DETAILED ACTION
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
Claims 1-24 are pending and examined on the merits.
Priority
The instant application filed on 3/16/2023 is a continuation of U.S. Patent Application No. 16/883,354 filed on 5/26/2020, which was not granted a patent until 4/18/2023. Thus, the effective filing date of the claims is 5/26/2020.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 6/21/2024 and 09/28/2026 have been entered and considered. A signed copy of the corresponding 1449 form has been included with this Office action.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) 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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-24 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, and 17 recite "projecting a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent". There is insufficient antecedent basis for “the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent”. The specific pest received is not required to be one of the different pests associated with the models, and there may not be a model for that specific pest. Applicant needs to clarify the specific pest is one of the different pests associated with a model.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 9, and 17: “obtaining multiple spatiotemporal population projection models, different spatiotemporal population projection models associated with different pests, diseases, or biocontrol agents, each spatiotemporal population projection model defining how the associated pest, disease, or biocontrol agent spreads and contracts in a growing area over time” and “projecting a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent” provides an evaluation (projecting involves making determinations based on data or experience) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“receiving information associated with an actual presence of a specific pest, disease, or biocontrol agent at one or more monitored spatial locations in the growing area, different monitored spatial locations in the growing area associated with different plants” provides for organizing information (receiving/gathering information) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“each spatiotemporal population projection model defines, for each monitored spatial location in the growing area, an estimated pressure of the associated pest, disease, or biocontrol agent at that spatial location” (instant specification para.0044: “Here, ‘pressure’ generally refers to a measure of how bad a pest or disease presence is in a given location of a growing area 104. Pressure can be expressed in various ways, such as different amounts of pest or disease (like low, medium, and high) or different amounts of expected damage”) provides an evaluation (estimating pressures at locations involves evaluation of the received information) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. The limitation also provides a mathematical calculation (estimating pressures at locations requires model calculations per instant specification para.0045-54) that is considered a mathematical concept, which is an abstract idea.
Claims 2, 10, and 18: “generating each spatiotemporal population projection model further defines the estimated pressure of the associated pest, disease, or biocontrol agent at each monitored spatial location in the growing area based on: a climate in the growing area; and a treatment applied to the spatial location” is further limiting the abstract idea of the independent claims and therefore are part of the abstract idea for the same reasons.
Claims 3, 11, and 19: “selecting one or more parameters of the spatiotemporal population projection model to minimize errors between actual measurements of the associated pest, disease, or biocontrol agent and projected measurements of the associated pest, disease, or biocontrol agent” provides an evaluation (parameter selection involves evaluation of said parameters) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. The limitation also provides a mathematical calculation (minimizing errors requires calculating per instant specification para.0047) that is considered a mathematical concept, which is an abstract idea.
Claims 4, 12, and 20: “generating an estimated distribution of the specific pest, disease, or biocontrol agent across some or all monitored spatial locations in the growing area” provides an evaluation (estimating a distribution involves evaluation of the received information) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 6, 14, and 22: “provide insight into whether the specific pest, disease, or biocontrol agent is increasing or decreasing in the growing area and whether to apply at least one treatment to one or more of the monitored spatial locations in the growing area” provides an evaluation (determining an increase/decrease and determining whether to apply a treatment requires evaluation) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 7, 15, and 23: “identifying at least one treatment to be applied to one or more of the monitored spatial locations in the growing area” provides an evaluation (determining a treatment to be applied requires evaluation) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 8, 16, and 24: “identifying an effectiveness of at least one prior treatment previously applied to one or more of the monitored spatial locations in the growing area” provides an evaluation (determining effectiveness of a treatment requires evaluation) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“identifying at least one additional treatment to be applied to at least one of the monitored spatial locations in the growing area based on the effectiveness of the at least one prior treatment” provides an evaluation (determining a treatment to be applied requires evaluation) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 1 and 9 recite performing some aspects of the analysis on “An apparatus comprising: at least one processor configured to [. . .]” and “A non-transitory computer readable medium containing instructions that when executed cause at least one processor to [. . .]”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-24 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “An apparatus comprising: at least one processor configured to [. . .]” provides mere instructions to apply the judicial exception (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 9: “A non-transitory computer readable medium containing instructions that when executed cause at least one processor to [. . .]” provides mere instructions to apply the judicial exception (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 5, 13, and 21: “outputting the estimated distribution of the specific pest, disease, or biocontrol agent to at least one electronic device of at least one human scout; and generating at least one notification or alert based on the estimated distribution of the specific pest, disease, or biocontrol agent and at least one location of the at least one human scout” provides insignificant extra-solution activities (outputting data and generating notifications is a post-solution activity) that do not serve to integrate the judicial exceptions into a practical application.
Claim 6, 14, and 22: “outputting the estimated distribution of the specific pest, disease, or biocontrol agent to at least one electronic device of at least one user” provides insignificant extra-solution activities (outputting data is a post-solution activity) that do not serve to integrate the judicial exceptions into a practical application.
Claim 7, 15, and 23: “controlling at least one actuator in order to initiate the at least one identified treatment” provides insignificant extra-solution activities (outputting/transmitting data to an actuator is a post-solution activity) that do not serve to integrate the judicial exceptions into a practical application.
The steps for outputting data and automatically controlling an actuator are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering, data manipulation, and sample manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-24 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “An apparatus comprising: at least one processor configured to [. . .]” and “A non-transitory computer readable medium containing instructions that when executed cause at least one processor to [. . .]” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for outputting data and automatically controlling an actuator are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional as evidenced by Romero, Rachel, et al. (Agricultural water management 114 (2012): 59-66): Page 2 col 1 paragraph 4 "Modern industry has been extensively relying on automated control systems. This realization has motivated extensive research, over the last fifty years, on the development of advanced model based operation and control strategies to achieve safe, environmentally friendly and economically optimal plant operation. Classical control systems, like proportional-integral-derivative (PID) control, utilize measurements of a single output variable (e.g., temperature, pressure, level, or product species concentration) to compute the control action needed to be implemented by a control actuator so that this output variable can be regulated at a desired set-point value".
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-24 are not patent eligible.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 9-10, and 17-18 rejected under 35 U.S.C. 103 as being unpatentable over Jagyasi et al. (US-10555461).
Regarding claims 1, 9, and 17, Jagyasi teaches obtaining multiple spatiotemporal population projection models, different spatiotemporal population projection models associated with different pests, diseases, or biocontrol agents, each spatiotemporal population projection model defining how the associated pest, disease, or biocontrol agent spreads and contracts in a growing area over time (Page 7 col 1 line 40 "Systems and methods of the present disclosure aggregate all data that can possibly influence pest control and facilitate prediction of pest severity and natural enemies population by generating a pest forecasting model and a natural enemies forecasting model which in turn are used to estimate the effective pest severity index").
Jagyasi also teaches receiving information associated with an actual presence of a specific pest, disease, or biocontrol agent at one or more monitored spatial locations in the growing area, different monitored spatial locations in the growing area associated with different plants (Page 6 col 1 line 65 "receive a first set of inputs pertaining to weather associated with a geo-location under consideration; receive a second set of inputs pertaining to agronomic information").
Jagyasi also teaches projecting a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent (Page 8 col 2 line 53 "Natural enemies forecasting model for Stethorus gilvifrons, as shown below was generated to predict its population 7 days in advance")
Jagyasi also teaches each spatiotemporal population projection model defines, for each monitored spatial location in the growing area, an estimated pressure (the effective pest severity index of Jagyasi, mentioned above) of the associated pest, disease, or biocontrol agent at that spatial location based on:
a prior pressure of the associated pest, disease, or biocontrol agent at the spatial location, and a rate of overall change of all pressures of the associated pest, disease, or biocontrol agent at multiple monitored spatial locations in the growing area (Page 8 col 1 line 36 "In an embodiment, the generated pest forecasting model 102A and the natural enemies forecasting model 102B are enhanced by historical data. For instance, the memory 102 can include a historical data lookup table (not shown) that stores the received weather inputs 10, the agronomic inputs 12 and the estimated effective pest severity index EP(n). In accordance with the present disclosure, the historical data lookup table is appended by the actual effective pest severity index detected for the corresponding weather inputs 10 and the agronomic inputs 12. The historical data lookup table is then used to dynamically update the pest forecasting model 102A and the natural enemies forecasting model 102B")
a growth parameter defining how quickly the associated pest, disease, or biocontrol agent is able to grow and spread in the growing area (Page 8 col 1 line 34 "α: scaling factor (dependent on season, stage (egg, adult, lava) of pest as well as natural enemy)")
one or more pressures of the associated pest, disease, or biocontrol agent in one or more neighboring spatial locations (Page 8 col 1 line 55 "In another exemplary method, ground truth in the form of images with geo-coordinates of leaves, trees, pest affected areas with tags or comments from farmers or the local people may be solicited by way of crowdsourcing. Such additional inputs increase effectiveness and reliability of the generated pest forecasting model 102A and the natural enemies forecasting model")
a maximum limit of the associated pest, disease, or biocontrol agent at the spatial location (this maximum limit is inherent property of the entity being modeled and would therefore be naturally incorporated into any population model. Additionally, a threshold level is suggested as well on page 9 col 1 line 34 "In an embodiment, quantity and timing of pesticide application can be recommended when the estimated effective pest severity index is greater than the Economic Threshold Level (ETL)").
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Jagyasi et al. explicitly motivates doing so at least on page 9 col 2 line 43 "Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments", and otherwise motivating experimentation and optimization. Additionally, doing so merely combines prior art elements according to known methods to yield predictable results.
Regarding claims 2, 10, and 18, Jagyasi teaches the methods of Claims 1, 9, and 17 on which this claim depends/these claims depend, respectively. Jagyasi also teaches each spatiotemporal population projection model further defines the estimated pressure of the associated pest, disease, or biocontrol agent at each monitored spatial location in the growing area based on: a climate in the growing area; and a treatment applied to the spatial location (Page 8 col 1 line 21 "W: Weather parameters like temperature, humidity, rainfall, etc." and page 7 col 2 line 54 "In an embodiment, various vegetation, soil and water related indices are derived from the remote sensing data received as agronomic inputs 12. In an embodiment, vegetation index can be Normalized Difference Vegetation Index (NDVI) and soil and water related indices can be Soil brightness index and Normalized Difference Water Index (NDWI)", because this NDWI index would naturally include any water and/or soil treatments previously applied).
Claims 3-8, 11-16, and 19-24 rejected under 35 U.S.C. 103 as being unpatentable over Jagyasi et al. (US-10555461) as applied to claims 1-2, 9-10, and 17-18 above, and further in view of Sotiroudas et al. (US-20180322436).
Jagyasi et al. is applied to claims 1-2, 9-10, and 17-18.
Regarding claims 3, 11, and 19, Jagyasi teaches the method of Claims 2, 10, and 18 on which this claim depends/these claims depend, respectively. Jagyasi does not explicitly teach each spatiotemporal population projection model is commissioned by selecting one or more parameters of the spatiotemporal population projection model to minimize errors between actual measurements of the associated pest, disease, or biocontrol agent and projected measurements of the associated pest, disease, or biocontrol agent
However, Sotiroudas teaches using machine learning methods to match real-time insect population data correlations to match actual data streams, as well as determining distances between clusters of predicted and actual data points (Para.0036 "this system can achieve further unsupervised learning by correlating real-time insect population data to fumigant levels. Additionally, we can combine CFD [Computational Fluid Dynamics] simulation with sensor data and automatically (i.e. via machine learning methods) adjust the correction factors in CFD to match actual data streams, and subsequently use the ‘trained’ CFD simulation to make accurate long-term predictions. This applies to both predictive pest management and crop spoilage detection use cases" and para.0263 "To conclude the training process, a small amount of historical data serves as a testing set where the performance of the clustering, the centroids and the prediction process is tested, resulted in an error that needs to be as small as possible, meaning that the predicted values and the actual are close").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Jagyasi as taught by Sotiroudas in order to optimize crop management processes (Sotiroudas , para.0073 "Thus, a large amount of data can efficiently be used in optimization of processes, e.g. to determine the exact duration of treatments such as fumigations, to issue alarm conditions and to predict time and conditions of successful treatments. The collected readings can be used to predict events and resolve or prevent potential quality issues, along with effecting productivity improvements based on historical and simulated data. Applying predictive models in real time means an end user can directly intervene early on in a long-lasting process, predict outcome and avoid failures"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with modeling and prediction of crop pests.
Regarding claims 4, 12, and 20, Jagyasi teaches the method of Claims 1, 9, and 17 on which this claim depends/these claims depend, respectively. Sotiroudas also teaches generating an estimated distribution of the specific pest, disease, or biocontrol agent across some or all monitored spatial locations in the growing area (Para.0033 "The inventions achieve these goals by providing fully automated, real-time, in situ (i.e. inside product storage areas) monitoring of fumigants and storage conditions, by coupling sensors with data analytics and cognitive methods, and by thus driving predictions and prescriptions of fumigant distribution, pest treatment parameters, insect mortality and repopulation, spoilage risks, stored crops quality metrics and several other related parameters and end user guidelines" and para.0126 "Accordingly, the system may be simulated to approximate its behavior throughout the mesh/crop storage area at later time points, including approximating the fumigant concentration profile for the crop storage area at later time points" suggests modeling throughout monitored storage locations).
Regarding claims 5, 13, and 21, Jagyasi in view of Sotiroudas teaches the method of Claims 4, 12, and 20 on which this claim depends/these claims depend, respectively. Sotiroudas also teaches outputting the estimated distribution of the specific pest, disease, or biocontrol agent to at least one electronic device of at least one human scout; and generating at least one notification or alert based on the estimated distribution of the specific pest, disease, or biocontrol agent and at least one location of the at least one human scout (Para.0077 "Certain embodiments of the invention concern a service platform that may be a cloud-based application which provides all the necessary architecture elements for aggregating and presenting data, executing and presenting cognitive predictive and prescriptive analysis, interacting with third-party applications, and generating efficient interfaces for end-user interaction" and para.0073 "Thus, a large amount of data can efficiently be used in optimization of processes, e.g. to determine the exact duration of treatments such as fumigations, to issue alarm conditions and to predict time and conditions of successful treatments).
Regarding claims 6, 14, and 22, Jagyasi in view of Sotiroudas teaches the method of Claims 4, 12, and 20 on which this claim depends/these claims depend, respectively. Sotiroudas also teaches outputting the estimated distribution of the specific pest, disease, or biocontrol agent to at least one electronic device of at least one user to provide insight into whether the specific pest, disease, or biocontrol agent is increasing or decreasing in the growing area and whether to apply at least one treatment to one or more of the monitored spatial locations in the growing area (Para.0036 "Further, this system can achieve further unsupervised learning by correlating real-time insect population data to fumigant levels. Additionally, we can combine CFD simulation with sensor data and automatically (i.e. via machine learning methods) adjust the correction factors in CFD to match actual data streams, and subsequently use the ‘trained’ CFD simulation to make accurate long-term predictions. This applies to both predictive pest management and crop spoilage detection use cases" which can easily be compared to previous predictions to determine an increase or decrease, likewise for treatment application, e.g. para.0073 "Thus, a large amount of data can efficiently be used in optimization of processes, e.g. to determine the exact duration of treatments such as fumigations, to issue alarm conditions and to predict time and conditions of successful treatments").
Regarding claims 7, 15, and 23, Jagyasi teaches the method of Claims 1, 9, and 17 on which this claim depends/these claims depend, respectively. Sotiroudas also teaches identifying at least one treatment to be applied to one or more of the monitored spatial locations in the growing area (para.0073 "Thus, a large amount of data can efficiently be used in optimization of processes, e.g. to determine the exact duration of treatments such as fumigations, to issue alarm conditions and to predict time and conditions of successful treatments").
Regarding the limitation of controlling at least one actuator in order to initiate the at least one identified treatment, in In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplish the same result is not sufficient to distinguish over the prior art (see also Manual of Patent Examining Procedure, U.S. Trademark and Patent Office, section 2144.04, III). In the instant case, the claimed invention merely makes the process of Sotiroudas et al. as computer-implemented or automatic and indeed accomplishes the same result. It is thus not sufficient to distinguish over Sotiroudas et al. Therefore, the limitation would have been obvious to a person of ordinary skill in the art at the time the invention was made over the process disclosed by Sotiroudas et al. There would have been a reasonable expectation of success because the court held regarding software that “writing code for such software is within the skill of the art, not requiring undue experimentation, once its functions have been disclosed.” Fonar Corp., 107 F.3d at 1549, 41 USPQ2d at 1805.
Regarding claims 8, 16, and 24, Jagyasi in view of Sotiroudas teaches the method of Claims 7, 15, and 23 on which this claim depends/these claims depend, respectively. Sotiroudas also teaches identifying an effectiveness of at least one prior treatment previously applied to one or more of the monitored spatial locations in the growing area; and identifying at least one additional treatment to be applied to at least one of the monitored spatial locations in the growing area based on the effectiveness of the at least one prior treatment (Para.0127 "The amount of living invertebrate pests (i.e., undesirable insects, arachnids, nematodes, and gastropods), and their pre-adult stages (e.g. eggs, larvae, pupae) may be determined for the range of time points based on the simulated amounts of fumigant (1210). The time points associated with an amount of living pests that is less than or equal to an acceptable amount may be identified (1212). More specifically, the effect of a fumigant on the mortality of invertebrate pests is based on the level of fumigant concentration and the duration of exposure", and applying an additional treatment is effectively a repetition of previously recited steps and/or elements (of claim 23) which would have been prima facie obvious (MPEP 2143 E., Example 9 pertains)).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-24 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of U.S. Patent No. 11631475. Although the claims at issue are not identical, they are not patentably distinct from each other because both involve obtaining multiple spatiotemporal population projection models, receiving information regarding pressure(s) at particular growing area location(s), and projecting presence of the pressure using a model that processes location-based data regarding: a prior pressure, a maximum limit of the pressure, a growth parameter defining how quickly the pressure is able to increase and spread, a rate of overall change of all pressures of the at multiple monitored locations, and pressures in one or more neighboring locations.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Singh et al., US-20210350295, PGPub 11/11/2021, filed 5/11/2020, estimating crop pest risk and/or crop disease risk at sub-farm level
Whish et al., Integrating pest population models with biophysical crop models to better represent the farming system,
Environmental Modelling & Software, Volume 72, 2015, Pages 418-425, ISSN 1364-8152, doi.org/10.1016/j.envsoft.2014.10.010
Conclusion
No claims are allowed.
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/R.A.P./Examiner, Art Unit 1686
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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