Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/376,421 received on 01/12/2026.
In accordance with Applicant’s amendment. Claims 1-21 are amended, currently pending, and have been examined.
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119 or 35 U.S.C. 120.
Response to Amendment
The amendment filed on 01/12/2026 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of amendment, the specification objection previously applied to the title in the specification is withdrawn.
Upon review of amendment, the §112(f) claim interpretation previously applied is withdrawn.
Upon review of amendment, the §112(b) rejections previously applied are withdrawn.
Upon review of amendment, the §112(a) rejections previously applied are withdrawn.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the original claims have been considered and are unpersuasive.
Applicant argues (Remarks at pgs. 9-10): “The claims are analogous to those in USPTO Subject Matter Eligibility Example 46 (Livestock Management), where claims reciting data collection and analysis for detecting animal health issues (e.g., disease like grass tetany) were deemed eligible under Step 2A Prong 2 when integrated into a practical application via automated control of farm equipment, such as dispensing feed/supplements or operating sorting gates based on the analysis. Similarly, here, the claims integrate the alleged abstract idea (data analysis) into a practical application by using the aggregated damaging factor to generate specific and tailored outputs that control spraying equipment for crop treatment, addressing a specific agricultural problem (disease threats exacerbated by consecutive weather conditions) in a way that effects a real-world improvement (see, Applicant's specification at paras. [0016]-[0017], [0044]). This is not merely applying the idea on a computer but transforming weather data into actionable control of physical processes, akin to the eligible claims in Diamond v. Diehr, 450 U.S. 175 (1981), where mathematical calculations were integrated to control a rubber-curing process and the Court found that the inclusion of a mathematical formula did not preclude patent eligibility. In response, Examiner respectfully disagrees with Applicant’s assertions that the present claims are directed to statutory subject matter in view of Example 46. Claim 2 and 3 of Example 46 were deemed eligible because the claims are beyond merely generally linking. In particular, the October 2019 Patent Eligibility Guide states that “Limitation (d) in combination with the feed dispenser enables the control of appropriate farm equipment based on the automatic detection of grass tetany, which goes beyond merely automating the abstract idea.” In contrast, the present claims merely recite generic computing equipment used as a tool to perform abstract steps, which is not sufficient to prove integration into a practical application or anything significantly more. Even when considering the limitation for “automatically spraying the field”, this limitation merely recites generic control of equipment, which is not sufficient to prove integration into a practical application or anything significantly more. Examiner respectfully asserts that the selective control of appropriate equipment integrated the judicial exception into a practical application because the control is performed in a particular way that is an “other meaningful limitation” that integrates the judicial exception into the overall livestock management scheme and accordingly practically applies the exception. Therefore, Examiner respectfully disagrees with Applicant’s assertions in view of Example 46.
Applicant argues (Remarks at pg. 10): “Assuming arguendo, even if the claims were directed to an abstract idea, the ordered combination of elements amounts to significantly more than the idea itself under Step 2B. The specific aggregation step - weighting consecutive threat intervals (e.g., via the formula in Applicant's specification at para. [0036]: "R(wk)=Isk P(L(F(h(x),t(x))))" where L( transforms to lengths of continuous intervals and P( weights them) - is unconventional and provides an inventive concept not well-understood or routine, as evidenced by the improved accuracy over prior methods (see, Applicant's specification at para. [0057]: Performance lines 304.1-304.2 showing greater area under the curve compared to baselines). Combined with the practical output to farm equipment, this ordered combination transforms the claims into an eligible invention (see, Applicant's specification at para. [0038]: Example calculation yielding a threat factor of 39 by weighting groups like "one four-consecutive threat interval set" higher).”. In response, Examiner respectfully disagrees and notes that Applicant’s claims for aggregating threat intervals doesn’t integrate the abstract idea into a practical application or otherwise add significantly more because the steps for aggregating the threat intervals recites a judicial exception directed to the “Mathematical Concepts” abstract idea grouping. Furthermore, the other items discussed above are irrelevant to the analysis because these features are not recited or required by the claim. For example, the claims do not recite or require “yielding a threat factor of 39”. Applicant’s argument lacks merit because is relies on limitations not required by the claims and it would be improper to import such limitations from the Specification. See Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also, CollegeNet, Inc. v. Apply Yourself Inc., 418 F.3d 1225, 1231 (Fed. Cir. 2005) (while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims).
Applicant argues (Remarks at pgs. 10-11): “The claims are further supported by recent USPTO decisions finding eligibility for inventions that provide specific technical improvements through data analysis and processing. For example, in Ex parte Desjardins (Appeal 2024-000567, PTAB Apps. Rev. Sept. 26, 2025), the USPTO Director reversed a PTAB § 101 rejection for an invention involving continual learning in machine learning models, holding that improvements in model efficiency, training strategies, and processing requirements constitute technological advancements eligible under §101, rather than abstract ideas, consistent with Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) (recognizing software innovations as eligible when directed to specific improvements in computer functionality). Similarly, here, the claimed method's specific aggregation of threat intervals to weight consecutive periods more heavily provides a technological improvement in the accuracy of crop disease assessment over conventional summing approaches (see, Applicant's specification at paras. [0016], [0057]), enabling precise control of agricultural equipment (see, Applicant's specification at para. [0044]), much like the data-driven efficiencies deemed eligible in Desjardins and the improved data structures in Enfish. Thus, the ordered combination integrates any alleged abstract idea into a practical application in precision agriculture.”. In response, Examiner respectfully disagrees and notes that the present claims do not provide an analogous improvement to the machine learning model (e.g. a machine learning model). Examiner respectfully asserts that the claims are unlike the Des Jardins decision because the claims are directed to an abstract idea versus being directed to an improvement to computer functionality. The present claims do not provide an analogous technical solution to that of Des Jardins because the claims do not “address challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” The machine learning model of the present claims is merely a tool to perform the abstract process. An improvement to threat analysis would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1 and not to the reinforcement learning model itself. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”. Furthermore, the improvement of Des Jardins is related to catastrophic forgetting, whereas the instant claims merely use machine learning at a high level to perform an abstract idea to merely employ generic techniques of data generation.
Therefore, the §101 rejections previously applied are maintained and updated to address Applicant’s amendments to claims 1-21. See §101 rejections section below for further details.
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the original claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 13): “Further, it would not have been obvious to one of ordinary skill in the art to modify or combine Ethington, Dail, and Carroll in the first place to arrive at the claimed invention because there is no motivation or teaching to aggregate threat intervals into a damaging factor based specifically on consecutiveness of intervals. The Office's motivation for Dail - to determine a risk day (Office Action at page 10) - does not suggest basing aggregation on consecutiveness, as Dail aggregates without regard to it. The Office's motivation for Carroll - to aggregate for onset timing (Office Action at page 10) - does not suggest the claimed consecutive-based aggregation, because Carroll's aggregation is for probability modeling, not threat intervals. Combining would require impermissible hindsight reconstruction using the claims as a blueprint, as the references lack any suggestion to emphasize or specially treat consecutive intervals in aggregation. Moreover, the combination would not yield the claimed damaging factor based on consecutiveness, as none teaches this feature.”. In response, Examiner respectfully disagrees and notes that one of ordinary skill in the art would reasonably interpret aggregating risk hours during the day as being equivalent to aggregating the multiple threat intervals into a damaging factor, based, in part, on ones of the multiple intervals being consecutive intervals during the time period. This is further supported by Dail in at least par. [0147], where Dail discloses “agricultural intelligence computer system 130 continuously monitors values for a particular field”. The same analysis applies to claims 1, 16, and 20.
Applicant’s arguments related to the §103 rejections previously applied to dependent claims 2-15, 17-19, and 21 are raised in support of the previous arguments. Therefore, §103 rejections previously applied to the dependent claims are maintained.
Therefore, the §103 rejections previously applied are maintained and updated to address Applicant’s amendments to claims 1-21. See §103 rejections section below for further details.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claims 1-15 are directed to a Method (i.e., Process), claims 16-19 are directed to a System (i.e., Machine), and claims 20-21 are directed to a Computer-Readable Storage Medium (i.e., Item of Manufacture). Therefore, claims 1-21 are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claim 1 recites a method for assessing disease threat. As drafted, the limitations recited by claim 1 fall under the “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion).
Independent claim 1 recites a method for assessing disease threat with limitations for:
accessing, by a computing device, weather data for a field, the field including a crop and the weather data including a weather condition for the field during a time period, the time period including multiple intervals; (But for the additional elements recited by the claim limitation (underlined), the step for “accessing the weather data” could be accomplished mentally, such as by human observation, evaluation, judgment, opinion, or with the help of pen and paper.);
identifying, by the computing device, the multiple intervals within the time period as threat intervals, based on the weather condition of the field during each of the multiple intervals being within a first range; (But for the additional elements recited by the claim limitation (underlined), the step for “identifying the multiple intervals” could be accomplished mentally, such as by human observation, evaluation, judgment, opinion, or with the help of pen and paper.);
aggregating, by the computing device, the multiple threat intervals into a damaging factor, based, in part, on ones of the multiple intervals being consecutive intervals during the time period; (But for the additional elements recited by the claim limitation (underlined), the step for “aggregating the multiple intervals” could be accomplished mentally, such as by human observation, evaluation, judgment, opinion, or with the help of pen and paper.);
comparing the damaging factor to a threat threshold; (The step for “comparing the damaging factor to a threat threshold” could be accomplished mentally, such as by human observation, evaluation, judgment, opinion, or with the help of pen and paper.);
in response to the damaging factor satisfying the threat threshold, identifying, by the computing device, a specific disease indicated by the comparison of damaging factor to the threat threshold; (But for the additional elements recited by the claim limitation (underlined), the step for “aggregating the multiple intervals” could be accomplished mentally, such as by human observation, evaluation, judgment, opinion, or with the help of pen and paper.);
generating and transmitting, by the computing device, an output indicative of the identified disease and a treatment specific to the identified disease; (But for the additional elements recited by the claim limitation (underlined), the step for “generating and transmitting an output” could be accomplished mentally, such as by human observation, evaluation, judgment, opinion, or with the help of pen and paper.);
and in response to the output, spraying, by farm equipment, the field with the treatment indicated in the output. (This limitation is an additional element, which will be analyzed further in Step 2A, Prong 2, and Step 2B.).
Independent claims 16, and 20 recite a system and a computer-readable storage medium for assessing disease threat with limitations that are largely similar to those set forth in claim 1. Therefore, the same analysis applies to claims 16, and 20.
Independent claims 1, 16, and 20 recite additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B below. The additional elements are: computing device, computer-readable storage medium, processor, and the step for spraying, by farm equipment, the field with the treatment indicated in the output.
Additionally, dependent claims 11, and 19 further narrow the abstract idea from the independent claims and recite additional limitations that fall under the “Mathematical Concepts” abstract idea grouping for mathematical relationships, mathematical formulas or equations, mathematical calculations. The limitation, similarly recited in both claims, is: “wherein aggregating the threat intervals is based, at least in part, on:
R
w
k
=
∑
s
k
(
P
L
F
h
x
,
t
x
where FO generates a threat interval based on relative humidity, h(x), and temperature, t(x), for a give time point, x, L() transforms the threat interval to a length of continuous threat intervals and respective counts, and P() weights the length of the group, as summed.”. Dependent claims 11, and 19 do not introduce any additional elements for consideration.
Dependent claims 5/21 further narrow the abstract idea and introduces the following additional element for consideration: using a machine learning model.
Dependent claims 2-4, 6-10, 12-15, and 17-18 further narrow the abstract idea and do not introduce any additional elements for consideration under said steps. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely computing device, computer-readable storage medium, processor from the independent claims. These additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
With respect to the limitations for spraying, by farm equipment, the field with the treatment indicated in the output from the independent claims, these limitations fail to integrate the abstract idea into a practical application because they amount to insignificant extra-solution activity (e.g., insignificant application/post solution activity).
Regarding the limitations: further comprising generating, using a machine learning model, an augmented damaging factor based on the damaging factor and at least one feature related to the crop and/or the field from claims 5/21, they provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not represent an improvement to technology or otherwise integrate the abstract idea into a practical application. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Dependent claims 2-4, 6-15, and 17-19 recite the same abstract ideas as the independent claims along with further steps/details falling under the scope of the abstract idea itself (in addition to the limitations that fall under the “Mathematical Concepts” abstract idea grouping for mathematical relationships, mathematical formulas or equations, mathematical calculations from claims 11/19) and do not introduce additional elements for consideration.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely computing device, computer-readable storage medium, processor from the independent claims, these additional element(s) has/have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for spraying, by farm equipment, the field with the treatment indicated in the output from the independent claims, these limitations fail to integrate the abstract idea into a practical application because they amount to insignificant extra-solution activity (e.g., insignificant application/post solution activity) (See MPEP 2106.05(g) Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential).
With respect to the limitations further comprising generating, using a machine learning model, an augmented damaging factor based on the damaging factor and at least one feature related to the crop and/or the field from claims 5/21, they provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not represent an improvement to technology or otherwise add significantly more to the abstract idea. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Dependent claims 2-4, 6-15, and 17-19 recite the same abstract ideas as the independent claims along with further steps/details falling under the scope of the abstract idea itself (in addition to the limitations that fall under the “Mathematical Concepts” abstract idea grouping for mathematical relationships, mathematical formulas or equations, mathematical calculations from claims 11/19), along with the same or substantially same generic computing element addressed above under Step 2A Prong Two and Step 2B, which is incorporated herein.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-10, 14-17, and 20 are rejected under 35 U.S.C. §103 as unpatentable over Ethington et al. (US 20160232621 A1, hereinafter “Ethington“), in view of Dail et al. (WO 2019103850 A1, hereinafter “Dail”), in frther view of Carroll (US 20190156255 A1, hereinafter “Carroll”).
Regarding Claims 1/16/20: Ethington teaches a computer-implemented method ([Abstract] computer-implemented method for recommending agricultural activities), a system ([0002] systems and methods for managing and recommending agricultural activities at the field level based on crop-related data and field-condition data), and a non-transitory computer-readable storage medium ([0285] the term “non-transitory computer-readable media” is intended to be representative of any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device) for use in assessing disease threat in a field with limitations for:
accessing, by a computing device, weather data for a field, the field including a crop and the weather data including a weather condition for the field during a time period, the time period including multiple intervals; ([0230] the harvest advisor computing module 425 assists the grower in projecting approximately when a given field will be ready for harvest by projecting moisture values over time, and considering both past weather data and future weather predictions at the given field.);
identifying, by the computing device, the multiple intervals within the time period as threat intervals, based on the weather condition of the field during each of the multiple intervals being within a first range; ([0048] The agricultural intelligence computer system also determines forecasted weather conditions including field temperature, wind, humidity, and dew point for hourly projected intervals, daily projected intervals, or any interval specified by the user. The forecasted weather conditions are also used to forecast field precipitation, field workability, and field growth stage.; [0056] The value of “Stop” workability indicates that field conditions are not suitable for work or a specified activity during an upcoming time interval.);
comparing the damaging factor to a threat threshold; ([0072] When field condition data indicates that the thresholds have been exceeded, the user device will receive alerts.).
Ethington doesn’t teach:
aggregating, by the computing device, the multiple threat intervals into a damaging factor, based, in part, on ones of the multiple intervals being consecutive intervals during the time period;
in response to the damaging factor satisfying the threat threshold, identifying, by the computing device, a specific disease indicated by the comparison of damaging factor to the threat threshold;
generating and transmitting, by the computing device, an output indicative of the identified disease and a treatment specific to the identified disease
and in response to the output, spraying, by farm equipment, the field with the treatment indicated in the output.
Dail teaches:
aggregating, by the computing device, the multiple threat intervals into a damaging factor, based, in part, on ones of the multiple intervals being consecutive intervals during the time period; ([0118] As another method of computing risk day values, agricultural intelligence computer system 130 may aggregate risk hours through the day.; [0147] In an embodiment, agricultural intelligence computer system 130 continuously monitors values for a particular field in order to determine when to apply a damage mitigating chemical. Examiner notes that one of ordinary skill in the art would reasonably consider continuously monitoring values for a particular field as equivalent as equivalent to on ones of the multiple intervals being consecutive intervals.);
in response to the damaging factor satisfying the threat threshold, identifying, by the computing device, a specific disease indicated by the comparison of damaging factor to the threat threshold; ([0129] 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.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Ethington with Dail’s feature(s) listed above. One would’ve been motivated to do so in order to determine that the risk value is above a risk value threshold and, in response, determines that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor (Dail; [Abstract]). By incorporating the teachings of Dail, one would’ve been able to analyze consecutive intervals of field threat data and use it to identify a disease.
Dail doesn’t teach:
generating and transmitting, by the computing device, an output indicative of the identified disease and a treatment specific to the identified disease;
and in response to the output, spraying, by farm equipment, the field with the treatment indicated in the output.
Carroll teaches:
generating and transmitting, by the computing device, an output indicative of the identified disease and a treatment specific to the identified disease; ([0146] In an embodiment, the model of disease probability computes the probability of disease onset over time. For example, a survival regression model, such as the Cox Proportional Hazard model, may be trained using one or more of the above described factors as covariates. As a survival regression model computes the probability of disease onset over time, environmental risk hours and/or risk days may be used as a duration variable for the model. Additionally or alternatively, agricultural intelligence computer system 130 may use growing degree days as the duration variable. When the model is run for a particular field, data may be aggregated to identify a particular time of onset. For example, if the output of a Cox Proportional Hazard model identifies a high risk of disease after a given day, agricultural intelligence computer system 130 may select the given day as the likely onset of the disease.; [0150] In an embodiment, the probabilities of disease are used to update models of crop yield and/or reduce a prior estimate of crop yield. For example, agricultural intelligence computer system 130 may use prior computations of crop yield and prior identifications of disease to determine an effect on crop yield of a particular disease. Based on the determination that the particular disease is currently affecting the field or has affected the field, agricultural intelligence computer system 130 may adjust the crop yield for the crop using the determined effect on crop yield of the particular disease. The reduced yield value may be sent to a field manager computing device for display to a field manager or may be used to recommend fungicide use and/or fungicide trials for future years.; [0153] In an embodiment, the fungicide recommendations are sent to a field manager computing device. For example, agricultural intelligence computer system 130 may cause a notification to be displayed on the field manager computing device identifying one or more fields and/or one or more portions of the field that are likely to present with a particular disease, thereby giving the field manager the opportunity to prevent or limit the progression of the disease.
and in response to the output, spraying, by farm equipment, the field with the treatment indicated in the output. ([0033] Systems and methods for tracking disease onset in one or more fields are described herein. In an embodiment, weather data is used to determine an environmental risk of disease presenting on the crop. Using the environmental risk, data relating to the crop such as the hybrid susceptibility and/or relative maturity, and data relating to the management of the field, such as tillage, harvesting, and/or product application, the server computer models a risk of the disease presenting on the crop over a particular timeframe. If the server computer determines that the disease has or will present on the crop, the server computer is able to make recommendations for preventing the disease and/or generate a script which is used to control an implement on the field, thereby causing the implement to spray the field with a fungicide or take other disease preventative measures.; [0083] 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.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with Carroll’s feature(s) listed above. One would’ve been motivated to do so in order to determine a benefit to crop yield and/or revenue of applying the fungicide (Carroll; [0153]). By incorporating the teachings of Carroll, one would’ve been able to output disease and treatment information to a user.
Regarding Claim 2: Ethington further teaches:
wherein the crop includes corn; ([0059] As part of the field condition data provided, the agricultural intelligence computer system provides field growth stage conditions (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages) for the crops being grown in each listed field.);
and wherein the time period includes a time period between a first date or first growth stage of the crop and a second date or second growth stage of the crop. (Vegetative growth stages for corn typically are described as follows. The “VE” stage indicates emergence, the “V1” stage indicates a first fully expanded leaf with a leaf collar.).
Regarding Claim 3: Ethington further teaches:
wherein the crop includes corn, soybeans, and/or wheat. ([0059] As part of the field condition data provided, the agricultural intelligence computer system provides field growth stage conditions (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages) for the crops being grown in each listed field.).
Regarding Claim 4: Ethington further teaches:
wherein the time period includes one day, two days, fourteen days, or twenty-eight days. ([0059] the “R2” or blister stage (occurring 10-14 days after R1) indicates that the kernel of corn is visible and resembles a blister; the “R3” or milk stage (occurring 18-22 days after R1) indicates that the kernel is yellow outside and contains milky white fluid; the “R4” or dough stage (occurring 24-28 days after R1) indicates that the interior of the kernel has thickened to a dough-like consistency.).
Regarding Claim 7: Ethington further teaches:
further comprising initiating an assessment of a threat to the field for a disease, ([0106] The agricultural intelligence computer system is additionally configured to provide agricultural intelligence services related to pest and disease.).
prior to accessing the weather data, ([0161] Agricultural intelligence computer system 150 uses field weather data module 411…; [0180] Agricultural intelligence computer system 150 is additionally configured to provide alerts based on weather and field-related information. One of skill in the art would reasonably interpret “uses” as being an action performed as needed, in other words, once the assessment has started, if the weather data is needed to complete the assessment, then it would be accessed. Likewise, one of skill in the art would reasonably interpret the alerts as a signal generated by the system after accessing the weather data and determining that the weather attribute is outside of a threshold.).
Ethington doesn’t teach:
wherein the first range is associated with the disease.
Dail further teaches:
wherein the first range is associated with the disease. ([0107] For example, some diseases, such as northern leaf blight, are harmed by the ultraviolet radiation of the sun. Thus, agricultural intelligence computer system 130 may be programmed or configured to only identify hours as risk hours if the temperatures are within a first range.; [0109] 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.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with the Dail’s feature(s) listed above. One would’ve been motivated to do so, so that environmental data may be used alone and/or in combination with temperature and relative humidity to produce risk hours (Dail; [0109]). By incorporating the teachings of Dail, one would’ve been able to associate the first range with a disease.
Regarding Claim 8: Ethington further teaches:
wherein each of the multiple intervals includes an hour; ([0161] dew point for hourly projected intervals);
Ethington doesn’t teach: wherein identifying the multiple intervals of the time period as threat intervals includes identifying each interval of the multiple intervals within the time period as a threat interval when the weather condition of the field during the interval is within a first range.
Dail further teaches:
and wherein identifying the multiple intervals of the time period as threat intervals includes identifying each interval of the multiple intervals within the time period as a threat interval when the weather condition of the field during the interval is within a first range. ([0105] 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.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with the Dail’s feature(s) listed above. One would’ve been motivated to do so in order to increment a value indicating a number of risk hours for that day (Dail; [0105]). By incorporating the teachings of Dail, one would’ve been able to identify intervals when the weather condition on the field during the interval is within a range.
Regarding Claim 9: Ethington further teaches:
wherein the weather condition includes temperature and humidity; ([0032] weather conditions (temperature, precipitation (current and over time), soil moisture, crop growth stage, wind velocity, relative humidity, dew point, black layer));
Ethington doesn’t teach:
and wherein identifying the multiple intervals as threat intervals is based on: the temperature of the field during each of the multiple intervals being within the first range, and the humidity of the field during each of the multiple intervals being within a second range.
Dail teaches:
and wherein identifying the multiple intervals as threat intervals is based on: the temperature of the field during each of the multiple intervals being within the first range; ([0107 agricultural intelligence computer system 130 may be programmed or configured to only identify hours as risk hours if the temperatures are within a first range, the relative humidity is within a second range.).
and the humidity of the field during each of the multiple intervals being within a second range. ([0107 agricultural intelligence computer system 130 may be programmed or configured to only identify hours as risk hours if the temperatures are within a first range, the relative humidity is within a second range.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with the Dail’s feature(s) listed above. One would’ve been motivated to do so in order to base the determination of a risk hour on one or more additional factors (Dail; [0107]). By incorporating the teachings of Dail, one would’ve been able to identify intervals based on temperature and humidity values.
Regarding Claim 10: Ethington further teaches:
wherein the temperature of each interval includes an average temperature during the interval; ([0165] temperature can be displayed as high temperatures, average temperatures and low temperatures over time. Temperature can be shown during a specific time and/or date range and/or harvest year and compared against prior times, years, including a 5 year average, a 15 year average, a 30 year average or as specified by the user.).
Ethington doesn’t teach:
and wherein a humidity of each interval includes an average humidity during the interval.
Dail further teaches:
and wherein a humidity of each interval includes an average humidity during the interval. ([0105] 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.)
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with the Dail’s feature(s) listed above. One would’ve been motivated to do so in order to compute the total risk for the day as a summation of each risk hour weighted by proximity to optimal temperature divided by twenty -four (Dail; [0119]). By incorporating the teachings of Dail, one would’ve been able to assign weights based on consecutive intervals.
Regarding Claim 14: Ethington doesn’t explicitly teach:
wherein generating and transmitting the output includes generating and transmitting executable instructions to the farm equipment to spray the field with the treatment specific to the identified disease;
and wherein spraying the field includes automatically spraying the field with the treatment, by the farm equipment, in response to the executable instructions.
Carroll teaches:
wherein generating and transmitting the output includes generating and transmitting executable instructions to the farm equipment to spray the field with the treatment specific to the identified disease; ([0033] If the server computer determines that the disease has or will present on the crop, the server computer is able to make recommendations for preventing the disease and/or generate a script which is used to control an implement on the field, thereby causing the implement to spray the field with a fungicide or take other disease preventative measures.; [0075] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement.);
and wherein spraying the field includes automatically spraying the field with the treatment, by the farm equipment, in response to the executable instructions. ([Fig. 1] 111 Agricultural Apparatus, 114 Application Controller; [0033] If the server computer determines that the disease has or will present on the crop, the server computer is able to make recommendations for preventing the disease and/or generate a script which is used to control an implement on the field, thereby causing the implement to spray the field with a fungicide or take other disease preventative measures.; [0075] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement.; [0080] In an embodiment, examples of sensors 112 that may be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, area counters or distance counters that interact with GPS or radar signals, PTO (power take-off) speed sensors, tractor hydraulics sensors configured to detect hydraulics parameters such as pressure or flow, and/or and hydraulic pump speed, wheel speed sensors or wheel slippage sensors. In an embodiment, examples of controllers 114 that may be used with tractors include hydraulic directional controllers, pressure controllers, and/or flow controllers; hydraulic pump speed controllers; speed controllers or governors; hitch position controllers; or wheel position controllers provide automatic steering.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with Carroll’s feature(s) listed above. One would’ve been motivated to do so in order to generate a recommendation to apply fungicide to the crop, thereby reducing the probability of disease (Carroll; [0152]). By incorporating the teachings of Carroll, one would’ve been able to generate instructions for a field treatment to be automatically applied.
Regarding Claim 15: Ethington further teaches:
wherein the treatment includes a fungicide ([0035] (f) pesticide data (e.g., pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant).
Regarding Claim 17: Ethington further teaches:
further comprising the farm equipment, ([0032] In an example embodiment, an agricultural machine (e.g., combine, tractor, cultivator, plow, subsoiler, sprayer or other machinery used on a farm to help with farming) may be coupled to a computing device (“agricultural machine computing device”) that interacts with the agricultural intelligence computer system in a similar manner as the user device.);
Ethington doesn’t explicitly teach:
and wherein the output includes executable instructions to apply the treatment to the field;
wherein the at least one computing device is configured to transmit the executable instructions included in the output to the farm equipment;
and wherein, in response to receipt of the executable instructions included in the output, the farm equipment is configured to execute the instructions and apply the treatment to the field to address the disease threat represented by the damaging factor.
Carroll teaches:
and wherein the output includes executable instructions to apply the treatment to the field; ([0033] If the server computer determines that the disease has or will present on the crop, the server computer is able to make recommendations for preventing the disease and/or generate a script which is used to control an implement on the field, thereby causing the implement to spray the field with a fungicide or take other disease preventative measures.; [0075] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement.);
wherein the at least one computing device is configured to transmit the executable instructions included in the output to the farm equipment; ([0075] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130.);
and wherein, in response to receipt of the executable instructions included in the output, the farm equipment is configured to execute the instructions and apply the treatment to the field to address the disease threat represented by the damaging factor. ([Fig. 1] 111 Agricultural Apparatus, 114 Application Controller; [0033] If the server computer determines that the disease has or will present on the crop, the server computer is able to make recommendations for preventing the disease and/or generate a script which is used to control an implement on the field, thereby causing the implement to spray the field with a fungicide or take other disease preventative measures.; [0075] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement.; [0080] In an embodiment, examples of sensors 112 that may be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, area counters or distance counters that interact with GPS or radar signals, PTO (power take-off) speed sensors, tractor hydraulics sensors configured to detect hydraulics parameters such as pressure or flow, and/or and hydraulic pump speed, wheel speed sensors or wheel slippage sensors. In an embodiment, examples of controllers 114 that may be used with tractors include hydraulic directional controllers, pressure controllers, and/or flow controllers; hydraulic pump speed controllers; speed controllers or governors; hitch position controllers; or wheel position controllers provide automatic steering.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with Carroll’s feature(s) listed above. One would’ve been motivated to do so in order to generate a recommendation to apply fungicide to the crop, thereby reducing the probability of disease (Carroll; [0152]). By incorporating the teachings of Carroll, one would’ve been able to generate instructions for a field treatment to be automatically applied by agricultural equipment to treat a disease.
Claims 5, 6, 18, and 21 are rejected under 35 U.S.C. §103 as unpatentable over Ethington et al. (US 20160232621 A1, hereinafter “Ethington“), in view of Dail et al. (WO 2019103850 A1, hereinafter “Dail”), in further view of Carroll (US 20190156255 A1, hereinafter “Carroll”) as applied to Claims 1, 16, and 20 above, in further view of Bull et al. (US 20200005401 A1, hereinafter “Bull”).
Regarding Claim 5/18/21: Ethington further teaches:
generating… an augmented damaging factor based on the damaging factor and at least one feature related to the crop and/or the field; ([0087] The planting advisor module receives and processes the sets of data points to simulate possible yield potentials. Possible yield potentials are calculated for various planting dates. The planting advisor module additionally utilizes additional data to generate such simulations. The additional data may include simulated weather between the planting data and harvesting date, field workability, seasonal freeze risk, drought risk, heat risk, excess moisture risk, estimated soil temperature, and/or risk tolerance. One of ordinary skill in the art would reasonably interpret the simulation of the different scenarios as augmenting the factors in the scenarios.);
and wherein comparing the damaging factor to the threat threshold includes comparing the augmented damaging factor to the threat threshold. (([0087] The planting advisor module receives and processes the sets of data points to simulate possible yield potentials. Possible yield potentials are calculated for various planting dates. The planting advisor module additionally utilizes additional data to generate such simulations. The additional data may include simulated weather between the planting data and harvesting date, field workability, seasonal freeze risk, drought risk, heat risk, excess moisture risk, estimated soil temperature, and/or risk tolerance.; [0088] the planting advisor module recommends or excludes planting dates based on predicted workability. For example, dates at which a predicted planting-specific workability value is “Stop” may either be excluded or not recommended. In some examples, the planting advisor recommends or excludes planting dates based upon predicted weather events (e.g., temperature or precipitation). For examples, planting dates may be recommended after which likelihood of freezing is lower than associated threshold values.);
Ethington doesn’t teach: …using a machine learning model…
Bull teaches:
…using a machine learning model… ([0179] machine learning models may be configured to use the range 910 and threshold 915 when calculating probability of success scores between 0 and 1.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Ethington, Dail, and Carroll with Bull’s feature listed above. One would’ve been motivated to do so, so that logistic regression may be implemented as the machine learning technique to determine probability of success (Bull; [0180]). By incorporating the teachings of Bull, one would’ve been able to use machine learning to augment the damaging factors with a high probability of success.
Regarding Claim 6: Ethington further teaches:
wherein the at least one feature includes a relative maturity of the crop, a susceptibility rating of the crop, a seeding rate of the crop in the field, and an earth observation residue for the field based on satellite images of the field. ([0060] The agricultural intelligence computer system determines a relative maturity value of the crops; [0246] The module 427 preferably additionally enables the user to select or modify one or more farm practice criteria (e.g., seeding rate, seed type) on a per-management-zone basis).
Claims 12-13 are rejected under 35 U.S.C. §103 as unpatentable over Ethington et al. (US 20160232621 A1, hereinafter “Ethington“), in view of Dail et al. (WO 2019103850 A1, hereinafter “Dail”), in further view of Carroll (US 20190156255 A1, hereinafter “Carroll”) as applied to Claim 1, 16, and 20 above, in further view of Friedberg et al. (US 20130332205 A1, hereinafter “Friedberg”).
Regarding Claim 12: Ethington doesn’t teach:
wherein aggregating the multiple intervals includes weighting the consecutive intervals of the multiple intervals more than the individual ones of the multiple intervals.
Friedberg further teaches:
wherein aggregating the multiple intervals includes weighting the consecutive intervals of the multiple intervals more than the individual ones of the multiple intervals. ([0092] Consecutive days of heat stress result in more significant yield loss. To account for this, daily payouts may be doubled for each day that is at least the third consecutive day of heat stress.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with Friedberg’s feature(s) listed above. One would’ve been motivated to do so, so that daily payouts may be increased as described above for a day that is more than the second consecutive Nighttime Heat Stress Day (Friedberg; [0093]). By incorporating the teachings of Friedberg, one would’ve been able to give more weight to consecutive threat intervals.
Regarding Claim 13: Ethington doesn’t teach:
wherein aggregating the multiple intervals includes weighting the consecutive intervals by a power associated with a number of the consecutive intervals.
Friedberg further teaches:
wherein aggregating the multiple intervals includes weighting the consecutive intervals by a power associated with a number of the consecutive intervals. ([0092] If a heat stress day is more than the second consecutive Heat Stress Day, this is a Heat Wave Day and the payout for that day may be twice the amount listed below. This coverage will thus pay for each day during the coverage period when the maximum temperature meets or exceeds the Daytime Heat Stress Maximum Temperature, once the Events Before Yield Impact is exceeded. One of ordinary skill in the art would reasonably interpret the larger payouts from consecutive days as equivalent to weighting the consecutive intervals by a power associated with a number of consecutive intervals.)
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ethington with Friedberg’s feature listed above. One would’ve been motivated to do so in order to specify one or more weather-related perils that may affect the crop, a threshold for each weather-related peril, and a payout schedule for each occurrence of the weather-related peril (Friedberg; [0090]). By incorporating the teachings of Friedberg, one would’ve been able to associate the weights with the number of consecutive intervals.
Discussion of Closest Prior Art
Regarding Claims 11 and 19: the claims are rendered neither obvious nor anticipated by the available field of prior art. With respect to claims 11 and 19, the prior art of the record teaches:
Wherein aggregating the threat intervals (Dail; [0118] As another method of computing risk day values, agricultural intelligence computer system 130 may aggregate risk hours through the day.);
However, the prior art of the record does not teach the claim limitation of: is based, at least in part, on:
R
W
k
=
∑
s
k
P
L
(
F
h
x
,
t
x
)
where F() generates a threat interval based on relative humidity, h(x), and temperature, t(x), for a give time point, x, L() transforms the threat interval to a length of continuous threat intervals and respective counts, and P() weights the length of the group, as summed.
As drafted, claims 11 and 19 are rendered neither obvious nor anticipated by the available field of prior art. The closest prior art of the record discloses:
Dail et al. (WO 2019103850 A1) discloses in par. [0118]: As another method of computing risk day values, agricultural intelligence computer system 130 may aggregate risk hours through the day. Additionally or alternatively, agricultural intelligence computer system 130 may use an average of the risk hours. For example, the risk for a single day may be computed as:
PNG
media_image1.png
87
182
media_image1.png
Greyscale
where R.sub.d is the daily risk and R.sub.h is the hourly risk. In embodiments where the hourly risk is not weighted, each hourly risk may comprise either a “1” to indicate presence of risk or “0” to indicate an absence of risk.
Therefore, the present claims are rendered neither obvious nor anticipated by the available field of prior art. However, these claims are not in condition for allowance because they remain rejected under 35 USC 101, as set forth in the instant office action. See the detailed rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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