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 .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 19/196,029 received on 04/28/2026.
In accordance with Applicant’s amendment. Claims 1-6 are amended, currently pending, and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Response to Amendment
The amendment filed on 04/28/2026 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of Applicant’s amendment, the §112(b) rejection previously applied to the original claims is withdrawn.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the original claims are primarily raised in support of the amendments to claim 1, which are believed to be fully addressed in the updated §101 rejections below.
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the original claims are primarily raised in support of the amendments to claim 1, which are believed to be fully addressed in the updated §103 rejections below.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claim(s) 1-6 is/are directed to a method (i.e., Process. Therefore, the claims are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claim 1 recites a method for incorporating a future crop production into a safe climatic space. As drafted, the limitations recited by the claims fall under the “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claim 1 recites a method for incorporating a future crop production into a safe climatic space with the following limitations:
receiving, by a computer, climatic data of a target region in a baseline period and current planting areas of crops in the target region; (But for the additional elements – underlined – recited in this claim limitation, the step for “receiving climatic data” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
calculating indicator data according to the climatic data of the target region in the baseline period, (The step for “calculating indicator data” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and constructing a first SCS by combining the indicator data with production data of a crop in the baseline period; (The step for “constructing a first SCS” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
adjusting the climatic data, such that the first SCS moves, repeating the adjusting for many times and for each adjusting, a moving range of the first SCS is combined with the first SCS to form a corresponding second SCS; (The steps for “adjusting the climatic data”, and “combining a moving range of the first SCS with the first SCS to form a second SCS” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and through a crop production simulation model in the computer, according to climatic data in a future period, screening out an optimal second SCS with a maximum crop production from a plurality of second SCS; (But for the additional elements – underlined – recited in this claim limitation, the step for “screening out an optimal second SCS” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and optimizing, by a genetic algorithm module and the crop production simulation model, a planting area distribution of crops in the target region based on the optimal second SCS and the current planting areas of the crops in the target region to obtain an optimal crop planting area distribution that improves a total production of the crops in the optimal second SCS, wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation; and parameters constraints of the genetic algorithm module comprise a variation of irrigation water and a variation of a planting area; (But for the additional elements – underlined – recited in this claim limitation, the steps for “optimizing a planting area” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and outputting, by the computer, the optimal crop planting area distribution, and dividing a planting land in the target region into a plurality of planting plots for respective crops in the target region according to the optimal crop planting area distribution to improve a future total production of the crops in the target region. (But for the additional elements – underlined – recited in this claim limitation, the steps for “outputting the optimal crop planting area distribution”, and “dividing a planting land in the target region” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
The additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B recited by the independent claim(s) is/are:
receiving, by a computer, climatic data
through a crop production simulation model in the computer, according to climatic data in a future period, screening out an optimal second SCS
optimizing, by a genetic algorithm module and the crop production simulation model, a planting area distribution of crops in the target region
wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation
outputting, by the computer, the optimal crop planting area distribution
Dependent claims 2-6 further narrow the abstract idea and introduce limitations that fall under the “Mathematical Concepts” (claims 3-5: for calculating annual precipitation, biotemperature, and aridity, using a series of formulas), for mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I). The dependent claims do not introduce further additional elements for consideration under Step 2A, Prong 2, and Step 2B.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely receiving, by a computer, climatic data, and outputting, by the computer, the optimal crop planting area distribution from independent claim 1, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Regarding the limitations: through a crop production simulation model in the computer, according to climatic data in a future period, screening out an optimal second SCS; optimizing, by a genetic algorithm module and the crop production simulation model, a planting area distribution of crops in the target region; and wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation from claim1, these limitations fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely receiving, by a computer, climatic data, and outputting, by the computer, the optimal crop planting area distribution from independent claim 1, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing) and does not amount to significantly more than the abstract idea itself. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Regarding the limitations: through a crop production simulation model in the computer, according to climatic data in a future period, screening out an optimal second SCS; optimizing, by a genetic algorithm module and the crop production simulation model, a planting area distribution of crops in the target region; and wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 5, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Kummu et al. Climate change risks pushing one-third of global food production outside the safe climatic space. One Earth. 2021 May 21 (hereinafter “Kummu), in view of Chemura et al. (2020) Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLOS ONE (hereinafter “Chemura”), in further view of Seats (US 20230289683 A1, hereinafter “Seats”), in further view of Li et al. "Benchmarks for Evaluating Optimization Algorithms and Benchmarking MATLAB Derivative-Free Optimizers for Practitioners’ Rapid Access," in IEEE Access, vol. 7, pp. 79657-79670, 2019 (hereinafter “Li”).
Regarding claim 1: Kummu teaches a method for incorporating a future crop production into a safe climatic space (SCS) ([Highlights] Safe climatic space method to assess climatic niche for global food production) with limitations for:
receiving, by a computer, climatic data of a target region in a baseline period and current planting areas of crops in the target region; ([Introduction; Pg. 721] In this study we aim to go beyond the existing studies by first defining the novel concept safe climatic space (SCS) by using a combination of three climatic parameters in an integrated way, instead of assessing a single indicator at the time.; [Page 723] Under the low-emissions scenario (SSP1-2.6), the areas under most critical risk (i.e., lowest 25th percentile of resilience and top 25th percentile of change in HLZ) lie in the Sahel and the Middle East, covering around 1 % of global crop and livestock production (Figure 3A).);
calculating indicator data according to the climatic data of the target region in the baseline period, ([Introduction; Pg. 721] In this study we aim to go beyond the existing studies by first defining the novel concept safe climatic space (SCS) by using a combination of three climatic parameters in an integrated way, instead of assessing a single indicator at the time.);
and constructing a first SCS by combining the indicator data with production data of a crop in the baseline period; ([Introduction; Pgs. 721-722] Our suggested SCS framework using Holdridge zoning provides thus a novel concept to define the climatic niche for current food production and allows us to holistically study the multifaceted and spatially heterogeneous risks of climate change on it. To assess these risks, we link the climate-change-induced alterations to HLZs over the coming 80 years with spatial gridded global datasets of (1) current production of 27 major food crops; [Results; Pg. 722] We estimated the HLZs for baseline conditions (1970–2000) as well as for future conditions (2021–2040, 2041–2060, 2061 2080, and 2081–2100);
adjusting the climatic data, such that the first SCS moves, repeating the adjusting for many times and for each adjusting, a moving range of the first SCS is combined with the first SCS to form a corresponding second SCS; ([Results; Pg. 722] We estimated the HLZs for baseline conditions (1970–2000) as well as for future conditions (2021–2040, 2041–2060, 2061 2080, and 2081–2100; [Page 726, Data]. HLZ is an ensemble of 38 life zones that were merged here to 13 zones (following Leemans30 and further combining two tropical forest classes) (Figure 1 D). HLZs are based on the following variables: annual precipitation, aridity indicator (ratio between average annual potential evapotranspiration [PEl] and precipitation), and biotemperature (see maps in Figure S1) using data from WorldClim v.2.1, based on approximately 9,000 and 60,000 weather stations. 35 HLZs are especially useful for assessing spatiotemporal and climatic changes locally. To estimate the current and future distribution of these zones, we calculated the parameters needed for determining the HLZ based on the open access WorldClim v .2.1 dataset, 35 which provides monthly climate data averaged over the baseline period of 1970-2000 as well as future scenarios. We used data for these baseline climate conditions and future climate change predictions for four time steps: 2021-2040, 2041-2060, 2061-2080, and 2081-2100.).
Kummu doesn’t teach:
and through a crop production simulation model in the computer, according to climatic data in a future period, screening out an optimal second SCS with a maximum crop production from a plurality of second SCS;
and optimizing, by a genetic algorithm module and the crop production simulation model, a planting area distribution of crops in the target region based on the optimal second SCS and the current planting areas of the crops in the target region to obtain an optimal crop planting area distribution that improves a total production of the crops in the optimal second SCS, wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation;
and parameters constraints of the genetic algorithm module comprise a variation of irrigation water and a variation of a planting area;
and outputting, by the computer, the optimal crop planting area distribution, and dividing a planting land in the target region into a plurality of planting plots for respective crops in the target region according to the optimal crop planting area distribution to improve a future total production of the crops in the target region.
Chemura teaches:
and through a crop production simulation model in the computer, according to climatic data in a future period, screening out an optimal second SCS with a maximum crop production from a plurality of second SCS; ([Fig. 2] Variable importance of each of the parameters used in determining the suitability for maize, sorghum, groundnut, and cassava in Ghana.; ([3.4 Suitability and suitability changes of individual crops; Pg. 9] Under projected climatic conditions the areas that have optimal suitability for maize production will decrease by 12% (6084 km2) and by 14% (7171 km2) under RCP2.6 and RCP8.5 respectively as suitability transition from being optimal to moderately suitable and marginal.);
and optimizing, by a genetic algorithm module and the crop production simulation model, a planting area distribution of crops in the target region based on the optimal second SCS and the current planting areas of the crops in the target region to obtain an optimal crop planting area distribution that improves a total production of the crops in the optimal second SCS, ([2.4 Modelling Approach; Pg. 5] Suitability models or their variants have been used in assessing the geography of crop suitability and in modelling impacts of climate change on agriculture for different crops. While the common approach is to use a 2 class (suitable/unsuitable) approach for modelling crop suitability [44–47], we propose a method that models four suitability classes (optimal, moderate, marginal and limited) as a 2 class system may over-estimate climate impacts by not scaling the suitability. Scaled four-class (high, moderate, marginal and unsuitable) suitability models are an alternative for determining suitability classes of agricultural crops from machine learning algorithms [31, 48–51]. To model the four suitability classes of the four crops, we applied the eXtreme Gradient Boosting (XGBoost) machine learning approach to the variables.; [Page 5] For future climatic conditions, the same climatic variables used in model fitting were derived from data on projected climatic conditions for Ghana.; [4.2 Individual and multiple crop suitability under climate change; pg. 15] In addition to these crop-specific climate responses, the predominant outcome of the suitability modeling is that the impacts of climate change are site and crop-specific. The impacts are determined by both the biophysical factors that influence crop viability and the specific genetic characteristics of the crops.; [Fig. 7] Area fractions suitable for (a) maize and sorghum (b) Maize and cassava (c) Maize and Groundnut (d) Cassava and Sorghum, (e) cassava and groundnut and (f) Sorghum and groundnut. The lines are Gaussian distribution fit for each climatic scenario.);
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Kummu with Chemura’s feature(s) listed above. One would’ve been motivated to do so in order to plan for food transfer systems that distribute food (Chemura; [pg. 15]). By incorporating the teachings of Chemura, one would’ve been able to build an optimal SCS and use a genetic algorithm.
Chemura doesn’t teach:
wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation;
and parameters constraints of the genetic algorithm module comprise a variation of irrigation water and a variation of a planting area;
and outputting, by the computer, the optimal crop planting area distribution, and dividing a planting land in the target region into a plurality of planting plots for respective crops in the target region according to the optimal crop planting area distribution to improve a future total production of the crops in the target region.
Seats teaches:
and parameters constraints of the genetic algorithm module comprise a variation of irrigation water and a variation of a planting area; ([0098] By way of example and not limitation, grouping objectives within an exemplary sub-MOPF model may be beneficial for valuing and comparing choices for agricultural practices. In a specific non-limiting example, a farmer may consider any number of different factors, including by way of example and not limitation, product demand, potential profits, costs of planting, what crops were grown in the last growing season (e.g., relevant to soil chemistry), and where on the property were said crops grown, land slope, water availability (e.g., irrigation access), some combination thereof, or the like when deciding which new crops to plant and where on the farmer's property to plant them.).
and outputting, by the computer, the optimal crop planting area distribution, and dividing a planting land in the target region into a plurality of planting plots for respective crops in the target region according to the optimal crop planting area distribution to improve a future total production of the crops in the target region. ([0072] The organized data from the MOPF calculations may be displayed on at least one monitor 46 or the like comprising an electronic display 12. The display 12 may comprise at least one configurable area 16 permitting a user to operate the exemplary software. Decision maker(s) 18 may utilize the exemplary system for valuing and comparing parcels for planning and development 10 by observing the organized data from the MOPF calculations; [0006] As a specific non-limiting example, where an overarching goal of resource allocation/project development is food security, specific planning objectives thereof may include crop diversity, land utilization, crop revenue, some combination thereof, or the like.; [0009] A problem (e.g., a non-limiting problem may include evaluating resource allocation for various parcels of land for crop development) is identified, defined, data is collected, alternatives are listed, and options are ranked by a quantitative model to be expressed to a decision maker.; [0022] In another example embodiment, the present invention may be utilized to execute MO planning to evaluate land slope, crop diversity, crop yield and sale price for the purpose of determining which existing and expandable arable lands sites are most ideal for agricultural development.; [0078] In one preferred embodiment, exemplary software may be utilized to execute MO planning to evaluate land slope, crop diversity, crop yield and sale price for the purpose of determining which existing and expandable arable lands sites are most ideal for agricultural development. An objective addressed by such embodiment is increased food production for a particular region.; [0118] Referring now to FIGS. 26-34, in addition to representing favorability/desirability magnitude of a choice with respect to project goals, MOPF output may also represent unfavourability of a choice with respect to project goals. The MO planning framework shown in these figures may permit two numeric values to be expressed for each project choice, a favorability/suitability percentage (“% Good” or “% G”), and an unfavourability/unsuitability percentage (“% Bad” or “% B”). Referring specifically to FIG. 26, as a non-limiting example of an exemplary embodiment, a farmer may seek to plant 3 different crops on three different plots of land. Relevant variable attributes with respect to the three plots of land may include ground slope, percent of total nutrients available for each crop, and distance of the plot of land to a nearest town. Accordingly, three goals (g1: slope) (g2: total nutrients) (g3: distance to town) are shown, wherein g1 may be best achieved by a certain land slope for each crop, g2 may be best achieved by a certain soil nutrient content for each crop, and g3 may be best achieved by a certain proximity to a town for each crop. Each attribute may have variable significance to each crop.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Kummu with Seats’ feature(s) listed above. One would’ve been motivated to do so in order to readily determine which parcel(s) of land are most desirable for a particular objective (Seats; [0072]). By incorporating the teachings of Seats, one would’ve been able to produce an output to inform decision makers what to plant, and where to plant it.
Seats doesn’t teach:
wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation;
Li teaches:
wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation; ([Introduction] Included in the benchmarking tests are five MATLAB built-in DFO functions, i.e. simulated annealing (SA), particle swarm optimization (PSO), the genetic algorithm (with elitism, GAe), simplex search (SS), and pattern search (PS), plus one third-party implementation of the widely used Powell’s conjugate (PC) method (as an open-source m-file available from MATLAB’s official user repository) recommended by MathWorks®.; [Benchmarking Matlab DFOs] MATLAB’s genetic algorithm uses elitism, hence named GAe, which guarantees the best 5% of a generation to survive to the next generation. For the GAe, the population size is 20D.; [Abstract] It is expected that the benchmarking system would help select optimizers for practical applications.; [Benchmarking Matlab DFOs] Thecrossover rate was 0.8, and the mutation rate is 0.2.).
Li teaches:
wherein the genetic algorithm module performs operations of population generation, selection, crossover, and mutation; ([Introduction] Included in the benchmarking tests are five MATLAB built-in DFO functions, i.e. simulated annealing (SA), particle swarm optimization (PSO), the genetic algorithm (with elitism, GAe), simplex search (SS), and pattern search (PS), plus one third-party implementation of the widely used Powell’s conjugate (PC) method (as an open-source m-file available from MATLAB’s official user repository) recommended by MathWorks®.; [Benchmarking Matlab DFOs] MATLAB's genetic algorithm uses elitism, hence named GAe, which guarantees the best 5% of a generation to survive to the next generation. For the GAe, the population size is 20D. The maximum number of generations is also 20D; so the maximum number of function evaluations is the same as PSO. The crossover rate was 0.8, and the mutation rate is 0.2.; [Benchmarking Matlab DFOs] Thecrossover rate was 0.8, and the mutation rate is 0.2.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Kummu with Li’s feature(s) listed above. One would’ve been motivated to do so in order to help practitioners rapidly to select a numerical optimizer (Li; [Introduction]). By incorporating the teachings of Li, one would’ve been able to consider population generation, selection, crossover and mutation as criteria in the genetic algorithm.
Regarding claim 2: Kummu teaches:
wherein the climatic data comprises temperature and precipitation data; and the indicator data comprises annual precipitation, biotemperature and aridity. ([Summary] Here, we address this gap by introducing the concept of safe climatic space (SCS), which incorporates the decisive climatic factors of agricultural production: precipitation, temperature, and aridity.; [Introduction] The HLZ concept divides the Earth into 38 zones based on three climatic factors: annual precipitation, biotemperature, and aridity).
Regarding claim 5: Kummu teaches:
wherein the aridity is calculated as follows:
PNG
media_image1.png
79
114
media_image1.png
Greyscale
where R is the aridity; EVP is potential evapotranspiration, mm; and P is the annual precipitation, mm; ([Data] ratio between average annual potential evapotranspiration [PET] and precipitation);
and the potential evapotranspiration is calculated as follows: EVP = 58.93 x bioT wherein bioT is the biotemperature, °C. ([Methods for Holdridge life zone calculations] PET was estimated using the method described in Holdridge, i.e., by multiplying biotemperature by a constant value of 58.93. Kummu describes all temperatures being in °C.
Regarding claim 6: Kummu teaches:
wherein corresponding indicator data is calculated according to the climatic data in the future period, to determine whether a future production of the crop in the preset region is affected by a climate change. ([Summary] Food production on our planet is dominantly based on agricultural practices developed during stable Holocene climatic conditions. Although it is widely accepted that climate change perturbs these conditions, no systematic understanding exists on where and how the major risks for entering unprecedented conditions may occur. Here, we address this gap by introducing the concept of safe climatic space (SCS), which incorporates the decisive climatic factors of agricultural production: precipitation, temperature, and aridity.).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kummu et al. Climate change risks pushing one-third of global food production outside the safe climatic space. One Earth. 2021 May 21 (hereinafter “Kummu), in view of Chemura et al. (2020) Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLOS ONE (hereinafter “Chemura”), in further view of Seats (US 20230289683 A1, hereinafter “Seats”), in further view of Li et al. "Benchmarks for Evaluating Optimization Algorithms and Benchmarking MATLAB Derivative-Free Optimizers for Practitioners’ Rapid Access," in IEEE Access, vol. 7, pp. 79657-79670, 2019 (hereinafter “Li”), as applied to claims 1 and 2, in further view of Stacey et al. (US 20200302555 A1, hereinafter “Stacey”).
Regarding claim 3: Kummu teaches:
wherein the annual precipitation is calculated… ([Methods for Holdridge life zone calculations] Annual precipitation (mm year−1) was calculated from monthly precipitation data).
Kummu doesn’t explicitly teach:
as follows: P=
PNG
media_image2.png
56
40
media_image2.png
Greyscale
, wherein P is the annual precipitation, mm; p is daily precipitation, mm; and days are a number of days in a year, days.
Stacey teaches:
…as follows: P=
PNG
media_image2.png
56
40
media_image2.png
Greyscale
, wherein P is the annual precipitation, mm; p is daily precipitation, mm; and days are a number of days in a year, days. ([0107] Example rainfall information obtained may include measured values of daily rainfall for the town (e.g., inches or millimeters of rain per day) for a time period of months or years.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Kummu with Stacey’s feature(s) listed above. One would’ve been motivated to do so in order to obtain rainfall information (Stacey; [0107]). By incorporating the teachings of Stacey, one would’ve been able to calculate precipitation as described by Applicant’s limitation.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kummu et al. Climate change risks pushing one-third of global food production outside the safe climatic space. One Earth. 2021 May 21 (hereinafter “Kummu), in view of Chemura et al. (2020) Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLOS ONE (hereinafter “Chemura”), in further view of Seats (US 20230289683 A1, hereinafter “Seats”), in further view of Li et al. "Benchmarks for Evaluating Optimization Algorithms and Benchmarking MATLAB Derivative-Free Optimizers for Practitioners’ Rapid Access," in IEEE Access, vol. 7, pp. 79657-79670, 2019 (hereinafter “Li”), as applied to claims 1 and 2, in further view of Leemans (1990) Possible Changes in Natural Vegetation Patterns due to a Global Warming, Publication Number 108 of the Biosphere Dynamics Project (hereinafter “Leemans”).
Regarding claim 4: Kummu doesn’t teach:
wherein the biotemperature is calculated as follows:
PNG
media_image3.png
70
141
media_image3.png
Greyscale
wherein bioT is the biotemperature, °C; t is a daily average temperature less than 35°C and greater than 0°C, °C; and days are a number of days in a year, days.
Leemans teaches:
wherein the biotemperature is calculated as follows:
PNG
media_image3.png
70
141
media_image3.png
Greyscale
wherein bioT is the biotemperature, °C; t is a daily average temperature less than 35°C and greater than 0°C; and days are a number of days in a year, days. ([Page 2] The biotemperature is a temperature sum, related to growing degree days, and is here defined as the sum of daily mean temperatures between 0' and 30' C divided by 365.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Kummu with Leemans’ feature(s) listed above. One would’ve been motivated to do so in order to create a global life zone map (Leemans; [Page 2]). By incorporating the teachings of Leemans, one would’ve been able to calculate biotemperature as described by Applicant’s limitation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Trim et al. (US 20230281924 A1), which discloses an approach for improving operational efficiencies in IoT agricultural ecosystems, including developing a map of applicable land options that the user may interact with to select the one or more modifications to be made to the one or more values of the set of values.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.J.T./Examiner, Art Unit 3625
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
Prosecution Insights