DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 02/18/2026 has been entered. Claim(s) 1, 3-7, 12, 13, 15, 16, 24-26 is/are now pending in the application. Applicant's amendments have addressed all informalities as previously set forth in the non-final action mailed on 10/31/2025.
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.
Claim(s) 1, 3-7, 12, 13, 15, 16, 24-26 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1 recites
receiving information associated with the location;
providing, by a computing device, the information to one or more trained machine- learning models;
determining, by the computing device, based on the one or more trained machine- learning models:
the predicted crop yield of the location comprising a probabilistic distribution of the predicted crop yield of the location,
wherein
the probabilistic distribution includes
a plurality of simulated sinh-arcsinh (SHASH) probabilistic distributions,
each of which is defined by a plurality of parameters including center, skew, scale, and kurtosis; and
an uncertainty measure, based on a moment, which is associated with a plurality of moment values, for the plurality of the simulated SHASH probabilistic distributions of the predicted crop yield, the moment specific to one or more of the plurality of parameters;
outputting, by the computing device, the predicted crop yield of the location and the uncertainty measure; and
based on the uncertainty measure not satisfying a predefined threshold:
obtaining additional training data to further train the one or more machine-learning models,
wherein
the additional training data includes
field operation data
and
environmental condition data;
retraining, by the computing device, the one or more machine-learning models based on the additional training data; and
repeating the determining, based on the retrained one or more machine learning models, of the predicted crop yield and the uncertainty measure, to
determine a second predicted crop yield and a second uncertainty measure;
determining, by the computing device, that the second uncertainty measure satisfies the predefined threshold; and
based on the determining that the second uncertainty measure satisfies the predefined threshold,
determining, by the computing device, a farming recommendation based on the second predicted crop yield
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
Independent Claim(s) 24 recites
receive information associated with the location;
provide the information to one or more trained machine-learning models; determine, based on the one or more trained machine-learning models:
the predicted crop yield of the location comprising a probabilistic distribution of the predicted crop yield of the location,
wherein
the probabilistic distribution is defined by a plurality of parameters; and
an uncertainty measure associated with a moment of the probabilistic distributions of the predicted crop yield;
output the predicted crop yield of the location and the uncertainty measure; and
in response to the uncertainty measure not satisfying a predefined threshold,
obtain additional training data and
retrain the one or more machine-learning models based on the additional training data
wherein
the additional training data includes
field operation data
and
environmental condition data;
and
after retraining the one or more machine learning models:
determine, based on the one or more retrained machine-learning models:
a second predicted crop yield of the location comprising a second probabilistic distribution of the second predicted crop yield of the location,
wherein
the second probabilistic distribution is defined by the plurality of parameters; and
a second uncertainty measure associated with a second moment of the second probabilistic distribution of the second predicted crop yield;
determine that the second uncertainty measure satisfies the predefined threshold; and
in response to the second uncertainty measure satisfying the predefined threshold,
determine a farming recommendation based on the second predicted crop yield
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
In combination with Independent Claim(s) 1, Claim(s) 3-7, 12, 13, 15, 16, 25, 26 recite(s)
wherein
the farming recommendation is related to
crop type, irrigation, planting, fertilizer, fungicide, pesticide, harvesting, or any combination thereof.
further comprising:
determining a risk associated with the farming recommendation based on the uncertainty measure.
wherein
the one or more models are trained based on
harvest data, soil data, planting data, fertilizing data, chemical application data, irrigation data, weather data, imagery data, scouting observations, or any combination thereof.
wherein
the one or more trained machine- learning models comprise
one or more neural network models.
wherein
the one or more trained machine- learning models comprises
a neural network trained with a dropout layer.
further comprising:
running a plurality of simulations using a neural network model of the one or more machine-learning models to obtain the plurality of moment values for the simulated SHASH probabilistic distributions.
wherein running the plurality of simulations comprises
performing T stochastic forward passes through the neural network model,
wherein
a network unit of the neural network model is
perturbed in each simulation of the plurality of simulations.
wherein
the uncertainty measure is a standard deviation calculated based on the plurality of moment values for the simulated SHASH probabilistic distributions.
wherein
the one or more machine- learning model comprise
a first model and a second model,
wherein the first model is used to
determine the plurality of simulated SHASH probabilistic distribution of the predicted crop yield of the location, and
wherein the second model is used to
determine the uncertainty measure.
further comprising
generating one or more executable scripts specific to the determined farming recommendation;
and
executing the one or more executable scripts
to automatically
adjust an operating parameter of the agricultural implement.
wherein
the predefined threshold is a first predefined threshold;
and
determining, based on the one or more trained machine-learning models, a model error based on a difference between the predicted crop yield and an actual crop yield at the location;
and then
based on the uncertainty measure not satisfying the first predefined threshold and the model error not satisfying a second predefined threshold,
obtaining the additional data to further train the one or more machine learning models
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. by a computing device; an application controller; adjust an operating parameter of the agricultural implement to perform a physical farming operation at the location in accordance with the determined farming recommendation);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output (e.g., transmitting the one or more executable scripts to an application controller communicatively coupled to an agricultural implement)); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. adjust an operating parameter of the agricultural implement to perform a physical farming operation at the location in accordance with the determined farming recommendation).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements).
Allowable Subject Matter (over Prior Art)
See the prior OA, mailed 04/07/2025, for the statement of reasons for the indication of allowable subject matter over prior art.
Response to Arguments
Applicant’s amendments, filed on 02/18/2026, have been entered and fully considered. In light of the applicant’s amendments changing the scope of the claimed invention, the rejection(s) have been withdrawn or updated. However, upon further consideration, a new or updated ground(s) of rejection(s) have been made, and applicant's argument(s)/remark(s) pertaining to the amended language have been rendered moot.
Applicant's argument(s)/remark(s), see page(s) 7-12, filed 02/18/2026, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“II. Claim Rejection under 35 U.S.C. § 101
Claims 1, 3-7, 12-13, 15-16, and 24 are rejected under 35 U.S.C. § 101 as allegedly directed to non-statutory subject matter. In particular, the Office argues that the claims are directed to an abstract idea without reciting additional elements sufficient to amount to significantly more than the abstract idea. This rejection is respectfully traversed for at least the following reasons.”.
-Applicant states
“A. The Pending Claims Are Not Directed to an Abstract Idea (Step 2A, Prong 1)
The Office maintains that the core limitations of the pending claims recite mathematical concepts (e.g., probabilistic modeling with SHASH distributions, moment calculations) and/or mental processes (e.g., observation, evaluation, judgment of thresholds). Applicant respectfully disagrees.
With regard to the characterization of the claims as reciting mental processes, the pending claims recite specific, computer-implemented operations that cannot practically be performed in the human mind, including:
Determining a probabilistic distribution comprising a plurality of simulated sinh- arcsinh (SHASH) probabilistic distributions, each defined by parameters including center, skew, scale, and kurtosis;
Calculating an uncertainty measure based on a moment associated with a plurality of moment values from those simulated distributions;
Obtaining additional training data (comprising harvest data, soil data, planting data, fertilizing data, chemical application data, irrigation data, weather data, imagery data, scouting observations, or any combination thereof);
Retraining the one or more machine-learning models based on that additional training data; and
Repeating the determination using the retrained models to obtain a second predicted crop yield and second uncertainty measure before determining a farming recommendation.
As explained in the October 2019 PEG Update (p. 7), 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." The human mind is not equipped to perform stochastic simulations of SHASH distributions, compute moment-based uncertainty across multiple simulated values, retrain neural networks with dropout, or iteratively refine predictions using newly obtained training data of the recited types (harvest, soil, planting, fertilizing, chemical application, irrigation, weather, imagery, scouting observations, or combinations thereof).
These are inherently computational processes requiring machine execution.
Likewise, with regard to the characterization of the claims as reciting mathematical concepts, while the claims do involve mathematical elements, they do not recite a mathematical concept per se. MPEP § 2106.04(a)(2) states that a claim does not recite a mathematical concept "if it is only based on or involves a mathematical concept." Here, the mathematics (SHASH parameterization, moment uncertainty) are applied tools within a larger process directed to improving agronomic modeling and decision-making-not the abstract idea itself. See, e.g., 11. [0003] and [0185] of the filed application. Further, the output is a real-world farming recommendation refined through uncertainty-driven retraining, not a mere mathematical result.
For these reasons, the pending claims are not directed to an abstract idea under Step 2A, Prong 1.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Examiner maintains response to arguments provided in the OA mailed 10/31/2025.
Examiner maintains ‘Examiner’s BRI of the claimed invention is utilizing generic computer structure as a tool to perform analysis on generically acquired information and outputting the result of the analysis corresponding to crop yield. Examiner does not interpret the claimed invention to be directed towards improving the function of how computers operate. Applicant has failed to persuade the examiner to how predicting a crop yield improves the general function of the computer beyond utilizing the computer as a tool to facilitate the programmed instructions.’.
When examining step 2A Prong 1, Examiner determines if there is an abstract idea present. One skilled in the art can at least perform the identified abstract idea utilizing Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation. One skilled in the art can at least perform the identified abstract idea utilizing Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The arguments, in light of the specification, fail to convince the Examiner that utilizing Mathematical Concepts and/or Mental Processes does not fit within the scope of the identified abstract limitations.
-Applicant states
“B. Even if the Claims Were Directed to an Abstract Idea, They Integrate It into a Practical Application (Step 2A, Prong 2)
Even assuming, arguendo, that the Claims recite a judicial exception, they integrate it into a practical application by improving the technical field of precision agriculture.
The Claims recite a specific and unique, threshold-driven process for finetuning the models being used to generate the farming recommendation. If the uncertainty measure does not satisfy a predefined threshold (indicating model unreliability), the system obtains additional agronomic training data, retrains the models, and repeats the prediction until the second uncertainty measure satisfies the threshold. Only then is a farming recommendation determined and used. This iterative, uncertainty-aware retraining solves a specific technical problem in agronomic modeling: overconfidence or under confidence in probabilistic yield predictions, which can lead to inaccurate or risky farming decisions (see, Applicant's specification at 1 [0146]-[0150], [0188]-[0191]). To this point, new dependent Claim 26 goes further in reciting use of the second, error-based threshold as an additional basis for retraining the model(s) (addressing situations where the model may be confident but simply wrong). In this way, a very specific determination is made with regard to whether the predicted farming recommendation should be used, or whether it should be re-determined.
This is not "mere instructions to implement an abstract idea on a computer" or "insignificant extra-solution activity" (e.g., generic data acquisition/output), as the Office asserts. The retraining is triggered by and conditioned, in a very particular manner, on the uncertainty evaluation, uses specific types of agronomic data, and directly improves the reliability of the output before it is applied to farming recommendations. This reflects an improvement to the functioning of agronomic prediction systems, analogous to the neural network improvements in USPTO Example 39 (eligible because specific training enhances facial detection accuracy) and the real-time integration in Example 42, Claim 1 (eligible because data processing enables meaningful remote updates).
The independent claims are also limited to a particular technological environment: precision agriculture, where unreliable predictions can waste resources or reduce yields. In connection therewith, the claims use any alleged abstract idea in a "specific manner" (threshold- based iteration with retraining) to achieve a practical outcome-more accurate, lower-risk farming recommendations-not merely linking the prediction to the field of agriculture generally.
Further, new dependent Claim 25 recites an automated implementation of the specific farming recommendation at the location, through the executable script(s). In particular, Claim 25 recites executing, by the application controller, the one or more executable scripts to automatically adjust an operating parameter of the agricultural implement to perform a physical farming operation at the location in accordance with the determined farming recommendation. This automated operation of the agricultural implement to actually carry out the farming recommendation transforms the prediction and recommendation process into physical control of agricultural machinery, which surely provides for a practical application of any alleged abstract idea.
To this feature, Courts have consistently recognized that claims integrating abstract ideas into physical processes or transformations are patent-eligible, as they go beyond mere computation to achieve tangible, real-world results. For example, in Diamond v. Diehr, 450 U.S. 175 (1981), the Supreme Court held that a process using mathematical calculations to control the timing and temperature of rubber curing was eligible under § 101, because the calculations were not performed in isolation but were instead applied to transform raw rubber into a cured product through precise physical adjustments. In that case, the Court emphasized that the invention as a whole effected a physical change, rendering it more than an abstract idea. Similarly, here, the claimed predictions and uncertainty measures are not abstract endpoints; they are used to determine recommendations and cause direct control of agricultural machinery by generating and transmitting scripts/signals that automatically adjust operating parameters, such as seeding rates or fertilizer application, to perform physical farming operations.
For the above reasons, the pending claims integrate any alleged abstract idea into a practical application under Step 2A, Prong 2.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
When examining step 2A Prong 2, Examiner examines the additional elements to determine if the identified abstract idea has been practically applied in a particular way in a particular technology. Limitations that are not indicative of integration into a practical application: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)); or Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). The additional elements, when viewed individually and in combination with the identified abstract idea, do not add anything beyond mere instructions to implement an abstract idea on a computer, adding generic ‘apply it’ language, and generically linking the identified abstract idea to a technological environment or field of use.
It is important to note, the judicial exception alone cannot provide the improvement. An improved abstract idea is still an abstract idea.
-Applicant states
“C. The Claims Amount to Significantly More Than Any Alleged Abstract Idea (Step 2B)
Even if the alleged abstract idea is not integrated into a practical application under Prong 2 above, the ordered combination of features in the claims recites significantly more than the alleged judicial exception.
For instance, the pending claims combine (at the least):
Probabilistic SHASH modeling with simulated distributions and moment-based uncertainty;
Iterative retraining conditioned on uncertainty thresholds using specific agronomic data types (harvest, soil, planting, fertilizing, chemical application, irrigation, weather, imagery, scouting observations, or combinations thereof); and
Determination of a farming recommendation only after the second uncertainty measure satisfies the threshold.
This combination is not well-understood, routine, or conventional. The specification describes it as a novel solution to epistemic uncertainty in agronomic models (11 [0146]-[0150]), enabling detection of overconfidence/underconfidence and iterative improvement before recommendations are made-not routine data analysis or generic ML output. Courts have found similar non-conventional integrations eligible. For example, in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), the Federal Circuit held eligible a method using specific automated rules to generate intermediate facial expressions for animation. Those rules replaced subjective manual judgments with objective criteria that synchronized mouth movements to phonemes to yield consistent, efficient animation that improved the animation process itself. The Court stressed that the particular rules, when applied as claimed, transformed the abstract idea of automating facial animation into a specific, non-preemptive technological improvement.
Similarly, Applicant's specific uncertainty-threshold rules-combined with iterative retraining triggered by those thresholds-replace unreliable or overconfident yield predictions with objectively refined, lower-risk recommendations. This rule-driven process improves the accuracy and safety of agronomic decisions in precision agriculture, much like the rules in McRO enhanced animation consistency.
The Office compares the claims to Example 47, Claim 2 (ineligible generic math/output). However, the claims more closely align with Claim 3: Like dropping malicious packets and blocking future traffic (proactive, field-specific remediation), the claims use the uncertainty result to proactively retrain models with targeted agronomic data to determine reliable predictions before generating recommendations-improving the technical field of precision agriculture, not merely outputting a result.
For all of the foregoing reasons, pending Claims 1, 3-7, 12-13, 15-16, and 24 (as well as new Claims 25-26) involve patent eligible subject matter. Reconsideration and withdrawal of the § 101 rejection of these claims are therefore respectfully requested.”.
When examining step 2B, Examiner examines the additional elements to determine if they amount to significantly more than the abstract idea. The only additional element(s) is/are the generic computer structure being used as a tool to perform the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
It is important to note, the judicial exception alone cannot provide the improvement. An improved abstract idea is still an abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit