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 s