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. DETAILED ACTION Election/Restrictions Applicant’s election without traverse of Group III comprising claims 11-36 in the reply filed on 1/22/2026 is acknowledged. 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 11-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to the abstract idea of mental processes and/ or certain methods of organizing human activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below. Step 1 of the 2019 Revised Patent Subject Matter More specifically, regarding Step 1, of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a machine, process, and/or an article of manufacturer, which are statutory categories of invention. Step 2a – Prong 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance Next, the claims are analyzed to determine whether it is directed to a judicial exception. T he claim involves: Gathering heart rate variability (HRV) data from an animal using sensors. Transmitting the data to a computing subsystem. Transforming the data into a formatted form, creating or modifying features derived from HRV (e.g., measured over adjustable intervals). Accessing baselines for the targeted individual. Comparing differences in values to generate predictive indicators. Using these indicators for activities such as creating/evaluating bets, formulating strategies, distributing products, or mitigating risks. This recites abstract ideas in multiple categories such as : Mathematical concepts: The claim involves mathematical calculations, such as deriving features from HRV data (e.g., R-R intervals, differences, variability values), comparisons to baselines, and generating indicators based on those computations. See USPTO guidance (e.g., mathematical relationships/formulas in claims like SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018), where statistical analysis of data was abstract). Mental processes: The steps of observing data (gathering HRV), evaluating it (comparing to baselines, calculating differences), and forming a judgment (generating predictive indicators) could be performed mentally or with pen and paper. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), where collecting, analyzing, and displaying data from measurements was a mental process. Certain methods of organizing human activity: The predictive indicators are used for commercial or business activities (e.g., betting markets, strategy formulation, risk mitigation), resembling managing economic risks or commercial interactions. See Bilski v. Kappos, 561 U.S. 593 (2010), where hedging risks was abstract. The focus is on data collection, analysis, and prediction for business/decision-making purposes, without tying to a specific technological improvement . Step 2a – Prong 2 of the 2019 Revised Patent Subject Matter Eligibility Guidance The second prong of step 2a is the consideration if the claim limitations are directed to a practical application. Limitations that are indicative of integration into a practical application: -Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a) -Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo -Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b) -Effecting a transformation or reduction of a particular article to a different state or thing – see MPEP 2106.05(c) -Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo Limitations that are not indicative of integration into a practical application: -Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea- see MPEP 2106.05(f) -Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) -Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) T he recited judicial exception is not integrated into a practical application. Additional elements include: Sensors (generic source sensors for gathering HRV data electronically). Transmission subsystem (generic data provision to a computing subsystem). Computing subsystem (generic computer configured to perform the data processing steps). These are conventional tools applied in a routine way to implement the abstract idea. There is no improvement to computer functionality. The sensors and transmission are off-the-shelf for physiological monitoring, and the processing is high-level data manipulation without specificity. The application to "animal data" or "targeted events" (e.g., potentially animal racing or performance) is field-of-use limitation, not a technological solution. The end uses (e.g., betting, risk mitigation) are extra-solution activity, not transforming the idea into something practical. The claim as a whole merely automates an abstract process using generic technology, without meaningful limitations. Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. T he claim does not add an inventive concept amounting to "significantly more" than the abstract idea. The elements, individually and in combination, are well-understood, routine, and conventional: HRV data collection via sensors and electronic transmission is standard in physiological monitoring (e.g., as evidenced by prior art like Ross US 8,412,315 B2, which describes real-time HRV sensors and processing for animals). Data transformation, feature creation (e.g., from R-R intervals), baseline comparisons, and indicator generation are conventional mathematical operations in signal processing and predictive analytics. No unconventional arrangement or non-generic implementation is claimed; the computing is at a high level of generality. This is akin to using a generic computer to perform abstract steps, which is insufficient. The claim lacks "something more" to transform it into eligible subject matter. Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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. Claim(s) 11-18, 26-29, 31-34 are rejected under 35 U.S.C. 102(a)( 1 ) as being anticipated by Ross (US 2008/0004539 A1) or, in the alternative, under 35 U.S.C. 103 as obvious over Ross (US 2008/0004539 A1) in view of Seder ( US 2009/0192975 A 1). 11. Ross discloses a system for generating dynamic real-time predictions using heart rate variability comprising: one or more source sensors that gather animal data from a targeted individual in real-time or near real-time prior to or during a targeted event, wherein at least a portion of the gathered animal data is heart rate variability data, the animal data being transmitted by the one or more source sensors electronically ( sensors ( 108: Fig. 1 , 3 ) on targeted animals gather real-time HRV/IBI (R-R) data prior/during events and transmitted electronically to analysis unit ) , [0044], [0092]-[0094] ; a transmission subsystem that provides transmitted animal data to a computing subsystem ( electronic transmission to computing subsystem ) , (304: Fig. 3), [0092]-[0094]; and a computing subsystem that gathers the animal data and associated contextual data related to the gathered animal data, the associated contextual data including event data from the targeted event associated with the targeted individual, wherein the computing subsystem is configured to ( computing unit gathers HRV + contextual activity, environment, events ) , ( 102: Fig. 1- 2 ) , [ 0 042] , [0045] : take one or more actions to transform the gathered animal data and the associated contextual data as transformed data, including the event data from the targeted event, into a data format wherein at least a portion of the transformed data is used to create, modify, or enhance one or more features, the one or more features including information derived from heart rate variability data measured over an adjustable time interval, and wherein each of the features is measured in an adjustable time period prior to an outcome of the targeted event being determined ( transforms raw IBI into time-domain (SDNN/RMS - SD-like) & frequency-domain (LF/HF over windows) features; adjustable intervals (15s– 24h); measured pre-outcome (pre-race, pre-treatment) ) , [0042], [0055], [0095] , [0118], [0120], [0137], [0154]-[0155] ; take one or more actions to transform the gathered animal data and associated contextual data into reference data, wherein at least a portion of the reference data is used to create, modify, or enhance one or more features, the one or more features including information derived from heart rate variability data measured over an adjustable time interval, and wherein each of the one or more features is measured in an adjustable time period prior to an outcome associated with the event being determined ( historical healthy-animal data becomes reference for feature enhancement/baselines ) , [0051] ; access one or more baselines for the targeted individual prior to each targeted event, the computing subsystem utilizing the one or more baselines and the real-time or near real-time animal data or its one or more derivatives to perform one or more calculations to derive a difference in values ( species/individual healthy baselines accessed pre-event; computes differences vs. current ) , [0051] ; and compare the difference in values to create, modify, or enhance at least one predictive indicator related to the outcome of the targeted event or another targeted event, and wherein the at least one predictive indicator is used by one or more computing devices to at least one of: (1) create, modify, enhance, or evaluate one or more odds ; (2) create, modify, enhance, or evaluate one or more bets; (3) as a market upon which one or more bets are placed or accepted; (4) formulate one or more strategies ; (5) create, modify, enhance, acquire, offer, or distribute one or more products; or (6) mitigate, prevent, or take one or more risks ( the data identified herein as indicating animal health and condition can be used as a general predictor of performance or fitness, and as a predictor of potential injury… The animal health condition indication techniques disclosed herein can be useful as a risk stratification tool, to use the HRV data analysis in conjunction with HRV parameters to identify animals (such as horses) who might be at risk of injury, catastrophic breakdown, or illness… can be useful as a predictive tool for animal performance, utilization, and care ), [0081] . Alternatively, Seder teaches obtaining physiological heart-health data from horses (ventricular septal wall thickness (SW), LVD, LVS, PS, SPLN, HTWT), normalizing against large database baselines (age/sex/weight-matched, n>5,000), transforming into predictive features/classifications (high-earner vs. low-earner, router vs. sprinter, 75th percentile thresholds), and directly applying those predictions to wagering . Seder also discloses discriminant analysis/classification models, multiple outcomes (high/low earnings, race-type success), and risk mitigation via selective betting (reject low-percentile horses to reduce loss probability) , [0011] , [0025], [0040]-[0045]. It would have been obvious to a person of ordinary skilled in the art before the effective filing date to modify Ross’s real-time HRV sensor and computing system by incorporating Seder’s equine physiological-prediction-to-betting framework and would have been motivated to do so because Ross already targets performance animals (horses/dogs) and uses HRV for fitness/injury prediction and risk mitigation; Seder shows that analogous cardiac/health metrics are routinely fed into handicapping systems to generate betting odds, markets, strategies, and wagering products in the exact same industry (horse racing). Combining them yields a single real-time HRV system whose outputs directly power live betting markets and risk-managed wagering on animal events which would produc e predictable commercial improvement with no undue experimentation . 12. Ross discloses the system of claim 11, wherein the computing subsystem is configured to execute one or more steps to create, modify, or enhance the at least one predictive indicator in the real-time or near real-time [0066], [0092], [0106] . 13. Ross discloses the system of claim 11, wherein the computing subsystem is configured to execute one or more steps of: gathering or calculating R-R intervals derived from one or more source sensors from a targeted individual; calculating differences in successive R-R intervals; calculating one or more heart rate variability values from successive differences between normal heartbeats; establishing a heart rate variability baseline for the targeted individual using at least a portion of the calculated one or more heart rate variability values for an event associated with the targeted individual with a definable, quantifiable, measurable, or observable outcome, wherein the heart rate variability baseline is created, modified, or enhanced based upon values collected prior to a start of the event; gathering or calculating subsequent R-R intervals derived from the one or more source sensors from the targeted individual; calculating differences in subsequent successive R-R intervals; calculating one or more subsequent heart rate variability values from the successive differences between normal heartbeats, wherein one value of the one or more subsequent heart rate variability values is calculated for each sub-event amongst two or more sub-events that comprise at least a portion of the event; calculating a HRV difference based upon the difference between the heart rate variability values for consecutive sub-events and the heart rate variability baseline, divided by the heart rate variability baseline; calculating the difference between two successive HRV differences to create at least one variability indicator; creating a threshold utilizing at least a portion of contextual data to characterize information derived from one or more variability indicators; creating at least one primary insight by comparing the threshold and the at least one variability indicator; accessing one or more reference insights; and comparing the at least one primary insight and the one or more reference insights to create one or more predictive indicators , [0015], [0045], [0050], [0055], [0079], [0120], [0122], [0127] . 14. Ross discloses the system of claim 11, wherein the computing subsystem is configured to execute one or more steps for a plurality of targeted individuals, either collectively or individually, to create, modify, or enhance at least one predictive indicator in the real-time or near real-time (i.e. multiple horses), [0066], [0081] . 15. Ross discloses the system of claim 11, wherein the computing subsystem gathers the reference data directly or indirectly associated with the targeted individual and/or the targeted event from one or more other computing devices [0066], [0081]. 16. Ross discloses the system of claim 11, wherein the computing subsystem does not have access to reference data but instead uses newly-gathered animal data from the targeted individual to create the one or more baselines that are representative of reference data and used for analysis [0051] . 17. Ross discloses the system of claim 11, wherein the computing subsystem uses one or more lagged values for one or more select features, wherein the one or more lagged values range from 1 to any number of seconds or time intervals [0109], [0114], [0118], [0124] . 18. Ross discloses the system of claim 11, wherein the computing subsystem is further operable to calculate one or more additional metrics, the one or more additional metrics including SPIKE_RMSSD_COUNT [0090] . 26. Ross discloses the system of claim 11, wherein the one or more features include at least one of: moving average heart rate over an adjustable time interval, rolling time domain HRV features RMSSD and SDNN over an adjustable time interval, rolling frequency domain features LF, HF, and LF/HF Ratio over an adjustable time interval, or one or more cumulative metrics [0042], [0120], [0127], [0137], (Fig. 23) . 27. Ross discloses the system of claim 11, wherein Seder further discloses the one or more bets include at least one of: a proposition bet, spread bet, a line bet, a future bet, a parlay bet, a round- robin bet, a handicap bet, an over/under bet, a full cover bet, an accumulator bet, an outright bet, or a teaser bet [0045] . 28. Ross discloses the system of claim 11, wherein creation, modification, enhancement, or evaluation of the one or more odds occurs dynamically and in real-time or near real-time as new animal data, contextual data, or a combination thereof is gathered by the computing subsystem , [0061], [0106], [0122], [0129] . 29. Ross discloses the system of claim 11, wherein the one or more features includes information derived from heart rate data [004 4 ], [0092]-[0094] . 31. Ross discloses the system of claim 11, wherein the targeted event is comprised of a plurality of targeted events [0087], [0124] . 32. Ross discloses the system of claim 11, wherein the computing subsystem uses a single input variable to create, modify, or enhance one or more outcome predictions via the one or more predictive indicators (i.e. user determines which input/inputs to use), [00 98 ] , [0118] . 33. Ross discloses the system of claim 11, wherein the computing subsystem uses multiple input variables to create, modify, or enhance one or more outcome predictions via the one or more predictive indicators (i.e. user determine s which input/inputs to use), [0098] , [0118] . 34. Ross discloses the system of claim 11, wherein the computing subsystem is configured to operate in a multi-dimensional space with one or more inputs from gathered data that include animal data, its associated contextual data, event outcome data, or a combination thereof, with at least a portion of the one or more inputs being orthogonal data (i.e. user determines which input/inputs to use), [0098] , [0118] . Claim(s) 19-25, 30, 35-36 are rejected under 35 U.S.C. 103 as obvious over Ross (US 2008/0004539 A1) and Seder ( US 2009/0192975 A1) as discussed above and further in view of H uke ( US 2022/0148364 A 1). 19 -20 . Ross and Seder discloses the system of claim 11, but does not expressly disclose wherein performances of wherein one or more Artificial Intelligence-based models are measured by using one or more AI performance measures or indices which include at least one of: confusion matrices, accuracy percentages per event, or distribution of mis-classifications over a spectrum of events , wherein the reference data used to design and train the one or more Artificial Intelligence-based models for each targeted individual or group of targeted individuals is organized by a higher tier in an event hierarchy, a lower tier in the event hierarchy, or a subset of tiers on the event hierarchy. Huke teaches an AI/machine-learning module that takes real-time sensor/physiological inputs, generates crossed odds formulas, and dynamically creates/modifies/enhances/evaluates bets, operates as a betting market, formulates strategies, distributes betting products, and mitigates risk in horse racing [0068], [0076], [0079]- [0080] . It would have been obvious to a person of ordinary skilled in the art to combine Ross’s real-time HRV system with Seder’s horse-racing betting/handicapping framework and Huke’s AI odds-generation engine and would have been motivated to do so because Ross already predicts performance/risk in racehorses using HRV; Seder shows the industry feeds heart-performance metrics into handicapping/betting markets; Huke demonstrates that real-time sensor/physiological data is routinely fed into an ML module to power live odds, betting markets, and risk-managed wagering in the identical horse-racing field. The combination yields a single system whose HRV predictions directly drive AI-optimized betting products with predictable commercial improvement . 21 -22 . Ross , Seder and Huke discloses the system as discussed above , wherein the computing subsystem is trained to learn one or more correlations between animal data gathered from a plurality of individuals and the associated contextual data from the plurality of individuals, the associated contextual data including one or more associated event outcomes, wherein the computing subsystem utilizes the real-time or the near real-time animal data, or its one or more derivatives, and the associated contextual data gathered from the targeted individual or a group of targeted individuals, a group of targeted individuals being sourced from the plurality of individuals, to create, modify, or enhance the at least one predictive indicator for the targeted individual or a group of targeted individuals related to one or more outcomes of the targeted event or another one or more events , Huke [0068], [0076], [0079]-[0080]. 23 -24 . Ross, Seder and Huke discloses the system as discussed above , wherein the one or more outcomes of the targeted event is a binary outcome or a multiclass outcome (i.e. win/loss or multi-outcome betting resolutions ), Huke [0080] . 25. Ross, Seder and Huke discloses the system as discussed above , wherein the at least one predictive indicator is derived utilizing at least one classification algorithm selected from the group consisting of Random Forest classification algorithm, Random Forest/Decision Trees, Support Vector Machine classifier, K-Nearest Neighbors, Naive Bayes, Linear Discriminant Analysis, Logistic Regression, Neural Networks, and Gradient Boosting Machine Classifier ( Huke’s AI engine inherently uses classification/regression (ML correlation of odds formulas) . Furthermore, a pplication of known classifiers such as Random Forest, SVM, neural networks are standard in 2021 ML betting engines and the combined dataset is routine and obvious . 30. Ross, Seder and Huke discloses the system as discussed above , wherein the computing subsystem is trained with reference data from one or more other individuals, at least a portion of the reference data from the one or more other individuals being utilized to create, modify, or enhance the at least one predictive indicator related to the targeted individual , [0068], [0076], [0079]-[0080] . 35 -36 . Ross, Seder and Huke discloses the system as discussed above , wherein the dimensionality of the gathered data is reduced using one or more Artificial Intelligence techniques, the one or more techniques including one or more Linear and Non-Linear Dimensionality Reduction techniques or methods, to identify and extract at least one contributing factor towards making a prediction and wherein one or more predictions are generated using a Multivariate Time Series forecasting technique in a multi-dimensional space via the use of one or more Classification or Regression techniques. ( Huke explicitly uses multivariate time-series historical data + ML forecasting/regression to generate and optimize odds (“machine learning … correlate the crossed odds with … previous similar plays”) ), (Abstract) . The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached USPTO form PTO-892. Filing of New or Amended Claims The examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the original disclosure a description of the invention defined by the claims. See Wertheim, 541 F.2d at 263, 191 USPQ at 97 (“[T]he PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). However, when filing an amendment an applicant should show support in the original disclosure for new or amended claims. See MPEP § 714.02 and § 2163.06 (“Applicant should specifically point out the support for any amendments made to the disclosure.”). Please see MPEP 2163 (II) 3. (b) Correspondence Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SENG H LIM whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3301 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday (9-5) . 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, FILLIN "SPE Name?" \* MERGEFORMAT David L. Lewis can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-7673 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Seng H Lim/ Primary Examiner, Art Unit 3715