DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed February 18, 2026. Claims 1, 15, and 19-20 have been amended. Claim 21 has been added. Claims 1-8 and 15-21 are presented for examination.
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 .
Response to Arguments
Applicant's arguments filed February 18, 2026 have been considered but they are not fully persuasive.
Regarding the rejection under 35 U.S.C. § 101, Applicant submits that a human could not perform the various calculations and script generation listed on pages 8-9 of Applicant’s response. The Examiner respectfully disagrees. While a human user might need the assistance of pen and paper, aside from the general link to the technology of machine learning, a human user could indeed perform the various calculations and a human user can write software code to generate script. The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
On pages 9-10 of the response, Applicant submits that the claims involve agricultural recommendations, but do not recite organizing human activity. Organizing human activity also includes business management. Paragraph 4 of Applicant’s Specification explains that “[t]he introduction of the conditions, by growers, for example, is known to impact the growth of the seeds in the fields, and as a result, the overall yield of the crops from the fields, which in turn impacts an amount of crops available for sale for business purposes and/or profit.” This supports the Examiner’s assertion that the claims are used for making business decisions, including those affecting sales and profit, which is an example of business management.
Applicant submits that the claims do not recite any specific mathematical calculations (page 10 of the response). The Examiner points out that calculating a yield difference (as recited in claims 1 and 15) presents an example of a mathematical concept (e.g., as a subtraction problem).
On page 10 of the response, Applicant submits that the integration of machine learning presents more than mere instructions to implement the abstract ideas on a generic computer. The Examiner respectfully disagrees. The use of machine learning is presented at a very high level in the independent claims and as a general link to technology.
Applicant argues that claims 19 and 20 recite automated implementation of controlling the sprayer via scripts (page 11 of Applicant’s response). The Examiner maintains that the scripts generated and executed in claim 19 are still presented as high-level instructions to define operating parameters for the operation of the sprayer. Specific control details are not presented in the claims. However, claim 20 is deemed to be patent eligible under 35 U.S.C. § 101 for the reasons explained below.
Applicant argues that new claim 21 presents a specific interaction with a user to update a treatment map (page 11 of Applicant’s response). There are no specific technical details presented in these limitations. At best, only integration of a generic processing device would be required to implement the otherwise abstract ideas associated with updating a treatment map.
Applicant submits that the combination of claim limitations is not well-understood, routine, or conventional (pages 12-13 of Applicant’s response). Such combination should be found in the details of the additional elements and their respective operations. At present, the processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Regarding the art rejection, the claim amendments are deemed to overcome the art rejections.
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 1-8, 15-19, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-8, 15-19, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “systems and methods for use in applying one or more treatments to crops in fields, or growing spaces, and in particular, to systems and methods for use in application of one or more treatments, at one or more times, to crops planted in fields, based on modeling of data associated with application windows of the one or more treatments to the crops in the fields” (Spec: ¶ 2) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-8, 19, 21), Apparatus (claims 15-18)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 15] A method for use in applying a treatment to crops in one or more fields, the method comprising:
receiving a request to recommend an application of a treatment for a field, the field including a crop, which is associated with a planting date indicative of a day the crop was planted, the request including a field ID for the field;
accessing data including planting data and weather data for the field;
calculating, using a phenology model, an accumulated thermal time for the field for a defined date, based on the weather data for the field;
calculating a growth stage of the crop in the field on the defined date, using the phenology model, based on the planting data and the calculated accumulated thermal time;
identifying a spray window for the crop in the field based on the calculated growth stage, wherein the spray window is defined by a range of multiple growth stages for a growth stage index;
defining a plurality of different synthetic treatments for application to the crop in the field within the identified spray window, wherein each of the different synthetic treatments is a hypothetical treatment for the crop in the field for application in the crop on a different day within the spray window; and then
for each one of the plurality of defined synthetic treatments:
calculating a disease risk for the crop in the field for each of multiple different diseases, using separate disease risk models specific to each of the multiple different diseases, wherein each calculated disease risk is indicative of a potential occurrence and/or a severity of the corresponding disease for which the disease risk is calculated; and
calculating a response to the synthetic treatment, using a response model, based on the calculated disease risk for each of the multiple different diseases and the calculated growth stage of the crop in the field, wherein each calculated response includes a yield difference between a predicted crop yield for the crop subject to the synthetic treatment and the predicted crop yield for the crop without the synthetic treatment; and then
compiling a report including a selected one or more of the calculated responses, based on the yield differences of the responses, as a recommendation for applying the treatment to the crop consistent with the synthetic treatment associated with the selected one or more of the calculated responses; and
transmitting the report in response to the request.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106.04(a)(1)(III), “[t]he courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human can perform the operations identified above. For example, a human user could gather data, perform the various calculations and determinations, and compile and transmit a report. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “systems and methods for use in applying one or more treatments to crops in fields, or growing spaces, and in particular, to systems and methods for use in application of one or more treatments, at one or more times, to crops planted in fields, based on modeling of data associated with application windows of the one or more treatments to the crops in the fields” (Spec: ¶ 2), which (under its broadest reasonable interpretation) is an example of business management (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Calculating a yield difference is recited in independent claims 1 and 15 and this is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Independent process claim 1 recites that the method is computer-implemented and includes a computer system to generally perform the operations of the invention. Furthermore, data is included in a data structure. Claim 1 recites that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression.
Independent apparatus claim 15 includes a system comprising a data structure including data and at least one processor coupled in communication to the data structure to generally perform the operations of the invention. Claim 15 recites that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 64, 88, 205-207). The recitation of “wherein the agricultural apparatus includes a sprayer apparatus, which includes a boom, one or more sprayers coupled to the boom, and a tank in fluid communication with the one of more sprayers, the tank configured to hold said treatment” in claim 2 simply presents a general link to a field of use.
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
The use of an agricultural apparatus (in communication with the computing device) to apply the treatment to the field is simply recited at a high level of generality and merely presents a general link to technology and to a field of use.
Claims 1 and 15 recite that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression. Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 126, 166, 169). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] applying the treatment to the field consistent with the recommendation included in the report, wherein the report further includes an indication of the treatment.
[Claim 3] wherein the crop includes winter wheat, and the treatment includes a fungicide.
[Claim 4] wherein accessing the data includes accessing the planting data in the data specific to the field based on the field ID.
[Claim 5] wherein the weather data includes air temperature values for the field on a daily basis; and
wherein calculating the accumulated thermal time for the field for the defined date includes adjusting one or more of the air temperature values based on at least one stress factor for the field corresponding to one or more days on which the one or more air temperature values were recorded, and then calculating the accumulated thermal time based on the adjusted one or more of the air temperature values.
[Claim 6] wherein the separate disease risk models include a septoria model, a leaf rust model, a stripe rust model, and a fusarium model.
[Claim 7] wherein at least one of the multiple disease risk models is based on one or more of: daily temperature, average temperature for an interval, daily relative humidity, average relative humidity for an interval, and a combination of temperature and relative humidity.
[Claim 8] wherein at least one of the multiple disease risks includes a time series severity risk of the disease from the defined date to a harvest date for the crop.
[Claim 16] determine daily weather for the field based on the planting data and the weather data; and
wherein the weather data includes observed actual weather data for the field, forecasted weather data for the field, and climatology data associated with the field.
[Claim 17] wherein the separate disease risk models include a septoria model, a leaf rust model, a stripe rust model, and a fusarium model.
[Claim 18] wherein at least one of the multiple disease risk models is based on one or more of: daily temperature, average temperature for an interval, daily relative humidity, average relative humidity for an interval, and a combination of temperature and relative humidity; and/or
wherein at least one of the multiple disease risks includes a time series severity risk of the disease from the defined date to a harvest date for the crop.
[Claim 21] wherein the report includes a treatment program including the synthetic treatment associated with the one or more calculated responses, a treatment map for applying the treatment to the crop in the field consistent with the synthetic treatment of the treatment program, and one or more user input features; and
wherein the method further comprises:
receiving, from a user, through the report, a change to the one or more user input features included in the report selecting a different synthetic treatment; and
in response to the change, dynamically updating the treatment map of the report to illustrate application of a different treatment to the crop in the field consistent with the selected different synthetic treatment.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims above.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106.04(a)(1)(III), “[t]he courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human can perform the operations identified above. For example, a human user could gather data, perform the various calculations and determinations, and compile and transmit a report. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “systems and methods for use in applying one or more treatments to crops in fields, or growing spaces, and in particular, to systems and methods for use in application of one or more treatments, at one or more times, to crops planted in fields, based on modeling of data associated with application windows of the one or more treatments to the crops in the fields” (Spec: ¶ 2), which (under its broadest reasonable interpretation) is an example of business management (i.e., organizing human activity), which is also an example of organizing human activity; therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Claim 19 defines operating parameters in scripts used to execute sprayer operations. Aside from the use of script (which is recited at a high level of generality), a human user could be given instructions to use the sprayer in accordance with the recited at least one of a spray rate, a spray timing, and/or a position of a boom of the sprayer, thereby providing another example of organizing human activity.
Calculating a yield difference is recited in independent claims 1 and 15 and this is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims incorporate the additional elements of the independent claims.
Process claims 1-8 recite that the method is computer-implemented and include a computer system to generally perform the operations of the invention. Furthermore, data is included in a data structure. Claim 2 recites that a sprayer (in communication with the computer system) applies the treatment to the field, wherein the sprayer includes a boom, one or more sprayers coupled to the boom, and a tank in fluid communication with the one of more sprayers, the tank configured to hold said treatment.
Claim 19 recites generating, by the computer system, one or more executable scripts based on the selected one or more of the calculated responses, the one or more executable scripts including instructions to control an operating parameter of a sprayer including at least one of a spray rate, a spray timing, and/or a position of a boom of the sprayer; and transmitting, by the computer system, the one or more executable scripts to an application controller of the sprayer to thereby control the operating parameter of the sprayer to apply the treatment to the crop consistent with the synthetic treatment associated with the selected one or more of the calculated responses.
The scripts generated and executed in claim 19 are presented as high-level instructions to define operating parameters for the operation of the sprayer. Specific control details are not presented in the claims.
Apparatus claims 15-18 include a system comprising a data structure including data and at least one processor coupled in communication to the data structure to generally perform the operations of the invention.
Claims 1 and 15 recite that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 64, 88, 205-207). The recitation of “wherein the agricultural apparatus includes a sprayer apparatus, which includes a boom, one or more sprayers coupled to the boom, and a tank in fluid communication with the one of more sprayers, the tank configured to hold said treatment” in claim 2 simply presents a general link to a field of use.
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
The use of an agricultural apparatus (in communication with the computing device) to apply the treatment to the field is simply recited at a high level of generality and merely presents a general link to technology and to a field of use.
Claims 1 and 15 recite that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression. Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 126, 166, 169). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
NOTE: Claim 20 falls into at least one class of statutory subject matter. In contrast to claim 19, claim 20 presents specific technical details regarding how the scripts are generated to then control specific operations of the sprayer by the application controller, thereby presenting more than a general link to technology and placing meaningful limitations on the abstract ideas. Thus, claim 20 is deemed to be patent eligible under 35 U.S.C. § 101.
Allowable Subject Matter
Claims 1-8, 15-19, and 21 are allowed over the prior art of record. These claims remain rejected under 35 U.S.C. § 101.
Claim 20 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Dail et al. (US 2019/0156437) in view of Moser et al. (US 2005/0102896) in view of Ethington (US 2016/0232621) most closely address the various concepts recited in each of the independent claims, as seen in the last art rejection of claims 1 and 15 in the Office action dated November 5, 2025. Dail, Moser, and Ethington do not address the specific claim details reciting that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression. Briancon et al. (US 2020/0356900) describes the related art as including “predicting a stochastic time-series descriptive of weather, markets, or industrial processes, and the like.” (Briancon: ¶ 3) Briancon explains that logistic regression methods using Lasso and Elastic Net penalty functions may be used to reduce the number of features (Briancon: ¶¶ 42, 94). However, Briancon does not explicitly disclose the specific claim details reciting that the phenology model is trained via machine learning, that the separate disease risk models are trained via machine learning to process time-series weather data including real-time observed weather data and forecasted weather data, and the response model is trained via machine learning including elastic net regression. Additionally, the Examiner finds that one of ordinary skill in the art prior to Applicant’s invention would not have, in light of the teachings of the aforementioned references, found it obvious to create the claimed invention with the level of detail and specific manner of integration of operations as they are presented in each of the independent claims. Therefore, claims 1-8 and 15-21 are deemed to be allowable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Qin et al. (US 2023/0025712) – Uses elastic nets and analyzes changes in temperature (¶¶ 7, 12, 96, 99).
Singh, K N et al. "Forecasting Crop Yield Through Weather Indices Through LASSO." Indian Journal of Agricultural Sciences 89 (3): 540-4, March 2019. – Forecasts crop yield based on weather using lasso and ridge regression techniques.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights