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 .
Response to Arguments
Regarding the specification objection, Examiner has fully considered Applicant’s arguments and amendments. The specification amendment has been entered. Accordingly, the specification objection has been withdrawn.
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The claims recite a specific, technical process for determining crop quality using a unique ordered combination of computational models (a growth stage model and a quality model) applied to planting and weather data, followed by directing physical agricultural operations (e.g., harvesting via an agricultural machine). This is not merely a mental process performable in the human mind, as it requires implementing specific models on a computing device to process correlated data in a manner that cannot practically be done mentally-e.g., determining growth stages via a growth stage model, correlating weather data to durations of those stages, and using a crop-type-specific quality model to output a quality determination independent of physical crop examination. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (claims not abstract where they recite a specific, structured way of achieving a result, rather than a result itself).,” Examiner respectfully disagrees. The models of the claim, under consideration of the broadest reasonable interpretation, are part of the abstract limitations for consideration under Step 2A, Prong 1. Therefore, these abstract limitations are not part of the additional elements for consideration under Step 2A, Prong 2 or Step 2B. With respect to the harvesting limitations of the independent claims, these limitations, as drafted, are nothing more than mere generally linking the use of the judicial exception to a particular technological environment, namely agricultural machines. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
With respect to Bascom, the claims of Bascom “presented a "technology-based solution" of filtering content on the Internet that overcame the disadvantages of prior art filtering systems and that amounted to significantly more than the recited abstract idea, it also would be reasonable for an examiner to have found these claims eligible at Pathway A or B if the examiner had considered the technology-based solution to be an improvement to computer functionality (See MPEP 2106.06(b)).” The present claims do not provide an analogous improvement over prior art systems. The present claims, as drafted, are directed to a generic computing system. The present claims, as drafted, recite abstract limitations performed “by the computing device” or with a system comprising at least one computing device. There is no particular arrangement within the claims such that the claims recite a technical improvement. Therefore, Examiner respectfully disagrees with Applicant’s assertions in view of Bascom.
Regarding Applicant’s assertion of “The claims recite an improvement to agricultural technology by automating crop quality determination through a novel integration of models (in a particular ordered combination): the growth stage model determines a plurality of growth stages based on accessed data (e.g., amended claims 1, 12, 19), its output is then used to correlate weather data to durations of those stages, and this correlated data serves as input to a quality model specific to the crop type, yielding a quality determination independent of physical crop examination. This unique chaining of models-where the output of the growth stage model directly informs the input to the quality model-improves the accuracy and efficiency of crop quality determination, as supported by the specification's description of predicting growth stages and correlating data thereto for model-based quality prediction,” Examiner respectfully disagrees. The models of the claim, under consideration of the broadest reasonable interpretation, are part of the abstract limitations for consideration under Step 2A, Prong 1. These abstract limitations are not part of the additional elements for consideration under Step 2A, Prong 2 or Step 2B. Therefore, these abstract limitations cannot prove integration into a practical application or anything significantly more. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “This enables precise, pre-harvest quality predictions that were previously infeasible without costly, time-intensive manual inspections or post-harvest testing, reducing inefficiencies like re-tagging/re-bagging harvested crops and avoiding manual entry into the field for physical crop examination (see, Applicant's specification at [0017]). The determination then directs real-world agricultural operations, such as harvesting the crop via an agricultural machine based on the quality (e.g., amended Claims 1, 12, 19)-a tangible improvement akin to USPTO Example 42 (Method for Transmission of Notifications When Medical Records Are Updated), where the claims integrate data processing into a practical application by enabling real-time, actionable outputs.,” Examiner respectfully disagrees. Examiner respectfully asserts that improvements related to quality predictions, as drafted, are not improvements to the additional elements of the claims. This purported improvement is reflected in the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
With respect to example 42, the claims of Example 42 were deemed to integrate the judicial exception into a practical application because they provided an improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the initially received format. The present claims do not recite an analogous solution. Furthermore, the present claims do not recite a real time availability of data in a standardized format regardless of the format it was received in. The present claims do not recite analogous improvements over the prior art system because the claims recite nothing more than use of a computer as a tool to perform the abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Regarding Applicant’s assertion of “For instance, in Example 47 (Anomaly Detection), claim 3 is eligible because it uses an artificial neural network to detect network anomalies and take specific action (denying access), integrating the AI model into a practical application of improving network security without requiring manual intervention. Similarly, here, the chained models determine crop quality and direct machine harvesting, improving agricultural operations by enabling non- invasive, automated quality assessment and action. See also Example 48 (Speech Separation), where AI signal processing integrates into a practical application by enhancing speech recognition technology, and Example 49 (Fibrosis Treatment), where an AI model personalizes medical treatment, eligible when tied to specific, actionable steps. The present claims likewise tie the model outputs to directing physical harvesting, providing a technological improvement in precision agriculture (see, Applicant's specification at 1 [0018], [0093]).,” Examiner respectfully disagrees. The claims of Example 47 are directed to monitoring network traffic data in such a way that takes proactive measures to remediate danger, which results in a specific improvement over existing systems in the field of network intrusion detection. The present claims do not provide an analogous improvement. Furthermore, the models of the claims, as drafted, is not sufficient to prove integration into a practical application or anything significantly more because they are part of the abstract limitations for consideration under Step 2A, Prong 1. The quality assessment of the claims is not akin to a specific improvement to a technical field, such as network intrusion detection. Therefore, Examiner respectfully disagrees with Applicant’s assertion.
Examiner respectfully disagrees with Applicant’s argument that the claims are analogous to Example 48. While multiple steps of Example 48 were found to contain abstract ideas, claims 2 and 3 were eligible as steps (f) and (g) are directed to creating a new speech signal that no longer contains extraneous speech signals from unwanted sources. This integrated the judicial exception into a practical application as an improvement to existing computer technology or to the technology of speech separation. Here, the claims do not create a new speech signal that no longer contains extraneous speech signals from unwanted sources. The claims contain the abstract limitations of related to evaluating the quality of crops based on various data, which does not make the claim eligible at Step 2A, Prong 2. Therefore, the Examiner believes the claims are not analogous to Example 48 as they do not contain similar limitations and contain abstract steps that need to be identified under Step 2A prong 2.
Examiner respectfully disagrees with Applicant’s assertions in view of Example 49 because the claims of said example are directed to an artificial intelligence model that is designed to assist in personalizing medical treatment to the individual characteristics of a particular patient. Claim 1 was ineligible as it contained abstract limitations and the appropriate treatment in limitation (c) does not require any particular application of the patient risk determination and is at most an instruction to “apply” the abstract idea. Claim 2 was eligible as relying on the determination of patient risk to administer Compound X eye drops to glaucoma patients at high risk of PI after microstent implant surgery is therefore a particular treatment for a medical condition such that the claim as a whole integrates the judicial exception into a practical application. See MPEP 2106.04(d)(2) Applicant’s claims are directed to evaluating crop quality, do not provide a particular treatment for a medical condition, and therefore are not analogous to Example 49. Thus, the present claims are not analogous to example 49.
Regarding Applicant’s assertion of “The Office suggests (see Office Action at 1 13-17) that the recited models and machine direction are "mere instructions to implement an abstract idea" (MPEP § 2106.05(f)) or "generally linking" to agricultural machines (§ 2106.05(h)). This is different from SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018), where the Federal Circuit determined the claims at issue were directed to an abstract idea because they offered no technological improvement-just generic data analysis. In contrast, Applicant's claims advance crop management by automating quality checks in a non-invasive way (no need to physically enter the field) and using that info to guide real-world actions like machine harvesting, which reduces manual work and mistakes (see, Applicant's specification at 1 [0018]-[0019]). This is similar to McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), where claims reciting the automation of a specific animation process using rules that improved computer technology were ruled patent-eligible. Here, the specific model chaining automates agricultural decision-making in a way that enhances field operations without requiring physical crop inspection, consistent with McRO.,” Examiner respectfully disagrees. The present claims do not provide a clear improvement to technology or computer functionality. The claims of McRo recite automatic lip synchronization and facial expression animation, which provided a clear improvement to a computer functionality (i.e. computer animation). In contrast, the instant claims do not recite an analogous improvement to a computer functionality. The claims recite an abstract modeling process to evaluate and predict the quality of crops based on input data. The claims employ generic computer functions to execute the abstract idea that, even while limiting the use of the idea to a particular technical environment, do not integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Shankar fails to anticipate Claim 1 because it does not disclose these elements,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Jarugumilli reference to cure the deficiencies of the prior art combination of the record. Therefore, the 35 USC 102 rejection has been withdrawn; however, the present claims remain rejected under 35 USC 103.
Accordingly, the present claims are rejected under 35 USC 103.
Claim Objections
Claims 9, 17, and 20 are objected to because of the following informalities: Examiner suggests amending the claim to correct the minor typographical error from “form” to “from.”
Claim 12 is objected to because of the following informalities: Examiner suggests amending the claim for the sake of clarity to recite: “and in response to direct at least one agricultural operation consistent with the determined quality of the crop in the target field, control an agricultural machine to harvest the crop from the target field, based at least in part on the determined quality of the crop in the target field.”
Appropriate correction is required.
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 1-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-11 are directed to a method, claims 12-18 are directed to a non-transitory computer readable medium, and claims 19-20 are directed to a system. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 12, and 19 recite determining the quality of a crop, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites limitations, similarly recited in claims 12 and 19, including “the data including planting data for the crop and weather data for the target field; determining, by the computing device implementing a growth stage model, based on the accessed data, a plurality of growth stages for the crop in the field; for each of the determined plurality of growth stages for the crop in the target field, correlating, the weather data to a duration of the of growth stage; prior to a harvest of the crop from the target field, determining, implementing a quality model specific to a type of the crop in the target field, a quality of the crop in the target field, based on the correlated weather data independent of a physical examination of the crop in the target field. These limitations, as drafted, but for the recitation of “by the computing device,” is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the “by the computing device” language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the “by the computing device” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2-5, 7-8, 10-11, 13-14, 16, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 6, 9, 15, 17, and 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 1, 12, and 19 do not integrate the judicial exception into a practical application. Claim 1 is directed to a computer implemented method performed “by a computing device.” Claim 12 is directed to “a non-transitory computer-readable storage medium including executable instructions for providing agricultural operations, which when executed by at least one processor, cause the at least one processor to,” which is recited in the preamble of the claim. Claim 19 is directed to a system that recites “a system for use in providing harvest operation(s) for a crop in a target field, based on a determined quality of the crop in the target field, the system comprising at least one computing device configured to” within the preamble of the claim. Claim 1 recites the additional element, similarly recited in claims 12 and 19, of “accessing, by a computing device, data associated with a crop in a target field, the data including planting data for the crop and weather data for the target field.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Independent claims 1, 12, and 19 further recite the additional element of “harvesting the crop from the target field, by an agricultural machine, based at least in part on the determined quality of the crop in the target field.” These limitations, as drafted, are nothing more than mere generally linking the use of the judicial exception to a particular technological environment, namely agricultural machines. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-5, 7-8, 10-11, 13-14, 16, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 6 introduces the additional element of “further comprising: training, by the computing device, the quality model based on a training set of data, the training set of data including, for a given time period and multiple fields, weather data, planting data and quality data for harvested crops from the multiple fields; and validating, by the computing device, the quality model, prior to determining the quality of the crop in the target field.” Dependent claim 15 introduces the additional element of “wherein the executable instructions, when executed by the at least one processor, further cause the at least one processor to: train the quality model based on a training set of data, the training set of data including, for a given time period and multiple fields, weather data, planting data and quality data for harvested crops from the multiple fields; and validate the quality model, prior to determining the quality of the crop in the target field.” These limitations do not integrate the judicial exception into a practical application because they are nothing more than mere use of a computer to perform training to perform the abstract idea of predicting quality. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 9 introduces the additional element of “wherein harvesting the crop in the target field includes harvesting, by the agricultural machine: the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field..” Dependent claim 17 introduces the additional element of “wherein the executable instructions, when executed by the at least one processor to control an agricultural machine to harvest the crop from the target field, cause the at least one processor to control the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field..” Dependent claim 20 introduces the additional element of “further comprising the agricultural machine in communication with the at least one computing device; wherein the at least one computing device is configured, in order to direct the agricultural machine to harvest the crop from the target field, to transmit executable instructions to the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field, whereby the agricultural machine executes the instructions at the target field to thereby control operation of the agricultural machine to harvest the field.” These limitations, as drafted, are nothing more than mere generally linking the use of the judicial exception to a particular technological environment, namely agricultural machines. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not sufficient to prove integration into a practical application.
Step 2B: Claims 1, 12, and 19 do not comprise anything significantly more than the judicial exception. Claim 1 is directed to a computer implemented method performed “by a computing device.” Claim 12 is directed to “a non-transitory computer-readable storage medium including executable instructions for providing agricultural operations, which when executed by at least one processor, cause the at least one processor to,” which is recited in the preamble of the claim. Claim 19 is directed to a system that recites “a system for use in providing harvest operation(s) for a crop in a target field, based on a determined quality of the crop in the target field, the system comprising at least one computing device configured to” within the preamble of the claim. Claim 1 recites the additional element, similarly recited in claims 12 and 19, of “accessing, by a computing device, data associated with a crop in a target field, the data including planting data for the crop and weather data for the target field.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Independent claims 1, 12, and 19 further recite the additional element of “harvesting the crop from the target field, by an agricultural machine, based at least in part on the determined quality of the crop in the target field.” These limitations, as drafted, are nothing more than mere generally linking the use of the judicial exception to a particular technological environment, namely agricultural machines. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2-5, 7-8, 10-11, 13-14, 16, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 6 introduces the additional element of “further comprising: training, by the computing device, the quality model based on a training set of data, the training set of data including, for a given time period and multiple fields, weather data, planting data and quality data for harvested crops from the multiple fields; and validating, by the computing device, the quality model, prior to determining the quality of the crop in the target field.” Dependent claim 15 introduces the additional element of “wherein the executable instructions, when executed by the at least one processor, further cause the at least one processor to: train the quality model based on a training set of data, the training set of data including, for a given time period and multiple fields, weather data, planting data and quality data for harvested crops from the multiple fields; and validate the quality model, prior to determining the quality of the crop in the target field.” These limitations are not anything significantly more than the judicial exception because they are nothing more than mere use of a computer to perform training to perform the abstract idea of predicting quality. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 9 introduces the additional element of “wherein harvesting the crop in the target field includes harvesting, by the agricultural machine: the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field..” Dependent claim 17 introduces the additional element of “wherein the executable instructions, when executed by the at least one processor to control an agricultural machine to harvest the crop from the target field, cause the at least one processor to control the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field..” Dependent claim 20 introduces the additional element of “further comprising the agricultural machine in communication with the at least one computing device; wherein the at least one computing device is configured, in order to direct the agricultural machine to harvest the crop from the target field, to transmit executable instructions to the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field, whereby the agricultural machine executes the instructions at the target field to thereby control operation of the agricultural machine to harvest the field.” These limitations, as drafted, are nothing more than mere generally linking the use of the judicial exception to a particular technological environment, namely agricultural machines. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-2, 7, 9, 12, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shankar et al. (US 20230110849 A1) in view of Jarugumilli et al. (US 20220138868 A1).
Regarding claim 1, Shankar teaches a computer-implemented method for use in providing agricultural operation(s) for a crop in a target field, based on a determined quality of the crop in the target field, the method comprising ([0005] teaches a computer implemented method for determining a plant protection treatment plan of a crop; see also: [0006-0007]):
accessing, by a computing device, data associated with a crop in a target field, the data including planting data for the crop and weather data for the target field ([0091] teaches the system is adapted to collect data to be input to the devices, wherein the data sources include a weather station, a database comprising observed plant data, wherein the collected data includes plant observation data indicative of a current state of health for an agricultural plant and weather data associated with a location at which the agricultural plant is cultivated, wherein [0020] teaches the location at which the plant is cultivated may be a field that is divided into a number of sub-fields, and wherein the disease probability may be predicted for at least a part of the number of sub-fields, wherein the disease probability may be predicted for each sub-field, wherein the plant treatment may be controlled individually for each one of the sub-fields; see also: [0090, 0094, 0096]);
determining, by the computing device implementing a growth stage model, based on the accessed data, a plurality of growth stages for the crop in the field ([0037] teaches the plant observation data comprises field data and a growth stage associated with the agricultural plant, wherein these data may be combined and correlated, wherein [0049] teaches the field specific data may be correlated with the growth stage and the weather data, wherein the field specific data includes the planting date and crop data, wherein crop data includes crop type, as well as in [0050-0051] teach the growth stage data may be obtained by determining disease severity at different growth stages of the agricultural plant throughout the growing period, wherein the growth stage data and observed disease severity may be correlated with weather data, wherein the weather data refers to historical weather data and the weather data may be correlated to the growth stage data, wherein [0033-0035] teach predicting the disease probability of the agricultural plant comprises predicting, by the computation model, a disease progression window in which a probably course of disease of the agricultural plant over a time period is computed and being indicative for the disease probability, wherein the predicted disease probability may be extracted from the disease progression window; see also: [0007-0010, 0048, 0052]; Examiner’s Note: See the combination below for teachings pertaining to the unbolded claim language.);
for each of the determined plurality of growth stages for the crop in the target field, correlating, by the computing device, the weather data to a duration of the of growth stage ([0037] teaches the plant observation data comprises field data and a growth stage associated with the agricultural plant, wherein these data may be combined and correlated, wherein [0049] teaches the field specific data may be correlated with the growth stage and the weather data, wherein the field specific data includes the planting date and crop data, wherein crop data includes crop type, as well as in [0050-0051] teach the growth stage data may be obtained by determining disease severity at different growth stages of the agricultural plant throughout the growing period, wherein the growth stage data and observed disease severity may be correlated with weather data, wherein the weather data refers to historical weather data and the weather data may be correlated to the growth stage data, wherein [0033-0035] teach predicting the disease probability of the agricultural plant comprises predicting, by the computation model, a disease progression window in which a probably course of disease of the agricultural plant over a time period is computed and being indicative for the disease probability, wherein the predicted disease probability may be extracted from the disease progression window; see also: [0007-0010, 0048, 0052]);
prior to a harvest of the crop from the target field ([0002] teaches in agriculture, cultivated plants, in particular crops, can be affected by diseases occurring between seeding and harvest, which may diminish yield, wherein [0050] teaches observing the plant throughout the growing period; see also: [0058-0062]), determining, by the computing device implementing a quality model specific to a type of the crop in the target field ([0048] teaches providing, to the computational model, training data as in put data comprising field specific data, observed disease severity, growth stage data, and weather data, wherein [0049] teaches the field specific data refers to data collected with experimental trials, wherein the field specific data includes observed disease severity, growth stage, weather data, planting date, crop type, and more, wherein the field trials include different plot designs and different aspects of the fields are recorded throughout the growing period by trial operators, which is then later analyzed, which can be used to provide input data, wherein [0053] teaches the training data is gathered through observations made in trials that aim to capture the temporal disease development of plant populations, wherein [0054] teaches the model can be adjusted using backpropagation based on the training data, parameters, or weights of the computational model to adapt the computational model to the changes conditions of cultivation of an agricultural plant; see also: [0055-0056, 0101]),
a quality of the crop in the target field ([0101] teaches the adapted computational model is used for determining the plant protection treatment plan of agricultural plant by predicting, by the computational model, is used for determining the plant protection treatment plan of the agricultural plant by predicting at least a time-related disease probability of the agricultural plant, wherein [0094] teaches obtaining plant observation data indicative of a current state of health of an agricultural plant and weather data associated with a location at which the agricultural plant is cultivated, wherein the system is adapted to predict, by the computational model and based on the obtained observation data and weather data, a time-related disease probability of the agricultural plant, wherein [0095] teaches the location at which the agricultural plant is cultivated may be a field that is divided into a number of sub-fields, and wherein the disease probability may be predicted for at least a part of the number of sub-fields in a sub-field specific manner, wherein [0033-0035] teach predicting the disease probability of the agricultural plant comprises predicting, by the computation model, a disease progression window in which a probably course of disease of the agricultural plant over a time period is computed and being indicative for the disease probability, wherein the predicted disease probability may be extracted from the disease progression window, wherein [0049] teaches the field data includes crop type, crop species, crop variety, and more, wherein the field specific data can allow for studying the dynamics of disease in order to devise better management strategies based on the crop data including crop type, which is provided as input data; see also: [0056, 0071-0078, 0097-0098]), based on the correlated weather data independent of a physical examination of the crop in the target field ([0101] teaches the adapted computational model is used for determining the plant protection treatment plan of agricultural plant by predicting, by the computational model, is used for determining the plant protection treatment plan of the agricultural plant by predicting at least a time-related disease probability of the agricultural plant, wherein [0094] teaches obtaining plant observation data indicative of a current state of health of an agricultural plant and weather data associated with a location at which the agricultural plant is cultivated, wherein the system is adapted to predict, by the computational model and based on the obtained observation data and weather data, a time-related disease probability of the agricultural plant, wherein [0095] teaches the location at which the agricultural plant is cultivated may be a field that is divided into a number of sub-fields, and wherein the disease probability may be predicted for at least a part of the number of sub-fields in a sub-field specific manner, wherein [0033-0035] teach predicting the disease probability of the agricultural plant comprises predicting, by the computation model, a disease progression window in which a probably course of disease of the agricultural plant over a time period is computed and being indicative for the disease probability, wherein the predicted disease probability may be extracted from the disease progression window, wherein [0049] teaches the field data includes crop type, crop species, crop variety, and more, wherein the field specific data can allow for studying the dynamics of disease in order to devise better management strategies based on the crop data including crop type, which is provided as input data, wherein the field-specific data can be obtained through measurements that include remote sensing, wherein [0094] teaches the computational model may be adapted to process the input data to compute the disease probability as a quantitative value; see also: [0056, 0059-0061, 0071-0078, 0097-0098]),
However, Shankar does not explicitly teach determining, implementing a growth stage model, a plurality of growth stages for the crop in the field; harvesting the crop from the target field, by an agricultural machine, based at least in part on the determined quality of the crop in the target field.
From the same or similar field of endeavor, Jarugumilli teaches
determining, implementing a growth stage model, a plurality of growth stages for the crop in the field ([0030] teaches the moisture interval may be defined by one or more models, wherein the moisture interval is defined by a growth stage model, or GSM, that is specific to a particular crop variety that is planted in the given field, wherein the GSM is designed to identify moisture content for each field and use that estimate in order to determine information about the crops based on the duration of the given time from planting the crop in the field, wherein Fig. 7 and [0116] teach modeling the harvest dates and harvest lateness in days for the expected moisture of the field/crop at harvest; see also: [0032, 0116]);
harvesting the crop from the target field, by an agricultural machine, based at least in part on the determined quality of the crop in the target field ([0035] teaches each of the fields exhibits a number of characteristics that may be measured or estimated, wherein the characteristics of the fields include a quality of the crops growing in the field, wherein [0036] teaches combine harvesters may be used to harvest fields of corn based on the different characteristics of the corn and the fields in which the corn is planted that may be taken into account in making the harvest determinations, wherein during the growing season, the fields of corn can be screened, wherein if there are major quality issues, the field may not be selected for the combine, wherein [0039] teaches estimating the harvest time for the particular field based on the operational data, as well as in [0049-0050] teach determining the harvest date of a field based on the quality of the crops, wherein [0079] teaches the harvest plan includes the listing of the fields and designation of the harvest date, wherein [0080] teaches the platform can implement the harvest plan in the one or more fields with pickers that automatically response to directions and travel the appropriate fields for harvest; see also: [0037-0038, 0052, 0065]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Shankar to incorporate the teachings of Jarugumilli to include determining, implementing a growth stage model, a plurality of growth stages for the crop in the field; harvesting the crop from the target field, by an agricultural machine, based at least in part on the determined quality of the crop in the target field. One would have been motivated to do so in order to uniquely schedule, for a given yield from a production operation from season to season, less total acres that may be required to achieve a desired result including improved satisfaction on demand for the harvested crops (Jarugumilli, [0022]). By incorporating the teachings of Jarugumilli, one would have been able to promote efficiencies by harvesting fields with similar quality at the same time (Jarugumilli, [0037]).
Regarding claims 12 and 19, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 12, Shankar teaches a non-transitory computer-readable storage medium including executable instructions for providing agricultural operations, which when executed by at least one processor, cause the at least one processor to ([0005-0006] teach the method can be carried out by a data processing unit, which may be a processor, a computer device, or the like, wherein the computer system may be implemented centrally or remotely, wherein the method may be implemented in computer program instructions and provided as a computer program element and performed by one or more data processing units and computing devices; see also: [0080, 0089-0090]). Regarding claim 19, Shankar teaches a system for use in providing harvest operation(s) for a crop in a target field, based on a determined quality of the crop in the target field ([0005-0006] teach the method can be carried out by a data processing unit, which may be a processor, a computer device, or the like, wherein the computer system may be implemented centrally or remotely; see also: [0089-0090]), the system comprising at least one computing device configured to. Accordingly, claims 12 and 19 are rejected as being unpatentable over Shankar in view of Jarugumilli.
Regarding claim 2, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
Shankar further teaches wherein the planting data includes a planting date of the crop in the field, and/or a location of the target field ([0049] teaches the field specific data includes the planting date and location details, wherein [0020] teaches the location of the agricultural plant being cultivated is one of a number of sub-fields; see also: [0052]);
wherein the weather data includes a temperature, a solar radiation, wherein the planting data is identified for the target field per interval ([0052] teaches weather data may be pre-processed including observations that may be made per observation date and the values from same day may be averaged, wherein [0009] teaches the weather data comprises temperatures, wherein [0022-0026] teach the soil moisture value can be derived from microwave radiation measurements; see also: [0024]);
and wherein the interval includes a day ([0052] teaches weather data may be pre-processed including observations that may be made per observation date and the values from same day may be averaged, wherein [0009] teaches the weather data comprises temperatures, wherein [0022-0026] teach the soil moisture value can be derived from microwave radiation measurements; see also: [0024]).
Regarding claim 7, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
Shankar further teaches wherein the weather data for the multiple fields includes actual weather data ([0091] teaches the system comprises training data comprising field specific data, weather data, observed disease severity, growth stage data, and more, wherein the training data is associated with changed conditions of cultivation of the agricultural plant, wherein the plant observation data indicative of a current state of health of the plant associated with the location at which the agricultural plant is being cultivated, wherein [0048] teaches providing, to the computational model, training data as in put data comprising field specific data, observed disease severity, growth stage data, and weather data, wherein [0049] teaches the field specific data refers to data collected with experimental trials, wherein the field specific data includes observed disease severity, growth stage, weather data, planting date, crop type, and more, wherein the field trials include different plot designs and different aspects of the fields are recorded throughout the growing period by trial operators, which is then later analyzed, which can be used to provide input data, wherein [0053] teaches the training data is gathered through observations made in trials that aim to capture the temporal disease development of plant populations; see also: [0050, 0055-0056, 0101]);
and wherein the accessed weather data for the target field, or a region in which the field is located, includes a combination of at least two of: actual weather data for a present season, and historical weather data for prior seasons ([0091] teaches the system comprises training data comprising field specific data, weather data, observed disease severity, growth stage data, and more, wherein the training data is associated with changed conditions of cultivation of the agricultural plant, wherein the plant observation data indicative of a current state of health of the plant associated with the location at which the agricultural plant is being cultivated and weather data associated with the location at which the plant is cultivated, wherein [0048] teaches providing, to the computational model, training data as in put data comprising field specific data, observed disease severity, growth stage data, and weather data, wherein [0049] teaches the field specific data refers to data collected with experimental trials, wherein the field specific data includes observed disease severity, growth stage, weather data, planting date, crop type, and more, wherein the field trials include different plot designs and different aspects of the fields are recorded throughout the growing period by trial operators, which is then later analyzed, which can be used to provide input data, wherein [0053] teaches the training data is gathered through observations made in trials that aim to capture the temporal disease development of plant populations; see also: [0050, 0055-0056, 0101]).
Regarding claim 9, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
However, Shankar does not explicitly teach wherein harvesting the crop in the target field includes harvesting, by the agricultural machine: the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field.
From the same or similar field of endeavor, Jarugumilli further teaches wherein harvesting the crop in the target field includes harvesting, by the agricultural machine: the crop form the target field at a specific time indicated by the determined quality of the crop in the target field ([0035] teaches each of the fields exhibits a number of characteristics that may be measured or estimated, wherein the characteristics of the fields include a quality of the crops growing in the field, wherein [0036] teaches combine harvesters may be used to harvest fields of corn based on the different characteristics of the corn and the fields in which the corn is planted that may be taken into account in making the harvest determinations, wherein during the growing season, the fields of corn can be screened, wherein if there are major quality issues, the field may not be selected for the combine, wherein [0039] teaches estimating the harvest time for the particular field based on the operational data, as well as in [0049-0050] teach determining the harvest date of a field based on the quality of the crops, wherein [0079] teaches the harvest plan includes the listing of the fields and designation of the harvest date, wherein [0080] teaches the platform can implement the harvest plan in the one or more fields with pickers that automatically response to directions and travel the appropriate fields for harvest; see also: [0037-0038, 0052, 0065]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the further teachings of Jarugumilli to include wherein directing at least one agricultural operation includes directing an agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field. One would have been motivated to do so in order to uniquely schedule, for a given yield from a production operation from season to season, less total acres that may be required to achieve a desired result including improved satisfaction on demand for the harvested crops (Jarugumilli, [0022]). By incorporating the teachings of Jarugumilli, one would have been able to promote efficiencies by harvesting fields with similar quality at the same time (Jarugumilli, [0037]).
Regarding claim 20, the combination of Shankar and Jarugumilli teaches all the limitations of claim 19 above.
However, Shankar does not explicitly teach further comprising the agricultural machine in communication with the at least one computing device; wherein the at least one computing device is configured, in order to direct the agricultural machine to harvest the crop from the target field, to transmit executable instructions to the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field, whereby the agricultural machine executes the instructions at the target field to thereby control operation of the agricultural machine to harvest the field.
From the same or similar field of endeavor, Jarugumilli further teaches further comprising the agricultural machine in communication with the at least one computing device (Fig. 1 and [0024-0025] teach a field site including multiple pickers, such as harvesting machines, wherein each field scenario may be associated with a hub that includes coordination in connection with a harvest, wherein [0113] teaches the harvest plan can be implemented through instructions to individual field sites, wherein the platform and hub may direct particular pickers in particular fields and the pickers may then harvest crops/plants from the fields, wherein [0080] teaches the platform can implement the harvest plan in the one or more fields with pickers that automatically response to directions and travel the appropriate fields for harvest; see also: [0037-0038, 0052, 0065]);
wherein the at least one computing device is configured, in order to direct the agricultural machine to harvest the crop from the target field, to transmit executable instructions to the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field ([0035] teaches each of the fields exhibits a number of characteristics that may be measured or estimated, wherein the characteristics of the fields include a quality of the crops growing in the field, wherein [0036] teaches combine harvesters may be used to harvest fields of corn based on the different characteristics of the corn and the fields in which the corn is planted that may be taken into account in making the harvest determinations, wherein during the growing season, the fields of corn can be screened, wherein if there are major quality issues, the field may not be selected for the combine, wherein [0039] teaches estimating the harvest time for the particular field based on the operational data, as well as in [0049-0050] teach determining the harvest date of a field based on the quality of the crops, wherein [0079] teaches the harvest plan includes the listing of the fields and designation of the harvest date, wherein [0080] teaches the platform can implement the harvest plan in the one or more fields with pickers that automatically response to directions and travel the appropriate fields for harvest; see also: [0037-0038, 0052, 0065]),
whereby the agricultural machine executes the instructions at the target field to thereby control operation of the agricultural machine to harvest the field (Fig. 1 and [0024-0025] teach a field site including multiple pickers, such as harvesting machines, wherein each field scenario may be associated with a hub that includes coordination in connection with a harvest, wherein [0113] teaches the harvest plan can be implemented through instructions to individual field sites, wherein the platform and hub may direct particular pickers in particular fields and the pickers may then harvest crops/plants from the fields, wherein [0080] teaches the platform can implement the harvest plan in the one or more fields with pickers that automatically response to directions and travel the appropriate fields for harvest; see also: [0037-0038, 0052, 0065]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the further teachings of Jarugumilli to include further comprising the agricultural machine in communication with the at least one computing device; wherein the at least one computing device is configured, in order to direct the agricultural machine to harvest the crop from the target field, to transmit executable instructions to the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field, whereby the agricultural machine executes the instructions at the target field to thereby control operation of the agricultural machine to harvest the field. One would have been motivated to do so in order to uniquely schedule, for a given yield from a production operation from season to season, less total acres that may be required to achieve a desired result including improved satisfaction on demand for the harvested crops (Jarugumilli, [0022]). By incorporating the teachings of Jarugumilli, one would have been able to promote efficiencies by harvesting fields with similar quality at the same time (Jarugumilli, [0037]).
Claim(s) 3-6, 13-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shankar et al. (US 20230110849 A1) in view of Jarugumilli et al. (US 20220138868 A1) in view of Farah et al. (US 20170351790 A1).
Regarding claims 3 and 13, the combination of Shankar and Jarugumilli teaches all the limitations of claims 1 and 12 above.
However, Shankar does not explicitly teach further comprising determining the plurality of growth stages of the crops, based on a planting date and a maturity group of the crop in the target field from the planting data; and wherein each growth stage is associated with a number of days from the planting date; and wherein determining the quality of crop in the target field includes: generating, by the computing device, via the quality model, a score for the crop in the target field; and comparing, by the computing device, the score to at least one threshold specific to the maturity group of the crop in the target field to thereby determine the quality of the crop.
From the same or similar field of endeavor, Farah teaches further comprising determining the plurality of growth stages of the crops ([0134] teaches determining crop growth stages using agricultural data based on received and observed growth stage data, as well as in [0147] teaches the agricultural intelligence computing system is configured to derive the GDD values for the observed growth stage thresholds by receiving temperature data, days between planting the hybrid seed and the observation dates, as well as the date of planting, and more, wherein [0139] teaches calculating crop phenology thresholds for specific hybrid seeds based on planting date and relative maturity, as well as in [0138] teaches the growing degree days (GDD) are used to track the different developmental stages of the corn plant growth and are calculated from the maximum and minimum daily air temperatures, wherein a particular growth stage is reached when the accumulated growing degree day crosses a specified threshold for that stage; see also: [0154, 0233-0236]),
based on a planting date and a maturity group of the crop in the target field from the planting data ([0134] teaches determining crop growth stages using agricultural data based on received and observed growth stage data, as well as in [0147] teaches the agricultural intelligence computing system is configured to derive the GDD values for the observed growth stage thresholds by receiving temperature data, days between planting the hybrid seed and the observation dates, as well as the date of planting, and more, wherein [0139] teaches calculating crop phenology thresholds for specific hybrid seeds based on planting date and relative maturity, as well as in [0138] teaches the growing degree days (GDD) are used to track the different developmental stages of the corn plant growth and are calculated from the maximum and minimum daily air temperatures, wherein a particular growth stage is reached when the accumulated growing degree day crosses a specified threshold for that stage; see also: [0154, 0233-0236]);
and wherein each growth stage is associated with a number of days from the planting date ([0134] teaches determining crop growth stages using agricultural data based on received and observed growth stage data, as well as in [0147] teaches the agricultural intelligence computing system is configured to derive the GDD values for the observed growth stage thresholds by receiving temperature data, days between planting the hybrid seed and the observation dates, as well as the date of planting, and more, wherein [0139] teaches calculating crop phenology thresholds for specific hybrid seeds based on planting date and relative maturity, as well as in [0138] teaches the growing degree days (GDD) are used to track the different developmental stages of the corn plant growth and are calculated from the maximum and minimum daily air temperatures, wherein a particular growth stage is reached when the accumulated growing degree day crosses a specified threshold for that stage; see also: [0154, 0233-0236]);
and wherein determining the quality of crop in the target field includes: generating, by the computing device, via the quality model, a score for the crop in the target field (Fig. 7 and [0132] teach generating a set of growing stage thresholds using an assimilated crop data model that incorporates historical growth stage data and observed growth stage data from particular agricultural fields, wherein [0116] teaches generating threshold values that define the estimated crop growth stages based upon the posterior distribution of growth stages, wherein the generated posterior distribution of the growth stages is generated for a particular seed at a particular location based on the various growth durations, wherein the system generates correlation parameters that describe correlations between different growth stages for hybrid seeds, as well as in [0212] teaches sending the crop growth stage threshold values in order to help determine and recommend more accurate times for deploying nitrogen in the field and determine the optimal date for harvesting the crop based on the crop growth stage threshold; see also: [0134-0135, 0141, 0213-0216]); and
comparing, by the computing device, the score to at least one threshold specific to the maturity group of the crop in the target field to thereby determine the quality of the crop (Fig. 7 and [0132] teach generating a set of growing stage thresholds using an assimilated crop data model that incorporates historical growth stage data and observed growth stage data from particular agricultural fields, wherein [0116] teaches generating threshold values that define the estimated crop growth stages based upon the posterior distribution of growth stages, wherein the generated posterior distribution of the growth stages is generated for a particular seed at a particular location based on the various growth durations, wherein the system generates correlation parameters that describe correlations between different growth stages for hybrid seeds, as well as in [0212] teaches sending the crop growth stage threshold values in order to help determine and recommend more accurate times for deploying nitrogen in the field and determine the optimal date for harvesting the crop based on the crop growth stage threshold; see also: [0134-0135, 0141, 0213-0216]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the teachings of Farah to include further comprising determining the plurality of growth stages of the crops, based on a planting date and a maturity group of the crop in the target field from the planting data; and wherein each growth stage is associated with a number of days from the planting date; and wherein determining the quality of crop in the target field includes: generating, by the computing device, via the quality model, a score for the crop in the target field; and comparing, by the computing device, the score to at least one threshold specific to the maturity group of the crop in the target field to thereby determine the quality of the crop. One would have been motivated to do so in order to use estimated crop growth thresholds to improve the accuracy of the historical crop growth model for future crop growth stage estimations (Farah, [0214]). By incorporating the teachings of Farah, one would have been able to accurately predict the growth stage thresholds that may improve watering efficiency and accuracy as it applies to different growth stages (Farah, [0213]).
Regarding claim 4, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
However, Shankar does not explicitly teach wherein correlating the weather data to the duration of the growth stage includes aggregating, by the computing device, the weather data for the duration of the growth stage.
From the same or similar field of endeavor, Farah teaches wherein correlating the weather data to the duration of the growth stage includes aggregating, by the computing device, the weather data for the duration of the growth stage ([0236] teaches measuring GDDs is particularly useful when determining specific weather indices that correlate to different developmental stages in corn plant growth, wherein the number of growing degree days are used to define the start and end of different phenological development stages, wherein stages can be defined after a number of GDD days based on different phenological development stages and daily average temperature, wherein [0235] teaches the phenology stages of the corn plant can be tracked based upon factors outside appearance including growing degree days that are used based on the daily average temperature calculated from the daily maximum and minimum temperatures; see also: [0138]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the teachings of Farah to include wherein correlating the weather data to the duration of the growth stage includes aggregating, by the computing device, the weather data for the duration of the growth stage. One would have been motivated to do so in order to use estimated crop growth thresholds to improve the accuracy of the historical crop growth model for future crop growth stage estimations (Farah, [0214]). By incorporating the teachings of Farah, one would have been able to accurately predict the growth stage thresholds that may improve watering efficiency and accuracy as it applies to different growth stages (Farah, [0213]).
Regarding claim 5, the combination of Shankar, Jarugumilli, and Farah teaches all the limitations of claim 4 above.
However, Shankar does not explicitly teach wherein aggregating the weather data includes: averaging temperature data from the weather data for the duration of the growth stage; and/or summing precipitation data from the weather data for the duration of the growth stage.
From the same or similar field of endeavor, Farah teaches wherein aggregating the weather data includes: averaging temperature data from the weather data for the duration of the growth stage; and/or summing precipitation data from the weather data for the duration of the growth stage ([0236] teaches measuring GDDs is particularly useful when determining specific weather indices that correlate to different developmental stages in corn plant growth, wherein the number of growing degree days are used to define the start and end of different phenological development stages, wherein stages can be defined after a number of GDD days based on different phenological development stages and daily average temperature, wherein [0235] teaches the phenology stages of the corn plant can be tracked based upon factors outside appearance including growing degree days that are used based on the daily average temperature calculated from the daily maximum and minimum temperatures; see also: [0138]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar, Jarugumilli, and Farah to incorporate the further teachings of Farah to include wherein aggregating the weather data includes: averaging temperature data from the weather data for the duration of the growth stage; and/or summing precipitation data from the weather data for the duration of the growth stage. One would have been motivated to do so in order to use estimated crop growth thresholds to improve the accuracy of the historical crop growth model for future crop growth stage estimations (Farah, [0214]). By incorporating the teachings of Farah, one would have been able to accurately predict the growth stage thresholds that may improve watering efficiency and accuracy as it applies to different growth stages (Farah, [0213]).
Regarding claims 6 and 15, the combination of Shankar and Jarugumilli teaches all the limitations of claims 1 and 14 above.
Shankar further teaches further comprising: training, by the computing device, the quality model based on a training set of data ([0048] teaches providing, to the computational model, training data as in put data comprising field specific data, observed disease severity, growth stage data, and weather data, wherein [0049] teaches the field specific data refers to data collected with experimental trials, wherein the field specific data includes observed disease severity, growth stage, weather data, planting date, crop type, and more, wherein the field trials include different plot designs and different aspects of the fields are recorded throughout the growing period by trial operators, which is then later analyzed, which can be used to provide input data, wherein [0053] teaches the training data is gathered through observations made in trials that aim to capture the temporal disease development of plant populations, wherein [0054] teaches the model can be adjusted using backpropagation based on the training data, parameters, or weights of the computational model to adapt the computational model to the changes conditions of cultivation of an agricultural plant; see also: [0055-0056, 0101]),
the training set of data including, for a given time period and multiple fields, weather data, planting data and quality data for harvested crops from the multiple fields ([0091] teaches the system comprises training data comprising field specific data, weather data, observed disease severity, growth stage data, and more, wherein the training data is associated with changed conditions of cultivation of the agricultural plant, wherein the plant observation data indicative of a current state of health of the plant associated with the location at which the agricultural plant is being cultivated, wherein [0048] teaches providing, to the computational model, training data as in put data comprising field specific data, observed disease severity, growth stage data, and weather data, wherein [0049] teaches the field specific data refers to data collected with experimental trials, wherein the field specific data includes observed disease severity, growth stage, weather data, planting date, crop type, and more, wherein the field trials include different plot designs and different aspects of the fields are recorded throughout the growing period by trial operators, which is then later analyzed, which can be used to provide input data, wherein [0053] teaches the training data is gathered through observations made in trials that aim to capture the temporal disease development of plant populations, wherein [0054] teaches the model can be adjusted using backpropagation based on the training data, parameters, or weights of the computational model to adapt the computational model to the changes conditions of cultivation of an agricultural plant; see also: [0050, 0055-0056, 0101]).
However, Shankar fails to explicitly teach and validating, by the computing device, the quality model, prior to determining the quality of the crop in the target field.
From the same or similar field of endeavor, Farah teaches and validating, by the computing device, the quality model, prior to determining the quality of the crop in the target field ([0105] teaches the agricultural intelligence system is configured to create an agronomic model, wherein [0106] teaches the agronomic model is cross validated in order to ensure accuracy of the model, wherein cross validation may include comparison to ground truthing that compares predicted results with actual results at a nearby location, wherein [0110] teaches the agricultural intelligence system is configured to use cross validation techniques to cross validate agronomic models, wherein the models are compared against historical agronomic property values, wherein [0111] teaches implementing agronomic model created based upon cross validated agronomic datasets, wherein [0112] teaches the agricultural intelligence computer system is configured to store the preconfigured agronomic data models for future field data evaluation; see also: [0114-0115, 0213-0214]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the teachings of Farah to include and validating, by the computing device, the quality model, prior to determining the quality of the crop in the target field. One would have been motivated to do so in order to use estimated crop growth thresholds to improve the accuracy of the historical crop growth model for future crop growth stage estimations (Farah, [0214]). By incorporating the teachings of Farah, one would have been able to accurately predict the growth stage thresholds that may improve watering efficiency and accuracy as it applies to different growth stages (Farah, [0213]).
Regarding claim 14, the combination of Shankar, Jarugumilli, and Farah teaches all the limitations of claim 13 above.
However, Shankar does not explicitly teach wherein the executable instructions, when executed by the at least one processor to correlate the weather data to the duration of the growth stage, cause the at least one processor to aggregate the weather data for the duration of the growth stage.
From the same or similar field of endeavor, Farah further teaches wherein the executable instructions, when executed by the at least one processor to correlate the weather data to the duration of the growth stage, cause the at least one processor to aggregate the weather data for the duration of the growth stage ([0236] teaches measuring GDDs is particularly useful when determining specific weather indices that correlate to different developmental stages in corn plant growth, wherein the number of growing degree days are used to define the start and end of different phenological development stages, wherein stages can be defined after a number of GDD days based on different phenological development stages and daily average temperature, wherein [0235] teaches the phenology stages of the corn plant can be tracked based upon factors outside appearance including growing degree days that are used based on the daily average temperature calculated from the daily maximum and minimum temperatures; see also: [0138]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar, Jarugumilli, and Farah to incorporate the further teachings of Farah to include wherein the executable instructions, when executed by the at least one processor to correlate the weather data to the duration of the growth stage, cause the at least one processor to aggregate the weather data for the duration of the growth stage. One would have been motivated to do so in order to use estimated crop growth thresholds to improve the accuracy of the historical crop growth model for future crop growth stage estimations (Farah, [0214]). By incorporating the teachings of Farah, one would have been able to accurately predict the growth stage thresholds that may improve watering efficiency and accuracy as it applies to different growth stages (Farah, [0213]).
Regarding claim 17, the combination of Shankar, Jarugumilli, and Farah teaches all the limitations of claim 15 above.
However, Shankar does not explicitly teach wherein the executable instructions, when executed by the at least one processor to control an agricultural machine to harvest the crop from the target field, cause the at least one processor to control the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field.
From the same or similar field of endeavor, Jarugumilli further teaches wherein the executable instructions, when executed by the at least one processor to control an agricultural machine to harvest the crop from the target field, cause the at least one processor to control the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field ([0035] teaches each of the fields exhibits a number of characteristics that may be measured or estimated, wherein the characteristics of the fields include a quality of the crops growing in the field, wherein [0036] teaches combine harvesters may be used to harvest fields of corn based on the different characteristics of the corn and the fields in which the corn is planted that may be taken into account in making the harvest determinations, wherein during the growing season, the fields of corn can be screened, wherein if there are major quality issues, the field may not be selected for the combine, wherein [0039] teaches estimating the harvest time for the particular field based on the operational data, as well as in [0049-0050] teach determining the harvest date of a field based on the quality of the crops, wherein [0079] teaches the harvest plan includes the listing of the fields and designation of the harvest date, wherein [0080] teaches the platform can implement the harvest plan in the one or more fields with pickers that automatically response to directions and travel the appropriate fields for harvest; see also: [0037-0038, 0052, 0065]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar, Jarugumilli, and Farah to incorporate the further teachings of Jarugumilli to include wherein the executable instructions, when executed by the at least one processor to control an agricultural machine to harvest the crop from the target field, cause the at least one processor to control the agricultural machine to harvest the crop form the target field at a specific time indicated by the determined quality of the crop in the target field and/or by multiple qualities determined for other fields associated with a grower of the target field. One would have been motivated to do so in order to uniquely schedule, for a given yield from a production operation from season to season, less total acres that may be required to achieve a desired result including improved satisfaction on demand for the harvested crops (Jarugumilli, [0022]). By incorporating the teachings of Jarugumilli, one would have been able to promote efficiencies by harvesting fields with similar quality at the same time (Jarugumilli, [0037]).
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shankar et al. (US 20230110849 A1) in view of Jarugumilli et al. (US 20220138868 A1) in view of Rawlins et al. (US 5845229 A).
Regarding claim 8, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
However, Shankar does not explicitly teach further comprising designating a tag and/or a bag specific to the determined quality of the crop in the target field to be used in bagging the crop harvested from the target field and appending the determined quality to the tag and/or the bag.
From the same or similar field of endeavor, Rawlins teaches further comprising designating a tag and/or a bag specific to the determined quality of the crop in the target field to be used in bagging the crop harvested from the target field and appending the determined quality to the tag and/or the bag (Col 5 line 37 to Col 6 line 30 teach information regarding the quality and position of plants in an individual field area can be stored in the memory of the computer, wherein the crops are marked with an electronic tag that can be correlated with the quality of the crops, as well as in Col 7 lines 29-47 teach mapping the crop quality of an orchard comprising a plurality of individual field areas, wherein the quality factors of the fruits can be mapped through the use of a bar-code label which is scanned with a barcode scanner, wherein Col 7 lines 48-64 teach each tree can be marked with a permanent ID that is a marker in the form of an electronically scannable tag, which can be used to identify information regarding the tree, wherein Col 8 lines 14-56 teach filling a bag with fruit from a particular tree, wherein the tree comprises a marker for scanning which identifies the tree, wherein a quality test is run on the crop sample within the bag in order to determine the quality of fruit from the tree; see also: Col 7 lines 14-28, Col 8 lines 57-67, Figs. 5-6).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the teachings of Rawlins to include further comprising designating a tag and/or a bag specific to the determined quality of the crop in the target field to be used in bagging the crop harvested from the target field and appending the determined quality to the tag and/or the bag. One would have been motivated to do so in order to keep data on the quality of crops with respect to selected field locations in order to maximize the yield plan based on the profits for the quality of the crops (Rawlins, Col 7 lines 15-28). By incorporating the teachings of Rawlins, one would have been able to allow a farmer to select an individual field area to produce the quality of product that will maximize profits by storing information regarding the quality of crops from year to year (Rawlins, Col 9 lines 5-16).
Claim(s) 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Shankar et al. (US 20230110849 A1) in view of Jarugumilli et al. (US 20220138868 A1) in view of Dobbins et al. (US 11636414 B1).
Regarding claim 10, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
However, Shankar does not explicitly teach further comprising extending one or more contracts during a present growing season of the crop in the target field, in response to defined portions of the target field being of a certain quality.
From the same or similar field of endeavor, Dobbins teaches further comprising extending one or more contracts during a present growing season of the crop in the target field, in response to defined portions of the target field being of a certain quality (Col 10 lines 39-65 teach the automated crop management program may suggest information including selecting products, preparing a baseline application recommendation, obtaining approval of the recommendation, or revising the recommendation and obtaining approval of the revised recommendation, wherein the approved recommendation can be incorporated into a commercial contract, and obtaining approval and signature for the commercial contract, wherein Col 16 lines 33-65 teach the automated program application can collect crop yield and pack out information for each harvest for crops in the client location, wherein the automated nutrient application program can generate recommendations for approval by the client, wherein if the client approves a repeat of the automated nutrient application program for the next crop life cycle, the process can start again, wherein Col 11 lines 27-61 teach recommending initial products based upon past historical performance including quality in order to receive approval for commercial contract documentation, and wherein Col 17 lines 13-34 teach the automated analysis can utilize predictions for subsequent seasons; see also: Col 16 line 66 to Col 17 line 12).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the teachings of Dobbins to include further comprising extending one or more contracts during a present growing season of the crop in the target field, in response to defined portions of the target field being of a certain quality. One would have been motivated to do so in order to provide improved yield and improved profitability for the grower by gathering data to address issues such as performance during a post-season period (Dobbins, Col 6 lines 19-47). By incorporating the teachings of Dobbins, one would have been able to automatically collect data relevant to field factors in order to generate recommendations that maximize yield and profitability (Dobbins, Col 17 lines 26-45).
Regarding claim 11, the combination of Shankar and Jarugumilli teaches all the limitations of claim 1 above.
However, Shankar does not explicitly teach further comprising, after harvesting the crop from the target field, based on the determined quality of the crop in the target field one of: planting the crop in the target field in a following growing season; not planting the crop in the target field in the following growing season; planting a different crop in the target field in the following growing season; or not planting the different crop in the target field in the following growing season.
From the same or similar field of endeavor, Dobbins teaches further comprising, after harvesting the crop from the target field, based on the determined quality of the crop in the target field one of: planting a different crop in the target field in the following growing season (Fig. 6 and Col 17 lines 13-25 teach making future input decisions based upon previous/historical sources pertaining to the site/farm location in order to generate information for future crops including planting corn in the given field in order to gain a desired yield, as well as in Col 3 lines 25-59 teach combining current collected crop yield information with past crop yield information in order to generate recommendations for future crops based upon crop yield information; see also: Col 6 lines 3-41).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar and Jarugumilli to incorporate the teachings of Dobbins to include further comprising, after harvesting the crop from the target field, based on the determined quality of the crop in the target field one of: planting the crop in the target field in a following growing season; not planting the crop in the target field in the following growing season; planting a different crop in the target field in the following growing season; or not planting the different crop in the target field in the following growing season. One would have been motivated to do so in order to provide improved yield and improved profitability for the grower by gathering data to address issues such as performance during a post-season period (Dobbins, Col 6 lines 19-47). By incorporating the teachings of Dobbins, one would have been able to automatically collect data relevant to field factors in order to generate recommendations that maximize yield and profitability (Dobbins, Col 17 lines 26-45).
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Shankar et al. (US 20230110849 A1) in view of Jarugumilli et al. (US 20220138868 A1) in view of Farah et al. (US 20170351790 A1) in view of Rawlins et al. (US 5845229 A).
Regarding claim 16, the combination of Shankar, Jarugumilli, and Farah teaches all the limitations of claim 15 above.
However, Shankar does not explicitly teach wherein the executable instructions, when executed by the at least one processor further cause the at least one processor to designate a tag and/or a bag specific to the determined quality of the crop in the target field to be used in bagging the crop harvested from the target field and append the determined quality to the tag and/or the bag.
From the same or similar field of endeavor, Rawlins teaches wherein the executable instructions, when executed by the at least one processor further cause the at least one processor to designate a tag and/or a bag specific to the determined quality of the crop in the target field to be used in bagging the crop harvested from the target field and append the determined quality to the tag and/or the bag (Col 5 line 37 to Col 6 line 30 teach information regarding the quality and position of plants in an individual field area can be stored in the memory of the computer, wherein the crops are marked with an electronic tag that can be correlated with the quality of the crops, as well as in Col 7 lines 29-47 teach mapping the crop quality of an orchard comprising a plurality of individual field areas, wherein the quality factors of the fruits can be mapped through the use of a bar-code label which is scanned with a barcode scanner, wherein Col 7 lines 48-64 teach each tree can be marked with a permanent ID that is a marker in the form of an electronically scannable tag, which can be used to identify information regarding the tree, wherein Col 8 lines 14-56 teach filling a bag with fruit from a particular tree, wherein the tree comprises a marker for scanning which identifies the tree, wherein a quality test is run on the crop sample within the bag in order to determine the quality of fruit from the tree; see also: Col 7 lines 14-28, Col 8 lines 57-67, Figs. 5-6).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar, Jarugumilli, and Farah to incorporate the teachings of Rawlins to include wherein the executable instructions, when executed by the at least one processor further cause the at least one processor to designate a tag and/or a bag specific to the determined quality of the crop in the target field to be used in bagging the crop harvested from the target field and append the determined quality to the tag and/or the bag. One would have been motivated to do so in order to keep data on the quality of crops with respect to selected field locations in order to maximize the yield plan based on the profits for the quality of the crops (Rawlins, Col 7 lines 15-28). By incorporating the teachings of Rawlins, one would have been able to allow a farmer to select an individual field area to produce the quality of product that will maximize profits by storing information regarding the quality of crops from year to year (Rawlins, Col 9 lines 5-16).
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Shankar et al. (US 20230110849 A1) in view of Jarugumilli et al. (US 20220138868 A1) in view of Farah et al. (US 20170351790 A1) in view of Dobbins et al. (US 11636414 B1).
Regarding claim 18, the combination of Shankar, Jarugumilli, and Farah teaches all the limitations of claim 15 above.
However, Shankar does not explicitly teach wherein the executable instructions, when executed by the at least one processor to further cause the at least one processor to extend one or more contracts during a present growing season of the crop in the target field, in response to defined portions of the target field being of a certain quality.
From the same or similar field of endeavor, Dobbins teaches wherein the executable instructions, when executed by the at least one processor to further cause the at least one processor to extend one or more contracts during a present growing season of the crop in the target field, in response to defined portions of the target field being of a certain quality (Col 10 lines 39-65 teach the automated crop management program may suggest information including selecting products, preparing a baseline application recommendation, obtaining approval of the recommendation, or revising the recommendation and obtaining approval of the revised recommendation, wherein the approved recommendation can be incorporated into a commercial contract, and obtaining approval and signature for the commercial contract, wherein Col 16 lines 33-65 teach the automated program application can collect crop yield and pack out information for each harvest for crops in the client location, wherein the automated nutrient application program can generate recommendations for approval by the client, wherein if the client approves a repeat of the automated nutrient application program for the next crop life cycle, the process can start again, wherein Col 11 lines 27-61 teach recommending initial products based upon past historical performance including quality in order to receive approval for commercial contract documentation, and wherein Col 17 lines 13-34 teach the automated analysis can utilize predictions for subsequent seasons; see also: Col 16 line 66 to Col 17 line 12).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shankar, Jarugumilli, and Farah to incorporate the teachings of Dobbins to include wherein the executable instructions, when executed by the at least one processor to further cause the at least one processor to extend one or more contracts during a present growing season of the crop in the target field, in response to defined portions of the target field being of a certain quality. One would have been motivated to do so in order to provide improved yield and improved profitability for the grower by gathering data to address issues such as performance during a post-season period (Dobbins, Col 6 lines 19-47). By incorporating the teachings of Dobbins, one would have been able to automatically collect data relevant to field factors in order to generate recommendations that maximize yield and profitability (Dobbins, Col 17 lines 26-45).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Foster et al. (US 8531300 B2) discloses record, correlating, and analyzing information at crops associated with various phases of production to improve crop yield and quality including directly tagging the crop
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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