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 First Office Action on the merits in reply to application number 19/196,029 filed on 05/01/2025.
Claims 1-6 are 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.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 12/16/2025 has been considered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 contains the trademarks/trade name “MATLAB". Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a programming and numeric computing platform (i.e., “an optimization program is edited with Matlab”) and, accordingly, the identification/description is indefinite.
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Claims 2-6 depend from claim 1, and are therefore indefinite based on their inheritance of the deficiencies of their respective parent claim.
Appropriate correction is required.
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:
calculating indicator data according to climatic data of a preset region in a 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. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
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. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
adjusting the climatic data, such that the first SCS moves, and a moving range of the first SCS is combined with the first SCS to form a second SCS; (The step for “adjusting the climatic data”, “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. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
and according to climatic data in a future period, screening optimal indicator data when a production of the crop is maximum; (The step for “screening optimal indicator data” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
and constructing a third SCS of the crop with the optimal indicator data of the crop, and optimizing a planting area distribution of the crop to improve a production of the crop in the third SCS, wherein the planting area distribution of the crop is optimized by a genetic algorithm (GA); (The step for “constructing a third SCS”, and “optimizing a planting area distribution of the crop” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and an optimization program is edited with Matlab, comprising population generation, selection, crossover, and mutation; (This limitation is an additional element to be analyzed under Step 2A, Prong 2, and Step 2B);
and parameters of the GA, comprising a variation of irrigation water and a variation of a planting area, are constrained. (The step for “constraining parameters of the GA”, and “optimizing a planting area distribution of the crop” 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: an optimization program is edited with Matlab, comprising population generation, selection, crossover, and mutation.
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 limitation an optimization program is edited with Matlab, comprising population generation, selection, crossover, and mutation, this limitation fails 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 limitation an optimization program is edited with Matlab, comprising population generation, selection, crossover, and mutation, this limitation fails 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 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”), in further view of Seats (US 20230289683 A1, hereinafter “Seats”).
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:
calculating indicator data according to climatic data of a preset region in a 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, and a moving range of the first SCS is combined with the first SCS to form a 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);
Kummu doesn’t explicitly teach:
and according to climatic data in a future period, screening optimal indicator data when a production of the crop is maximum; and constructing a third SCS of the crop with the optimal indicator data of the crop, and optimizing a planting area distribution of the crop to improve a production of the crop in the third SCS,
wherein the planting area distribution of the crop is optimized by a genetic algorithm (GA);
and an optimization program is edited with Matlab, comprising population generation, selection, crossover, and mutation;
and parameters of the GA, comprising a variation of irrigation water and a variation of a planting area, are constrained.
Chemura teaches:
and according to climatic data in a future period, screening optimal indicator data when a production of the crop is maximum; and constructing a third SCS of the crop with the optimal indicator data of the crop, and optimizing a planting area distribution of the crop to improve a production of the crop in the third 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. These are the largest changes from the optimal suitable areas of the crops modelled in this study. Areas that have marginal suitability are projected to increase by 8% (6885 km2) under RCP2.6 and by 7% (5703 km2) under RCP8.5 scenario, with limited areas decreasing by 11% or 5800 km2 (RCP2.6) and by 8% or 4508 km2 (RCP8.5) (Figs 3B and 3C and 4A and Table 4).
wherein the planting area distribution of the crop is optimized by a genetic algorithm (GA); ([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.; [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.).
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:
and an optimization program is edited with Matlab, comprising population generation, selection, crossover, and mutation;
and parameters of the GA, comprising a variation of irrigation water and a variation of a planting area, are constrained.
Li teaches:
and an optimization program is edited with Matlab, comprising 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.).
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 use Matlab and consider population generation, selection, crossover and mutation as criteria.
Li doesn’t teach:
and parameters of the GA, comprising a variation of irrigation water and a variation of a planting area, are constrained.
Seats teaches:
and parameters of the GA, comprising a variation of irrigation water and a variation of a planting area, are constrained. ([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.).
having a degenerate Pareto Front forming
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 reduce having a degenerate Pareto Front forming (Seats; [Introduction]). By incorporating the teachings of Seats, one would’ve been able to use Matlab and consider population generation, selection, crossover and mutation as criteria.
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:
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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 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”), in further view of Seats (US 20230289683 A1, hereinafter “Seats”), 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=
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, 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=
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, 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 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”), in further view of Seats (US 20230289683 A1, hereinafter “Seats”), 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:
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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:
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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. ([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:
Azuma et al. (Doc. ID: WO 2017068782 A1| Published Date: 04/27/2017), which discloses an index value calculation unit that calculates index values at which the growth of crops is hindered, during a period including at least an observation period in which the effects of farming work performed in order to grow crops are observed; and a schedule generation unit that, in order to reduce the index values calculated by the index value calculation unit, generates a schedule for farming work performed on crops during a prescribed farming period being a period prior to the observation period.
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.
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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.
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/G.J.T./Examiner, Art Unit 3625
/TIMOTHY PADOT/Primary Examiner, Art Unit 3625