DETAILED ACTION
Status of the Application
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 .
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
This action is a Final Action on the merits in response to the application filed on 03/09/2026.
Claims 1, 5, 16, 30, 34-35, and 41 have been amended.
Claims 3-4, 18, and 32-33 have been cancelled.
Claims 1, 2, 5, 6, 8, 11, 16, 21, 25, 26, 30, 34, 35, 40, and 41 remain pending in this application.
Foreign Priority
The Examiner/office acknowledges that the applicant claims foreign priority to the date 10/29/2020.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 103 rejections of claims in the previous office action have been maintained.
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, 2, 5, 6, 8, 11, are directed towards a system, claims 16, 18, 21, 25, 26 are directed towards a method, and claims 30, 34, 35, 40 and 41 are directed towards a device, both of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including training data. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1, 2, 5, 6, 8, 11, 16, 21, 25, 26, 30, 34, 35, 40, and 41, the independent claims (claims 1, 16, and 30) are directed to managing agricultural data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
1. A pest management system, implemented on a network- enabled computing platform, for managing application of pest treatment materials to one or more crop locations based on a phenological model, the phenological model being a stored digital representation of a pest phenology in a particular region associated with the crop locations, the system comprising:
a digital data storage component for receiving over a network and storing, over time, in digital association with the one or more crop locations:
said digital sensed data received from said one or more network- interfacing sensors;
the phenological model for the one or more crop locations that provides the one or more pest treatment application suggestions based at least in part on said digital sensed data indicative of said phenological conditions at a given time; and
a digital data processor in network communication with said digital data storage component and operable to calculate a correlation for at least some of the one or more crop locations between said crop outcome data and one or more of said pest treatment application data and said pest treatment application suggestions.
these steps fall and recite an abstract ideas because they are directed to Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation.
If a claim limitation, under its broadest reasonable interpretation observation and evaluations, then it falls within the ”mental processes”; “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of network, phenological model, sensors, storage component, processor (Claim 16 network, phenological model, sensors, storage component, processor, device; Claim 30 network, phenological model, sensors, storage component, processor, device, , communication bus). The claims recite the steps are performed by the network, phenological model, sensors, storage component, processor.
The limitations of
one or more network-interfacing sensors configured to acquire and communicate digital sensed data associated with the one or more crop locations and indicative at least in part of a phenological condition relating to a pest phenology;
pest treatment application data being a digital representation of information representative of pest treatments applied in connection with one or more pest treatment application suggestions provided by the phenological model;
crop outcome data relating to observed crop outcomes and stored as digital data; and
wherein said digital data processor dynamically applies corrections to suggestions from the phenological model for at least some of the one or more crop locations based on said correlation.
are mere data processing and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by a network, phenological model, sensors, storage component, processor. The network, phenological model, sensors, storage component, processor are recited at a high level of generality. In limitation (a), network, phenological model, sensors, storage component, processor are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The network, phenological model, sensors, storage component, processor are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the network, phenological model, sensors, storage component, processor. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data processing and outputting.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
one or more network-interfacing sensors configured to acquire and communicate digital sensed data associated with the one or more crop locations and indicative at least in part of a phenological condition relating to a pest phenology;
pest treatment application data being a digital representation of information representative of pest treatments applied in connection with one or more pest treatment application suggestions provided by the phenological model;
crop outcome data relating to observed crop outcomes and stored as digital data; and
wherein said digital data processor dynamically applies corrections to suggestions from the phenological model for at least some of the one or more crop locations based on said correlation.
are recited at a high level of generality. These elements amount to processing and transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2, 5, 6, 8, 11, 21, 25, 26, 34, 35, 40, and 41 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 11, 25, 26 recite devices for controlling signals; claims 40, 41 recite communication bus and devices for controlling signals. Claims 2, 5, 6, 8, 11, 21, 25, 26, 34, 35, 40, and 41 recites network, phenological model, sensors, storage component, processor which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2, 5, 6, 8, 11, 21, 25, 26, 34, 35, 40, and 41 recites network, phenological model, sensors, storage component, processor , which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2, 5, 6, 8, 11, 21, 25, 26, 34, 35, 40, and 41 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 16, and 30. Therefore claims 2, 5, 6, 8, 11, 21, 25, 26, 34, 35, 40, and 41 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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 1-6, 8, 11, 16, 18, 21, 25-26, 30, 32-35, 40 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20180322590, Sundararajan, et al. to hereinafter Sundararajan in view of United States Patent Publication US 20190246549, Peters, et al.
Referring to Claim 1, Sundararajan teaches a pest management system implemented on a network- enabled computing platform, for managing application of pest treatment materials to one or more crop locations based on a phenological model (See Peters), the system comprising: one or more network-interfacing sensors configured to acquire and communicate digital sensed data associated with the one or more crop locations and indicative at least in part of a phenological condition relating to a pest phenology (See Peters) (
Sundararajan: Sec. 0024, seed selection application 132 may be configured to assist a farmer, a social entrepreneur, or their consultant(s) (hereinafter, simply referred generically as “the farmer”) in selecting seed/crop for the soil at a location of an agricultural region. Soil management application 134 may be configured to assist the farmer to manage the nutrient of the soil at the location of the agricultural region
Sundararajan: Sec. 0090, wherein operating the soil management recommendation application to provide soil management recommendations for crops being grown at the location includes operating the soil management recommendation application to: receive inputs on nutrient, crop type or soil type; perform signal analysis or nutrient level analysis, on the sensor data, based at least in part on the received inputs; and generate fertilizer recommendations, based at least in part on results of the signal analysis or results of the nutrient analysis.
Sundararajan: Sec. 0113, performing agricultural testing and optimization, comprising: means for receiving a container of soil nutrient solution sample of a location in an agricultural region; means for collecting sensor data from the soil nutrient solution sample; and means for performing agricultural testing and optimization for the location, based at least in part on the sensor data collected from the soil nutrient solution sample.);
a digital data storage component for receiving over a network and storing, over time, in digital association with the one or more crop locations:
said digital sensed data received from said one or more network- interfacing sensors (
Sundararajan: Sec. 0025, Cloud/fog servers 120, as will be described in more detail below with references to FIGS. 5-8, may include a number of repositories of agricultural data for a plurality of agricultural regions, and a plurality of cloud/fog data analytics tracking and services to provide agricultural testing and optimization support services to a plurality of portable integrated agricultural testing and optimization devices 110 to operate in a plurality of locations of the plurality of agricultural regions to provide in field agricultural testing and optimization. In embodiments, cloud/fog servers 120 may include a number of storage devices to store the plurality of repositories, and processors and memory arrangements to host and operate the cloud/fog data analytics tracking and services. Further, cloud/fog servers 120 may include a plurality of communication interfaces to communicate with the portable integrated agricultural testing and optimization devices 110 operating in the field at various locations of the various agricultural regions.
Sundararajan: Sec. 0031, electronic components 302-318 may further include global network satellite system (GNSS) sensors 306 configured to sense location of device 300, and other sensors 308 like gyroscope, magnetometer, accelerometer etc. to collect and provide various sensor data for the control and operation of portable integrated agricultural testing and optimization device 300.
Sundararajan: Sec. 0037, Repository 524 may be configured to store crop information suitable for the agricultural region where device 504 is being used. Repository 526 may be configured to store the seed information for growing such crops. Data in repository 524 and 526 may be downloaded from repository 514 and 516 in cloud server 502. Further, each device 504 may include local repository 522 of soil measurements, and local repository 528 of previous seed/crop recommendations.);
pest treatment application data being a digital representation of information representative of pest treatments applied in connection with one or more pest treatment application suggestions provided by the phenological model (See Peters) (Sundararajan: Sec. 0046, On receipt of the completed survey, pesticide/additive management application 706 may analyze the survey results to provide a crop pesticide or disease diagnosis 740. Based on the diagnosis, pesticide/additive management application 706 may determine whether pesticide(s) or additive(s) are needed for treatment of the soil 742. If pesticide(s) or additive(s) are not needed for treatment of the soil, pesticide/additive management application 706 proceeds to recommend treatment for the crop 754. On the other hand, if pesticide(s) or additive(s) are needed for treatment of the soil, pesticide/additive management application 706 further determines whether the needed pesticide(s)/additive(s) are available in the local suppliers 744. If pesticide(s) or additive(s) needed for treatment of the soil are not available in local suppliers, pesticide/additive management application 706 proceeds to recommend immediate action, and later treatment with pesticide(s) and additive(s) 756. On the other hand, if pesticide(s) or additive(s) needed for treatment of the soil are available in local suppliers, pesticide/additive management application 706 recommends immediate treatment with pesticide(s) and additive(s) 746 from local suppliers. In embodiments, the recommendations are further reported to cloud/fog data analytics tracking and services 712 of cloud server 702.);
crop outcome data relating to observed crop outcomes and stored as digital data (
Sundararajan: Sec. 0004, One approach to maximizing crop yield is to optimize the numerous nutrients found in soil; however, soil nutrients are not monitored in many parts of the world largely due to lack of affordable, easy-to-use sensing tools at or near the field);
a digital data processor in network communication with said digital data storage component and operable to calculate a correlation for at least some of the one or more crop locations between said crop outcome data and one or more of said pest treatment application data and said pest treatment application suggestions,
wherein said digital data processor dynamically applies corrections to suggestions from the phenological model (See Peters) for at least some of the one or more crop locations based on said correlation. (
Sundararajan: Sec. 0004, One approach to maximizing crop yield is to optimize the numerous nutrients found in soil; however, soil nutrients are not monitored in many parts of the world largely due to lack of affordable, easy-to-use sensing tools at or near the field
Sundararajan: Sec. 0024, Each portable integrated agricultural testing and optimization device 110 may be configured to accept and analyze soils samples (at the not visible side; see FIG. 2). Further, each portable integrated agricultural testing and optimization device 110 may be configured with a number of crop cycle related applications 130, e.g., seed selection application 132, soil management application 134, pest & disease control application 136, and/or buyer and seller linking application 138. In embodiments, seed selection application 132 may be configured to assist a farmer, a social entrepreneur, or their consultant(s) (hereinafter, simply referred generically as “the farmer”) in selecting seed/crop for the soil at a location of an agricultural region. Soil management application 134 may be configured to assist the farmer to manage the nutrient of the soil at the location of the agricultural region. Pest & disease control application 136 may be configured to assist the farmer in managing pest & disease in the soil at the location of the agricultural region, while the seed germinates and the crop grows. Buyer and seller linking application 138 may be configured to assist the farmer in managing the harvesting and selling of the crops. Resultantly, farmers (including small farmers and/or famers in underdeveloped regions of the world) may have access to advanced technology to assist them in seed/crop selection, soil management, pest and disease control, harvesting and selling, improving their productivity, profitability, and/or sustainability. In alternate embodiments, crop cycle related applications 130 may include more or less applications.
Sundararajan: Sec. 0044, Still referring to FIG. 7, portable integrated agricultural testing and optimization device 704 may be any one of portable integrated agricultural testing and optimization devices 110, 200, 300, or 400 of FIG. 1, 2, 3 or 4. Pesticide/additive management application 706 may be pesticide/additive management application 136 of FIG. 1. In embodiments, pesticide/additive management application 706 on each device 704 may include repository 722, 724 and 726. Repository 722 may be configured to store crop information and pest and disease diagnostic information. Repository 724 may be configured to store crop problems and associated pesticide and additive treatment information. Repository 726 may be configured to store the local supplier of pesticides and additives. Data in repository 722, 724 and 726 may be downloaded from repository 714 and 716 in cloud server 702.
corrections to suggestions
Sundararajan: Sec. 0036, FIG. 1, may include cloud/fog data analytics tracking and services 512, cloud/fog repository 514 of optimum soil and crop combination for various agricultural regions, and cloud/fog repository 516 of registered seeds. Cloud/fog data analytics tracking and services 512 may be configured to maintain and update the optimum soil and crop combination data for various agricultural regions, and seed data in repository 514 and 516, and interact with various portable integrated agricultural testing and optimization devices (e.g., device 504) to provide support services, including but not limited to the provision of the relevant subset of these data to corresponding repository (e.g., repository 524 and 526) to various portable integrated agricultural testing and optimization devices (e.g., device 504).
Sundararajan: Sec. 0040, Cloud/fog data analytics tracking and services 612 may be configured to maintain and update the optimum soil and crop combination data for various agricultural regions, and nutrient measurements in repository 614 and 616, and provide the relevant subset of these data to corresponding repository (e.g., repository 640) in various portable integrated agricultural testing and optimization devices (e.g., device 604).
Sundararajan: Sec. 0043, FIG. 1, may include cloud/fog data analytics tracking and services 712, cloud/fog repository 714 of crop problems and associated pesticides and additives, and cloud/fog repository 716 of various suppliers of various pesticides and additives. Cloud/fog data analytics tracking and services 712 may be configured to maintain and update the crop problems and associated pesticides and additives data, and suppliers of various pesticides and additives in repository 714 and 716, and provide the relevant subset of these data to corresponding repository (e.g., repository 722, 724 and 726) in various portable integrated agricultural testing and optimization devices (e.g., device 704).
Sundararajan describes the updating of collected crop information with is equivalent to Applicant’s spec. at 00173).
Sundararajan does not explicitly teach phenological model; the phenological model being a stored digital representation of a pest phenology in a particular region associated with the crop locations; the phenological model for the one or more crop locations that provides the one or more pest treatment application suggestions based at least in part on said digital sensed data indicative of said phenological conditions at a given time.
However, Peters teaches these limitations
phenological model (
Peters: Sec. 0080, also conceivable to use for determination a requirement prediction model, e.g. for predicting pest infestations. Such prediction models have been extensively described in the prior art and are also commercially available. The decision support system proPlant Expert uses for prediction purposes data on the cultivated crop plant (development stage, growth conditions, plant protection measures), the weather (temperature, sunshine duration, wind speed, precipitation) and known pests/diseases (economic limit values, pest/disease pressure) and calculates an infection risk based on these data.
Peters: Sec. 0138, the time of the agricultural measure are so far apart that useful planning is not longer possible, e.g. because the conditions in the field have changed so much in the interim that the planned agents and amounts are no longer sufficient to meet the requirements, prediction models can be used to calculate the current state. In such a case, an earlier digital image is received and a prediction model is added, which then preferably calculates the conditions in the field for the time (or period) of the planned agricultural measure. In this case, the planning takes place not directly based on the digital image, but based on data that correspond to a digital image at the time of prediction.)
Peters describes the generating and use of modeling base on the prediction and application of collected data, which is similar to the Applicant’s spec at 00156.
the phenological model being a stored digital representation of a pest phenology in a particular region associated with the crop locations (
Peters: Sec. 0022, planning a partial-area-specific agricultural measure in the field based on the digital image of the field and providing means for partial-area-specific implementation of the measure,
Peters: Sec. 0045, The vegetation state of the crop plants can be determined from the digital images e.g. by calculating a vegetation index. A known vegetation index is for example the normalized difference vegetation index (NDVI, also known as the normalized density vegetation Index). The NDVI is calculated from the reflectance values in the near infrared region and the red visible region of the light spectrum. The index is based on the fact that healthy vegetation reflects a relatively small amount of radiation in the red region of the visible spectrum (wavelength of approximately 600 to 700 nm) and a relatively large amount of radiation in the adjacent near infrared region (wavelength of approximately 700 to 1300 nm)
Peters: Sec. 0080, It is also conceivable to use for determination a requirement prediction model, e.g. for predicting pest infestations. Such prediction models have been extensively described in the prior art and are also commercially available. The decision support system proPlant Expert uses for prediction purposes data on the cultivated crop plant (development stage, growth conditions, plant protection measures), the weather (temperature, sunshine duration, wind speed, precipitation) and known pests/diseases (economic limit values, pest/disease pressure) and calculates an infection risk based on these data
Peters: Sec. 0080, cultivated crop plants for nutrients can be determined for example by means of local sensors and/or remote sensors and/or inspection and/or prediction models (such as plant growth models).),
the phenological model for the one or more crop locations that provides the one or more pest treatment application suggestions (
Peters: Sec. 0080, It is also conceivable to use for determination a requirement prediction model, e.g. for predicting pest infestations. Such prediction models have been extensively described in the prior art and are also commercially available. The decision support system proPlant Expert uses for prediction purposes data on the cultivated crop plant (development stage, growth conditions, plant protection measures), the weather (temperature, sunshine duration, wind speed, precipitation) and known pests/diseases (economic limit values, pest/disease pressure) and calculates an infection risk based on these data
Peters: Sec. 0082, Analogously, the requirement of the cultivated crop plants for nutrients can be determined for example by means of local sensors and/or remote sensors and/or inspection and/or prediction models (such as plant growth models).
Peters: Sec. 0138, the time of the agricultural measure are so far apart that useful planning is not longer possible, e.g. because the conditions in the field have changed so much in the interim that the planned agents and amounts are no longer sufficient to meet the requirements, prediction models can be used to calculate the current state. In such a case, an earlier digital image is received and a prediction model is added, which then preferably calculates the conditions in the field for the time (or period) of the planned agricultural measure. In this case, the planning takes place not directly based on the digital image, but based on data that correspond to a digital image at the time of prediction.
Peters: Sec. 0139, If the growth stage of the cultivated crop plants is of decisive importance for an appropriate agricultural measure, e.g. because the measure involves application of an agent in an amount that depends on the leaf area or the amount of biomass present, one can for example use a plant growth model to predict the growth stage for the time of the planned agricultural measure based on the earlier digital image of the field.
the one or more pest treatment application suggestions based at least in part on said digital sensed data indicative of said phenological conditions at a given time.
Peters: Sec. 0111, Accordingly, the planning can be such that the infested area is treated with a pest control agent as rapidly as possible. Areas immediately adjacent to the infested area are preferably also included in the treatment, while unaffected areas far away do not have to be treated.
Peters: Sec. 0117, It is preferably a digital application map that can be read into a control unit of the application device. If the application device moves in and/or over the field, the position of the application device can be determined by means of a GPS sensor or a comparable sensor. By comparing the actual position with the corresponding position on the digital application map, the respective amount of one or a plurality of plant protection agents and/or nutrients required at the actual position can be determined.
Peters: Sec. 0118, the planned agricultural measure is carried out. During implementation of the measure, one or a plurality of sensors (field sensor(s)) is/are used that detect one or a plurality of local parameters in the field. The detected local parameters are then included in implementation of the agricultural measure so that the implementation is adapted to the local requirements in the field.
Peters: Sec. 0119, By means of the at least one field sensor, at least one parameter is determined locally in the field that is to be taken into account for implementation of the agricultural measure in order to ensure appropriate treatment.
Peters: Sec. 0124, Field sensors for determining local parameters in the field are commercially available in a variety of forms (cf. for example https://www.decagon.com/en/canopy/canopy-measurements/spectral-reflectance sensor-srs/; http://plantstress.com/methods/Greenseeker.pdf; http://dx.doi.org/10.1155/2012/582028; N. Srivastava et al.: Pest Monitor and Control System Using Wireless Sensor Network (with Special Reference to Acoustic Device Wireless Sensor); International Conference on Electrical and Electronics Engineering, 27 Jan. 2013, Goa, ISBN: 978-93-82208-58-7, pp. 40-46; Lucia Quebrajo et al.: An Approach to Precise Nitrogen Management Using Hand-Held Crop Sensor Measurements and Winter Wheat Yield Mapping in a Mediterranean Environment, Sensor 2015, 15, 5504-5517, doi:10.3390/s150305504).
Peters: Sec. 0157, the remote sensor and field sensor can detect different parameters respectively; for example, a remote sensor can use a first parameter to recognize the requirement for treatment of the field with a plant protection agent, while the field sensor can use a second parameter to determine the local currently required amounts. The remote sensor covers the entire field, while the field sensor covers only a local area.);
Peters describes the use of digital obtained data to analyze crop information that consist of pest treatment, in which Peters disclose the use of sensors to collect and analyze crop information that would manage the pest treatments, in which the Examiner is interpreting as phenological conditions. As, Phenological refers to the study or relating to the timing of cyclical and seasonal events in nature, such as plant flowering, insect emergence, and animal migration, wherein Peters at 0045, 0080, 0105, 0123, teaches the period for when crops are: developing, growing, and harvesting.
Sundararajan and Peters are both directed to the analysis of agricultural data (See Sundararajan at 0078-0082, 0114; Peters at 0088, 0094, 0104-0107). Sundararajan discloses that additional elements, such as a portable integrated agricultural testing and optimization device can be considered (See Sundararajan at 0018). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sundararajan, which teaches detecting and updating agricultural information technology problems in view of Peters, to efficiently apply analysis of agricultural data to enhancing the capability to determine the prediction of plants blooms and growth. (See Peters at 0082, 0083, 0161).
Referring to Claim 2, Sundararajan teaches the pest management system of claim 1, wherein the one or more crop locations comprise the location of one or more of a farm, a field, a crop area, a portion of a field, a block, a portion of a block, a row of plants, a portion of a row of plants, a group of plants, a plant, and a portion of a plant (
Sundararajan: Sec. 0025, Further, cloud/fog servers 120 may include a plurality of communication interfaces to communicate with the portable integrated agricultural testing and optimization devices 110 operating in the field at various locations of the various agricultural regions.
Sundararajan: Sec. 0026, In alternate embodiments, container 210 and integrated cavity 202 may be of other geometry sizes and/or shapes. In embodiments, if portable integrated agricultural testing and optimization device 200 is used by social entrepreneurs or consultants for multiple farmers, e.g., in forward/regional laboratories/offices, the soil samples may be geo-tagged to identify the associated fields/farmers of the soil samples.).
Referring to Claim 5, Sundararajan teaches the pest management system of claim 1, Sundararajan does not explicitly teach wherein at least some of said digital sensed data comprises at least one of environmental data, insect monitoring data, weed monitoring data, a crop stage, and observational data.
However, Peters teaches wherein at least some of said digital sensed data comprises at least one of environmental data, insect monitoring data, weed monitoring data, a crop stage, and observational data (
Peters: Sec. 0039, By means of the remote sensors, digital images of areas of the Earth's surface are produced from which information on the vegetation and/or the environmental conditions prevailing in said areas can be obtained
Peters: Sec. 0078, Determination of a requirement is preferably carried out using sensors in and/or over the field that register the presence of a harmful organism in the field and/or register the presence of environmental conditions conducive to the spread of a harmful organism).
Sundararajan and Peters are both directed to the analysis of agricultural data (See Sundararajan at 0078-0082, 0114; Peters at 0088, 0094, 0104-0107). Sundararajan discloses that additional elements, such as a portable integrated agricultural testing and optimization device can be considered (See Sundararajan at 0018). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sundararajan, which teaches detecting and updating agricultural information technology problems in view of Peters, to efficiently apply analysis of agricultural data to enhancing the capability to determine the prediction of plants blooms and growth. (See Peters at 0082, 0083, 0161).
Referring to Claim 6, Sundararajan teaches the pest management system of claim 1, wherein at least some of said crop outcome data comprises observational crop data, and said observational crop data comprises at least one of pre-harvest crop data and post-harvest crop data (
Sundararajan: Sec. 0018, how to deal with pests, and/or when to harvest and what price to sell. The portable integrated agricultural testing and optimization system of the present disclosure may be used by the farmers, social entrepreneurs, government or non-governmental officials, or their consultants, in the fields or in forward/regional laboratories or offices near the fields.
Sundararajan: Sec. 0024, Buyer and seller linking application 138 may be configured to assist the farmer in managing the harvesting and selling of the crops. Resultantly, farmers (including small farmers and/or famers in underdeveloped regions of the world) may have access to advanced technology to assist them in seed/crop selection, soil management, pest and disease control, harvesting and selling, improving their productivity, profitability, and/or sustainability.
Sundararajan: Sec. 0039, The sorting may be further based on user inputs on target seed sowing and harvest months 530. Thereafter, seed recommendation application 506 may recommend the seeds for growing the selected crops 548 (from the list of seeds, and/or in view of the further user inputs). In embodiments, the recommendations may be recorded in local repository 528, e.g., to take into consideration for future recommendations.
Sundararajan: Sec. 0049, If the selected offer is not a spot trade, harvest and sale management application 806 further solicits and receive user inputs on harvesting date, frequency and duration of harvest, and so forth 844. Next, harvest and sale management application 806 generate a seller harvest chart and schedule for a user to complete 846.).
Referring to Claim 8, Sundararajan teaches the pest management system of claim 1, Sundararajan does not explicitly teach wherein at least some of said crop outcome data relates to at least one of a yield, a grade, and a crop damage.
However, Peters teaches wherein at least some of said crop outcome data relates to at least one of a yield, a grade, and a crop damage (
Peters: Sec. 0012, Differences in the field with respect to e.g. soil or weather conditions should also be taken into consideration in sowing a crop plant in order to achieve a maximum yield. For example, it is conceivable that in some areas of the field, denser planting is more favorable, while planting is to be less dense in other areas. It is also conceivable to select the crop plant or type to be cultivated based on the respective soil properties present.
Peters: Sec. 0013, There is thus a need to be able to recognize inhomogeneities in the field that can have an effect on the subsequent yield and/or that require correspondingly adapted treatment of the field in order to sustainable achieve a maximum yield.).
Sundararajan and Peters are both directed to the analysis of agricultural data (See Sundararajan at 0078-0082, 0114; Peters at 0088, 0094, 0104-0107). Sundararajan discloses that additional elements, such as a portable integrated agricultural testing and optimization device can be considered (See Sundararajan at 0018). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sundararajan, which teaches detecting and updating agricultural information technology problems in view of Peters, to efficiently apply analysis of agricultural data to enhancing the capability to determine the prediction of plants blooms and growth. (See Peters at 0082, 0083, 0161).
Referring to Claim 11, Sundararajan teaches the pest management system of claim 1, further comprising:
one or more pest treatment deployment devices configured to apply the pest treatment materials in response to a control signal generated in response to said pest treatment application suggestions (
Sundararajan: Sec. 0044, Still referring to FIG. 7, portable integrated agricultural testing and optimization device 704 may be any one of portable integrated agricultural testing and optimization devices 110, 200, 300, or 400 of FIG. 1, 2, 3 or 4. Pesticide/additive management application 706 may be pesticide/additive management application 136 of FIG. 1. In embodiments, pesticide/additive management application 706 on each device 704 may include repository 722, 724 and 726. Repository 722 may be configured to store crop information and pest and disease diagnostic information. Repository 724 may be configured to store crop problems and associated pesticide and additive treatment information. Repository 726 may be configured to store the local supplier of pesticides and additives. Data in repository 722, 724 and 726 may be downloaded from repository 714 and 716 in cloud server 702.
Sundararajan: Sec. 0046, On receipt of the completed survey, pesticide/additive management application 706 may analyze the survey results to provide a crop pesticide or disease diagnosis 740. Based on the diagnosis, pesticide/additive management application 706 may determine whether pesticide(s) or additive(s) are needed for treatment of the soil 742. If pesticide(s) or additive(s) are not needed for treatment of the soil, pesticide/additive management application 706 proceeds to recommend treatment for the crop 754. On the other hand, if pesticide(s) or additive(s) are needed for treatment of the soil, pesticide/additive management application 706 further determines whether the needed pesticide(s)/additive(s) are available in the local suppliers 744. If pesticide(s) or additive(s) needed for treatment of the soil are not available in local suppliers, pesticide/additive management application 706 proceeds to recommend immediate action, and later treatment with pesticide(s) and additive(s) 756. On the other hand, if pesticide(s) or additive(s) needed for treatment of the soil are available in local suppliers, pesticide/additive management application 706 recommends immediate treatment with pesticide(s) and additive(s) 746 from local suppliers. In embodiments, the recommendations are further reported to cloud/fog data analytics tracking and services 712 of cloud server 702
Sundararajan: Sec. 0057, Executable code of programming instructions (or bit streams for encoding hardware accelerators) 1004 may be configured to enable a device, e.g., computer device 900, in response to execution of the executable code/programming instructions (and/or operation of hardware accelerators), to perform, e.g., various operations associated with seed/crop selection, soil management, pesticide and disease control, and harvest/sales, described with references to FIGS. 1-8.),
wherein said one or more pest treatment deployment devices are further configured to selectively apply the pest treatment materials at specific locations of the one or more crop locations in response to said control signal (
Sundararajan: Sec. 0024, Pest & disease control application 136 may be configured to assist the farmer in managing pest & disease in the soil at the location of the agricultural region, while the seed germinates and the crop grows. ).
Claims 16, 21, 25, 26 recite limitations that stand rejected via the art citations and rationale applied to claims 1, 6, 11. Referring to
automatically modifying the digital data (
Sundararajan: Sec. 0019, portable integrated agricultural testing and optimization system of the present disclosure may provide the benefit of automating both the measurement stage and the connection between the data and the fertilizer recommendation.)
modifying the digital data storage component by amending, for at least some of the one or more crop locations based on said correlation, at least one of the phenological model or future pest treatment application suggestions based thereon (
Peters: Sec. 0111, Accordingly, the planning can be such that the infested area is treated with a pest control agent as rapidly as possible. Areas immediately adjacent to the infested area are preferably also included in the treatment, while unaffected areas far away do not have to be treated.
Peters: Sec. 0117, It is preferably a digital application map that can be read into a control unit of the application device. If the application device moves in and/or over the field, the position of the application device can be determined by means of a GPS sensor or a comparable sensor. By comparing the actual position with the corresponding position on the digital application map, the respective amount of one or a plurality of plant protection agents and/or nutrients required at the actual position can be determined.
Peters: Sec. 0118, the planned agricultural measure is carried out. During implementation of the measure, one or a plurality of sensors (field sensor(s)) is/are used that detect one or a plurality of local parameters in the field. The detected local parameters are then included in implementation of the agricultural measure so that the implementation is adapted to the local requirements in the field.
Peters: Sec. 0119, By means of the at least one field sensor, at least one parameter is determined locally in the field that is to be taken into account for implementation of the agricultural measure in order to ensure appropriate treatment.
Peters: Sec. 0124, Field sensors for determining local parameters in the field are commercially available in a variety of forms (cf. for example https://www.decagon.com/en/canopy/canopy-measurements/spectral-reflectance sensor-srs/; http://plantstress.com/methods/Greenseeker.pdf; http://dx.doi.org/10.1155/2012/582028; N. Srivastava et al.: Pest Monitor and Control System Using Wireless Sensor Network (with Special Reference to Acoustic Device Wireless Sensor); International Conference on Electrical and Electronics Engineering, 27 Jan. 2013, Goa, ISBN: 978-93-82208-58-7, pp. 40-46; Lucia Quebrajo et al.: An Approach to Precise Nitrogen Management Using Hand-Held Crop Sensor Measurements and Winter Wheat Yield Mapping in a Mediterranean Environment, Sensor 2015, 15, 5504-5517, doi:10.3390/s150305504).
Peters: Sec. 0157, the remote sensor and field sensor can detect different parameters respectively; for example, a remote sensor can use a first parameter to recognize the requirement for treatment of the field with a plant protection agent, while the field sensor can use a second parameter to determine the local currently required amounts. The remote sensor covers the entire field, while the field sensor covers only a local area.
Peters: Sec. 0065, it is planned to treat the field with one or a plurality of plant protection agents and/or nutrients, wherein the amounts of the plant protection agents and/or nutrients to be used are adapted to the differing needs of the crop plants in different areas of the field.
Peters: Sec. 0167, A period is planned in which the plant protection agent is to be applied. Weather data and weather forecast data are used to identify a period that is in the near future (in order to prevent further spreading of the harmful organisms and thus damage to the crop plants), but in which no precipitation is expected and which is followed by a period of at least one day in which also no precipitation is expected, so that the plant protection agent can exert its action without first being washed away.);
Peters describes the use of digital obtained data to analyze crop information that consist of pest treatment, in which Peters disclose the use of sensors to collect and analyze crop information that would manage the pest treatments, in which the Examiner is interpreting as phenological conditions. As, Phenological refers to the study or relating to the timing of cyclical and seasonal events in nature, such as plant flowering, insect emergence, and animal migration, wherein Peters at 0045, 0080, 0105, 0123, teaches the period for when crops are: developing, growing, and harvesting. Additionally, Peters teaches the use of predictive modeling crop data for future planned treatments at 0157, 0065, 0167
Claims 30, 34, 35, 40, 41 recite limitations that stand rejected via the art citations and rationale applied to claims 1, 6, 11. Regarding a pest management device (
Sundararajan: Sec. 0044, Still referring to FIG. 7, portable integrated agricultural testing and optimization device 704 may be any one of portable integrated agricultural testing and optimization devices 110, 200, 300, or 400 of FIG. 1, 2, 3 or 4. Pesticide/additive management application 706 may be pesticide/additive management application 136 of FIG. 1. In embodiments, pesticide/additive management application 706 on each device 704 may include repository 722, 724 and 726. Repository 722 may be configured to store crop information and pest and disease diagnostic information. Repository 724 may be configured to store crop problems and associated pesticide and additive treatment information. Repository 726 may be configured to store the local supplier of pesticides and additives. Data in repository 722, 724 and 726 may be downloaded from repository 714 and 716 in cloud server 702.);
Response to Arguments
Applicant’s arguments filed 03/09/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 03/09/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 6-11 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
1. (Currently Amended) A pest management system, implemented on a network- enabled computing platform, for managing application of pest treatment materials to one or more crop locations based on a phenological model, the phenological model being a stored digital representation of a pest phenology in a particular region associated with the crop locations, the system comprising:
one or more network-interfacing sensors configured to acquire and communicate digital sensed data associated with the one or more crop locations and indicative at least in part of a phenological condition relating to a pest phenology;
a digital data storage component for receiving over a network and storing, over time, in digital association with the one or more crop locations:
said digital sensed data received from said one or more network- interfacing sensors;
pest treatment application data being a digital representation of information representative of pest treatments applied in connection with one or more pest treatment application suggestions provided by the phenological model;
crop outcome data relating to observed crop outcomes and stored as digital data; and
the phenological model for the one or more crop locations that provides the one or more pest treatment application suggestions based at least in part on said digital sensed data indicative of said phenological conditions at a given time; and
a digital data processor in network communication with said digital data storage component and operable to calculate a correlation for at least some of the one or more crop locations between said crop outcome data and one or more of said pest treatment application data and said pest treatment application suggestions,
wherein said digital data processor dynamically applies corrections to suggestions from the phenological model for at least some of the one or more crop locations based on said correlation.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations regarding managing agricultural data, which constitutes methods related to mental processes which include concepts performed in the human mind (including an observation, evaluation, judgment, opinion) which are still considered an abstract idea under the 2019 PEG. Additionally, Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation. The managing agricultural data on the equipment is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 12 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
pg. 12, “As described in Applicant's Specification "a network of sensors (e.g. hundreds of sensors) may be distributed across a large farm or growing area comprising many different crops, topologies, geographical features, or the like. Each sensor may be associated with a particular crop location having potentially unique location-specific characteristics (e.g. crop stage, degree days, soil type, crop type, crop distribution, levels of sunlight, wind, environmental conditions, or the like) and/or pest phenologies. A pest management platform in networked communication with the sensor network may access/store data in association with each sensor (e.g. each crop location) as a function of, for instance, time, or degree day. Based on a phenological model (or phenological models each associated with a respective crop location), the pest management platform may, in view of the sensed data at each crop location, prescribe a particular application or regimen for each respective crop location. For example, the pest management system may prescribe, for each crop location, an optimal time to apply a pesticide, which may differ between crop locations." Applicant's Specification at [00104]. The integration of the sensor network, the location- specific characteristics, the pest management platform, and the phenological model are technologically intertwined in a way that cannot merely be performed as a mental process.”
These arguments seems to describe a “particular way” of managing agricultural data of the abstract idea. “
Moreover, at pg. 9the Applicant admission that the application is directed to improving the user’s experience and not the computer itself. The Applicant is basically relying on the system elements (sensor) as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner.
Regarding in CardioNet, Examiner notes the device claimed in CardioNet is used to more accurately detect the occurrence of atrial fibrillation and atrial flutter of cardiac activity, thus providing an improvement to the function of a cardiac monitoring device and the relevant technology. Examiner finds the pending claims do not recite a comparable improvement. Specifically, utilizing sensors to monitoring, analyzing, and managing agricultural data, that improves and existing business process (e.g. agricultural management) and not a technology, computer-related technology or technological field. Examiner maintains the claims are directed to an abstract idea.
Moreover, the Examiner would like to point the Applicant to the 2019 PEG, in which ”managing agricultural data”. will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 103 rejection, at pg. 13-14 Applicant argues “Applicant submits that none of the cited documents, whether considered alone or in combination teach, suggest, or render obvious at least the features of "the phenological model for the one or more crop locations that provides the one or more pest treatment application suggestions based at least in part on said digital sensed data indicative of said phenological conditions at a given time...wherein said digital data processor modifies, for at least some of the one or more crop locations based on said correlation, one or more of the phenological model or future pest treatment application suggestions based thereon," as recited in amended independent claim 1 and as similarly recited in independent claims 16 and 30.”
In response, the Examiner respectfully disagrees. Peters describes the use of digital obtained data to analyze crop information that consist of pest treatment, in which Peters disclose the use of sensors to collect and analyze crop information that would manage the pest treatments, in which the Examiner is interpreting as phenological conditions. As, Phenological refers to the study or relating to the timing of cyclical and seasonal events in nature, such as plant flowering, insect emergence, and animal migration, wherein Peters at 0045, 0080, 0105, 0123, teaches the period for when crops are: developing, growing, and harvesting. Additionally, Peters teaches the use of predictive modeling crop data for future planned treatments at 0157, 0065, 0167
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xiang et al., U.S. Pub. 20170090068, (discussing the analyzing of soil with the use of sensors).
Datta et al., W.O. Pub. 2016193898, (discussing the recommending of crop yields and harvest).
Castle et al., Factors Influencing Producer Propensity for Data Sharing & Opinions Regarding Precision Agriculture and Big Farm Data, https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1051&context=ageconworkpap, University of Nebraska – Lincoln Presentations, Working Papers, and Gray Literature: Agricultural Economics Department, 2016, (discussing the monitoring and producing of agriculture.).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/UCHE BYRD/Examiner, Art Unit 3624
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