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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/03/2026 has been entered.
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
predicting a crop yield for a location and uncertainty associated with the predicted crop yield,
the method comprising:
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;
wherein
determining the uncertainty measure includes:
performing, by the computing device, a plurality of simulations using the one or more trained machine-learning models to obtain the plurality of simulated SHASH probabilistic distributions of predicted crop yield of the location;
determining, by the computing device, the plurality of moment values from the plurality of simulated SHASH probabilistic distributions;
and
determining, by the computing device, the uncertainty measure associated with the moment based on the plurality of determined moment values;
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;
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 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
predicting a crop yield for a location and uncertainty associated with the predicted crop yield,
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;
perform a plurality of simulations using the one or more trained machine-learning models to obtain a plurality of simulated probabilistic distributions of predicted crop yield of the location;
determine a plurality of moment values from the plurality of simulated probabilistic distributions;
determine an uncertainty measure associated with a moment of the probabilistic distributions of the predicted crop yield based on the plurality of determined moment values;
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)
the farming recommendation is related to
crop type, irrigation, planting, fertilizer, fungicide, pesticide, harvesting, or any combination thereof.
determining a risk associated with the farming recommendation based on the uncertainty measure.
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.
the one or more trained machine- learning models comprise
one or more neural network models.
the one or more trained machine- learning models comprises
a neural network trained with a dropout layer.
performing the plurality of simulations includes
running the 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.
the uncertainty measure is a standard deviation calculated based on the plurality of moment values for the simulated SHASH probabilistic distributions.
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.
generating, by the computing device, one or more executable scripts specific to the determined farming recommendation;
transmitting, by the computing device, the one or more executable scripts to an application controller communicatively coupled to an agricultural implement;
and
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.
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. A computer-implemented method of; by a computing device; an application controller; A non-transitory computer-readable storage medium storing one or more programs for; 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 06/03/2026, 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) 7-9, filed 06/03/2026, 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 24-26 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.
The amended claims are patent-eligible under 35 U.S.C. § 101. They are not directed to an abstract idea. And, even assuming, arguendo, that they recite a judicial exception, the claims as a whole integrate any such exception into a practical application and amount to significantly more.
The Federal Circuit's recent precedential decision in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), cert. denied, 146 S. Ct. 891 (2025), is applicable here. In Recentive, the Court addressed "a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible." Id. at 1211. The Court held that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Id. at 1216. The court made clear that eligibility turns on whether the claims recite a specific technological improvement to the models, algorithms, architecture, or training methods themselves. Id. at 1212, 1216.
The present amendments place Claims 1 and 24 squarely on the eligible side of the line drawn in Recentive. Both independent claims now expressly recite, as part of "determining the uncertainty measure", a specific, non-generic implementation:
"wherein determining the uncertainty measure includes: performing, by the computing device, a plurality of simulations using the one or more trained machine-learning models to obtain the plurality of simulated SHASH probabilistic distributions of predicted crop yield of the location; determining, by the computing device, the plurality of moment values from the plurality of simulated SHASH probabilistic distributions; and determining, by the computing device, the uncertainty measure associated with the moment based on the plurality of determined moment values;
Applicant's specification teaches that this integrated simulation is a concrete technical solution to a specific problem in the art: representing and quantifying epistemic (model) uncertainty in probabilistic agronomic predictions without sacrificing computational efficiency or accuracy. See, Applicant's specification at 1 [0147]-[0149]. In connection therewith, as part of the simulations, the same plurality of stochastic forward passes simultaneously generates both the final predicted SHASH distribution and the moment-based uncertainty measure, enabling the system to detect overconfident or under-confident models and to improve them through targeted retraining (see, e.g., Claim 13). See, Applicant's specification at 1 [0188]-[0191].
This is precisely the type of specific technological improvement to models, algorithms, architecture, or training methods themselves that Recentive holds confers eligibility. The claims do not merely apply a generic neural network or "machine learning" to crop-yield prediction. They recite the specific, non-conventional mechanism by which the model itself computes and reports epistemic uncertainty-a technical advance and improvement in the functioning of the machine-learning system.
Further, in the precedential decision Ex Parte Desjardins, No. 16/319,040, 2025 WL 3095778 (P.T.A.B. Sept. 26, 2025), the Board held that claims reciting specific improvements to machine-learning training methods-including reduced system complexity and improved computational performance-are patent-eligible. On December 5, 2025, the USPTO issued an advance notice of change to the MPEP (available at It> :www .usu -'v tcsA1etauk(fiesi Uocu m: :'ern&Jc;- ir ) to incorporate the Desjardins decision into MPEP § 2106 and to add new eligibility examples based on the decision addressing improvements to learning systems and computer functionality. Accordingly, MPEP § 2106.04(d)(1) will now instruct that, in light of Desjardins, improvements such as reduced use of storage capacity and reduced complexity in the system "were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation."
The simulations recited in the present claims is an analogous specific improvement to the training and inference process for uncertainty quantification in a machine learning model. To this point, the Application describes that the system may determine or optimize a value for T (i.e., the number of forward passes) such that it is relatively small (and thus computationally efficient) while large enough to obtain a sufficiently accurate model uncertainty (see, again, Claim 13).
Because the amended claims now recite a specific improvement to the machine-learning models and training/simulation methods themselves-the exact distinction drawn by the Federal Circuit in Recentive and analogous to the examples/guidance in the revised MPEP (in view of Ex Parte Desjardins)-they are not directed to an abstract idea under Step 2A, Prong 1. What's more, they integrate any alleged exception into a practical application by improving the functioning of the computer/ML system under Step 2A, Prong 2 and amount to significantly more under Step 2B.
For all of the foregoing reasons, pending Claims 1, 3-7, 12-13, 15-16, and 24-26 involve patent eligible subject matter. Reconsideration and withdrawal of the § 101 rejection of these claims are therefore respectfully requested.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Examiner relies on the 2019 Patent Eligibility Guidance (2-Prong Analysis) and precedential cases utilizing said guidance. Any remarks pertaining to case law not utilizing the most current Patent Eligibility Guidance is moot. Any remarks pertaining to non-precedential case law is also moot.
After review of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), there does not appear to any language indicating this case is precedential. Precedential cases have clear language in the document indicating such. Therefore, any remarks pertaining to non-precedential case law is also moot.
It is important to note that the December 5th, 2025 memorandum, Advance notice of change to the MPEP in light of Ex Parte Desjardins, does not change the current 2-Prong 101 eligibility guidance. The memorandum reminds examiners that claimed inventions directed to improving the function of machine learning technology itself, therefore an improvement in computer technology, is patent eligible.
The memorandum concludes that Ex Parte Desjardins is directed towards ‘protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation’.
Examiner’s BRI of the claimed inventions is generic computer structure being used as a tool to mathematically process generically acquired data, not improving how the machine learning model itself would function in operation.
The examined claims align with Example 47, claim 2, which an abstract idea was identified as being present and was found to be patent ineligible.
Further, Examiner maintains previous response:
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.
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.
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
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
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