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 .
Claim Objections
Claims 2-11, 15-19 are objected to because of the following informalities: minor lack of antecedent basis.
Claims 2-11, 15 recite “the method” appears “the computer-implemented method”.
Appropriate correction is required.
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 1-19 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-13 and 15-19, 12, 13, 14 are directed to a method, a system, a medium, a method of generating a zone specific application map for treating an agricultural field with products. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 12-14 these claims recite
providing a hypermodel comprising- a product recommendation model, PRM; and a biophysical parameter model, BPM, wherein the hypermodel controls an interdependence of the product recommendation model and the biophysical parameter model in an iterative procedure, the iterative procedure comprising setting initial input parameters for the product recommendation model and the biophysical parameter model, iteratively running the product recommendation model and the biophysical parameter model, and collecting output of the product recommendation model and the biophysical parameter model; providing PRM input parameters for the product recommendation model and generating PRM output by the product recommendation model; providing BPM input parameters for the biophysical parameter model and generating BPM output by the biophysical parameter model; and generating the zone specific application map by the hypermodel, using at least parts of the PRM output and parts of the BPM output.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec. page 10, Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 12-14 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and 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 2106.0S(f))
MPEP § 2106.05(f): Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent)
or are mere instructions to implement an abstract idea or other exception on a computer? (Yes)
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 12-14: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “providing..”, “providing..”, “providing..”, “generating…” etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2-3, 7, 9
Claim 2-3, 7, 9 merely recite other additional elements that define the models which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4
Claim 4 merely recite other additional elements that define the application map which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5
Claim 5 merely recite other additional elements that define the products which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 8
Claim 6, 8 merely recite other additional elements that define the input and output parameters of models which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 10
Claim 10 merely recite other additional elements that define generating zone specific control data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 11
Claim 11 merely recite other additional elements that define determining common solution of the product for the agricultural field which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 15
Claim 15 merely recite other additional elements that define providing stat to the agricultural equipment which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 16
Claim 16 merely recite other additional elements that define the condition for stopping the hypermodel which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 17
Claim 17 merely recite other additional elements that define the hypermodel execution which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 18
Claim 18 merely recite other additional elements that define the PRM output which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 19
Claim 19 merely recite other additional elements that define the iterative procedure which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims 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 1-16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Avey US 2014/0067745 in view of Perry et al. (Perry) US 2019/0050948
In regard to claim 1, Avey disclose A computer-implemented method for generating a zone specific application map for treating an agricultural field with products, the method comprising: ([0006]-[0010] [0020]-[0032] generating recommendations regarding agricultural inputs, such as intra-field management zone to optimizing a crop production with treatments)
providing a hypermodel comprising- a product recommendation model, PRM; and a biophysical parameter model, BPM; ([0006]-[0013] [0020]-[0032] [0046]-[0050][0058] receiving input with various dataset and data models. TAIR system with multiple models, such as provide recommendations with ML algorithms and data model with localized usage context data)
wherein the product recommendation model and the biophysical parameter model in the iterative procedure, ([0030]-[0032] [0052]-[0058] the TAIR system control the recommendation with ML algorithms and the data model to iteratively improve its recommendations based on the input of the localized usage context data)
the iterative procedure comprising setting initial input parameters for the product recommendation model and the biophysical parameter model, ([0030]-[0032] [0047]-[0058] the iterative procedure starts with an initial indication of the localized usage context from the user and the product suggestions based on the initial localized usage context)
iteratively running the product recommendation model and the biophysical parameter model, and collecting output of the product recommendation model and the biophysical parameter model; ([0030]-[0032] [0047]-[0058] iteratively improve its recommendations based on the continuous input of the localized usage context data and display output of the recommendations at results viewable area also with env. conditions, etc.)
providing PRM input parameters for the product recommendation model and generating PRM output by the product recommendation model; providing BPM input parameters for the biophysical parameter model and generating BPM output by the biophysical parameter model; ([0006]-[0013] [0020]-[0032] [0046]-[0050][0058] receiving input with various dataset and data models. TAIR system query one or more datasets and/or data models, such as crop models soil dataset, product datasets, historical data, insect, weed and/or disease dataset, and weather models, weather dataset and/models, etc, and generate output with crop price forecasts, etc.) and
generating the zone specific application map by the hypermodel, using at least parts of the PRM output and parts of the BPM output. ([0006]-[0013] [0020]-[0032] [0046]-[0058] generating map based on the models outputs. recommendations may be presented)
But Avey fail to explicitly disclose “wherein the hypermodel controls an interdependence of the product recommendation model and the biophysical parameter model in a iterative procedure,”
Perry disclose wherein the hypermodel controls an interdependence of the product recommendation model and the biophysical parameter model in a iterative procedure, (Fig. 8, [0119]-[0141][0176]-[0178] iteratively update the prediction model with accessed field information from the request to optimize crop productivity)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Perry’s ML in agricultural into Avey’s invention as they are related to the same field endeavor of agricultural recommendation. The motivation to combine these arts, as proposed above, at least because Perry’s ML model would help to provide more machine learning capability to Avey’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing machine learning capability would help to optimize crop production.
In regard to claim 2, Avey and Perry disclose The method according to claim 1,
Avey disclose wherein
the hypermodel further comprises a growth stage model, GSM; the method further comprises providing GSM input parameters for the growth stage model and generating GSM output by the growth stage model; and the PRM input parameters optionally comprise at least parts of the GSM output. ([0006]-[0013] [0020]-[0032] [0046]-[0050][0058] receiving input with various dataset and data models. TAIR system query one or more datasets and/or data models, such as crop models soil dataset, product datasets, historical data, insect, weed and/or disease dataset, and weather models, weather dataset and/models, etc, and generate various output, etc. such as a time for a crop or plant to reach maturity, a reach a defined growth stage)
In regard to claim 3, Avey and Perry disclose The method according to claim 2,
Avey disclose wherein
the hypermodel further comprises a disease and infection risk model, DIRM; the method further comprises providing DIRM input parameters, comprising at least parts of the GSM output, for the disease and infection risk model and generating DIRM output by the disease and infection risk model; and the PRM input parameters (4) comprise at least parts of the DIRM output. ([0006]-[0013] [0020]-[0032] [0046]-[0050][0058] receiving input with various dataset and data models. TAIR system query one or more datasets and/or data models, such as crop models soil dataset, product datasets, historical data, insect, weed and/or disease dataset, and weather models, weather dataset and/models, etc, and generate various output, etc. such as data models for various pests and/or pathology, such as historical or predicted insect and/or disease (fungal, bacterial, viral, and abiotic) infestation levels and treatment thresholds, weed growth models, nematode models, etc. may be used)
In regard to claim 4, Avey and Perry disclose The method according to claim 1,
Avey disclose wherein the zone specific application map comprises a selection of products and a product rate per zone of the agricultural field, wherein the zone is in particular a polygon- shaped cell, more particularly a square cell. ([0020]-[0032] [0046]-[0058] user can select a location on the map with a latitude and a longitude and therefore can select a square cell and seed products (corn, soybeans, etc.) and planting density or planting rate, etc. in the zone)
In regard to claim 5, Avey and Perry disclose The method according to claim 1,
Avey disclose wherein the products comprise at least one out of a group, the group consisting of chemical products, biological products, fertilizers, nutrients and water. ([0020]-[0034] [0046]-[0058] chemical treatments, biological products, fertilizers, nutrients and water content)
In regard to claim 6, Avey and Perry disclose The method according to claim 2,
Avey disclose wherein:
the GSM input parameters comprise at least one out of a group, the group consisting of crop, variety, variety characteristics, raw weather data, seeding date and growth stage observation; ([0020]-[0034] [0039] [0046]-[0058] crop types and/varieties, or combinations of varieties, weather data, planting windows, growth stage detections)
the GSM output comprises the distribution of growth stages over the season, in particular with a daily resolution; ([0024] [0046]-[0058] growth stage with a time of year or specific data)
the DIRM input parameters comprise at least one out of a group, the group consisting of crop, previous crop, variety, variety characteristics, raw weather data, seeding date, infection rules, tillage and disease observations; ([0020]-[0034] [0039] [0046]-[0058] crop types and/varieties, or combinations of varieties, weather data, planting windows, disease and or pest infestation information, and tillage practices, disease database or historical insect or disease infestation level, etc.)
the DIRM output comprises disease and infection data, in particular disease and infection risk and disease and infection events, particularly for the past, the present and the future; ([0020]-[0034] [0039] [0046]-[0058] disease and or pest infestation information, disease database or historical or predicted insect or disease infestation levels and treatment thresholds, with risk level, etc. the measurement can be associated with a specific date and time)
the PRM input parameters comprise at least one out of a group, the group consisting of crop, variety, variety characteristics, indication, product registration, efficacy requirements of products and observational data; ([0020]-[0034] [0039] [0046]-[0058] crop types and/varieties, or combinations of varieties, various indications, product registration, a time for a crop to reach a defined growth rate, or maturity, and sensor data, etc.) the PRM output comprises a selection of products and a product rate; ([0020]-[0034] [0039] [0046]-[0058] recommend agricultural products and product density or planting rate)
the BPM input parameters comprise remote image data, particularly multi-spectral image data, of the agricultural field, in particular provided by a satellite, an aircraft and/or a drone; and/or the BPM output comprises the zone specific distribution of a biophysical parameter, in particular a leaf area index and/or a canopy density. ([0020]-[0032] [0046]-[0058] image data from satellite, and output zone specific planting densities or planting rate with variable or fixed in the zone for the biophysical products)
In regard to claim 7, Avey and Perry disclose The method according to claim 1,
Avey disclose wherein:
the growth stage model is a process model or a machine learning model; the disease and infection risk model is a process model or a machine learning model; the product recommendation model is a process model or a machine learning model; and the biophysical parameter model is a process model or a machine learning model. ([0020]-[0032] [0046]-[0058] various models are utilizing ML algorithms)
In regard to claim 8, Avey and Perry disclose The method according to claim 1,
Avey disclose further comprising at least one out of a group, the group consisting of; using at least parts of the GSM output as some of the BPM input parameters; using at least parts of the DIRM output as some of the GSM input parameters; using at least parts of the DIRM output as some of the BPM input parameters; using at least parts of the PRM output as some of the GSM input parameters; using at least parts of the PRM output as some of the DIRM input parameters; and using at least parts of the PRM output as some of the BPM input parameters. ([0006]-[0013] [0020]-[0032] [0046]-[0050][0058] receiving input with various dataset and data models and the models can be combined. TAIR system query one or more datasets and/or data models, and generate output and recommendations with various models.)
In regard to claim 9, Avey and Perry disclose The method according to claim 1,
Avey disclose wherein the hypermodel further comprises a weather model. ([0006]-[0013] [0020]-[0032] [0046]-[0050][0058] weather models, weather dataset and/models, etc)
In regard to claim 10, Avey and Perry disclose The method according to claim 1,
Avey disclose further comprising generating zone specific control data and/or a zone specific control map configured to be used for controlling an agricultural equipment to apply the products to the agricultural field. ([0020]-[0034] [0039] [0046]-[0058] generating recommendations to control an agricultural equipment to apply the products to the agricultural field)
In regard to claim 11, Avey and Perry disclose The method according to claim 1,
Avey disclose further comprising determining one common solution of the products for the agricultural field by the hypermodel, wherein the zone specific application map specifies the amount per unit area of the common solution to be applied per zone of the agricultural field. ([0020]-[0032] [0046]-[0058] recommend by the model, zone specific planting densities or planting rate with variable or fixed in the zone for the biophysical products)
In regard to claim 12, Avey and Perry disclose A system for generating a zone specific application map, configured to carry out a method according to claim 1 and comprising: ([0006]-[0010] [0020]-[0032] generating recommendations regarding agricultural inputs, such as intra-field management zone to optimizing a crop production with treatments)
at least one input interface for providing input parameters, the input parameters comprising at least one out of a group, the group consisting of the GSM input parameters, DIRM input parameters, PRM input parameters and BPM input parameters; ([0042]-[0050][0058] user interface for providing inputs from various models)
at least one processing unit configured to generate the zone specific application map; ([0042]-[0050][0058] a processor to generate zone map)
and at least one output interface for outputting at least one out of a group, the group consisting of the zone specific application map, zone specific control data and the zone specific control map. ([0006]-[0013] [0020]-[0032] [0046]-[0058] generating map, recommend zone specific planting densities or planting rate with variable or fixed in the zone, etc)
In regard to claim 13, Avey and Perry disclose A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processor in a system, cause the processor carry out a method according to claim 1. ([0006]-[0011][0020]-[0032] [0037]-[0040] medium, generating recommendations regarding agricultural inputs, such as intra-field management zone to optimizing a crop production with treatments)
In regard to claim 14, Avey and Perry disclose A method of applying products to an agricultural field, comprising:
generating a zone specific application map, zone specific control data and/or a zone specific control map generated according to a method according to claim 1; ([0006]-[0013] [0020]-[0032] [0046]-[0058] generating map based on the models outputs, and various map or various data related to control and recommendations according to claim 1) and
applying the products to the agricultural field in accordance with the zone specific application map, zone specific control data and/ or zone specific map. ([0006]-[0011] [0020]-[0032] [0037]-[0054] taking management actions such as a crop production with treatments, etc. based on the map and data generated)
In regard to claim 15, Avey and Perry disclose The method according to claim 1, further comprising providing the zone specific application map, zone specific control data and/or a zone specific control map to an agricultural equipment, wherein the agricultural equipment performs a zone specific application of the products to the agricultural field based on the zone specific application map, zone specific control data and/or zone specific control map. ([0006]-[0011] [0020]-[0032] [0037]-[0040] [0046]-[0058] generating recommendations regarding agricultural inputs, such as intra-field management zone to optimizing a crop production with treatments to control equipment for applying the products to an agricultural field based on the map, control data)
In regard to claim 16, Avey and Perry disclose The method according to claim 1,
But Avey fail to explicitly disclose “wherein the hypermodel is configured to stop the iterative procedure after a pre-defined number of iterations or after a pre-defined accuracy has been reached.”
Perry disclose wherein the hypermodel is configured to stop the iterative procedure after a pre-defined number of iterations or after a pre-defined accuracy has been reached. (Fig. 8, [0119]-[0141][0176]-[0178] iteratively update the prediction model with accessed field information until the a threshold number of iterations)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Perry’s ML in agricultural into Avey’s invention as they are related to the same field endeavor of agricultural recommendation. The motivation to combine these arts, as proposed above, at least because Perry’s ML model would help to provide more machine learning capability to Avey’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing machine learning capability would help to optimize crop production.
In regard to claim 18, Avey and Perry disclose The method according to claim 1,
Avey wherein the PRM output further comprises a dependence of selected products and/or a product rate on a biophysical parameter, and wherein the hypermodel determines a recommended product and product rate for each zone of the agricultural field using the PRM output with said dependence on the biophysical parameter together with the BPM output. (0006]-[0013][0030]-[0036] [0047]-[0058] the product suggestions based on the localized usage context and the recommended product and product rate for each zone of the field using recommended product based on the updated localized usage context outputted from the model)
In regard to claim 19, Avey and Perry disclose The method according to claim 1,
Avey disclose wherein the iterative procedure further comprises using said collected output of the product recommendation model and the biophysical parameter model as input for the product recommendation model and the biophysical parameter model in a next iteration. ([0030]-[0032] [0052]-[0058] iteratively improve its recommendations based on the continuous input of the localized usage context data with the product recommendations to the recommendation model and data model repeatedly)
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Avey US 2014/0067745 and Perry et al. (Perry) US 2019/0050948 as applied to claim 1, further in view of Hassanzadeh et al. (Hassanzadeh) US 2018/0132422
In regard to claim 17, Avey and Perry disclose The method according to claim 1,.
But Avey and Perry fail to explicitly disclose “wherein parts of the hypermodel are executed on separate processors, parallelizing the execution of the method.”
Hassanzadeh disclose wherein parts of the hypermodel are executed on separate processors, parallelizing the execution of the method. (Fig. 1, [0070]-[0090] [0181]-[0186] the model executed on separate processors and can be processed in parallel)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hassanzadeh’s identifying management zones in agricultural fields and generating plans for the zones in agricultural into Perry and Avey’s invention as they are related to the same field endeavor of agricultural recommendation. The motivation to combine these arts, as proposed above, at least because Hassanzadeh’s parallel processing with multiple processors would help to provide more execution capability to Perry and Avey’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more execution capability with parallel processing and multiple processors would help to optimize crop production.
Response to Arguments
Applicant’s arguments with respect to claims 1-15 filed on 8/27/2026 have been considered but are moot because the arguments do not apply to the current rejection.
With respect to 35 USC § 101, please see above for the rejection detail.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20200160459 A1 2020-05-21 Coolidge et al.
CROP MANAGEMENT METHOD AND SYSTEM
Coolidge et al. disclose A computer-implemented cloud-based agricultural enterprise management system and methods. The system comprises a plurality of modular components for receiving and processing data pertaining to agricultural production of commodities by an agricultural producer and for centralizing and storing the received and/or processed data in a single cloud-based database. The producer can provide to one or more third-party suppliers and/or service providers, authorized but restricted access to selected components of their agricultural enterprise management system and cloud-based database so that together, the producer, suppliers and service providers can effectively and cost-efficiently plan and manage the delivery of products and services during a crop production cycle, and the sale of harvested agricultural commodities. Separate modular components may be provided for inputs exemplified by agronomy data, crop production inputs data, and crop growth and performance tracking… see abstract.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights