Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on 02/11/2025. Claims 1-20 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/15/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Perry et al. (WO2019032648A1; hereafter Perry).
Regarding claim 1, Perry discloses a method for cultivating a field, comprising:
generating for each portion of a plurality of portions of the field, a set of values for a plurality of planting parameters before or during planting the portion of the field, the plurality of planting parameters including at least a planting depth, a planting spacing, a type of treatment applied when planting, and an amount of treatment applied when planting, ([0041]; “Databases including inputs from growers or grower client devices 102 describing crops planted (including crop variety, hybrid crop types, and chemical/microbial coatings of crop seed), actions taken during past years or seasons, planting dates, planting depths, row spacing, planting speed, seed application rate (including minimum, average, and maximum rates), locations and identification of portions of land on which the crops were planted (e.g., management zones), and actions taken before, during, and after planting;”) wherein the set of values for the plurality of planting parameters for at least one of the plurality of portions are generated based on input received from a user on an interface ([0041]; “in other embodiments, the information is manually entered (for instance, by a grower, an agronomist, or an entity associated with the crop prediction system (for instance, via a GUI generated by the interface module 130 and displayed via the grower client device 102, the agronomist client device 108, or the like).”);
planting each of the plurality of portions of the field, each portion of the field planted using one or more agricultural vehicles based on a corresponding set of values generated for the plurality of planting parameters ([0042]; “In some embodiments, sensor data sources are one or more sources of data taken from sensors describing past or current measurements associated with crop growth, the environment and/or portions of land.”
Note: One of ordinary skill in the art would recognize that in order to sense past or current measurements within a portion of land, planting in that portion of land must have occurred.);
determining, for each portion of the field, a resulting crop outcome across the portion of the field ([0042]; “The sensor data sources 114 are one or more sources of data taken from sensors describing past or current measurements associated with crop production that can be used by machine learning processes of the crop prediction system 125 to train crop prediction models to apply crop prediction models to predict future crop production, and to identify farming operations that optimize future crop production.”);
generating a training set of data comprising the set of values generated for the plurality of planting parameters and the determined crop outcome for each of the portions of the field ([0041]; “The external databases 112 are one or more sources of data describing past or present actions, events, and characteristics associated with crop production that can be used by machine learning processes of the crop prediction system 125 to train crop prediction models”);
training a machine-learned model using the generated training set of data, the machine- learned model configured to predict a crop outcome for a portion of the field based on a set of values for the plurality of planting parameters used to plant the portion of the field ([0045]; “The crop prediction system 125 receives data from the external databases 112, sensor data sources 114, and image data sources 116, and performs machine learning operations on the received data to produce one or more crop prediction models.”);
for a subsequent iteration, for a target portion of the field, applying the machine-learned model to identify a set of planting parameters for planting the target portion of the field to optimize for a desired crop outcome ([0034]; “The geographic and agricultural data from the grower client device 102 can be used by the crop prediction system 125, for instance to train one or more crop prediction models and/or as an input to previously trained crop prediction models in order to predict crop production and identify a set of farming operations that can optimize crop production.”); and
planting, in the subsequent iteration, the target portion of the field based on the identified set of planting parameters. ([0066]; “The training module 410 stores generated crop prediction models in the model store 415 for subsequent access and application, for instance by the crop prediction module 425.”)
Regarding claim 2, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses generating a recommended set of planting parameters for the plurality of portions of the field ([00102]; “For example, the crop prediction engine 155 applies a Gaussian network machine learning model to the accessed field information in order to iterate through various combinations of farming operations to predict corresponding crop productions.”); and
receiving, from the user on the interface, a selection of planting parameters from the recommended set of planting parameters, the selection corresponding to the plurality of planting parameters with respect to which the set of values are generated for each portion. ([00102]; “Based on an output from the crop prediction engine 155, the crop prediction system 125 selects 915 a set of farming operations to optimize crop productivity for the portion of land and modifies 920 a user interface displayed by a client device associated with the request for predicted crop production information to displayed a crop growth program for the portion of land based on the selected set of farming operations.”)
Regarding claim 3, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses receiving, based on input from the user, safe bounds or factor types associated with one or more of the plurality of planting parameters, wherein the set of values for the plurality of planting parameters are generated based on the safe bounds or factor types received from the user. ([0048]; “Various types and formats of data may be stored in the geographic database 135 for access by the other components of the crop prediction system 125, on which to perform one or more machine learning operations in order to train a crop prediction model, to predict a crop production for a portion of land, and to identify a set of farming operations that optimize crop production.”)
Regarding claim 4, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses prompting the user to confirm the generated set of values for the plurality of planting parameters for each portion of the plurality of portions of the field ([0099]; “The crop prediction system 125 applies 835 the crop prediction engine 155 to the accessed field information and a first set of farming operations (such as a set of farming operations selected by the requesting entity, or by the crop prediction engine itself) to produce a first predicted crop production.”); and
receiving input from the user on the interface confirming or modifying the generated set of values for the plurality of planting parameters for each portion of the plurality of portions of the field. ([00100]; “The crop prediction system 125 then applies 840 the crop prediction engine 155 to the accessed field information and a second set of farming operations (such as a set of farming operations selected by the crop prediction engine) to produce a second predicted crop production.”)
Regarding claim 5, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses the plurality of planting parameters further includes at least one of: a seed rate, a seed type, an amount of irrigation, an environmental condition, a sun exposure, a fertilizer type and/or rate, an herbicide type and/or rate, a pesticide type and/or rate, a soil composition, a land use type, a crop rotation, or a cover crop type. ([0070]; “Operations describing planting, including one or more of: a planting rate operation, a planting depth operation, a planting date range operation, an operation to plant an identified crop variant, an operation to not plant a crop, an operation to plant a different type of crop than an identified type of crop (e.g., an operation to plant an intercrop, an operation to plant a cover crop), and the like;”)
Regarding claim 6, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses each of the plurality of portions of the field is associated with a same crop type being planted within the portion of the field. ([0091]; “FIG. 7 illustrates an example process for using machine learning to predict updated crop production information and identify an updated set of farming operations that further optimize crop production for a portion of land after planting a crop within the portion of land. One or more planted fields 705 (corresponding to fields 605 of FIG. 6) are associated with a grower 610, a crop variant planted on the field or fields, and a set of factors describing the field or fields.”)
Regarding claim 7, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses the desired crop outcome includes at least one of: a desired yield, a desired yield rate, a desired cost to plant, a desired amount of fertilizer used, a desired amount of herbicide used, a desired amount of pesticide used, a desired amount of irrigation used, a desired time to harvest, a desired soil condition, a desired nitrogen efficiency, a desired environmental impact, a desired water quality, a land use, or a financial subsidy. ([0084]; “For example, the crop prediction module 425 can output a numerical value representing a predicted crop yield; a probability distribution ( e.g., a 20% chance of a decrease in crop production and an 80% chance of an increase in productivity); a map overlay representing an area of land including a planting region corresponding to a request showing management zones of application of one or more farming operations and a predicted productivity corresponding to the management zones; a predicted cost/profit ratio per acre; a yield or profitability delta relative to similar or nearby groups of fields; a yield or profitability delta relative to historical practices or productivity of the same planting region; and the like.”)
Regarding claim 8, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses values of one or more of the plurality of planting parameters for the plurality of portions of the field are evenly distributed among a range of values. ([0091]; “In the example shown in FIG. 7, the planted fields 705 are sub-portions of a portion of land 702, and are represented by unique plot indices 705A, 705B, 705C, and 705D. In one instance, each planted field (planted field 705A, planted field 705B, planted field 705C, and planted field 705D) is associated with a unique set of parameters and conditions that may impact the crop production of the field. For example, the planted field 705A may be planted with a first crop variant that is different from a second crop variant planted in the planted field 705B. In the embodiment of FIG. 7, the grower 610 selected the set of operations 620C from the example of FIG. 6, and began implementing the set of operations 620C in the planted field 705A.”)
Regarding claim 9, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses a variation in values of one or more of the plurality of planting parameters for the plurality of portions of the field is distributed according to a Gaussian distribution, a face-centered cubic design, or a Box-Behnken design. ([0065]; “the crop prediction models can perform various machine learning operations when applied, including but not limited to: a generalized linear model, a generalized additive model, nonparametric regression, random forest, spatial regression, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, decision tree learning, artificial neural networks, recurrent neural network, reinforcement learning, linear/non-linear”)
Regarding claim 10, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses sub-optimal values for one or more of the plurality of planting parameters for the plurality of portions of the field are less likely to be selected in the generated set of values. ([0038]; “In one embodiment, the broker client device 104 accesses the crop prediction system 125 via an interface 130 generated by the crop prediction system 125 that allows the user of the broker client device 104 to identify predicted crop production information from one or more growers, to identify sets of farming operations to suggest or provide to the one or more growers in order to optimize crop production”).
Regarding claim 11, Perry discloses all of the limitations of claim 1. Additionally, Perry discloses identifying, for each of a plurality of sub-portions of the target portion, a set of varied planting parameters based on the identified set of planting parameters for planting the target portion of the field ([0088]; “In the example shown in FIG. 6, the unplanted fields are sub-portions of a portion of land 602 and are represented by unique plot indices, such that a field 605A, a field 605B, a field 605C, and a field 605D are each associated with one or more sets of field parameters.”); and
planting, in the subsequent iteration and for each of the plurality of sub-portions of the target portion, the sub-portion with a corresponding set of varied planting parameters. ([0089]; “The crop prediction system 125 iterates through multiple sets of farming operations 620 and identifies the set of farming operations associated with an optimized predicted crop production. In the example of FIG. 6, a first set of farming operations 620A specifies that the crop variety is the corn variant "CORN_ 008", the plant date is 06/05/2018, a pesticide application date is 08/01/2018, and so forth. Likewise, a second set of farming operations 620B specifies that the crop variety is the corn variant "CORN_ 015", the plant date is 06/05/2018, a nitrogen application date is 6/25/2018, and so forth. Finally, a third set of farming operations 620C specifies that the crop variety is the corn variant "CORN_ 11 0", the planting date is 06/01/2018, and so forth.”)
Regarding claim 12, Perry discloses all of the limitations of claim 11. Additionally, Perry discloses the set of varied planting parameters for each of the plurality of sub-portions of the target portion are identified based on an input received from the user on the interface. ([0088]; “The grower 610 accesses the crop prediction system 125 via a grower client device 102 and transmits a request to the crop prediction system including a set of field parameters associated with the field 605A.”).
Claim 13 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 1. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 13 is rejected for the same reasoning.
Claim 14 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 2. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 14 is rejected for the same reasoning.
Claim 15 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 3. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 15 is rejected for the same reasoning.
Claim 16 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 4. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 16 is rejected for the same reasoning.
Claim 17 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 5. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 17 is rejected for the same reasoning.
Claim 18 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 8. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 18 is rejected for the same reasoning.
Claim 19 recites a non-transitory computer readable medium comprising memory with instructions to perform the operations of claim 11. Perry discloses a non-transitory computer readable medium comprising memory with instructions ([00104]; “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”). Therefore claim 19 is rejected for the same reasoning.
Claim 20 recites a vehicle to perform the methods of claim 1. Perry discloses a vehicle ([0010]; “In an embodiment, accessing the field information comprises collecting images of the first portion of land from one or more a satellite, an aircraft, an unmanned aerial vehicle, a land-based vehicle, and a land-based camera system.”). Therefore claim 20 is rejected for the same reasoning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON SUNG EUN LEE whose telephone number is (571)272-5684. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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/B.S.L./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668
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