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 .
DETAILED ACTION
1. This Office Action is in response to the Amendment filed on February 25, 2026, which paper has been placed of record in the file.
2. Claims 1-23 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-22 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 1, which is analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a method for directing seed products to growing spaces. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a method for directing seed products to growing spaces. The claim recites the steps: receiving a request for a planting recommendation…, accessing the seed placement prediction models…, determining a prediction output, based at last on the location data and on weather data…, accessing an optimization model…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…, and planting the multiple seed products at the target growing space, based on the seed planting recommendation output, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps: determining a prediction output, based at last on the location data and on weather data…, and determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter associated with the target growing space…, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
The claim also recites the steps: using the seed placement prediction models and using the optimization model, that falls within the “Mathematical Concepts” grouping of abstract ideas (mathematical relationships, mathematical formulas or equations, mathematical calculations). See MPEP 2106.04(a)(2), subsection III.
Accordingly, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “receiving, by a computer device, a request for a plating recommendation”, “accessing, by the computing device, seed prediction models”, “accessing, by the computing device, an optimization model”, “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output.” The claim also recites that the steps of “receiving a request for a planting recommendation…, accessing the seed placement prediction models…, determining a prediction output, based at last on the location data and on weather data…, accessing an optimization model…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…”, are performed by a computing device.
The additional elements “receiving, by a computer device, a request for a plating recommendation”, “accessing, by the computing device, seed prediction models”, “accessing, by the computing device, an optimization model” are mere data gathering and transmitting, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitation amounts to necessary data gathering and transmitting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, improvements to the computing device, they just merely used as general means for gathering and transmitting data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
The additional elements “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output” 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.
The additional elements “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output”, invoked the agricultural apparatuses merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on an agricultural apparatus without any recitation of details of how to carry out the abstract idea. See MPEP 2106.05(f).
Further, the limitations “receiving a request for a planting recommendation…, accessing the seed placement prediction models…, determining a prediction output, based at last on the location data and on weather data…, accessing an optimization model…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…, and directing planting of the multiple seed products at the target growing space, based on the seed planting recommendation output”, are recited as being performed by the computing device. The computing device is recited at a high level of generality. In limitations “receiving a request for a planting recommendation…, accessing the seed placement prediction models…, accessing an optimization model…”, the computing device is used as a tool to perform the generic computer function of gathering and transmitting data. See MPEP 2106.05(f). In limitations “determining a prediction output, based at last on the location data and on weather data…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…, and directing planting of the multiple seed products at the target growing space, based on the seed planting recommendation output”, the computing device is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the computing device, memory, and software component, that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the computing device, improvement to the farm equipment, or other technology. They just merely used as general means for collecting, displaying data and performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional elements of “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “receiving, by a computer device, a request for a plating recommendation”, “accessing, by the computing device, seed prediction models”, “accessing, by the computing device, an optimization model”, were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data transmitting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “receiving, by a computer device, a request for a plating recommendation”, “accessing, by the computing device, seed prediction models”, “accessing, by the computing device, an optimization model” are recited at a high level of generality. This element amounts to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: 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).
As discussed in Step 2A, Prong Two above, the recitation of the computing device to perform limitations “receiving a request for a planting recommendation…, accessing the seed placement prediction models…, determining a prediction output, based at last on the location data and on weather data…, accessing an optimization model…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claim 15, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 15 directed to a system, is also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Regarding dependent claims 2-14 and 16-23, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2 and 16, the claims recite the additional element training the one or more seed placement prediction models…, is used to generally apply the abstract idea without placing any limits on how the training the model functions. Rather, this limitation only recites the outcome of “to determine the seed planting recommendation output” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). The additional element “training the one or more seed placement prediction models” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “training the one or more seed placement prediction models” limits the identified judicial exceptions “to determine the seed planting recommendation output”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (training the model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3, 4 and 17, the claims recite wherein the set of multiple seed products includes the multiple seed products, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 1. Moreover, the claims recite the additional element wherein inputs for training the one or more seed placement prediction models include…, which are used to generally apply the abstract idea without placing any limits on how the training the model functions. Rather, these limitations only recite the outcome of “to determine the seed planting recommendation output” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 2 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5 and 18, the claims recite additional element wherein inputs for training the one or more seed placement prediction models include…, which are used to generally apply the abstract idea without placing any limits on how the training the model functions. Rather, these limitations only recite the outcome of “to determine the seed planting recommendation output” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 2 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 6, 7 and 19, the claims recite the additional elements training the optimization model, based on historical seed portfolio data…, wherein inputs for training the optimization model include, for each of the multiple growing spaces…, are used to generally apply the abstract idea without placing any limits on how the training the model functions. Rather, these limitations only recite the outcome of “to determine the seed planting recommendation output” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (see claim 2 above) Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 8, the claim recites the additional element wherein the inputs for training the optimization model include, for each of the multiple growing spaces…, is used to generally apply the abstract idea without placing any limits on how the training the model functions. Rather, this limitation only recites the outcome of “to determine the seed planting recommendation output” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). (see claim 2 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 9, the claim recites wherein the grower constraint information includes at least one of a grower seeding rate preference…, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 10 and 20, simply refines the abstract idea by further reciting determining, by the computing device, using the one or more seed placement prediction models, the prediction output includes generating a three-dimensional matrix output of yield predictions…, that fall under the category of Mental process and Mathematical Concepts groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 11, the claim simply refines the abstract idea by further reciting wherein the one or more seed placement prediction models include a multilayer perceptron neural network and/or an XGBoost model., that fall under the category of Mental process and Mathematical Concepts groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 12, the claim simply refines the abstract idea by further reciting wherein the seed planting recommendation output includes a portfolio having more than one of the multiple seed product types available for planting at the target growing space, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 13, the claim recites the additional element seeding the target growing space in response to the seed planting recommendation output, that is invoked the agricultural apparatus merely as tool to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on an agricultural apparatus without any recitation of details of how to carry out the abstract idea. See MPEP 2106.05(f). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 14, the claim recites the additional element receiving, at a communication device of a user associated with the target growing space, the seed planting recommendation output, which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05 (See claim 1 above). Moreover, the claim recites the additional element causing operation of one or more agricultural apparatuses at the target growing space to apply the at least one of the multiple seed products to the target growing space, that is invoked the agricultural apparatus merely as tool to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on an agricultural apparatus without any recitation of details of how to carry out the abstract idea. See MPEP 2106.05(f). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 21, the claim recites the additional elements an agricultural apparatus configured to plant the at least one of the multiple seed products at the target growing space; and wherein the agricultural apparatus is configured to receive the seed planting recommendation output from the at least one computing device and plant the at least one of the multiple seed products at the target growing space, that are invoked the agricultural apparatus merely as tool to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on an agricultural apparatus without any recitation of details of how to carry out the abstract idea. See MPEP 2106.05(f). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 22, the claim recites the additional element a communication device associated with the user, the communication device configured to receive the seed planting recommendation output from the at least one computing device, which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 23, the claim recites the additional elements generating, by the computing device, one or more executable scripts specific to the determined seed planting recommendation output; and transmitting, by the computing device, the one or more executable scripts to an application controller communicatively coupled to the farm equipment; and wherein planting the at least one of the multiple seed products at the targe growing space includes executing, by the application controller, the one or more executable scripts to automatically adjust an operating parameter of the agricultural implement to plant the at least one of the multiple seed products at the target growing space in accordance with the determined seed planting recommendation output, which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-23 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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 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.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (hereinafter Jiang, US 2020/0327603) in view of Sood et al. (hereinafter Sood, US 2020/0134485).
Regarding to claim 1, Jiang discloses a computer-implemented method for directing seed products to growing spaces, the computer-implemented method comprising:
receiving, by a computing device, a request for a planting recommendation related to seeding of a target growing space, the request including seed product data and location data relating to the target growing space, the seed product data including multiple identifiers each associated with a different one of multiple seed products available for planting at the target growing space; (para [0058], a target success yield group of seeds may be generated using a server computer system that is configured to receive, over a digital data communication network, one or more agricultural data records that represent crop seed data describing seed and yield properties of one or more seeds and first field geo-location data for one or more agricultural fields where the one or more seeds were planted);
accessing, by the computing device, one or more seed placement prediction models consistent with the location data (para [0088], The model may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields. Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data);
determining, by the computing device executing, the one or more seed placement prediction models, a prediction output, based, at least, on the location data and on weather data, the prediction output defining a matrix of seed placement data including a predicted yield for each of the multiple seed products [at each of multiple different weather conditions] (para [0176], Machine learning techniques are implemented to determine probability of success scores for the seeds at the geo-locations associated with the target fields. In an embodiment, the normalized yield values and assigned relative maturity values are used as predictor variables for machine learning models. In other embodiments, additional properties such as, crop rotations, tillage, weather observations, soil composition, may also be used as additional predictor variables for the machine learning models. The target variable of the machine learning models is a probabilistic value ranging from 0 to 1, where 0 equals a 0% probability of a successful yield and 1 equals a 100% probability of a successful yield. In other embodiments, the target variable may be a probabilistic value that may be scaled from 0 to 10, 1 to 10, or any other scale of measurement. A successful yield is described as the likelihood that the yield of a specific seed is a certain value above the mean yield for similarly classified seeds. For example, a successful yield may be defined as a yield that is 5 bushels per acre above the mean yield of seeds that have the same assigned relative maturity value; para [0182], support vector machine (SVM) modelling may be implemented as the machine learning technique to determine probability of success scores for each of the seeds for the target fields. Support vector machine modelling is a supervised learning model used to classify whether input using classification and regression analysis. Input values for the support vector machine model are the normalized yield values and the environmental classification relative maturity values for each seed. The output is a set of probability scores for each seed between 0 and 1. In yet another embodiment, gradient boost (GBM) modelling may be implemented as the machine learning technique, where the input values are the normalized yield values and the environmental classification relative maturity values for each seed. Gradient boost is a technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, such as decision trees; para [0187], the presentation layer 134 may display additional seed property data and other agricultural data that may be relevant to the grower. The presentation layer 134 may also sort the seed in the target success yield group based on the probability of success values. For example, the display of seeds may be sorted in descending order of probability of success values such that the grower is able to view the most successful seeds for his target fields first);
accessing, by the computing device, an optimization model consistent with the target growing space (para [0210], the optimization solver may also input target field data describing size, shape, and geo-location of each of the target fields, in order to determine allocation instructions that include placement instructions for each of the allotments of target seeds. For example, if a particular target field is shaped or sized in a particular way, the optimization solver may determine that allotment of one target seed is preferable on the particular field as opposed to planting multiple target seeds on the particular field. The optimization solver is not limited to the CPLEX Optimizer, other embodiments may implement other optimization solvers or other optimization algorithms to determine sets of allocation instructions for the set of target seeds);
determining, by the computing device executing, the optimization model, a seed planting recommendation output, based on at least the matrix of seed placement data defined by the prediction output from the one or more seed placement prediction models and at least one grower constraint parameter associated with the target growing space, the seed planting recommendation output including at least one of the multiple seed products (para [0067], a computer-implemented method comprises receiving, over a digital data communication network at a server computer system, agricultural data records comprising a set of yield properties for a set of seeds grown in a set of environments, wherein the set of yield properties includes yield properties generated by applying genetic relationship data between the seeds. The method further includes receiving, over the digital data communication network, feature data for one or more target fields where seeds are to be planted. The server computer system may then be used to generate seed recommendations for the one or more target fields based on the set of yield properties and the feature data. And, the method may also include causing display, on a display device communicatively coupled to the server computer system, of the seed recommendations; para [0257], the system 130 uses the processed agricultural data records to generate G×E relationships between genetic features of seeds, field features, and yield properties using, for instance, some form of a BLUP model (e.g., an environmental best linear unbiased prediction (eBLUP) model), T-stat, and/or a kernel smoothing using a Gaussian process. The system 130 may also use the processed agricultural data to fill-in data gaps according to block 1206 of FIG. 12, for example. As discussed above in relation to blocks 1206, 1208, and 1210, for instance, the system 130 is configured to use the G×E relationships and/or imputed data to generate predicted yield performance for one or more seeds and one or more specific target fields with particular field conditions or features, and from the predicted yield performance, to generate field-level yield improvement product recommendations); and
planting the at least one of the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output (para [0257], To facilitate implementation of the product recommendations and improve resulting yield, the system 130 is further configured to cause display of the product recommendations on a display device communicatively coupled to the system. A grower may select or confirm a product recommendation and instruct the system 130 to control one or more machines to implement the product recommendation).
Jiang does not disclose, however, Sood discloses:
the prediction output defining a matrix of seed placement data including a predicted yield for each of the multiple seed products at each of multiple different weather conditions (para [0147], model output 712 (FIG. 7A) is further transformed to yield initial model predictions 714 (FIG. 7B) as a set of dates during which harvest is predicted to occur at kernel moisture content of 20% to 25% by volume. The initial model predictions 714 are used to determine which fields to sample for actual kernel moisture content. In-season field measurements 716 are obtained from those fields and may include actual moisture values and crop-stage values indicating the stage of crops for which samples were obtained. In-season field measurements 716 may be received at any stage in a growing season including after harvesting starts. In some cases, in-season field measurements 716 may indicate that the crop has reached growth stage R6. Therefore, in an embodiment, a computer implementing aspects of FIG. 7A, FIG. 7B may be programmed to compare the predicted values and the actual observed R6 date, from in-season measurements 716, at block 718 to yield new window predictions 720 for after the R6 date; para [0151], Nitrogen Advisor may be called to execute a fertility model to produce as outputs weather data and corn growth stage data. Operations data, product/genetics data, corn phenology data including R6 date, which is key to harvest, and weather data all may be obtained as output from such API calls. This approach may be used as an alternative to the transmission of programmatic calls to a digital weather data service directly as it produces corn phenology data including R6 dates, which indicate maturity. Growth after R6 typically is confined to dry-down, which is less relevant to determining harvest date. Thus, any date after the R6 date is a potential harvest date).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Jiang’s to incorporate the features taught by Sood above, for the purpose of including a predicted yield for each of the multiple seed products at each of multiple different weather conditions in Jiang’s prediction output, thus providing more effectiveness in planting based on weather conditions. Since Jiang discloses the prediction output defining a matrix of seed placement data including a predicted yield for each of the multiple seed products, Sood discloses the prediction output defining a matrix of seed placement data including a predicted yield for each of the multiple seed products at each of multiple different weather conditions, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Jiang and Sood would have yield predictable results in generating the prediction output.
Regarding to claim 2, Jiang discloses the computer-implemented method of claim 1, further comprising training the one or more seed placement prediction models, based on historical data associated with multiple growing spaces and a set of multiple seed products (para [0181], a random forest algorithm may be implemented as the machine learning technique to determine probability of success scores for each of the seeds for the target fields. Random forest algorithm is an ensemble machine learning method that operates by constructing multiple decision trees during a training period and then outputs the class that is the mean regression of the individual trees).
Regarding to claim 3, Jiang discloses the computer-implemented method of claim 2, wherein the set of multiple seed products includes the multiple seed products (para [0224], FIG. 13 illustrates an example of received agricultural data records and further processing to impute data values. In FIG. 13, the received agricultural data records include seed products G1, G2, G3, G4, G5, G6 and yield data is provided in bushels per acre (bu/ac), for example, associated with different fields or environments E1, E2, E3, E4; para [0135], identifying seeds that will optimally perform on target fields is based on input received by the agricultural intelligence computer system 130 including, but not limited to, agricultural data records for multiple different seeds and geo-location data related to the fields where the agricultural data records were collected. For example, if agricultural data records are received for one-hundred seeds, then the agricultural data records would include growth and yield data for the one-hundred seeds and geo-location data about the fields where the one-hundred seeds were planted).
Regarding to claim 4, Jiang discloses the computer-implemented method of claim 2, wherein inputs for training the one or more seed placement prediction models include, for each of the multiple growing spaces: a location of the growing space; and a yield of one or more of the multiple seed products at the growing space (para [0126], the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data).
Regarding to claim 5, Jiang discloses the computer-implemented method of claim 4, wherein the inputs for training the one or more seed placement prediction models include, for each of the multiple growing spaces, at least one of soil data of the growing space, weather data associated with the growing space, and hybrid/genetic seed data associated with seed products planted in the growing space (para [0126], the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data).
Regarding to claim 6, Jiang discloses the computer-implemented method of claim 1, further comprising training the optimization model, based on historical seed portfolio data associated with multiple growing spaces and multiple seed product types (para [0167], the agricultural data records may include historical crop seed data and field specific data from sets of fields owned and operated by individual growers. 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).
Regarding to claim 7, Jiang discloses the computer-implemented method of claim 6, wherein inputs for training the optimization model include, for each of the multiple growing spaces: a location of the growing space; and a yield of a portfolio including at least two of the multiple seed product types at the growing space (para [0126], the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data).
Regarding to claim 8, Jiang discloses the computer-implemented method of claim 7, wherein the inputs for training the optimization model include, for each of the multiple growing spaces, at least one of field information of the growing space, available seed supply list information associated with the growing space, weather information associated with the growing space, and grower constraint information associated with the growing space (para [0135], identifying seeds that will optimally perform on target fields is based on input received by the agricultural intelligence computer system 130 including, but not limited to, agricultural data records for multiple different seeds and geo-location data related to the fields where the agricultural data records were collected. For example, if agricultural data records are received for one-hundred seeds, then the agricultural data records would include growth and yield data for the one-hundred seeds and geo-location data about the fields where the one-hundred seeds were planted. In an embodiment, the agricultural intelligence computer system 130 also receives geo-location and agricultural data for a second set of fields. The second set of fields are the target fields where the grower intends to plant selected seeds. Information about the target fields are particularly relevant for matching specific seeds to the environment of the target fields).
Regarding to claim 9, Jiang discloses the computer-implemented method of claim 8, wherein the grower constraint information includes at least one of a grower seeding rate preference, a grower relative maturity spread preference, a preferred range of different varieties, a minimum and maximum product volume preference, a trait mix preference, and a brand mix preference (para [0100], digital map book instructions 206 comprise field map data layers stored in device memory and are programmed with data visualization tools and geospatial field notes. This provides growers with convenient information close at hand for reference, logging and visual insights into field performance. In one embodiment, overview and alert instructions 204 are programmed to provide an operation-wide view of what is important to the grower, and timely recommendations to take action or focus on particular issues. This permits the grower to focus time on what needs attention, to save time and preserve yield throughout the season. In one embodiment, seeds and planting instructions 208 are programmed to provide tools for seed selection, seed placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield or return on investment through optimized seed purchase, placement and population).
Regarding to claim 10, Jiang discloses the computer-implemented method of claim 1, wherein: determining, by the computing device, using the one or more seed placement prediction models, the prediction output includes generating a three-dimensional matrix output of yield predictions, the three dimensions of the matrix output including seed type, weather condition(s), and predicted yield (figure 11 and para [0206], FIG. 11 depicts an example graph 1105 of yield versus risk for the subset of one or more seeds. The y-axis 1110 represents the representative yield, as expected yield, for the seeds and the x-axis 1115 represents the risk values for the seeds expressed as standard deviation. By representing risk values as standard deviation, the unit of the risk values may be the same as the units for representative yield, which is bushels per acre. Dots on graph 1105, represented by group 1125 and group 1130 represent each of the seeds from the subset of one or more seeds).
Regarding to claim 11, Jiang discloses the computer-implemented method of claim 1, wherein the one or more seed placement prediction models include a multilayer perceptron neural network and/or an XGBoost model (para [0179], Different machine learning models may include, but are not limited to, logistic regression, random forest, vector machine modelling, and gradient boost modelling).
Regarding to claim 12, Jiang discloses the computer-implemented method of claim 1, wherein the seed planting recommendation output includes a portfolio having more than one of the multiple seed product types available for planting at the target growing space (para [0101], 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).
Regarding to claim 13, Jiang discloses the computer-implemented method of claim 1, further comprising seeding the target growing space in response to the seed planting recommendation output (para [0188], after receiving the information displayed, a grower may act on the information and plant the suggested seeds).
Regarding to claim 14, Jiang discloses the computer-implemented method of claim 13, further comprising:
receiving, at a communication device of a user associated with the target growing space, the seed planting recommendation output (para [0187], the presentation layer 134 may display additional seed property data and other agricultural data that may be relevant to the grower. The presentation layer 134 may also sort the seed in the target success yield group based on the probability of success values. For example, the display of seeds may be sorted in descending order of probability of success values such that the grower is able to view the most successful seeds for his target fields first); and
causing operation of one or more agricultural apparatuses at the target growing space to apply the at least one of the multiple seed products to the target growing space (para [0188], after receiving the information displayed, a grower may act on the information and plant the suggested seeds. In some embodiments, the growers may operate as part of the organization that is determining the target success yield group, and/or may be separate. For example, the growers may be clients of the organization determining the target success yield group and may plant seed based on the target success yield group; para [0257], To facilitate implementation of the product recommendations and improve resulting yield, the system 130 is further configured to cause display of the product recommendations on a display device communicatively coupled to the system. A grower may select or confirm a product recommendation and instruct the system 130 to control one or more machines to implement the product recommendation).
Regarding claims 15-20, Jiang discloses a system for use in directing seed products to growing spaces, the system comprising at least one computing device (see figure 1, Agricultural Intelligence Computer System 130) configure to performed the method as described in claims 1-2, (3, 4), 5, (6, 7), and 10 above, therefore are rejected by the same rationale.
Regarding to claim 21, Jiang discloses system of claim 15, further comprising an agricultural apparatus configured to plant the at least one of the multiple seed products at the target growing space; and wherein the agricultural apparatus is configured to receive the seed planting recommendation output from the at least one computing device and plant the at least one of the multiple seed products at the target growing space in response to the seed planting recommendation output (para [0074], 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; para [0257], To facilitate implementation of the product recommendations and improve resulting yield, the system 130 is further configured to cause display of the product recommendations on a display device communicatively coupled to the system. A grower may select or confirm a product recommendation and instruct the system 130 to control one or more machines to implement the product recommendation).
Regarding to claim 22, Jiang discloses the system of claim 21, further comprising a communication device associated with the user, the communication device configured to receive the seed planting recommendation output from the at least one computing device, and whereby the agricultural apparatus is configured to plant the at least one of the multiple seed products at the target growing space in response to the seed planting recommendation (para [0067], The method further includes receiving, over the digital data communication network, feature data for one or more target fields where seeds are to be planted. The server computer system may then be used to generate seed recommendations for the one or more target fields based on the set of yield properties and the feature data. And, the method may also include causing display, on a display device communicatively coupled to the server computer system, of the seed recommendations).
Regarding to claim 23, Jiang discloses computer-implemented method of claim 1, further comprising
generating, by the computing device, one or more executable scripts specific to the determined seed planting recommendation output (para [0101], 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); and
transmitting, by the computing device, the one or more executable scripts to an application controller communicatively coupled to the farm equipment (para [0101], 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); and
wherein planting the at least one of the multiple seed products at the target growing space includes executing, by the application controller, the one or more executable scripts to automatically adjust an operating parameter of the agricultural implement to plant the at least one of the multiple seed products at the target growing space in accordance with the determined seed planting recommendation output (para [0101], 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; para [0103], the nitrogen graph may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen graph. The user may then use his optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen map, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted).
Response to Arguments/Amendment
7. Applicant's arguments with respect to claims 1-23 have been fully considered but are not persuasive.
I. Claim Rejections - 35 USC § 101
Claims 1-23 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more. (See details above).
Step 2A, Prong 1:
In response to the Applicant’s arguments that “The Pending Claims Are Not Directed to an Abstract Idea” the Examiner respectfully disagrees and submits that:
The claims recite systems and methods for use in selection of types of seed products for planting in the growing spaces based on predicted yield and variance for multiple seed products (See Specification, para [0002]). The claims recite the steps: receiving a request for a planting recommendation…, accessing the seed placement prediction models…, determining a prediction output, based at last on the location data and on weather data…, accessing an optimization model…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…, and planting the multiple seed products at the target growing space, based on the seed planting recommendation output, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps: determining a prediction output, based at last on the location data and on weather data…, and determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter associated with the target growing space…, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
The claim also recites the steps: using the seed placement prediction models and using the optimization model, that falls within the “Mathematical Concepts” grouping of abstract ideas (mathematical relationships, mathematical formulas or equations, mathematical calculations). See MPEP 2106.04(a)(2), subsection III.
Accordingly, the claim recites an abstract idea.
The limitations “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output” are additional elements and are analyzing under Step 2A, Prong 2.
Step 2A, Prong 2:
In response to the Applicant’s arguments that “Even if the Claims Were Directed to an Abstract Idea, They Integrate It into a Practical Application” the Examiner respectfully disagrees and submits that:
The additional element recited in claims 2-9 and 16-19 “training the one or more seed placement prediction models…”, is used to generally apply the abstract idea without placing any limits on how the training the model functions. Rather, this limitation only recites the outcome of “to determine the seed planting recommendation output” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). The additional element “training the one or more seed placement prediction models” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “training the one or more seed placement prediction models” limits the identified judicial exceptions “to determine the seed planting recommendation output”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (training the model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
The additional elements “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output”, invoked the agricultural apparatuses merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on an agricultural apparatus without any recitation of details of how to carry out the abstract idea. See MPEP 2106.05(f).
Regarding dependent new claim 23, the claim recites the additional elements generating, by the computing device, one or more executable scripts specific to the determined seed planting recommendation output; and transmitting, by the computing device, the one or more executable scripts to an application controller communicatively coupled to the farm equipment; and wherein planting the at least one of the multiple seed products at the targe growing space includes executing, by the application controller, the one or more executable scripts to automatically adjust an operating parameter of the agricultural implement to plant the at least one of the multiple seed products at the target growing space in accordance with the determined seed planting recommendation output, which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05 (See claim 1 above).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the computing device, improvement to the farm equipment, or other technology. They just merely used as general means for collecting, displaying data and performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B:
In response to the Applicant’s arguments that “The Claims Amount to Significantly More Than Any Alleged Abstract Idea” the Examiner respectfully disagrees and submits that:
As explained with respect to Step 2A, Prong Two, the additional elements of “planting the multiple seed products at the target growing space, by farm equipment, based on the seed planting recommendation output” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
As discussed in Step 2A, Prong Two above, the additional elements of “receiving, by a computer device, a request for a plating recommendation”, “accessing, by the computing device, seed prediction models”, “accessing, by the computing device, an optimization model” are recited at a high level of generality. This element amounts to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: 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).
As discussed in Step 2A, Prong Two above, the recitation of the computing device to perform limitations “receiving a request for a planting recommendation…, accessing the seed placement prediction models…, determining a prediction output, based at last on the location data and on weather data…, accessing an optimization model…, determining a seed planting recommendation output, based on at least the prediction output and one grower constraint parameter…”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claims are not patent eligible.
According, the 101 rejection is maintained.
II. Claim Rejections - 35 USC § 102
Applicant’s arguments with respect to claims 1-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. New ground of 103 rejection described above.
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
9. Claims 1-23 are rejected.
10. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Xu et al. (US 2024/0046187) disclose one or more fields can be divided into portions, and different sets of interventions can be performed during the course of planting and growing a crop in each field portion. The effect of each set of interventions on the crop outcome of the corresponding field portion can be determined using a best performing model of a set of predictive models applied to the set of interventions.
Lee et al. (US 2022/0067027) disclose a server computer (“server”) identifies planting datasets of planting data values that correspond to separate planting passes in a field and harvesting datasets of harvesting data values that correspond to separate harvesting passes in the field, each planting data value and each harvesting data value including a location value.
Eckel et al. (US 2020/0309994) disclose a computer-implemented method of generating and displaying a comprehensive depiction of a weather element comprises: based on archived forecast model and observed data, training a machine learning model; calibrating current forecast data by applying the machine learning model to yield a calibrated forecast probability density function; displaying graphical representation of recently observed data and calibrated forecast probability density.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NGA B NGUYEN/Primary Examiner, Art Unit 3625 May 26, 2026
Prosecution Insights