DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1 and 7 are amended, claims 2, 6, 10-12, and 14-20 are cancelled, and claims 21-25 are new. Claims 1, 3-5, 7-9, 13, and 21-25 are pending.
Response to Arguments
Applicant's arguments filed 9/22/2025 have been fully considered.
Regarding the rejections of claim 1 under 101, Applicant contends on pages 6-7 of the response that the claim does not merely recite abstract activities and instead integrates ideas into a specific and practical application. In support, Applicant notes that the claim requires the hypothesis be based on simulations produced by a virtual research model and further that the claim requires use of cluster analysis to identify smart farms for data collection and real-time data collection from sensors located around the livestock and that the data is then analyzed using statistical models and a customized report is generated, transmitted, and either accepted or denied by a smart farm operator.
The Examiner submits that the foregoing functions includes some functions that have been found to fall within the mental processes judicial exception because they may be performed, individually or in combination, via mental processes and Applicant’s arguments do not appear to directly address which elements and/or combinations of elements that have been found to fall within the judicial exception may have been incorrectly categorized in this way. The Examiner further notes that while some of the foregoing functions cited by Applicant (e.g., generating and transmitting a report) fall outside the judicial exception and therefore qualify as “additional elements,” Applicant’s arguments do not specifically address the manner in which such additional elements, either individually or in combination, may integrate the functions falling within the judicial exception into a practical application such as via improvements to the functioning of a computer, or to any other technology or technical field; applying the judicial exception with, or by use of, a particular machine; and/or effecting a transformation or reduction of a particular article to a different state or thing.
On page 7 of the response, Applicant contends that features such as implementation of recommendations followed by transmission of implementation data to a cloud configuration, wherein the transmitted data becomes historical data for future virtual modeling forms a feedback loop between the digital and virtual research systems, enabling continuous improvement and machine learning. Applicant further asserts that the feedback mechanism, coupled with real-time sensor integration, distinguishes the claim from abstract data analysis and reporting and demonstrate that claim 1 is not directed to a judicial exception but rather to a practical application of digital technology for a specific and useful purpose.
The Examiner respectfully disagrees that the various components and functions, individually and in combination, that fall outside the judicial exception act/function to integrated the steps falling within the judicial exception into a practical application. Applicant generally alludes to an overall practical application, but has not described a particular manner in which the additional elements in claim 1 are not merely extra solution activity but rather functions/structures that play a significant functional role in the manner in which the steps falling within the judicial exception are performed (in contrast to being routine, convention data processing activities/components that serve the substantially a similar role with respect to any type of underlying computer processing).
In further regard to claim 1, Applicant contends on page 7 of the response that even if claim 1 is found to recite an abstract idea, claim 1 recites additional features that amount to significantly more than the judicial exception. In support Applicant cites the use of real-time livestock sensors, wireless data transmission, integration with a cloud-based virtual research model, and application of artificial intelligence models to real-world data as providing an inventive concept that is not routine, conventional, or generic.
The Examiner submits that the recited additional elements, either individually or in combination do not result in the claim as a whole amounting to more than the judicial exception because the additional elements constitute extra-solution activity (i.e., activity/components having no particularized functional relation to the elements falling within the judicial exception). Furthermore, as explained in the grounds for rejecting amended claim 1 under 103, the combination of elements falling within the judicial exception and elements falling outside the judicial exception are taught or otherwise rendered obvious by the set of prior arts utilized in the rejection such that no inventive concept is presented by claim 1.
Regarding the rejection of independent claim 7 under 101, Applicant notes that claim 7 recites that the data analysis is performed by applying artificial intelligence to livestock data using multiple data processing modules within a cloud computing environment and that these modules are executed in conjunction with a processor and are configured to continuously evaluate data from a plurality of producers to estimate/predict health improvements by modifying variables. Applicant contends on page 8 of the response that this configuration is not a generic computing environment, but rather a specific infrastructure for managing and analyzing complex real-world livestock data requiring implementation in a distributed, cloud-based system.
The Examiner acknowledges that the combination of all of the additional elements recited in claim 7 may fall outside what may be considered a “generic” computing environment. However, as explained in the grounds for rejecting claim 7 many of these elements and in particular the processor/processing, sensor-based collection, cloud processing/networking environment appear to be substantially generic in the similar applications disclosed in the prior arts including Kuper (US 2018/0350010 A1), Stroman (US 2008/0059264 A1), Singh (US 2019/0053470 A1), Richt (US 2018/0075546 A1), and Kopic (US 2014/0116341 A1). Furthermore, the additional elements in claim 7, individually and in combination, appear to constitute extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
In further regard to claim 7, Applicant contends on page 8 that claim 7 recites a non-abstract improvement to an existing technological process. In support, Applicant notes that the claimed method overcomes efficiency problems encountered by traditional method by enabling scalable, predictive simulations and feedback-based learning that reduce the need for repetitive trials and enhance the efficiency and accuracy of research outcomes. Applicant further asserts that the claim recites additional features that amount to significantly more than an abstract idea, including integration of cloud-based computing, real-time data input, artificial intelligence model training and retraining, and application-specific improvements for agricultural systems.
The Examiner submits that these arguments are somewhat generalized and do not specifically describe the manner in which one or more specific additional elements, individual or in combination, act to integrate the elements falling within the judicial exception into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception. The Examiner submits that as set forth in the current grounds for rejecting claim 7, cloud-based computing represents routine, convention data processing infrastructures for storing and distributing data, real-time data input represents high-level data collection, and artificial intelligence modeling and training represents routine, convention program instruction implementation of underlying functions, such that these elements, individually or in combination, represent extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding the rejection of claim 7 under 103, Applicant contends on page 9 of the response that the combination of references fails to teach or suggest the structured and iterative framework set forth in claim 7. Specifically, Applicant asserts that Kuper ‘010 does not describe a formalized virtual research process that includes simulation, hypothesis generation, report generation, user feedback, field trail validation, and model training.
The Examiner submits that Applicant’s argument regarding what the “combination” of references teaches does not set forth the specific reasons as to why/in what manner the previous combination of prior art applied to reject claim 7 fails to teach the combination of recited elements and is therefore essentially conclusory. For example, Applicant contends, on page 9, that the step of implementing a recommendation in a controlled field trial following acceptance of a report is not disclosed by any of the references, without supporting this contention by showing how the particular references fails to actually disclose particular features and/or why the references are not properly combinable. Therefore, the Examiner can only point to the grounds for rejecting claim 7 to indicate why the combination of Kuper (US 2018/0350010 A1), “Kuper ‘010,” in view of Stroman (US 2008/0059264 A1), and in further view of Singh (US 2019/0053470 A1), Kopic (US 2014/0116341 A1), and Park (WO 2019216647A1) teaches or otherwise renders obvious the combination of elements in claim 7.
Regarding the amendments to claim 7, Applicant contends on page 9 that the previously cited references do not teach the application of artificial intelligence in a distributed, modular, cloud-based system designed for continuous real-time evaluation and adaptation. Applicant further contends that the cited references to not disclose a model retraining step in connection with specific field trial outcomes resulting from a recommendation generated by earlier virtual research. These arguments are again essentially conclusory and generalized in terms of the cited prior art references failing to teach combinations of elements (in which there appears to be some inference of features that are not actually recited in the claim), and the Examiner notes that as set forth in the current grounds for rejecting claim 7, the combination of Kuper ‘010, Stroman, Singh, Kopic, and Park teach or otherwise render obvious these combinations of elements. The Examiner further notes that, as explained in the grounds for rejecting claim 7 under 112(a), Applicant’s original disclosure does not appear to support, with reasonable clarity, “an artificial intelligence supervised model trained on livestock-specific real-time data obtained from the field trial.”
Regarding the grounds for rejecting claim 1 under 103, Applicant sets forth arguments on pages 10-11 including that the Kuper references “do not describe a simulation-based research framework that generates a hypothesis later tested through real-time sensor data collection at selected farms,” that the combination of references do not disclose “a cluster analysis of historical data be performed to determine which smart farms are appropriate for real-time data collection,” that the cited references do not teach or suggest “the integration of real-time livestock data analysis with a feedback loo that includes wireless transmission of a customized research report, confirmation or denial of acceptance by the receiving smart farm, and the implementation of recommendations derived from the initial simulation-based hypothesis,” and that the cited references fail to teach or suggest “the final limitations of claim 1.”
The Examiner submits that these arguments are generalized and incorporate inferences that are not recited in the claim language. Furthermore, and similar to the arguments relating to claim 7, these arguments do not address with any specificity how/why the cited prior art references fail to teach particular elements and/or how/why the references are not properly combinable as set forth in the rejections and are therefore essentially conclusory. Therefore, the Examiner cites the current grounds for rejecting claim 1 under 103 as indicating why the subject matter of claim 1 is unpatentable over Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry (US 2019/0050948 A1).
Note: The Examiner recognizes that the application of multiple references for the various aspects of the claims results in numerous complexities and has undertaken to make the rejections as clear as possible. Given the relative complexity of the rejections, the Examiner earnestly welcomes the Applicant’s representative to contact the Examiner for the purpose of scheduling an Examiner’s Interview to discuss any questions or concerns that may arise in review of the rejections such that prosecution of this application may be advanced as efficiently as possible.
Claim Objections
Claim 24 is objected to because of the following informalities:
In claim 24 line 1, “the step of storing” should read “a step of storing.”
In claim 24 line 3, “the updated model version” does not have sufficient antecedent basis and should be amended to recite “the retrained model.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 3-5, 7-9, 13, and 21-25 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor had possession of the claimed invention.
Claim 7 lines 9-12 recites “analyzing the livestock data … with one or more analysis models, including a trained artificial intelligence model configured to generate predictive outcomes by applying statistical correlations, normalization, and aggregation techniques to the livestock data,” which does not appear to be adequately supported by Applicant’s original disclosure. The support for this element appears in Applicant’s specification on page 6 lines 27-29, which discloses that analyzing the livestock data includes “correlation, normalization, aggregation, and modeling of the data” (i.e., modeling in addition to correlation, normalization, and aggregation is used for analyzing the data). While this disclosure generally associates modeling and the functions of statistical correlation, normalization, and data aggregation, Applicant’s original disclosure does not appear to disclose with reasonable clarity that the model itself applies statistical correlations, normalization, and aggregation techniques to the livestock data.
Claims 1, 3-5, 8-9, 13, and 21-25 depend from claim 7 and are likewise rejected for the same reasons.
Claim 7 lines 19-20 recites “an artificial intelligence supervised model trained on livestock-specific real-time data obtained from the field trial,” which does not appear to be adequately supported by Applicant’s original disclosure. The support for this element appears in Applicant’s specification on page 2 lines 30-32 and in original claim 12, which discloses analyzing field trial data and retraining the one or more analysis models. While this disclosure appears to associates field trail data and model training in terms of an overall process, and may suggest to one of ordinary skill in the art that the field trial data may be used for training the model, this disclosure does not expressly disclose that the field trial data is used for training a model. Moreover, Applicant’s original disclosure does not appear to disclose using real-time data obtained from a field trial for training a model. Therefore, Applicant’s original disclosure does not appear to disclose with reasonable clarity an artificial intelligence supervised model trained on livestock-specific real-time data obtained from the field trial.”
Claims 1, 3-5, 8-9, 13, and 21-25 depend from claim 7 and are likewise rejected for the same reasons.
Claim 24 recites “storing the retrained model in a version-controlled model registry,” which does not appear to be adequately supported by Applicant’s original disclosure. The support for this element appears to be the “database” function/component referenced throughout Applicant’s specification such as on page 8 lines 13-15, page 8 line 15 through page 9 line 12, and page 9 lines 29-33, which disclose use a database for storing information relating to modeling (e.g., inputs/outputs) and other storage functions. However, Applicant’s original disclosure does not appear to disclose, with reasonable clarity, storing any model (trained and/or retrained) “in a version-controlled model registry.”
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 3-5, 7-9, 13, and 21-25 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
In claim 7 lines 9-12, “analyzing the livestock data … with one or more analysis models, including a trained artificial intelligence model configured to generate predictive outcomes by applying statistical correlations, normalization, and aggregation techniques to the livestock data” renders claim 7 indefinite because this language potentially suggests that the “model” itself applies statistical correlations, normalization, and aggregation techniques to the livestock data. This potential interpretation does not appear entirely consistent with the specification description on page 6 lines 27-29, which discloses that analyzing the livestock data includes “correlation, normalization, aggregation, and modeling of the data” (i.e., modeling in addition to correlation, normalization, and aggregation is used for analyzing the data). As noted in the grounds for rejecting claim 7 under 103, correlation and aggregation may be considered inherently incident to artificial intelligence modeling but normalization implemented by the model itself is not. It appears that the intent of the language in lines 9-12 of claim 7 is to characterize the “analyzing” as entailing using “one or more analysis models, including a trained artificial intelligence model configured to generate predictive outcomes in which the overall modeling process, including data processes not strictly implemented by the model itself, applies statistical correlations, normalization, and aggregation techniques, which is how claim 7 is interpreted for the purposes of prior art examination.
Claims 1, 3-5, 8-9, 13, and 21-25 depend from claim 7 and are likewise rejected for the same reasons.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-9, 13, and 21-25 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Claim 7 recites:
“[a] method for conducting virtual research to predict the outcome of a selected variable of interest and improve livestock health outcomes, the method comprising the steps of:
(a) automatically obtaining livestock data from a plurality of livestock producers, the livestock data related to one or more variables pertaining to livestock nutrition, livestock environment, livestock health or livestock performance;
(b) transmitting the livestock data to a cloud computing environment comprising at least one server, at least one processor, and a memory component;
(c) analyzing the livestock data in the cloud computing environment with one or more analysis models, including a trained artificial intelligence model configured to generate predictive outcomes by applying statistical correlations, normalization, and aggregation techniques to the livestock data;
(d) generating a customized report that includes the results of the analysis, the results including an estimate or prediction of an expected improvement in livestock health outcome by changing one or more variables;
(e) transmitting the report to an end user, wherein the end user may accept or deny the report;
(f) implementing the change to one or more variables in a field trial;
(g) building an artificial intelligence supervised learning model trained on livestock-specific real-time data obtained from the field trial;
(h) analyzing the data produced from the field trial using at least one trained machine learning model selected from the group consisting of ensemble learning, deep learning, time series modeling, and Bayesian statistics; and
(i) retraining the one or more of the analysis models,
wherein the step of analyzing the livestock data includes applying artificial intelligence to the livestock data in a plurality of data processing modules within a cloud computing environment in which the plurality of data processing modules are executed in conjunction with at least one processor, the data processing modules and artificial intelligence configured to continuously evaluate data from a plurality of livestock producers to estimate or predict an expected improvement in livestock health outcome by changing one or more variables,
wherein the cloud computing environment executes a plurality of modularized data processing components, each configured to perform a specific transformation on the data, and wherein the data analysis is performed in conjunction with at least one processor to generate a livestock-specific output that enables automated feedback and iterative improvement in livestock health outcomes.”
Claim 1 recites:
“[a] method of conducting digital research to improve livestock health outcomes, the method comprising
forming, using a processor, a hypothesis regarding what changes may be made to one or more variables related to livestock nutrition, environment, health status, or animal performance to improve livestock health outcomes for a particular livestock producer, the hypothesis based on simulations produced according to the virtual research method of claim 7;
performing, using a statistical or machine learning algorithm, a cluster analysis of historical data related to the one or more variables to determine one or more smart farms for real-time livestock data collection;
obtaining real-time livestock data from one or more sensors or cameras located on or around a plurality of livestock animals at the determined one or more smart farms;
analyzing the real-time livestock data using a trained artificial intelligence model to test the hypothesis;
generating, via a cloud-based processing module, a customized research report that includes results of the analysis of the real- time livestock data including one or more recommendations to improve livestock health outcomes,
transmitting the research report wirelessly to the one or more smart farms;
confirming or denying acceptance of the research report by the one or more smart farms;
implementing the one or more recommendations by the smart farm; and
transmitting data related to an outcome of implementation of the one or more recommendations to a cloud configuration or central database, wherein the transmitted data is made available for use as historical data in a virtual research model,
wherein upon implementation of the one or more recommendations by the smart farm, livestock health outcomes are improved.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 7 recites a method and therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claims 7 and 1 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
Regarding claim 7, the recited functions:
“conducting virtual research to predict the outcome of a selected variable of interest and improve livestock health outcomes”
“automatically obtaining livestock data from a plurality of livestock producers, the livestock data related to one or more variables pertaining to livestock nutrition, livestock environment, livestock health or livestock performance”
“analyzing the livestock data in the cloud computing environment”
“generate predictive outcomes by applying statistical correlations, normalization, and aggregation techniques to the livestock data”
“estimate or prediction of an expected improvement in livestock health outcome by changing one or more variables,”
“implementing the change to one or more variables in a field trial”
“analyzing the data produced from the field trial,” and
“continuously evaluate data from a plurality of livestock producers to estimate or predict an expected improvement in livestock health outcome by changing one or more variables” may be performed, individually and in combination, as mental processes.
Conducting virtual research to predict an outcome of a selected variable of interest and improve livestock health outcomes may be performed via mental processes (e.g., evaluation of data, including data from online sources, relating to factors affecting livestock and applying judgment to such evaluations to formulate outcome predictions). Automatically obtaining livestock data from a plurality of livestock producers, the livestock data related to one or more variables pertaining to livestock nutrition, livestock environment, livestock health or livestock performance may also be performed via mental processes (e.g., observation, automatically in terms of some prompting, of data generated in some manner from livestock producers). Analyzing the livestock data in the cloud computing environment may also be performed via mental processes (e.g., evaluation of livestock data in a manner entailing mental comparison of the data with example livestock data (analysis models) or, mental evaluation of livestock data that is generated using a computer-implemented analysis model). Generating predictive outcomes by applying statistical correlations, normalization, and aggregation techniques to the livestock data may be performed via mental processes (e.g., applying analytic techniques, possibly aided by pen-and-paper, including statistical correlation, normalization, and aggregation and evaluation of results to determine (via judgement) predictions). Estimating or predicting an expected improvement in livestock health outcome by changing one or more variables may be performed via mental processes (e.g., evaluation and judgement). Implementing the change to one or more variables in a field trial (e.g., implementation beginning with ascertaining course of action based on a changed variable involved in field trial implementation), and analyzing the data produced from the field trial (e.g., evaluation) may also be performed via mental processes. Continuously evaluating data from a plurality of livestock producers to estimate or predict an expected improvement in livestock health outcome by changing one or more variables may be performed via mental processes (e.g., evaluation and judgment).
Regarding claim 7, the recited functions “applying statistical correlations, normalization” are further found by the Examiner to fall within the mathematical relations subcategory of the mathematical concepts judicial exception because statistical correlations and normalization are each fundamentally characterized by mathematical relations/calculations.
Regarding claim 1, the recited functions:
“conducting digital research to improve livestock health outcomes”
“forming” “a hypothesis regarding what changes may be made to one or more variables related to livestock nutrition, environment, health status, or animal performance to improve livestock health outcomes for a particular livestock producer, the hypothesis based on simulations produced according to the virtual research method of claim 7;
performing a cluster analysis of historical data related to the one or more variables to determine one or more smart farms for real-time livestock data collection;
obtaining real-time livestock data from one or more sensors or cameras located on or around a plurality of livestock animals at the determined one or more smart farms;
analyzing the real-time livestock data to test the hypothesis,”
“confirming or denying acceptance of the research report by the one or more smart farms,” and
“implementing the one or more recommendations by the smart farm,” may be performed as mental processes.
Conducting digital research to improve livestock health outcomes and forming hypotheses regarding changes to variables related to livestock nutrition, environment, health status, or animal performance to improve livestock health outcomes for a particular livestock producer in which the hypothesis based on simulations produced according to the virtual research method of claim 7 may be performed via mental processes (e.g., evaluation of data, including data from digital sources, relating to factors affecting livestock and applying judgment to such evaluations to formulate hypotheses). Performing a cluster analysis of historical data related to the one or more variables to determine one or more smart farms for real-time livestock data collection may also be performed via mental processes (e.g., evaluation/judgment of historical variable data to mentally formulate patterns such as commonalities defining clusters of such information relating to smart farms). Obtaining real-time livestock data from one or more sensors or cameras located on or around a plurality of livestock animals at one or more smart farms may also be performed via mental processes (e.g., observation either of data in a real-time stream from sensors or observation of data that may be stored and was collected by sensors in real-time). Analyzing real-time livestock data to test the hypothesis may also be performed via mental processes (e.g., evaluation and judgment applied to livestock data conducted for the purpose of testing a related hypothesis). Confirming or denying acceptance of the research report by the one or more smart farms may also be performed via mental processes (e.g., evaluation/review of research report and judgement regarding the same). Implementing the one or more recommendations by the smart farm may also be performed via mental processes (e.g., implementation beginning with ascertaining course of action based on a recommendation and performed by a smart farm personnel).
The type of high-level information analysis and deduction recited in the foregoing elements of claims 7 and 1 has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claims 7 and 1 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” of claims 7 and 1 in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted step or a device for implementing the highlighted step such as a signal processing device or a generic computer. For example, “transmitting the livestock data to a cloud computing environment comprising at least one server, at least one processor, and a memory component,” “one or more analysis models, including a trained artificial intelligence model configured to generate predictive outcomes” for performing the livestock data analysis, “generating a customized report that includes the results of the analysis,” and “transmitting the report to an end user, wherein the end user may accept or deny the report” in claim 7, and “using a processor” to form a hypothesis, “using a statistical or machine learning algorithm” to perform a cluster analysis, “generating, via a cloud-based processing module, a customized research report that includes results of the analysis of the real-time livestock data including one or more recommendations to improve livestock health outcomes,” “transmitting the research report wirelessly to the one or more smart farms,” and “transmitting data related to an outcome of implementation of the one or more recommendations to a cloud configuration or central database, wherein the transmitted data is made available for use as historical data in a virtual research model,” in claim 1, represent well-known data processing functions that do not influence or rely on the functions entailing the abstract idea and do not otherwise technologically improve any aspect of a device or system. Therefore, these elements constitute extra-solution activity (post-solution activity for “generating a customized research report).
Similarly, “building an artificial intelligence supervised learning model trained on livestock-specific real-time data obtained from the field trial,” “using at least one trained machine learning model selected from the group consisting of ensemble learning, deep learning, time series modeling, and Bayesian statistics” to analyze the data produced from the field trial, and “retraining the one or more of the analysis models, wherein the step of analyzing the livestock data includes applying artificial intelligence to the livestock data in a plurality of data processing modules within a cloud computing environment in which the plurality of data processing modules are executed in conjunction with at least one processor, the data processing modules and artificial intelligence configured to continuously evaluate data from a plurality of livestock producers” in claim 7, individually and collectively with the other additional elements represent standard data collection, transmission, processing functions (high-level model generation and training) that are well-known and do not directly influence the functions entailing the abstract idea and therefore constitute extra solution activity that does not otherwise technologically improve any aspect of a device or system.
Regarding claim 1, the Examiner notes that “wherein upon implementation of the one or more recommendations by the smart farm, livestock health outcomes are improved” recites an intended result/purpose that does not further characterize the function performed by the method and is therefore does not constitute an additional element that integrates the abstract idea into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the foregoing additional elements of claims 7 and 1 are configured and implemented in a conventional rather than a particularized manner of implementing livestock monitoring.
Regarding a transformation or reduction of a particular article to a different state or thing, claims 7 and 1 do not include any such transformation or reduction. Instead, each of these claims as a whole entails steps and components for receiving/collecting livestock input information, applying standard processing techniques (i.e., computer-implemented execution of instruction) to the information to obtain and deduce livestock health-related information with the additional elements failing to provide a meaningful integration of the abstract idea in an application that transforms an article to a different state. Instead, the additional elements represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claims 7 and 1 do not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, each of claims 7 and 1 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, the additional elements in claims 7 and 1 represent extra-solution activity as set forth in the Step 2A Prong Two analysis and therefore do not result in the claim as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements appear to be generic and well understood as evidenced by the disclosures of Kuper (US 2018/0350010 A1), “Kuper ‘010”, Stroman (US 2008/0059264 A1), Singh (US 2019/0053470 A1), Richt (US 2018/0075546 A1), and Kopic (US 2014/0116341 A1) each of which disclose livestock/farming monitoring methods/systems utilizing substantially the same data collection, transmission, and processing operations.
Regarding claims 7 and 1, Kuper ‘010 teaches generating an output report that includes analysis results (FIG. 1 output data 170 that includes average daily gain 171, growth rate predictions 172, and livestock recommendations 173) as does Stroman (FIG. 9 block 350).
Regarding claims 7 and 1, Singh teaches using a cloud computing environment for transmitting and processing livestock data ([0035]) as does Richt ([0217] analogously applied for plant farming rather than livestock farming).
Regarding claim 7, “transmitting the report to an end user” is well-understood and conventional in the art as taught by Stroman (FIG. 1 depicting networked systems including client computers 110 and server 107 providing access to data within central database 109 via links 115, [0070]; [0129] the adaptive reasoning system 140 (that is part of overall platform depicted in FIG. 1) electronically available at ranches; [0130] output from adaptive reasoning system 140 in the form of a notification; [0133]-[0134] user may retrieve analyzed information from the database via the Internet; [0104] “users” may include ranchers) and Singh (FIG. 1B depicting networked connection between remote server 108 and users 110a-110b; [0035], FIG. 3A block 314).
In claim 7, the functions “building an artificial intelligence supervised learning model,” “retraining the one or more of the analysis models,” and “applying artificial intelligence to the livestock data in a plurality of data processing modules within a cloud computing environment in which the plurality of data processing modules are executed in conjunction with at least one processor” (effectively conveying application of artificial intelligence to the livestock data in a plurality of data processing modules in a cloud computing environment in conjunction with a processor) are well-understood and conventional in the art as taught by Kuper ‘010 (FIG. 1 AI layer 146 and adaptive machine learning methods 147 configured to processing variable input data), Stroman (FIG. 1 adaptive reasoning system 140 and other networked components, [0132]), Singh (FIG. 3A block 312; [0035] cloud computing environment), and Richt (FIG. 1A, [0125] machine learning applied, [0217] cloud computing environment). Building/training models including AI models is further well-known and conventional in the art as disclosed by Stroman ([0129] analytic and empirical models may be refined, improved, recalibrated. Examiner notes that improving and recalibrating models of the system that per [0128] is “adaptive” and has “learning capabilities” entails retraining the model), Kuper ‘010 (FIG. 1 adaptive machine learning methods 147 (Examiner notes that a machine learning method is inherently implemented by some form of data structure (model) that has been built), [0033]), and Kopic ([0076] discloses a method for analytic management of dairy production that includes generating a production model (Abstract) that may be a supervised machine learning model).
Regarding claim 1, transmitting the research report wirelessly to the one or more smart farms and transmitting data related to an outcome of implementation of the one or more recommendations to a cloud configuration or central database, are well-understood and conventional in the art as taught by Stroman (FIG. 1 depicting networked systems including client computers 110 and server 107 providing access to data within central database 109 via links 115 that per [0073] may be wireless, [0070]; [0129] the adaptive reasoning system 140 (that is part of overall platform depicted in FIG. 1) electronically available at ranches; [0130] output from adaptive reasoning system 140 in the form of a notification; [0133]-[0134] user may retrieve analyzed information from the database via the Internet; [0104] “users” may include ranchers) and Singh (FIG. 1B depicting networked connection between remote server 108 and users 110a-110b; [0035], FIG. 3A block 314).
Therefore, the additional elements in claims 7 and 1 are insufficient to amount to significantly more than the judicial exception and consequently claims 7 and 1 are not patent eligible.
Regarding claim 7, The Examiner notes that even if “automatically obtaining livestock data from a plurality of livestock producers, the livestock data related to one or more variables pertaining to livestock nutrition, livestock environment, livestock health or livestock performance” is interpreted more narrowly to fall outside the mental processes exception, this element represents high-level data collection and therefore constitutes extra-solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception. Similarly, even if “implementing the change to one or more variables in a field trial” is interpreted more narrowly to fall outside the mental processes exception, this element represents convention, routine data processing activity having no particularized functional relation to the steps falling within the judicial exception and therefore constitutes extra-solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding claim 3, the characterization of the sensors as transmitting a particular code associated with an animal neither integrates the abstract idea into a practical application nor results in claim 3 as a whole amounting to significantly more than the judicial exception because it constitutes extra solution activity (high-level data collection) and is furthermore well-understood and conventional in the art as taught by Stroman (FIG. 1 tracking system 145 that per [0097]-[0099] includes livestock sensors; FIG. 2 block 215 depicting electronic identification of animals based on RFID detection at block 210, [0028] RFID detection/identification/data collection; [0097]-[0099] RFID tagging using RFID tags on animals. Examiner notes that RFID tag sensing inherently entails sending an RF identification code to a reader), and Singh (FIG. 1B tag assemblies 102 including sensor 114, [0047]).
Claim 3 therefore also constitutes ineligible subject matter.
Regarding claim 4, using one or more statistical models to implement the analysis of real-time livestock data constitutes extra solution activity (well-known, conventional data processing for implementing the judicial exception) that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 5 does not recite any further additional elements because it only characterizes the data that is processed (cluster analysis).
Claim 5 therefore also constitutes ineligible subject matter for the same reasons as set forth for claim 1.
Claims 8-9 do not recite any further additional elements because each only characterizes the data that is processed and therefore also constitute ineligible subject matter for the same reasons as set forth for claim 7.
Claim 13 further recites that steps (a)-(d) are repeated upon denial of the report. As set forth in the grounds for rejecting claim 7, steps (a) and (c) fall within the judicial exception and the additional elements (b) and (d) do not integrate the judicial exception into a practical application or result in the claim overall as amounting to significantly more than the judicial exception. Therefore, the additional element newly introduced by claim 13 is essentially repeating processing cycles in response to a state or condition, which constitutes well-understood computer data processing that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 21 further recites “the step of obtaining livestock data comprises receiving real-time sensor data from a plurality of smart farms, the sensor data including at least one of environmental temperature, humidity, water pH, water temperature, ammonia levels, or animal body weight,” which represents high-level data collection having no particularized relation to the steps falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 22 further recites “the artificial intelligence supervised learning model comprises one or more machine learning models selected from the group consisting of decision trees, regression trees, random forests, gradient boosting machines, support vector machines, neural networks, and Bayesian networks,” which represents application of known artificial intelligence modeling program configurations having no particularized relation to the steps falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 23 further recites “the step of retraining one or more of the analysis models comprises modifying at least one model parameter based on performance data from the field trial using a semi-supervised or transfer learning approach,” which represents program instruction preparation using known machine learning training techniques having no particularized relation to the steps falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a w