DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/18/2025 has been entered.
Response to Amendment
Applicant’s amendments to the claims, filed 9/18/2025, are accepted and appreciated by the examiner.
Response to Arguments
Applicant’s arguments filed 9/18/2025 have been fully considered. With regards to the 35 U.S.C. 101 Rejection, Applicant’s amendments recite a practical application and thus the rejection has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4, 7, 9-10, 12, 14-18, 22-25, 27-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 23, 28 recite “at least one of: (i) nutritional deficit or toxicity, (ii) water deficit, (iii) photosynthesis rate”. This limitation recites at least one of at least one of A or B, C, and D. It is not clear what is actually required and is therefore indefinite. It is not clear why (i) nutritional decicit or toxicity, has two item if the claim is trying to claim only a single item (at least one of). Examiner recommends amending the claims to recite “at least one of: nutritional deficit, toxicity, water deficit, and photosynthesis rate” Claims 1, 28 recite “the client terminal”. There is insufficient antecedent basis for this limitation in the claim. Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
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.
Claim(s) 1-4, 9, 10, 12, 14, 16-18, 22-24, 28 is/are rejected under 35 U.S.C. 103as being unpatentable over Ethington (US 2016/0078375 A1) in view of Mathur(US 2016/0202227 A1) and Freitag (US 2018/0189564 A1) and Guan (US 2018/0211156 A1).Examiner notes that herein Mathur ( US 2016/0202227 A1) will be referred to as Mathur’227.
With respect to Claim 1 Ethington teaches A computer implemented method of administration of at least one agricultural practice to a target field, comprising of: (See Abstract) transmitting through a communication network (See Abstract), by a hardware processor of a computing device: (See Fig 1) receiving, by the computing device, through said communication network said plurality of measurements; (See Fig 1) receiving a selection of at least one agricultural practice for administration to the target field; (See Abstract and Para[0028] The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.) computing based on said received plurality of measurements, at least one state parameter indicative of a state of a target crop at the target field and comprising at least one stress parameter indicative of the target crop experiencing at least one of: (i) nutritional deficit or toxicity,(ii) water deficit, and (iii) photosynthesis rate; (See Para[0028] and Para[0053]) the at least one state parameter of the target field and the at least one selected agricultural practice, wherein said at least one trained classifier is trained to: (See Fig 4) compute instructions for administration of the at least one selected agricultural practice to the target field, such that at least one of yield and quality of the target crop at the future target event is predicted to be increased when the instructions for administration of the at least one selected agricultural practice to the target field are implemented compared to the at least one of yield and quality of the target crop that is predicted at the future target event when an alternative administration of the at least one selected agricultural practice is implemented; (See Abstract and Para[0057] and Fig 6 ); receiving as an output from the at least one trained classifier the computed instructions for administration of the at least one selected agricultural practice to the target field, said computed instructions comprising machine readable instructions for controlling an agricultural controller for automatic implementation of the at least one selected agricultural practice; (See Fig 4) and providing the computed instructions for administration of the at least one selected agricultural practice to the target field to the client terminal; (See Fig 4) and However Ethington is silent to the language of by at least one crop physiological sensor continuously monitoring in real time a target crop of the target field a plurality of measurements taken by said at least one crop physiological sensor; inputting into at least one trained classifier, search a plurality of records in a training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields corresponding to a future target event and associated with a respective at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field, and automatically implementing the at least one selected agricultural practice to the target field, by at least one agricultural controller, automatically executing said provided machine readable instructions Nevertheless Mathur’227 teaches by at least one crop physiological sensor continuously monitoring in real time a target crop of the target field a plurality of measurements taken by said at least one crop physiological sensor; (See Para[0050],[0072]) automatically implementing the at least one selected agricultural practice to the target field, by at least one agricultural controller, automatically executing said provided machine readable instructions (See Para[0029],[0033]) While Mathur’227 teaches The machine learning techniques may include, for example, supervised and/or unsupervised techniques, such as artificial networks, Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, decision trees, association rule learning, or the like. The created models may include models that are specific to a particular farm and models that are generally applicable to all farms. (See Para[0054]) Once the models have been created, precision agriculture system 250 may further train the models and/or create new models, based on receiving new training data. The new training data may include, in addition to the data discussed above in relation to the corpus of data, data from user devices 210 that are being used by farmers. (See Para[0057]) However Mathur’227 is silent to the language of inputting into at least one trained classifier, search a plurality of records in a training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields corresponding to a future target event and associated with a respective at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field, and Nevertheless Freitag teaches inputting into at least one trained classifier, (See Fig 5 Component 512) search a plurality of records in a training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, (See Para[0047]-[0050] ) obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields corresponding to a future target event and associated with a respective at least one state parameter according to the records associated (See Para[0019]) However Freitag is silent to the language of with a highest yield and quality of a reference crop in a respective reference field, and Nevertheless Guan teaches with a highest yield and quality of a reference crop in a respective reference field, and (See Para[0207]) It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and have physiological sensor continuously monitoring in real time and automate such as that of Mathur’227. One of ordinary skill would have been motivated to modify Ethington because monitoring in real time would produce accurate results and automating would improve efficiency. It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and input into a trained classifier and classify such as that of Freitag. One of ordinary skill would have been motivated to modify Ethington, because using a trained classifier would produce accurate results. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ethington and identify a highest yield and quality such as that of Guan. One of ordinary skill would have been motivated to modify Ethington, because having a highest yield and quality would improve profits. With respect to Claim 2 Ethington teaches The method of claim 1, wherein the at least one state parameter includes at least one of: at least one stress parameter indicative of stress experienced by the target crop, at least one growth parameters indicative of growth of the target crop, and at least one physiological parameters indicative of a physiological condition of the crop. (See Para[0060],[0085]) With respect to Claim 3 Ethington teaches The method of claim 1, wherein the instructions for administration comprises a certain time for administration of the at least one agricultural practice to the target crop. (See Para[0053])
With respect to Claim 4 Ethington teaches The method of claim 3, wherein the certain time is selected from the group consisting of: a certain phenological stage of the target crop, degree days, and a calendar date. (See Para[0053])
With respect to Claim 9 Ethington teaches The method of claim 1, further comprising: monitoring administration of the at least one agricultural practice according to the instructions by iterating the inputting into the at least one classifier, and the classifying, for a plurality of state parameters associated with different sequential time intervals obtained at least one of: during administration of the at least one agricultural practice according to the instructions classified by the at least one classifier and after administration of the at least one agricultural practice according to the instructions classified by the at least one classifier, wherein the classifying the plurality of state parameters dynamically adjusts the instructions for administration of the at least one agricultural practice. (See Para[0046],[0050],[0065]) With respect to Claim 10 Ethington teaches The method of claim 1, wherein the at least one state parameter is further associated with a timestamp including one or more members selected from the group consisting of: calendar day and time, phenological stage of the target crop, and degree day within a growing season, wherein the classifier further performs the classification according to the timestamp. (See Para[0044]-[0047],[0049],[0056-0057],[0089]) With respect to Claim 12 Ethington teaches The method of claim 1, wherein the at least one classifier searches records of a dataset by matching the at least one state parameter of the target field to at least one state parameter of at least one reference field, wherein the dataset stores records each including: indications of at least one state parameter of respective reference fields, indications of agricultural practices administered to respective reference fields, and at least one of yield and quality of respective reference crops of the respective reference fields at historical reference events, wherein the instructions for administration of the at least one agricultural practice to the target field are obtained according to the indication of agricultural practices administered to the reference field of at least one matched record. (See Para[0096],[0198])
With respect to Claim 14 Ethington teaches The method of claim 1, wherein the at least one state parameter is selected from the group consisting of: nutritional deficit, toxicity level, water deficit, and photosynthesis blockage. (See Para[0031],[0098]) With respect to Claim 16 Ethington teaches The method of claim 1, wherein the at least one state parameter comprises a plurality of state parameters each associated with a respective sequential timestamp over a time interval, wherein the plurality of state parameters denote dynamic changes for the target field over the time interval. (See Para[0038],[0044-[0047],[0056]-[0057]) With respect to Claim 17 Ethington teaches The method of claim 1, wherein the instructions include instructions for administration of another at least one agricultural practice to the target field, wherein the instructions for administration of another at least one agricultural practice are selected for adjustment of the at least one state parameter(s) of the target field associated with a prediction of at least one of yield and quality of the target crop at the future target event according to the at least one adjusted state parameter(s) relative to the at least one of yield and quality of the target crop at the future target event according to the at least one state parameter(s) without the adjustment. (See Para[0029],[0066],[0078],[0192]) With respect to Claim 18 Ethington teaches The method of claim 1, wherein the at least one crop physiological sensor is selected from the group consisting of: dendrometer, stem diameter sensor, fruit diameter sensor, leaf diameter sensor, crop growth rate sensor, leaf temperature sensor, soil moisture sensor, environmental temperature sensor, solar radiation sensor, wind sensor, relatively humidity sensor, and airborne or satellite image sensor. (See Para[0029],[0066],[0078],[0192])
With respect to Claim 22 Ethington teaches The method of claim 1, wherein the at least one agricultural practice is selected from the group consisting of: irrigation, chemical pesticide, chemical fertilizer, pruning, thinning, harvesting, and bio-stimulant. (See Para[0028],[0053],[0062],[0097]) With respect to Claim 23 Ethington teaches A computer implemented method of training at least one classifier for classifying at least one agricultural practice and at least one state parameter of a target field to provide instructions for administration of the at least one agricultural practice to the target field, with improved crop quality and crop yield, comprising: (See Abstract) providing a training dataset, including a plurality of records for a plurality of reference fields, each record of each respective reference field storing: instructions used for administering of at least one agricultural practice to the respective reference field, at least one stress parameter indicative of a reference crop experiencing at least (i) nutritional deficit or toxicity, (ii) water deficit, and (iii) photosynthesis rate, indicative of a state of the reference crop at the respective reference field computed based on, and at least one of yield and quality of the target crop at a historical reference event; and (See Para[0028] and Para[0053]) training at least one classifier to: receive as inputs at least one selected agricultural practice and at least one state parameter indicative of a target crop of a target field, experiencing at least one of: (i) nutritional deficit or toxicity, (ii) water deficit, and (iii) photosynthesis rate; (See Para[0028] and Para[0053]) output, according to the training dataset, instructions for administering the at least one selected agricultural practice to the target field, wherein the outputted instructions comprise machine readable instructions for controlling an agricultural controller for However Ethington is silent to the language of measurements taken and transmitted by at least one crop physiological sensor continuously monitoring the reference crop a respective at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field search the plurality of records in the training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields at historical reference events corresponding to a future target event and associated with, and automatic implementation of the at least one selected agricultural practice, wherein said trained at least one classifier provides agricultural practice administration instructions that improve at least one of yield and quality of the target crop at a future target event, relative to the at least one of yield and quality of the target crop that is predicted at the future target event when an alternative administration of the at least one selected agricultural practice is implemented Nevertheless Mathur’227 teaches measurements taken and transmitted by at least one crop physiological sensor continuously monitoring the reference crop (See Para[0050],[0072]) automatic implementation of the at least one selected agricultural practice, wherein said trained agricultural practice administration instructions that improve at least one of yield and quality of the target crop at a future target event, relative to the at least one of yield and quality of the target crop that is predicted at the future target event when an alternative administration of the at least one selected agricultural practice is implemented. (See Para[0029],[0033]) While Mathur’227 teaches The machine learning techniques may include, for example, supervised and/or unsupervised techniques, such as artificial networks, Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, decision trees, association rule learning, or the like. The created models may include models that are specific to a particular farm and models that are generally applicable to all farms. (See Para[0054]) Once the models have been created, precision agriculture system 250 may further train the models and/or create new models, based on receiving new training data. The new training data may include, in addition to the data discussed above in relation to the corpus of data, data from user devices 210 that are being used by farmers. (See Para[0057]) However Mathur’227 is silent to the language of at least one classifiersearch the plurality of records in the training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields at historical reference events corresponding to a future target event and associated with, and a respective at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field Nevertheless Freitag teaches at least one classifier (See Fig 5 Component 512) search the plurality of records in the training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, (See Para[0047]-[0050] ) obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields at historical reference events corresponding to a future target event and associated with, and (See Para[0019]) However Freitag is silent to the language of with a highest yield and quality of a reference crop in a respective reference field, and Nevertheless Guan teaches with a highest yield and quality of a reference crop in a respective reference field, and (See Para[0207]) It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and have physiological sensor continuously monitoring in real time and automate such as that of Mathur’227. One of ordinary skill would have been motivated to modify Ethington because monitoring in real time would produce accurate results and automating would improve efficiency. It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and input into a trained classifier and classify such as that of Freitag. One of ordinary skill would have been motivated to modify Ethington, because using a trained classifier would produce accurate results. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ethington and identify a highest yield and quality such as that of Guan. One of ordinary skill would have been motivated to modify Ethington, because having a highest yield and quality would improve profits. With respect to Claim 24 Ethington teaches The method of claim 23, wherein each record of each respective reference fields stores a plurality of at least one state parameter computed at each of a plurality of sequential time intervals spanning an entire growing season of the respective reference crop growing at the respective reference field. (See Para[0038], [0043]) With respect to Claim 28 Ethington teaches A system for administration of at least one agricultural practice to a target field, comprising: (See Abstract) a non-transitory memory having stored thereon a code for execution by at least one hardware processor, the code comprising: code for receiving through a communication network, (See Abstract), code for receiving a selection of at least one agricultural practice for administration to the target field; (See Abstract and Para[0028] The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.) code for computing based on said received plurality of measurements, at least one state parameter indicative of a state of a target crop at the target field and comprising at least one stress parameter indicative of the target crop experiencing at least one of (i)_nutritional deficit or toxicity, (ii) water deficit, and (iii) photosynthesis rate; (See Para[0028] and Para[0053]) compute instructions for administration of the at least one selected agricultural practice to the target field, such that at least one of yield and quality of the target crop at the future target event is predicted to be increased when the instructions for administration of the at least one selected agricultural practice to the target field are implemented compared to the at least one of yield and quality of the target crop that is predicted at the future target event when an alternative administration of the at least one selected agricultural practice is implemented; (See Abstract and Para[0057] and Fig 6 ); code for receiving as an output from the at least one trained classifier, the computed instructions for administration of the at least one selected agricultural practice to the target field, said computed instructions comprising machine readable instructions for controlling an agricultural controller for automatic implementation of the at least one selected agricultural practice; (See Fig 4) code for providing the computed instructions for administration of the at least one selected agricultural practice to the target field to the client terminal; and (See Fig 4) However Ethington is silent to the language of from at least one crop physiological sensor continuously monitoring in real time a target crop of the target field a plurality of measurements taken by said at least one crop physiological sensor; code for inputting into at least one trained classifier, the at least one state parameter of the target field and the at least one selected agricultural practice, wherein said at least one trained classifier is trained to: search a plurality of records in a training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields corresponding to a future target event and associated with a respective at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field, and code for instructing at least one agricultural controller to automatically implement the at least one selected agricultural practice to the target field by automatically executing said provided machine readable instructions Nevertheless Mathur’227 teaches from at least one crop physiological sensor continuously monitoring in real time a target crop of the target field a plurality of measurements taken by said at least one crop physiological sensor; (See Para[0050],[0072]) code for instructing at least one agricultural controller to automatically implement the at least one selected agricultural practice to the target field by automatically executing said provided machine readable instructions(See Para[0029],[0033]) While Mathur’227 teaches The machine learning techniques may include, for example, supervised and/or unsupervised techniques, such as artificial networks, Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, decision trees, association rule learning, or the like. The created models may include models that are specific to a particular farm and models that are generally applicable to all farms. (See Para[0054]) Once the models have been created, precision agriculture system 250 may further train the models and/or create new models, based on receiving new training data. The new training data may include, in addition to the data discussed above in relation to the corpus of data, data from user devices 210 that are being used by farmers. (See Para[0057]) However Mathur’227 is silent to the language of code for inputting into at least one trained classifier, the at least one state parameter of the target field and the at least one selected agricultural practice, wherein said at least one trained classifier is trained to: search a plurality of records in a training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields corresponding to a future target event and associated with a respective at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field, and Nevertheless Freitag teaches code for inputting into at least one trained classifier, the at least one state parameter of the target field and the at least one selected agricultural practice, wherein said at least one trained classifier is trained to: (See Fig 5 Component 512) search a plurality of records in a training dataset to identify records associated with state parameters that match to the at least one state parameter of the target field, and records associated with agricultural practices previously applied to a plurality of reference fields, that match the at least one selected agricultural practice, (See Para[0047]-[0050] ) obtain instructions used in historical reference events for administering the selected agricultural practice to reference fields corresponding to a future target event and associated with a respectiveand(See Para[0019]) However Freitag is silent to the language of at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field, Nevertheless Guan teaches at least one state parameter according to the records associated with a highest yield and quality of a reference crop in a respective reference field, (See Para[0207]) It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and have physiological sensor continuously monitoring in real time and automate such as that of Mathur’227. One of ordinary skill would have been motivated to modify Ethington because monitoring in real time would produce accurate results and automating would improve efficiency. It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and input into a trained classifier and classify such as that of Freitag. One of ordinary skill would have been motivated to modify Ethington, because using a trained classifier would produce accurate results. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ethington and identify a highest yield and quality such as that of Guan. One of ordinary skill would have been motivated to modify Ethington, because having a highest yield and quality would improve profits.
Claim(s) 7, 15, 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ethington (US 2016/0078375 A1) in view of Mathur (US 2016/0202227 A1) and Freitag (US 2018/0189564 A1) and Guan (US 2018/0211156 A1) and Mathur (US 2016/0253595 A1).Examiner notes herein Mathur (US 2016/0253595 A1) will be referred to as Mathur’595
With respect to Claim 7 Ethington teaches The method of claim 1, further comprising: providing a target field profile of the target field including a plurality of parameters remaining substantially static over the growing season of the target crop growing in the target field, and wherein the classifier performs the classification according to reference field profiles of respective reference fields correlated to the target field profile according to a correlation requirement; (See Para[0019]-[0021],[0028],[0043],[0046]) However Ethington is silent to the language of selecting a subset of reference fields that correlate to the target field according to the correlation of the target field profile of the target field and the reference field profiles of the reference fields; and dynamically training the at least one classifier according to the subset of reference fields. Nevertheless Mathur’595 teaches selecting a subset of reference fields that correlate to the target field according to the correlation of the target field profile of the target field and the reference field profiles of the reference fields; and (See Para[0024],[0057],[0059]) dynamically training the at least one classifier according to the subset of reference fields. (See Para[0024],[0057],[0059]) It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and select and dynamically train such as that of Mathur’595. One of ordinary skill would have been motivated to modify Ethington because dynamically training would result in accurate results. With respect to Claim 15 Ethington is silent to the language of The method of claim 1, wherein the at least one state parameter is computed by at least one state classifier trained according to a training dataset of output of crop physiological sensors and associated data indicative of a certain value of the state. Nevertheless Mathur’595 teaches wherein the at least one state parameter is computed by at least one state classifier trained according to a training dataset of output of crop physiological sensors and associated data indicative of a certain value of the state. It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington computer by a classifier trained such as that of Mathur’595. One of ordinary skill would have been motivated to modify Ethington because using a trained classifier would result in accurate results. With respect to Claim 27 Ethington is silent to the language of The method of claim 23, wherein each record of each respective field stores a reference field profile including a plurality of parameters remaining substantially static over the growing season of the reference crop growing in the reference field, and wherein the at least one classifier is trained according to the reference field profiles. Nevertheless Mathur’595 teaches wherein each record of each respective field stores a reference field profile including a plurality of parameters remaining substantially static over the growing season of the reference crop growing in the reference field, and wherein the at least one classifier is trained according to the reference field profiles (See Para[0081]) It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and store a profile or determine consequences of actions based on static data, such as that of Mathur’595. One of ordinary skill would have been motivated to modify Ethington, because storing a profile would allow one to determine the consequences of such a set of parameters to predict future values.
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ethington (US 2016/0078375 A1) in view of Mathur (US 2016/0202227 A1) and Freitag (US 2018/0189564 A1) and Guan (US 2018/0211156 A1) and Mathur (US 2016/0253595 A1) and Allison (US 2011/0040660 A1).
With respect to Claim 25 Ethington is silent to the language of The method of claim 24, wherein the training dataset is updated based on an indication of the at least one state parameter for each of the plurality of sequential time intervals transmitted by each of a plurality of reference client terminals associated with each respective reference field to a server storing the training dataset, wherein the classifier is trained in real time according to the updated version of the training dataset. Nevertheless Mathur’595 teaches wherein the training dataset is updated based on an indication of the at least one state parameter for each of the plurality of sequential time intervals transmitted by each of a plurality of reference client terminals associated with each respective reference field to a server storing the training dataset, (See Para[0057]) However Mathur is silent to the language of wherein the classifier is trained in real time according to the updated version of the training dataset. Neverthless Allison teaches wherein the classifier is trained in real time according to the updated version of the training dataset (See Para[0082]). It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington and updating the training set such as that of Mathur’595. One of ordinary skill would have been motivated to modify Ethington because updating the training set would improve accuracy. It would have been obvious to one of ordinary skill in the art at the time of filing to modify Ethington with train in real-time such as that of Allison. One of ordinary skill would have been motivated to modify Ethington because real-time training would improve accuracy and implement the most recent data.
Conclusion
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YOSHIHISA . ISHIZUKA
Examiner
Art Unit 2863
/YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2863