DETAILED ACTION
Notice of 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 .
Claims 1-15 are pending and are rejected.
Priority
Foreign priority:
Acknowledgment is made of applicant’s claim for foreign priority to application no. EP21175343.9 filled on 05/21/2021. The certified copy has been received.
PCT:
The current application is a 371 of the PCT application no. PCT/EP2022/063797 filled on 05/20/2022.
Information Disclosure Statement
The information disclosure statements (IDSs) filled on 11/14/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Preliminary Amendments
Claims 1-15 are amended in the preliminary claim amendments filed on 11/14/2023. Accordingly, the amended claims are being fully considered by the examiner.
Drawings
Figures 1 and 7-11 in drawings filled on 11/14/2023 are acceptable for examination purpose.
Objections:
The drawings are objected to because in figures 2-6, the words in the table are inverted (upside down).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claimed invention is directed to abstract idea without significantly more:
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed is directed to an abstract idea without significantly more.
Step 1:
Claims 1-15 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A:
The claims 1-15 fall within the judicial exception of an abstract idea. Specifically, Mental Processes such that concepts performed in the human mind or with pen and paper including observation, evaluation, judgment, calculation, determination, and presentation (MPEP 2106.04(a)(2)(III)); and Mathematical Concepts such as mathematical formulas or equations, or mathematical calculations (MPEP 2106.04(A)(a)(I)).
Step 2A – Prong 1:
Claims 1 and 14.
providing/provide an application rate model for a plant, wherein the application rate model is configured to describe a relationship between a nitrogen content and an application rate of a glutamine synthetase inhibitor (S10);
determining/determine the application rate of the glutamine synthetase inhibitor of the at least one section of the agricultural field using the application rate model (S40);
These limitations describe providing/determination of a model and calculation of application rate.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is a determination, and Mathematical Concepts such as mathematical calculation (modeling and using the model).
Claim 2.
wherein the nitrogen content data is derived by a measurement of the nitrogen content of a plant or a part of the plant which is within the at least one section of the agricultural field.
Claim 3.
wherein the nitrogen content data is derived by a measurement of the nitrogen content of a soil or a part of the soil which is within the at least one section of the agricultural field.
These limitations of claims 2 and 3 describes determination/deriving of data via measurements.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is a determination and derivation.
Claim 4.
wherein the application rate model is based on the correlation that within a minimum value and a maximum value of an application rate of the glutamine synthetase inhibitor for the section of the agricultural field, a higher application rate is determined for sections with higher nitrogen content compared to sections with lower nitrogen content.
These limitations describe providing/determination of model using max/min values and determination based on comparison.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is observation, analysis and determination.
Claim 5.
wherein the application rate model describes a negative correlation of the nitrogen content and a phytotoxic effect of the glutamine synthetase inhibitor.
This limitation describes analysis and determination of model.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is observation, analysis and determination.
Claim 6.
wherein the nitrogen content data is derived by an analysis of satellite image data of the at least one section of the agricultural field; and wherein the satellite image data comprises geographical location data and image data of the least one section of the agricultural field.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is observation, analysis and determination/derivation.
Claim 7.
wherein the nitrogen content data is derived by…the nitrogen content in the soil around or at least partially around a plant.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is observation, analysis and determination/derivation.
Claim 8.
providing an application rate map for the agricultural field, wherein the application rate map is determined based on the at least one determined application rate for the section of the agricultural field, wherein the application rate map comprises different sections of the agricultural field with different application rates.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is observation, analysis and determination/derivation.
Claim 9.
wherein the application rate model is further based on environmental data comprising humidity data, light data, and/or temperature data.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is observation, analysis and determination/derivation.
Claim 12.
Use of satellite image data and/or nitrogen content data in a method according to claim 1.
These limitations given its broadest reasonable interpretation in light of the specification is a mental process since this is a concept that can be performed in the human mind and is using data for determination of results.
As described above, these limitations describe Mental Process that can be performed in the human mind, or by a human using a pen and paper (Please see MPEP 2106.04(a)(2), III.), and Mathematical Concepts such that mathematical relationships, and mathematical calculations etc. (Please see MPEP 2106.04(a)(2), I.).
Step 2A – Prong 2 and Step 2B:
This judicial exception is not integrated into a practical application because the additional elements such as computer-implemented, control device, providing unit (11), an obtaining unit, providing unit (13), a determining unit (14), a providing unit (15), non-transitory computer-readable medium and processor that are mere instructions to implement an abstract idea on a general purpose computer (apply it; corresponding structure disclosed in the specification is a general purpose computer implementing the claimed functions characterized as abstract ideas above. See MPEP 2106.05(a)). The claim limitations are implemented on these generic elements such that the following are merely applying the abstract idea on a generic computer: providing, executing, selecting, associating, distinguishing, saving, observing, searching identifying etc.
The claims further recite:
Claim(s) 1:
A computer-implemented method for providing application data for an agricultural field, comprising the following steps.
providing the application rate of the glutamine synthetase inhibitor for the at least one section of the agricultural field (S50).
Claim(s) 7:
the nitrogen content data is derived by a measurement with a near-infrared (NIR) spectrometry sensor of the nitrogen content of a plant, a part of a plant and/or the nitrogen content in the soil around or at least partially around a plant
Claim(s) 10:
application rate model comprises an evaluation algorithm, which is based on the results of a machine-learning algorithm, and wherein as training data for such a machine- learning algorithm, test result data are used showing the dependency of the glutamine synthetase inhibitor efficacy on nitrogen bioavailability of a plant.
Claim(s) 11:
wherein the application rate is provided to a control device of an agricultural application vehicle comprising a smart sprayer, a drone and/or a tractor with an application device.
Claim(s) 13:
Use of test result data showing the dependency of the glutamine synthetase inhibitor efficacy on nitrogen bioavailability of a plant as training data of a machine-learning algorithm.
Claim(s) 14:
A system (10) for providing application rate data for an agricultural field, comprising:
provide the application rate of the glutamine synthetase inhibitor for the at least one section of the agricultural field.
Claim(s) 15:
A non-transitory computer-readable medium having instructions encoded that, when executed by a processor in a system, cause the processor to carry out a method according to claim 1.
The limitations as described above is generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) (in the field of providing application data for an agricultural field). The use of an evaluation algorithm that is based on the results of a machine-learning algorithm is generally linking the use of machine learning techniques in the field of providing application data for an agricultural field. Further, application rate is provided to the control device, but no actual control is performed with that application rate, such that the providing of application rate to a control device is generally linking the use of a judicial exception to the field of providing application data for an agricultural field.
The claims recite the additional elements:
Claim(s) 1 and 14:
obtaining/obtain nitrogen content data of at least a section of the agricultural field (S20);
providing/provide the nitrogen content data of the at least one section of the agricultural field to the application rate model (S30);
Claim(s) 5, 15, 20:
the flagging includes marking, annotating, or setting aside to save in a separate portion of the database.
These additional elements are recited at a high level of generality and as a form of insignificant extra solution activity recognized by court as well-understood, routine, conventional activity. The claims recite these additional elements as described above that are recited at a high level of generality and amounts to mere data gathering which is a form of insignificant extra solution activity (MPEP 2106.05(g)). Further, the use of the claimed invention in the field of providing application data for an agricultural field is simply an attempt to limit the use of the abstract idea to a particular technological environment.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Even when combined with all of the claim limitations as a whole, it is still directed to the abstract idea of mental process. Therefore, the claims are not patent eligible.
Dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Claims 1-15 do not include any further additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the claim(s) 1-15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4:
Claim recites the phrase, the correlation that within a minimum value and a maximum value of an application rate of the glutamine synthetase inhibitor for the section of the agricultural field… is not clear and seems incomplete due to the word “that within”. Applicant’s specification ¶14 describes, the application rate of the glutamine synthetase inhibitor, e.g. glufosinate or a salt thereof, for the section of the agricultural field comprises a minimum value and a maximum value. Further claim 1 describes a relationship and not “the correlation,” thus it isn’t clear what correlation is referred and seems to be a typographical error.
For the examination purpose, this limitation is construed as, wherein the application rate model is based on the relationship between the nitrogen content and the application rate of the glutamine synthetase inhibitor, wherein the application rate of the glutamine synthetase inhibitor within a minimum value and a maximum value for the section of the agricultural field…..
Appropriate correction is required.
Claim Interpretation
It is noted that, at this time, claim 14 is not examined under the claim interpretation 35 USC § 112(f) and therefore the 35 USC §101 rejections are applied. See the 35 USC §101 rejections as applied above.
Claim Rejections - 35 USC § 112
35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 13 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim doesn’t state what claim it depends from. Claim fails to include all the limitations of the claim upon which it depends from. Therefore, it’s unclear whether this claim supposed to be an independent claim, or supposed to depend from method claim 1.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 filling 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Casas et al. (US20200184214A1) [hereinafter Casas] and further in view of Perry et al. (US20190050948A1) [hereinafter PERRY].
Regarding claim 1 (amended):
Casas discloses, A computer-implemented method for providing application data for an agricultural field, comprising the following steps: [¶49: identifying management zones for agricultural fields for the purpose of determining recommendations for the fields. Based on the identified zones, a computer system may automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for seeding rate and hybrids, seed placement, variable rate nitrogen scripts, variable rate crop protection, soil sampling, and irrigation management and diagnosing yield stressors.];
providing an application rate model for a plant, wherein the application rate model is configured to describe a relationship between a nitrogen content and an application rate… [¶70: Machine learning model instructions 138 may include instructions for training one or more ML models for determining soil property for a field based on input imagery, porting input images to the ML models to generate predictions of the soil property based on the input images, output the predictions….
¶93: optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…. optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs…
¶49: automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for…variable rate nitrogen scripts,…
¶58: field data 106 include… (c) soil data (composition, pH, organic matter (OM),… (e) fertilizer data (for example, nutrient type (Nitrogen,…(f) chemical application data (for example, pesticide, herbicide,…application amount),…
¶60: Sensor data may consist of the same type of information as field data 106…
¶63: Agricultural intelligence computer system 130 is programmed…to receive field data 106…and sensor data from remote sensor 112…];
obtaining nitrogen content data of at least a section of the agricultural field (S20); [¶95: field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include…determining nitrogen indices based on field images;.];
providing the nitrogen content data of the at least one section of the agricultural field to the application rate model (S30); [¶139: the predictions of the subfield soil properties may be used to generate maps for the agricultural fields….the maps may be used to generate variable rate nitrogen scripts for the fields,…irrigation management….
¶95: field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include…determining nitrogen indices based on field images;…
¶93: optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…. optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts];
determining the application rate…of the at least one section of the agricultural field using the application rate model (S40); and [¶139: the predictions of the subfield soil properties may be used to generate maps for the agricultural fields….the maps may be used to generate variable rate nitrogen scripts for the fields,…irrigation management….
¶49: automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for…variable rate nitrogen scripts,…];
providing the application rate…for the at least one section of the agricultural field (S50). [¶139: the predictions of the subfield soil properties may be used to generate maps for the agricultural fields….the maps may be used to generate variable rate nitrogen scripts for the fields,…irrigation management….
¶49: automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for…variable rate nitrogen scripts,], but doesn’t explicitly disclose, and
PERRY discloses, ….the application rate model is configured to describe a relationship between a nitrogen content and an application rate of a glutamine synthetase inhibitor (S10);
determining the application rate of the glutamine synthetase inhibitor of the at least one section of the agricultural field using the application rate model (S40); and
providing the application rate of the glutamine synthetase inhibitor for the at least one section of the agricultural field (S50).
[¶55: Sensor data from sensor data sources 114 may be taken at one or more times during a growing season…
¶77: The crop prediction system 125 receives data from…sensor data sources 114,…and performs machine learning operations on the received data to produce one or more crop prediction models…
¶4: The system applies the crop prediction engine to the accessed field information to identify a second set of farming operations that can produce a second expected crop productivity and modifies the first set of farming operations based on the second set of farming operations to produce a modified set of farming operations…
¶5: one or more farming operations to crop productivities by performing one or more machine learning operations. Based on an output of the prediction model, the system selects a set of farming operations…
¶12: the second set of operations includes one or more of:… an herbicide application operation,…
¶14: The selected set of farming operations can identify one or more of:…a rate of application for an agricultural chemical…
Examiner notes that, in broadest reasonable interpretation, claim requires modeling/determining delivery/application rate of glutamine synthetase inhibitor based on nitrogen content such that the model describes relationship between nitrogen content and glutamine synthetase inhibitor (chemical/herbicide), and providing the application rate to any section of the agricultural field where proving can be any of determining/storing/using etc.
Examiner notes that even though PERRY doesn’t explicitly disclose, relationship between glutamine synthetase inhibitor and nitrogen, PERRY implicitly discloses, using a model, based on nitrogen content, determining farming operations including application rate of chemical such as herbicide; and using the farming operation such as application rate of chemical/herbicide determined using the model in an agricultural field.
Using the teachings of PERRY, one of the ordinary skilled in the art will understand that, application rate of herbicide/chemical glutamine synthetase inhibitor such as Glufosinate-ammonium salt that is a broad-spectrum, contact-based, post-emergent herbicide used to control broadleaf and grass weeds in resistant crops can be determine using the model based on nitrogen content].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the model that describes relationship between chemical/herbicide application rate and nitrogen content; determining herbicide/chemical application rate from the model; and using the application rate in the agricultural field in order to maximize crop productivity using the prediction model for the determination of application rate taught by PERRY and use the teachings of PERRY to apply glutamine synthetase inhibitor using the determined application rate with the method taught by Casas as discussed above in order to have reasonable expectation of success such as to maximize crop productivity [PERRY, ¶5: Based on an output of the prediction model, the system selects a set of farming operations that maximize crop productivity].
Regarding claim 2 (amended):
Casas and PERRY disclose, The method according to claim 1, and
Casas further discloses, the nitrogen content data is derived by a measurement of the nitrogen content of a plant or a part of the plant which is within the at least one section of the agricultural field. [¶116: comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement.].
Regarding claim 3 (amended):
Casas and PERRY disclose, The method according to claim 1, and
PERRY further discloses, wherein the nitrogen content data is derived by a measurement of the nitrogen content of a soil or a part of the soil which is within the at least one section of the agricultural field. [¶55: Sensor data from sensor data sources 114 may be taken at one or more times during a growing season…
¶59: Soil composition sensors for measuring components of soil including…nitrogen)];
Regarding claim 6 (amended):
Casas and PERRY disclose, The method according to claim 1, and
Casas further discloses, wherein the nitrogen content data is derived by an analysis of satellite image data of the at least one section of the agricultural field; and
wherein the satellite image data comprises geographical location data and image data of the least one section of the agricultural field. [¶68: Data obtaining instructions 136 may include instructions for receiving various types of image data for an agricultural field, including satellite digital images,…
¶69: Instructions 137 may be configured to, for example, perform atmospheric correction to the satellite images, filter out images, detect image data outliers, reprojection data, perform data interpolation, perform data subsampling…
¶70: Machine learning model instructions 138 may include instructions for training one or more ML models for determining soil property for a field based on input imagery, porting input images to the ML models to generate predictions of the soil property based on the input images, output the predictions…
¶95: determining nitrogen indices based on field images;].
Regarding claim 7 (amended):
Casas and PERRY disclose, The method according to claim 1, and
PERRY further discloses, wherein the nitrogen content data is derived by a measurement with a near-infrared (NIR) spectrometry sensor of the nitrogen content of a plant, a part of a plant and/or the nitrogen content in the soil around or at least partially around a plant.
[Examiner notes that claim requires one of the optional limitations separated by “or,” and thus only one of them is given the patentable weight.
Accordingly PERRY teaches, nitrogen content data is derived by a measurement with a near-infrared (NIR) spectrometry sensor of nitrogen content in the soil around or at least partially around a plant as described below.
¶59: Soil composition sensors for measuring components of soil including…nitrogen);…
¶55: Sensor data from sensor data sources 114 may be taken at one or more times during a growing season…. Examples of sensor data sources 114 can include:…
¶64 Spectrometers, spectrophotometers, spectrographs, and spectral analyzers for measuring properties of light, including reflectance or transmission, over one or more portions of the electromagnetic spectrum;].
Regarding claim 8 (amended):
Casas and PERRY disclose, The method according to claim 1, and
PERRY further discloses, further comprising providing an application rate map for the agricultural field, wherein the application rate map is determined based on the at least one determined application rate for the section of the agricultural field, wherein the application rate map comprises different sections of the agricultural field with different application rates. [¶13: The prediction model can be applied to the accessed field information before planting a crop within the first portion of land…
¶12: the second set of operations includes one or more of:… an herbicide application operation,…
¶14: The selected set of farming operations can identify one or more of:…a portion of the first portion of land on which to plant a crop… a portion of the first portion of land on which to apply a microbial composition…an agricultural chemical to apply, a portion of the first portion of land on which to apply an agricultural chemical, a date to apply an agricultural chemical, a rate of application for an agricultural chemical].
Regarding claim 9 (amended):
Casas and PERRY disclose, The method according to claim 1, and
Casas further discloses, wherein the application rate model is further based on environmental data comprising[¶58: Examples of field data 106 include… (h) weather data (for example,…temperature,…humidity,…sunrise, sunset)…
¶63: Agricultural intelligence computer system 130 is programmed…to receive field data 106].
Regarding claim 11 (amended):
Casas and PERRY disclose, The method according to claim 1, and
Casas further discloses, wherein the application rate is provided to a control device of an agricultural application vehicle comprising a smart sprayer, a drone and/or a tractor with an application device. [¶61: The apparatus 111 may comprise a cab computer 115 that is programmed with a cab application, which may comprise a version or variant of the mobile application for device 104…Cab computer 115 may implement some or all of the operations and functions that are described further herein for the mobile computer device 104….
¶60: agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture.].
Regarding claim 12 (amended):
Casas and PERRY disclose, method according to claim 1, and
Casas further discloses, Use of satellite image data and/or nitrogen content data in a method according to claim 1 [Examiner notes that claim requires one of the optional limitations separated by “or,” and thus only one of them is given the patentable weight.
Accordingly Casas teaches, Use of satellite image data in the method of claim 1 as described below:
¶68: Data obtaining instructions 136 may include instructions for receiving various types of image data for an agricultural field, including satellite digital images, on-ground collected image data, image data received from national and/or research agricultural databases, and the like...
¶51: An ML model may be configured to receive model inputs and to generate model predictions. In some embodiments, the inputs may include processed images derived from satellite remote sensing data that may include data collected for different optical bands and spectrums].
Regarding claim 13 (amended):
Casas and PERRY disclose, The method according to claim 1, and
Casas discloses, Use of test result data showing….nitrogen bioavailability of a plant as training data of a machine-learning algorithm. [¶95: field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include…determining nitrogen indices based on field images;….
¶93: optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…. optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…
¶51: An ML model may be configured to receive model inputs and to generate model predictions. In some embodiments, the inputs may include processed images derived from satellite remote sensing data that may include data collected for different optical bands and spectrums…
Examiner notes that, as described in the 35 U.S.C. 112(d) section, the dependency of claim 13 is not clear. Therefore, it is construed that claim 13 depends from the method of claim 1.
Examiner notes that, in broadest reasonable interpretation claim describes, training a machine-learning algorithm using any data including nitrogen bioavailability of a plant], but doesn’t explicitly disclose, and
PERRY discloses, Use of test result data showing the dependency of the glutamine synthetase inhibitor efficacy on nitrogen bioavailability….as training data of a machine learning algorithm. [¶59: Soil composition sensors for measuring components of soil including…nitrogen);…
¶77: The crop prediction system 125 receives data from…sensor data sources 114,…and performs machine learning operations on the received data to produce one or more crop prediction models…
¶5: one or more farming operations to crop productivities by performing one or more machine learning operations. Based on an output of the prediction model, the system selects a set of farming operations…
¶12: the second set of operations includes one or more of:… an herbicide application operation,…
¶14: The selected set of farming operations can identify one or more of:…a rate of application for an agricultural chemical…
¶112: The crop prediction system 125 can comprehensively account for this large quantity of crop production information, and can perform one or more machine learning operations identify a set farming operations and decisions for growers…The result is that the grower is able to efficiently harness the power of complex machine learning processes to identify farming operations that, when performed, can increase or optimize the grower's crop yield, overhead, and profitability.
Examiner notes that, in broadest reasonable interpretation, claim requires use of data for training a machine learning model, where data includes glutamine synthetase inhibitor efficacy on nitrogen bioavailability that efficiency of the inhibitor/herbicide/chemical on available/measured nitrogen.
Examiner notes that even though PERRY doesn’t explicitly disclose, use of training data of machine learning includes glutamine synthetase inhibitor efficacy on nitrogen bioavailability, PERRY implicitly discloses, use of data for machine learning that describes best/efficient chemical/herbicide rate for the measured/available nitrogen content.
Using the teachings of PERRY, one of the ordinary skilled in the art will understand that, using the data related to efficiency of chemical/herbicide application based on the available nitrogen content, where the herbicide/chemical can be glutamine synthetase inhibitor such as Glufosinate-ammonium salt that is a broad-spectrum, contact-based, post-emergent herbicide used to control broadleaf and grass weeds in resistant crops, and one of ordinary skilled in the art can make a choice of herbicide/chemical to apply based on nitrogen content and use these data for the machine learning purpose.].
Regarding claim 14 (amended):
Casas discloses, A system (10) for providing application rate data for an agricultural field, comprising: [¶49: identifying management zones for agricultural fields for the purpose of determining recommendations for the fields. Based on the identified zones, a computer system may automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for seeding rate and hybrids, seed placement, variable rate nitrogen scripts, variable rate crop protection, soil sampling, and irrigation management and diagnosing yield stressors.];
a providing unit (11) to provide an application rate model for a plant, wherein the application rate model is configured to describe a relationship between a nitrogen content and an application rate… [¶70: Machine learning model instructions 138 may include instructions for training one or more ML models for determining soil property for a field based on input imagery, porting input images to the ML models to generate predictions of the soil property based on the input images, output the predictions….
¶93: optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…. optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs…
¶49: automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for…variable rate nitrogen scripts,…
¶58: field data 106 include… (c) soil data (composition, pH, organic matter (OM),… (e) fertilizer data (for example, nutrient type (Nitrogen,…(f) chemical application data (for example, pesticide, herbicide,…application amount),…
¶60: Sensor data may consist of the same type of information as field data 106…
¶63: Agricultural intelligence computer system 130 is programmed…to receive field data 106…and sensor data from remote sensor 112…];
an obtaining unit (12) configured to obtain nitrogen content data of at least a section of the agricultural field; [¶95: field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include…determining nitrogen indices based on field images;.];
a providing unit (13) configured to provide the nitrogen content data of the at least one section of the agricultural field in the application rate model; [¶139: the predictions of the subfield soil properties may be used to generate maps for the agricultural fields….the maps may be used to generate variable rate nitrogen scripts for the fields,…irrigation management….
¶95: field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include…determining nitrogen indices based on field images;…
¶93: optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts…. optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts];
a determining unit (14) configured to determine the application rate… of the at least one section of the agricultural field using the application rate model; and [¶139: the predictions of the subfield soil properties may be used to generate maps for the agricultural fields….the maps may be used to generate variable rate nitrogen scripts for the fields,…irrigation management….
¶49: automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for…variable rate nitrogen scripts,…];
a providing unit (15) configured to provide the application rate… for the at least one section of the agricultural field. [¶139: the predictions of the subfield soil properties may be used to generate maps for the agricultural fields….the maps may be used to generate variable rate nitrogen scripts for the fields,…irrigation management….
¶49: automatically generate one or more agricultural prescriptions for each management zone. The prescription may include scripts for…variable rate nitrogen scripts,], but doesn’t explicitly disclose, and
PERRY discloses, ….the application rate model is configured to describe a relationship between a nitrogen content and an application rate of a glutamine synthetase inhibitor;
determine the application rate of the glutamine synthetase inhibitor of the at least one section of the agricultural field using the application rate model; and
provide the application rate of the glutamine synthetase inhibitor for the at least one section of the agricultural field. [¶55: Sensor data from sensor data sources 114 may be taken at one or more times during a growing season…
¶77: The crop prediction system 125 receives data from…sensor data sources 114,…and performs machine learning operations on the received data to produce one or more crop prediction models…
¶4: The system applies the crop prediction engine to the accessed field information to identify a second set of farming operations that can produce a second expected crop productivity and modifies the first set of farming operations based on the second set of farming operations to produce a modified set of farming operations…
¶5: one or more farming operations to crop productivities by performing one or more machine learning operations. Based on an output of the prediction model, the system selects a set of farming operations…
¶12: the second set of operations includes one or more of:… an herbicide application operation,…
¶14: The selected set of farming operations can identify one or more of:…a rate of application for an agricultural chemical…
Examiner notes that, in broadest reasonable interpretation, claim requires modeling/determining delivery/application rate of glutamine synthetase inhibitor based on nitrogen content such that the model describes relationship between nitrogen content and glutamine synthetase inhibitor (chemical/herbicide), and providing the application rate to any section of the agricultural field where proving can be any of determining/storing/using etc.
Examiner notes that even though PERRY doesn’t explicitly disclose, relationship between glutamine synthetase inhibitor and nitrogen, PERRY implicitly discloses, using a model, based on nitrogen content, determining farming operations including application rate of chemical such as herbicide; and using the farming operation such as application rate of chemical/herbicide determined using the model in an agricultural field.
Using the teachings of PERRY, one of the ordinary skilled in the art will understand that, application rate of herbicide/chemical glutamine synthetase inhibitor such as Glufosinate-ammonium salt that is a broad-spectrum, contact-based, post-emergent herbicide used to control broadleaf and grass weeds in resistant crops can be determine using the model based on nitrogen content].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the model that describes relationship between chemical/herbicide application rate and nitrogen content; determining herbicide/chemical application rate from the model; and using the application rate in the agricultural field in order to maximize crop productivity using the prediction model for the determination of application rate taught by PERRY and use the teachings of PERRY to apply glutamine synthetase inhibitor using the determined application rate with the system taught by Casas as discussed above in order to have reasonable expectation of success such as to maximize crop productivity [PERRY, ¶5: Based on an output of the prediction model, the system selects a set of farming operations that maximize crop productivity].
Regarding claim 15 (amended):
Casas and PERRY disclose, method according to claim 1.
Casas further discloses, A non-transitory computer-readable medium having instructions encoded that, when executed by a processor in a system, cause the processor to carry out a method according to claim 1. [¶126: Computer system 400 also includes….Main memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in non-transitory storage media accessible to processor 404,].
Allowable Subject Matter
Claims 4-5 and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Further, examiner notes that, claims 4-5 and 10 would be allowable if they overcome the 35 U.S.C. 101 rejections set forth in this Office action.
Claim 4 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892 Notice of Reference Cited document.
Zyskowski et al. (US20100306012A1) - System and method for managing and predicting crop performance:
¶13: Aspect 1 improves upon other nitrogen and irrigation management systems by generating an easily-used schedule for the application of water and nitrogen that takes into account both the current status of the crop and predictions for future crop requirements. Aspect 2 improves upon current methods of analysing and monitoring crop yields because the invention quantifies the effects of interacting weather, soil and management factors to predict yields and identify factors that may be reducing yield. Aspect 3 provides a means of analysis and monitoring that is otherwise not available.
Xu et al. (US20200320647A1) - Generating digital models of relative yield of a crop based on nitrate values in the soil:
¶39: agricultural intelligence computing system is programmed or configured to receive, over a network, crop yield data for a plurality of fields with corresponding nitrate measurements during a portion of the crop's development. The agricultural intelligence computing system identifies a maximum yield for each location of a plurality of locations and converts the crop yield data into relative crop yields using the maximum yield for each location. The system then models the relative yield of the crop as a function of nitrate in the soil. When the system receives, over a network, a nitrate measurement for a particular field, the system computes a relative crop yield for the particular field using the model of relative yield.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED SHAFAYET whose telephone number is (571)272-8239. The examiner can normally be reached M-F 8:30 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116