Office Action 1: Non-Final Rejection (2023-02-08)
Rejection types: 101 - Patent-Eligible Subject Matter, 102 - Lack of Novelty, 103 - Obviousness

CLAIMS:
WHAT IS CLAIMED IS:

1\. 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;
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
providing the simulated animal data to a computing device.
2\. The method of claim 1 wherein one or more simulations are executed to generate simulated animal data.
3\. The method of claim 2 wherein at least a portion of the generated 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\. The method of claim 2 wherein at least a portion of the generated 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\. 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\. The method of claim 2 wherein at least a portion of the generated simulated animal data is used as one or more inputs in one or more simulations to generate simulated animal data.
7\. The method of claim 6 wherein at least a portion of the generated 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\. The method of claim 6 wherein at least a portion of the generated 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. The method of claim 1 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.

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

23\. 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\. The method of claim 21 wherein the trained neural network is a recurrent neural network.

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

26\. The method of claim 21 wherein the trained neural network is a Generative Adversarial Network.

27\. The method of claim 21 wherein the trained neural network utilized to generate 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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 the simulation user's data to a computing device, which includes at least a portion of the user's animal data or one or more derivatives thereof.

38\. 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 the one or more 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. 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\. The method of claim 1 wherein the system detects 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\. 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\. 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\. 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\. A system for generating and distributing simulated animal data, the system including a computing device operable 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;
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
providing the simulated animal data to the computing device.

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

53\. 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\. The system of claim 53 wherein the trained neural network is trained with at least a portion of simulated data.
55\. 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\. 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\. 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\. 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. 91 92 93 94 95 96 97 98 99 100 SUBSTITUTE SHEET (RULE 26)

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 .
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-25 and 27-58 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2018/0036591 A1 to King et al. (hereinafter “King”).
Concerning claim 1, King discloses 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 (paragraphs [0065], [0066] – sensor data is received and stored with respect to individuals);
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 (paragraphs [0184], [0200], [0205]- simulated data is generated and variables of individuals are modified); and
providing the simulated animal data to a computing device (paragraph [0198] – data is provided).
Concerning claims 2 and 52, King discloses wherein one or more simulations are executed to generate simulated animal data (paragraphs [0182], [0205]).
Concerning claim 3, King discloses wherein at least a portion of the generated 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 4, King discloses wherein at least a portion of the generated 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 (paragraphs [0107], [0133], [0205]).
Concerning claim 5, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 6, King discloses wherein at least a portion of the generated simulated animal data is used as one or more inputs in one or more simulations to generate simulated animal data (paragraph [0205]).
Concerning claim 7, King discloses wherein at least a portion of the generated 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 8, King discloses wherein at least a portion of the generated 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 (paragraphs [0107], [0133], [0205]).
Concerning claim 9, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 10, King discloses wherein the one or more parameters or variables modified to generate simulated data are comprised of non-animal data (paragraph [0136]).
Concerning claim 11, King discloses wherein the simulated animal data is generated by randomly sampling at least a portion of the real animal data (paragraphs [0106], [0205]).
Concerning claim 12, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 13, King discloses wherein the function is a line, polynomial, exponential, a Gaussian, Lorentzian, piecewise linear, or a spline between real data points (paragraph [0106]).
Concerning claim 14, King discloses 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 (paragraph [0064]).
Concerning claim 15, King discloses wherein the simulated animal data is generated by adding one or more offset values to each value of real animal data (paragraph [0079]).
Concerning claim 16, King discloses 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 (paragraph [0106]).
Concerning claim 17, King discloses wherein at least a portion of the simulated animal data is transformed into a lookup table to be used by a simulation (paragraph [0065]).
Concerning claim 18, King discloses 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 (paragraph [0127]).
Concerning claim 19, King discloses 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 (paragraph [0127]).
Concerning claim 20, King discloses 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 (paragraph [0136]).
Concerning claims 21 and 53, King discloses 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 (paragraphs [0126], [0181], [0205]).
Concerning claims 22 and 54, King discloses wherein the trained neural network is trained with at least a portion of simulated data (paragraph [0205]).
Concerning claim 23, King discloses wherein the one or more parameters or variables modified to generate simulated data are comprised of non-animal data (paragraph [0136]).
Concerning claim 24, King discloses wherein the trained neural network is a recurrent neural network (paragraph [0126]).
Concerning claim 25, King discloses wherein the trained neural network is a Long Short- Term Memory recurrent neural network (paragraphs [0126], [0181]).
Concerning claim 27, King discloses wherein the trained neural network utilized to generate 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 (paragraphs [0126], [0181]).
Concerning claim 28, King discloses 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 (paragraph [0205]).
Concerning claims 29 and 55, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 30, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claims 31 and 56, King discloses 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 (Figs. 10, 11, paragraphs [0012], [0029], [0140], [0184]).
Concerning claim 32, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 33, King discloses 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 (paragraph [0148]).
Concerning claim 34, King discloses 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 (paragraphs [0107], [0133], [0205], [0207]).
Concerning claim 35, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 36, King discloses 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 (paragraphs [0066], [0107], [0205], [0207]).
Concerning claim 37, King discloses 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 the simulation user's data to a computing device, which includes at least a portion of the user's animal data or one or more derivatives thereof (paragraphs [0066], [0205], [0207]).
Concerning claim 38, King discloses 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 the one or more 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 39, King discloses 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 40, King discloses 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 41, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claims 42 and 58, King discloses 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 (paragraph [0056]).
Concerning claim 43, King discloses 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 (paragraphs [0097], [0176], [0205]).
Concerning claim 44, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 45, King discloses 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 (paragraphs [0064], [0100], [0205]).
Concerning claim 46, King discloses 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 (paragraph [0032]).
Concerning claim 47, King discloses wherein the system detects 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 (paragraphs [0066], [0130]).
Concerning claim 48, King discloses wherein the one or more artificial values align in a time series of generated values and fit within a preestablished threshold or range (paragraphs [0032], [0130]).
Concerning claim 49, King discloses wherein the one or more artificial values are used, at least in part, as one or more inputs to derive animal data (paragraphs [0130]).
Concerning claim 50, King discloses 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 (paragraphs [0132]).
Concerning claim 51, see the rejection of claim 1.
Concerning claim 57, King discloses 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 (paragraph [0148]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 26 is rejected under 35 U.S.C. 103 as being unpatentable over King in view of  US Publication No. 2019/0220975 A1 to Hsieh et al. (hereinafter “Hsieh”).
Concerning claim 26, King lacks disclosing, however,  Hsieh discloses wherein the trained neural network is a Generative Adversarial Network (paragraph [0115]).  It would have been obvious to one of ordinary skill in the art at the time the invention was made to include wherein the trained neural network is a Generative Adversarial Network as taught by Hsieh into the system of King for the purpose of monitoring and improving an imaging system and during operation, neural network classifications confirmed or denied in order to continue to improve neural network behavior.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892.

APPLICANT REBUTTAL: Remarks
Claims 1-58 are pending in this application. Claims 1-58 now stand rejected as follows.
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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.
Claims 1 is amended to call for "the one or more sensors including a biological sensor configured to measure electrical signals in or derived from a targeted subject's body" and "transforming the electrical signals into one or more heart rate values and/or electrocardiogram (ECG) data." Claim 54 is amended to explicitly call for "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."
With respect to the present rejection, the Office Action states:
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.
The amendments to claim 1 clarify that the sequence of steps cannot be performed in the mind. In addition to this feature, claim 54 also calls for one or more sensors. Among these sensors is a "biological sensor configured to measure electrical signals in or derived from a targeted subject's body."
Accordingly, claims 1-58 are now allowable under 35 U.S.C. 101.
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Claims 1-25 and 27-58 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2018/0036591 A 1 to King et al. (hereinafter "King").
Applicant respectfully traverses the present rejection for the following reasons. In addition to the amendments set forth above, claims 1 and 54 are amended to call for "the simulated animal data including artificially-created data that shares at least one biological function with a human or another animal." In this regard, the Specification explains:
The term "artificial data" refers to artificially-created data that is derived from, based on, or generated using, at least in part, real animal data or one or more derivatives thereof. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and can include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data can include any artificially- created data that shares at least one biological function with a human or another animal (e.g., artificially-created vision data, artificially-created movement data). It is inclusive of "synthetic data," which can be any production data applicable to a given situation that is not obtained by direct measurement. Synthetic data can be created by statistically modeling original data and then using those models to generate new data values that reproduce at least one of the original data's statistical properties. In a refinement, the term "artificial data" is inclusive of any derivative of artificial data. For the purposes of the presently disclosed and claimed subject matter, the terms "simulated data" and "synthetic data" are synonymous and used interchangeably with "artificial data," and a reference to any one of the terms should not be interpreted as limiting but rather as encompassing all possible meanings of all the terms. In a refinement, the term "artificial data" is inclusive of the
term "artificial animal data."
Specification, paragraph [0049]
King does not generate simulated animal data, and in particular, simulated animal data including artificially-created data that shares at least one biological function with a human or another animal as claimed.
With respect to the present rejection, the Office Action alleges that King discloses:
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 (paragraphs [0184], [0200], [0205]simulated data is
generated and variables of individuals are modified);
The Office Action
Contrary to the assertions of the Office action, King does not disclose the generation of simulated animal data. Instead, as disclosed in the paragraphs cited by the Office Action, the alleged simulations are directed to "making prescriptions" which are assignments for fitness-related activities. Notwithstanding, claims 1 and 54 are amended to "the simulated animal data including artificially-created data that shares at least one biological function with a human or another animal." King does not disclose artificially-created data. Moreover, King does not disclose artificially-created data that shares at least one biological function with a human or another animal."
Accordingly, claims 1-25 and 27-58 are allowable under 35 U.S.C. 102(a)(1) over King.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over King in view of US Publication No. 2019/0220975 A 1 to Hsieh et al. (hereinafter "Hsieh").
Claim 26 depends from claim 1 which is show above to be allowable over King. Hsieh does not correct the deficiencies in King set forth above. Accordingly, claim 26 is allowable over the present rejection.
Conclusion

NEXT 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 7/10/23.  Claims 1-4, 6-8, 21, 27, 37, 38, 47, 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-25 and 27-58 are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0036591 A1 to King et al. (hereinafter “King”) in view of US Patent No. 10,861,170 B1 to Li et al. (hereinafter “Li”).
Concerning claim 1, King discloses 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 (paragraphs [0065], [0066] – sensor data is received and stored with respect to individuals);
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 (paragraphs [0184], [0198], [0200], [0205]- simulated data is generated and variables of individuals are modified).
King lacks specifically disclosing, however, Li discloses the one or more sensors including a biological sensor configured to measure electrical signals in or derived from a targeted subject’s body; transforming the electrical signals into one or more heart rate values and/or electrocardiogram (ECG) data; and the simulated animal data including artificially-created data that shares at least one biological function with a human or another animal (column 11, lines 1-14, 48-54; column 12, lines 20-47; column 16, lines 20-52 – data is input into the system including heart rate data which is input into a CNN in order to simulate an avatar in a game).  It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the use of the sensor input into a CNN as disclosed by Li in the game of King in order to create avatars that more realistically reflect a human.
Concerning claims 2 and 52, King discloses wherein one or more simulations are executed to generate the simulated animal data (paragraphs [0182], [0205]).
Concerning claim 3, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 4, King discloses 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 (paragraphs [0107], [0133], [0205]).
Concerning claim 5, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 6, King discloses wherein at least a portion of the simulated animal data is used as one or more inputs in one or more simulations (paragraph [0205]).
Concerning claim 7, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 8, King discloses wherein at least a portion of the generated 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 (paragraphs [0107], [0133], [0205]).
Concerning claim 9, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 10, King discloses wherein the one or more parameters or variables modified to generate simulated data are comprised of non-animal data (paragraph [0136]).
Concerning claim 11, King discloses wherein the simulated animal data is generated by randomly sampling at least a portion of the real animal data (paragraphs [0106], [0205]).
Concerning claim 12, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 13, King discloses wherein the function is a line, polynomial, exponential, a Gaussian, Lorentzian, piecewise linear, or a spline between real data points (paragraph [0106]).
Concerning claim 14, King discloses 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 (paragraph [0064]).
Concerning claim 15, King discloses wherein the simulated animal data is generated by adding one or more offset values to each value of real animal data (paragraph [0079]).
Concerning claim 16, King discloses 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 (paragraph [0106]).
Concerning claim 17, King discloses wherein at least a portion of the simulated animal data is transformed into a lookup table to be used by a simulation (paragraph [0065]).
Concerning claim 18, King discloses 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 (paragraph [0127]).
Concerning claim 19, King discloses 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 (paragraph [0127]).
Concerning claim 20, King discloses 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 (paragraph [0136]).
Concerning claims 21 and 53, King discloses 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 (paragraphs [0126], [0181], [0205]).
Concerning claims 22 and 54, King discloses wherein the trained neural network is trained with at least a portion of simulated data (paragraph [0205]).
Concerning claim 23, King discloses wherein the one or more parameters or variables modified to generate simulated data are comprised of non-animal data (paragraph [0136]).
Concerning claim 24, King discloses wherein the trained neural network is a recurrent neural network (paragraph [0126]).
Concerning claim 25, King discloses wherein the trained neural network is a Long Short- Term Memory recurrent neural network (paragraphs [0126], [0181]).
Concerning claim 27, King discloses 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 (paragraphs [0126], [0181]).
Concerning claim 28, King discloses 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 (paragraph [0205]).
Concerning claims 29 and 55, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 30, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claims 31 and 56, King discloses 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 (Figs. 10, 11, paragraphs [0012], [0029], [0140], [0184]).
Concerning claim 32, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 33, King discloses 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 (paragraph [0148]).
Concerning claim 34, King discloses 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 (paragraphs [0107], [0133], [0205], [0207]).
Concerning claim 35, King discloses 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 (paragraphs [0107], [0205], [0207]).
Concerning claim 36, King discloses 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 (paragraphs [0066], [0107], [0205], [0207]).
Concerning claim 37, King discloses 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 38, King discloses 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 39, King discloses 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 40, King discloses 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 (paragraphs [0066], [0205], [0207]).
Concerning claim 41, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claims 42 and 58, King discloses 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 (paragraph [0056]).
Concerning claim 43, King discloses 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 (paragraphs [0097], [0176], [0205]).
Concerning claim 44, King discloses 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 (paragraphs [0184], [0200], [0205]).
Concerning claim 45, King discloses 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 (paragraphs [0064], [0100], [0205]).
Concerning claim 46, King discloses 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 (paragraph [0032]).
Concerning claim 47, King discloses 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 (paragraphs [0066], [0130]).
Concerning claim 48, King discloses wherein the one or more artificial values align in a time series of generated values and fit within a preestablished threshold or range (paragraphs [0032], [0130]).
Concerning claim 49, King discloses wherein the one or more artificial values are used, at least in part, as one or more inputs to derive animal data (paragraphs [0130]).
Concerning claim 50, King discloses 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 (paragraphs [0132]).
Concerning claim 51, see the rejection of claim 1.
Concerning claim 57, King discloses 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 (paragraph [0148]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 26 is rejected under 35 U.S.C. 103 as being unpatentable over King, Li, and further in view of  US Publication No. 2019/0220975 A1 to Hsieh et al. (hereinafter “Hsieh”).
Concerning claim 26, King lacks disclosing, however,  Hsieh discloses wherein the trained neural network is a Generative Adversarial Network (paragraph [0115]).  It would have been obvious to one of ordinary skill in the art at the time the invention was made to include wherein the trained neural network is a Generative Adversarial Network as taught by Hsieh into the system of King for the purpose of monitoring and improving an imaging system and during operation, neural network classifications confirmed or denied in order to continue to improve neural network behavior.
Response to Arguments
Applicant's arguments filed 7/10/23 have been fully considered but they are not persuasive. 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 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.
Applicant’s arguments with respect to claim(s) 1-58 have been considered but are moot based on the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  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 date of this final action.

CITED REFERENCE SUMMARIES: Ref US20190220975A1: Claims:
An apparatus comprising: a first deployed machine learning network associated with an acquisition engine, the acquisition engine associated with an imaging device, the first deployed machine learning network configured to operate with the acquisition engine to generate a configuration for the imaging device, the first deployed machine learning network generated and deployed from a first training machine learning network; a second deployed machine learning network associated with a reconstruction engine, the reconstruction engine to receive acquired image data from the imaging device via the acquisition engine and to reconstruct an image from the acquired image data, the second deployed machine learning network to operate with the reconstruction engine based on the acquired image data, the second deployed machine learning network generated and deployed from a second training machine learning network; a first assessment engine with a third deployed machine learning network, the assessment engine to receive output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine, the third deployed machine learning network generated and deployed from a third training machine learning network.
a first deployed machine learning network associated with an acquisition engine, the acquisition engine associated with an imaging device, the first deployed machine learning network configured to operate with the acquisition engine to generate a configuration for the imaging device, the first deployed machine learning network generated and deployed from a first training machine learning network;
a second deployed machine learning network associated with a reconstruction engine, the reconstruction engine to receive acquired image data from the imaging device via the acquisition engine and to reconstruct an image from the acquired image data, the second deployed machine learning network to operate with the reconstruction engine based on the acquired image data, the second deployed machine learning network generated and deployed from a second training machine learning network;
a first assessment engine with a third deployed machine learning network, the assessment engine to receive output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine, the third deployed machine learning network generated and deployed from a third training machine learning network.
The apparatus of claim 1 further including: a fourth deployed machine learning network associated with a diagnosis engine, the diagnosis engine to facilitate diagnosis using the reconstructed image from the reconstruction engine, the fourth deployed machine learning network to operate with the diagnosis engine, the fourth deployed machine learning network generated and deployed from a fourth training machine learning network; and a second assessment engine with a fifth deployed machine learning network, the assessment engine to receive output from at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine to assess operation of the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine and to provide feedback to the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine.
a fourth deployed machine learning network associated with a diagnosis engine, the diagnosis engine to facilitate diagnosis using the reconstructed image from the reconstruction engine, the fourth deployed machine learning network to operate with the diagnosis engine, the fourth deployed machine learning network generated and deployed from a fourth training machine learning network; and
a second assessment engine with a fifth deployed machine learning network, the assessment engine to receive output from at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine to assess operation of the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine and to provide feedback to the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine.
The apparatus of claim 2 , wherein the acquisition engine, the reconstruction engine, and the diagnosis engine exchange feedback to generate an indication of system health.
The apparatus of claim 2 , wherein the reconstruction engine is to generate the reconstructed image for human viewing and to process the acquired image data for computer analysis of the image data by the diagnosis engine.
The apparatus of claim 1 , wherein the first deployed machine learning network is to generate a configuration for the imaging device based on the acquisition engine, the imaging device, and a patient variable associated with the patient to be imaged.
The apparatus of claim 1 , wherein at least one of the first deployed machine learning network, the second deployed machine learning network, or the third deployed machine learning network includes a convolutional neural network.
The apparatus of claim 1 , wherein at least one of the first training machine learning network, second training machine learning network, or third training machine learning network is provided with one or more features of interest in training of the corresponding first, second, or third deployed machine learning network.
A method comprising: generating a configuration for the imaging device for image acquisition via a first deployed machine learning network associated with an acquisition engine associated with the imaging device, the first deployed machine learning network generated and deployed from a first training machine learning network; monitoring, using a second deployed machine learning network, image reconstruction by a reconstruction engine of image data acquired by the imaging device via the acquisition engine, the second deployed machine learning network associated with the reconstruction engine and to operate with the reconstruction engine based on the acquired image data, the second deployed machine learning network generated and deployed from a second training machine learning network; assessing, using a third deployed machine learning network, operation of respective at least one of the acquisition engine or the reconstruction engine based on output received from the respective at least one of the acquisition engine or the reconstruction engine, the third deployed machine learning network generated and deployed from a third training machine learning network; and providing feedback to the respective at least one of the acquisition engine or the reconstruction engine.
generating a configuration for the imaging device for image acquisition via a first deployed machine learning network associated with an acquisition engine associated with the imaging device, the first deployed machine learning network generated and deployed from a first training machine learning network;
monitoring, using a second deployed machine learning network, image reconstruction by a reconstruction engine of image data acquired by the imaging device via the acquisition engine, the second deployed machine learning network associated with the reconstruction engine and to operate with the reconstruction engine based on the acquired image data, the second deployed machine learning network generated and deployed from a second training machine learning network;
assessing, using a third deployed machine learning network, operation of respective at least one of the acquisition engine or the reconstruction engine based on output received from the respective at least one of the acquisition engine or the reconstruction engine, the third deployed machine learning network generated and deployed from a third training machine learning network; and
providing feedback to the respective at least one of the acquisition engine or the reconstruction engine.
The method of claim 8 , further including: facilitating, using a fourth deployed machine learning network, diagnosis using the reconstructed image from the reconstruction engine, the fourth deployed machine learning network to operate with the diagnosis engine, the fourth deployed machine learning network generated and deployed from a fourth training machine learning network; and assessing operation of respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine based on output received from the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine to provide feedback to the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine.
facilitating, using a fourth deployed machine learning network, diagnosis using the reconstructed image from the reconstruction engine, the fourth deployed machine learning network to operate with the diagnosis engine, the fourth deployed machine learning network generated and deployed from a fourth training machine learning network; and
assessing operation of respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine based on output received from the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine to provide feedback to the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine.
The method of claim 8 , further including generating an indication of system health based on an exchange of feedback among the acquisition engine, the reconstruction engine, and the diagnosis engine.
The method of claim 8 , wherein the reconstruction engine is configured to generate the reconstructed image for human viewing and to process the acquired image data for computer analysis of the image data by the diagnosis engine.
The method of claim 8 , wherein the first deployed machine learning network is to generate a configuration for the imaging device based on the acquisition engine, the imaging device, and a patient variable associated with the patient to be imaged.
The method of claim 8 , wherein at least one of the first deployed machine learning network, the second deployed machine learning network, or the third deployed machine learning network includes a convolutional neural network.
The method of claim 8 , wherein at least one of the first training machine learning network, second training machine learning network, or third training machine learning network is provided with one or more features of interest in training of the corresponding first, second, or third deployed machine learning network.
A non-transitory computer readable medium comprising instructions which, when executed, cause a machine to at least: generate a configuration for the imaging device for image acquisition via a first deployed machine learning network associated with an acquisition engine associated with the imaging device, the first deployed machine learning network generated and deployed from a first training machine learning network; monitor, using a second deployed machine learning network, image reconstruction by a reconstruction engine of image data acquired by the imaging device via the acquisition engine, the second deployed machine learning network associated with the reconstruction engine and to operate with the reconstruction engine based on the acquired image data, the second deployed machine learning network generated and deployed from a second training machine learning network; assess, using a third deployed machine learning network, operation of respective at least one of the acquisition engine or the reconstruction engine, or the diagnosis engine based on output received from the respective at least one of the acquisition engine or the reconstruction engine, the third deployed machine learning network generated and deployed from a third training machine learning network; and provide feedback to the respective at least one of the acquisition engine or the reconstruction engine.
generate a configuration for the imaging device for image acquisition via a first deployed machine learning network associated with an acquisition engine associated with the imaging device, the first deployed machine learning network generated and deployed from a first training machine learning network;
monitor, using a second deployed machine learning network, image reconstruction by a reconstruction engine of image data acquired by the imaging device via the acquisition engine, the second deployed machine learning network associated with the reconstruction engine and to operate with the reconstruction engine based on the acquired image data, the second deployed machine learning network generated and deployed from a second training machine learning network;
assess, using a third deployed machine learning network, operation of respective at least one of the acquisition engine or the reconstruction engine, or the diagnosis engine based on output received from the respective at least one of the acquisition engine or the reconstruction engine, the third deployed machine learning network generated and deployed from a third training machine learning network; and
provide feedback to the respective at least one of the acquisition engine or the reconstruction engine.
The computer readable medium of claim 15 , wherein the instructions, when executed, further cause the machine to: facilitate, using a fourth deployed machine learning network, diagnosis using the reconstructed image from the reconstruction engine, the fourth deployed machine learning network to operate with the diagnosis engine, the fourth deployed machine learning network generated and deployed from a fourth training machine learning network; and assess operation of respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine based on output received from the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine to provide feedback to the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine.
facilitate, using a fourth deployed machine learning network, diagnosis using the reconstructed image from the reconstruction engine, the fourth deployed machine learning network to operate with the diagnosis engine, the fourth deployed machine learning network generated and deployed from a fourth training machine learning network; and
assess operation of respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine based on output received from the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine to provide feedback to the respective at least one of the acquisition engine, the reconstruction engine, or the diagnosis engine.
The computer readable medium of claim 15 , wherein the instructions, when executed, further cause the machine to generate an indication of system health based on an exchange of feedback among the acquisition engine, the reconstruction engine, and the diagnosis engine.
The computer readable medium of claim 15 , wherein the reconstruction engine is configured to generate the reconstructed image for human viewing and to process the acquired image data for computer analysis of the image data by the diagnosis engine.
The computer readable medium of claim 15 , wherein the first deployed machine learning network is to generate a configuration for the imaging device based on the acquisition engine, the imaging device, and a patient variable associated with the patient to be imaged.
The computer readable medium of claim 15 , wherein at least one of the first deployed machine learning network, the second deployed machine learning network, or the third deployed machine learning network includes a convolutional neural network.
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 fitne
[Truncated to input limit]