Office Action 5: Notice of Allowance (2025-05-02)

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 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; and
(f) modifying one or more parameters of simulated animal data to alter physiological, biomechanical, or environmental conditions for predictive analysis.
2\. (Currently Amended) The method of claim 1 wherein one or more simulations are executed to generate animal data by one or more simulation systems.
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\. (Currently Amended) 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, 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; and
(f) modifying one or more parameters of simulated animal data to alter physiological, biomechanical, or environmental conditions for predictive analysis.

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.

59\. (New) The method of claim 1 further comprising providing predicted fatigue levels or expected caloric expenditure for the player in an athletic event.

60\. (New) The method of claim 1, wherein the modifying of one or more parameters of simulated animal data comprises adjusting physiological conditions selected from heart rate, hydration level, respiratory rate, or body temperature to simulate real-world performance variations.

61\. (New) The method of claim 1, wherein the modifying of one or more parameters of simulated animal data is performed in real-time or near real-time, enabling continuous recalibration of predictive models during an ongoing athletic event.
62\. (New) The method of claim 4, further comprising forecasting future biological readings for an activity.

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

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


CITED REFERENCE SUMMARIES: Ref US20180036591A1: Claims:
A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations to prescribe fitness-related activities responsive to events, the operations comprising: receiving, with one or more processors, with a fitness prescription engine, an event indicative of a current or future state of a user, wherein: the user is associated with a mobile computing device upon which a native application executes; the native application is configured to send events to the fitness prescription engine via a network; and the fitness prescription engine is configured to prescribe fitness-related activities responsive to events for a user-base with a plurality of users including the user; determining, with one or more processors, with the fitness prescription engine, a fitness need of the user based on a user profile of the user, wherein: the fitness need is determined based on a fitness goal of the user in the user profile; and the fitness need is determined based on a previous fitness-related prescription in the user profile; determining, with one or more processors, with the fitness prescription engine, a current fitness-related prescription in response to both the fitness need and the event, wherein: determining the current fitness-related prescription comprises selecting a fitness resource from among a plurality of candidate fitness resources; and the selected fitness resource is designated in the current prescription as a fitness resource to be accessed by the user in accordance with the current prescription; and causing, with one or more processors, with the fitness prescription engine, the current fitness-related prescription to be presented to the user.
receiving, with one or more processors, with a fitness prescription engine, an event indicative of a current or future state of a user, wherein: the user is associated with a mobile computing device upon which a native application executes; the native application is configured to send events to the fitness prescription engine via a network; and the fitness prescription engine is configured to prescribe fitness-related activities responsive to events for a user-base with a plurality of users including the user;
the user is associated with a mobile computing device upon which a native application executes;
the native application is configured to send events to the fitness prescription engine via a network; and
the fitness prescription engine is configured to prescribe fitness-related activities responsive to events for a user-base with a plurality of users including the user;
determining, with one or more processors, with the fitness prescription engine, a fitness need of the user based on a user profile of the user, wherein: the fitness need is determined based on a fitness goal of the user in the user profile; and the fitness need is determined based on a previous fitness-related prescription in the user profile;
the fitness need is determined based on a fitness goal of the user in the user profile; and
the fitness need is determined based on a previous fitness-related prescription in the user profile;
determining, with one or more processors, with the fitness prescription engine, a current fitness-related prescription in response to both the fitness need and the event, wherein: determining the current fitness-related prescription comprises selecting a fitness resource from among a plurality of candidate fitness resources; and the selected fitness resource is designated in the current prescription as a fitness resource to be accessed by the user in accordance with the current prescription; and
determining the current fitness-related prescription comprises selecting a fitness resource from among a plurality of candidate fitness resources; and
the selected fitness resource is designated in the current prescription as a fitness resource to be accessed by the user in accordance with the current prescription; and
causing, with one or more processors, with the fitness prescription engine, the current fitness-related prescription to be presented to the user.
The medium of claim 1 , wherein: the event is an external event received from a third-party application program interface; the event is one of two received events for which prescriptions are provided by the fitness prescription engine, the two received events including a first event generated by a fitness-center-membership management system indicating the user is checked in at a fitness center and a second event generated by network-connected workout equipment in use by the user indicating use of the network-connected workout equipment by the user; and at least one of the two events is received without being relayed by the mobile computing device.
the event is an external event received from a third-party application program interface;
the event is one of two received events for which prescriptions are provided by the fitness prescription engine, the two received events including a first event generated by a fitness-center-membership management system indicating the user is checked in at a fitness center and a second event generated by network-connected workout equipment in use by the user indicating use of the network-connected workout equipment by the user; and
at least one of the two events is received without being relayed by the mobile computing device.
The medium of claim 1 , wherein: the event is one of three or more received events for which prescriptions are provided by the fitness prescription engine among the following: an event generated by an airline-reservation system indicating that the user has booked or checked into a flight; an event generated by a hotel-reservation system indicating that the user has reserved or checked into a hotel room; an event generated by a wedding or baby registry management system; an event indicative of weather; an event indicative of a calendar entry of the user; an event generated by a meal-logging application an event indicative of the user signing up for or attending an athletic activity; or an event indicative of the user's geolocation.
the event is one of three or more received events for which prescriptions are provided by the fitness prescription engine among the following: an event generated by an airline-reservation system indicating that the user has booked or checked into a flight; an event generated by a hotel-reservation system indicating that the user has reserved or checked into a hotel room; an event generated by a wedding or baby registry management system; an event indicative of weather; an event indicative of a calendar entry of the user; an event generated by a meal-logging application an event indicative of the user signing up for or attending an athletic activity; or an event indicative of the user's geolocation.
an event generated by an airline-reservation system indicating that the user has booked or checked into a flight;
an event generated by a hotel-reservation system indicating that the user has reserved or checked into a hotel room;
an event generated by a wedding or baby registry management system;
an event indicative of weather;
an event indicative of a calendar entry of the user;
an event generated by a meal-logging application
an event indicative of the user signing up for or attending an athletic activity; or
an event indicative of the user's geolocation.
The medium of claim 1 , wherein: the event is one of three or more events for which prescriptions are provided by the fitness prescription engine among the following: an event indicative of the user waking up or going to bed; an event indicative of the user's heart rate or change thereof; an event indicative of the user's blood-oxygen level or change thereof; an event indicative of the user's physical activity or change thereof; an event indicative of the user's hydration state or change thereof; and an event indicative of the user completing or starting a previously prescribed fitness activity; or at least some of the three or more events are generated responsive to measurements by a wearable computing device of the user.
the event is one of three or more events for which prescriptions are provided by the fitness prescription engine among the following: an event indicative of the user waking up or going to bed; an event indicative of the user's heart rate or change thereof; an event indicative of the user's blood-oxygen level or change thereof; an event indicative of the user's physical activity or change thereof; an event indicative of the user's hydration state or change thereof; and an event indicative of the user completing or starting a previously prescribed fitness activity; or
an event indicative of the user waking up or going to bed;
an event indicative of the user's heart rate or change thereof;
an event indicative of the user's blood-oxygen level or change thereof;
an event indicative of the user's physical activity or change thereof;
an event indicative of the user's hydration state or change thereof; and
an event indicative of the user completing or starting a previously prescribed fitness activity; or
at least some of the three or more events are generated responsive to measurements by a wearable computing device of the user.
The medium of claim 1 , wherein: the event is one of a plurality of events for which prescriptions are provided by the fitness prescription engine including all of the following: an event generated by a fitness-center-membership management system indicating the user is at a fitness center; an event generated by network-connected workout equipment in use by the user; an event generated by an airline-reservation system indicating that the user has booked or checked into a flight; an event generated by a hotel-reservation system indicating that the user has reserved or checked into a hotel room; an event generated by a wedding or baby registry management system; an event indicative of weather; an event indicative of a calendar entry of the user; an event generated by a meal-logging application an event indicative of the user signing up for or attending an athletic event; an event indicative of the user's geolocation; an event indicative of the user waking up or going to bed; an event indicative of the user's heart rate or change thereof; an event indicative of the user's blood-oxygen level or change thereof; an event indicative of the user's physical activity or change thereof; an event indicative of the user's hydration state or change thereof; and an event indicative of the user completing or starting a previously prescribed fitness activity.
the event is one of a plurality of events for which prescriptions are provided by the fitness prescription engine including all of the following: an event generated by a fitness-center-membership management system indicating the user is at a fitness center; an event generated by network-connected workout equipment in use by the user; an event generated by an airline-reservation system indicating that the user has booked or checked into a flight; an event generated by a hotel-reservation system indicating that the user has reserved or checked into a hotel room; an event generated by a wedding or baby registry management system; an event indicative of weather; an event indicative of a calendar entry of the user; an event generated by a meal-logging application an event indicative of the user signing up for or attending an athletic event; an event indicative of the user's geolocation; an event indicative of the user waking up or going to bed; an event indicative of the user's heart rate or change thereof; an event indicative of the user's blood-oxygen level or change thereof; an event indicative of the user's physical activity or change thereof; an event indicative of the user's hydration state or change thereof; and an event indicative of the user completing or starting a previously prescribed fitness activity.
an event generated by a fitness-center-membership management system indicating the user is at a fitness center;
an event generated by network-connected workout equipment in use by the user;
an event indicative of the user signing up for or attending an athletic event;
an event indicative of the user's geolocation;
an event indicative of the user completing or starting a previously prescribed fitness activity.
The medium of claim 1 , wherein: more than 1,000 events are received per hour; prescriptions responsive to events are provided within five minutes of respective events to which respective prescriptions are responsive; more than 10,000 users are in the userbase; and the plurality of candidate resources comprises more than 2,000 candidate fitness resources.
more than 1,000 events are received per hour;
prescriptions responsive to events are provided within five minutes of respective events to which respective prescriptions are responsive;
more than 10,000 users are in the userbase; and
the plurality of candidate resources comprises more than 2,000 candidate fitness resources.
The medium of claim 1 , wherein: determining the fitness need comprises: accessing a sequence of types of prescriptions, the types including at least three of the following: user guidance; user motivation; user affirmation; post-activity celebration; user education; user accountability; connections between users; user entertainment; fitness equipment; and professional fitness assistance; determining a current state of the user in the sequence after a previously prescription in the sequence; and determining a category of a subsequent prescription from the sequence based on the current state of the user in the sequence.
determining the fitness need comprises: accessing a sequence of types of prescriptions, the types including at least three of the following: user guidance; user motivation; user affirmation; post-activity celebration; user education; user accountability; connections between users; user entertainment; fitness equipment; and professional fitness assistance; determining a current state of the user in the sequence after a previously prescription in the sequence; and determining a category of a subsequent prescription from the sequence based on the current state of the user in the sequence.
accessing a sequence of types of prescriptions, the types including at least three of the following: user guidance; user motivation; user affirmation; post-activity celebration; user education; user accountability; connections between users; user entertainment; fitness equipment; and professional fitness assistance;
user motivation;
user affirmation;
post-activity celebration;
user accountability;
connections between users;
user entertainment;
fitness equipment; and
professional fitness assistance;
determining a current state of the user in the sequence after a previously prescription in the sequence; and
determining a category of a subsequent prescription from the sequence based on the current state of the user in the sequence.
The medium of claim 1 , wherein: determining the fitness need comprises inputting the previous fitness-related prescription or result thereof into a hidden Markov model or recurrent neural network trained to infer sequences of fitness related needs.
determining the fitness need comprises inputting the previous fitness-related prescription or result thereof into a hidden Markov model or recurrent neural network trained to infer sequences of fitness related needs.
The medium of claim 1 , wherein: determining the current fitness-related prescription comprises: determining which of a plurality of prescription-selection rules apply based on the previous fitness-related prescription; and selecting a prescription-template based on applicable prescription-selection rules; and populating the prescription-template with the fitness resource based on the user profile to at least partially form the current fitness-related prescription.
determining the current fitness-related prescription comprises: determining which of a plurality of prescription-selection rules apply based on the previous fitness-related prescription; and selecting a prescription-template based on applicable prescription-selection rules; and populating the prescription-template with the fitness resource based on the user profile to at least partially form the current fitness-related prescription.
determining which of a plurality of prescription-selection rules apply based on the previous fitness-related prescription; and
selecting a prescription-template based on applicable prescription-selection rules; and
populating the prescription-template with the fitness resource based on the user profile to at least partially form the current fitness-related prescription.
The medium of claim 9 , wherein: the selected fitness-template specifies an intangible resource and a tangible resource are to be combined in the current fitness-related prescription; and the intangible resource is selected based on an intensity associated with the intangible resource and the user profile; and the tangible resource is selected without regard to the intensity associated with the intangible resource.
the selected fitness-template specifies an intangible resource and a tangible resource are to be combined in the current fitness-related prescription; and
the intangible resource is selected based on an intensity associated with the intangible resource and the user profile; and
the tangible resource is selected without regard to the intensity associated with the intangible resource.
The medium of claim 1 , wherein: the current fitness-related prescription is determined based on feedback from previous fitness-related prescriptions.
the current fitness-related prescription is determined based on feedback from previous fitness-related prescriptions.
The medium of claim 1 , wherein: the current fitness-related prescription is determined based on a machine learning model trained on logged outcomes of previous fitness-related prescriptions.
the current fitness-related prescription is determined based on a machine learning model trained on logged outcomes of previous fitness-related prescriptions.
The medium of claim 12 , wherein the operations comprise: obtaining a training data set, the training data set comprising logged records of previous fitness-related prescriptions and outcomes of the previous fitness-related prescriptions, the outcomes including an indication of whether corresponding users acted on the previous fitness-related prescriptions; and configuring parameters of the machine learning model by adjusting the parameters to at least partially optimize an objective function based, at least in part, on the outcomes.
obtaining a training data set, the training data set comprising logged records of previous fitness-related prescriptions and outcomes of the previous fitness-related prescriptions, the outcomes including an indication of whether corresponding users acted on the previous fitness-related prescriptions; and
configuring parameters of the machine learning model by adjusting the parameters to at least partially optimize an objective function based, at least in part, on the outcomes.
The medium of claim 13 , wherein: optimizing comprises determining a plurality of partial derivatives of the objective function with respect to the respective parameters of the machine learning model; and adjusting the parameters by respective increments in directions that the partial derivatives indicate tend to optimize the objective function.
optimizing comprises determining a plurality of partial derivatives of the objective function with respect to the respective parameters of the machine learning model; and
adjusting the parameters by respective increments in directions that the partial derivatives indicate tend to optimize the objective function.
The medium of claim 1 , wherein: the plurality of candidate fitness r
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