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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/15/2026 has been entered.
Response to Amendment
Claims 1, 7, and 23 are amended and claims 2, 6, 10-12, 14-20, and 24 are cancelled. Claims 1, 3-5, 7-9, 13, 21-23, and 25 are pending.
Response to Arguments
Applicant's arguments filed 6/15/2026 have been fully considered.
Regarding the rejections of claims 1, 3-5, 7-9, 13, 21-23, and 25 under 112(a), and as noted by Applicant on pages 6-7 of the response, the amendments overcome the rejections which are withdrawn.
Regarding the rejection of claim 7 under 112(b), and as noted by Applicant on page 7 of the response, the amendment to claim 7 overcomes the rejection, which is withdrawn.
Regarding the rejections of claims 1, 3-5, 7-9, 13, 21-23, and 25 under 101 as directed to an abstract idea without significantly more, Examiner agrees that the amendments overcome the rejections. In particular, Examiner finds that the amendments including the implementing of recommendations and tracking of evolution using daily collected sensor data from sensors from smart farms selected based on an initial clustering combined with the other elements of claim 7 effectively integrates the abstract idea into a practical application and results in the claim as a whole amounting to significantly more than the abstract idea. Therefore, the rejection of claim 7 and all claims depending therefrom are withdrawn.
Regarding the rejections of independent claim 7 under 103, Examiner acknowledges that the amendment to claim 7 overcomes the previous grounds of rejection. Based on further search and analysis of the references new grounds for rejecting claim 7 under 103 as being unpatentable over 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), Park (WO 2019216647A1), and Perry (US 2019/0050948 A1) as set forth herein.
On pages 12-13 of the response, Applicant contends that Kuper ‘010 does not teach a paraphrasing of a combination of multiple elements, which does not address the combined disclosures of the cited prior art references in terms of disputing what these references teach or whether they are properly combinable. For example, Examiner acknowledges as asserted by Applicant on page 12 that Kuper ‘010 alone does not teach the combined simulation-based virtual research process that generates a hypothesis, produces a report with recommendations that a user may accept or deny, and upon acceptance implements variable changes in a controlled field trial at cluster-selected representative farms with daily sensor tracking and iterative model retraining fed back into historical data for future virtual research. Instead, this combination is taught/rendered obvious by the teachings of Kuper ‘010 in combination with the teachings of Stroman, Singh, Kopic, Park, and Perry as set forth in the new grounds of rejection.
Similarly, on pages 12-13 Applicant contends that the framework of Kuper ‘010 is not designed to select representative farms from a system of farms such as to conduct trials across multiple locations and provide outcome data, and that Kuper ‘010 does not teach the combination of (i) a virtual research phase that generates simulation-based hypotheses before any field activity; (ii) user acceptance or denial of a report as a condition precedent to field-trial implementation; (iii) cluster-based selection of smart farms that represent variability within an entire system of smart farms; (iv) assigning or installing sensors at cluster-selected farms specifically for controlled field trials; (v) daily tracking of field-trial evolution via real-time sensor data; or (vi) transmitting field-trial outcome data to a cloud database for iterative use as historical data in a virtual research model for future virtual research simulations.
Examiner acknowledges that Kuper ‘010 itself does not teach these combined features and submits these features are taught by the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry (in particular Perry as combined with Kuper ‘010 as teaching selection via clustering) as set forth in the new grounds of rejection.
On page 12 Applicant further contends that Kuper ‘010’s model adjustment is fundamentally different from the claimed model retraining. In support, Applicant asserts that Kuper ‘010’s artificial intelligence layer adjusts weights or biases assigned to variables based on differences between predicted and actual growth rates at a single feedlot such that bias adjustments improve predictions for a particular producer, pen, or animal, which is in contrast to the amended claims reciting retraining analysis models based on field trial outcome data.
Examiner acknowledges that Kuper ‘010 does not teach these combined (and paraphrased) features. However, Examiner submits that retraining analysis models based on field trial outcome data is taught/rendered obvious by the combined teachings of Kuper ‘010 and Stroman as set forth in the current grounds of rejection (Stroman: [0128]-[0129] analytic and empirical models may be refined, improved, recalibrated based on outcomes compared with expected results).
On page 12 of the response, Applicant contends that Perry does not disclose or suggest the combination of cluster-based selection of representative smart farms, daily sensor-based field-trial tracking, or feedback of outcome data for iterative use in a virtual model. Examiner acknowledges that Perry does not itself teach this combination, which is instead taught by the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry (Perry as combined with Kuper ‘010 as teaching selection via clustering).
On page 13 of the response, Applicant contends that the combination of cited references does not teach the inventive core entailing “a unified virtual-to-digital research pipeline where cluster analysis selects representative farms, sensor data tracks field-trial evolution daily, and outcome data feeds back to retrain models and enrich the historical data used for future virtual research simulations,” which Examiner notes is a high-level paraphrasing of the claimed elements
The Examiner submits that Applicant’s argument regarding what the “combination” of references teaches is overly generalized, failing to specifically set forth reasons as to why/in what manner the previous combination of prior arts applied to reject claims 7 and/or 1 fails to teach the combination of recited elements and is therefore essentially conclusory. Therefore, the Examiner can only point to the grounds for rejecting claims 7 and 1 to indicate why the combination of Kuper, “Kuper ‘010,” in view of Stroman, and in further view of Singh, Kopic, Park, and Perry teaches or otherwise renders obvious the combination of elements in claims 7 and 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5, 7-9, 13, 21-23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over 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), Park (WO 2019216647A1), and Perry (US 2019/0050948 A1).
As to claim 7, Kuper ‘010 teaches “[a] method for conducting virtual research to predict the outcome of a selected variable of interest and improve livestock health outcomes (Abstract disclosing adaptive framework method for modeling livestock growth including optimizing and recommending feed operations (feed operation being the variable of interest and outcome being growth); FIG. 2 including block 290, [0008] nutrition recommendations are predictive), the method comprising the steps of:
(a) automatically obtaining livestock data from” “livestock producers (FIG. 1 input data 110 relating to livestock includes nutrition, weather, animal-specific data, location, etc.; [0037] and [0039]-[0040] data inputs relating to livestock conditions are provided automatically), the livestock data related to one or more variables pertaining to livestock nutrition, livestock environment, livestock health or livestock performance (FIG. 1 input data 110 relating to livestock includes nutrition, weather, animal-specific data, location, etc.);”
“(c) analyzing the livestock data” “by applying correlation (statistical correlation inherent to machine learning modeling),” “aggregation (aggregation inherent to machine learning modeling such as is entailed in generating (training) a model and also in AI layer 146, adaptive machine learning methods, and selected model 160 processing the multi-variate/aggregate input data 110), and modeling with one or more analysis models, including a trained artificial intelligence model configured to generate predictive outcomes (FIG. 1 depicting data processing components 140 configured to analyze via modeling input data 110 including AI layer 146, adaptive machine learning methods 147 (application of machine learning inherently entails the model having been trained), and selected growth model 160; [0015] AI layer 146 and selected model 160 generate output data 170 such as predicted growth rate 172, average gain 171 and recommendations 173 based on processing input data described in [0019]);
(d) generating a customized report that includes the results of the analysis (FIG. 1 output data 170 that includes average daily gain 171, growth rate predictions 172, and livestock recommendations 173 (Examiner notes the output data constitutes a report that is customized in terms of being dynamically adjusted in accordance with changing input data 110 and per [0026] in accordance with model weight adjustments)), the results including an estimate or prediction of an expected improvement in livestock health outcome by changing one or more variables (FIG. 1 output data 170 that includes growth rate predictions 172 and corresponding livestock recommendations 173; [0033] modeling including machine learning used to predict and recommend livestock feeding and management (Examiner notes the predictions are determined based on (dependent on) modeling/processing of variable input data such that the predictions themselves constitute an estimate/prediction of expected improvement in which the “changing one or more variables” aspect of the predictions are manifested in corresponding recommendations).”
Kuper ‘010 further suggests automatically obtaining livestock data from “a plurality of” livestock producers ([0005], [0023], and [0035] disclosing that producer-specific information utilized to optimize workflow, which suggests discerning among multiple producers (farms)).”
Stroman teaches automatically obtaining livestock data from multiple livestock producers ([0002] Internet-based platform shared by members of livestock production chain including producers; FIGS. 1 and 8 tracking system 145; [0016] tracking system used to document information from different ranches).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have combined Stroman’s teaching of collecting livestock data from multiple farms/livestock producers with the method taught by Kuper ‘010.
The motivation would have been to expand the comprehensive coverage of livestock data available for processing such as to more accurately determine monitoring processing results from such varied information as disclosed by Stroman.
Neither Kuper ‘010 nor Stroman expressly teaches “transmitting the livestock data to a cloud computing environment comprising at least one server, at least one processor, and a memory component,” analyzing the livestock data “in the cloud computing environment,” generating predictive outcomes by applying “normalization,” and “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.”
Singh discloses a method for tracking health in animal populations (Abstract) in which livestock data ([0036]-[0038] tag assemblies 102 may include sensors 114 that collect livestock information) is transmitted to a cloud computing environment comprising at least one server, at least one processor, and a memory component (FIGS. 1A-1B depicting network-based computing environment comprising network 106 as information portal between user devices 110, tag assemblies 102 via concentrator 104, and remote server 108; [0043] tag assembly information provided to concentrator 104; [0050] concentrator 104 communicatively coupled with remote server 108 via network 106; [0052] network interface of concentrator transmits livestock data to remote server; [0035] network 106 may comprise a cloud-based architecture. Examiner notes that the computer-based cloud computing environment depicted in FIGS. 1A and 1B that implements data processing function such as performed by remote server 108 inherent includes a processor and memory for execution of functions) and analyzing the livestock data in the cloud computing environment (FIG. 1B remoter server 108 forming part of cloud computing environment in terms of being provisioned data via network 106 and including processor 130 configured to process data received in the cloud computing environment; [0052]), and wherein the cloud computing environment executes a plurality of modularized data processing components (FIG. 1B environment includes multiple data processing components including remote server 108, user devices 110a-110b, concentrator 104; [0052] remote server 108 itself may include multiple processors (modularized data processing components)), each configured to perform a specific transformation on data (each of the processing components within the cloud environment depicted in FIGS. 1A and 1B perform some particularized data processing), and wherein the data analysis is performed in conjunction with at least one processor to generate a livestock-specific output (each of the processing components, including the multiple processors within remote server 108, within the cloud environment depicted in FIGS. 1A and 1B inherently perform processor-driven processing in conjunction to determine livestock condition per [0052]).
Singh further teaches normalizing an input dataset (aggregated data) for use with machine learning ([0064] and [0180] processors scale/normalize the dataset to facilitate machine learning in terms of prediction accuracy).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Singh’s teaching of transmitting livestock data to a cloud computing environment in which the livestock data is analyzed to the method taught by Kuper ‘010 as modified by Stroman, such that in combination the method includes transmitting the livestock data from a plurality of livestock producers to a cloud computing environment (e.g., cloud-based architecture as an input portal for Kuper ‘010 input data 110 and analyzes the data (e.g., modeling within Kuper ‘010 adaptive framework 100) within the cloud computing environment (e.g., the model-based processing would be within a cloud-based environment by virtue of receiving the inputs through a cloud-based network architecture).
The motivation would have been to provide the combined network connectivity and processing-resource provisioning flexibility characteristics of cloud computing to enhance livestock data processing as suggested by Singh.
It would also have been obvious to one of ordinary skill in the art before the filing date, to have applied Singh’s teaching of normalizing the input dataset to the method taught by Kuper ‘010 as modified by Stroman, such that in combination the method includes generating predictive outcomes by applying normalization in addition to the statistical correlations and aggregation incident to machine learning modeling.
The motivation would have been to optimize accuracy of output predictions by the artificial intelligence modeling as disclosed by Singh.
While as set forth above, the preamble language “to predict the outcome of a selected variable of interest and improve livestock health outcomes” is taught by Kuper ‘010, this feature recites an intended result/purpose that does not further characterize the function performed by the method and is therefore not given patentable weight.
Kuper ‘010 suggests, but does not expressly teach that the method further includes “transmitting the report to an end user ([0036] output data 170 (constituting “report”) including recommendations regarding livestock feeding and veterinary care. The formulation of a recommendation inferentially suggests a need for communicating such recommendation to a user (user at farm) that may implement the recommendation),” and does not appear to teach steps (e), (f), (i), and (j).
Regarding steps (e), (f), (i), and (j) Stroman discloses (e) transmitting the report to an end user (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 “wherein the end user may accept or deny the report ([0133] recommendation sent to user may be reviewed by the user (i.e., user may confirm acceptance via reviewing or deny acceptance via not reviewing; [0129] adaptive reasoning system 140 recommends actions (transmission inherently required) and the system processes results of implementation of the recommendations (Examiner notes that user implementation of recommendations entails a form of “acceptance” of the recommendation/report));
(f) upon acceptance of the report, implementing the change to one or more variables in a field trial ([0022] user performs action to implement recommendation from adaptive reasoning system (i.e., implements some change in variable factor); FIG. 15 depicting adaptive reasoning system 140 as providing recommendation 1015 with corresponding action 1020 and 1030; [0133]-[0134]) at one or more smart farms (FIG. 2 electronics and automated communications for monitoring livestock; FIG. 8 ranch and feedlot including processing mechanisms for livestock monitoring)” “wherein the selected smart farms represent variability within an entire system of smart farms (one or more farms inherently entail forms of variability with respect to other farms);”
“(i) analyzing the data produced from the field trial ([0129] and [0134] outcome of the recommended actions monitored and compared (analyzed) with respect to expected results) using at least one trained machine learning model ([0128]-[0129], and [0132] adaptive reasoning system implemented via artificial intelligence learning (training/trained model))” and
“(j) retraining the one or more of the analysis models based on outcome data produced from the field trial ([0129] analytic and empirical models may be refined, improved, recalibrated based on outcomes compared with expected results. Examiner notes that improving and recalibrating models of the system that per [0128] is “adaptive” and has “learning capabilities” and entails retraining the model).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of transmitting an output report an end user (e.g., smart farm) wherein the user may accept or deny the report, and wherein upon acceptance of the report, the method includes implementing the change to one or more variables in a field trial, analyzing the data produced from the field trial, and retraining the one or more of the analysis models based on outcome data produced from the field trial to the method taught by Kuper ‘010 as modified by Stroman and Singh such that Kuper ‘010 output data (output data 170) that includes livestock health-related recommendations is transmitted to a user (e.g., client at a smart farm) in a manner in which the user may accept or deny the report, and whereupon acceptance of the report, the method includes implementing the change to one or more variables in a field trial, analyzing the data produced from the field trial, and retraining the one or more of the analysis models based on outcome data produced from the field trial.
The motivation would have been to provide the recommendation within the report to the onsite user (user at ranch/farm) to effectuate consideration of the recommendation by the user, who may accept or deny acceptance of the report and undertake logically corresponding actions such as implementing the change to one or more variables in a field trial, analyzing the data produced from the field trial, and retraining the one or more of the analysis models as disclosed by Stroman.
Regarding the one or more smart farms being “selected based on a cluster analysis of historical data” Kuper ‘010 teaches utilizing artificial intelligence and machine learning for analyzing livestock related input data such as location data and facility information (FIG. 1 feedlot location 120, GPS tracking 121, and facility type 117) but does not appear to expressly teach that the changes are implemented on farms selected based on a cluster analysis of historical data, characterized in Applicant’s specification as potentially entailing artificial intelligence and/or machine learning.
Perry discloses a machine learning based agricultural method for evaluating historical agricultural data to predict particular outcomes (Abstract) that includes using grouping/clustering analysis to determine sources (farms or portions thereof) for the collection of agricultural input information to be used for modeling/predictive analysis (Abstract disclosing selective access of agricultural condition data ( data relevant to crop health) corresponding to request including parameters such as location, weather, and soil composition; [0004] and [0007] crop growth information including multiple growth-related characteristics collected (historical) and then grouped/clustered in accordance with similarities based on a particular request; [0009] growth information collected from a plurality of fields/locations associated with (grouped/clustered in accordance with) a threshold geographic and/or environmental diversity; [0019] system identifies/selects a cluster of sub-portions of land having a threshold similarity (identification/selection requires considering known/historical data) and associated with one or more common field characteristics (i.e., selected via clustering of field characteristic variables)).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Perry’s teaching of selecting sources of collected agricultural input information based on threshold matching with agricultural farm parameters (grouping/clustering) as a selection means for collecting relevant input information to the method taught by Kuper ‘010 as modified by Singh and Stroman which teaches real-time collection of livestock information from one or more farms, such that in combination the method includes performing a “cluster analysis” of historical data related to the one or more variables, and “selecting, based on the cluster analysis, one or more smart farms that represent variability within an entire system of smart farms” and assigning or installing one or more sensors at “the selected” one or more smart farms.
The motivation would have been to enable selective collection of data across one or more farms having shared characteristics that are determined either by request or otherwise to be most relevant to the subsequent processing analysis, including collection of necessary data, of agricultural/livestock health including growth as disclosed by Perry.
Regarding implementing the change to one or more variables at one of more smart farms “selected based on a cluster analysis of historical data,” and as set forth above, Kuper ‘010 teaches assigning variable/treatment changes to farms and further teaches that such changes are applied for farms/producers from which the data is collected and modeling performed (Abstract and [0005], and [0023] adaptive framework generates recommendations for producers based on information (e.g., pen, animal, historical performance) associated with the producers).
Therefore, in view of the foregoing obvious combination of Perry with Kuper ‘010 of using cluster analysis to select one or more farms that are then used to collect data for modeling analysis, it would have been further obvious to have further applied Kuper ‘010 teaching of implementing variables changes to the same producers from which data is collected to the combination such that the method is configured to implement changes to one or more variables or treatments to the smart farms identified by the cluster analysis.
The motivation would have been to leverage the modeling to improve livestock management for any of a variety of farms correlated to the data used in the modeling, particularly the farms from which the modeled data was collected.
Regarding the trained machine learning model being “selected from the group consisting of ensemble learning, deep learning, time series modeling, and Bayesian statistics,” Stroman teaches that the model may be a neural network which frequently includes multiple layers (deep learning) [0132]) but does not disclose the type of neural network. Park discloses use of a deep learning (multi-layer) type of neural network that may be utilized for estimating livestock-related data (page 1, Claims (13), paragraph beginning with “An oil production forecasting method”; page 3, Description, paragraph beginning with “According to an embodiment of the present disclosure, the first prediction model is based on a Long-Short-Term Recurrent Neural Network (RNN) algorithm”. Examiner notes that an RNN is known as a multi-layer/deep learning type neural network model).
It would have been obvious to one of ordinary skill in the art before the filing date, to have applied Park’s teaching of an RNN as a type of neural network modeling (deep learning) to the method disclosed by Kuper ‘010 as modified by Stroman, Perry, and Singh to include using neural network modeling, such that in combination the neural network modeling implements multi-layer modeling (e.g., RNN).
Such a combination would amount to selecting a known design option for implementing neural network modeling to achieve predictable results.
The combination of Kuper ‘010, Stroman, Perry, Singh, and Park further teaches “wherein the step of analyzing the livestock data includes applying artificial intelligence to the livestock data in a plurality of data processing modules (Kuper ‘010: FIG. 1 adaptive framework 100 that processes input data 110 includes AI layer 146 and adaptive machine learning methods 147) within a cloud computing environment in which the plurality of data processing modules are executed (Singh: cloud-computing environment as combined with Kuper ‘010 for claim 7) in conjunction with at least one processor (Kuper ‘010: FIG. 1 computing environment 130 that includes AI layer 146 and machine learning methods 147 includes processors 132; [0016]), the data processing modules and artificial intelligence configured to continuously evaluate data from a plurality of livestock producers (Kuper ‘010: [0006]-[0007] real-time assessments of data inputs (Examiner notes that real-time processing of input data implicitly entails continuousness of such processing); claim 1 reciting continuous evaluation of input data) to estimate or predict an expected improvement in livestock health outcome by changing one or more variables (Kuper ‘010: FIG. 1 output data 170 that includes growth rate predictions 172 and corresponding livestock recommendations 173; [0033] modeling including machine learning used to predict and recommend livestock feeding and management (Examiner notes the predictions are determined based on (dependent on) modeling/processing of variable input data such that the predictions themselves constitute an estimate/prediction of expected improvement in which the “changing one or more variables” aspect of the predictions are manifested in corresponding recommendations).”
Claim 7 further recites a step “(g) building or training an artificial intelligence supervised learning model configured to explain variability in performance based on collected real-time livestock data from the field trial.” Kuper ‘010 effectively discloses building or training an artificial intelligence model that explains performance variability based on collected real-time livestock data from a field trial (FIG. 1 adaptive machine learning methods 147 and selected growth model 160 (Examiner notes that a machine learning method is inherently implemented by some form of data structure (model) that has been built and learns (is trained)), [0032]-[0033] artificial intelligence modeling used for predicting livestock growth based on the variable inputs (in this manner explains the effects of inputs on outcomes); [0006]-[0007] conditions assessments may be performed in real-time).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have combined Kuper ‘010 teaching of building or training an artificial intelligence model that explains performance variability based on collected real-time livestock data from a field trial with the combined teachings of Kuper ‘010, Stroman, Perry, and Singh, which as set forth above teaches implementing the change to one or more variables in a field trial, such that in combination the method includes building or training an artificial intelligence model configured to explain variability in performance based on collected real-time livestock data from the field trial.
The motivation would have been to leverage artificial intelligence modeling to ascertain variations in predicted outcomes based on variations of input condition data to better manage livestock conditions.
Kuper ‘010 is silent regarding whether the model is supervised or unsupervised and therefore does not teach that the artificial intelligence model “supervised learning model.”
Supervised learning models were a well-known type of artificial intelligence model prior to the effective filing date. For example, Kopic discloses a method for analytic management of dairy production that includes generating a production model (Abstract) that may be a supervised machine learning model ([0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Kopic’s teaching of generating a production model that may be a supervised machine learning model to the method taught by Kuper ‘010 as modified by Stroman, Perry, Singh, and Park such that the combined method includes building an artificial intelligence supervised learning model.
The motivation would have been to generate a type of artificial intelligence model that may be usefully employed in the context of monitoring livestock activity as disclosed by Kopic.
Regarding “tracking evolution of the field trial using real-time livestock data collected daily from sensors at the selected one or more smart farms,” Stroman further teaches tracking evolution of a field trial using real-time livestock data collected from the one or more sensors at selected one or more farms (FIG. 15 adaptive reasoning system 140 including processing/tracking results 1025 of actions 1020; [0131] adaptive reasoning system 140 collects information from animals including sensor information such as imaging and temperature measurements that would be performed in real-time; [0133]-[0134] user implements recommended actions and outcomes of actions are monitored. Examiner notes that in context the “results” are clearly animal condition results that as disclosed in [0131] may be collected using sensor data), and further teaches that livestock data monitoring may be performed/collected daily ([0016] and [0096] monitoring system may collect daily average weight gain).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of tracking evolution of an implementation using real-time livestock data collected from the one or more sensors at selected one or more farms, and furthermore teaches that livestock data monitoring may be performed/collected daily to the method taught by Kuper ‘010 as modified by Perry, Singh, and Stroman, which teaches that the farms may be smart farms such that in combination the method includes tracking evolution of the field trial using real-time livestock data collected daily from the one or more sensors at the selected one or more smart farms.
The motivation would have been to monitor changes to livestock to both provide desired livestock condition data and to determine whether changes to the livestock treatment/health actions require adjustments as disclosed by Stroman.
Regarding “transmitting outcome data from the field trial to a cloud configuration or central database, wherein the transmitted outcome data is made available for use as historical data in a virtual research model for future virtual research,” Stroman further teaches transmitting outcome data for a field trial of one or more recommendations (FIG. 15 depicting adaptive reasoning system 140 in which Result 1025 data resulting from an Action 1020 that is based on a Recommendation 1015 is transmitted as Feedback 1030 to step 1010) to a central database (step 1010 depicting Expert Rules, Corrective Actions, Recommendations, Best Practices in bi-directional communicative contact with a Central Database; [0128] “Specific health regimens and their causes and effects may entered into … adaptive reasoning system 140”), wherein the transmitted outcome data is made available for use as historical data ([0128] “Specific health regimens and their causes and effects may entered into … adaptive reasoning system 140, which allows the system to learn and pass the information on to the users; [0129] historical information archived in the central database) in a virtual research model (FIG. 15 adaptive reasoning system 140 includes remotely accessible data storage (Central Database), processing functionality (Expert Rules, Diagnosis, Corrective Actions, Recommendations, Best Practices), and multivariate inputs (step 105) that constitute and implement a virtual research model (that is inherently available for future virtual research) in terms of the step 1010 implementing recommendations and modifying actions based on results (i.e., data modeling that implements research). Examiner notes that while “for future virtual research” is an inherent attribute of the virtual research model, this feature conveys an intended purpose/result that does not positively limit the functions of the recited method and is therefore not given patentable weight.
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of transmitting data related to an outcome of implementation of one or more recommendations to a central database, wherein the transmitted data is made available for use as historical data in a virtual research model to the method taught by Kuper ‘010 as modified by Stroman, Singh, Kopic, and Perry such that in combination the method includes 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.
The motivation would have been to store implementation outcome data to a widely accessible storage from which it may be accessed and usefully applied to improve future recommendations as suggested by Stroman.
As to claim 8, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7, wherein the selected variable of interest is selected from the group consisting of microbiome, feeding method (Kuper ‘010: Abstract disclosing adaptive framework method for modeling livestock growth including optimizing and recommending feed operations (feed operation being the variable of interest and outcome being growth); FIG. 2 including block 290, [0008] nutrition recommendations include feed rations and feed additives that are part of “feeding method”), feeding schedule, medical history, breed, breeding status, age, body condition, genetics, gender, environmental temperature, humidity, water pH, water temperature, water quality, ammonia levels in environment, air quality, barn flooring quality/type, feeder type, feed quality, feed size, nutritional levels and requirements, disease, medication, medication delivery method, distance of livestock from other barns and/or packing plants, growth performance, and carcass characteristics.”
As to claim 9, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7,” and Stroman further teaches that livestock data may be available from an Internet source serving animal information (“Internet of Animals”) (FIG. 26 depicting Internet Service Provider (hence Internet server architecture) in/over which livestock information is available; [0088] animal information may be obtained via the Internet).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of using animal/livestock information stored at and served from the Internet (Internet of Animals) to provision at least some of the livestock data used for livestock analysis to the method taught by Kuper ‘010 as modified by Stroman and Singh, such that the combined method entails the livestock data further comprising data obtained from such Internet source (Internet of Animals).
The motivation would have been to provide a widely utilized networking source (the Internet) to which relevant animal/livestock information may be stored and readily obtained using standard networking protocols as suggested by Stroman.
As to claim 13, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7,” and as set forth in the grounds for rejecting claim 7 Kuper ‘010 teaches generating an output report including a recommendation (FIG. 1 output data 170 that includes average daily gain 171, growth rate predictions 172, and livestock recommendations 173).
In view of Kuper ‘010 teaching of generating an output report including recommendation, it would have been obvious to one of ordinary skill in the art before the effective filing date, to have sent the output report including recommendation to an end user for acceptance or denial as this step is inferentially a part of making a recommendation and would be required to effectuate the intent of making a recommendation (i.e., the motivation for sending the report/recommendation is inherent in the generating of the recommendation disclosed by Kuper ‘010).
Kuper ‘010 further teaches repeating steps in the acquisition and analysis of livestock data (obtaining, transmitting, and analyzing livestock data as recited in steps (a)-(c)) until one or more variables dependent on improvement through several simulations is fulfilled (FIG. 1 feedback loop 162, [0024] framework 100 incorporates a feedback loop for validating models and consequently obtaining improved predictions from the model(s) such that the framework is self-improving over time (i.e., as more data is processed); [0007] feedback is provided in an ongoing manner as real-time information received).
In view of Kuper ‘010 teaching of a repeating process via feedback in which the analysis process, which includes collection and transmission of new livestock information, is repeated on an ongoing manner, it would have been obvious to one of ordinary skill in the art before the effective filing date, to have also repeated the steps including the reporting step (step (d)) as part of the overall process regarding of whether the user accepts or denies the report.
Such a combination would be a logical extension of the data collection/analysis/reporting process that is performed in an ongoing manner using real-time data to improve and/or otherwise adjust recommendations based on changes in the input data necessitating modeling adjustments as suggested by Kuper ‘010.
As to claim 21, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7, wherein the step of obtaining livestock data comprises receiving real-time sensor data (Kuper ‘010: FIG. 1 input data 110 relating to livestock includes nutrition, weather, animal-specific data, location, etc.; [0006]-[0007] and [0020] real-time assessments of data; [0037] and [0039]-[0040] data inputs relating to livestock conditions are provided in real-time; FIG. 1 input data 110 including scale data 127, weather information 115, and GPS/tracking 121) from a” “farm[s] (Kuper ‘010: [0023] and [0035] workflow processing optimized in accordance with particular facility and producer (farm)), the sensor data including at least one of environmental temperature (Kuper ‘010: [0020] real-time weather information may include temperature), humidity (Kuper ‘010: [0021] weather information may include humidity), water pH, water temperature, ammonia levels, or animal body weight.”
Regarding the sensor data being received from “a plurality” of smart farms, Kuper ‘010 further suggests automatically obtaining livestock data from “a plurality of” livestock producers ([0005], [0023], and [0035] disclosing that producer-specific information utilized to optimize workflow, which suggests discerning among multiple producers (farms)), and Stroman teaches automatically obtaining livestock data from multiple livestock producers ([0002] Internet-based platform shared by members of livestock production chain including producers; FIGS. 1 and 8 tracking system 145; [0016] tracking system used to document information from different ranches).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have combined Stroman’s teaching of collecting livestock-related data from multiple farms/livestock producers with the method taught by Kuper ‘010 in which the data is real-time sensor data, such that in combination the real-time sensor data is received from a plurality of farms.
The motivation would have been to expand the comprehensive coverage of livestock data available for processing such as to more accurately determine monitoring processing results from such varied information as disclosed by Stroman.
Regarding the farms being “smart” farms from which the real-time sensor data is received, Kuper ‘010 does not appear to clearly characterize the subject “producers” (farms) as being “smart farms.”
Perry further discloses that the agricultural monitoring method including collecting agricultural information from farm regions equipped with automated monitoring/sensing devices and thus constitute a “smart” farm ([0010] and [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Perry’s teaching of applying agricultural monitoring processes for smart farms to the method taught by Kuper ‘010 such that the farms (producers) from which the real-time sensor data is received are smart farms. The motivation would have been to leverage the automated data gathering capabilities of smart farms to improve data gathering efficiency as disclosed by Perry.
As to claim 22, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7,” and as set forth in the grounds for rejecting claim 7, the combination of Kuper ‘010 and Kopic teaches building the “artificial intelligence supervised learning model.”
Kuper ‘010 and Kopic are largely silent regarding the particular type of supervised learning model and therefore neither expressly discloses “wherein 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.”
Stroman teaches application of neural networks as a type of machine learning model/algorithm applicable in the general context of livestock monitoring ([0132], claim 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of the utility of using neural networks for monitoring/processing livestock related data to the method taught by Kuper ‘010 as modified by Stroman, Singh, Kopic, and Park, such that in combination the artificial intelligence supervised learning model comprises a neural network.
Such a combination would amount to selecting a known design option for an artificial intelligence supervised learning model to achieve predictable results.
As to claim 23, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7, wherein the step of retraining one or more of the analysis models comprises modifying at least one model parameter (Stroman: [0129] analytic and empirical models may be refined, improved, recalibrated. Examiner notes that modifying the models necessarily and inherent entails modifying a model parameter) based on performance data from the field trial using a semi-supervised or transfer learning approach.”
Regarding the analysis models being trained (parameters modified) “based on performance data from the field trial,” Stroman teaches that field trial performance data may be used to improve rules, logic of an adaptive reasoning system that may be implemented via machine learning ([0022] user performs action to implement recommendation from adaptive reasoning system (i.e., implements some change in variable factor) and resulting outcome information used to improve rules, logic; FIG. 15 depicting adaptive reasoning system 140 as providing recommendation 1015 with corresponding action 1020 and result 1025 that is provided as feedback 1030 to update rules 1010; [0129], [0132] adaptive reasoning system may implement artificial intelligence (learning) models such as neural networks; [0133]-[0134]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of using field trial data for updating/training adaptive data processing models to the method taught by Kuper ‘010 as modified by Stroman, Singh, Kopic, Park, and Perry such that the retraining including modifying of a model parameter is based on performance data from the field trial.
The motivation for using the field trial performance data for model training/parameter updating would have been to leverage field trial data to improve modeling accuracy as suggested by Stroman.
Regarding the training/learning technique being “a semi-supervised or transfer learning approach,” Kopic further teaches that semi-supervised learning is an option for training/retraining models ([0076) semi-supervised learning for updating model].
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Kopic’s teaching of using semi-supervised learning/training for updating/training models to the method taught by Kuper ‘010 as modified by Stroman, Singh, Kopic, and Park such that a semi-supervised learning approach is used for training/parameter update for the model.
Such a combination would amount to selecting a known design option for updating/training a model to achieve predictable results.
As to claim 25, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 7, wherein the customized report includes a prediction of improvement in at least one performance metric selected from feed conversion ratio, mortality rate, average daily gain (Kuper ‘010: [0005]; FIG. 1 output data 170 that includes average daily gain 171, growth rate predictions 172, and livestock recommendations 173 (Examiner notes the output data constitutes a report that is customized in terms of being dynamically adjusted in accordance with changing input data 110 and per [0026] in accordance with model weight adjustments)), or carcass yield, based on changing one or more variables (Kuper ‘010: FIG. 1 output data 170 that includes growth rate predictions 172 and corresponding livestock recommendations 173; [0033] modeling including machine learning used to predict and recommend livestock feeding and management (Examiner notes the predictions are determined based on (dependent on) modeling/processing of variable input data such that the predictions themselves constitute an estimate/prediction of expected improvement in which the “changing one or more variables” aspect of the predictions are manifested in corresponding recommendations).
As to claim 1, Kuper ‘010 teaches “[a] method of conducting digital research to improve livestock health outcomes (Abstract disclosing adaptive framework method for modeling livestock growth including optimizing and recommending feed operations; FIG. 2), the method comprising
forming, using a processor (FIG. 1 processors 132), 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 (FIG. 1 ensemble of models 150 in combination with AI layer 146 and adaptive machine learning methods 147, [0015] input data applied to models 150 for determining and predicting livestock growth rate; [0023] adaptive machine learning methods used to predict and recommend livestock feeding and management based on input data 110 (Examiner notes the models themselves constitute a formulated hypothesis regarding how livestock outcomes are affected by livestock-related input variables such that the models by their function inherently demonstrate/predict how a change in variable input data affects livestock health outcomes); [0028] results of comparison of predicted performance and actual performance may be used to adjust the weights of the model (the premise/hypothesis represented by the model) to improve modeling accuracy),”
“performing, using a statistical or machine learning algorithm, a” “analysis of historical data related to the one or more variables ([0005] and [0023] workflow processing is optimized using machine learning methods in accordance with particular facility and producer (farm) that are evaluated based on historical performance, genetics, and management practices of producer) to determine one or more” “farms for real-time livestock data collection ([0023] and [0035] workflow processing optimized in accordance with particular facility and producer (farm). In this manner of optimizing/customizing workflow in accordance with producer (farm) historical data, the producers/farms are themselves determined as part of performing the analysis/evaluation);”
“assigning or installing one or more sensors at” “one or more smart farms (FIG. 1 input data 110 obtained including scale data 127, weather information 115, and GPS/tracking 121 (onsite sensors));
obtaining real-time livestock data (FIG. 1 input data 110 relating to livestock includes nutrition, weather, animal-specific data, location, etc.; [0006]-[0007] real-time assessments of data; [0037] and [0039]-[0040] data inputs relating to livestock conditions are provided in real-time) from one or more sensors (FIG. 1 input data 110 including scale data 127, weather information 115, and GPS/tracking 121)” “at the determined one or more” “farms ([0023] and [0035] workflow processing optimized in accordance with particular facility and producer (farm));
analyzing the real-time livestock data using a trained artificial intelligence model (FIG. 1 depicting data processing components 140 configured to analyze via modeling input data 110 including AI layer 146, adaptive machine learning methods 147 (machine learning inherently entails training/trained model), and selected growth model 160; [0015] AI layer 146 and selected model 160 generate output data 170 such as predicted growth rate 172, average gain 171 and recommendations 173 based on processing input data described in [0019]) to test the hypothesis ([0026] the predictions resulting from modeling analysis may be tested such as by comparison with actual results; [0028] results of comparison of predicted performance and actual performance may be used to adjust the weights of the model (the premise/hypothesis represented by the model));
generating” “a customized research report that includes results of the analysis of the real-time livestock data (FIG. 1 output data 170 that includes average daily gain 171, growth rate predictions 172, and livestock recommendations 173 (Examiner notes the output data constitutes a report that is customized in terms of being dynamically adjusted in accordance with changing input data 110 and per [0026] in accordance with model weight adjustments)) including one or more recommendations to improve livestock health outcomes (FIG. 1 livestock recommendations 173; [0008] recommendations include recommended nutrition; [0032]),” and
“implementing the one or more recommendations at the selected one or more smart farms, including assigning changes to one or more variables or treatments to the” “farms (FIG. 1 output data 170 that includes average daily gain 171, growth rate predictions 172, and livestock recommendations 173 (Examiner notes the output data constitutes a report that is customized in terms of being dynamically adjusted in accordance with changing input data 110)).”
Kuper ‘010 does not expressly teach that the customized research report is generated “via a cloud-based processing module.”
Singh discloses a method for tracking health in animal populations (Abstract) in which generating and processing of livestock data report data is performed via a cloud-based processing module (FIGS. 1A-1B depicting network-based computing environment comprising network 106 as information portal between user devices 110, tag assemblies 102 via concentrator 104, and remote server 108; [0052] remote server generates livestock data that per FIG. 4 blocks 414 and 416 is reported; [0035] network 106 may comprise a cloud-based architecture).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Singh’s teaching that livestock report data may be generated via a cloud-based processing module to the method taught by Kuper ‘010 such that in combination the method includes generating the customized research report via a cloud-based processing module.
The motivation would have been to provide the combined network connectivity and processing-resource provisioning flexibility characteristics of cloud computing to enhance livestock data processing as suggested by Singh.
Kuper ‘010 teaches utilizing artificial intelligence and machine learning for analyzing livestock related input data such as location data and facility information (FIG. 1 feedlot location 120, GPS tracking 121, and facility type 117) but does not appear to expressly teach that the analysis of historical data related to the one or more variables is a “cluster” analysis, characterized in Applicant’s specification as potentially entailing artificial intelligence and/or machine learning, and that the method includes “selecting, based on the cluster analysis, one or more smart farms that represent variability within an entire system of smart farms” and assigning or installing one or more sensors at “the selected” one or more smart farms.
Perry discloses a machine learning based agricultural method for evaluating historical agricultural data to predict particular outcomes (Abstract) that includes using grouping/clustering analysis to determine sources (farms or portions thereof) for the collection of agricultural input information to be used for modeling/predictive analysis (Abstract disclosing selective access of agricultural condition data ( data relevant to crop health) corresponding to request including parameters such as location, weather, and soil composition; [0004] and [0007] crop growth information including multiple growth-related characteristics collected (historical) and then grouped/clustered in accordance with similarities based on a particular request; [0009] growth information collected from a plurality of fields/locations associated with (grouped/clustered in accordance with) a threshold geographic and/or environmental diversity; [0019] system identifies/selects a cluster of sub-portions of land having a threshold similarity and associated with one or more common field characteristics (i.e., selected via clustering of field characteristic variables)).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Perry’s teaching of selecting sources of collected agricultural input information based on threshold matching with agricultural farm parameters (grouping/clustering) as a selection means for collecting relevant input information to the method taught by Kuper ‘010 as modified by Singh, which teaches real-time collection of livestock information from one or more farms, such that in combination the method includes performing a “cluster analysis” of historical data related to the one or more variables, and “selecting, based on the cluster analysis, one or more smart farms that represent variability within an entire system of smart farms” and assigning or installing one or more sensors at “the selected” one or more smart farms.
The motivation would have been to enable selective collection of data across one or more farms having shared characteristics that are determined either by request or otherwise to be most relevant to the subsequent processing analysis, including collection of necessary data, of agricultural/livestock health including growth as disclosed by Perry.
Regarding “assigning changes to one or more variables or treatments to the smart farms identified by the cluster analysis,” and as set forth above, Kuper ‘010 teaches assigning variable/treatment changes to farms and further teaches that such changes are applied for farms/producers from which the data is collected and modeling performed (Abstract and [0005], and [0023] adaptive framework generates recommendations for producers based on information (e.g., pen, animal, historical performance) associated with the producers).
Therefore, in view of the foregoing obvious combination of Perry with Kuper ‘010 of using cluster analysis to select one or more farms that are then used to collect data for modeling analysis, it would have been further obvious to have further applies Kuper ‘010 teaching of assigning variables changes to the same producers from which data is collected to the combination such that the method is configured to assign changes to one or more variables or treatments to the smart farms identified by the cluster analysis.
The motivation would have been to leverage the modeling to improve livestock management for any of a variety of farms correlated to the data used in the modeling, particularly the farms from which the modeled data was collected.
Regarding determining one or more “smart” farms for real-time livestock data collection and obtaining real-time livestock data from the selected one or more “smart” farms, Kuper ‘010 does not appear to clearly characterize the subject “producers” (farms) as being “smart farms.”
Perry further discloses that the agricultural monitoring method including collecting agricultural information from farm regions equipped with automated monitoring/sensing devices and thus constitute a “smart” farm ([0010] and [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Perry’s teaching of applying agricultural monitoring processes for smart farms to the method taught by Kuper ‘010 as modified by Singh and Perry, such that the farms (producers) from which livestock data is obtained are smart farms. The motivation would have been to leverage the automated data gathering capabilities of smart farms to improve data gathering efficiency as disclosed by Perry.
It should be noted that, while as set forth above, the preamble language “to improve livestock health outcomes” is taught by Kuper ‘010, this feature recites an intended result/purpose that does not further characterize the function performed by the method and is therefore not given patentable weight.
As set forth above, the combination of Kuper ‘010, Singh, and Perry teaches “obtaining real-time livestock data from one or more sensors (Kuper ‘010: FIG. 1 input data 110 including scale data 127, weather information 115, and GPS/tracking 121),” and Stroman discloses a livestock monitoring method (Abstract) in which the ranches (animal farms) subject to (determined for) livestock monitoring in which the livestock management method includes obtaining real-time livestock data from “one or more sensors” “located on or around a plurality of livestock animals” at a smart farm (FIG. 2 blocks 210 and 215, [0081] RFID tags used to detect, identify, and otherwise collect information about one or more animals. Examiner notes that RFID tag detection inherently utilizes tag and reader for sensing such that each pair formed by a respective tag on an animal and the reader constitutes an individual “sensor” such that for multiple sensors result from multiple tagged animals; [0097]-[0099] rectal thermometers and a digital camera may be included as sensors along with RFID sensors).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of using multiple, on-site sensors (sensors on or around the animals) to collect livestock information for livestock management processing to the method taught by Kuper ‘010 as modified by Singh and Perry.
The motivation would have been to provide more comprehensive sensor coverage such as in terms of geographical coverage and data type coverage that may be individually correlated to each of multiple individual animals as disclosed by Stroman.
Regarding “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,” and “upon acceptance, implementing the one or more recommendations at the selected one or more smart farms,” and “tracking evolution of the implementation using real-time livestock data collected daily from the one or more sensors at the selected one or more smart farms” Kuper ‘010 suggests, but does not expressly teach that the method further includes, “transmitting the research report” “to the one or more smart farms ([0036] output data 170 (constituting “report”) including recommendations regarding livestock feeding and veterinary care. The formulation of a recommendation inferentially suggests a need for communicating such recommendation to a place (farm) at which the recommendation may be performed).”
Stroman discloses transmitting an output report wirelessly to the one or more smart farms (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), confirming or denying acceptance of the report by the one or more smart farms ([0133] recommendation sent to user may be reviewed by the user (i.e., user may confirm acceptance via reviewing or deny acceptance via not reviewing; [0129] adaptive reasoning system 140 recommends actions (transmission inherently required) and the system processes results of implementation of the recommendations (Examiner notes that user implementation of recommendations entails a form of “acceptance” of the recommendation/report)), and upon acceptance, implementing the one or more recommendations at the selected one or more smart farms ([0022] user performs action to implement recommendation from adaptive reasoning system (i.e., implements some change in variable factor); FIG. 15 depicting adaptive reasoning system 140 as providing recommendation 1015 with corresponding action 1020 and 1030; [0133]-[0134]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of transmitting an output report wirelessly to the one or more smart farms, confirming or denying acceptance of the research report by the one or more smart farms, and implementing the one or more recommendations (in the report) to the method taught by Kuper ‘010 as modified by Singh, Perry and Stroman, in which smart farms are identified by cluster analysis and the changes to variables/treatments are assigned to “the selected” farms, such that Kuper ‘010 output data (output data 170) that includes livestock health-related recommendations is transmitted wireless to a smart farm in a manner in which the smart farm may confirm or deny acceptance of the report and further determine to implement the recommendation(s).
The motivation would have been to provide the recommendation within the report to the onsite user (user at ranch/farm) who may be remote (hence wireless transmission) to effectuate consideration of the recommendation by the user, who may accept or deny acceptance of the report such as by or including implementation as disclosed by Stroman.
Stroman further teaches tracking evolution of an implementation using real-time livestock data collected from the one or more sensors at selected one or more farms (FIG. 15 adaptive reasoning system 140 including processing/tracking results 1025 of actions 1020; [0131] adaptive reasoning system 140 collects information from animals including sensor information such as imaging and temperature measurements that would be performed in real-time; [0133]-[0134] user implements recommended actions and outcomes of actions are monitored. Examiner notes that in context the “results” are clearly animal condition results that as disclosed in [0131] may be collected using sensor data), and further teaches that livestock data monitoring may be performed/collected daily ([0016] and [0096] monitoring system may collect daily average weight gain).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of tracking evolution of an implementation using real-time livestock data collected from the one or more sensors at selected one or more farms, and furthermore teaches that livestock data monitoring may be performed/collected daily to the method taught by Kuper ‘010 as modified by Perry, Singh, and Stroman, which teaches that the farms may be smart farms such that in combination the method includes tracking evolution of the implementation using real-time livestock data collected daily from the one or more sensors at the selected one or more smart farms.
The motivation would have been to monitor changes to livestock to both provide desired livestock condition data and to determine whether changes to the livestock treatment/health actions require adjustments as disclosed by Stroman.
Regarding “transmitting outcome data related to the implementation to a cloud configuration or central database, wherein the transmitted outcome data is made available for use as historical data in the virtual research model for future virtual research,” Stroman further teaches transmitting data related to an outcome of implementation of one or more recommendations (FIG. 15 depicting adaptive reasoning system 140 in which Result 1025 data resulting from an Action 1020 that is based on a Recommendation 1015 is transmitted as Feedback 1030 to step 1010)” to a central database (step 1010 depicting Expert Rules, Corrective Actions, Recommendations, Best Practices in bi-directional communicative contact with a Central Database; [0128] “Specific health regimens and their causes and effects may entered into … adaptive reasoning system 140”), “wherein the transmitted data is made available for use as historical data ([0128] “Specific health regimens and their causes and effects may entered into … adaptive reasoning system 140, which allows the system to learn and pass the information on to the users; [0129] historical information archived in the central database) in a virtual research model (FIG. 15 adaptive reasoning system 140 includes remotely accessible data storage (Central Database), processing functionality (Expert Rules, Diagnosis, Corrective Actions, Recommendations, Best Practices), and multivariate inputs (step 105) that constitute and implement a virtual research model (that is inherently available for future virtual research) in terms of the step 1010 implementing recommendations and modifying actions based on results (i.e., data modeling that implements research). Examiner notes that while “for future virtual research” is an inherent attribute of the virtual research model, this feature conveys an intended purpose/result that does not positively limit the functions of the recited method and is therefore not given patentable weight.
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Stroman’s teaching of transmitting data related to an outcome of implementation of one or more recommendations to a central database, wherein the transmitted data is made available for use as historical data in a virtual research model to the method taught by Kuper ‘010 as modified by Stroman, Singh, Kopic, and Perry such that in combination the method includes 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.
The motivation would have been to store implementation outcome data to a widely accessible storage from which it may be accessed and usefully applied to improve future recommendations as suggested by Stroman.
The element “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 not given patentable weight.
Regarding “the hypothesis based on simulations produced according to the virtual research method of claim 7”, Kuper ‘010 teaches adjustment to the models (hypothesis) based on the simulations in a feedback manner ([0028] results of comparison of predicted performance and actual performance may be used to adjust the weights of the model (the premise/hypothesis represented by the model) to improve modeling accuracy).
Furthermore, and per the grounds for rejecting claim 7, simulations based on hypotheses performed according to virtual research method of claim 7 are taught by the combination of Kuper ‘010, Stroman, Singh, Kopic, and Park.
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have combined the teaching of Kuper ’010 of determining (e.g., by adjustment) the hypothesis based on simulations with the teachings of Kuper ‘010, Stroman, Singh, and Kopic such that the simulations produced according to the virtual research method of claim 7 are utilized to form the hypothesis.
Such a combination would amount to applying a known design option for formulating and applying a hypothesis regarding changes to variables may affect livestock performance to achieve predictable results.
As to claim 3, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 1, wherein the one or more sensors transmits a particular code associated with an individual livestock animal (Stroman: 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) or individual smart farm.”
As to claim 5, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 1,” and Kuper ‘010 further suggests “wherein the cluster analysis utilizes historic data obtained from a plurality of smart farms located throughout one or more global regions ([0005], [0023], and [0035] disclosing that producer-specific information utilized to optimize workflow, which suggests discerning among multiple producers (farms)).”
Furthermore, Stroman teaches collecting historical information relating to factors affecting livestock condition from multiple smart farms ([0002] Internet-based platform shared by members of livestock production chain including producers; FIGS. 1 and 8 tracking system 145; [0016] tracking system used to document information from different ranches (Examiner notes that the information relates to events that have occurred and are thus “historical”); [0098]-[0099] ranches are smart farms in terms of using automated livestock information collection) located throughout one or more global regions (Examiner notes that ranches are inherently located on at least one or more global regions).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have combined Stroman’s teaching of collecting historical health-related livestock data from multiple smart farms with Kuper ‘010 teaching of analyzing historical livestock data from potentially differing farms, such that the combined method includes the cluster analysis utilizing historic data obtained from a plurality of smart farms located throughout one or more global regions.
The motivation would have been to expand the comprehensive coverage of historical livestock data available for processing and to provide access within a centralized livestock monitoring platform as disclosed by Stroman.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kuper ‘010 in view of Stroman, Singh, Kopic, Park, and Perry as applied to claim 1 above, and further in view of Kuper (US 10,628,756 B1), “Kuper ‘756.”
As to claim 4, the combination of Kuper ‘010, Stroman, Singh, Kopic, Park, and Perry teaches “[t]he method of claim 1, wherein the step of analyzing the real-time livestock data is accomplished by one or more statistical models (Kuper ‘010: FIG. 1 data processing components for processing/analyzing input data 110 include AI layer 146 and adaptive machine learning methods 147 (Examiner notes that AI inherently entails statistical processing (probabilities) consistent with description of statistical models in Applicant’s specification).”
Furthermore, even if Kuper ‘010 disclosure is considered not to expressly teach analyzing livestock data by statistical models, Kuper ‘756 explicitly teaches using statistical analysis for analyzing input livestock data (FIG. 1 statistical analysis 153 applied to input data, col. 2 lines 15-24, col. 4 lines 40-53, col. 8 lines 14-23).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Kuper ‘756 teaching of using statistical-type modeling for analyzing livestock data to the method taught by Kuper ‘010 as modified by Stroman, Singh, Kopic, Park, and Perry such that the analysis of livestock data includes using statistical analysis. Such a combination would amount to selecting a known design option for analyzing livestock data to achieve predictable results.
ConclusionAny inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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