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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 has been entered.
Status of Claims
This communication is a Non-Final Office Action in response to Applicant’s RCE for application number 18/202,250 received on 05/15/2026.
In accordance with Applicant’s amendment, claims 1-6, 8-11, 13-18, and 20-24 are amended, currently pending and have been examined.
Priority
Applicants claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of Applicant’s amendments, the §112(f) claim interpretations previously applied to the claims, are withdrawn.
Upon review of Applicant’s amendments, the §112(b) rejections previously applied to claims 4 and 16, are withdrawn.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 13): “As such, even though some features of Claim 1 could be performed in the human mind (which they are not), Claim 1 is still not directed to a mental process. As such, the pending claims, as a whole, are directed to a technical solution and NOT a mental process.”. In response, Examiner respectfully disagrees and notes that regarding the limitations for “identifying multiple seeds for a grower, the multiple seeds suitable to be selected by the grower for planting in one or more growing spaces associated with the grower; accessing data, the data including seed data representative of each of the multiple seeds; identifying candidate seeds from the multiple seeds for the grower; outputting the identified candidate seeds to the grower or a user associated with the grower; receiving a selection by the grower or the user associated with the grower of at least one seed from the identified candidate seeds; generating executable planting instructions specific to the selected at least one seed; wherein the planting instructions include at least one planting operation particular to a manner of planting the selected at least one seed”, but for the additional elements recited in the claims, these limitations could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper. For example, one of ordinary skill in the art would be able to reasonably perform “generating executable planting instructions specific to the selected at least one seed“ with the help of pen and paper. Therefore, the claims recite abstract ideas that fall under “Mental Processes”. Examiner further reminds Applicant that the additional elements of the claim limitations are analyzed in Step 2A, Prong 2 and Step 2B, and are not part of the analysis under Step 1, or Step 2A, Prong 1. See 101 rejections below for further details.
Applicant argues (Remarks at pg. 14): “The pending claims integrate the alleged idea into a practical application”. In response, Examiner respectfully disagrees and notes that the additional elements recited in the claim limitations fail to integrate the abstract idea into a practical application because they amount to using generic computing components as a tool, as well as instructions to perform the abstract idea in a computer (used as a tool) (See MPEP 2106.05(f)), and insignificant extra solution activity (e.g., mere data gathering and insignificant application) (See MPEP 2106.05(g)), none of which integrate the abstract idea into a practical application. See 101 rejections below for further details.
Applicant argues (Remarks at pg. 14): “Additionally, the subject matter eligibility examples published by the Office further support this conclusion, that the pending claims integrate the alleged idea into a practical application. See MPEP § 2106.04(d); October 2019 Update: Subject Matter Eligibility (pp. 37- 38) (example of a monitoring component that analyzes data and then automatically sends a control signal to a feed dispenser to dispense a therapeutically effective amount of supplemental salt and minerals-precisely analogous to the present claims' generation and transmission of planting instructions that cause physical planting apparatus to operate).”. In response, Examiner respectfully disagrees and notes that the claim limitations fail to integrate the abstract idea into a practical application for the reasons listed above, as well as in the 101 rejections below. Furthermore, per Applicant’s own disclosure, Applicant’s claims are directed to “identifying candidate seeds for a growing space” (per Claims 1/13/22). Therefore, the steps to cause “the agricultural planter to plant the selected at least one seed in the one or more growing spaces associated with the grower” amount to insignificant extra-solution activity (insignificant application), which does not integrate the abstract idea into a practical application. Furthermore, the present claims do not provide improvement analogous to the improvements in Classen Immunotherapies, Inc. v. Biogen IDEC, specifically because the present claims do not improve the computer where the method is implemented. The present claims are directed to identifying candidate seeds for a growing space, which is not an improvement to the computer itself. Rather, this is an improvement to the abstract idea associated with “Mental Processes”. See 101 rejections below for further details.
Applicant argues (Remarks at pg. 15): “Even assuming, arguendo, that the pending claims are directed to an abstract idea, they surely recite something significantly more than the abstract idea itself under Step 2B.
The amended claims include meaningful additional elements (as noted above) that amount to significantly more, particularly the generation and transmission of planting instructions that are specific to the selected seed, and that then cause the agricultural planting apparatus to operate in a particular manner, to plant the selected seed in a manner particular to the seeds themselves. This is not insignificant extra-solution activity or mere post-solution application. It is a concrete, machine-controlling step that is specific to the identified candidate seeds (from application of the grower-specific model) and that improves the technical field of precision agriculture by translating the grower-specific model's output (trained on planted/unplanted historical selections independent of performance) into real-world control of planting hardware (see Applicant's specification at 1 [0007], [0068], [0087], [0093]).
To be sure, the ordered combination of (1) the grower/region-specific model trained on historical selected and unselected seed data and (2) the direct control of physical planting apparatus via generated and transmitted instructions provides an inventive concept that is not well-understood, routine, or conventional. This solves the recognized problem identified in the specification where growers replace higher-performing seeds and retain lower-performing ones for unidentified reasons (see Applicant's specification at [ 0020]-[0021]). This is analogous to Diamond v. Diehr, 450 U.S. 175 (1981), in which the Supreme Court held patent-eligible claims that used a computer to control a physical industrial process (rubber curing) in a novel way, even though the underlying mathematical calculations were abstract.”. In response, Examiner respectfully disagrees and notes that the claim limitations fail to add significantly more because the additional elements recited in the claim limitations amount to using generic computing components as a tool, as well as instructions to perform the abstract idea in a computer (used as a tool) (See MPEP 2106.05(f)), and insignificant extra solution activity (e.g., mere data gathering and insignificant application) (See MPEP 2106.05(g)), none of add significantly more to the claims. See 101 section below for details.
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the claims are moot based on the new grounds of rejections set forth in the instant office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-11, 13-18, and 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claim(s) 1-6, and 8-11 is/are directed to a method (i.e., Process), claim(s) 13-18, and 20-21 is/are directed to a non-transitory computer-readable storage medium (i.e., Manufacture), and claim(s) 22-24 is/are directed to a system (i.e., Machine). Therefore, all claims are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claims 1, 13, and 22 recite a method, a system, and a computer-readable storage medium for processing data to conduct an attribution analysis. As drafted, the limitations recited by the independent claims fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claim 1recites a method for use in identifying candidate seeds with the following abstract limitations: “identifying multiple seeds for a grower, the multiple seeds suitable to be selected by the grower for planting in one or more growing spaces associated with the grower; accessing data, the data including seed data representative of each of the multiple seeds; identifying candidate seeds from the multiple seeds for the grower; outputting the identified candidate seeds to the grower or a user associated with the grower; receiving a selection by the grower or the user associated with the grower of at least one seed from the identified candidate seeds; generating executable planting instructions specific to the selected at least one seed; wherein the planting instructions include at least one planting operation particular to a manner of planting the selected at least one seed”. But for the additional elements recited in claim 1, the limitations of claim 1 could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper.
Independent claims 13 and 22 recite a non-transitory computer-readable storage medium including executable instructions and a system comprising at least one computing device for use in identifying candidate seeds for a growing space with the following abstract limitations: “identify multiple seeds for a grower, the multiple seeds suitable to be selected by the grower for planting in one or more growing spaces associated with the grower; access data, the data including seed data representative of each of the multiple seeds; identify candidate seeds from the multiple seeds for the grower; output the identified candidate seeds to the grower or a user associated with the grower; based on a selection of one(s) of the identified candidate seeds from the grower or the user associated with the grower, generate executable planting instructions specific to the selected one(s) of the identified candidate seeds”. But for the additional elements recited in claims 13/22, the limitations of claim 1 could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper.
The dependent claims (2-6, 8-11, 14-18, 20-21, and 23-24) further narrow the abstract idea and do not introduce further additional elements for consideration.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely by a computing device, and from a data server from claims 1/13/22, and a non-transitory computer-readable storage medium including executable instructions and a processor from claim 13, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (generic computing environment).
With respect to the limitations based on a model specific to the grower, wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower from claims 1/13/22, these limitations fail to integrate the abstract idea into a practical application because they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitations and transmitting the planting instructions to an application controller of an agricultural planter from claims 1/13/22, these limitations do not integrate the judicial exception into a practical application because they add insignificant extra-solution activity to the judicial exception. These limitations merely recite receiving and/or sending data over a network, which is extra-solution activity. See MPEP 2106.05(g).
With respect to the limitations wherein the application controller executes the planting instructions and causes the agricultural planter to plant the selected at least one seed in the one or more growing spaces associated with the grower from claims 1/13/22, these limitations do not integrate the judicial exception into a practical application because they add insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).
The dependent claims (2-6, 8-11, 14-18, 20-21, and 23-24) further narrow the abstract idea and do not introduce further additional elements for consideration.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely by a computing device, and from a data server from claims 1/13/22, and a non-transitory computer-readable storage medium including executable instructions and a processor from claim 13, these additional element(s) has/have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations based on a model specific to the grower, wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower from claims 1/13/22, these limitations fail to add significantly more because they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitations and transmitting the planting instructions to an application controller of an agricultural planter from claims 1/13/22, these limitations fail to add significantly more because they add insignificant extra-solution activity to the judicial exception. These limitations merely recite receiving and/or sending data over a network (e.g., mere data gathering), which is extra-solution activity. See MPEP 2106.05(g). Additionally, the mere data gathering extra-solution activity has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
With respect to the limitations wherein the application controller executes the planting instructions and causes the agricultural planter to plant the selected at least one seed in the one or more growing spaces associated with the grower from claims 1/13/22, these limitations fail to add significantly more because they add insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g) (Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55).
The dependent claims (2-6, 8-11, 14-18, 20-21, and 23-24) further narrow the abstract idea and do not introduce further additional elements for consideration.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Accordingly, claims 1-6, 8-11, 13-18, and 20-24 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6, 9-11, 13, 17, 18, and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Reich et al. (US 20200005166 A1), in view of Berg et al. (US 20220392213 A1).
Regarding claims 1/13/22: Reich teaches a computer-implemented method ([0057] a computer-implemented method), a non-transitory computer-readable storage media ([0145] Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400), and a system ([0145] Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400) for use in identifying seeds with limitations for:
identifying, by a computing device, multiple seeds for a grower, the multiple seeds suitable to be selected by the grower for planting in one or more growing spaces associated with the grower; ([0059] A computer system and a computer-implemented method that are disclosed herein for generating a set of target success yield group of hybrid seeds that have a high probability of a successful yield on one or more target fields.);
accessing, by the computing device, data from a data server, the data including seed data representative of each of the multiple seeds; ([0057] receiving, over a digital data communication network at a server computer system, grower datasets specifying agricultural fields of growers and inventories of hybrid products or seed products of the growers);
identifying, by the computing device, candidate seeds from the multiple seeds for the grower ([0191] At step 1015, the hybrid seed filtering instructions 182 provide instruction to select a subset of one or more hybrid seeds from the candidate set of hybrid seeds that have a probability of success value greater than or equal to a target probability filtering threshold.);
based on a model specific to the grower ([0064] Hybrid seed filtering instructions within the server computer system are configured to select a subset of the hybrid seeds that have probability of success values greater than a target probability filtering threshold. The server computer system includes hybrid seed normalization instructions configured to generate representative yield values for hybrid seeds in the subset of the one or more hybrid seeds based on the historical agricultural data. Examiner notes that of ordinary skill in the art would reasonably interpret the historical agricultural data disclosed in Reich to be specific to the grower using the claimed invention, as a result, the model created and its hyperparameters will be specific to the grower.);
outputting, by the computing device, the identified candidate seeds to the grower or a user associated with the grower ([Fig. 22]; [0033] FIG. 22 illustrates an example graphical screen display for displaying output recommendations.);
receiving a selection by the grower or the user associated with the grower of at least one seed from the identified candidate seeds; ([Fig. 6] Planting 1-4, Apply; [0084] FIG. 6 depicts an example embodiment of a spreadsheet view for data entry. Using the display depicted in FIG. 6, a user can create and edit information for one or more fields. The data manager may include spreadsheets for inputting information with respect to Nitrogen, Planting, Practices, and Soil as depicted in FIG. 6. To edit a particular entry, a user computer may select the particular entry in the spreadsheet and update the values. For example, FIG. 6 depicts an in-progress update to a target yield value for the second field. Additionally, a user computer may select one or more fields in order to apply one or more programs. In response to receiving a selection of a program for a particular field, the data manager may automatically complete the entries for the particular field based on the selected program. As with the timeline view, the data manager may update the entries for each field associated with a particular program in response to receiving an update to the program. Additionally, the data manager may remove the correspondence of the selected program to the field in response to receiving an edit to one of the entries for the field.);
generating executable planting instructions specific to the selected at least one seed, wherein the planting instructions include at least one planting operation particular to a manner of planting the selected at least one seed; ([0098] script generation instructions 205 are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts. The interface enables growers to create scripts for field implements, such as nutrient applications, planting, and irrigation. For example, a planting script interface may comprise tools for identifying a type of seed for planting. Upon receiving a selection of the seed type, mobile computer application 200 may display one or more fields broken into management zones, such as the field map data layers created as part of digital map book instructions 206. In one embodiment, the management zones comprise soil zones along with a panel identifying each soil zone and a soil name, texture, drainage for each zone, or other field data. Mobile computer application 200 may also display tools for editing or creating such, such as graphical tools for drawing management zones, such as soil zones, over a map of one or more fields. Planting procedures may be applied to all management zones or different planting procedures may be applied to different subsets of management zones.);
and transmitting the planting instructions to an application controller of an agricultural planter, ([0098] When a script is created, mobile computer application 200 may make the script available for download in a format readable by an application controller, such as an archived or compressed format. Additionally, and/or alternatively, a script may be sent directly to cab computer 115 from mobile computer application 200 and/or uploaded to one or more data servers and stored for further use.).
Reich doesn’t explicitly teach:
wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower;
wherein the application controller executes the planting instructions and causes the agricultural planter to plant the selected at least one seed in the one or more growing spaces associated with the grower.
Berg teaches:
wherein the model is trained on historical selected and unselected ones of the candidate seeds by the grower, ([0003] Growers and trusted advisors struggle to gain an understanding of the growing behavior of soybeans in agricultural fields. Conventionally used soybean characteristics, such as tall, bushy, etc., are subjective and do not lend themselves to analysis when trying to understand which soybean varieties will grow well in which fields and under which growing conditions. Thus, growers and trusted advisors are often unsure which soybean variety to plant, a consideration only complicated by the variability among different agricultural fields.; [0006] computing system for training a machine learning model to characterize soybean plants includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: (i) access an initial machine data set corresponding to an agricultural field; (ii) label the initial machine data set with one or more known variety profile index values to generate a labeled machine data set; (iii) process the labeled machine data set with a machine-learned model to generate one or more predicted variety profile index values; and (iv) modify one or more parameters of the machine-learned model based at least in part on one or more differences between the known variety profile index values and the predicted variety profile index values.; [0064] Multiple different types of artificial neural networks may be employed, including without limitation, recurrent neural networks, convolutional neural networks, and deep learning neural networks. Data sets used to train the artificial neural network(s) may be divided into training, validation, and testing subsets; these subsets may be encoded in an N-dimensional tensor, array, matrix, or other suitable data structures. Training may be performed by iteratively training the network using labeled training samples (e.g., training samples labeled using one or more observed/measured VPI values). Training of the artificial neural network may produce byproduct weights, or parameters which may be initialized to random values. The weights may be modified as the network is iteratively trained, by using one of several gradient descent algorithms, to reduce loss and to cause the values output by the network to converge to expected, or “learned”, values.; [0063] Returning to FIG. 3, the VPI determining module 300 may train the ML models 302 by implementing a comparator 310. When the VPI determining module 300 is training the ML models 302, the data transformer 308 may generate input data vectors 306 using the machine data 305 that include known VPI values 312.; [0064] Training may be performed by iteratively training the network using labeled training samples (e.g., training samples labeled using one or more observed/measured VPI values).; [0065] Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs. For example, a deep learning artificial neural network may be trained using historical machine data to generalize about previously unseen machine data.;; [0091] the trusted advisor, for example, may recommend planting of a variety, such as Variety 1, that consistently performs at or above a baseline VPI of 0.4.; See also [Fig. 6B] Chart 604, [Fig. 6E] [0079, 0095]. Examiner notes that one of ordinary skill in the art would reasonably consider accessing known VPI values corresponding to the agricultural field as equivalent to accessing data related to seeds available for planting. This is further supported by chart 604, where variety 1 is selected for planting based on VPI. Therefore, the ML model disclosed by Berg, is trained with data containing all available seeds, to select specific seeds for planting based on their VPI value.)
independent of historical performance of the candidate seeds in the one or more growing spaces associated with the grower; ([Abstract] A computing system for predicting a variety profile index includes a processor; and a non-transitory, computer-readable media including a trained machine-learned model; and instructions that, when executed by the one or more processors, cause the computing system to process a second machine data set to generate one or more predicted variety profile index values; and provide the one or more predicted variety profile index values.; [0008] a machine-learned model trained using an initial machine data set corresponding to a first agricultural field, the initial machine data set labeled with one or more known variety profile index values; [0025] A disclosed example objective characterization of the bushiness of a soybean plant is a variety profile index (VPI) value. A VPI value for a soybean plant may be computed as a ratio of branch bean weight and stem bean weight, where branch bean weight is the total weight of the beans associated with the branches of a soybean plant, and stem bean weight is the total weight of the beans associated with the stem of the soybean plant.; [0045] the control module 120 may execute an agricultural prescription that specifies, for a given agricultural field, a varying application rate of a chemical (e.g., a fertilizer, an herbicide, a pesticide, etc.) or a seed to apply at various points along the path based on the clay characteristics of the field.; [0085] For example, the method 500 may include generating an agricultural prescription for the agricultural field, including at least one treatment based on the VPI values (block 506). An example prescription includes variety selection, seeding rate, field planting priority, in season management including fertility (e.g., macro or micronutrients spread or foliar sprayed), crop protection (e.g., fungicide, insecticide), biologicals, etc.; [Fig. 4]; See also [0003, 0006-0007]);
wherein the application controller executes the planting instructions and causes the agricultural planter to plant the selected at least one seed in the one or more growing spaces associated with the grower. ([0069] The prescription module 156 may include generating one or more agricultural prescriptions, or scripts. The agricultural prescriptions may include computer-executable instructions for causing an implement (e.g., the implement 104) to perform one or more tasks (e.g., instruct a planter to switch from product A to product B). In some embodiments, the prescription may include instructions for performing the tasks in response to a clay type at a location within a given field. For example, the implement control module 120 may analyze a field map layer received from the topographic module 152 and a clay map layer. The implement control module 120 may execute the prescription. The prescription may include instructions causing the implement 104 to perform the task in a predetermined way (e.g., plant seeds at a specific rate) when 1) the location of the implement 104 coincides with a minimum VPI value; and 2) the location of the implement 104 coincides with a particular field, as determined by reference to the field map layer.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Reich with Berg’s feature(s) listed above. One would’ve been motivated to do so in order to perform the generated agricultural prescription by, for example, transmitting the prescription in the form of an electronic prescription file to the client computing device 102 for execution in the implement control module 120 (block 508). The agricultural prescription may include sets of instructions for automatically applying a treatment in portions of the field that correspond with certain VPI values (Berg; [0086]) and generate prescriptions executable by a client device (Berg; [0069]). By incorporating the teachings of Berg, one would’ve been able to train the model and receive instructions that cause a planter to plant a seed in a growing space.
Regarding claim 6: Reich further teaches:
further comprising receiving an input from the grower or the user associated with the grower prior to identifying the multiple seeds, the input indicative of the grower and a type of seed ([Fig. 17] Data Inputs 1702, Grower Data (seed order, equipment types, and operations management).; [0242] teaches: data inputs 1702 may include data obtained from client computers used by growers, such as data collected via client computers 104 (FIG. 1) and/or the FIELD VIEW software that has been previously described; such data may specify field locations, size, density and harvest metrics. Data inputs 1702 also may include grower data such as seed orders, equipment types and operations management parameters. Unlike prior approaches, embodiments use grower-specific inputs that assist in tailoring field assignments to particular growers. Data inputs 1702 may also include programmed models to calculate placement POS values and assignment POS values.);
wherein identifying the multiple seeds includes identifying the multiple seeds for the grower based on the type of seed indicated in the input ([Fig. 17] Recommendation 1706.; [0245] Output recommendations 1706 may be presented in a graphical user interface 1708 showing hybrid-to-field assignments, and/or in a hybrid farm placement data table 1710.; [0033] FIG. 22 illustrates an example graphical screen display for displaying output recommendations.; [0239] teaches: The table 2202 shows recommended assignments of hybrids to fields. In an embodiment, each row of the table represents a discrete recommended assignment, and columns of the table specify a farm, field, number of acres, product, number of bags and trial type for each assignment. Examiner’s Note: Based on description from the abstract – automatically assigning hybrid products or seed products to agricultural fields – Examiner interprets “product” as being equivalent to a seed.).
Regarding claims 9/21: Reich further teaches:
identify/identifying seeds planted by the grower in year Y as the historical selections of the candidate seeds by the grower ([0069] Examples of field data 106 include: (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population).; [0158] At step 705, the agricultural intelligence computer system 130 receives agricultural data records from one or more fields for multiple different hybrid seeds. In an embodiment, the agricultural data records may include crop seed data for one or more hybrid seeds. Crop seed data can include historical agricultural data related to the planting, growing, and harvesting of specific hybrid seeds on one or more fields. Examples of crop seed data may include, but are not limited to, historical yield values, harvest time information, and relative maturity of a hybrid seed, and any other observation data about the plant life cycle. For example, the agricultural data records may include hybrid seed data for two hundred (or more) different types of available corn hybrids. The crop seed data associated with each of the corn hybrids would include historical yield values associated with observed harvests, harvest time information relative to planting, and observed relative maturity for each of the corn hybrids on each of the observed fields. For instance, corn hybrid-001 may have agricultural data records that include historical yield data collected from twenty (or more) different fields over the past ten (or more) years.; [0240] data from research growing fields for seeds or hybrids with similar relative maturity over a period of years is obtained. In one example, yield data from Illinois for 2014-2017 with RM ranging from 105 to 110 was used. The data is grouped in cycles; for example, data up to 2014 is used for predictions for 2015, data up to 2015 is used for predictions up to 2016, etc.);
access/accessing data for the historical selections of the candidate seeds by the grower from the data server ([0057] receiving, over a digital data communication network at a server computer system, grower datasets specifying agricultural fields of growers and inventories of hybrid products or seed products of the growers.; [0060] The server computer system includes hybrid seed normalization instructions configured to generate a dataset of hybrid seed properties that describe a representative yield value and an environmental classification for each hybrid seed from the one or more agricultural data records.);
identify/identifying multiple unplanted seeds for the grower, based on the accessed data for the planted seeds ([0161] In an embodiment, specific field data within the agricultural data records may also include crop rotation data. For example, some historical observations have shown that a “rotation effect” of rotating between different crops on a field may increase crop yield by 5 to 15% over planting the same crop year over year. As a result, crop rotation data within the agricultural data records may be used to help determine a more accurate yield estimation. Examiner notes that one of ordinary skill in the art would reasonably consider crop rotation data refers to planting cycles where different crops are planted in the same growing area. One of ordinary skill in the art would reasonably interpret crop rotation data as including both planted and unplanted seeds from the grower.);
access/accessing data for the identified unplanted seeds from the data server ([0057] receiving, over a digital data communication network at a server computer system, grower datasets specifying agricultural fields of growers and inventories of hybrid products or seed products of the growers.; [0060] The server computer system includes hybrid seed normalization instructions configured to generate a dataset of hybrid seed properties that describe a representative yield value and an environmental classification for each hybrid seed from the one or more agricultural data records. Examiner notes that one of ordinary skill in the art would reasonably interpret the inventories of multiple growers as being inclusive of planted and unplanted seeds from the perspective of a single grower.);
join/joining the accessed data for the planted and unplanted seeds into a training data set for the grower ([Fig. 1] Model Data Field Data Repository 160.; [0060] The server computer system includes hybrid seed normalization instructions configured to generate a dataset of hybrid seed properties that describe a representative yield value and an environmental classification for each hybrid seed from the one or more agricultural data records.);
wherein training the model includes training the model based on at least a portion of the training data set ([0240] teaches: A machine learning model is trained with the following features extracted from the data: previous two years' yield for the pairs as defined, from a specified data source; product POS difference; product RM difference.).
Regarding claim 10: Reich further teaches:
wherein the accessed data for the planted and unplanted seeds includes, for each seed, one or more of: commercial name, brand, product identifier, ratings, trait stack(s), product category, relative maturity, stalk strength, wilt, green snap, leaf blight, dry down, harvest appearance, emergence, root strength, test weight, seeding growth, leaf spot, stalk rot, and/or drought tolerance ([0069] Examples of field data 106 include: (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population).).
Regarding claim 11: Reich further teaches:
further comprising training the model, based on historical data specific to the at least one other grower in a region of said grower, the historical data indicative the historical selections of the candidate seeds by the at least one other grower in a region of said grower ([0237] FIG. 16 illustrates how the present techniques combine a predictive model and an operations research model to result in accurate hybrid field assignments. In an embodiment, output data from the predictive model that has just been described is fed to an operations research model to result in a hybrid field assignment. The example of FIG. 16 involves three (3) products 1602 and three (3) fields 1604 as well as various assumptions to support illustrating a clear example. POS values for various permutations of products A, B, C are assumed to be 0.8, 0.6 and 0.5, as seen for products 1602. Three grower fields 1604 denoted 1, 2 and 3 are presumed to have yields at the 35th, 60th, and 75th percentile as compared to regional or state-wide mean yield. Examiner notes that one of ordinary skill in the art would reasonably interpret the three grower fields to come from at least one grower, and the various permutations of products A, B, and C to be part of the historical selections from the grower.).
Regarding claim 17: Reich further teaches:
wherein the data representative of the multiple seeds includes, for each seed, one or more of: commercial name, brand, product identifier, ratings, trait stack(s), product category, relative maturity, stalk strength, wilt, green snap, leaf blight, dry down, harvest appearance, emergence, root strength, test weight, seeding growth, leaf spot, stalk rot, and/or drought tolerance ([0069] Examples of field data 106 include: (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population). Examiner notes that one of ordinary skill in the art would reasonably interpret crop type to being equivalent to product category.).
Regarding claim 18: Reich further teaches:
wherein the executable instructions, when executed by the at least one processor, cause the at least one processor to receive an input from the grower or the user associated with the grower, the input indicative of the grower and a type of seed ([Fig. 17] Data Inputs 1702, Grower Data (seed order, equipment types, and operations management).; [0242] data inputs 1702 may include data obtained from client computers used by growers, such as data collected via client computers 104 (FIG. 1) and/or the FIELD VIEW software that has been previously described; such data may specify field locations, size, density and harvest metrics. Data inputs 1702 also may include grower data such as seed orders, equipment types and operations management parameters. Unlike prior approaches, embodiments use grower-specific inputs that assist in tailoring field assignments to particular growers. Data inputs 1702 may also include programmed models to calculate placement POS values and assignment POS values.);
identify the multiple seeds for the grower based on the type of seed indicated in the input ([Fig. 17] Recommendation 1706.; [0245] Output recommendations 1706 may be presented in a graphical user interface 1708 showing hybrid-to-field assignments, and/or in a hybrid farm placement data table 1710.; [0033] FIG. 22 illustrates an example graphical screen display for displaying output recommendations.; [0239] The table 2202 shows recommended assignments of hybrids to fields. In an embodiment, each row of the table represents a discrete recommended assignment, and columns of the table specify a farm, field, number of acres, product, number of bags and trial type for each assignment. Examiner’s Note: Based on description from the abstract – automatically assigning hybrid products or seed products to agricultural fields – Examiner interprets “product” as being equivalent to a seed.).
Regarding Claim 23: Reich further teaches:
further comprising the one or more growing spaces in which the one(s) of the identified candidate seeds is(are) planted ([0059] The server computer system then receives second geo-locations data for one or more target fields where hybrid seeds are to be planted.).
Regarding Claim 24: Reich further teaches:
further comprising the agricultural planter ([0071] An agricultural apparatus 111 may have one or more remote sensors 112 fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural intelligence computer system 130 and are programmed or configured to send sensor data to agricultural intelligence computer system 130. Examples of 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.).
Claims 5, 8, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Reich et al. (US 20200005166 A1), in view of Berg et al. (US 20220392213 A1), as applied to claims 1/13/22 above, in further view of Bull et al. (US 20200005401 A1).
Regarding Claim 5: Reich further teaches:
wherein the data representative of the multiple seeds includes, for each seed, one or more of: commercial name, brand, product identifier, ratings, trait stack(s), product category, relative maturity, stalk strength, wilt, green snap, leaf blight, dry down, harvest appearance, emergence, root strength, test weight, seeding growth, leaf spot, stalk rot, and/or drought tolerance: [0069] Examples of field data 106 include: (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population).
However, Reich doesn’t explicitly teach:
and wherein the model is further trained on historical selected ones of the candidate seeds by at least one other grower in a region of said grower
Bull teaches:
and wherein the model is further trained on historical selected ones of the candidate seeds by at least one other grower in a region of said grower ([0097] teaches: the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare.; [0122] In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. Additionally, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvesting recommendations and other crop management recommendations.; [0124] FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. FIG. 3 may serve as an algorithm or instructions for programming the functional elements of the agricultural intelligence computer system 130 to perform the operations; [0163] These sets of fields where agricultural data records are collected may also be the same fields designated as target fields for planting newly selected crops. In yet other embodiments, sets of fields owned and operated by a grower may provide agricultural data records used by other growers; [0228] At step 1215 of FIG. 12, the server computer 108 generates one or more yield ranking scores for the grower's one or more fields using the first set of historical agricultural data.; [0230] FIG. 14 illustrates aspects of integration of assignment permutations into a machine learning algorithm. In the example of FIG. 14, a trained ML classifier 1400 is used to calculate the outcome of an assignment of a product to a field.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Reich with Bull’s additional feature(s) listed above. One would’ve been motivated to do so in order to train the machine learning model and/or to modify the machine learning model to be more specific to the grower's field (Bull; [0258]). By incorporating the teachings of Bull, one would’ve been able to train the ml model using grower specific historical data that includes past selection of seeds for the growing spaces.
Regarding Claim 8: Reich doesn’t teach:
further comprising training the model, based on historical data specific to the grower, the historical data indicative of the historical selections of the candidate seeds by the grower.
Bull teaches:
further comprising training the model, based on historical data specific to the grower, the historical data indicative of the historical selections of the candidate seeds by the grower. ([0097] the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare.; [0228] At step 1215 of FIG. 12, the server computer 108 generates one or more yield ranking scores for the grower's one or more fields using the first set of historical agricultural data.; [0258] The computer system may store a trained machine learning model. The training data may include yield values at similar locations and features of the hybrid seeds used. In an embodiment, yield data from the grower's field is additionally used to train the machine learning model. Examiner’s notes that one of ordinary skill in the art would’ve reasonably interpreted the seeds used in the training data as historical selections of seeds planted in previous seasons.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Reich with Bull’s feature(s) listed above. One would’ve been motivated to do so in order to train the machine learning model and/or to modify the machine learning model to be more specific to the grower's field (Bull; [0258]). By incorporating the teachings of Bull, one would’ve been able to train the ml model using grower specific historical data that includes past selection of seeds for the growing spaces.
Regarding Claims 20: Reich doesn’t teach:
wherein the executable instructions, when executed by the at least one processor, further cause the at least one processor to train the model, based on historical data specific to the grower, the historical data indicative of the historical selections of the candidate seeds by the grower
Bull teaches:
wherein the executable instructions, when executed by the at least one processor, further cause the at least one processor to train the model, based on historical data specific to the grower, the historical data indicative of the historical selections of the candidate seeds by the grower ([0097] the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare.; [0228] At step 1215 of FIG. 12, the server computer 108 generates one or more yield ranking scores for the grower's one or more fields using the first set of historical agricultural data.; [0258] The computer system may store a trained machine learning model. The training data may include yield values at similar locations and features of the hybrid seeds used. In an embodiment, yield data from the grower's field is additionally used to train the machine learning model.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Reich with Bull’s feature(s) listed above. One would’ve been motivated to do so in order to train the machine learning model and/or to modify the machine learning model to be more specific to the grower's field (Bull; [0258]). By incorporating the teachings of Bull, one would’ve been able to train the ml model using grower specific historical data that includes past selection of seeds for the growing spaces.
Accordingly, claims 1, 5, 6, 8-11, 13, 17, 18, and 20-24 are rejected under 35 USC 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Perry et al. (US 20190050948 A1), which discloses a machine learning crop production system.
Bull et al. (WO 2020009963 A1), which discloses techniques to generate a yield range improvement recommendation for each of the one or more fields, wherein the yield improvement recommendation comprises a recommended change in seed population or a recommended change in seed density.
P. Tummala, M. Sobhana and S. Kakumani, "Predicting crop yield with NDVI and Backscatter values using Deep Neural Networks," 2022 International Mobile and Embedded Technology Conference (MECON), Noida, India, 2022, pp. 390-394, which discloses providing accurate yield prediction with a focus on key crops by taking inputs such as crop, season, Normalized Difference Vegetation Index (NDVI), backscatter values, and area, which are then analyzed in the hidden layers using weights that are adjusted throughout training.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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