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.
Claim Re
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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. Claim 1 is amended to call for:
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.
Claim 51 is similarly amended. The neural network (RNN) component of claims 1 and 51 are analogous to Example 47, claim 2 the USPTO's July 2024 Subject Matter Eligibility Examples- artificial neural network (ANN) used for detecting malicious network packets which is determined to encompass patent eligible subject matter. The following table summarizes the correlation with
Both claims employ neural networks for data-driven predictions and real-time decision-making and transform abstract data into actionable insights. In particular, both claim to train a neural network by a specifically claimed method, both apply the neural network, and both conclude with a final step that is a result of the application of the trained neural network.
In both claims, the neural network is trained using real data to form a predictive model that is capable of making decisions based on input data. In the malicious network packet detection claim, the ANN is trained using network traffic data and a specific algorithm (backpropagation and gradient descent) to detect anomalies. Similarly, in the animal data claim, the RNN is trained using real animal data (e.g., heart rate) and then used to generate simulated animal data and predict future physiological conditions. Just as the ANN enables detection of anomalies in network traffic to identify malicious packets, the RNN allows the system to detect trends or future physiological outcomes in the animal data. Both systems leverage neural networks to enhance predictive accuracy and move beyond human-driven evaluation.
In the malicious network packet claim, the ANN makes real-time decisions about which packets to block or drop based on detected anomalies. Similarly, in the animal data system, the RNN enables real-time or near real-time analysis of physiological trends and generates artificial data for decision-making and monetization. In amended claim 1, the RNN, like the ANN in the network security claim, supports automated decision-making, where the output of the neural network (simulated animal data) is used for further analysis, potentially leading to real-time actions in health monitoring or other animal-related applications. This automates and enhances the decision-making process in the same way the ANN automatically drops malicious packets.
In the network packet detection claim, the ANN improves the security system by allowing for real-time detection of threats and automated responses, such as dropping malicious packets and blocking future traffic. Similarly, in the animal data system, the RNN improves the data collection system by allowing for the generation of artificial animal data that simulates future outcomes based on real data, enabling better predictions and increasing the value of the data for stakeholders. Just
as the ANN represents a technical improvement in network intrusion detection, the RNN in the animal data system represents a technical improvement in data forecasting and analysis. Both systems improve their respective technical fields by enhancing the ability to manage complex, real-time data and make predictions based on it.
In the malicious network packet claim, the ANN is not just an abstract concept; it is integrated into a larger system that improves network security by detecting anomalies and automatically taking actions to protect the network. In the animal data system, the RNN is similarly integrated into a broader system that collects, processes, and monetizes animal data by generating artificial data for further analysis and decision-making. In both cases, the neural network is not an abstract idea but a crucial part of the overall system that improves its practical utility. In the same way the ANN improves network security by enabling real-time automated responses, the RNN improves the animal data system by enabling more sophisticated data predictions and providing stakeholders with valuable insights for monetization.
The ANN in the network packet detection claim provides a real-world benefit by preventing network intrusions and improving security. Similarly, the RNN in the animal data system provides real-world benefits by predicting animal physiological conditions and enabling stakeholders to monetize the data.
Both systems use neural networks to solve real-world technical problems. In the network packet detection claim, the problem is protecting the network from malicious activity, while in the animal data system, the problem is accurately predicting physiological trends for commercial or health-related applications. In both cases, the neural network enhances the system's ability to manage large amounts of data and make decisions based on it.
In conclusion, the neural network (RNN) component in the animal data claim is patentable because it provides similar benefits and technological improvements as the ANN in the malicious network packet detection claim. Both claims involve the use of a neural network to perform tasks that enhance the functionality of the system, such as real-time predictions, automated decision- making, and improved data handling. The RNN transforms the system for monetizing animal data into a practical, technical application that leverages AI to solve complex, real-world problems in
a manner analogous to the ANN improving network security in the other claim. Therefore, the RNN component contributes significantly to the patentability of the claim.
Accordingly, claims 1-58 are allowable under 35 U.S.C. 101.
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Claims 1-25 and 27-58 are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0036591 Al to King et al. in view of US Patent No. 10,861,170 B1 to Li et al. in view of US Publication 2014/0278220 Al to Yuen.
Applicant respectfully traverses the present rejection for the following reasons. As set forth above, claim 1 is amended to call for:
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.
Claim 51 is similarly amended. The combination of King, Li, and Yuen do not teach simulated animal data generated the now claimed method using a recurrent neural network (RNN).
Accordingly, claims 1-25 and 27-58 are allowable under 35 U.S.C. 103 over King in view of Li and in view of Yuen.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over King, Li, Yuen, and further in view of US Publication No. 2019/0220975 Al to Hsieh et al.
Claim 26 depends from claim 1 which is shown above to be allowable. Accordingly, claim 26 is allowable over the present rejection.
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