system 116, based on the training set (which includes the original observations from the accessed data and also the synthetic observations). It should be appreciated that other models may be used in other embodiments.
[0087] As indicated above, in this example embodiment, the model is an ensemble model. The ensemble model is defined by a number of members, which is controlled by the parameters in the model configuration. In this example embodiment, theensemble model may be defined by ten ensemble members, yet the number of ensemble members may be more or less in other embodiments. In connection therewith, the number of XGBRegressor ensembles determines the number of models that are part of the model ensemble. Each ensemble member is trained on the training data and also defined by different values for the parameters and trained decision trees (and other trained characteristics) thereof. The prediction from the ensemble is then averaged to generate, at 310, the model D x Y curve, for each region and seed type, for example, of prediction of yield versus seeding rate. The curve or D x Y curve, which is indicative of a yield response per seeding rate (or Density (D) by Yield (Y)), may be employed in the next steps. Alternatively, the curve, which may include a non-continuous yield density response showing a jagged response and often flat steps, may be smoothed by the agricultural computer system 116, at 312. In particular, the agricultural computer system 116 may generate a smoothed D x Y curve as a fitted model, consistent with the above, to the predicted D x Y curve.
[0091] The seeding rate recommendation may then be selected by the user 104, via a field manager computing device 110 associated with the user 104. This may include the agricultural computer system 116 generating a prescription for a field (e.g., one or more of the fields 103, etc.) and transmitting the prescription to the user 104. In addition, based on the selection, the user 104 may order and/or purchase particular seeds/hybrids, for instance, via the agricultural computer system 116, etc. (e.g., whereby the agricultural computer system 116 receives the order, purchase request, etc. from the grower/user, in response to output of the seeding rate to the grower/user 104 and a corresponding selection by the grower/user; etc.), and then the agricultural computer system 116 directs the seeds/hybrids to the user 104 (e.g., delivering the selected seeds to the target field, etc.), for planting via the planting equipment 106a-b, etc. In some embodiments, following the selection by the user 104, the order may be implemented automatically by the agricultural computer system 116. In addition, in some embodiments, following the selection by the use 104, the agricultural computer system 116 may update (or modify) and existing order (or a standing order) for the user 104, based on the recommended seeding rate(s).
[0092] In addition, or alternatively, the agricultural computer system 116 may transmit the recommendation(s) and/or generated prescriptions to a computing device associated with the planting devices 106a-b (e.g., a cab computer associated therewith, etc.). In doing so, broadly, the desired seeds are included (e.g., planted, etc.) in the fields 103, by the planting equipment 106a-b, at the recommended seeding rate, based on one or more scripts generated and/or compiled by the agricultural computer system 116. This may include the agricultural computer system 116 generating planting instructions - as the script(s) - based on the seeding rate recommendation and providing the instructions to the planting equipment 106a-b whereby the planting equipment 106a-b operates, in response to the instructions, to plant the seeds at that rate in the fields 103, with only limited additional input from the user 104 (e.g., upon delivery of the selected seeds to the planting equipment 106a-b, etc.). In one or more embodiments, the planting equipment 106a-b may be controlled automatically, through the scripts generated, by the agricultural computer system 116, in response to the user's selection and/or the identification by the agricultural computer system 116.
[0103] Applications having instructions configured in this way may be implemented for different computing device platforms while retaining the same general user interface appearance. For example, the mobile application may be programmed for execution on tablets, smartphones, or server computers that are accessed using browsers at client computers. Further, the mobile application as configured for tablet computers or smartphones may provide a full app experience or a cab app experience that is suitable for the display and processing capabilities of cab computer 115.
[0104] For example, FIG. 5 is a block diagram that illustrates a computer system 500 upon which one or more embodiments of the present disclosure may be implemented. Computer system 500 includes a bus 502 or other communication mechanism for communicating information, and a hardware processor 504 coupled with bus 502 for processing information. Hardware processor 504 may be, for example, a general purpose microprocessor.
[0105] Computer system 500 also includes a main memory 506, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 502 for storing information and instructions to be executed by processor 504. Main memory 506 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 504. Such instructions, when stored in non-transitory storage media accessible to processor 504, render computer system 500 into a special-purpose machine that is customized to perform the operations specified in the instructions.
[0117] It should also be appreciated that one or more aspects of the present disclosure transform a general-purpose computing device into a special-purpose computing device when configured to perform the functions, methods, and/or processes described herein.
[0118] As will be appreciated based on the foregoing specification, the above- described embodiments of the disclosure may be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or subset thereof, wherein the technical effect may be achieved by performing at least one of the steps/operations recited in the claims
[0119] Examples and embodiments are provided so that this disclosure will be thorough, and will fully convey the scope to those who are skilled in the art. Numerous specific details are set forth such as examples of specific components, devices, and methods, to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that example embodiments may be embodied in many different forms and that neither should be construed to limit the scope of the disclosure. In some example embodiments, well-known processes, well-known device structures, and well-known technologies are not described in detail. In addition, advantages and improvements that may be achieved with one or more example embodiments disclosed herein may provide all or none of the above mentioned advantages and improvements and still fall within the scope of the present disclosure.
[0124] The foregoing description of the embodiments has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but, where applicable, are interchangeable and can be used in a selected embodiment, even if not specifically shown or described. The same may also be varied in many ways. Such variations are not to be regarded as a departure from the disclosure, and all such modifications are intended to be included within the scope of the disclosure.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-19 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN MAX LEE whose telephone number is (571)272-3821. The examiner can normally be reached on Mon-Thurs 8:00 am - 7:00 pm.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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