Jump to content

Patent Application 17866028 - SYSTEMS AND METHODS FOR USE IN APPLICATION OF - Rejection

From WikiPatents

Patent Application 17866028 - SYSTEMS AND METHODS FOR USE IN APPLICATION OF

Title: SYSTEMS AND METHODS FOR USE IN APPLICATION OF TREATMENTS TO CROPS IN FIELDS

Application Information

  • Invention Title: SYSTEMS AND METHODS FOR USE IN APPLICATION OF TREATMENTS TO CROPS IN FIELDS
  • Application Number: 17866028
  • Submission Date: 2025-05-16T00:00:00.000Z
  • Effective Filing Date: 2022-07-15T00:00:00.000Z
  • Filing Date: 2022-07-15T00:00:00.000Z
  • National Class: 047
  • National Sub-Class: 057700
  • Examiner Employee Number: 76267
  • Art Unit: 3625
  • Tech Center: 3600

Rejection Summary

  • 102 Rejections: 0
  • 103 Rejections: 2

Cited Patents

The following patents were cited in the rejection:

Office Action Text



    DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed March 19, 2025. Claims 1-2, 6-8, 15, and 17-18 have been amended. Claims 9-14 are cancelled. Claims 1-8 and 15-18 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 March 19, 2025 have been fully considered but they are not persuasive.
Regarding the claim interpretation under 35 U.S.C. § 112(f), Applicant submits that the structural aspects of the limitations are disclosed in the Specification and, thus, the corresponding structural limitations recited in the claims do not invoke interpretations under 35 U.S.C. § 112(f) (page 7 of Applicant’s response). Applicant’s Specification does not present special definitions for the computing device and agricultural apparatus that would limit them to specific structural elements. These terms present generic placeholders. The corresponding disclosure in the Specification meets the appropriate requirements for support under 35 U.S.C. § 112(f), but does not obviate the interpretation of the computing device and the agricultural apparatus under 35 U.S.C. § 112(f).
	Regarding the rejection under 35 U.S.C. § 101, Applicant points out that a lot of data is processed and submits that the “pending claims eliminate the imprecision of the conventional estimation, and therefore, define a technical solution to a technical problem” (pages 7-9 of Applicant’s response). Even if a human user would need a relatively lengthy period of time to process the data, a human user can still process the type of data recited in the claims. Additionally, as explained in the rejection, 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)).
	Applicant states, “As such, with the claimed technical operations, assess the individual diseases through specific modeling, whereby the output is expressed in combination, which is more accurate and more efficient than separately assessing the same, thereby improving technology for associated with the treatment recommendation. With that said, claims that are directed to improvements in technology, as here, are not actually directed to abstract ideas.” (Page 9 of Applicant’s response) A human can evaluate, schedule, and implement the treatment recommendation. Treating crops is not necessarily a technology. The claims do not present specific technological details, much less details of a technical solution to a technical problem.
	Applicant submits that claim 2 is like the 101 example regarding control of a gate (pages 9-10 of Applicant’s response). Claim 2 does not recite specific details regarding how a fully automated device is controlled in response to the treatment recommendation, for example. Simply defining details of the agricultural apparatus does not equate to directly and autonomously controlling the active operation of the agricultural apparatus in direct response to treatment recommendations, for example.
	Regarding the art rejection, Applicant argues that Dail does not “disclose calculating, by the computing device, a growth stage of the crop in the field on a defined date.” (Page 13 of Applicant’s response) The Examiner respectfully disagrees. The following paragraphs explain how various growth stages may be correlated to certain risk days and risk hours and how the prospect of spraying may be evaluated during certain growth stages:
[0126] In an embodiment, agricultural intelligence computer system 130 assigns weights to risk hours based on a growth stage of the crop. For example, agricultural intelligence computer system 130 may initially receive data identifying a relative maturity of a crop and/or a number of growing degree days until maturity for the crop. Agricultural intelligence computer system 130 may use growing degree days to model the growth of the crop through different various growth stages. Agricultural intelligence computer system 130 may additionally assign weights to different growing stages. For example, a higher weight may be assigned to risk hours that occur during the V6-V12 growth stages for a particular crop damaging factor. Thus, risk hours and/or risk days that were identified during the V6-V12 stages may be multiplied by a first weight when computing risk values.
[0127] Different types of weights may be used in combination. For example, agricultural intelligence computer system 130 may apply weights based on proximity to optimal temperatures or humidity. Agricultural intelligence computer system 130 may then apply a second set of weights based on growth stage. Thus, a risk hour at the optimal temperature and humidity during an optimal growth stage may have a value of “1” while a risk hour at a suboptimal temperature during a suboptimal growth stage may be associated with a value of the weighting for the suboptimal temperature multiplied by the weighting for the suboptimal growth stage.
…
[0160] Additionally and/or alternatively, agricultural intelligence computer system 130 may determine risk values at particular portions of the crop development in order to generate a damage mitigating chemical application recommendation for particular portions of the crop development. For example, agricultural intelligence computer system 130 may be programmed or configured to generate recommendations for spraying fungicide during the V6-V12 portion of a crop's growth stage.
[0161] Agricultural intelligence computer system 130 may identify risk values prior to the V6-V12 growth stage using observed temperature and humidity data and/or risk values during or after the V6-V12 growth stage using observations and/or forecasts. Based on the risk values, agricultural intelligence computer system 130 may determine whether to generate a damaging mitigating chemical application recommendation for the crop and/or field. For example, if a risk value computed for an agronomic field for a particular day exceeds a risk threshold for that day, agricultural intelligence computer system 130 may generate a recommendation to spray the agronomic field with a damage mitigating chemical on the field.

	Applicant further submits that Dail does not address the claimed “multiple disease risks, each based on a separate disease risk model.” (Page 14 of Applicant’s response) The Examiner respectfully disagrees. In ¶ 121, Dail states, “The different types of environmental data may be used for determining risk of different diseases or insects. For example, environmental risk factors for disease may be based on temperature and relative humidity while environmental risk factors for insects may be based on temperature, relative humidity, and one or more of the examples listed above.” Additionally, Dail states, “In an embodiment, agricultural intelligence computer system 130 stores different ranges for different crop damaging factors. For example, agricultural intelligence computer system 130 may store a first set of ranges for the Gray Leaf Spot disease which includes a temperature range of 22° C.-30° C. and a relative humidity range of 87%-100%. Agricultural intelligence computer system 130 may additionally store a second set of ranges for the Northern Leaf Blight disease which includes a temperature range of 15° C.-24° C. and a relative humidity range of 87%-100%. In embodiments, agricultural intelligence computer system 130 may additionally store ranges pertaining to optimal conditions for other diseases or insects.” (Dail: ¶ 118)
	Applicant makes a general assertion that the last Office action provided no motivation to combine the references in the rejection (page 14 of Applicant’s response). Applicant does not specifically address the rationale for combining the cited teachings of Dail with those of Ethington, which was and continues to be explained as follows:
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Dail such that the various calculations, risk assessment, and report generation (as recited in the claim) are performed for each one of the plurality of synthetic treatments (such as each of Dail’s possible fungicides and insecticides), wherein each synthetic treatment is a hypothetical treatment for the crop in the field in order to allow a grower to make more informed decisions regarding identified disease risk and the best treatments, particularly in light of model parameters that the grower may prioritize, such as return on investment (ROI), yield, etc. (all of which are of importance to Dail, as seen in ¶ 157 (“Based on the estimate of loss, agricultural intelligence computer system 130 may determine a benefit to crop yield and/or revenue of applying a particular fungicide. A fungicide recommendation may identify the likely increase in crop yield and/or revenue for applying the particular fungicide.”) and in ¶ 68 (“This enables growers to maximize yield or return on investment through optimized nitrogen application during the season.”) of Dail).

	Regarding claim 2, Applicant submits that neither Dail nor Ethington discloses 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. The Examiner points out that ¶ 84 of Dail does indeed address this new claim limitation, as seen in the following statement: “In an embodiment, examples of sensors 112 that may be used in relation to apparatus for applying fertilizer, insecticide, fungicide and the like, such as on-planter starter fertilizer systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors indicating which spray head valves or fluid line valves are open; sensors associated with tanks, such as fill level sensors; sectional or system-wide supply line sensors, or row-specific supply line sensors; or kinematic sensors such as accelerometers disposed on sprayer booms. In an embodiment, examples of controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.”
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 

The following is a quotation of pre-AIA  35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.  The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, is invoked. 
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph:
(A)	the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; 
(B)	the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and 
(C)	the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. 
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. 
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. 
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.  Such claim limitation(s) is/are:
“computing device” for receiving (in claim 1), for calculating (multiple recitations in claim 1), for determining (in claim 1), and for compiling (in claim 1)
“agricultural apparatus” for applying (in claim 2)
“computing device” for performing each of the functions recited in claims 15-18

** Applicant’s Specification describes hardware corresponding to computing devices as follows:
[0064] Applications having instructions configured in this way may be implemented for different computing device platforms while retaining the same general user interface appearance. For example, the mobile application may be programmed for execution on tablets, smartphones, or server computers that are accessed using browsers at client computers…
[0088] According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special- purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.
[0206] It should also be appreciated that one or more aspects of the present disclosure transform a general-purpose computing device into a special-purpose computing device when configured to perform the functions, methods, and/or processes described herein.

** Applicant’s Specification describes hardware corresponding to agricultural apparatus as follows:
[0033] As shown in FIG. 1, an agricultural apparatus 111 is disposed in the field 105, and may be configured, generally, to perform one or more operations at the field 105 (e.g., seed planting, application of treatment, harvesting, etc.). In this specific embodiment, the agricultural apparatus 111 is a sprayer apparatus, which is configured to apply one or more treatments to the field 105. In particular, for example, the sprayer apparatus includes one or more holding tanks 107 for a treatment and one or more sprayers 117 (e.g., sprayer nozzles, etc.), which are positioned on a boom 113 extending from each side of a cab to apply (e.g., spray, etc.) the treatment in a manner prescribed for the specific treatment, etc. It should be appreciated that the agricultural apparatus 111 may be of a different type of machine and/or configured otherwise in other embodiments. In addition, other agricultural apparatus may be included in the system 100, for example, in the field 105 or other fields, in other embodiments. Generally, examples of agricultural apparatus as used herein may include, without limitation, tractors, combines, harvesters, planters, trucks, fertilizer equipment, sprayers, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. 
[0034] In this example embodiment, as shown, the agricultural apparatus 111 in the system 100 may have one or more remote sensors 112 fixed thereon or coupled thereto, where the sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural computer system 130 and which are programmed or configured to send sensor data to agricultural computer system 130. In some embodiments, apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the agricultural apparatus 111, where a controller area network (CAN) is an example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of the agricultural apparatus 111 and/or implementation from the agricultural computer system 130. For instance, a CAN bus interface may be used to enable communications from the agricultural computer system 130 to the agricultural apparatus 111, for example, via the CLIMATE FIELDVIEW DRIVE, available from The Climate Corporation, San Francisco, California, etc. Sensor data may include the same type of information as field data 106. In some embodiments, remote sensors 112 may not be fixed to the agricultural apparatus 111, but may be remotely located in the field (e.g., the field 105, etc.) and/or may communicate with the agricultural apparatus 111 or the network 109 directly. 
[0035] The agricultural apparatus 111 may further comprise a cab computer 115 that is programmed with a cab application, which may comprise a version or variant of the mobile application for communication device 104 (or otherwise) that is further described in other sections herein. In an embodiment, cab computer 115 may include a compact computer, often a tablet-sized computer or smartphone, with a graphical screen display, such as a color display, that is disposed and/or mounted within the cab of the apparatus 111.Cab computer 115 may implement some or all of the operations and functions that are described further herein for the communication device 104. 

Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, applicant may:  (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph.
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 and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.  
Claims 1-8 and 15-18 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), Apparatus (claims 15-18)
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 1]	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 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, 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; 
determining whether the calculated growth stage is within a spray window for the crop, wherein the spray window is defined by a range of a growth stage index; 
in response to the growth stage being within the spray window, defining a plurality of synthetic treatments within the window for the field, wherein each synthetic treatment is a hypothetical treatment for the crop in the field; and then 
for each one of the plurality of synthetic treatments: 
	calculating multiple disease risks for the crop in the field, each based on a separate disease risk model, which is indicative of a potential occurrence and/or a severity of at least one disease; and 
	calculating a response to the synthetic treatment, via a response model, based on the calculated disease risks and the calculated 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 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.  
[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]	determining daily weather for the field based on the planting data and the weather data, the weather data including observed actual weather data for the field and forecasted weather data for the field; and 
wherein the growth stage is based on the daily weather for the field, from the planting date to the defined date, and at least one stress factor for the field.  
[Claim 6]	wherein the disease risk models include one or more of: 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 15]	identifying treatments for crops in one or more fields, comprising: 
data including planting data and weather data associated with multiple fields; and 
	receive 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; 
	access the planting data and the weather data for the field in the data; 
	calculate a growth stage of the crop in the field on a defined date, via a phenology model, based on the planting date and weather data included in the data structure, which is indicative of a thermal time associated with the crop as defined by temperatures associated with the field from about the planting date to about the defined date; 
	determine whether the calculated growth stage is within a spray window for the crop, wherein the spray window is defined by a range of a growth stage index; 
	in response to the growth stage being within the spray window, define a plurality of synthetic treatments within the window for the field, wherein each synthetic treatment is a hypothetical treatment for the crop in the field; and then 
	for each one of the plurality of synthetic treatments: 
		calculate multiple disease risks for the crop in the field, each based on a separate disease risk model, which is indicative of a potential occurrence and/or a severity of at least one disease; and 
		calculate a response to the synthetic treatment, via a response model, based on the calculated disease risks and the calculated 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 treatment and the predicted crop yield for the crop without the synthetic treatment; and then 
compile 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 
transmit the report in response to the request.  
[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 disease risk models include one or more of: 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.

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 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) and a human can receive instructions to apply a treatment (as recited in claim 10), 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.

Various calculating steps are recited throughout the claims and these are examples of mathematical concepts. Additional evidence of this assessment, based on broadest reasonable interpretation, is found in the various calculations described throughout Applicant’s Specification, including in ¶¶ 114-126, 135-166).
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Process claims 1-8 recite that the method is computer-implemented and include a computing device to generally perform the operations of the invention. Furthermore, data is included in a data structure. Claim 2 recites that an agricultural apparatus (in communication with the computing device) applies the treatment to the field, 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.
  
Apparatus claims 15-18 include a system comprising a data structure including data and at least one computing device coupled in communication to the data structure to generally perform the operations of the invention.

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.

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.




Claim Rejections - 35 USC § 103
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, 4-5, 7-8, 15-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dail et al. (US 2019/0156437) in view of Ethington (US 2016/0232621).
[Claim 1]	Dail discloses a computer-implemented method for use in applying a treatment to crops in one or more fields (¶ 34 – “The computer system may then attempt to mitigate the risk by recommending application of a damage mitigating chemical and/or determine new practices for the future to reduce the risk of damage due to a crop damaging factor.”), the method comprising: 
receiving, at a computing device, 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 (¶ 45 – “Communication layer 132 may be programmed or configured to perform input/output interfacing functions including sending requests to field manager computing device 104, external data server computer 108, and remote sensor 112 for field data, external data, and sensor data respectively.”; ¶ 46 – “The GUI may comprise controls for inputting data to be sent to agricultural intelligence computer system 130, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.”; ¶ 50 – “Using the display depicted in FIG. 5, a user computer can input a selection of a particular field and a particular date for the addition of event. Events depicted at the top of the timeline may include Nitrogen, Planting, Practices, and Soil.”; ¶ 38 – “Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range),...(d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population)…”); 
accessing a data structure including planting data and weather data for the field (¶ 54 – “Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.”; ¶ 91 – “In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields.”; ¶ 37 – “The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.”; ¶ 38 -- (d) planting data, (h) weather data); 
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 (¶ 35 – “In an embodiment, a method comprises receiving, for each hour of a first day, weather data identifying temperature values and humidity values associated with a geographic location; determining, for a particular hour of the first day, that a temperature value is within a first range of values and a humidity value is within a second range of values and, in response, identifying the particular hour as a risk hour; computing, for a second day, a risk value for one or more agronomic fields at the geographic location based, at least in part, on one or more identified risk hours between a day of planting a crop on the one or more agronomic fields and the second day; determining that the risk value is above a risk value threshold and, in response, determining that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor; storing data indicating that the crop is at risk of suffering damage from the particular crop damaging factor.”; ¶ 92 – “In an embodiment, the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data. The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement.”; ¶ 38 -- crop phenology; Crop phenology is the study of cyclic growth in plants, which (as discussed throughout the rejection) Dail analyzes.); 
determining, by the computing device, whether the calculated growth stage is within a spray window for the crop, wherein the spray window is defined by a range of a growth stage index (¶ 141 – “At 708, the process determines that the risk value is above a risk value threshold and, in response, a determination is made that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor. For example, the agricultural intelligence computer system 130 may store a threshold value indicating a high level of risk of crop damage from disease. If the computed risk value is above the threshold value, agricultural intelligence computer system 130 may determine that a crop on the one or more fields is at risk of suffering damage from the particular crop damaging factor. As used herein, suffering damage refers to a negative impact on crop yield, crop health, and/or crop quality.”; ¶ 142 – “In an embodiment, the threshold is established for a particular period of time. For example, an optimal time for application of fungicide and/or insecticide may occur at a particular growth stage for a crop. Agricultural intelligence computer system 130 may store a risk value threshold for the particular stage of the crop. If, when the crop is at the particular growth stage, the risk value is above the risk value threshold, agricultural intelligence computer system 130 may determine that the crop is at risk and may benefit from application of an insecticide or fungicide.”; The following paragraphs explain how various growth stages may be correlated to certain risk days and risk hours and how the prospect of spraying may be evaluated during certain growth stages:
[0126] In an embodiment, agricultural intelligence computer system 130 assigns weights to risk hours based on a growth stage of the crop. For example, agricultural intelligence computer system 130 may initially receive data identifying a relative maturity of a crop and/or a number of growing degree days until maturity for the crop. Agricultural intelligence computer system 130 may use growing degree days to model the growth of the crop through different various growth stages. Agricultural intelligence computer system 130 may additionally assign weights to different growing stages. For example, a higher weight may be assigned to risk hours that occur during the V6-V12 growth stages for a particular crop damaging factor. Thus, risk hours and/or risk days that were identified during the V6-V12 stages may be multiplied by a first weight when computing risk values.
[0127] Different types of weights may be used in combination. For example, agricultural intelligence computer system 130 may apply weights based on proximity to optimal temperatures or humidity. Agricultural intelligence computer system 130 may then apply a second set of weights based on growth stage. Thus, a risk hour at the optimal temperature and humidity during an optimal growth stage may have a value of “1” while a risk hour at a suboptimal temperature during a suboptimal growth stage may be associated with a value of the weighting for the suboptimal temperature multiplied by the weighting for the suboptimal growth stage.
…
[0160] Additionally and/or alternatively, agricultural intelligence computer system 130 may determine risk values at particular portions of the crop development in order to generate a damage mitigating chemical application recommendation for particular portions of the crop development. For example, agricultural intelligence computer system 130 may be programmed or configured to generate recommendations for spraying fungicide during the V6-V12 portion of a crop's growth stage.
[0161] Agricultural intelligence computer system 130 may identify risk values prior to the V6-V12 growth stage using observed temperature and humidity data and/or risk values during or after the V6-V12 growth stage using observations and/or forecasts. Based on the risk values, agricultural intelligence computer system 130 may determine whether to generate a damaging mitigating chemical application recommendation for the crop and/or field. For example, if a risk value computed for an agronomic field for a particular day exceeds a risk threshold for that day, agricultural intelligence computer system 130 may generate a recommendation to spray the agronomic field with a damage mitigating chemical on the field.
); 
in response to the growth stage being within the spray window, defining a plurality of synthetic treatments within the window for the field (¶ 142 – “In an embodiment, the threshold is established for a particular period of time. For example, an optimal time for application of fungicide and/or insecticide may occur at a particular growth stage for a crop. Agricultural intelligence computer system 130 may store a risk value threshold for the particular stage of the crop. If, when the crop is at the particular growth stage, the risk value is above the risk value threshold, agricultural intelligence computer system 130 may determine that the crop is at risk and may benefit from application of an insecticide or fungicide.”); and then 
for at least one of the plurality of synthetic treatments (¶¶ 149-151): 
	calculating, by the computing device, multiple disease risks for the crop in the field, each based on a separate disease risk model, which is indicative of a potential occurrence and/or a severity of at least one disease (¶ 141 – “At 708, the process determines that the risk value is above a risk value threshold and, in response, a determination is made that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor. For example, the agricultural intelligence computer system 130 may store a threshold value indicating a high level of risk of crop damage from disease. If the computed risk value is above the threshold value, agricultural intelligence computer system 130 may determine that a crop on the one or more fields is at risk of suffering damage from the particular crop damaging factor. As used herein, suffering damage refers to a negative impact on crop yield, crop health, and/or crop quality.”; ¶ 144 – “By correlating risk values with a quantification of loss of crop yield, agricultural intelligence computer system 130 is able to generate useful risk value thresholds. For example, if the test data indicates that a particular risk value during the V4 stage of development is highly correlated with a loss of 10 bu/acre of yield, agricultural intelligence computer system 130 may set the particular risk value to be a risk value threshold. Thus, if agricultural intelligence computer system 130 computes a risk value above the risk value threshold during the V4 stage of development, agricultural intelligence computer system 130 may determine that the agronomic field runs a high risk of suffering a loss of 10 bu/acre due to the one or more crop damaging factors.”; ¶ 146 – “a first range of risk values may be identified as little to no risk, a second range of risk values may be identified as moderate risk, and a third range of values may be identified as severe risk.”; ¶ 121 – “The different types of environmental data may be used for determining risk of different diseases or insects. For example, environmental risk factors for disease may be based on temperature and relative humidity while environmental risk factors for insects may be based on temperature, relative humidity, and one or more of the examples listed above.”; ¶ 118 -- “In an embodiment, agricultural intelligence computer system 130 stores different ranges for different crop damaging factors. For example, agricultural intelligence computer system 130 may store a first set of ranges for the Gray Leaf Spot disease which includes a temperature range of 22° C.-30° C. and a relative humidity range of 87%-100%. Agricultural intelligence computer system 130 may additionally store a second set of ranges for the Northern Leaf Blight disease which includes a temperature range of 15° C.-24° C. and a relative humidity range of 87%-100%. In embodiments, agricultural intelligence computer system 130 may additionally store ranges pertaining to optimal conditions for other diseases or insects.”); and 
	calculating, by the computing device, a response to the synthetic treatment, via a response model, based on the calculated disease risks 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 treatment and the calculated predicted crop yield for the crop without the synthetic treatment (¶¶ 141, 144, 146; ¶ 143 – “Using the test data, agricultural intelligence computer system 130 may correlate differences in yields between protected crops and unprotected crops with a risk value at a particular stage of development.”; ¶ 92 – “In an embodiment, the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data. The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement.”; ¶¶ 118, 121 – multiple disease risks and corresponding analyses; ¶¶ 126-127, 160-161 – defined stages and time frames for growth and treatment applications); and then
compiling, by the computing device, 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 (¶ 143 – “Using the test data, agricultural intelligence computer system 130 may correlate differences in yields between protected crops and unprotected crops with a risk value at a particular stage of development.”; ¶ 144 – “By correlating risk values with a quantification of loss of crop yield, agricultural intelligence computer system 130 is able to generate useful risk value thresholds. For example, if the test data indicates that a particular risk value during the V4 stage of development is highly correlated with a loss of 10 bu/acre of yield, agricultural intelligence computer system 130 may set the particular risk value to be a risk value threshold.”; ¶ 151 – “At step 710, data indicating that the crop is at risk of suffering damage from the particular crop damaging factor is stored. Agricultural intelligence computer system 130 may store the data in order to correlate yield loss with the risk hour computation, recommend application of a fungicide and/or insecticide, generate a script that causes application of a fungicide and/or insecticide on a field, and/or recommend different planting and/or management activities in the future.”); and
transmitting the report in response to the request (¶ 143 – “Using the test data, agricultural intelligence computer system 130 may correlate differences in yields between protected crops and unprotected crops with a risk value at a particular stage of development.”; ¶ 151 – “At step 710, data indicating that the crop is at risk of suffering damage from the particular crop damaging factor is stored. Agricultural intelligence computer system 130 may store the data in order to correlate yield loss with the risk hour computation, recommend application of a fungicide and/or insecticide, generate a script that causes application of a fungicide and/or insecticide on a field, and/or recommend different planting and/or management activities in the future.”; ¶ 72 – “In one embodiment, performance instructions 216 are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield-limiting factors. The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others.”).

While Dail performs disease risk assessment and treatments in relation to fungicide and/or insecticide (Dail: ¶¶ 149-151), Dail does not explicitly disclose that the various calculations, risk assessment, and report generation (as recited in the claim) are performed for each one of the plurality of synthetic treatments, wherein each synthetic treatment is a hypothetical treatment for the crop in the field. However, Ethington displays various pesticide application scenarios so that a user can compare various scenarios, including with the ability for the user to customize some of the model parameters, as seen in ¶ 113 of Ethington:
[0113] The pest and disease advisor module is configured to receive and process all such sets of data points and received user data and simulate possible pesticide application practices. The simulation of possible pesticide practices includes, dates, rates, and next date on which workability for a pesticide application is “Green” taking into account predicted workability. The pest and disease advisor module generates and displays on the user device a scouting and treatment recommendation for the user. The scouting recommendation includes daily (or as specified by the user) times to scout for specific pests and diseases. The user has the option of displaying a specific subset of pests and diseases as well as additional information regarding a specific pest or disease. The treatment recommendation includes the list of fields where a pesticide application is recommended, including for each field the recommended application practice, recommended application dates, recommended application rate, and next data on which workability for the pesticide application is “Green.” The user has the option of modeling and displaying estimated return on investment for the recommended pesticide application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The alternative pesticide application scenarios may be displayed and graphed on the user device along with the original recommendation. The user has the further option of modeling and displaying estimated yield benefit (minimum, average, and maximum) for the recommended pesticide application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user.

The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Dail such that the various calculations, risk assessment, and report generation (as recited in the claim) are performed for each one of the plurality of synthetic treatments (such as each of Dail’s possible fungicides and insecticides), wherein each synthetic treatment is a hypothetical treatment for the crop in the field in order to allow a grower to make more informed decisions regarding identified disease risk and the best treatments, particularly in light of model parameters that the grower may prioritize, such as return on investment (ROI), yield, etc. (all of which are of importance to Dail, as seen in ¶ 157 (“Based on the estimate of loss, agricultural intelligence computer system 130 may determine a benefit to crop yield and/or revenue of applying a particular fungicide. A fungicide recommendation may identify the likely increase in crop yield and/or revenue for applying the particular fungicide.”) and in ¶ 68 (“This enables growers to maximize yield or return on investment through optimized nitrogen application during the season.”) of Dail).
[Claim 2]	Dail discloses applying, by an agricultural apparatus, in communication with the computing device, the treatment to the field consistent with the recommendation included in the report, wherein the report further includes an indication of the treatment (¶ 40 -- Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130. For instance, a controller area network (CAN) bus interface may be used to enable communications from the agricultural intelligence computer system 130 to the agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE, available from The Climate Corporation, San Francisco, Calif., is used.”; ¶ 69 – nitrogen map to guide application instructions; ¶ 151 – “At step 710, data indicating that the crop is at risk of suffering damage from the particular crop damaging factor is stored. Agricultural intelligence computer system 130 may store the data in order to correlate yield loss with the risk hour computation, recommend application of a fungicide and/or insecticide, generate a script that causes application of a fungicide and/or insecticide on a field, and/or recommend different planting and/or management activities in the future.”; ¶ 155 – “Damage mitigating chemical application recommendations may include recommendations to apply fungicide or insecticide to a field to reduce and/or prevent damage to a crop on the field. For example, agricultural intelligence computer system 130 may determine that a risk value for the crop in the next fourteen days is highly correlated with a particular level of crop damage using the methods described herein.”); and
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 (¶ 84 – “In an embodiment, examples of sensors 112 that may be used in relation to apparatus for applying fertilizer, insecticide, fungicide and the like, such as on-planter starter fertilizer systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors indicating which spray head valves or fluid line valves are open; sensors associated with tanks, such as fill level sensors; sectional or system-wide supply line sensors, or row-specific supply line sensors; or kinematic sensors such as accelerometers disposed on sprayer booms. In an embodiment, examples of controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.”).
[Claim 4]	Dail discloses wherein accessing the data structure includes accessing the planting data in the data structure specific to the field based on the field ID (¶ 54 – “Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.”; ¶ 38 – Fields are assigned field identifiers; ¶ 50 – “Using the display depicted in FIG. 5, a user computer can input a selection of a particular field and a particular date for the addition of event.”; ¶¶ 50, 53, 151 – Application of a fungicide and/or insecticide is recommended for a particular field.).  
[Claim 5]	Dail discloses determining daily weather for the field based on the planting data and the weather data, the weather data including observed actual weather data for the field and forecasted weather data for the field (¶ 70 – “In one embodiment, weather instructions 212 are programmed to provide field-specific recent weather data and forecasted weather information. This enables growers to save time and have an efficient integrated display with respect to daily operational decisions.”; ¶ 75 – “The weather data may include past and present weather data as well as forecasts for future weather data.”; ¶¶ 115, 135, 154, 159, 161 – weather forecasts, observed temperatures); and 
wherein the growth stage is based on the daily weather for the field, from the planting date to the defined date, and at least one stress factor for the field (¶¶ 117, 161 – growth stage, daily temperature/weather; ¶ 35 – “In an embodiment, a method comprises receiving, for each hour of a first day, weather data identifying temperature values and humidity values associated with a geographic location; determining, for a particular hour of the first day, that a temperature value is within a first range of values and a humidity value is within a second range of values and, in response, identifying the particular hour as a risk hour; computing, for a second day, a risk value for one or more agronomic fields at the geographic location based, at least in part, on one or more identified risk hours between a day of planting a crop on the one or more agronomic fields and the second day; determining that the risk value is above a risk value threshold and, in response, determining that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor; storing data indicating that the crop is at risk of suffering damage from the particular crop damaging factor.”; ¶ 117 – “At step 704, the process determines that, for a particular hour of the first day, a temperature value is within a first range of values and a humidity value is within a second range of values and, in response, the particular hour is identified as a risk hour. For example, agricultural intelligence computer system 130 may store a range of temperature and humidity values for a particular disease that describe optimal temperature and humidity for growth of a disease. If the average temperature and humidity for a particular hour is within the two ranges, agricultural intelligence computer system 130 may identify the hour as a risk hour. Identifying the hour as a risk hour may comprise incrementing a value indicating a number of risk hours for that day and/or storing data identifying the particular hour as a risk hour.” Disease factors are examples of stress factors.).  
[Claim 7]	Dail discloses 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 (¶ 117 – “At step 704, the process determines that, for a particular hour of the first day, a temperature value is within a first range of values and a humidity value is within a second range of values and, in response, the particular hour is identified as a risk hour. For example, agricultural intelligence computer system 130 may store a range of temperature and humidity values for a particular disease that describe optimal temperature and humidity for growth of a disease. If the average temperature and humidity for a particular hour is within the two ranges, agricultural intelligence computer system 130 may identify the hour as a risk hour. Identifying the hour as a risk hour may comprise incrementing a value indicating a number of risk hours for that day and/or storing data identifying the particular hour as a risk hour.”; ¶ 121 – “The different types of environmental data may be used for determining risk of different diseases or insects. For example, environmental risk factors for disease may be based on temperature and relative humidity while environmental risk factors for insects may be based on temperature, relative humidity, and one or more of the examples listed above.”; ¶ 118 -- “In an embodiment, agricultural intelligence computer system 130 stores different ranges for different crop damaging factors. For example, agricultural intelligence computer system 130 may store a first set of ranges for the Gray Leaf Spot disease which includes a temperature range of 22° C.-30° C. and a relative humidity range of 87%-100%. Agricultural intelligence computer system 130 may additionally store a second set of ranges for the Northern Leaf Blight disease which includes a temperature range of 15° C.-24° C. and a relative humidity range of 87%-100%. In embodiments, agricultural intelligence computer system 130 may additionally store ranges pertaining to optimal conditions for other diseases or insects.”).  
[Claim 8]	Dail discloses 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 (¶ 126 – “In an embodiment, agricultural intelligence computer system 130 assigns weights to risk hours based on a growth stage of the crop. For example, agricultural intelligence computer system 130 may initially receive data identifying a relative maturity of a crop and/or a number of growing degree days until maturity for the crop. Agricultural intelligence computer system 130 may use growing degree days to model the growth of the crop through different various growth stages. Agricultural intelligence computer system 130 may additionally assign weights to different growing stages. For example, a higher weight may be assigned to risk hours that occur during the V6-V12 growth stages for a particular crop damaging factor. Thus, risk hours and/or risk days that were identified during the V6-V12 stages may be multiplied by a first weight when computing risk values.” Reaching “maturity for the crop” corresponds to a harvest date for the crop.; ¶¶ 34-35 – crop damage; ¶ 146 – Risk may be categorized by a level of severity.; ¶ 121 – “The different types of environmental data may be used for determining risk of different diseases or insects. For example, environmental risk factors for disease may be based on temperature and relative humidity while environmental risk factors for insects may be based on temperature, relative humidity, and one or more of the examples listed above.”). 
[Claims 15-16, 18]	Claims 15-16, 18 recite limitations already addressed by the rejections of claims 1, 5, 7-8, and 11 above; therefore, the same rejections apply.
	Furthermore, regarding claim 15, Dail discloses a system for use in identifying treatments for crops in one or more fields, the system comprising: 
a data structure including planting data and weather data associated with multiple fields (¶ 54 – “Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.”; ¶ 91 – “In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields.”; ¶ 37 – “The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.”; ¶ 38 -- (d) planting data, (h) weather data; ¶ 51 – “In an embodiment, the data manager provides an interface for creating one or more programs. ‘Program,’ in this context, refers to a set of data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information that may be related to one or more fields, and that can be stored in digital data storage for reuse as a set in other operations. After a program has been created, it may be conceptually applied to one or more fields and references to the program may be stored in digital storage in association with data identifying the fields.”); and 
at least one computing device coupled in communication to the data structure, the at least one computing device configured to perform the disclosed operations (Dail: ¶¶ 100-112 – hardware/software; ¶ 51 – “In an embodiment, the data manager provides an interface for creating one or more programs. ‘Program,’ in this context, refers to a set of data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information that may be related to one or more fields, and that can be stored in digital data storage for reuse as a set in other operations. After a program has been created, it may be conceptually applied to one or more fields and references to the program may be stored in digital storage in association with data identifying the fields.”; ¶ 54 – “Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.”; ¶ 91 – “In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields.”).


Claims 3, 6, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dail et al. (US 2019/0156437) in view of Ethington (US 2016/0232621), as applied to claims 1-2 and 15-16 above, in view of Karunakaran et al. (WO 2015/179985 A1).
[Claims 3, 6, 17]	Dail discloses wherein the treatment includes a fungicide (Dail: ¶ 151 – A fungicide is a possible treatment), as per claim 3. However, Dail does not explicitly disclose:
[Claim 3]	wherein the crop includes winter wheat;  
[Claim 6]	wherein the disease risk models include one or more of: a septoria model, a leaf rust model, a stripe rust model, and a fusarium model;
[Claim 17]	wherein the disease risk models include one or more of: a septoria model, a leaf rust model, a stripe rust model, and a fusarium model.  

Karunakaran screens crop plants for disease resistance (Karunakaran: ¶ 1) and Karunakaran specifically evaluates crops like winter wheat (Karunakaran: ¶¶ 39, 51) as these crops relate to diseases including septoria, leaf rust, stripe rust, and fusarium (Karunakaran: ¶¶ 41, 53). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Dail:
[Claim 3]	wherein the crop includes winter wheat;  
[Claim 6]	wherein the disease risk models include one or more of: a septoria model, a leaf rust model, a stripe rust model, and a fusarium model;
[Claim 17]	wherein the disease risk models include one or more of: a septoria model, a leaf rust model, a stripe rust model, and a fusarium model
in order to adapt Dail’s models to crops commonly planted in a particular geographical area (like winter wheat) and diseases commonly affecting those crops (like septoria, leaf rust, stripe rust, and fusarium), thereby making Dail’s invention more marketable to a wider range of customers (e.g., growers) in various regions.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.

/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A


Retrieved from "https://wikipatents.org/index.php?title=Patent_Application_17866028_-_SYSTEMS_AND_METHODS_FOR_USE_IN_APPLICATION_OF_-_Rejection&oldid=592892"
Cookies help us deliver our services. By using our services, you agree to our use of cookies.