DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
2. Claims 1-42 have been examined in this application. This communication is the first action on the merits.
Priority
3. The Examiner has noted the Applicants claiming Priority from Provisional (PRO) Application #63/499,952 filed on 05/03/2023. Therefore, the earliest effective filing date considered for this case is of 05/03/2023.
IDS Statements
4. The 1 Information Disclosure Statement (IDS) filed on 09/18/2024 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Claim Objections
5. Claims 4-5, 18-19 and 32-33 are objected to because of the following informalities:
(A). The 3rd claim limitation of Dependent Claims 4, 18 and 32 recites the following: “the optimization results comprise a schedule identifying resources that are scheduled to perform harvesting of the crops in the growing areas or in lots within the growing areas and when the resources scheduled to perform harvesting of the crops in the growing areas or in the lots within the growing areas.” Examiner notes a minor typo or claim informality in the phrase “the growing areas” when referring back to the 1st limitation of Dependent Claims 4, 18 and 32 which recite “multiple growing areas”. To correct the consistency, Examiner suggests to Applicant to amend the 3rd claim limitation of Dependent Claims 4, 18 and 32 to recite the following: “the optimization results comprise a schedule identifying resources that are scheduled to perform harvesting of the crops in the multiple growing areas or in lots within the multiple growing areas and when the resources scheduled to perform harvesting of the crops in the multiple growing areas or in the lots within the multiple growing areas.”
(B). The 2nd claim limitation of Dependent Claims 5, 19 and 33 recites the following: “the optimizer determines a scheduling status for use of the resources over time.” Examiner notes a minor typo or claim informality in the phrase “the resources” when referring back to the 1st limitation of Dependent Claims 5, 19 and 33 which recite “multiple resources”. To correct the consistency, Examiner suggests to Applicant to amend the 2nd claim limitation of Dependent Claims 5, 19 and 33 to recite the following: “the optimizer determines a scheduling status for use of the multiple resources over time.” Appropriate corrections are required.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-42 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-42 are each focused to a statutory category namely, a “method” or a “process” (Claims 1-14), an “apparatus” or a “system” (Claims 15-28) and a “non-transitory computer readable medium” or an “article of manufacture” (Claims 29-42).
Step 2A Prong One: Independent Claims 1, 15 and 29 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“obtaining, , data , wherein the data is associated with or affects to be optimized” (see Independent Claim 1);
“generating, , predictions based on the obtained data, wherein the predictions represent estimated values associated with one or more time-varying parameters associated with ” (see Independent Claim 1);
“providing, , the predictions ” (see Independent Claim 1);
“executing, , to generate optimization results based on the predictions, the optimization results associated with ” (see Independent Claim 1);
“” (see Independent Claim 15);
“” (see Independent Claim 29);
“obtain data , wherein the data is associated with or affects to be optimized” (see Independent Claims 15 and 29);
“generate predictions based on the obtained data, wherein the predictions represent estimated values associated with one or more time-varying parameters associated with ” (see Independent Claims 15 and 29);
“provide the predictions ” (see Independent Claims 15 and 29);
“execute to generate optimization results based on the predictions, the optimization results associated with ” (see Independent Claims 15 and 29).
These abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments or opinions) or (2) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior (including teachings or following rules or instructions) or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations or (5) mathematical relationships.
That is, other than reciting the additional elements of (e.g., “one or more data sources” & “an optimizer” & “underlying system” & “one or more processors” & “at least one processing device”), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments or opinions) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations or (5) mathematical relationships.
Moreover, the mere recitation of generic computer components such as (e.g., “one or more processors” & “at least one processing device”) does not take the claims out of “Certain Methods of Organizing Human Activities” or “Mental Processes” or “Mathematical Concepts” Groupings.
Therefore, at step 2a prong 1, Yes, Claims 1-42 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1 and 15 recites additional elements directed to: (e.g., “at least one processing device” & “one or more data sources”). Independent Claim 29 recites additional elements directed to: (e.g., “one or more processors” & “one or more data sources”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1, 15 and 29: With respect to reliance on (e.g., “an optimizer” & “underlying system”) as additional elements when considered individually and as an ordered combination (as a whole) for the claim limitations for Independent Claims 1, 15 and 29, these additional elements do not provide limitations that are indicative of integration into a practical application due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) limiting to a particular field of use or technological environment pertaining to providing predictions to an optimizer whereby the optimizer generates optimization results based on the predictions association with an underlying system using a computer in an agricultural environment (see MPEP § 2106.05 (h)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-42 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1 and 15 recites additional elements directed to: (e.g., “at least one processing device” & “one or more data sources”). Independent Claim 29 recites additional elements directed to: (e.g., “one or more processors” & “one or more data sources”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (h) and See MPEP § 2106.05 (f). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0032]: “The processing device 202 includes any suitable number(s) and type(s) of processors or other processing devices in any suitable arrangement. Example types of processing devices 202 include one or more microprocessors, microcontrollers, reduced instruction set computers (RISCs), complex instruction set computers (CISCs), graphics processing units (GPUs), data processing units (DPUs), virtual processing units, associative process units (APUs), tensor processing units (TPUs), vision processing units (VPUs), neuromorphic chips, artificial intelligence (AI) chips, quantum processing units (QPUs), cerebras wafer-scale engines (WSEs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.”).
Independent Claims 1, 15 and 29: With respect to reliance on (e.g., “an optimizer” & “underlying system”) as additional elements when considered individually and as an ordered combination (as a whole) for the claim limitations for Independent Claims 1, 15 and 29, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) limiting to a particular field of use or technological environment pertaining to providing predictions to an optimizer whereby the optimizer generates optimization results based on the predictions association with an underlying system using a computer in an agricultural environment (see MPEP § 2106.05 (h)). Additionally, certain/particular claim limitations in Independent Claims 1, 15 and 29 recite steps of “receiving data” (e.g., “obtaining, using at least one processing device, data from one or more data sources, wherein the data is associated with or affects an underlying system to be optimized”) when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), which have been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or Transmitting Data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-14, 16-28 and 30-42 recite additional elements directed to: (e.g., “agricultural system” & “processing facility” & “reinforcement learning” & “Monte Carlo simulations” & “equipment”), and when considered individually and as an ordered combination (as a whole) with the limitations recite the same abstract idea(s) as shown in Independent Claims 1, 15 and 29 along with further steps/details that could be performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations or (5) mathematical relationships.
Dependent Claims 2-3, 5-6, 10, 12, 14, 16-17, 19-20, 24, 26, 28, 30-31, 33-34, 38, 40 and 42 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and 2B for Independent Claims 1, 15 and 29. Dependent Claims 4, 7, 11, 18, 21, 25, 32, 35 and 39: With respect to reliance on (e.g., “agricultural system” (see Dependent Claims 4, 18 and 32) & “processing facility” (see Dependent Claims 7, 21 and 35) & “reinforcement learning” & “Monte Carlo Simulations” (see Dependent Claims 11, 25 and 39)) as additional elements shown in Dependent Claims 4, 7, 11, 18, 21, 25, 32, 35 and 39 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting to a particular field of use or technological environment pertaining to providing predictions to an optimizer whereby the optimizer generates optimization results based on the predictions association with an underlying system using a computer in an agricultural environment or an agricultural field of use (see MPEP § 2106.05 (h)).
Dependent Claims 8-9, 13, 22-23, 27, 36-37 and 41: With respect to reliance on (e.g., “machine learning model”) as additional elements shown in Dependent Claims 8-9, 13, 22-23, 27, 36-37 and 41 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: limiting to a particular field of use or technological environment pertaining to training to generated the predictions when obtained data includes imperfections and using the feedback to compensate for prediction errors associated with the predictions using a computer in an agricultural environment or an agricultural field of use (see MPEP § 2106.05 (h)).
The additional element of “machine learning” or “machine learning model” in certain/particular claims does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. See for example; US PG Pub (US 2020/0090107 A1) hereinafter McKeeman. McKeeman at ¶ [0163]: “Intelligent matching agent 290 may be based on an artificial intelligence model, neural network, or other machine learning algorithm which may, in examples, be trained to recognize and identify compatibility between information fields comprised within producer records, equipment owner records and operator records held within storages 201, 202 and 203.” See for example; US PG Pub (US 2020/0272971 A1) hereinafter Ruff, et. al. See Ruff at ¶ [0225]: “Other machine-learning methods known to someone skilled in the art for capturing various relationships between the seeding rate (in conjunction with other attributes) and the crop yield lift, such as neural networks or regression techniques, can also be used.” See Ruff at ¶ [0347]: “The model may be a regression model, such as a generalized additive model (GAM), a tree-based model, a machine learning model, and/or a neural network model. The model may be configured to estimate a distribution, such as a sinh-arcsinh (SHASH) distribution. Alternatively, the agricultural intelligence computer system may use alternative methods of quantifying uncertainty, such as Monte Carlo sampling.”
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-42 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-42 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 102
8. 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.
9. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
10. Claims 1, 3, 6, 8-9, 12-13, 15, 17, 20, 22-23, 26-27, 29, 31, 34, 36-37 and 40-41 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by US PG Pub (US 2019/0057461 A1) hereinafter Ruff, et. al.
Regarding Independent Claim 1, Ruff method for a machine learning-based production optimizers teaches the following:
- obtaining (see at least Ruff: ¶ [0083] & ¶ [0091] & ¶ [0198]. Ruff teaches that the system 130 may obtain or ingest data under user 102 control, on a mass basis from a large number of growers who have contributed data to a shared database system. This form of obtaining data may be termed “manual data ingest” as one or more user-controlled computer operations are requested or triggered to obtain data for use by the system 130. See also Ruff at ¶ [0083]: The nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs. See also Ruff at ¶ [0198]: The system 130 is programmed to present summaries, tips, or further recommendations generated from analyzing the data obtained from the multitude of prescribed experiments across grower fields.), using at least one processing device (see at least Ruff: ¶ [0114-0116] & Fig. 1 & Fig. 4.), data from one or more data sources (see at least Ruff: ¶ [0107-0108] & ¶ [0201]. Ruff teaches that the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise, distorting effects, and confounding factors within the agronomic data including measured outliers that could adversely affect received field data values. The historical data can be obtained from internal trials and experiments or from external data sources. The number of fields can have common values in certain characteristics, such as the crop hybrid grown in a field, the location of a field, or the yield lift management practice for a field.), wherein the data is associated with or affects an underlying system to be optimized (see at least Ruff: ¶ [0105-0107] & ¶ [0109] & ¶ [0201]. Ruff teaches that the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. seeds and planting instructions 208 are programmed to provide tools for seed selection, hybrid placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield or return on investment through optimized seed purchase, placement and population. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method.);
- generating (see at least Ruff: ¶ [0194-0197] & ¶ [0201] & ¶ [0208-0211].), using the at least one processing device (see at least Ruff: ¶ [0114-0116] & Fig. 1 & Fig. 4.), predictions based on the obtained data, wherein the predictions represent estimated values associated with one or more time-varying parameters associated with the underlying system (see at least Ruff: ¶ [0194-0197] & ¶ [0201] & ¶ [0208-0211]. Ruff teaches that the system 130 is programmed to further analyze the data, to adjust the predictions or the plans for the prescribed experiments, or to glean specific insight that can be used in designing future experiments. Such analysis can be performed periodically, at the end of a season or a year, or upon request by a grower. When a prescribed experiment was not properly carried out, the predicted result might not be obtained, and the system 130 can be programmed to adjust the prediction based on how the plan for the prescribed experiment was followed. For example, the system 130 can be configured to consider that the actual seeding rate was only 80% of the prescribed seeding rate overall, due to erroneous calibration of the agricultural implement, the skipping of certain planting steps, or other reasons, in determining the predicted crop yield might be only 80% of or otherwise less than the predicted or recommended crop yield. The system 130 can also be programmed to generate a series of remedial steps in order to realize the original prediction. For example, when the actual seeding rate was only 80% of the prescribed seeding rate overall, the system 130 can be configured to compensate for it by prescribing a seeding rate that was 20% or otherwise higher than originally prescribed for the rest of the experiment. See also Ruff at ¶ [0105]: The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop. See also Ruff at ¶ [0211]: The group of fields may be selected from those fields that share values with the grower's field in certain characteristics, such as the crop hybrid grown in a field, the predicted yield lift for a change in management practice for a field, or the location of a field. See also Ruff at ¶ [0242]: The agricultural intelligence computing system may additionally alter the predicted results of the trial based on identified modifications to the testing locations. For example, the agricultural intelligence computing system may predict an increase in yield of 30 bushels/acre for an application of 40 lbs/acre of nitrogen. If the agricultural intelligence computing system detects that only 30 lbs/acre of nitrogen has been applied to a field, the agricultural intelligence computing system may lower the predicted increase in yield of 30 bushels/acre.);
- providing, using the at least one processing device (see at least Ruff: ¶ [0114-0116] & Fig. 1 & Fig. 4.), the predictions to an optimizer (see at least Ruff: ¶ [0104-0106] & ¶ [0109] & ¶ [0201-0204]. Ruff teaches that the agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data. See also Ruff at ¶ [0201-0204] for system 130 as the optimizer and/or the trained model of Ruff at paragraphs ¶ [0104-0106].)
- executing, using the at least one processing device (see at least Ruff: ¶ [0114-0116] & Fig. 1 & Fig. 4.), the optimizer to generate optimization results based on the predictions, the optimization results associated with the underlying system (see at least Ruff: ¶ [0200-0208] & ¶ [0220]. Ruff notes the use of the field targeting and based on the inputs that have been fed into the system at the prior limitations. Some optimization results are depicted at Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.).
Regarding Independent Claim 15, Ruff apparatus for a machine learning-based production optimizers teaches the following:
- at least one processing device configured to (see at least Ruff: ¶ [0114-0116] & Fig. 1 & Fig. 4.);
- obtain (see at least Ruff: ¶ [0083] & ¶ [0091] & ¶ [0198]. Ruff teaches that the system 130 may obtain or ingest data under user 102 control, on a mass basis from a large number of growers who have contributed data to a shared database system. This form of obtaining data may be termed “manual data ingest” as one or more user-controlled computer operations are requested or triggered to obtain data for use by the system 130. See also Ruff at ¶ [0083]: The nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs. See also Ruff at ¶ [0198]: The system 130 is programmed to present summaries, tips, or further recommendations generated from analyzing the data obtained from the multitude of prescribed experiments across grower fields.) data from one or more data sources (see at least Ruff: ¶ [0107-0108] & ¶ [0201]. Ruff teaches that the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise, distorting effects, and confounding factors within the agronomic data including measured outliers that could adversely affect received field data values. The historical data can be obtained from internal trials and experiments or from external data sources. The number of fields can have common values in certain characteristics, such as the crop hybrid grown in a field, the location of a field, or the yield lift management practice for a field.), wherein the data is associated with or affects an underlying system to be optimized (see at least Ruff: ¶ [0105-0107] & ¶ [0109] & ¶ [0201]. Ruff teaches that the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. seeds and planting instructions 208 are programmed to provide tools for seed selection, hybrid placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield or return on investment through optimized seed purchase, placement and population. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method.);
- generate predictions based on the obtained data, wherein the predictions represent estimated values associated with one or more time-varying parameters associated with the underlying system (see at least Ruff: ¶ [0194-0197] & ¶ [0201] & ¶ [0208-0211]. Ruff teaches that the system 130 is programmed to further analyze the data, to adjust the predictions or the plans for the prescribed experiments, or to glean specific insight that can be used in designing future experiments. Such analysis can be performed periodically, at the end of a season or a year, or upon request by a grower. When a prescribed experiment was not properly carried out, the predicted result might not be obtained, and the system 130 can be programmed to adjust the prediction based on how the plan for the prescribed experiment was followed. For example, the system 130 can be configured to consider that the actual seeding rate was only 80% of the prescribed seeding rate overall, due to erroneous calibration of the agricultural implement, the skipping of certain planting steps, or other reasons, in determining the predicted crop yield might be only 80% of or otherwise less than the predicted or recommended crop yield. The system 130 can also be programmed to generate a series of remedial steps in order to realize the original prediction. For example, when the actual seeding rate was only 80% of the prescribed seeding rate overall, the system 130 can be configured to compensate for it by prescribing a seeding rate that was 20% or otherwise higher than originally prescribed for the rest of the experiment. See also Ruff at ¶ [0105]: The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop. See also Ruff at ¶ [0211]: The group of fields may be selected from those fields that share values with the grower's field in certain characteristics, such as the crop hybrid grown in a field, the predicted yield lift for a change in management practice for a field, or the location of a field. See also Ruff at ¶ [0242]: The agricultural intelligence computing system may additionally alter the predicted results of the trial based on identified modifications to the testing locations. For example, the agricultural intelligence computing system may predict an increase in yield of 30 bushels/acre for an application of 40 lbs/acre of nitrogen. If the agricultural intelligence computing system detects that only 30 lbs/acre of nitrogen has been applied to a field, the agricultural intelligence computing system may lower the predicted increase in yield of 30 bushels/acre.);
- provide the predictions to an optimizer (see at least Ruff: ¶ [0104-0106] & ¶ [0109] & ¶ [0201-0204]. Ruff teaches that the agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data. See also Ruff at ¶ [0201-0204] for system 130 as the optimizer and/or the trained model of Ruff at paragraphs ¶ [0104-0106].)
- execute the optimizer to generate optimization results based on the predictions, the optimization results associated with the underlying system (see at least Ruff: ¶ [0200-0208] & ¶ [0220]. Ruff notes the use of the field targeting and based on the inputs that have been fed into the system at the prior limitations. Some optimization results are depicted at Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.).
Regarding Independent Claim 29, Ruff non-transitory computer readable medium for a machine learning-based production optimizers teaches the following:
- storing computer readable program code that, when executed by one or more processors, causes the one or more processors to (see at least Ruff: ¶ [0114-0116] & Fig. 1 & Fig. 4.);
- obtain (see at least Ruff: ¶ [0083] & ¶ [0091] & ¶ [0198]. Ruff teaches that the system 130 may obtain or ingest data under user 102 control, on a mass basis from a large number of growers who have contributed data to a shared database system. This form of obtaining data may be termed “manual data ingest” as one or more user-controlled computer operations are requested or triggered to obtain data for use by the system 130. See also Ruff at ¶ [0083]: The nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs. See also Ruff at ¶ [0198]: The system 130 is programmed to present summaries, tips, or further recommendations generated from analyzing the data obtained from the multitude of prescribed experiments across grower fields.) data from one or more data sources (see at least Ruff: ¶ [0107-0108] & ¶ [0201]. Ruff teaches that the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise, distorting effects, and confounding factors within the agronomic data including measured outliers that could adversely affect received field data values. The historical data can be obtained from internal trials and experiments or from external data sources. The number of fields can have common values in certain characteristics, such as the crop hybrid grown in a field, the location of a field, or the yield lift management practice for a field.), wherein the data is associated with or affects an underlying system to be optimized (see at least Ruff: ¶ [0105-0107] & ¶ [0109] & ¶ [0201]. Ruff teaches that the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. seeds and planting instructions 208 are programmed to provide tools for seed selection, hybrid placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield or return on investment through optimized seed purchase, placement and population. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method.);
- generate predictions based on the obtained data, wherein the predictions represent estimated values associated with one or more time-varying parameters associated with the underlying system (see at least Ruff: ¶ [0194-0197] & ¶ [0201] & ¶ [0208-0211]. Ruff teaches that the system 130 is programmed to further analyze the data, to adjust the predictions or the plans for the prescribed experiments, or to glean specific insight that can be used in designing future experiments. Such analysis can be performed periodically, at the end of a season or a year, or upon request by a grower. When a prescribed experiment was not properly carried out, the predicted result might not be obtained, and the system 130 can be programmed to adjust the prediction based on how the plan for the prescribed experiment was followed. For example, the system 130 can be configured to consider that the actual seeding rate was only 80% of the prescribed seeding rate overall, due to erroneous calibration of the agricultural implement, the skipping of certain planting steps, or other reasons, in determining the predicted crop yield might be only 80% of or otherwise less than the predicted or recommended crop yield. The system 130 can also be programmed to generate a series of remedial steps in order to realize the original prediction. For example, when the actual seeding rate was only 80% of the prescribed seeding rate overall, the system 130 can be configured to compensate for it by prescribing a seeding rate that was 20% or otherwise higher than originally prescribed for the rest of the experiment. See also Ruff at ¶ [0105]: The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop. See also Ruff at ¶ [0211]: The group of fields may be selected from those fields that share values with the grower's field in certain characteristics, such as the crop hybrid grown in a field, the predicted yield lift for a change in management practice for a field, or the location of a field. See also Ruff at ¶ [0242]: The agricultural intelligence computing system may additionally alter the predicted results of the trial based on identified modifications to the testing locations. For example, the agricultural intelligence computing system may predict an increase in yield of 30 bushels/acre for an application of 40 lbs/acre of nitrogen. If the agricultural intelligence computing system detects that only 30 lbs/acre of nitrogen has been applied to a field, the agricultural intelligence computing system may lower the predicted increase in yield of 30 bushels/acre.);
- provide the predictions to an optimizer (see at least Ruff: ¶ [0104-0106] & ¶ [0109] & ¶ [0201-0204]. Ruff teaches that the agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data. See also Ruff at ¶ [0201-0204] for system 130 as the optimizer and/or the trained model of Ruff at paragraphs ¶ [0104-0106].)
- execute the optimizer to generate optimization results based on the predictions, the optimization results associated with the underlying system (see at least Ruff: ¶ [0200-0208] & ¶ [0220]. Ruff notes the use of the field targeting and based on the inputs that have been fed into the system at the prior limitations. Some optimization results are depicted at Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.).
Regarding Dependent Claims 3, 17 and 31, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Independent Claims 1, 15 and 29 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- wherein the optimizer (see at least Ruff: ¶ [0104-0106] noting the trained model and Ruff ¶ [0201-0204] system 130 as the optimizer.) is configured to perform production schedule optimization (see at least Ruff: ¶ [0186] & ¶ [0193] & Fig. 14-16. Ruff teaches that the system 130 is programmed to prescribe experiments to grower fields and the design or selection of experiments can be carried out automatically according to a predetermined schedule, such as at the beginning of every year or every growing season. The prescribing of experiments can also be performed automatically. The system 130 can be configured to generate the prescription, plan, or scheme for an experiment that is to be understood by a human, a machine, or a combination of both. See also Ruff at ¶ [0051] showing the Actual Production History (APH). See also Ruff at ¶ [0193]: The system 130 can be programmed to validate the execution of each prescribed experiment according to a predetermined schedule, such as every month, or as soon as error signals or application data are received. Some optimization results depicted at Ruff of Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.).
Regarding Dependent Claims 6, 20 and 34, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Independent Claims 1, 15 and 29 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- wherein the optimizer (see at least Ruff: ¶ [0104-0106] noting the trained model and Ruff ¶ [0201-0204] system 130 as the optimizer.) is configured to perform process optimization (see at least Ruff: ¶ [0147] & ¶ [0186-0190] & Figs. 14-16. Ruff notes identification of processing facility, and/or identification of one or more people processing and/or collecting the soil, additional soil chemistry data, bulk density of the soil, and/or buffer capacity. See also Ruff at ¶ [0186]: The design or selection of experiments can be carried out automatically according to a predetermined schedule, such as at the beginning of every year or every growing season. The prescribing of experiments can also be performed automatically. The system 130 can be configured to generate the prescription, plan, or scheme for an experiment that is to be understood by a human, a machine, or a combination of both. For example, one experiment may be to plant certain seeds at certain rates on a certain grower's fields. See also Ruff at ¶ [0190]: The system 130 is programmed to receive data from the same agricultural implements to which the experiment schemes or plans were transmitted, or from the same field manager computing device, including mobile devices, registered under the growers. The agricultural implements can be equipped with sensors that can capture many types of data. In addition to data related to the variables involved in the experiment, such as the volume of seeds actually planted, the time of actual planting, the actual moving or rotational speed of the agricultural implement, the route actually taken by the agricultural implement, or the crop yield actually achieved. The data can be transmitted by an agricultural implement or a personal computing device to the system 130 once the data becomes available, upon request by the system 130, or according to a predetermined schedule.);
Regarding Dependent Claims 8, 22 and 36, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Independent Claims 1, 15 and 29 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- wherein the predictions are generated using a trained machine learning model (see at least Ruff: ¶ [0104] & ¶ [0203-0206] & ¶ [0220]. Ruff teaches a Process Overview-Agronomic Model Training. See also modeled yield variability data based on predictors to a model trained on historic yield variability data. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all-subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. See also Ruff at ¶ [0203]: Other machine-learning methods known to someone skilled in the art for capturing various relationship between the seeding rate (in conjunction with other attributes) and the crop yield lift, such as neural networks or regression techniques can be used. See also Ruff at ¶ [0206]: The type of management practice can also be an input attribute for a machine learning method discussed above.).
Regarding Dependent Claims 9, 23 and 37, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Claims 1, 8, 15, 22, 29 and 36 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- wherein the trained machine learning model is trained (see at least Ruff: ¶ [0104] & ¶ [0203-0206] & ¶ [0220]. Ruff teaches a Process Overview-Agronomic Model Training. See also modeled yield variability data based on predictors to a model trained on historic yield variability data. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all-subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. See also Ruff at ¶ [0203]: Other machine-learning methods known to someone skilled in the art for capturing various relationship between the seeding rate (in conjunction with other attributes) and the crop yield lift, such as neural networks or regression techniques can be used. See also Ruff at ¶ [0206]: The type of management practice can also be an input attribute for a machine learning method discussed above.) to generate the predictions when the obtained data includes imperfections (see at least Ruff: ¶ [0108] & ¶ [0196]. Ruff teaches that the field data received from one or more data sources may be preprocessed for the purpose of removing noise, distorting effects, and confounding factors within the agronomic data including measured outliers that could adversely affect received field data values. Embodiments of agronomic data preprocessing may include, but are not limited to, removing data values commonly associated with outlier data values. See also Ruff at [0196]: The system 130 can also be configured to detect patterns from the outcomes of similar experiments, which can help identify outliers and point to field-specific issues. The reasons behind the discrepancies between the predicted outcomes and the actual outcomes can be used for designing future experiments or generating predictions for future experiments. Examiner Note: Examiner interprets that the “imperfections” are outliers determined when performing predictions when the ML model is trained.).
Regarding Dependent Claims 12, 26 and 40, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Independent Claims 1, 15 and 29 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- further comprising: iteratively obtaining the data (see at least Ruff: ¶ [0187] & ¶ [0199] & (Dependent Claim 8 of Ruff). Ruff notes that in one scheme, a grower's field can be divided into locations, and the prescription can indicate that the first location is to be used for the targeted trial, the second location is to be used for the control trial, and this pattern is to repeat three times geographically (the second time on the 3rd and fourth locations, and the 3 time on the 5th and the sixth locations). Some or all of these steps 1302 through 1312 can be executed repeatedly, iteratively, or out of order. For example, data capturing and execution validation can take place periodically during a season.), generating the predictions (see at least Ruff: ¶ [0194-0197] & ¶ [0201] & ¶ [0208-0211]. Ruff teaches that the system 130 is programmed to further analyze the data, to adjust the predictions or the plans for the prescribed experiments, or to glean specific insight that can be used in designing future experiments. Such analysis can be performed periodically, at the end of a season or a year, or upon request by a grower. When a prescribed experiment was not properly carried out, the predicted result might not be obtained, and the system 130 can be programmed to adjust the prediction based on how the plan for the prescribed experiment was followed. For example, the system 130 can be configured to consider that the actual seeding rate was only 80% of the prescribed seeding rate overall, due to erroneous calibration of the agricultural implement, the skipping of certain planting steps, or other reasons, in determining the predicted crop yield might be only 80% of or otherwise less than the predicted or recommended crop yield. The system 130 can also be programmed to generate a series of remedial steps in order to realize the original prediction. For example, when the actual seeding rate was only 80% of the prescribed seeding rate overall, the system 130 can be configured to compensate for it by prescribing a seeding rate that was 20% or otherwise higher than originally prescribed for the rest of the experiment. See also Ruff at ¶ [0105]: The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop. See also Ruff at ¶ [0211]: The group of fields may be selected from those fields that share values with the grower's field in certain characteristics, such as the crop hybrid grown in a field, the predicted yield lift for a change in management practice for a field, or the location of a field. See also Ruff at ¶ [0242]: The agricultural intelligence computing system may additionally alter the predicted results of the trial based on identified modifications to the testing locations. For example, the agricultural intelligence computing system may predict an increase in yield of 30 bushels/acre for an application of 40 lbs/acre of nitrogen. If the agricultural intelligence computing system detects that only 30 lbs/acre of nitrogen has been applied to a field, the agricultural intelligence computing system may lower the predicted increase in yield of 30 bushels/acre.), providing the predictions to the optimizer (see at least Ruff: ¶ [0104-0106] & ¶ [0109] & ¶ [0201-0204]. Ruff teaches that the agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data. See also Ruff at ¶ [0201-0204] for system 130 as the optimizer and/or the trained model of Ruff at paragraphs ¶ [0104-0106].), and executing the optimizer (see at least Ruff: ¶ [0200-0208] & ¶ [0220]. Ruff notes the use of the field targeting and based on the inputs that have been fed into the system at the prior limitations. Some optimization results are depicted at Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.).
- wherein at least one of the optimization results or the predictions from one iteration are provided as feedback for use during generation of the predictions during a subsequent iteration (see at least Ruff: ¶ [0108-0110] & (Dependent Claim 8 of Ruff). Ruff notes that the agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not meet configured quality thresholds are used during future data subset selection steps (block 310). The report can also outline possible experiments to apply to the grower's fields in the future and solicit feedback from the grower. See also (Dependent Claim 8 of Ruff): “Updating one or more predictions of a result of the trial on the agricultural field based, at least in part, on the one or more parameters of the application.”).
Regarding Dependent Claims 13, 27 and 41, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Claims 1, 12, 15, 26, 29 and 40 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- the predictions are generated using a trained machine learning model (see at least Ruff: ¶ [0104] & ¶ [0203-0206] & ¶ [0220]. Ruff teaches a Process Overview-Agronomic Model Training. See also modeled yield variability data based on predictors to a model trained on historic yield variability data. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all-subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. See also Ruff at ¶ [0203]: Other machine-learning methods known to someone skilled in the art for capturing various relationship between the seeding rate (in conjunction with other attributes) and the crop yield lift, such as neural networks or regression techniques can be used. See also Ruff at ¶ [0206]: The type of management practice can also be an input attribute for a machine learning method discussed above.);
- the trained machine learning model (see at least Ruff: ¶ [0104] & ¶ [0203-0206] & ¶ [0220]. Ruff teaches a Process Overview-Agronomic Model Training. See also modeled yield variability data based on predictors to a model trained on historic yield variability data. See also Ruff at ¶ [0109]: The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all-subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. See also Ruff at ¶ [0203]: Other machine-learning methods known to someone skilled in the art for capturing various relationship between the seeding rate (in conjunction with other attributes) and the crop yield lift, such as neural networks or regression techniques can be used. See also Ruff at ¶ [0206]: The type of management practice can also be an input attribute for a machine learning method discussed above.) is configured to use the feedback to compensate for prediction errors associated with the predictions (see at least Ruff: ¶ [0110] & ¶ [0193] & ¶ [0241]. Ruff notes that agronomic models may be compared and/or validated using one or more comparison techniques, such as, but not limited to, root mean square error with leave-one-out cross validation (RMSECV), mean absolute error, and mean percentage error. For example, RMSECV can cross validate agronomic models by comparing predicted agronomic property values created by the agronomic model against historical agronomic property values collected and analyzed. The agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not meet configured quality thresholds are used during future data subset selection steps (block 310). See also Ruff at ¶ [0193]: The system 130 can be programmed to validate the execution of each prescribed experiment according to a predetermined schedule, such as every month, or as soon as error signals or application data are received. See also Ruff at ¶ [0241]: The agricultural intelligence computing system may be programmed or configured to alter one or more trials in response to determining that a testing location does not comply with a trial. The agricultural intelligence computing system suggests alterations to one or more practices for other locations to offset errors in the testing location. If a control location was planted with a seeding rate that is ten percent higher than required by the trial, the agricultural intelligence computing system may modify the seeding rate for the other testing locations to be ten percent higher. See also (Dependent Claim 8 of Ruff): “Updating one or more predictions of a result of the trial on the agricultural field based, at least in part, on the one or more parameters of the application.”)
Claim Rejections - 35 USC § 103
11. 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.
12. 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.
13. 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.
14. Claims 2, 16 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0057461 A1) hereinafter Ruff, et. al, and in view of US PG Pub (US 2018/0181893 A1) hereinafter Basso.
Regarding Dependent Claims 2, 16 and 30, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does not explicitly disclose, but Basso teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does disclose the following:
- wherein executing the optimizer comprises performing optimization (see at least Basso: & ¶ [0054] & ¶ [0078] & ¶ [0108]. Basso teaches that the optimized parameters can be determined on a whole-field basis or on a portion of the field (e.g., a higher target crop yield in one portion of the field and a relatively lower target crop yield in another portion of the field). If two or more field state properties are being optimized (e.g., two or more properties related to the same field quantity; two or more properties related to the different field quantities), a weighted and/or constrained optimization system can be used by the crop model 212 to determine the crop management plan 210. See also Basso at ¶ [0078]: When the actual yield 180 is within about 5%, 10%, 20%, 30%, or 50% of the model yield 340 (e.g., expressed on a whole-field basis or based on one or more sub-regions of the field), it can be inferred that the grower substantially followed the crop management plans 210, 220 determined during the growing season and satisfied any compliance parameters, which were included as optimization constraints for the crop models 212, 222 to determine the corresponding plans 210, 220. See also Basso at Figs. 1, 1A, 1B and 1C.) using partial knowledge of first principles of the underlying system (see at least Basso: ¶ [0047] & Figs. 1, 1A, 1B and 1C. Basso teaches that the crop model, in combination with complete historical weather information and partial historical yield information can be used to simulate the missing yield periods (e.g., with knowledge of what plant was planted during the missing yield periods).).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: wherein executing the optimizer comprises performing optimization using partial knowledge of first principles of the underlying system, and in further view of Basso, whereby a crop management plan is determined using a crop model incorporating a variety of inputs and plant-specific material and energy balances to specify one or more grower-controlled management parameters. An updated plan for a given field (e.g., reflecting an updated prescription for fertilization, irrigation, and/or other grower-controlled management parameters) can be followed by a grower to increase crop yield and/or optimize one or more other crop or field parameters (e.g., crop quality, field (marginal) net return, etc.) (see at least Basso: ¶ [0041].).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Basso, the results of the combination were predictable.
15. Claims 4, 7, 18, 21, 32 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0057461 A1) hereinafter Ruff, et. al, and in view of US PG Pub (US 2009/0234695 A1) hereinafter Kapadi, et. al.
Regarding Dependent Claims 4, 18 and 32, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Claims 1, 3, 15, 17, 29 and 31 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- the underlying system comprises an agricultural system in which crops grow in multiple growing areas (see at least Ruff: Fig. 8 & ¶ [0130] & ¶ [0218-0220]. Ruff teaches an agricultural system in which crops grow in multiple zones shown in Fig. 8. See also Ruff at ¶ [0130]: The trial may require one or more testing locations to be placed in an area of the field with conditions differing from the rest of the field and/or areas of the field spanning different types of conditions. The trial may require one or more different management practices to be undertaken in one or more testing locations. See also Ruff at ¶ [0135]: The agricultural intelligence computing system may identify locations on the field for implementing a test location based on areas in the field capable of performing the trial, efficiency of performing the trial in each location, applicability of the trial to other locations, and/or benefit to the field of performing the trial. See also Ruff at ¶ [0161]: An agricultural intelligence computing system may determine the responsiveness of different areas for a particular field based on prior practices, prior yield data, and other field data from one or many fields. The agricultural intelligence computing system may then determine the effectiveness of applying the product to the responsive portions and the non-responsive portions of the field. See also Ruff at ¶ [0218]: Management zones are identified based on both responsiveness and total yield. The agricultural intelligence computing system may determine the responsiveness of areas in a field to applications of products and/or different management practices based on prior yield data, soil data, imagery, other crop data, and management practices. See also Ruff at ¶ [0225]: For example, the agricultural intelligence computing system may prioritize areas of the field that have had historically lower yields, thereby reducing any possible negative impacts on the yield of the field.);
- the optimization results (see at least Ruff: ¶ [0200-0208] & ¶ [0220]. Ruff notes the use of the field targeting and based on the inputs that have been fed into the system at the prior limitations. Some optimization results are depicted at Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.) comprises a schedule identifying resources that are scheduled to perform harvesting of the crops in the growing areas or in lots within the growing areas (see at least Ruff: ¶ [0146-0147] & ¶ [0186-0190] & Fig. 8. Ruff notes that the farm equipment use history may include identification of the tilling, planting, application, and harvesting equipment. The field operator data may identify one or more people, operations, or service providers who perform activities on the field. See also identification of processing facility, and/or identification of one or more people processing and/or collecting the soil, additional soil chemistry data, bulk density of the soil, and/or buffer capacity. The design or selection of experiments can be carried out automatically according to a predetermined schedule, such as at the beginning of every year or every growing season. The plan for the experiment can include a variety of details, such as the type of seeds, the destination of the seeds within the fields, the volume of seeds to plant each day, or the time to plant the seeds each day. See also Ruff noting growing areas or growing zones of harvesting of crops shown in Figs. 8-10.) and when the resources scheduled to perform harvesting of the crops in the growing areas or in the lots within the growing areas (see at least Ruff: ¶ [0146-0147] & ¶ [0186-0190] & ¶ [0225]. Ruff notes that the system 130 can be configured to generate the prescription, plan, or scheme for an experiment that is to be understood by a human, a machine, or a combination of both. For example, one experiment may be to plant certain seeds at certain rates on a certain grower's fields. The plan for the experiment can include a variety of details, such as the type of seeds, the destination of the seeds within the fields, the volume of seeds to plant each day, or the time to plant the seeds each day. See also Ruff at ¶ [0225]: Instead of transmitting the entire scheme for an experiment to a smart device, whether it is an agricultural implement or a person digital assistant, the system 130 is programmed to transmit the scheme incrementally and timely. For example, when the scheme involves the performance of daily tasks, the system 130 can be configured to send a portion of the scheme corresponding to each day's work every day. See also Ruff noting growing areas or growing zones of harvesting of crops shown in Figs. 8-10.)
Moreover, regarding Dependent Claims 4, 18 and 32, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does not explicitly disclose, but Kapadi in the analogous art for method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does disclose the following:
- the one or more time-varying parameters relate to one or more products recoverable in the crops (see at least Kapadi: ¶ [abstract] & ¶ [0007] & Fig. 2. Kapadi notes that sugarcane yield and sugar recovery within a sugar mill region vary with a combination of deterministic parameters (e.g., variety, crop class or ratoon type, age, harvest date indicating season) and stochastic parameters (e.g., weather conditions, soil type, farming practices, irrigation facilities). See also Kapadi at ¶ [abstract]: A harvesting and/or planting schedule is generated based on a product recovery model and a crop yield model. The product recovery model models recovery of a product, such as sugar, from a crop, such as sugarcane. The crop yield model models yield of the crop from land. First, second, third, and fourth data are used to generate the harvesting and/or planting schedule. The first input data is pertinent to predicting the recovery of the product by use of the product recovery model. See also Kapadi at ¶ [0029]: The combined model represented by equations (4) and (5) can be fitted to the production and harvest training data of an industry in order to estimate the parameters av, bv, cv, dv, and ev. See also Kapadi at Fig. 2 noting “recovery estimation models - 20” and “yield estimation models – 22”.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: the one or more time-varying parameters relate to one or more products recoverable in the crops, and in further view of Kapadi, in order for the sugar recovery and sugarcane yield models 20 and 22 as described are used by an optimizing procedure 24 of FIG. 2 according to an optimization framework in order to optimize the planting and harvesting schedules to maximize net farm returns (see at least Kapadi: ¶ [0080]).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Kapadi, the results of the combination were predictable.
Regarding Dependent Claims 7, 21 and 35, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Claims 1, 6, 15, 20, 29 and 34 above, and Ruff further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- the optimization results (see at least Ruff: ¶ [0200-0208] & ¶ [0220]. Ruff notes the use of the field targeting and based on the inputs that have been fed into the system at the prior limitations. Some optimization results are depicted at Figs. 14-15 and Fig. 16 elements 1612, 1614, 1616 and associated text.) comprise one or more settings for equipment in the processing facility (see at least Ruff: ¶ [0090] & ¶ [0146-0147] & ¶ [0154]. Ruff teaches that application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement. For example, an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve. See also Ruff at ¶ [0146]: Farm equipment use history may include identification of the tilling, planting, application, and harvesting equipment. The field operator data may identify one or more people, operations, or service providers who perform activities on the field. See also Ruff at ¶ [0147] regarding “identification of processing facility.” See also Ruff at ¶ [0154]: Tiling data may include presence of tiling, tiling system types, tiling system maps, tiling system flow conductances, and/or flow rates or fluid levels in tile lines.).
Moreover, regarding Dependent Claims 7, 21 and 35, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does not explicitly disclose, but Kapadi in the analogous art for method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does disclose the following:
- the underlying system comprises a processing facility configured to process harvested crops (see at least Kapadi: ¶ [abstract] & ¶ [0087] & ¶ [0107]. Kapadi notes that the third input data relates to capacity of a crop processing plant to process the crop to produce the product. See also Kapadi at ¶ [0087]: If sugarcane planting is not sufficient to run the processing plant during the special harvest season, the processing plant is run only during the main harvest season. Hence, the main harvest season should always start, but starting of the special harvest season is optional. See also Kapadi at ¶ [0107]: The return on investment models 42 model the investments that affect processing plant capacity such as cost of facilities associated with the milling and harvesting of sugarcane, expected prices of sugar, molasses, and/or bagasse, transportation costs, storage costs, and other costs that affect the return on the investment associated with the production of sugar, molasses, and/or bagasse. See also Kapadi at ¶ [0112]: The output device(s) is capable of outputting the planting schedule 26, the harvesting schedule 28, and the net farm returns 30. The output device(s) 88 is also capable of outputting the processing plant capacity 46.);
- the one or more time-varying parameters relate to one or more products recoverable in the crops (see at least Kapadi: ¶ [abstract] & ¶ [0007] & Fig. 2. Kapadi notes that sugarcane yield and sugar recovery within a sugar mill region vary with a combination of deterministic parameters (e.g., variety, crop class or ratoon type, age, harvest date indicating season) and stochastic parameters (e.g., weather conditions, soil type, farming practices, irrigation facilities). See also Kapadi at ¶ [abstract]: A harvesting and/or planting schedule is generated based on a product recovery model and a crop yield model. The product recovery model models recovery of a product, such as sugar, from a crop, such as sugarcane. The crop yield model models yield of the crop from land. First, second, third, and fourth data are used to generate the harvesting and/or planting schedule. The first input data is pertinent to predicting the recovery of the product by use of the product recovery model. See also Kapadi at ¶ [0029]: The combined model represented by equations (4) and (5) can be fitted to the production and harvest training data of an industry in order to estimate the parameters av, bv, cv, dv, and ev. See also Kapadi at Fig. 2 noting “recovery estimation models - 20” and “yield estimation models – 22”.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: the underlying system comprises a processing facility configured to process harvested crops & the one or more time-varying parameters relate to one or more products recoverable in the crops, and in further view of Kapadi, in order for the sugar recovery and sugarcane yield models 20 and 22 as described are used by an optimizing procedure 24 of FIG. 2 according to an optimization framework in order to optimize the planting and harvesting schedules to maximize net farm returns (see at least Kapadi: ¶ [0080]).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Kapadi, the results of the combination were predictable.
16. Claims 5, 19 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0057461 A1) hereinafter Ruff, et. al, and in view of US PG Pub (US 2020/0090107 A1) hereinafter McKeeman, et. al.
Regarding Dependent Claims 5, 19 and 33, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does not explicitly disclose, but McKeeman in the analogous art for method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does disclose the following:
- the underlying system (see at least McKeeman: ¶ [0107] & ¶ [0125] & Figs. 2A-2C. McKeeman notes the agricultural system 200 shown in Figs. 2A-2C.) comprises multiple resources who use is time-varying (see at least McKeeman: Figs. 2A-2C & ¶ [0065] & ¶ [0103]. McKeeman notes that producers may not own or have access to sufficient agricultural resources (for example, but not limited to, agricultural equipment, agricultural equipment operators, or agricultural equipment repair technicians), needed to service all of their needs, and the producers may be reliant on agricultural service providers. Agricultural service providers may provide agricultural equipment to the producer, and/or may provide agricultural equipment operators to the producer, and/or may provide repair services for agricultural equipment to the producer. See also McKeeman at ¶ [0005]: McKeeman notes that during critical windows of time during which services need to be completed, such as harvesting, where crop conditions and weather can change rapidly. See also McKeeman at ¶ [0103]: A scheduled service record may include a number of sub-fields comprising the information, such as (but not limited to) a producer identifier to whom the service is to be delivered, identifiers for one or more equipment owners scheduled to provide agricultural equipment for the service, identifiers for one or more operators scheduled to provide human resources to operate the service or to operate equipment for the service, one or more pieces of agricultural equipment that are to be used in providing the service, calendar times or days during which the service is to be performed or during which the equipment is to be hired, a date and time at which the booking was made, a booking identifier, pricing or payment information associated with the scheduled service, any conditions that must be met in order to trigger payment or a release of any escrowed funds.) and that are subject to one or more environmental factors (see at least McKeeman: ¶ [0065-0066] & ¶ [0174]. McKeeman notes that events that may impact a scheduled service may include (but are not limited to), a current weather condition, a change in a forecast weather condition, a transportation delay, an illness or other inability of a person to perform duties necessary for the service, an equipment breakdown, malfunction or need for maintenance, a theft of equipment. See also McKeeman at ¶ [0065-0066]: The demand for agricultural services varies greatly, depending on factors such as the seasons, weather patterns, the type of agricultural product for a particular producer (e.g., type of crop, type of livestock). There can be delays to the actual provision of services due to factors that are beyond the control of the producer, for example weather conditions, equipment transport delays, or lack of qualified agricultural equipment operators, since everything has to be simultaneously ready to deploy for the services to take place.);
- the optimizer (see at least McKeeman: Figs. 1C-1D & Figs. 2A-2C & ¶ [0063-0064]. McKeeman teaches that an agricultural management system is configured to optimize scheduling and facilitate asset tracking with a goal of maximizing the utilization of agricultural equipment and the agricultural equipment operators, while enabling producers to meet their needs in a timely manner with reliability and predictability.) determines a scheduling status for use of the resources over time (see at least McKeeman: ¶ [0165-0167] & ¶ [0172] & ¶ [0262]. McKeeman teaches that an availability of equipment and/or operator resources compared against the service scheduling requirements or preferences of a producer. A location of equipment and/or operator resources compared against any location-based service requirements or preferences of a producer. See also McKeeman at ¶ [0165-0167]: Coordinate the booking and scheduling of a service amongst users of the system involved in providing or receiving the service, to update availabilities of equipment or human resources in accordance with a booked service, to reschedule services or to notify users in response to any change. Also having received a selected pairing 262 or any booking confirmation or payment, booking manager and scheduler 294 may mark, label or indicate the associated equipment and operator resources as only “tentatively available” (as opposed to available) within any of storages 271 in order to help the system detect any potential conflicts or problems of double-booking. Booking manager and scheduler 294 may temporarily mark, label or indicate the associated equipment or operator resources as reserved or unavailable within one or more of the appropriate storages 271 via the sending of suitable scheduling and availability update information 265 to the appropriate storage(s). See also McKeeman at ¶ [0172]: Booking manager and scheduler 294 may send further scheduling and availability update information 265 to any storage within storages 271 to mark, label or indicate the associated equipment or operator resources as available or unavailable in accordance with the current status of the booking, scheduling or payment processes, or to record the current status for example in scheduled services storage 219. See also McKeeman at Fig. 2A noting “status manager 214”.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: the underlying system comprises multiple resources who use is time-varying and that are subject to one or more environmental factors and the optimizer determines a scheduling status for use of the resources over time, and in further view of McKeeman, in order to improve the utilization factor of agricultural machinery and equipment, and the human resources needed to operate such. The systems and methods enable seekers of an agricultural service (such as producers) to rapidly identify both equipment owners and operators and to contract out their use for predetermined scheduled periods as a farming service package. Moreover, the systems of McKeeman gather, track, store and analyze a plurality of different information types. The further analysis of this information may lead to many actionable uses, including real time scheduling of services, pairing service providers with one another to form service packages, pairing service providers and producers, finding the optimal service providers for a given producer in a search type format, providing real-time push notifications to available service providers when a producer need is identified, providing real-time push notifications to producers when there is service provider availability in their areas (see at least McKeeman: ¶ [0007-0008]).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by McKeeman, the results of the combination were predictable.
17. Claims 10-11, 24-25 and 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0057461 A1) hereinafter Ruff, et. al, and in view of US PG Pub (US 2019/0050948 A1) hereinafter Perry, et. al.
Regarding Dependent Claims 10, 24 and 38, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does not explicitly disclose, but Perry in the analogous art for method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does disclose the following:
- wherein the predictions are generated using stochastic optimization (see at least Perry: ¶ [0142-0144]. Perry teaches that the Bayesian classifier can select a set of farming operations associated with a predicted crop production probability distribution that stochastically dominates the predicted crop production probability distributions associated with other possible sets of farming operations. See also at least Perry at ¶ [0144]: If a predicted probable yield range of the set of probable yield ranges stochastically dominates the predicted probable range for the planted crop, the multivariable regression model can then recommend a replant, and can identify a set of farming operations (including the type of crop to plant, planting date, and the like) that will optimize crop production for the replant.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: wherein the predictions are generated using stochastic optimization and in further view of Perry, whereby a crop prediction model can be a multivariable regression model trained on one or more of historic planting dates, planting rates, harvest dates, weather conditions, and solar radiation. If a predicted probable yield range of the set of probable yield ranges stochastically dominates the predicted probable range for the planted crop, the multivariable regression model can then recommend a replant, and can identify a set of farming operations (including the type of crop to plant, planting date, and the like) that will optimize crop production for the replant (see at least Perry: ¶ [0144]). Moreover, having access to predicted crop production information can beneficially enable growers to efficiently and profitably allocate resources, improve crop yield stability, reduce or account for short- and long-term risks, and evaluate expected market conditions months or years in advance (see at least Perry: ¶ [0186]).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Perry, the results of the combination were predictable.
Regarding Dependent Claims 11, 25 and 39, Ruff / Perry method / apparatus / non-transitory computer readable medium for machine learning based production optimizers teaches the limitations of Claims 1, 10, 15, 24 and 29 above, and Perry further teaches the method / apparatus / non-transitory computer readable medium for machine learning based production optimizers comprising:
- wherein the predictions are generated using reinforcement learning or Monte Carlo simulations (see at least Perry: ¶ [0007] & ¶ [0116] & ¶ [0176]. Perry teaches that various machine learning operations may be performed in different contexts to train the crop prediction models, and the crop prediction models can perform various machine learning operations when applied, including but not limited to: a generalized linear model, a generalized additive model, non-parametric regression, random forest, spatial regression, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, decision tree learning, artificial neural networks, recurrent neural network, reinforcement learning, linear/non-linear regression, support vector machines, clustering operations, genetic algorithm operations, and any combination or order thereof. See also Perry at ¶ [0145]: The resulting Bayesian model can interpolate soil sample information over a portion of land, such as a grower's field, for instance using Markov Chain Monte Carlo sampling or variational inference estimations. See also Perry at ¶ [0176]: The crop prediction models can be learned with or without human guidance, and can be learned using supervised machine learning, unsupervised machine learning, and/or reinforcement machine learning. Examiner Note: Examiner interprets that the predictions in the Perry reference are primarily determined using “reinforcement learning” or “reinforcement machine learning”.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff / Perry method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: wherein the predictions are generated using reinforcement learning or Monte Carlo simulations and in further view of Perry, whereby a crop prediction model can be a multivariable regression model trained on one or more of historic planting dates, planting rates, harvest dates, weather conditions, and solar radiation. If a predicted probable yield range of the set of probable yield ranges stochastically dominates the predicted probable range for the planted crop, the multivariable regression model can then recommend a replant, and can identify a set of farming operations (including the type of crop to plant, planting date, and the like) that will optimize crop production for the replant (see at least Perry: ¶ [0144]). Moreover, having access to predicted crop production information can beneficially enable growers to efficiently and profitably allocate resources, improve crop yield stability, reduce or account for short- and long-term risks, and evaluate expected market conditions months or years in advance (see at least Perry: ¶ [0186]).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Perry, the results of the combination were predictable.
18. Claims 14, 28 and 42 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0057461 A1) hereinafter Ruff, et. al, and in view of US PG Pub (US 2017/0169523 A1) hereinafter Xu, et. al.
Regarding Dependent Claims 14, 28 and 42, Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does not explicitly disclose, but Xu in the analogous art for method / apparatus / non-transitory computer readable medium for machine learning based production optimizers does disclose the following:
- wherein generating the predictions comprises considering uncertainty in an objective function (see at least Xu: ¶ [0149] & ¶ [0163] & ¶ [0169]. Xu teaches that for example, agricultural intelligence computer system 130 may estimate total uncertainty in optimal nitrogen amounts, relative yield values, and total yield values based on model uncertainty, measurement uncertainty, and unknown weather situations. By estimating the uncertainty, agricultural intelligence computer system 130 may display probabilistic estimates for total yield as well as probabilistic recommendations for nitrogen applications. See also at least Xu at ¶ [0163]: The utility function typically represents a trade-off between exploration, using input values where the present knowledge of the objective function is very uncertain, and exploitation, using input values where the objective function is expected to be high. See also at least Xu at ¶ [0169]: The utility function is an information function that provides a numerical measure at every point x, of either the (1) uncertainty of the GP approximation of the objective function, or (2) likelihood that x is the maximum of the objective function, or (3) a combination of both.) used to generate the predictions (see at least Xu: ¶ [0095] & ¶ [0135] & ¶ [0149]. Xu teaches that the model data may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields. See also Xu at ¶ [0095]: Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge at the same location or an estimate of nitrogen content with a soil sample measurement. See also Xu at ¶ [0135]: By parameterizing the above equation for different portions of the crop's development, agricultural intelligence computer system 130 is better able to predict an effect on relative yield of nitrate in the soil at a particular time. Xu teaches that for example, agricultural intelligence computer system 130 may estimate total uncertainty in optimal nitrogen amounts, relative yield values, and total yield values based on model uncertainty, measurement uncertainty, and unknown weather situations. By estimating the uncertainty, agricultural intelligence computer system 130 may display probabilistic estimates for total yield as well as probabilistic recommendations for nitrogen applications.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Ruff method / apparatus / non-transitory computer readable medium for machine learning based production optimizers with the aforementioned teachings of: wherein generating the predictions comprises considering uncertainty in an objective function, and in further view of Xu, in order for the agricultural intelligence computer system may estimate total uncertainty in optimal nitrogen amounts, relative yield values, and total yield values based on model uncertainty, measurement uncertainty, and unknown weather situations. By estimating the uncertainty, agricultural intelligence computer system may display probabilistic estimates for total yield as well as probabilistic recommendations for nitrogen applications. The probabilistic recommendations for nitrogen applications may allow farmers who are more risk averse to make more informed decisions (see at least Xu: ¶ [0149]).
Further, the claimed invention is merely a combination of old elements in a similar field for machine learning based production optimizers and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Xu, the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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