Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claim Objections
Claims 1 and 10 are objected to because of the following informalities: the above claims recite a feature “executing additional modeling generating a recommendation …” As best understood by the Examiner, the feature should be changed to --executing additional modeling and generating a recommendation—as discussed in the Specification [0127], as published: “additional modeling or to generate a recommendation”.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A method comprising: receiving an input comprising a type of a crop, a planting date of the crop, and a geographic location for the crop to be grown; computing an ideal crop yield potential based at least partially on the type of the crop, the planting date, the geographic location, and a phenology model; computing a stress model based at least partially on one or more development stresses and one or more event stresses predicted to occur or observed to occur during a growing season; computing an actual crop yield by applying the stress model to the ideal crop yield potential as a reduction penalty; and transmitting the actual crop yield to one or more of: a first device of a user; a computing device adapted to perform one or more of: executing additional modeling generating a recommendation of an agent to apply to the crop; or a second device adapted to operate a tool for one or more of harvesting the crop or treating the crop..”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “computing an ideal crop yield potential based at least partially on the type of the crop, the planting date, the geographic location, and a phenology model; computing a stress model based at least partially on one or more development stresses and one or more event stresses predicted to occur or observed to occur during a growing season; computing an actual crop yield by applying the stress model to the ideal crop yield potential as a reduction penalty; … perform one or more of: executing additional modeling” are treated as belonging to the mathematical concepts grouping while the step of “generating a recommendation of an agent to apply to the crop” is treated as belonging to mental process grouping. This mental steps represents a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. In the context of this claim, encompasses a user evaluating/observing modeling results to develop a judgement related to how to apply the crop. This step, under the BRI, alternatively/additionally is treated as mathematical relationship step if mathematical modeling of the recommendation takes place (MPEP 2106.04.II: “construing the claims in accordance with their broadest reasonable interpretation”).
Similar limitations comprise the abstract ideas of Claims 10 and 19.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method comprising: receiving an input comprising a type of a crop, a planting date of the crop, and a geographic location for the crop to be grown;
In Claim 10: A system comprising: a memory device to store instructions; and a processing device operatively coupled to the memory device, wherein the processing device is configured to execute the instructions perform operations; receiving an input from a device of a user, from a database, or from a sensor, the input comprising a type of a crop, a planting date of the crop, and a geographic location for the crop to be grown;
In Claim 19: A non-transitory computer-readable medium comprising instructions, which when executed by a processing device, cause the processing device to perform operations; receiving an input comprising a type of a crop, a planting date of the crop, and a geographic location for the crop to be grown.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a system comprising: a memory device to store instructions; and a processing device operatively coupled to the memory device, wherein the processing device is configured to execute the instructions perform operations (Claim 10) and non-transitory computer-readable medium comprising instructions, which when executed by a processing device, cause the processing device to perform operations (Claim 19) are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application. The limitations that generically recite receiving an input comprising a type of a crop, a planting date of the crop, and a geographic location for the crop to be grown (all independent claims) represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record (Xu, Bulayevskaya, Andrejko, Chen).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-9, 11-18, and 20 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, additional elements in Claims 8 and 17 (using the actual crop yield at least partially as one or more of a direct control parameter or an indirect control parameter to control an agricultural machinery usable to treat the crop) are all recited in generality and not meaningful to indicate a practical application and/or qualify for significantly more.
Claim Rejections - 35 USC § 103
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 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 1-4, 8-13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ying Xu et al. (US 20170196171), hereinafter ‘Xu’ in view of Vera Bulayevskaya et al. (US 20230035413), hereinafter ‘Bulayevskaya’.
With regards to Claim 1, Xu discloses
A method comprising:
receiving an input comprising a type of a crop, a planting date of the crop, and a geographic location for the crop to be grown (Examples of field data 106 include… [0039]; [0040]; Table 7);
computing an ideal crop yield potential based at least partially on the type of the crop, the planting date, the geographic location, and a phenology ([0050]; [0139]; Step 714, Fig. 7; Fig.9):
computing a stress model based at least partially on one or more development stresses and one or more event stresses predicted to occur or observed to occur during a growing season (Crop stress index calculation instructions 138 when executed by one or more processors of agricultural intelligence computer system 130 cause agricultural intelligence computer system 130 to perform translation and storage of data values and construction of digital models of weather effects on crop yield [0068]; The crop stress indices, as described further herein, represent conditions that negatively affect the optimal yield of a crop. As the effects of each crop stress index may vary from location to location, the set of coefficients corresponding to the crop stress indices, γ.sub.c, may be parameterized separately for each location or with one or more location dependent terms. For example, if the crop stress index representing day heat stress has a large impact on the crop yields at a first location than at a second location, the corresponding coefficient for the first location may be higher than the corresponding coefficient for the second location [0142]; the agricultural intelligence computer system 130 may calculate the daytime heat stress over a period covering stages V10 through V16, [0160]; the crop stress index calculation instructions 138 provide instruction to determine a stage one daytime heat stress where, stage one includes phenology stages from V10 to V16 and a defined threshold temperature at 93 degrees [0161]; if crop stress due to high temperatures has been steadily increasing over the past five years, agricultural intelligence computer system 130 may model the increase in crop stress due to high temperatures and use a modeled crop stress index for heat stress in the estimated crop stress indices [0175]);
computing an actual crop yield by applying the stress model to the ideal crop yield potential as a reduction penalty (The model of actual yield may incorporate the potential yield and one or more values representing stress factors that limit the total yield of a crop [0142]; calculate a crop stress index may be derived from a crop's lifecycle called the crop phenology [0146]; agricultural intelligence computer system 130 uses estimated future weather and planting practices to estimate crop stress indices for a particular growing period [0176]); and
transmitting the actual crop yield to one or more of:
a first device of a user ([0057-0058]);
a computing device adapted to perform one or more of: executing additional modeling generating a recommendation of an agent to apply to the crop (agricultural intelligence computer system 130 may be programmed to treat the potential yield as a latent function in a model of actual yield. The model of actual yield may incorporate the potential yield and one or more values representing stress factors that limit the total yield of a crop [0142]; Using the actual production history values and the past planting dates and relative maturity values, agricultural intelligence computer system 130 may further improve the model of potential yield for a particular location [0169]); or
a second device adapted to operate a tool for one or more of harvesting the crop or treating the crop (agricultural intelligence computer system 130 may send a recommendation to plant a seed associated with the first relative maturity value on a particular date within the next five days. Additionally, agricultural intelligence computer system 130 may send data indicating that a farmer should instead plant a seed associated with the second relative maturity value if the farmer plans to plant the crop between five to ten days from the current time [0187]).
Xu also discloses using phenology data [0147].
However, Xu does not specifically disclose computing an ideal crop yield potential based at least partially on the a phenology model.
Bulayevskaya discloses computing an ideal crop yield potential based at least partially on the a phenology model (calculate a growth stage of the crop in the field on a defined date, via a phenology model, based on the planting data and weather data included in the data structure, the growth stage indicative of a thermal time associated with the crop as defined by multiple temperatures associated with the field from about the planting date to about the defined date; (d) determine whether the calculated growth stage is within a spray window for the crop; (e) in response to the growth stage being within the spray window, define a plurality of synthetic sprays within the spray window for the field; and then (f) for each one of the plurality of synthetic sprays: (i) calculate at least one disease risk for the crop in the field, based on at least one disease risk model, the at least one disease risk indicative of a potential occurrence and/or a severity of at least one disease; and/or (ii) calculate a response to the synthetic spray, via a response model, based on the calculated at least one disease risk and the growth stage of the crop in the field, wherein the calculated response includes a yield difference between a predicted crop yield for the crop subject to the synthetic spray and the predicted crop yield for the crop without the synthetic spray [0202]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu in view of Bulayevskaya to use a phenology model to calculate an ideal crop yield potential to reflect multiple factors associated with phenology as discussed above (Bulayevskaya [0202]).
With regards to Claim 2, Xu additionally discloses identifying a plurality of parameters utilized to compute the ideal crop yield potential, wherein the plurality of parameters are representative of weather conditions (precipitation measurements [0143]), soil properties (soil data [0143]), and crop specific growing degree days required for crop maturity (growing degree days (GDD) [0150]), and wherein the plurality of parameters are derived at least partially from the type of a crop, a planting date of the crop, and a geographic location in combination with historical weather and crop observations ([0137-0140], [0147-0151]).
With regards to Claim 3, Xu additionally discloses the ideal crop potential is computed as a profile representative of crop yield potential as a function of a date that is representative of a maximum potential yield computed with respect to the geographic location and without developmental stresses, environmental stresses, and event stresses (Based on the received relative maturity value, agricultural intelligence computer system 130 may be programmed to use the model described above to identify a planting date which maximizes the potential yield and/or total yield for the crop. For example, agricultural intelligence computer system 130 may compute potential yields and/or total yields for the received relative maturity value and each planting date within a particular time period. Agricultural intelligence computer system 130 may identify the planting date associated with the highest computed potential yield and/or total yield. In response to identifying the planting date associated with the highest computed potential yield and/or total yield, agricultural intelligence computer system 130 may send a recommendation of the planting date to field manager computing device [0182]).
With regards to Claim 4, Xu is silent on computing the phenology model by one of determining, computing, or simulating plant development stages of the crop based on the planting date, crop variety, an estimated emergence date, and a degree-day accumulation model.
Bulayevskaya discloses computing the phenology model by one of determining, computing, or simulating plant development stages of the crop based on the planting date (The method also includes calculating, by the computing device, a growth stage of the crop in the field on a defined date, via a phenology model, based on the planting data and weather data included in the data structure, the growth stage indicative of a thermal time associated with the crop as defined by multiple temperatures associated with the field from about the planting date to about the defined date [0006]), crop variety (variety of crop in the field 105 [0182]), an estimated emergence date (the phenology model 502 may include, or not, further logic for different stages of the growth of the particular crop. For example, a thermal time between germination and emergence may be specifically calculated, as defined in Equation 12 [00003]) and a degree-day accumulation model (In doing so, the photoperiod factor and the vernalization factor are applied to each daily thermal time (from 706), and the adjusted thermal times are then summed (or accumulated, etc.), at 712, for a desired period (e.g., from planting to a current day, etc.) to provide a cumulative thermal time (custom-character TT′). In this way, the phenology model 502 accounts for both the stress factors and the adjusted temperature(s) [0122]: It should be appreciated that devernalization may occur when daily maximum temperatures exceed 30° C. (or other suitable temperature) and the total accumulated vernalization days are less than 10 [0125]; The daily thermal times may then be determined based on the adjusted/penalized daily temperatures, and accumulated [0125]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu in view of Bulayevskaya to compute the phenology model by one of determining, computing, or simulating plant development stages of the crop based on the planting date, crop variety, an estimated emergence date, and a degree-day accumulation model as known in the art to account for all phenology factors in the model otherwise impossible to a human mind (the number of factors … and conditions of the field and/or surrounding environment (e.g., rainfall, temperature, humidity, soil, tillage, etc.), etc., … define a problem and set of parameters impossible to be computed and/or processed in the human mind, Bulayevskaya [0026]).
With regards to Claim 8, Xu additionally discloses using the actual crop yield at least partially as one or more of a direct control parameter or an indirect control parameter to control an agricultural machinery usable to treat the crop (a crop model may include a digitally constructed model of the development of a crop on the one or more fields. “Model,” in this context, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things [0066]; also [0097]).
With regards to Claim 9, Xu additionally discloses causing a tool to harvest or treat the crop based on the actual crop yield (an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, and harvesting recommendations. The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop [0102]; also [0050, 0091]).
With regards to Claim 10, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 1 and 9.
With regards to Claim 11, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 10 and 2.
With regards to Claim 12, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 10 and 3.
With regards to Claim 13, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 10 and 4.
With regard to Claims 17 and 18, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 10 and Claims 8 and 9, respectively.
With regards to Claim 19, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 1 and 10.
Xu additionally discloses a non-transitory computer-readable medium comprising instructions, which when executed by a processing device, cause the processing device to perform operations [0113, 0117].
With regards to Claim 20, Xu in view of Bulayevskaya discloses the claim limitations as discussed above in Claims 19 and 2.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Bulayevskaya, and further in view of Haoliang Yan et al., “Crop traits enabling yield gains under more frequent extreme climatic events”, Science of The Total Environment, Volume 808, 20 February 2022, 13 pages, hereinafter ‘Yan’.
With regards to Claim 5, Xu is silent with regards to computing the one or more development stresses based at least partially on radiation loss, extreme temperatures, and moisture loss conditions predicted during the growing season.
Yan discloses development stresses are based at least partially on radiation loss, extreme temperatures, and moisture loss conditions predicted during the growing season (The waterlogging functions are based on 0–1 multipliers (1=nostress,0=full stress) in the form of“ x/y pairs”, where X is the independent variable (e.g. soil moisture) and y is the response variable (e.g. photosynthesis and phenology, p.4), process-based crop growth models can assist breeding programs by identifying optimal hypothetical traits for various future scenarios (Hammer et al., 2006); empirical validation can then be refined after various iterations (Hammer et al., 2019). The current study exemplified how models can be used to design future climate-resilient wheat id types. Our results highlight the importance of including stress tolerance traits to alleviate the negative effects of future climate change, especially in the regions where crops are expected to experience severe waterlogging stress, p.8, Similar limitations may be perceived in the conceptualization of radiation-use efficiency in some crop models, p.10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu in view of Bulayevskaya, in view of Yan to compute the one or more development stresses based at least partially on radiation loss, extreme temperatures, and moisture loss conditions predicted during the growing season known in the art as affecting development stresses.
With regards to Claim 14, Xu in view of Bulayevskaya, in view of Yan discloses the claim limitations as discussed above in Claims 10 and 5.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Bulayevskaya, and further in view of Akanksha Sehgal et al., “Drought or/and Heat-Stress Effects on Seed Filling in Food Crops: Impacts on Functional Biochemistry, Seed Yields, and Nutritional Quality”, Frontiers in Plant Science I www.frontiersin.org November 2018 I Volume 9 I Article 1705, 19 pages, hereinafter ‘Seghal’.
With regards to Claim 6, Xu is silent with regards to computing the one or more event stresses based at least partially on seed set loss and seed fill loss models.
Seghal discloses that one or more event stresses based at least partially on seed set loss and seed fill loss (the seed filling stage is crucial for determining average seed weight, seed composition and, therefore, the final quantitative and qualitative yield, p.2; The duration of seed filling is inversely proportional to moisture loss and biomass deposition (Gambin et al., 2007), p.3; These studies collectively indicate that heat-stresses induces leaf senescence, inhibits net photosynthetic rate, chlorophylls, hastens seed filling, disrupts sucrose-starch conversion and causes loss of sink activity to decrease the seed weight and quality, p.9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu in view of Bulayevskaya, in view of Seghal to compute one or more event stresses based at least partially on seed set loss and seed fill loss models that account for seed development (Seed filling is a crucial growth stage for all crops, which involves mobilization and transport processes required for importing various constituents, and many biochemical processes for the synthesis of proteins, carbohydrates and lipids in the developing seeds, Seghal, p.2).
With regards to Claim 15, Xu in view of Bulayevskaya, in view of Seghal discloses the claim limitations as discussed above in Claims 10 and 6.
Claim 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Bulayevskaya, and further in view of Yang Lu et al., “Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model”, Agricultural Water Management 252 (2021) 18 pages, hereinafter ‘Lu’.
With regards to Claim 7, Xu is silent with regards to c computing the one or more event stresses based at least partially on soil moisture model and canopy development loss models.
Lu discloses analyzing the one or more event stresses based at least partially on soil moisture model and canopy development loss models (In dry environments, soil moisture is the major limiting factor in canopy development, and the water stress coefficient for leaf expansion is more sensitive to soil moisture variation than under wet conditions (Raes et al., 2012). When soil moisture was assimilated, the water stress was better depicted. As a result, the canopy cover dynamics were simulated more accurately, and the early senescence in some ensemble realizations in 2005 was corrected. In wet environments, soil moisture is not a strong constraint for canopy cover as the water stress is low, evidenced by the positive bias in OL estimates, p.8; The most frequently used variables in crop model assimilation include those that are relevant to crop canopy development and those that affect crop growth, p.2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu in view of Bulayevskaya, in view of Lu to compute the one or more event stresses based at least partially on soil moisture model and canopy development loss models (Assimilation of soil moisture and canopy cover observations effectively constrained simulation uncertainties and improved model performance, Lu, p.16).
With regards to Claim 16, Xu in view of Bulayevskaya, in view of Lu discloses the claim limitations as discussed above in Claims 10 and 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yaqi Chen et al. (US 20200202127) discloses computer systems and computer-implemented processes that are configured to receive aerial image data (e.g., satellite-based digital images) of agricultural fields, transform the image data into appropriate resolutions (e.g., spatial and/or temporal resolutions), and use the transformed image data together with other feature(s) (e.g., crop yield data) to address the challenge of accurately forecasting or modelling agricultural crop yield predications at a field level.
Erik Andrejko et al. (US 11930743) discloses a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
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