Office Action 4: Final Rejection (2024-11-18)
Rejection types: 101 - Patent-Eligible Subject Matter

CLAIMS:
Amendments to the Claims:

This listing of claims will replace all prior versions, and listings, of claims in the application:

1\. (Currently Amended) A method comprising:
receiving, by a computing device, one or more sets of real animal data at least partially obtained from one or more sensors that receive, store, and/or send information related to one or more targeted individuals, the one or more sensors including a biological sensor configured to measure electrical signals in or derived from a targeted subject's body, the one or more targeted individuals including a player in an athletic event;
transforming the electrical signals into one or more heart rate values and/or electrocardiogram (ECG) data;
generating, by the computing device, simulated animal data from at least a portion of real animal data or one or more derivatives thereof, wherein one or more parameters or variables of the one or more targeted individuals are modified, the simulated animal data including artificially- created data that shares at least one biological function with a human or another Animal; and
assessing fatigue for the player in the athletic event from the simulated animal data from a running total for an amount of time the player has an elevated heart rate, diastolic blood pressure, systolic blood pressure, perspiration rate and/or distance \[\[run.\]\] run wherein the simulated animal data is generated by a method using a recurrent neural network (RNN) comprising:
(a) receiving real animal data from multiple events, wherein training animal data readings are timestamped and recorded at predetermined time intervals;
(b) training an untrained recurrent neural network model using the real animal data to form a trained recurrent neural network model, wherein the training includes:(i) using a sequence of observations as input;
(ii) performing the training over a plurality epochs using an error metric and an optimizer for updating network weights;

(c) generating simulated animal data by applying the trained neural recurrent network model, wherein the trained recurrent neural network model predicts physiological data including heart rate based on the real animal data;
(d) performing in-sample forecasting, wherein the simulated animal data is generated from a subset of the real animal data to predict future values based on known outcomes; and
(e) performing out-of-sample forecasting, wherein the simulated animal data is generated from observations that were not part of the real animal data to predict future values based on previously unseen data.
2\. (Previously presented) The method of claim 1 wherein one or more simulations are executed to generate animal data.
3\. (Previously presented) The method of claim 2 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used to create, enhance, or modify one or more insights, computed assets, or predictive indicators.
4\. (Previously presented) The method of claim 2 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used in one or more simulation systems, whereby the one or more simulation systems are at least one of: a game-based system, augmented reality system, virtual reality system, mixed reality system, or an extended reality system.
5\. (Original) The method of claim 4 wherein one or more computing devices utilized as part of one or more simulation systems are operable to either directly or indirectly: (1) offer or accept one or more wagers; (2) create, enhance, modify, acquire, offer, or distribute one or more products; (3) evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) formulate one or more strategies; (5) take one or more actions; (6) mitigate or prevent one or more risks; (7) recommend one or more actions; (8) engage one or more users; (9) or a combination thereof.
6\. (Previously presented) The method of claim 2 wherein at least a portion of the simulated animal data is used as one or more inputs in one or more simulations.

7\. (Previously presented) The method of claim 6 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used to create, enhance, or modify one or more insights, computed assets, or predictive indicators.
8\. (Previously presented) The method of claim 6 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used in one or more simulation systems, whereby the one or more simulation systems are at least one of: a game-based system, augmented reality system, virtual reality system, mixed reality system, or an extended reality system.
9\. (Original) The method of claim 8 wherein one or more computing devices utilized as part of one or more simulation systems are operable to either directly or indirectly: (1) offer or accept one or more wagers; (2) create, enhance, modify, acquire, offer, or distribute one or more products; (3) evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) formulate one or more strategies; (5) take one or more actions; (6) mitigate or prevent one or more risks; (7) recommend one or more actions; (8) engage one or more users; or (9) a combination thereof.

10\. (Original) The method of claim 1 wherein the one or more parameters or variables modified to generate simulated data are comprised of non-animal data.

11\. (Original) The method of claim 1 wherein the simulated animal data is generated by randomly sampling at least a portion of the real animal data.

12\. (Original) The method of claim 1 wherein the simulated animal data is generated by fitting the real animal data to a function with one or more independent variables or one or more adjustable parameters that are optimized to provide a fit to real animal data.

13\. (Original) The method of claim 12 wherein the function is a line, polynomial, exponential, a Gaussian, Lorentzian, piecewise linear, or a spline between real data points.

14\. (Original) The method of claim 12 wherein the one or more independent variables or adjustable parameters include time such that one or more biological parameters are associated with one or more virtual participants in a simulation as a function of time.

15\. (Original) The method of claim 1 wherein the simulated animal data is generated by adding one or more offset values to each value of real animal data.

16\. (Original) The method of claim 1 wherein at least a portion of the real animal data is transformed into simulated data by adding one or more random numbers to each value of a real data set.

17\. (Original) The method of claim 1 wherein at least a portion of the simulated animal data is transformed into a lookup table to be used by a simulation.

18\. (Original) The method of claim 1 wherein at least a portion of the simulated animal data is generated by fitting the real animal data to a probability distribution and then randomly sampling the probability distribution to assign one or more biological parameters to one or more virtual subjects.

19\. (Original) The method of claim 18 wherein the probability distribution is selected from the group consisting of Bernoulli distributions, uniform distributions, binomial distributions, normal distributions, Poisson distributions, exponential distributions, and Lorentzian distributions.

20\. (Original) The method of claim 18 wherein one or more sets of real animal data include one or more non-animal data variables or parameters which are applied as one or more parameters or variables in a simulation.

21\. (Previously Presented) The method of claim 1 wherein a trained neural network generates the simulated animal data, the trained neural network having been trained with at least a portion of the real animal data or one or more derivatives thereof.

22\. (Original) The method of claim 21 wherein the trained neural network is trained with at least a portion of simulated data.

23\. (Original) The method of claim 21 wherein the one or more parameters or variables modified to generate simulated data are comprised of non-animal data.

24\. (Original) The method of claim 21 wherein the trained neural network is a recurrent neural network.

25\. (Original) The method of claim 21 wherein the trained neural network is a Long Short-Term Memory recurrent neural network.

26\. (Original) The method of claim 21 wherein the trained neural network is a Generative Adversarial Network.
27\. (Previously presented) The method of claim 21 wherein the trained neural network utilized to generate the simulated animal data consists of one or more of the following types of neural networks: Feedforward, Perceptron, Deep Feedforward, Radial Basis Network, Gated Recurrent Unit, Autoencoder (AE), Variational AE, Denoising AE, Sparse AE, Markov Chain, Hopfield Network, Boltzmann Machine, Restricted BM, Deep Belief Network, Deep Convolutional Network, Deconvolutional Network, Deep Convolutional Inverse Graphics Network, Liquid State Machine, Extreme Learning Machine, Echo State Network, Deep Residual Network, Kohenen Network, Support Vector Machine, Neural Turing Machine, Group Method of Data Handling, Probabilistic, Time delay, Convolutional, Deep Stacking Network, General Regression Neural Network, Self-Organizing Map, Learning Vector Quantization, Simple Recurrent, Reservoir Computing, Echo State, Bi-Directional, Hierarchal, Stochastic, Genetic Scale, Modular, Committee of Machines, Associative, Physical, Instantaneously Trained, Spiking,

Regulatory Feedback, Neocognitron, Compound Hierarchical-Deep Models, Deep Predictive Coding Network, Multilayer Kernel Machine, Dynamic, Cascading, Neuro-Fuzzy, Compositional Pattern-Producing, Memory Networks, One-shot Associative Memory, Hierarchical Temporal Memory, Holographic Associative Memory, Semantic Hashing, Pointer Networks, or Encoder- Decoder Network.

28\. (Original) The method of claim 21 wherein a plurality of neural networks are utilized on at least a portion of the same animal data or one or more derivatives thereof to create simulated data.

29\. (Original) The method of claim 21 wherein at least a portion of the simulated animal data is used either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to formulate one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) to recommend one or more actions; (8) as one or more signals or readings utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) as one or more core components or supplements to one or more mediums of consumption; (11) in one or more promotions; or (12) a combination thereof.

30\. (Original) The method of claim 21 wherein one or more simulations occur utilizing at least a portion of the real animal data or one or more derivatives thereof to create simulated data that is utilized either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to formulate one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) to recommend one or more actions; (8) as one or more signals or readings utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages

with one or more users; (10) as one or more core components or supplements to one or more mediums of consumption; (11) in one or more promotions; or (12) a combination thereof.

31\. (Original) The method of claim 1 wherein a simulation simulates based upon one or more targeted individuals engaged in at least one of: a fitness activity, a sporting event, a health assessment, or an insurance evaluation.

32\. (Original) The method of claim 31 wherein at least a portion of the simulated animal data is used by one or more computing devices either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to formulate one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks;
(7) to recommend one or more actions; (8) as one or more signals or readings utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) as one or more core components or supplements to one or more mediums of consumption; (11) in one or more promotions; or (12) a combination thereof.

33\. (Original) The method of claim 1 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used to create or modify one or more insurance services, identifications, classifications, rates, reimbursements, or a combination thereof.

34\. (Original) The method of claim 1 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used in one or more simulation systems, whereby the one or more simulation systems are at least one of: a game-based system, augmented reality system, virtual reality system, mixed reality system, or an extended reality system.

35\. (Original) The method of claim 34 wherein one or more computing devices utilized as part of one or more simulation systems are operable to either directly or indirectly: (1) offer or accept one or more wagers; (2) create, enhance, modify, acquire, offer, or distribute one or more products; (3) evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) formulate one or more strategies; (5) take one or more actions; (6) mitigate or prevent one or more risks; (7) recommend one or more actions; (8) engage one or more users; or (9) a combination thereof.

36\. (Original) The method of claim 34 wherein at least a portion of a simulation user's animal data or one or more derivatives thereof are utilized as part of one or more simulations, the simulation user's animal data being at least partially obtained from one or more sensors.
37\. (Previously presented) The method of claim 36 wherein an apparatus that is in communication with the one or more simulation systems has one or more sensors in contact with, embedded in, affixed to, lodged in, or integrated with the apparatus, and provides at least a portion of a simulation user's data to a computing device, which includes at least a portion of a user's animal data or one or more derivatives thereof.
38\. (Previously presented) The method of claim 1 wherein the one or more sensors and/or one or more appendices thereof are affixed to, are in contact with, or send one or more electronic communications in relation to or derived from, one or more targeted individuals including a targeted individuals' body, eyeball, vital organ, muscle, hair, veins, biological fluid, blood vessels, tissue, or skeletal system, embedded in the one or more targeted individuals, lodged or implanted in one or more targeted individuals, ingested by the one or more targeted individuals, integrated to comprise at least a portion of the one or more targeted individuals, or integrated into or as part of, affixed to, or embedded within, a fabric, textile, cloth, material, fixture, object, or apparatus that contacts or is in communication with one or more targeted individuals, either directly or via one or more intermediaries.

39\. (Original) The method of claim 1 wherein the one or more sensors includes at least one biosensor that gathers at least one of: physiological, biometric, chemical, biomechanical, location, environmental, genetic, genomic, or other biological data from one or more targeted individuals.

40\. (Original) The method of claim 1 wherein the one or more sensors gathers, or provides information that can be converted into, at least one of the following types of data: facial recognition data, eye tracking data, blood flow data, blood volume data, blood pressure data, biological fluid data, body composition data, biochemical data, pulse data, oxygenation data, core body temperature data, skin temperature data, galvanic skin response data, perspiration data, location data, positional data, audio data, biomechanical data, hydration data, heart-based data, neurological data, genetic data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic data, ambient temperature data, humidity data, barometric pressure data, or elevation data.

41\. (Original) The method of claim 40 wherein the simulated animal data is utilized either directly or indirectly by one or more computing devices that provide information related to one or more insights, computed assets, or predictive indicators gathered or derived from the one or more sensors.

42\. (Original) The method of claim 1 wherein a user chooses one or more parameters or variables for one or more simulations, one or more simulations occur, and one or more users acquire at least a portion of the simulated animal data or one or more derivatives thereof for consideration.

43\. (Original) The method of claim 1 wherein a user provides one or more commands, and a one or more computing devices take one or more actions that utilize at least a portion of the simulated animal data or one or more derivatives thereof to fulfill at least a portion of the one or more commands.

44\. (Original) The method of claim 1 wherein at least a portion of the simulated animal data is utilized to create, enhance, or modify one or more insights, computed assets, or predictive indicators.

45\. (Original) The method of claim 44 wherein the one or more insights is a personal score or other indicator related to one or more targeted individuals or groups of targeted individuals that utilizes at least a portion of simulated data to (1) evaluate, assess, prevent, or mitigate animal data-based risk, (2) to evaluate, assess, and optimize animal data-based performance, or (3) a combination thereof.

46\. (Original) The method of claim 1 wherein one or more computing devices take one or more actions on behalf of a user based upon one or more thresholds set by the user, with the one or more actions being initiated either directly or indirectly as a result of at least a portion of the simulated animal data or one or more derivatives thereof.
47\. (Previously Presented) The method of claim 1 wherein the computing device is configured to detect at least one of: one or more outlier values generated from the one or more sensors, or one or more missing values related to data generated from the one or more sensors, and replaces the one or more outlier values or missing values with one or more artificial data values.

48\. (Original) The method of claim 47 wherein the one or more artificial values align in a time series of generated values and fit within a preestablished threshold or range.

49\. (Original) The method of claim 47 wherein the one or more artificial values are used, at least in part, as one or more inputs to derive animal data.

50\. (Original) The method of claim 1 wherein one or more health classifications, treatments, procedures, identifications, rates, reimbursements, or services are created, modified, or assigned either directly or indirectly based upon at least a portion of the simulated animal data or one or more derivatives thereof.

51\. (Currently Amended) A system for generating and distributing simulated animal data, the system comprising:
one or more sensors that receive, store, and/or send information related to one or more targeted individuals, the one or more sensors including a biological sensor configured to measure electrical signals in or derived from a targeted subject's body, the one or more targeted individuals including a player in an athletic event; and
a computing device configured to execute steps of:receiving one or more sets of real animal data at least partially obtained from one or more sensors that receive, store, or send information related to one or more targeted individuals, the simulated animal data including artificially-created data that shares at least one biological function with a human or another animal;
generating simulated animal data from at least a portion of real animal data or one or more derivatives thereof, wherein one or more parameters or variables of the one or more targeted individuals are modified and;
assessing fatigue for the player in an athletic event from the simulated animal data from a running total for an amount of time the player has an elevated heart rate, diastolic blood pressure, systolic blood pressure, perspiration rate and/or distance \[\[run.\]\] run, wherein at least a portion of the artificially-created data is processed by a trained recurrent neural network model by a method comprising:
(a) receiving real animal data from multiple events, wherein training animal data readings are timestamped and recorded at predetermined time intervals;
(b) training an untrained recurrent neural network model using the real animal data to form a trained recurrent neural network model, wherein the training includes:(i) using a sequence of observations as input;
(ii) performing the training over a plurality epochs using an error metric and an optimizer for updating network weights;
(c) generating simulated animal data by applying the trained neural recurrent network model, wherein the trained recurrent neural network model predicts physiological data including heart rate based on the real animal data;

(d) performing in-sample forecasting, wherein the simulated animal data is generated from a subset of the real animal data to predict future values based on known outcomes; and
(e) performing out-of-sample forecasting, wherein the simulated animal data is generated from observations that were not part of the real animal data to predict future values based on previously unseen data.

52\. (Original) The system of claim 51 wherein one or more simulations are executed to generate simulated animal data.

53\. (Original) The system of claim 51 wherein a trained neural network generates simulated animal data, the trained neural network having been trained with at least a portion of the real animal data or one or more derivatives thereof.

54\. (Original) The system of claim 53 wherein the trained neural network is trained with at least a portion of simulated data.

55\. (Original) The system of claim 51 wherein the simulated animal data is used either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to formulate one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) to recommend one or more actions; (8) as one or more signals or readings utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) as one or more core components or supplements to one or more mediums of consumption; (11) in one or more promotions; or (12) a combination thereof.

56\. (Original) The system of claim 51 wherein a simulation simulates based upon one or more targeted individuals engaged in at least one of: a fitness activity, a sporting event, a health assessment, or an insurance evaluation.

57\. (Original) The system of claim 51 wherein at least a portion of the simulated animal data or one or more derivatives thereof are used to create, modify, or assign one or more health procedures, services, treatments, codes, identifications, classifications, rates, reimbursements, or a combination thereof.

58\. (Original) The system of claim 51 wherein a user chooses one or more parameters or variables for one or more simulations, one or more simulations occur, and one or more users acquire at least a portion of the simulated animal data or one or more derivatives thereof for consideration.

OFFICE ACTION:
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 .  This Office Action is in response to Applicant’s reply filed 11/1/24.  Claims 1 and 51 are amended.  Claims 1-58 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-58 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a method comprising: receiving one or more sets of real animal data at least partially obtained from one or more sensors that receive, store, or send information related to one or more targeted individuals. The limitation of : receiving one or more sets of real animal data at least partially obtained from one or more sensors that receive, store, or send information related to one or more targeted individuals, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “from one or more sensors,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “sensors” language, “receiving” in the context of this claim encompasses the user manually receiving information related to a user. Similarly, the limitations of: generating and providing are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind. The same interpretation is applied to the remaining steps in claim 1.  If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – sensors. The sensors are recited at a high-level of generality (i.e., as a generic processor implementing a step) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using sensors amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.  Similar reasoning is applied to claims 2-58.
Response to Arguments
Applicant's arguments filed 11/1/24 have been fully considered but they are not persuasive. Applicant provides examples of claims which were considered eligible subject matter according to the USPTO’s July 2024 Subject Matter Eligibility Examples, however, the analogy was found unpersuasive.  The claims recite a method comprising: receiving one or more sets of real animal data at least partially obtained from one or more sensors that receive, store, or send information related to one or more targeted individuals. Other than reciting “from one or more sensors,” nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The sensors and the computer language in the claim are recited at a high-level of generality (i.e., as a generic processor implementing a step) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.  The Applicant is invited to set up an interview with the Examiner to advance prosecution with respect to the 35 USC 101 rejection.
Conclusion
THIS ACTION IS MADE FINAL.  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.

APPLICANT REBUTTAL: Remarks
Claims 1-58 are pending in this application. Claims 1-58 now stand rejected.
New claims 59-62 are presented for examination.
Claim Rejections -
35 U
SC $
Claims 1-58 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Applicant respectfully traverses the present rejection for the following reasons. Independent claims 1 and 51 are amended such that the artificially-created data is processed by a trained recurrent neural network model by a method that includes a step of "(f) modifying one or more parameters of simulated animal data to alter physiological, biomechanical, or environmental conditions for predictive analysis." The step of modifying one or more parameters of simulated animal data to alter physiological, biomechanical, or environmental conditions for predictive analysis when combined with steps a) to e) presents a solution to a technical problem by enabling a dynamically adaptable predictive model that goes beyond conventional data analysis. Traditional physiological monitoring systems rely on static data sets or historical trends, which limit their ability to forecast conditions under unobserved scenarios. The claimed invention addresses this limitation by allowing the modification of simulated animal data, ensuring that predictive models can account for changing physiological states, environmental influences, and biomechanical variations. This capability is not merely data processing, but an active computational transformation that enables improved fatigue prediction, injury prevention, and performance optimization. By incorporating real-time parameter adjustments, the claimed method creates a feedback loop within the predictive model, making it responsive to new or hypothetical conditions that were not part of the original dataset. This step improves the reliability and accuracy of AI- driven physiological forecasting, making it a technical advancement over conventional static modeling techniques that fail to generalize beyond predefined conditions.
The claims recite a method and system that apply artificial intelligence, specifically a trained recurrent neural network (RNN), to process physiological data for predictive analysis, making them patent-eligible under 35 U.S.C. § 101. The claimed invention does not merely recite an abstract idea but instead integrates a machine-learning model into a technological process that transforms raw physiological signals into actionable insights for assessing fatigue in an athletic event. The claimed steps require a computing device to receive and transform electrical signals from biological sensors into physiological values such as heart rate and electrocardiogram (ECG) data, a process that inherently requires specialized data processing beyond human cognition. The invention further generates simulated animal data by modifying physiological and biomechanical parameters to predict conditions such as fatigue, which is not a mere mental process but a computationally intensive AI-driven simulation that enables forecasting of real-world conditions.
Unlike conventional data processing techniques, the claims train an RNN model using timestamped, event-based physiological data, incorporating multi-epoch training, an error metric, and an optimizer for updating network weights, which directly enhances the accuracy of predicting fatigue-related events. The invention is not merely using AI for data classification but is actively generating and modifying simulated data through in-sample and out-of-sample forecasting, allowing the model to predict future physiological values based on previously unseen data. For example, the dynamic simulation of fatigue indicators, including heart rate, diastolic blood pressure, systolic blood pressure, perspiration rate, and distance run, is not simply a passive computation but a fundamental improvement to predictive analytics that provides practical applications in sports performance monitoring and real-time physiological assessment. The modification of simulated animal data to alter physiological, biomechanical, or environmental conditions ensures that the generated data is not merely a derivative of collected real-world data but a transformed dataset tailored for predictive analysis, demonstrating a concrete real-world application beyond mere data collection and analysis.
The system claim further confirms that the invention is not an abstract idea but a practical application of machine learning in a structured data-processing environment. The claimed system integrates one or more sensors, including a biological sensor, into a computing device that executes
a trained RNN model to generate and assess simulated data for fatigue analysis. By specifying that at least a portion of the artificially-created data is processed through a trained recurrent neural network, the claim ensures that the model does not merely perform generic AI tasks but applies a specifically trained neural network to physiological data in a structured and meaningful way. The use of in-sample and out-of-sample forecasting further distinguishes the claim from generic AI applications by ensuring that the model can predict physiological conditions beyond previously collected data, directly enhancing real-world accuracy and usability.
The claimed invention is not a fundamental economic or mathematical principle but rather a machine-learning-based system that enhances physiological monitoring by transforming and predicting fatigue-related biometric data. Because the claims do not merely recite a mental process, mathematical calculation, or conventional data gathering, but instead apply an advanced neural network model in a structured and specific manner, they are not directed to an abstract idea and instead integrate AI into a practical application with clear technical benefits. The claims, therefore, satisfy both steps of the Alice test, as they are directed to a specific, technical improvement in physiological data modeling and predictive fatigue analysis and include elements that amount to significantly more than any potential abstract idea. Therefore, the rejection under 35 U.S.C. § 101 should be withdrawn.
The newly added claims 59-62 further enhance patentability under 35 U.S.C. § 101 by demonstrating practical applications and specific technical improvements that move beyond mere data collection or analysis. Claim 59 strengthens the patent eligibility argument by specifying that the predicted fatigue levels or expected caloric expenditure are delivered as actionable outputs, ensuring that the method is not directed to an abstract idea but to a real-world decision-making system with tangible benefits in athletic monitoring. Claim 60 explicitly defines how modifying simulated animal data alters key physiological conditions, making the claimed invention a dynamic predictive system rather than a static computational model. By specifically adjusting factors such as heart rate and hydration, the claim clarifies that the modification step simulates real-world conditions in a way conventional models do not, reinforcing that the method is not merely abstract but provides a concrete technological solution.
New claim 61 further solidifies patentability by emphasizing real-time or near real-time recalibration of predictive models, demonstrating that the method actively adapts and refines fatigue predictions as an athletic event progresses. This real-time adjustment capability highlights a significant technical improvement over conventional regression-based models, ensuring that the system continuously integrates new data and updates predictions dynamically rather than operating on historical data alone. Finally, claim 62 enhances the eligibility of the invention by explicitly incorporating forecasting of future biological readings, demonstrating that the system is not limited to past or present conditions but predicts future physiological states, a critical distinction from prior conventional monitoring techniques. These claims collectively reinforce that the invention is not an abstract mathematical model but a technological innovation that enhances predictive accuracy and real-time decision-making in athletic event monitoring.
Accordingly, claims 1-58 and new claims 59-62 are now allowable under 35 U.S.C. 101.
Conclusion

NEXT OFFICE ACTION: Reasons for Allowance
Claims 1-62 are allowed.  The following is an examiner’s statement of reasons for allowance: Applicant’s reply/amendments filed 2/18/25 was found persuasive to overcome the outstanding rejections.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion