Prediction system based on animal data

Abstract

The inference system for providing animal data and its predictive indicators includes one or more source sensors that collect animal data from individuals of interest. Characteristically, the animal data can be transmitted electronically. A computing subsystem receives the animal data. At least a portion of the animal data is converted by the computing subsystem or by one or more sensors into predictive indicators for a selected individual or group of individuals of interest. The computing subsystem is operable to provide the predictive indicators and, optionally, at least a portion of the animal data, to a user. Advantageously, a transmission subsystem transmits the animal data to the computing subsystem.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 62 / 833,970, filed April 15, 2019, and U.S. Provisional Application No. 62 / 912,822, filed October 9, 2019, the disclosures of which are incorporated by reference in their entireties herein.

[0002] In at least one embodiment, the invention relates to a system for making predictions based on information from animal data. [Background technology]

[0003] The ever-increasing accessibility of information via the Internet has substantially transformed the way business is conducted. Concurrent with this information explosion, sensor technology, and in particular biosensor technology, has also advanced. In particular, miniature biosensors that measure electrocardiogram signals, blood flow, body temperature, sweat rate, or respiratory rate are now available. The ability to transmit data from such sensors wirelessly or via the Internet has opened up new potential applications for collecting data sets.

[0004] Thus, there is a need for a system that collects, organizes, and further analyzes sensor data for new applications, including, but not limited to, gambling and probability scoring systems. Summary of the Invention

[0005] In at least one aspect, an inference system for providing animal data and a predictive indicator thereof is provided. The inference system includes one or more source sensors that collect animal data from one or more target individuals. Characteristically, the animal data can be transmitted wirelessly or via a wired connection. A computing subsystem receives the animal data, and at least a portion of the animal data is converted by the computing subsystem or by the one or more source sensors into at least one computing asset assigned to the selected target individual or to a group of selected target individuals. The one or more source sensors or the computing subsystem are operable to convert the at least one computing asset into a predictive indicator and further to provide the predictive indicator, the at least one computing asset, and / or at least a portion of the animal data to one or more users. A transmission subsystem transmits at least a portion of the animal data to the computing subsystem. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 presents a schematic diagram of an inference system that provides animal data together with predictive indicators to a user. [Diagram 2] FIG. 2 presents a schematic of an exemplary transmission subsystem that may be used in the system of FIG. [Diagram 3] FIG. 3 presents a schematic of a guessing system applied to sports betting. [Figure 4] FIG. 4 provides an example of a source page for an advertisement that may be displayed in an Iframe. [Diagram 5]FIG. 5A presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. FIG. 5B presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. FIG. 5C presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. FIG. 5D presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. FIG. 5E presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. FIG. 5F presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. FIG. 5G presents an example of an advertisement in an Iframe with respect to an opportunity associated with the guessing system. [Figure 6] FIG. 6 provides an example of user consumption media, such as pop-up or embedded media, that may be displayed when a user is watching media, such as a live sporting event, while inviting the user to place one or more bets. [Figure 7] FIG. 7 is an example of a gambling application home page. [Figure 8] FIG. 8 presents an example of a gambling interface that a user may visit to evaluate one or more probabilities and / or place one or more bets. [Figure 9] Figure 9 provides an example of the type of market that may be created using the output from the guessing system (labeled as a "Human Data Bet" for purposes of clarity). [Figure 10] FIG. 10 provides an example of a user selecting a new market on which to place a bet. [Figure 11] FIG. 11 provides an example of a health monitoring interface that may be created using one or more outputs from the inference system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Next, a detailed description will be given of currently preferred embodiments and methods of the present invention, which constitute the best mode of carrying out the invention currently known to the inventors. The drawings are not necessarily to scale. However, it will be understood that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various alternative forms. Therefore, the specific details disclosed herein should not be interpreted as limiting, but rather merely as a representative basis for any aspect of the present invention and / or to teach those skilled in the art that the present invention may be variously employed.

[0008] It is also to be understood that the invention is not limited to the specific embodiments and methods described below, as specific components and / or conditions may, of course, be varied. Further, the terminology used herein is used only for the purpose of describing particular embodiments of the invention, and is not intended to be limiting in any manner.

[0009] Although the terms "probability" and "odds" are mathematically distinct (e.g., "probability" can be defined as the occurrence of a particular event expressed as a percentage of all possible events, while "odds" can be defined as the occurrence of a particular event expressed as a percentage of the non-occurrence of that event), both describe the likelihood of an event occurring. They are used interchangeably to avoid redundancy, and a reference to one term should be interpreted as meaning a reference to both.

[0010] With respect to the terms "bet" and "gambling," both terms refer to the act of taking a risk (e.g., money, non-economic consideration) on the outcome of a future event. Risk includes both economic (e.g., money) and non-economic (e.g., health, life) risks. Risks can be taken against one or more other parties (e.g., an insurance company deciding whether to provide insurance) or against oneself (e.g., an individual deciding whether to purchase insurance) based on the outcome of a future event or the likelihood of such an outcome. The act of making a "bet" or "gambling" can be done in or as part of any system or subsystem in which one or more risks can be taken, including any system in which risk is gamified (e.g., gambling, sports betting). When the terms "bet" or "gambling" are used herein, the presently disclosed and claimed subject matter may use either of the other two terms interchangeably.

[0011] It should also be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. For example, reference to an element in the singular is intended to include the plural elements.

[0012] The term "comprising" is synonymous with "comprising," "having," "containing," or "characterized by." These terms are inclusive and open-ended and do not exclude additional, unrecited elements or method steps.

[0013] The phrase "consisting of" excludes any element or step or ingredient not specified in the claim. When this phrase appears in a section of the body of a claim rather than immediately following the introductory portion of the claim, it limits only the elements recited in that section and does not exclude other elements from the claim as a whole.

[0014] The phrase "consisting essentially of" limits the scope of a claim to specified materials or steps that, in addition to the specified materials or steps, do not materially affect the basic and novel characteristic or characteristics of the claimed subject matter.

[0015] Where a computing device is described as performing action or method steps, it will be understood that the computing device is operable to perform the action or method steps, typically by executing one or more lines of source code. The action or method steps may be encoded on non-transitory memory (e.g., a hard drive, optical drive, flash drive, and the like).

[0016] With respect to the terms "comprising," "consisting of," and "consisting essentially of," when one of these three terms is used in this specification, the presently disclosed claimed subject matter may include use of either of the other two terms.

[0017] The term "one or more" means "at least one," and the term "at least one" means "one or more." The terms "one or more" and "at least one" include the terms "multiple" and "multiple" as subsets.

[0018] Throughout this application, when publications are referenced, the disclosures of these publications in their entireties are hereby incorporated by reference into this application in order to more fully describe the state of the art to which this invention pertains.

[0019] The term "server" refers to any computer or computing device (including, but not limited to, desktop computers, notebook computers, laptop computers, mainframes, mobile phones, smart watches / smart glasses, AR / VR headsets, and the like), distributed system, blade, gateway, switch, processing device, or combination thereof, configured to perform the methods and functions described herein.

[0020] The term "computing device" generally refers to any device capable of performing at least one function, including communicating with another computing device. In a refinement, a computing device includes a central processing unit capable of executing program steps and a memory for storing data and program code. As used herein, a computing subsystem is a computing device.

[0021] The processes or methods or algorithms disclosed herein can be distributable to or implemented by computing devices or controllers or computers, which may include any existing programmable or dedicated electronic control units. Similarly, the processes or methods or algorithms can be stored in many forms, including but not limited to information stored persistently on non-rewritable storage media, such as ROM devices, and information stored mutably on rewritable storage media, such as floppy disks, magnetic tapes, CDs, RAM devices, other magnetic and optical media, shared or dedicated cloud computing resources, as data and instructions executable by the controller or computer. The processes or methods or algorithms can also be implemented in executable software objects. Alternatively, the processes or methods or algorithms can be embodied, in whole or in part, using suitable hardware components, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), state machines, controllers, or other hardware components or devices, or using a combination of hardware, software, and firmware components.

[0022] The terms "subject" or "individual" are synonymous and refer to humans or other animals, including birds and fish, and all mammals, including primates (especially higher primates), horses, sheep, dogs, rodents, guinea pigs, cats, whales, rabbits, and cows. One or more subjects may be, for example, humans participating in athletic training or competitions, horses racing on a track, humans playing video games, humans monitoring their own personal health, humans providing data to a third party, humans participating in a research or clinical study, or humans attending a fitness class. The subject or individual may also be a derivative of a human or other animal (e.g., a laboratory-generated organism derived at least in part from a human or other animal), or one or more individual components or members or processes of a human or other animal (e.g., cells, proteins, biological fluids, amino acid sequences, tissues, hair, limbs) that make up a human or other animal, or one or more artificial creations that share one or more characteristics with a human or other animal (e.g., human brain cells grown in a laboratory that generate electrical signals similar to human brain cells). In a refinement, the subject or individual may be a machine (e.g., a robot, an autonomous vehicle, a mechanical arm) or machine network programmable by one or more computing devices, and that shares at least one biological function with a human or other animal and from which one or more types of biological data may be derived, which may be at least in part, artificial in nature (e.g., data from artificial intelligence-derived activity that mimics biological brain activity).

[0023] The term "animal data" refers to any data obtained from an object or generated directly or indirectly by an object that can be converted into a form that can be transmitted to a server or other computing device. Typically, the animal data is transmitted electronically using wired or wireless connections. The animal data includes any data that can be obtained from one or more sensors or sensing devices / sensing systems, particularly biological sensors (biosensors). The animal data can also include descriptive data, auditory data, visually acquired data, neurologically generated data (e.g., brain signals from neurons), data that can be manually entered related to the object (e.g., medical history, social habits, emotions related to the object), and data that includes at least a portion of actual animal data. In a refinement, the term "animal data" includes any derivative of animal data. In another refinement, the animal data includes metadata collected with the animal data. In another refinement, the animal data includes at least a portion of simulated data. In yet another refinement, the animal data includes simulated data.

[0024] The term "insight" refers to one or more descriptors that may be assigned to a subject individual and that describe the subject individual's condition or status. Examples include descriptors such as stress level (e.g., high stress, low stress), energy level, fatigue level, and the like. Insights may be quantified by one or more numerical values ​​or by multiple numerical values, expressed as a probability, or similar odds-based indicator. Insights may also be characterized by one or more evaluation criteria or indicators related to pre-determined performance (e.g., visual, such as color, or physical, such as vibration).

[0025] The term "computational asset" refers to one or more numerical values, numerical values, values, metrics, measurements, insights, graphs, charts, or plots derived from at least a portion of the animal data or one or more derivatives thereof. As used herein, one or more sensors initially provide electronic signals. A computational asset is extracted or derived, at least in part, from one or more electronic signals or one or more derivatives thereof. A computational asset describes or quantifies an interpretable characteristic related to one or more individuals of interest. For example, electrocardiogram measurements can be derived from an analog front-end signal (electronic signal from a sensor), heart rate data (e.g., heart rate in beats per minute) can be derived from an electrocardiogram or PPG sensor, body temperature data can be derived from a temperature sensor, sweat data can be derived from a sweat sensor, glucose information can be derived from a biofluid sensor, DNA and RNA sequence information can be derived from sensors that obtain genomic and genetic data, brain activity data can be derived from a neurological sensor, hydration data can be derived from an intra-oral saliva sensor, position data can be derived from a GPS sensor or an RFID sensor, biomechanical data can be derived from an optical sensor or a translation sensor, and respiration rate data can be derived from a respiration sensor. In a refinement, the computing asset can include one or more signals or one or more measurements from one or more non-animal data sources as one or more inputs in its one or more operations or calculations. In another refinement, the computing asset is comprised of a plurality of computing assets.

[0026] The term "predictive indicator" refers to a metric or other indicator (e.g., one or more colors, codes, numbers, values, graphs, charts, plots, measurements, numerical representations, descriptors, text, physical responses, auditory responses, visual responses, kinesthetic responses) based on which one or more predictions or forecasts or probabilities or possibilities or recommendations related to one or more outcomes for one or more future events involving one or more target individuals or involving one or more groups of target individuals can be calculated, computed, derived, extracted, extrapolated, simulated, made, modified, enhanced, estimated, evaluated, inferred, established, determined, estimated, observed, communicated, or addressed. In a refinement, the predictive indicator is a computed computational asset derived from at least a portion of the animal data or one or more derivatives thereof. In another refinement, the predictive indicator comprises one or more signals or one or more measurements from one or more non-animal data sources as one or more inputs in one or more calculations, computations, derivations, extractions, extrapolations, simulations, making, modifying, enhancing, inferring, evaluating, inferring, establishing, determining, predicting, observing, or communicating regarding one or more predictions or forecasts or probabilities or possibilities or recommendations. In yet another refinement, the predictive indicator consists of a plurality of predictive indicators.

[0027] The term "artificial data" refers to artificially created data that is at least partially derived from or generated at least partially using real animal data or one or more derivatives thereof. Artificial data can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models and can include as one or more inputs one or more signals or one or more measurements from one or more non-animal data sources. Artificial data also includes any artificially created data that shares at least one biological function with humans or other animals (e.g., artificially created visual data, artificially created locomotion data). This includes "synthetic data," which can be any produced data that is applicable to a given situation and is not obtained by direct measurement. Synthetic data can be created by statistically modeling original data and using the model to generate new data values ​​that reproduce at least one statistical characteristic of the original data. For purposes of the presently disclosed and claimed subject matter, the terms "simulated data" and "synthetic data" are synonymous and are used interchangeably with "artificial data," and reference to any one of these terms should not be construed as limiting, but rather as encompassing all possible meanings of all terms.

[0028] Referring to FIG. 1, a schematic of a system for providing animal data and its predictive indicators is presented. The prediction system 10 receives animal data 14, which may be transmitted electronically. i Typically, the source of animal data 12 includes one or more sensors 18. i Includes the individual or object of interest 16 i The corresponding animal data 14 i are the objects to be collected. Label i is simply a number from 1 to i associated with each individual of interest. max are integer labels up to imax is the total number of individuals, which may range from one to several thousand or more. In this context, animal data refers to data related to the body of a subject obtained from sensors, particularly biological sensors (biosensors). In many useful applications, the subject is a human (e.g., an athlete, a soldier, a hospitalized or remote telemedicine patient, a fitness class participant, a video gamer) and the animal data is human data. Animal data can be obtained from an individual subject, or from multiple individuals, or from a group of individuals, or from multiple groups of individuals. Animal data can be obtained from a single source sensor on each individual subject, or from multiple source sensors on each individual subject. In some cases, a single source sensor can obtain data from multiple individuals, or from a group of individuals, or from multiple groups of individuals (e.g., an optical-based camera sensor that can identify location and measure distance traveled for a group of individuals). Each source sensor may provide a single type of animal data or multiple types of animal data. In a refinement, the one or more source sensors comprise at least one biosensor.

[0029] A biosensor collects a biological signal, which in the context of the present embodiments is any signal or characteristic in or from an animal that can be measured, monitored, observed, calculated, computed, input, or interpreted, either continuously or intermittently, including electrical and non-electrical signals, measurements, and artificially generated information. A biological sensor can collect biological data (e.g., including measurements and signals), such as physiological data, biometric data, chemical data, biomechanical data, genetic data, genomic data, location data, or other biological data, from one or more individuals of interest. For example, some biosensors may collect eye tracking data (e.g., pupil response, movement, EOG-related data), blood flow / blood volume data (e.g., PPG data, pulse transit time, pulse arrival time), biofluid data (e.g., analyses derived from blood, urine, saliva, sweat, cerebrospinal fluid), body composition data (e.g., BMI, body fat %, protein / muscle), biochemical composition data, biochemical structure data, pulse data, oxygenation data (e.g., SpO2), core body temperature data, skin temperature data, electrodermal response data, sweat data (e.g., rate, composition), blood pressure data (e.g., systolic, diastolic, MAP), hydration data (e.g., fluid balance I / O), cardiac-based data (e.g., heart rate, average HR, HR range, etc.), and other data that may be collected by the biosensor. Biological data may be measured or may provide information that can be converted into or derived from such biological data, such as ECG-related data including pulse width, heart rate variability, HRV time domain, HRV frequency domain, autonomic tone, ECG-related data including PR interval, QRS interval, QT interval, and RR interval, neural-related data (e.g., EEG-related data), gene-related data, genomic-related data, skeletal data, muscle data (e.g., EMG-related data including surface EMG and amplitude), respiratory data (e.g., respiratory rate, breathing pattern, inspiration / expiration ratio, tidal volume, spirometry data), thoracic electrical bioimpedance data, or combinations thereof. Some biosensors may detect biological data, such as biomechanical data that may include angular velocity, joint path, gait description, step count, or position or acceleration in various directions that may characterize the movement of an object of interest.Some biosensors may collect biological data, such as location and geolocation data (e.g., GPS-based data, RFID-based data, posture data), facial recognition data, kinesthetic data (e.g., physical pressure obtained from sensors placed on the soles of shoes), or auditory data, associated with one or more objects of interest. Some biological sensors are image-based or video-based and can collect and / or provide and / or analyze video or other visual data (e.g., still or moving images, including videos, MRIs, computed tomography scans, ultrasounds, x-rays) based on which biological data can be detected, measured, monitored, observed, extrapolated, calculated, or computed (e.g., biomechanical movement, position, fractures based on x-rays, or stress or disease based on video-based or image-based visual analysis of an object). Some biosensors can measure various biological parameters from biological fluids such as blood (e.g., venous, capillary), saliva, urine, sweat, and the like, such as triglyceride levels, red blood cell count, white blood cell count, adrenocorticotropic hormone levels, hematocrit, platelet count, ABO / Rh blood type, blood urea nitrogen levels, calcium levels, carbon dioxide levels, chloride levels, creatinine levels, glucose levels, hemoglobin A1c levels, lactate levels, sodium levels, potassium levels, bilirubin levels, alkaline phosphatase (ALP) levels, and the like. The biosensors may derive information including levels, alanine transaminase (ALT) levels, aspartate aminotransferase (AST) levels, albumin levels, total protein levels, prostate specific antigen (PSA) levels, microalbuminuria levels, immunoglobulin A levels, folate levels, cortisol levels, amylase levels, lipase levels, gastrin levels, bicarbonate levels, iron levels, magnesium levels, uric acid levels, folate levels, vitamin B-12 levels, and the like. In addition to biological data associated with one or more subject individuals, some biosensors may measure non-biological data conditions, such as ambient temperature and humidity, altitude, barometric pressure, and the like.In a refinement, the one or more sensors provide biological data including one or more calculations, operations, predictions, probabilities, possibilities, estimates, evaluations, inferences, determinations, estimates, observations, or predictions derived at least in part from the biosensor data. In another refinement, the one or more biosensors can provide two or more types of data, at least one of which is biological data (e.g., heart rate data and VO2 data, muscle activity data and accelerometer data, VO2 data and altitude data).

[0030] At least one sensor 18 i and / or one or more appendages thereof may be secured to, in contact with, transmit one or more electronic communications associated with, transmit one or more electronic communications derived from, be embedded in, placed or implanted within, be ingested by, or be integrated to form at least a portion of, a subject, including the skin, eye, vital organs, muscles, hair, veins, biological fluids, blood vessels, tissue, or skeletal system of the subject. For example, a saliva sensor fixed to a tooth or to a dentition or to an instrument in contact with one or more teeth, a sensor that extracts DNA information derived from a subject's biological fluids or hair, a wearable sensor (e.g., a sensor that can be worn on the human body), a sensor that is fixed to or implanted in a subject's brain and can detect brain signals from neurons, a sensor that is ingested by an individual to track one or more biological functions, a sensor attached to or integrated into a machine (e.g., a robot) that shares at least one characteristic with an animal (e.g., a robot arm capable of performing one or more tasks similar to a human, a robot with information processing capabilities similar to a human), and the like. Advantageously, the machine itself may be composed of one or more sensors and may be classified as both a sensor and an object. In an improvement, one or more sensors 18 iis integrated into, integrated as part of, affixed to, or embedded within a fabric, cloth, material, accessory, object, or device that is in contact with or in communication with the individual of interest directly, through one or more intermediate members, or through one or more interstitial members. Examples include sensors attached to the skin via an adhesive, sensors integrated into a watch or headset, sensors integrated or embedded into a shirt or jersey, sensors integrated into a steering wheel, sensors integrated into a video game controller, sensors integrated into a basketball that contacts the subject's hand, sensors integrated into a hockey stick or hockey puck that intermittently contacts an intermediate member held by the subject (e.g., a hockey stick), sensors integrated into or embedded into one or more handles or grips of a fitness machine (e.g., a treadmill, a bike, a bench press), sensors integrated into a robot (e.g., a robotic arm) that is controlled by the individual subject, sensors integrated into or embedded in a shoe that may contact the individual subject via a sock that forms an intermediate member or via adhesive tape wrapped around the individual subject's ankle, and the like. In another refinement, the sensor or sensors may be woven into, embedded in, integrated with, or fixed to a flooring or surface (e.g., artificial turf, basketball floor, soccer field, manufacturing / assembly line floor), a seat / chair, helmet, bed, or other object that contacts the subject directly or through one or more intermediate members (e.g., the subject contacts the sensor in the seat through a gap in the clothing). In another refinement, the sensor and / or one or more of its appendages may contact one or more particles or objects derived from the subject's body (e.g., tissue from an organ, hair from the subject), and the sensor or sensors derive or provide information from such particle or objects that may be converted into biological data.In yet another refinement, the one or more sensors may be optically based (e.g., camera based) and may provide an output from which biological data can be detected, measured, monitored, observed, extracted, extrapolated, inferred, estimated, evaluated, determined, calculated, or computed. In yet another refinement, the one or more sensors may be light based and may use infrared technology (e.g., temperature or heat sensors) to calculate the temperature of the individual or the relative heat for different parts of the individual.

[0031] In the variant shown in FIG. i is the number of target individuals 16 i Animal Data 14 i At least one sensor 18 for collecting i The computing subsystem 22 transmits the animal data 14 via a transmission subsystem 24. i The transmission subsystem 24 receives and collects one or more source sensors 18. iThe computing subsystem 22 may transmit data wirelessly via one or more transmission (e.g., communication) protocols. Advantageously, sensor communication may occur in real-time or near real-time. Near real-time in this context means that the transmission is not intentionally delayed except for necessary processing by the sensor and computing subsystem. The computing subsystem 22 is operable to receive animal data or groups of animal data from a single subject individual or from multiple subject individuals, either as raw animal data or as processed (e.g., manipulated) animal data. In an improvement, the computing subsystem 22 is operable to receive a single type of animal data (e.g., heart rate data) and / or multiple types (e.g., including groups / data sets) of animal data (e.g., raw analog front-end data, heart rate data, muscle activity data, accelerometer data, hydration data) from a single sensor and / or from multiple sensors from a single subject individual and / or from multiple subject individuals. In another refinement, the transmission subsystem 24 transmits the animal data 14 to the intermediate server 22. i a computing device 26 that mediates the transmission of data, i.e., collects and transmits the data to the computing subsystem 22; i For example, the computing device 26 i The computing device 26 may be a smartphone or a computer. i can be any computing device. Typically, computing device 26 iis local to the individual of interest or to a group of individuals of interest, although this is not a requirement of the invention. In a further refinement, the computing subsystem 22 communicates with the source of animal data 12 via a cloud 40 or a local server (e.g., localized or networked server / storage, localized storage devices, a distributed network of computing devices). The cloud 40 can be the Internet, a public cloud, a private cloud utilized by the organization operating the intermediate server 22, or another third party. Thus, in this context, the cloud 40 and / or the local server are part of the transmission subsystem 24. In another refinement, the transmission subsystem 24 includes a direct communication link. Thus, in this refinement, the computing subsystem 22 communicates with the sensor 18 ... i Alternatively, as shown by the communication link 34 to the computing device 26 i In one refinement, the sensor 18 communicates directly to the source of the animal data, as shown by the communication link 36 to the sensor 18. i The communication to the sensor 18 may be via one or more designated transmission protocols or networks (e.g., wired, WIFI, BLE, Zigbee, NFC, cellular networks). i Communication to the sensor may be via the sensor's native application or other data collection medium (e.g., cloud, server). In another refinement, communication to one or more sensors may be via direct contact between the sensor (e.g., a sensor at the bottom of the shoe) and the receiving technology (e.g., integrated as part of the floor or ground).

[0032] Still referring to FIG. 1, the computing subsystem 22 and / or one or more sensors 18 iThe computing subsystem 22 and / or the one or more sensors 18 convert at least a portion of the animal data into at least one computing asset assigned to the selected individual of interest or to a group of selected individuals of interest. i The computing subsystem 22 and / or one or more sensors 18 are operable to convert at least one computing asset into a predictive indicator. iis further operable to provide one or more outputs to one or more users. A user may be an end user of one or more outputs via the computing subsystem 22, such as a customer or acquirer of data (e.g., a person or group of persons placing one or more bets based on one or more outputs in an area such as sports betting, or using one or more outputs for their health monitoring). In this regard, a user may be one or more persons, one or more organizations, and the like. In an improvement, a user may consist of multiple users. A user may also be one or more systems, or one or more subsystems. A system may be one or more sets of one or more interrelated or interacting components that cooperate to achieve one or more common goals or to generate one or more desired outputs. One or more components of the system may include one or more applications, frameworks, platforms, or other subsystems, which may be integral to the system or may be separate but part of one or more networks linked to the system, and which are operable to achieve one or more common goals or to generate one or more desired outputs. For example, the computing subsystem 22 may provide one or more outputs to a user (system), such as a platform that provides insurance products based on the one or more outputs, such as a telemedicine application that provides real-time health statistics based on the one or more outputs, or such as a gambling system 28 or a probability scoring system 30.In these examples, the output or outputs provided by computing subsystem 22 may be to one or more third party systems or subsystems, or to one or more systems or subsystems that are directly or indirectly a part of computing subsystem 22 (e.g., integrated with computing subsystem 22, operated separately by one or more entities that execute computing subsystem 22). In refinements, the system or systems may be operated by the same entity that operates computing subsystem 22, or by one or more different entities. In another refinement, the system or systems may be other systems that are not part of computing subsystem 22, but are operated by the same entity that operates computing subsystem 22, or are other systems operated by one or more different entities.

[0033] In some variations, converting at least a portion of the animal data into computational assets and converting the computational assets into predictive indicators can occur via the computing subsystem 22 or via one or more source sensors. Conversion can occur using any animal data. For example, in the context of measuring heart rate, a biological sensor can be configured to measure electrical signals in a subject's body and convert (e.g., convert) the analog measurements into digital measurements and transmit the digital measurements. The computing subsystem can receive the digital measurements and convert them through one or more calculations into one or more heart rate values ​​based on the overlapping segments of the digital measurements by (i) identifying R-peaks in the overlapping segments, (ii) calculating a number of sample values ​​based on the time between adjacent R-peaks, (iii) discarding samples affected by false or missed peak detections, and (iv) calculating an average, which may be weighted, for the remaining sample values. The computing subsystem may determine that the sample is affected by erroneous or missed peak detection in response to the sample value differing from the previous heart rate value by more than a first threshold. If the standard deviation of the differences between the samples is greater than a second threshold, the server may determine that the sample is affected by erroneous or missed peak detection in response to the sample value differing from the previous heart rate value by more than a third threshold less than the first threshold. In a refinement, each step in a process of performing one or more actions on data may be considered a transformation for purposes of the present invention. In this context, one or more actions may include one or more of calculate, compute, derive, incorporate, simulate, extract, extrapolate, modify, enhance, create, evaluate, estimate, infer, determine, process, communicate, and the like.In another refinement, the one or more transformations are performed utilizing one or more signals or one or more measurements from the non-animal data.

[0034] In an improvement regarding one or more conversions related to measuring the heart rate, at least one biological sensor is configured to measure electrical signals in the subject's body and to convert one or more analog measurements into one or more digital measurements, and to transmit the one or more digital measurements. A computing subsystem is configured to receive the one or more digital measurements and is configured to calculate the heart rate based on one or more overlapping segments of the one or more digital measurements by identifying R-peaks in the one or more overlapping segments, by calculating one or more sample values ​​based on the time between adjacent R-peaks, by discarding one or more samples affected by erroneous or missing peak detections, and by calculating one or more averages for the remaining sample values. The computing subsystem may be operable to communicate one or more averages of the remaining sample values.

[0035] In another improvement of one or more conversions related to measuring the heart rate, at least one biological sensor is adapted to be fixed to the skin of the subject and configured to measure electrical signals in the skin, convert the analog measurement into a digital measurement, and transmit the digital measurement. A computing system receives the digital measurement and calculates one or more heart rate values ​​based on the one or more overlapping segments of the digital measurement by (i) identifying R-peaks in one or more overlapping segments, (ii) calculating a number of sample values ​​based on the time between adjacent R-peaks, (iii) selecting samples that are within a first threshold of previous heart rate values, and (iv) setting the current heart rate value to an average, which may be weighted, for the selected samples. Each sample value may be proportional to the inverse of the time between adjacent R-peaks. The computing system may select samples that are within a second threshold of previous heart rate values ​​in response to a standard deviation of the difference between consecutive samples being greater than a third threshold. The computing subsystem may set the current heart rate value equal to the previous heart rate value in response to the number of samples being less than a fourth threshold or in response to no sample being selected. The computing system may be operable to communicate the one or more current heart rate values ​​to one or more users. The system may operate in real-time or near real-time, where the computing system is operable to display each current heart rate value before each subsequent heart rate value is calculated, and the computing system calculates each current heart rate value before the sensor completes measurements for at least some or all of the measurements used to calculate the subsequent heart rate value.The computing system may calculate the initial heart rate value by receiving a preliminary segment of digital measurements that is longer than the overlapping segments, identifying R-peaks in the preliminary segment, calculating sample values ​​based on the time between adjacent R-peaks, and further calculating an average, which may be weighted, over the samples.

[0036] In yet another refinement regarding one or more transformations related to measuring the heart rate, at least one biological sensor is configured to measure one or more electrical signals in the subject's body and to convert (e.g., convert) the analog measurements into one or more digital measurements and to transmit the digital measurements. A computing subsystem is configured to receive the one or more digital measurements and to convert (e.g., calculate) one or more heart rate values ​​based on one or more overlapping segments of the one or more digital measurements by identifying R-peaks in the one or more overlapping segments, calculating one or more sample values ​​based on the time between adjacent R-peaks, selecting one or more samples that are within a first threshold of previous heart rate values, and setting the current heart rate value to an average with respect to the selected samples.

[0037] In yet another refinement of one or more transformations related to measuring the heart rate, one or more measurements are received by the computing subsystem from at least one biological sensor, the computing subsystem being operable to process the one or more measurements. For example, a first segment of measurements is received by the computing subsystem from one or more sensors. R-peaks in the first segment are then identified by the computing subsystem. A first plurality of sample values ​​is then calculated by the computing subsystem based on the time between adjacent R-peaks. For example, a constant may be divided by the time between adjacent R-peaks. A first subset of the first plurality of sample values ​​is selected to include only sample values ​​that are within a first threshold of previous heart rate values. A first updated heart rate value is then calculated by the computing subsystem based on an average of the first subset of sample values. The first updated heart rate value can then be displayed by the computing subsystem. In a subsequent iteration, a second segment of digital measurements may be received by the computing subsystem from one or more sensors. A third segment of the digital measurements may be formed by adding the second segment to the first segment. An R-peak may then be identified within the third segment. A second plurality of sample values ​​may be calculated based on the time between adjacent R-peaks. A plurality of differences between consecutive samples may then be calculated. In response to the standard deviation of the differences exceeding a second threshold, a second subset of the second plurality of sample values ​​may be selected to include only sample values ​​that are within a third threshold of the first updated heart rate value. A second updated heart rate value may then be calculated by the computing subsystem and displayed based on an average, which may be weighted, for the second subset of sample values. An initial heart rate value may be calculated based on the preliminary segment of the digital measurements.

[0038] In yet another refinement of the one or more transformations related to the measurement of the heart rate, the transformations can be performed when addressing issues related to signal quality. If the raw data has a very low signal-to-noise ratio, additional pre-filter logic can be applied to transform the data before calculating the heart rate value. The pre-filtering process detects any outliers and replaces the one or more outliers with values ​​that are aligned in the time series of generated values ​​and fall within a pre-established threshold / range using a look-ahead approach. These generated values ​​that fall within the pre-established threshold / range can be forwarded through the system for calculations related to one or more heart rate values.

[0039] In yet another refinement of the one or more transformations related to the measurement of the heart rate, the transformation can be performed when detecting and replacing one or more outliers generated from one or more biological sensors. The computing subsystem can be operable to receive one or more values ​​generated directly or indirectly by one or more biological sensors. One or more statistical tests can be applied by the computing subsystem to determine acceptable upper and / or lower limits for each value. Using a backfilling method, one or more outliers can be replaced by the next available value that falls within an established tolerance range within the current window of samples.

[0040] Additional details related to systems for measuring heart rate and other biological data are disclosed in U.S. Patent Application No. 16 / 246,923, filed January 14, 2019, and International Application No. PCT / US20 / 13461, filed January 14, 2020, the entire disclosures of which are incorporated herein by reference. The present invention is not limited to the method or system used to convert the animal data and / or one or more derivatives thereof, nor is the present invention limited to the type of data that is converted.

[0041] The output or outputs provided by the inference system 10 may include one or more of predictive indicators, computing assets, animal data (including signals and measurements), one or more derivatives thereof, and / or combinations thereof. In this context, "provided" includes "transmitted," "made available," and "allowed access." For example, a computing subsystem may transmit one or more outputs to another system or subsystem, including platforms and applications (e.g., gaming applications, health / telemedicine applications, fitness applications, insurance applications, predictive applications, rehabilitation applications), or allow access to one or more outputs if the other system or subsystem has access to the data through one or more mechanisms (e.g., access via the cloud 40).The computing subsystem 22 may use at least a portion of the one or more outputs from the computing subsystem 22 for: (1) use as a marketplace to place or accept one or more bets; (2) to accept one or more bets; (3) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; (5) to develop one or more strategies; (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) to generate, ... ) for use as one or more signals or one or more measurements (including a set of signals of measurements) utilized in one or more simulations or calculations or analyses, (9) for use as part of one or more simulations in which the output directly or indirectly involves one or more users, (10) to recommend one or more actions, (11) for use as one or more core components or one or more supplements to one or more consumption media, (12) in one or more promotions, or (13) as a combination thereof. In this context, a combination can include two or more iterations and can include all possible iterations. Additionally, an indirect use can include any derivative of one or more outputs or can include a non-direct application of one or more outputs. For example, if a probability is generated for object X based on predictive indicator X, and a probability is generated for object Y based on predictive indicator Y, then a probability for group Z (comprised of object X and object Y) may be generated without direct use of predictive indicator X and predictive indicator Y. Indirect use of one or more outputs may also include one or more actions not directly derived from the data.For example, an indirect use may include observing user interactions with the data, based on which the computing subsystem 22 or betting system 28 or probability evaluation system 30 may dynamically create or enhance or modify betting markets or odds, products acquired or consumed, strategies, predictions, recommendations, and the like, based on user interactions with the data, rather than based on the data itself. Finally, one or more uses may be interconnected and correlated. For example, an action may mitigate risk, the creation of probabilities may enable the development of strategies, the creation of products may be used for promotion, simulation outputs may provide the basis for predictions or recommendations, etc.

[0042] In a variation on application (1), the market can be a particular type or category of bet on a particular event. The market can be on any event. Often, an organization accepting one or more bets will offer multiple betting markets on each event and also provide listed odds for each market. The particular type or category can include a proposition bet, a spread bet, a line bet, a futures bet, a parlay bet, a round robin bet, a handicap bet, an over / under bet, a full coverage bet, or a teaser bet.

[0043] In variations on use (2), the underwriting of a bet may be, for example, underwriting a bet by a gaming system utilizing one or more outputs (e.g., bet types derived from the predictive index), underwriting by an insurance system (e.g., an insurance policy offered to an individual, which may or may not cost the company more based on the likelihood that the individual will experience any given biological event predicted by the predictive index), and so forth.

[0044] In a variation on use (3), the one or more products can be one or more products or services designed to be sold or distributed. The product can be any product that can be created, modified, enhanced, offered, or distributed in any industry or business, so long as the product directly or indirectly uses at least a portion of the one or more outputs. It also includes the one or more outputs that lead to (or result from) the creation of the product. For example, the product can be the one or more outputs themselves (e.g., predictive indicators), a betting market, an insurance offering, a health application that displays one or more outputs, a set of algorithms designed to provide specific insights related to an object, a sports betting application, a consumer product (e.g., beverage, food) that utilizes one or more outputs, and the like. For purposes of clarity, "enhancing" can include "becoming part of" the product if the enhancement creates added value. Additionally, in many cases, "creating" can include "deriving" and vice versa. Similarly, "creating" can include "generating" and vice versa. Additionally, "modify" can include "revise," "amend," "adjust," "change," and "refine." Finally, an "acquirer" of a product can be, for example, a consumer, an organization, another system, any other endpoint that can consume the product, and the like.

[0045] In a variation on application (4), one or more predictions or probabilities or possibilities can be associated with a future outcome or occurrence with which one or more predictions or probabilities or possibilities are connected. For example, a probability can be calculated to determine the likelihood that any given athlete will have a heart rate above 200 beats per minute in any given basketball game using various types of data including the athlete's current heart rate, average heart rate, maximum heart rate, past heart rates under similar conditions, fluid levels, sEMG data, minutes on the court, total distance traveled, and the like. Using this probability, another probability can be calculated to determine the likelihood that the athlete will make a basket from outside 25 feet (7.62 meters) more than 50% of the time. Additionally, "communication" can include visualization of one or more predictions or probabilities or possibilities (e.g., displaying probabilities via an application, displaying probabilities based on output for other individuals in an AR or VR system), verbal communication of one or more predictions or probabilities or possibilities (e.g., a voice-activated virtual assistant informing an individual of the likelihood that an event may occur or that an event will occur based on one or more outputs for the individual. Examples can be the likelihood of suffering hypoglycemia if a particular action is not taken, the likelihood of suffering a stroke within the next 120 days based on collected biological data, or that a biologically relevant event will occur based on one or more outputs). Finally, a modification to a prediction or probability or possibility can include revising a previously determined prediction or probability or possibility for an event.

[0046] In variations on application (5), the strategy may include any strategy that uses one or more outputs. A strategy may be, for example, a plan of action for determining whether to insure an individual, whether to place a bet, whether to take a particular action, and the like.

[0047] In a variation on use (6), the action can be any action directly or indirectly related to at least a portion of one or more outputs. Actions include actions derived from (or resulting from) one or more outputs. For example, actions can be to insure someone (e.g., the probability that a person will have a heart attack within the next 24 months is X, so the insurance premium is Y), actions related to an individual's biology (e.g., a passenger in a car has an output value that triggers an autonomous vehicle to drive to the nearest hospital), actions to place a bet (e.g., an athlete's energy level is X percent, so the user places a bet), actions to take a particular action (e.g., the system communicates an action to take a particular action such as "place a bet," "run for 20 minutes today," "eat X calories today," etc.), actions to not take any action at all, and the like.

[0048] In variations on use (7), mitigating or preventing a risk can include any action, inaction, strategy, recommendation, and the like, related to mitigating or preventing a risk, and can also include taking additional risks.

[0049] In variations on application (8), the signals or measurements may include information in any form and in any format (eg, included as one or more data sets).

[0050] In a variation on application (9), simulation includes both the creation of one or more computer models and the imitation of one or more situations or one or more processes. Simulation has a wide range of engagement applications, including simulations that are utilized to generate one or more outputs, any use of the outputs may be considered direct or indirect engagement, and inclusion of one or more outputs within one or more simulations may engage one or more users (e.g., video games, AR / VR systems).

[0051] In a variation on application (10), recommending one or more actions includes both recommendations inferred by one or more outputs (e.g., a predictive indicator providing a probability of a happening may infer an action to take) and recommendations directly written based on one or more outputs (e.g., recommending an action to take based on a predictive indicator derived from the probability of a happening). In a refinement, the recommendation may be composed of multiple recommendations.

[0052] In a variation on application (11), the one or more media for user consumption can be any media from which a user can directly or indirectly consume one or more outputs. Media can include, for example, a health monitoring application that communicates a cardiac status check via one or more outputs, a telerehabilitation or telemedicine platform that communicates one or more outputs with the platform during an activity (e.g., during a remote workout) while allowing a remote medical or rehabilitation professional to examine the patient via an integrated video display, an insurance application that communicates insurance adjustments based at least in part on animal data, a sports betting platform, and the like. It can also include media broadcasts that integrate one or more outputs, a sports streaming content platform (e.g., a video platform) that integrates one or more outputs as a supplement to a live sporting event being viewed (e.g., allowing a user to place a bet while viewing the live content), and the like.

[0053] In variations on use (12), the one or more promotions can be any promotion that provides assistance to facilitate the underwriting and acquisition (e.g., sale) of one or more products, including one or more advertisements, an offer using one or more outputs (e.g., an offer for insurance with the possibility of lowering premiums by providing one or more outputs), a discount scheme using one or more outputs, and the like.

[0054] In an embodiment, the computing subsystem 22 may be operable to provide one or more data outputs to one or more systems (e.g., the wagering system 28, the probability evaluation system 30, or other systems), where the one or more systems may use at least a portion of the one or more outputs (1) as a market for placing or accepting one or more bets; (2) to accept one or more bets; (3) to create, enhance, modify, obtain, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; or (5) to develop one or more strategies. (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) as one or more signals or one or more measurements used in one or more simulations or calculations or analyses; (9) as part of one or more simulations the output of which directly or indirectly involves one or more users; (10) to recommend one or more actions; (11) as one or more core components or one or more supplements to one or more consumer media; (12) in one or more promotions; or (13) any combination thereof.

[0055] In another variation, one or more outputs are dynamically created or modified or enhanced by computing subsystem 22, where at least a portion of the dynamically created or modified or enhanced output(s) are used to: (1) serve as a market for making or underwriting one or more bets; (2) to create, modify, enhance, obtain, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; (4) to develop one or more strategies; or (5) to take one or more actions. (6) used directly or indirectly to mitigate or prevent one or more risks, (7) as one or more signals or one or more measurements used in one or more simulations or calculations or analyses, (8) as part of one or more simulations whose output directly or indirectly involves one or more users, (9) to recommend one or more actions, (10) as one or more core components or one or more supplements to one or more consumption media, (11) in one or more promotions, or (12) any combination thereof. In an improvement, the dynamic creation or modification or enhancement may be made on another system (e.g., a system in a network of related systems, a third party system).

[0056] In another variation, one or more direct or indirect uses by the computing subsystem are at least partially dynamic and based on one or more user interactions with one or more outputs. For example, the computing subsystem 22 may be operable to dynamically create, enhance, or modify at least one of betting markets or odds, products to be acquired or consumed, and evaluations or calculations regarding probabilities or strategies or predictions or recommendations or actions to mitigate or prevent risk, based at least in part on one or more outputs from the computing subsystem 22. Such creation, enhancement, or modification may result from one or more direct or indirect observations of user engagement with data collected by the computing subsystem 22, or may result from new data being collected by the system. "Dynamic" in this case refers to the ability to be non-static, but change based on one or more factors or inputs. Such use cases may include the creation or enhancement of products or modifications to such products in the areas of sports betting (e.g., any type of bet or market including proposition bets, spread bets, parlays, futures, line bets, round robins, teasers), non-sports betting products (e.g., platforms utilizing predictive indicators based on animal data, such as health monitoring applications, telemedicine applications, fitness applications, insurance applications, rehabilitation applications, etc.), strategies based on user interaction with data, recommendations based on collected data, and the like. For example, if an individual has purchased data related to heart rate related to Team X, a proposition bet may be created and offered to the individual related to heart rate related to Team X.In another example, if a user of the data is viewing their cardiac health statistics (e.g., ECG readings) combined with other types of animal data (e.g., stress levels, fatigue) via a health application, the computing subsystem may dynamically create or adjust its recommended products based on the user's interaction with the user's health statistics (e.g., based on irregular ECG patterns and extremely high stress levels, a product may communicate, "User has a 7% chance of having a heart attack within the next 30 days. See a doctor" based on the system's analysis of previously collected data). Such one or more functions may also be performed by the wagering system 28 and / or the probability assessment system 30.

[0057] In yet another variation, the guessing system 10 interacts with a betting system 28 or a probability evaluation system 30. The betting system 28 can be configured, for example, to receive one or more bets from one or more individuals, in which case the computing subsystem 22 transmits data (e.g., animal data and / or one or more derivatives thereof) to the betting system. A betting system typically includes one or more computing devices that execute a betting application. Similarly, the probability evaluation system 30 can be configured, for example, to evaluate or calculate one or more probabilities, to make one or more predictions, to mitigate or prevent one or more risks, or to create, enhance, or modify one or more products for acquisition or consumption, in which case the computing subsystem 22 transmits data (e.g., animal data and / or one or more derivatives thereof) to the probability evaluation system. The betting system or probability evaluation system can be operated by the entity that manages the guessing system or by a third party. In an improvement, the betting system 28 or the probability evaluation system 30 or a combination thereof are part of the computing subsystem 22. Characteristically, the computing subsystem 22 is operable to provide one or more identical or substantially similar data outputs to multiple users (which may include, for example, multiple systems or end users such as bettors). Advantageously, the provision of data to multiple users may occur simultaneously. For example, the computing subsystem may provide the same "energy level" output for athlete X to multiple systems (e.g., broadcast partners), but one of the outputs may include a different graphics package or may include different metadata or formats (e.g., timestamps displayed differently). In another example, the computing subsystem may provide a heart rate output to multiple users (e.g., multiple systems) based on which various products may be created.In yet another example, the computing subsystem may provide wagering opportunities on a heart rate output for athlete X, but may display or communicate it in multiple ways (e.g., a heart rate display for bettor A, a heart rate vibration notification alert transmitted to bettor B's smartwatch, and a heart rate verbally communicated by a virtual assistant to bettor C) to one or more users (e.g., one or more bettors in this case). In a variation, bet types may be based on the same heart rate output of athlete X, but may be productized in different ways to accommodate one or more bettors and their preferred bet products (e.g., offering bets to bettors on heart rate as a numerical value versus heart rate as a color directly based on a numerical value). In an improvement, one or more outputs of the computing subsystem 22 are synchronized with one or more types of non-animal data and / or one or more consumable media. For example, the one or more outputs may be synchronized with media content, which may include video content (e.g., the data may be synchronized with one or more live broadcast streams of a sporting event to provide the possibility of placing one or more bets while watching the sporting event, the data may also be synchronized with streaming video of a patient during a telemedicine or remote monitoring or rehabilitation session in real time, the data may also be synchronized with visual content originating and utilized within a smart glass or AR / VR system), audio content, additional data measurements (e.g., sports statistics such as points won / lost, games won / lost, points, assists, goals, shooting percentage, and the like), simulated games (e.g., video games), and the like. In another refinement, the computing subsystem 22 or the betting system 28 or the probability evaluation system 30 is operable to generate one or more betting products from the animal data.

[0058] In addition, one or more of the betting systems and the probability assessment system may share one or more functions and / or one or more characteristics (e.g., both types of systems may be programmed to assess one or more probabilities, to develop one or more strategies, to inform one or more users to take one or more actions, to provide one or more recommendations, to mitigate one or more risks, to create or modify one or more products). Similarly, one or more of the betting systems may have one or more functions or one or more characteristics of a probability assessment system, and vice versa. In an improvement, the betting systems and the probability assessment system may operate together (e.g., within the same network or networks, or performing different tasks to solve the same use case), thereby providing one or more different proposals based on the same data for the same or similar use cases. In some cases, one or more of the betting systems may communicate 38 directly to one or more probability assessment systems, and vice versa. For example, upon receiving data from the computing subsystem 22, the probability assessment system may create a product that provides one or more odds for any given outcome to occur (e.g., in sports betting, insurance, healthcare), and the betting system may underwrite a bet based on the one or more odds (e.g., a sports betting platform that creates and underwrites a bet using odds created by a third party analytics company based on predictive indicators; an insurance company that creates and underwrites risk to insure someone with a premium adjusted based on predictive indicators created by a third party insurance analytics company; or a telemedicine company or telemedicine monitoring company that underwrites risk to provide a digital service product to patients based on predictive indicators provided by a third party).

[0059] As discussed above, predictive indicators provide multiple opportunities for value creation, including as a basis for new gambling markets and products, and for establishing predictions and associated decisions (e.g., probabilities, likelihoods) related to future occurrences. Predictive indicators can be derived in various manners, for example, by utilizing one or more statistical models, or by one or more artificial intelligence techniques (e.g., machine learning, deep learning techniques), or by one or more calculations or operations. For example, by utilizing one or more machine learning techniques, a system can analyze previously collected data sets and current data sets, thereby creating or modifying or enhancing one or more predictions. Since machine learning based systems are configured to learn from collected data rather than requiring explicitly programmed instructions, they can search for and recognize patterns that may be hidden within one or more data sets, which allows the machine learning based system to uncover insights from collected data, thereby enabling predictions to be made. Advantageously, machine learning based systems learn using data, often using an iterative approach to improve the model's predictions and accuracy as new data is input to the system, with improvements derived from feedback provided from previous operations performed by the system (which also enables the production of reliable and reproducible results).

[0060] Additionally, the predictive index can be expressed in n ways. For example, the predictive index may be expressed as a percentage (e.g., there is a 75% chance that subject X will have a heart attack within the next n years), as a text or description (e.g., a recommended action based on the predictive index, such as "subject X will become dehydrated if he does not drink water within the next n minutes," a description such as "subject X will experience symptom y within the next n days,"), as a physical response (e.g., a vibration via a watch programmed to alert the user to place a bet based on the predictive index), and the like. The predictive index can be derived from a single animal data type or from multiple animal data types. The predictive index can be derived from any signal or measurement or derivative of the animal data or any portion thereof. In an improvement, the predictive index is a computational asset calculated from at least a portion of the animal data, or is a composite calculated from two or more signals or measurements from one or more source sensors. For example, one or more physiological metrics may predict the fatigue level of a subject individual, which may predict whether the individual will be successful in a particular task at a particular time (e.g., in sports, making a free throw or kicking a field goal; as a pilot, safely driving or flying to a destination; as a surgeon, performing surgery). In this context, the predictive indicators may be used to, for example, determine whether to place a bet, determine the probability of an outcome occurring for an event, revise a previously determined probability for an event, or otherwise formulate a strategy in creating a market for an individual to place a bet or in taking an action. Thus, a user may be an organization that assesses risk or assumes risk for financial gain (e.g., a bookmaker, an insurance company), an analytics company, a sports team that analyzes player performance, a person placing a bet, or a company that creates a betting product.In a variation, the predictive index is calculated from computational assets including biological data selected from the group consisting of facial recognition data, eye tracking data, blood flow data, blood volume data, blood pressure data, biological fluid data, body composition data, biochemical composition data, biochemical structure data, pulse data, oxygenation data, core body temperature data, skin temperature data, electrodermal response data, sweat data, position data, location information data, audio data, biomechanical data, hydration data, cardiac-based data, neurological data, genetic data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic data, thoracic electrical bioimpedance data, or combinations thereof. In another refinement, the predictive index includes one or more signals or one or more measurements from non-animal data. The one or more non-animal signals or measurements may include, for example, ambient temperature data, humidity data, barometric pressure data, altitude data, wind speed, nutritional data, family history data, psychological data, non-animal statistical data (e.g., examples in a sports context include points, rebounds, assists, touchdowns, shots, goals, turnovers, passing yards, rushing yards, wins / losses, win percentage, and head-to-head information), other historical data, and the like.

[0061] In another refinement, at least a portion of the predictive indicators are used, directly or indirectly, (1) as a market for making or underwriting one or more bets; (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 or probabilities or possibilities; (4) to develop one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) as one or more measurements utilized in one or more simulations or calculations or analyses; (8) as part of one or more simulations whose output directly or indirectly involves one or more users; (9) to recommend one or more actions; (10) as one or more core components or one or more supplements to one or more consumption media; (11) in one or more promotions; or (12) any combination thereof. For purposes of explanation, one or more actions in this context may include taking on one or more gambles. In a medical scenario, a user (e.g., a patient) may agree to pay for a medicine or prescription written by a medical professional (e.g., a doctor), where the prescription or medicine is prescribed based on a predictive indicator (e.g., the predictive indicator may indicate that the patient may have an n percent chance of experiencing a medical condition, and therefore the doctor prescribes pill x to reduce the chance of the medical event based on the predictive indicator). The patient's action of accepting pill x prescribed based at least in part on the predictive indicator and accepting risks (e.g., costs, potential health-related issues with pill x) in exchange for the benefit of taking the medicine or prescription to improve health status may be a gamble taken and executed by the patient / user.

[0062] In another refinement, the predictive index can include multiple predictive indexes. For example, the predictive index may provide multiple predictive assessments within a single index (e.g., the index indicates the probability of X occurring within n months or indicates the probability of Y occurring within n+3 months). In another refinement, the one or more predictive indexes can be derived from or associated with an individual of interest, a plurality of individuals of interest, a group of interest of the plurality of individuals of interest, and / or a group of interest of the plurality of individuals of interest. This includes applicable, associated, assigned, and the like. For example, one or more predictive indicators can be attributed to an individual of interest, a group of individuals of interest, each of which has its own predictive indicator or indicators, a group of individuals of interest, each of which has its own predictive indicator or indicators, or a group of individuals of interest, each of which has its own predictive indicator or indicators, and / or a group of individuals of interest, each of which has its own predictive indicator or indicators, and / or a group of individuals of interest, each of which has its own predictive indicator or indicators. For example, in the context of a basketball team, player A is a guard for basketball team B in league C. Player A may have a predictive index associated with his or her performance, Team B may have a predictive index associated with team performance derived at least in part from members of the team (including Player A), League C may have a predictive index for all guards in the league derived at least in part from all guards in the league (including Player A), etc. In another refinement, the one or more individuals of interest or the group of individuals of interest includes one or more de-identified individuals.

[0063] In yet another refinement, the predictive indicators may include at least a portion of the biological data derived from one or more source sensors and provide predictions regarding the health status of the individual of interest, which may include feedback regarding life expectancy, risks associated with medical procedures (e.g., surgery, medication, etc.), or general health indicators including stress levels or energy levels. Users of such information include airlines, medical facilities (e.g., hospitals), pharmaceutical companies, automotive companies, transportation companies, rehabilitation facilities, military organizations, sports organizations, local government groups (e.g., police), oil and gas companies, construction companies, health care companies, financial groups, insurance companies, wellness companies, other technology companies, individuals, and the like. For example, airlines or transportation companies may use predictive indicators to monitor and predict pilot or driver fatigue. Insurance companies may apply predictive indicators to adjust an individual's insurance premiums based on collected and analyzed animal data. Financial institutions may apply predictive indicators to predict stress levels for a person based on transaction size and volume that may affect their decision-making abilities. A nursing home or care facility can apply predictive indicators to determine the expected future care needs for any given patient and can adjust the fees required to provide care for that individual accordingly. A telemedicine or telehealth monitoring company can apply predictive indicators to determine the likelihood of any given health outcome and can provide feedback to the patient and provide one or more recommended actions (e.g., take prescribed medication, take specific actions to prevent risk). A provider of home gym or fitness equipment can use predictive indicators to determine the future outcome of any given person's training with the aim of providing training-related recommendations (e.g., if you keep running on the treadmill at z miles per hour, your body will tire in n seconds. You need y minutes / seconds of recovery time before your next workout).A sports bettor may apply predictive indicators to assess the current or future biological state of one or more individuals that may affect the desire to place a bet. In another refinement, the predictive indicators may be used as indicators to enable one or more actions to be taken. For example, a taxi company may implement a system utilizing predictive indicators to certify that an individual is safe to drive, and such individual will operate the vehicle. A military organization may use predictive indicators to determine a soldier's "readiness" for combat or other missions, resulting in an action to be taken on or by the soldier. A rehabilitation facility may use predictive indicators to predict whether a subject may recover from any given injury, and what exercise (or exercises) and / or rehabilitation techniques may be most effective for the subject to maximize the chance of recovery. An airline or labor union may use predictive indicators to ensure that a pilot is ready to fly on any given day, or to extend the retirement age of a particular pilot, based on physiological characteristics or other collected animal data. In this example, the question may be whether to allow any given pilot of age n (e.g., age 65) whose data has been collected by the system the ability to continue flying beyond a certain age, even if that pilot exhibits certain biological characteristics, which may include physiological, biomechanical, and neurological characteristics. More specifically, it may be in the airline's best interest to determine a pilot's biological "fitness" and predict future biological fitness, rather than mandating deactivation (e.g., retirement) based on indicators such as a person's age, since pilot experience may result in an overall safer flying experience and / or allow for more routes to be flown in order to expand business.Thus, for any given pilot, the system may use one or more techniques (e.g., statistical models, running one or more simulations via one or more artificial intelligence techniques) on the collected animal data (e.g., cardiac / ECG data, age, weight and other data including habits and medical history) to generate one or more predictive indicators (e.g., predictive data sets for ascertaining the future cardiac activity of pilots from age 66-80 and for determining future biological "fitness" and future "fitness to fly" for a defined time period, based on which recommendations can be derived). In an improvement, the computing subsystem 22 or one or more gaming systems are operable to generate one or more gaming opportunities from the predictive indicators.

[0064] As mentioned above, the inference system 10 includes a transmission subsystem 24. Typically, the transmission subsystem 24 includes a transmitter and a receiver, or a combination thereof (e.g., a transceiver). The transmission subsystem 24 may include one or more receivers, transmitters, and / or transceivers, with a single antenna or multiple antennas (e.g., configured as part of a mesh network). The transmission subsystem and / or one or more of its components may be housed within the computing subsystem or may be external to the computing subsystem (e.g., a dongle connected to a computing device that is part of the transmission subsystem and is comprised of one or more hardware and / or software components that facilitate wireless communication). In a refinement, the transmission subsystem and / or one or more of its components are integrated into one or more sensors or are provided within such sensors. FIG. 2 shows a computing device 26 (or computing subsystem 22) receiving a signal from a sensor 18 attached to an individual of interest 16. The sensor 18 includes an integrated transmitter or receiver or transceiver 46. Advantageously, the transmission subsystem enables one or more source sensors to wirelessly transmit data for real-time or near real-time communication. Additionally, the transmission subsystem may utilize one or more transmission protocols to communicate with one or more source sensors. The present invention is not limited by the technology that the sensor 18 uses to transmit and / or receive signals. Currently, such transmission technologies and infrastructures include, but are not limited to, Bluetooth, Bluetooth Low Energy, Zigbee, Ant+, NFC, WIFI, cellular networks, and the like. The receiver 48 receives the signal from the transmitter 46.

[0065] In the illustrated variation, the receiver 48 includes an antenna and / or dongle (e.g., Bluetooth transceiver) 50 and the computing device 26 (or computing subsystem 22). In an improvement, the antenna / dongle 50 can be located at a large distance (e.g., 100 feet (30.5 meters), 1000 feet (305 meters), or more) from the computing device 26. Thus, the connection line 52 may include a converter 54 (e.g., USB to Ethernet, if desired) that can run longer lines as needed. Finally, an adapter 56 is used by the computing device 26 to transmit the animal data via the cloud 40 or a local server to the computing subsystem 22 of FIG. 1. The cloud 40 can be the Internet, a public cloud, or a cloud owned by the company that operates the prediction system or a third party.

[0066] In many cases, the communication distance between the sensor and the receiver of the sensor signal can be extended by the transmission subsystem for real-time or near real-time communication, thereby extending the range limitations of one or more sensors and their corresponding transmission protocol or protocols. In an improvement, the computing subsystem synchronizes the communication and real-time or near real-time streaming for one or more sensors in communication with the computing subsystem 22. Advantageously, the transmission subsystem allows real-time or near real-time streaming in an environment where potential radio frequency (RF) interference occurs. In an improvement, the computing subsystem 22 transmits at least a portion of the animal data to another location (e.g., a predetermined location within the system or another system) or stores the animal data for later use. In a variation, the system may provide a real-time or near real-time backup mechanism for input data from one or more source sensors with minimal impact on the real-time or near real-time transmission.

[0067] In another refinement, the transmission subsystem(s) or one or more components thereof, such as the antenna and / or dongle, may be wearable and may be fixed to, in contact with, or integrated into the object, either directly or via one or more intermediate members (e.g., clothing). The transmission subsystem or components of the transmission subsystem may also be mobile or personal to one or more individuals. In another refinement, the transmission subsystem 24 includes an on-body or in-body transceiver 60 ("on-body transceiver"), optionally functioning as another sensor or optionally integrated into a biological sensor. The on-body transceiver 60 is operable to communicate with one or more sensors 18 located on or across one or more objects, and may itself track one or more types of biological data (e.g., location data or location information data). In an improvement, the on-body transceiver is fixed to, integrated with, or in contact with the subject's skin, hair, vital organs, muscles, skeletal system, eyes, clothing, objects, or other devices on the subject. Advantageously, the on-body transceiver collects one or more data streams in real time or near real time from one or more sensors on the subject's body and communicates for each sensor using a transmission protocol for that particular sensor. The on-body transceiver may also function as a data collection hub. In an improvement, the on-body transceiver may minimize transmission overhead while improving transmission capabilities (e.g., increasing speed, reducing latency). In another improvement, the on-body transceiver may include logic that enables the on-body transceiver to perform at least one action on the animal data from the group consisting of collecting, normalizing, time stamping, aggregating, tagging, storing, manipulating, denoising, productizing, enhancing, organizing, visualizing, analyzing, summarizing, replicating, synthesizing, anonymizing, synchronizing, or distributing the animal data.Characteristically, the on-body transceiver is operable to transmit any collected and selected data (e.g., animal data, computational assets, predictive indicators, any derivatives, and the like) in real-time or near real-time to the n endpoints, while non-selected data can be stored on the transceiver for later download. Additionally, the summarization functionality can summarize data that may be sampled at a high frequency rate (e.g., 250hz-1000hz) and transmitted in a summarized form (e.g., data processed and / or summarized at 1hz) to accommodate any number of use cases or constraints (e.g., limited bandwidth).

[0068] In another variation, the transmission subsystem 24 includes an airborne transceiver 62 for continuous streaming and / or intermittent communication from one or more sensors located on one or more objects or objects of interest. Examples of the airborne transceiver 62 include, but are not limited to, one or more communication satellites or an unmanned aerial vehicle (e.g., high altitude pseudolite, drone) with a transceiver attached. Details regarding an unmanned aerial vehicle-based data collection and dissemination system are disclosed in U.S. Patent Application No. 16 / 517,012, filed July 19, 2019, the entire disclosure of which is incorporated herein by reference. In another variation, the transmission subsystem 24 includes a transceiver 63 embedded or integrated as part of a floor or ground (including a field), with transmission via direct contact with the surface (e.g., when the sensor is located on or near the bottom of a shoe).

[0069] In variations, the computing subsystem 22 synchronizes, timestamps, and tags the animal data with information related to the one or more subject individuals from whom the animal data was collected (e.g., name, age, weight, height, activity level, and / or associated group) and with information related to the one or more source sensors and including at least one characteristic of the one or more source sensors (e.g., characteristics). The at least one characteristic includes at least a sensor type, one or more sensor settings, a sensor brand, a sensor model, a sensor firmware, and the like. In refinements, the animal data includes metadata identifying one or more characteristics related to the animal data and the one or more source sensors. In some variations, the computing subsystem 22 and / or the gaming system 28 and / or the probability assessment system 30 take one or more further actions on the animal data upon receipt. Examples of such further actions include, but are not limited to, normalizing, time stamping, aggregating, storing, manipulating, denoising, enhancing, organizing, visualizing, analyzing, anonymizing, synthesizing, summarizing, replicating, productizing, and synchronizing the animal data. In a refinement, one or more actions can transform the animal data and / or one or more derivatives thereof. In another refinement, the computing subsystem 22 applies a schema suitable for real-time or near real-time data transfer, which reduces latency and provides error checking and a security layer, and further encrypts the animal data or a portion thereof.In another refinement, the computing subsystem or betting system or probability assessment system is operable to (1) monitor, receive, and record at least one request for predictive indicators and / or at least one computing asset and / or animal data by directly communicating with one or more systems, and (2) provide one or more users (e.g., systems) requesting access to predictive indicators and / or at least one computing asset and / or animal data with the ability to make one or more requests for data (e.g., by object or object group, one or more characteristics, data type, time, and the like), and (3) transmit and / or receive data. This may be achieved by utilizing technologies such as blockchain. By utilizing technologies such as blockchain, the system may have the ability to monitor the animal data and all transactions related to the data from the time the data is collected by the system. At any given time, a data provider, or an authorized user in the system, may view the conflict history tree of the individual's data, as well as any given use of the data, which may include the destination of the data, restrictions attached to the data, and other metadata associated with each data. In another refinement, the computing subsystem 22 and / or the betting system 28 and / or the probability evaluation system 30 are operable to associate at least one request for a predictive indicator and / or at least one computing asset and / or animal data with at least one user, or with a group of users, or with a class of users. For example, an association may be made by the computing subsystem or the betting system or the probability evaluation system between the system (e.g., its own system) and a person, group of people, or class of users requesting data (e.g., to place a bet), or between the system (e.g., its own system) and another system (e.g., a third party system).In another refinement, the computing subsystem or betting system or probability evaluation system can associate the animal data and one or more requests from the third party system to the person placing the bet. Furthermore, the computing subsystem or betting system or probability evaluation system can associate at least one request for data to one or more target individuals, or to one or more target groups, or to one or more target individuals from which the animal data was derived. For example, if a request for animal data is made from a target individual to ascertain the target individual's real-time blood pressure and heart rate vitals, or to ascertain the target individual's probability of experiencing a heart attack within the next n months, the computing subsystem or betting system or probability evaluation system can associate the particular request to the animal data, to the corresponding target individual, and to any other data or metadata required to fulfill the request. In another refinement, the animal data is grouped into one or more categories, each category having an associated computing asset or value.

[0070] In a variation, the computing subsystem 22 is operable to manage one or more source sensors and one or more data streams from the one or more source sensors by at least one characteristic from the group consisting of tissue, sensor type, sensor parameter, data type, data quality, timestamp, location, activity, individual of interest, grouping of individuals of interest, and data measurement. In a refinement, the management and / or management of the sensors can include functions such as scanning and pairing one or more sensors to the system, assigning one or more sensors to one or more individuals in the system (if necessary), assigning one or more sensors and / or one or more individuals to tissues or events, verifying that one or more source sensors are properly placed on the object and streaming desired data after application on the object, and the like. The system may also include functionality to support one or more sensors streaming in real-time or near real-time to the system, including automatic reconnection when one or more sensors lose connectivity or experience streaming interruptions. Additionally, the system may provide one or more alerts based on sensor connectivity loss, sensor failure (including battery failure), sensor degradation (e.g., producing data quality below a minimum established standard or threshold), and the like. In a refinement, the computing subsystem is operable to collect information from one or more source sensors by communicating directly to the one or more source sensors, to an associated cloud, or to a native application associated with the one or more source sensors. In another refinement, the computing subsystem is operable to change one or more sensor settings by transmitting one or more commands to the one or more sensors.For example, such commands may power on or off individual source sensors, put them into a battery saver mode to conserve energy, start or stop streaming, or increase or decrease the amount of data throughput to accommodate available bandwidth for streaming. As another example, such commands may increase or decrease the data collection frequency and / or sensor sensitivity gain of at least one source sensor. In another refinement, the computing subsystem 22 is operable to simultaneously communicate with multiple source sensors on the individual of interest, or with one or more source sensors on multiple individuals of interest. In another refinement, the computing subsystem 22 synchronizes communication with one or more data signals or one or more measurements from multiple sensors communicating with the computing subsystem. This may include one or more commands transmitted from the sensor to the system, which may include examples of a pre-streaming handshake between the sensor and the system to ensure authenticity of both, encryption protocols, and the like. Also included are issues of synchronization with one or more data signals or one or more measurements. For example, there may be mismatches in the timing utilized by each sensor. The sensor output received by a computing subsystem may differ from other sensors (e.g., by milliseconds) even if the computing subsystems receive them at the same time, so the computing subsystem may need to synchronize the data streams to ensure the integrity of both streams.

[0071] In another variation, when a data type or data set is requested by a user that is not within the computing subsystem 22, the computing subsystem 22 may obtain data from one or more third party organizations, apply or utilize one or more analytical tools (e.g., third party or in-house) to the collected or obtained data set to create the requested data to be provided to the one or more users, or artificially create one or more data types or data sets, such as may be done via one or more simulations. Alternatively, the computing subsystem 22 may transmit the animal data to another system (e.g., a third party analytical system for analysis), in which case the computing subsystem receives the analyzed data from the third party and provides it to the one or more users. When transmitting data to another system or source (e.g., a third party betting system, a third party probability rating system, a third party analytical company, a betting system or probability rating system or analytical system that is part of the computing subsystem), the computing subsystem is operable to record one or more characteristics of the animal data provided as part of the one or more distributions. These characteristics of the animal data may include at least one of the following: the source of the animal data, specific individual attributes of one or more individuals (e.g., name, weight, height, corresponding identification number or reference number), the type of sensor used, the specific sensor configuration, location, activity level, data format, type of data, algorithms used, data quality, when the data was collected, associated tissues, associated events, and the rate at which the data is provided. Alternatively, the recipient may transmit the animal data directly to the user that has already been transmitted by the computing subsystem and analyzed by the recipient. In another example, the animal data, including other data (e.g., non-biological statistics), may be obtained and made available for analysis.For example, the number of points scored by an athlete in a game may be obtained from a third party and may be used as part of an analysis to examine the athlete's heart rate, respiratory rate, and biological fluids to derive insights or other indicators. In an improvement, the computing subsystem may provide anonymized data output without any identification or association to an individual or group of individuals. Although animal data is often associated with one or more identifiable individuals of interest or with a group of identifiable individuals of interest, it will be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which may include computing assets and predictive indicators) may be anonymized or de-identified. De-identification involves removing personal identifying information to protect the privacy of individuals, but retaining the characteristics that make the data useful (e.g., in a human context, characteristics such as age, weight, height, medical condition, country of origin, blood type, information derived from biological fluids, etc.). In the context of the present invention, anonymization and de-identification are considered to be synonymous.

[0072] In other variations, the computing subsystem 22 is operable to allow one or more users to select the frequency at which the predictive indicators, computing assets, animal data, and / or one or more derivatives thereof are provided (e.g., one or more data packets per second, each data packet containing specified data). Moreover, the computing subsystem 22 can be operable to allow users to select one or more parameters, such as latency (e.g., real-time or near real-time vs. non-real-time) and duration, to maximize the value of any given data for their particular use case. In some cases, the system is operable even though meaningful predictive indicators cannot be initially derived due to insufficient data collected within the computing subsystem. The ability to provide animal data in real-time or near real-time, and in particular the ability to provide predictive indicators in real-time or near real-time, is particularly useful in both gaming and probability assessment applications. Such real-time or near real-time data is necessary for the computing subsystem 22 and / or the wagering system 28 and / or the probability evaluation system 30 to provide one or more wagering strategies or one or more markets, such as making or modifying a prediction or one or more odds, adjusting or modifying a probability evaluation, a strategy for mitigating or preventing risk, or other use cases (e.g., real-time health feedback). In another refinement, the computing subsystem 22 and / or the wagering system and / or the probability evaluation system are operable to allow a user to select at least one characteristic for which a predictive indicator, a computing asset, an animal data, and / or one or more derivatives thereof are provided.The characteristics may include one or more sources of animal data, specific individual attributes for one or more individuals or groups of individuals, the type of sensor used, sensor characteristics, classification, specific sensor configuration, location, activity level, data format, type of data, algorithms used, data quality, when the data was collected, associated organizations, associated events, and the rate at which the data is provided.

[0073] In some circumstances, the computing subsystem 22 provides and / or uses historical data of animal data. In an improvement, historical data from one or more similar events for an individual or one or more similar individuals may be useful to a user to predict performance associated with any given event. For example, if a gym equipment manufacturer wishes to create predictive metrics for users of its products (e.g., home cycling equipment) aimed at predicting fatigue (or the likelihood of fatigue occurring at any given time based on movement patterns) as part of a platform subscription service, using historical animal data derived from users of a cycling-specific fitness class may be useful in enabling the manufacturer to create predictive metrics for any given user to predict current or future biological performance when using the equipment. Advantageously, historical data for one or more subjects or groups of subjects allows the system to learn from that information and use that learning to provide more accurate results (e.g., by learning how a subject's heart rate performs for a given level of activity, the system can fine-tune its heart rate algorithm, thereby providing more accurate measurements). In another refinement, historical data from one or more similar individuals may be useful to a user to predict the performance of any given subject. For example, if an insurance company wants to understand the likelihood that any given subject will develop a particular medical condition (e.g., heart attack) within a given time period, the insurance company may perform one or more simulations by utilizing data from individuals who share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, ECG data history, heart rate history, blood pressure history, genomic / genetic history, history of data derived from biological fluids), thereby determining the likelihood that the subject will develop the medical condition within the required time period.In another refinement, historical data not derived from animal data (e.g., in a sports context, historical statistics such as points, rebounds, assists, goals, shots, win / loss percentages, and the like) may also be used.

[0074] The estimation system 10 may be operable to allow a user to adjust one or more parameters in one or more data sets to determine (or provide supporting information for) whether to place a bet, evaluate or calculate the probability of occurrence of an outcome of an event, make a prediction, modify a previously determined probability for an event, develop a strategy to create a market for one or more individuals to place a bet, or recommend an action, to run one or more simulations. In an improvement, the estimation system 10 provides one or more artificially created data sets derived at least in part from one or more sensors as a substitute for the actual data set. The artificial data may be beneficial when a user wishes to recreate (e.g., generate) data from previous historical data / events to establish trendlines and aid in the study and understanding of changes in any given performance for any given object in light of modifications (e.g., changes) to any given one or more inputs (e.g., variables) to predict future occurrences. In training the system to understand these one or more performance changes and variables associated with such changes (e.g., the changes may occur via one or more neural networks), the user may recreate the data (e.g., using one or more methodologies including within one or more simulation scenarios) to predict future events or occurrences, for example, based on an understanding of the subject's past performance, the past impact of one or more inputs, current performance, the current impact of one or more inputs, and trends seen by the system for similar events, subjects, and inputs. Advantageously, the one or more inputs may be biological data.In particular, the computing subsystem 22 or the betting system 28 or the probability assessment system 30 may create one or more artificially generated animal data sets, computing assets, or predictive indicators, which may be done via one or more simulations using at least a portion of the predictive indicators, computing assets, actually collected animal data, and / or one or more derivatives thereof. This may be done using one or more artificial intelligence techniques (e.g. one or more trained neural networks, machine learning systems) or statistical models. In a further refinement, the simulation may be composed of multiple simulations (e.g. n simulations are run simultaneously or sequentially to derive a single simulation output). In a variant, the computing subsystem 22 or the betting system 28 or the probability assessment system 30 may generate simulated data derived from at least a portion of the predictive indicators, at least one computing asset, and / or animal data for one or more target individuals or groups of target individuals (including one or more anonymized individuals or groups of anonymized individuals). In these contexts, "generate" can include "create" and vice versa, and "generate" can include "derive" and vice versa.The computing subsystem or betting system or probability assessment system uses at least a portion of the simulated data: (1) as a market for placing or underwriting one or more bets; (2) to underwrite one or more bets; (3) to create, enhance, modify, acquire, offer or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance or communicate one or more predictions or probabilities or possibilities; (5) to develop one or more strategies; (6) to take one or more actions; (7) to mitigate or (8) as one or more signals or one or more measurements utilized in one or more simulations, calculations, or analyses; (9) as part of one or more simulations the output of which directly or indirectly involves one or more users; (10) to recommend one or more actions; (11) as one or more core components or one or more supplements to one or more consumer media; (12) in one or more promotions; or (13) any combination thereof.

[0075] In addition to sensor data, other types of collected animal or non-animal data can be incorporated into one or more simulations depending on the use case. For example, personal information can be incorporated, which can include age, height, race, country / region of origin, ethnicity, sex, information from medical records, personal history, social history, health history, social habits, education records, criminal history, emotions, psychological evaluations, and the like. In the case of sports, the information can include win / loss records and other statistics (e.g., points won / lost, results, personal statistics such as points scored, rebounds, shots, forehands, backhands, and the like). In the case of sports betting, the information can also include past bets, user behavior, betting trends, and other user data. Featured, the ability to add or modify one or more inputs (e.g., variables) in a requested data set allows a user to determine one or more parameters from which an artificial data output is generated, while at the same time adapting the output to one or more specifications of the user. The one or more inputs may include any data, including as well one or more signals or one or more measurements from animal and non-animal data, relevant to understanding past behavior to predict future performance. The artificial data output may be, for example, artificial animal data, computing assets, and / or predictive indicators. In an alternative, the computing subsystem or gaming system or probability assessment system or other system may directly or indirectly use or apply at least a portion of the simulated data to create, enhance or modify a predictive indicator and / or at least one computing asset and / or animal data (including any derivatives thereof).At least a portion of the created, enhanced or modified predictive indicator and / or at least one computing asset and / or animal data may then be used (1) as a marketplace to place or underwrite one or more bets; (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 or probabilities or possibilities; (4) to develop one or more strategies; (5) to take one or more actions; or (6) to mitigate or prevent one or more risks. (7) as one or more signals or one or more measurements utilized in one or more simulations or calculations or analyses, (8) as part of one or more simulations whose output directly or indirectly involves one or more users, (9) to recommend one or more actions, (10) as one or more core components or one or more supplements to one or more consumption media, (11) in one or more promotions, or (12) as a combination thereof. In an improvement, the creation or enhancement or modification is dynamic. Advantageously, the one or more simulations and one or more applications of the output to one or more use cases can be performed in real time or near real time.

[0076] In an improvement, one or more inputs / parameters may be altered (e.g., randomized) within the simulation, thereby providing one or more new simulated data sets. In a further improvement, at least a portion of the generated artificial data output may be used as one or more data sets or as part of another one or more data sets utilized within one or more simulations or calculations or analyses. For example, one or more simulations may be run to determine what the future biological data (e.g., physiological data, biomechanical data, positional data) of an athlete may look like in any given match. Once the artificial data is generated, it may be utilized in one or more further simulations to determine one or more predictions related to whether the athlete will win a match. Such determinations may be made in real-time or near real-time. In another improvement, the predicted indicators may be fine-tuned by utilizing simulated data incorporating at least one type of animal data. Advantageously, one or more predictions may be modified or enhanced in real-time or near real-time as new data is collected and further simulations or statistical models are adjusted. For example, the system may conclude based on historical data that a certain outcome is likely, and the system may also make predictions related to the occurrence of that outcome.By running one or more simulations using at least a portion of the artificial data incorporating at least one type of animal data, the system can adjust or enhance the predictive index to determine the likely probability of an outcome occurring based on various data, which may include, for example, a tennis player's current match status (e.g., Player A is playing the 4th game of the 2nd set and losing 6-4, 3-2), historical data (e.g., all match outcomes when Player A is playing the 4th game of the 2nd set and losing 6-4, 3-2), current conditions (e.g., humidity, temperature, altitude), the same conditions (e.g., humidity, temperature, altitude) under which previous matches were played, relevant animal data (e.g., published physiological characteristics, biomechanical characteristics, biofluid-based characteristics, location-based characteristics, and the like), and other types of non-animal data. In another example, an insurance company may have hypotheses related to the characteristics of one or more individuals and may run one or more simulations utilizing at least a portion of the animal data to determine likely biological outcomes for those individuals. An outcome may be, for example, the likelihood that a person will get sick within the next n months, the likelihood that a given injury will achieve a given recovery rate, the likelihood that an individual will experience the onset of an illness (e.g., stroke, heart attack), and the like. Based on one or more simulations, artificial data may be created, whereby odds for any given outcome may be created or adjusted and provided to a prediction system (e.g., the likelihood that an individual will have a stroke based on one or more characteristics of the individual). In an insurance example, an insurance company may adjust premiums for individuals with those characteristics. In another example, an elderly care facility may determine likely health outcomes for any given individual by running one or more simulations by utilizing at least a portion of the animal data, and thus the amount of future care required for that particular individual. Based on the amount of likely future care required, the care facility may adjust prices for each individual based on the individual's profile.In another example, a manufacturer of an automobile or aircraft may wish to run simulations to fine-tune predictive indicators to provide one or more responses associated with objects within the automobile or aircraft to mitigate or prevent risk. More specifically, an automobile manufacturer may wish to determine whether a person exhibiting certain physiological or biomechanical characteristics while operating the vehicle is at risk of causing an accident. By utilizing the animal data and / or one or more derivatives thereof, the vehicle may take one or more actions (e.g., stop the vehicle, pull over, drive to a hospital) based on the predictive indicators and other animal data to mitigate or prevent risk (e.g., the vehicle may drive itself to a hospital if it determines based on collected sensor data that a person is having a heart attack, or the vehicle may stop itself if it determines that a person with a given profile and characteristics, such as a particular age, weight range, height range, cardiac condition, increased heart rate, elevated blood pressure, elevated stress levels, irregular biomechanical movements, etc., is more likely than a given percentage to have a heart attack while behind the wheel, or with a more absolute prediction via the predictive indicators that a subject with these given sets of characteristics and parameters will have a heart attack). In another example, an airline may monitor real-time biological characteristics of one or more pilots via one or more source sensors during flight and may take one or more actions (e.g., notify the airline, take control away from the pilot, put the aircraft on autopilot, enable remote control of the aircraft by the airline or the aircraft manufacturer) based on the probability of an occurrence related to at least a portion of the animal data.

[0077] In another refinement, the simulated data incorporating at least a portion of the animal data may be utilized to create one or more prop bets on the simulated event. For example, if the system previously collected the heart rate of Team A when playing against Team B, the system may create one or more new bets utilizing the previously collected data incorporated as part of one or more simulations. A bet may be "whether Team A's average maximum heart rate exceeds 170 beats per minute during the match time against Team B over 10,000 simulated matches." In another refinement, the simulated data incorporating at least a portion of the animal data may be utilized to create new prop bets on virtual events (including simulated events) or may be utilized as information utilized as part of a betting strategy for virtual wagering. For example, if the system collects respiration rates for one or more real objects (e.g., real horses) in one or more real races, the system may generate simulated data (e.g., simulated respiration rates) based on the respiration rate data collected from one or more simulated races, which may enable the system to create one or more prop bets or wagering products for one or more virtual objects (e.g., virtual horses) utilizing at least a portion of the simulated animal data generated in one or more virtual races (e.g., a bet may be "will the virtual horse's maximum respiration rate in the virtual race exceed metric X"). In variations, the generated simulated data may not share the same characteristics as those from which it was derived. For example, when generating an artificial respiration rate for a virtual horse, the simulation may characterize and display the generated artificial respiration rate as a different metric (e.g., a color, a different name such as "fatigue," and the like).In a further refinement, one or more virtual objects share at least one common characteristic with one or more real objects, and a virtual event shares at least one common characteristic with an event for which real animal data was collected (e.g., horse Z runs in a real race, virtual horse Z runs in a virtual race, and at least one characteristic of the real horse and the event exists in the system. The characteristic may be, for example, respiration rate, and the event may be a horse race. The bet is "will virtual horse Z's maximum respiration rate in the virtual race exceed index X?"). Characteristics of interest may include biological characteristics, physical characteristics, profile characteristics (e.g., same name, jersey number, team name, team colors), and the like. In another example, a simulation game (e.g., a video game) or a virtual world video game may create one or more bets or products (e.g., in-game virtual products for purchase) related to the real animal data of one or more users playing the game (e.g., using the real animal data of the users incorporated as part of the virtual video game to create rewards within the game for users who reach a goal while using simulated data incorporating at least a portion of their animal data within the game; allowing users to purchase artificial database virtual products generated at least in part from the animal data; creating bet types or products based on the artificial animal data utilized within the video game). In yet another refinement, simulated data may be created for a virtual event or simulation game (e.g., a video game) based at least in part on the animal data, thereby creating new value or assets for a betting system and / or probability evaluation system. For example, in the above scenario, a user may wish to know the probability of horse Z winning a race in a simulated event when horse Z's simulated respiration rate exceeds index X, and how frequently this occurs in any given simulated race.The system may generate the simulated data by utilizing various data including at least a portion of the animal data (e.g., horse Z's respiration rate collected from one or more source sensors for each available race, other horses' respiration rates from one or more source sensors if available, simulated respiration rate data generated from one or more simulated races, other factors collected in the real world that may be used as inputs for the simulated races, i.e., environmental conditions such as weather or temperature, injuries, biological fluid data, and the like). Based on this information, a user may place a bet on the virtual horse race. In yet another refinement, the simulated data incorporating at least one type of animal data may be utilized to create or adjust betting lines (e.g., adjust odds) with greater accuracy. For example, if a line has been set for Player A vs. Player B for a particular match, the computing subsystem may run one or more simulations using similar match conditions (e.g., temperature on the court, past win / loss record) and using inputs incorporating at least one animal data input for the players (e.g., all of Player A's animal data vs. Player B's animal data), allowing the system to determine the probability of the outcome with greater accuracy. This probability may then be used to create or adjust one or more odds, which may be done in real-time or near real-time.

[0078] In an improvement, one or more artificial data sets may be generated randomly or otherwise according to one or more parameters set by the user. This may be useful, for example, when the user is unable to obtain or capture or create the real data desired. When the user has a requirement for which obtaining real data may be infeasible (e.g., the requested data cannot be obtained within the requested time frame, the price associated with the real data set is too high, the use case required by the acquirer requires one or more data sets not found in the system or that cannot be obtained), the inference system 10 may generate artificial data that fits one or more parameters established by the user and may make it available for the creation, refinement, enhancement, acquisition, distribution, and / or consumption of products. The new one or more artificial data sets may be created by application of one or more artificial intelligence techniques that may analyze one or more previously acquired data sets that fit at least one of the characteristics required by the acquirer. One or more artificial intelligence techniques (e.g., machine learning based engine, one or more neural networks) recognize patterns in one or more real data sets and create artificial data that matches or conforms to the minimum requirements of a user (e.g., a gambling entity, a bettor, an organization that assesses odds or creates strategies to mitigate or prevent risk, a platform that provides recommendations or information to take action). The one or more data sets can be created based on a single individual, based on a group of one or more individuals with one or more similar characteristics, based on a random selection of one or more individuals within a group with one or more characteristics defined, based on a random selection of one or more characteristics within a group with one or more individuals defined, based on a defined selection of one or more individuals within a group with one or more characteristics defined, or based on a defined selection of one or more characteristics within a group with one or more individuals defined. In a refinement, the group can include multiple groups.Based on user requirements, the inference system may have the ability to ensure reproducibility by isolating a single variable or multiple variables in creating one or more artificial data sets in order to keep the data both relevant and random.

[0079] Another method for creating an artificial dataset includes augmenting a dataset consisting of a previously collected real dataset with simulated data. For example, a system may have access to a specified amount (e.g., 10 hours, 100 hours, 1000 hours or more) of play / match data for an athlete A, including different types of data and metadata (e.g., in the context of a sport such as tennis, court temperature, humidity, heart rate, distance traveled, swing speed, energy level, respiratory rate, muscle activity, hydration level, data from biological fluids, power of shots, length of points, positioning on the court, opponents, opponents' performance under specific environmental conditions, win percentage against opponents, win percentage against opponents under similar environmental conditions, current match statistics, past match statistics based on performance trends in the match, dates, timestamps, wins, etc.). The dataset may be expanded by using one or more artificial intelligence techniques to recreate at least a portion of an event (e.g., a match) that may not have been played by a given athlete, and / or to generate synthetic data for athlete A within the recreated event (e.g., athlete A plays a 2-hour tennis match, and heart rate data has been obtained for that match, but the user wants heart rate data for a match that has not yet been played but will be played in the future, at the 3rd hour mark. Thus, the computing subsystem may create the data by running one or more simulations). More specifically, one or more of these datasets may be used to train one or more neural networks to understand athlete A's biological function and how one or more variables affect any given biological function. The neural network may be further trained to understand what outcomes occur based on one or more biological functions and based on the influence of one or more variables, thereby enabling correlation and causal analysis.For example, one or more biological functions for athlete A in any given scenario including the current scenario, one or more variables that may affect one or more biological functions for athlete A in any given scenario including the current scenario, one or more results that have occurred previously in any given scenario including the current scenario based on one or more biological functions exhibited by athlete A and / or based on one or more current variables, one or more biological functions for athletes similar and dissimilar to athlete A in any given scenario including scenarios similar to the current scenario, one or more other variables that may affect one or more biological functions for athlete A in any given scenario including scenarios similar to the current scenario, one or more variables that may affect one or more biological functions for other athletes similar and dissimilar to athlete A in any given scenario including scenarios similar to the current scenario, one or more variables that may affect one or more biological functions for other athletes similar and dissimilar to athlete A in any given scenario including scenarios similar to the current scenario, When the data acquirer is trained to understand information such as one or more results that have occurred previously in any given scenario, including a scenario similar to the current scenario, based on one or more biological functions and / or one or more variables exhibited by a non-athlete, the data acquirer may request to run one or more simulations to extend the current collected data set with artificially generated data (e.g., athlete A has just played for two hours, and various biological data, including location-based data, have been acquired. The acquirer wants location-based data at the end of the third hour under the same match conditions, so the system may create data based on the previously collected data by running one or more simulations), or may predict the outcome that will occur for any given level of activity via a predictive indicator (e.g., based on looking only at athlete A's data, the system may predict the likelihood that athlete A will win a match against athlete B in the final set).In a variant, one or more neural networks may be trained using multiple animals (e.g., athletes) in a team or group or competing against each other, and one or more neural networks may be trained using one or more data sets from each animal to more accurately generate predictive indicators predicting one or more outcomes (e.g., whether athlete A will win a match against athlete B). In this example, one or more simulations may be run to first generate artificial sensor data based on actual sensor data for each athlete, and then at least a portion of the generated artificial sensor data may be utilized in one or more further simulations to determine the likelihood of any given outcome and / or to make predictions.

[0080] In another example, a sports team may wish to determine the appropriate time to take a player off the court during a game, or a hospital may wish to determine whether to allow a surgeon to continue operating after working for a certain number of hours in a day or week. By running one or more simulations utilizing at least a portion of real animal data, the sports team or hospital may generate one or more artificial data sets, based on which one or more predictive indicators may be derived, and the sports team or hospital may decide whether to take an action (e.g., for a sports team, considering whether to allow an athlete to stay on the court while he is still performing well, or to take him out of the game, knowing that fatigue may lead to injury or poor performance at a future suboptimal time, or for a hospital, the gamble is whether to allow a surgeon to continue operating after working continuously for a certain number of hours or while he is exhibiting certain characteristics, considering that one risk is a human life and one benefit is an experience that may lead to saving more lives). In yet another example, a remote patient monitoring or telemedicine platform may wish to provide both a medical professional (e.g., doctor) and a patient with the likelihood that the patient will experience any medical condition (e.g., flu, heart attack, diabetes, stroke) in the future based on one or more real-time vitals (e.g., heart rate, ECG, blood pressure, fatigue, stress, sleep data) provided to the application via one or more source sensors, and based on other data (nutritional status, age, weight, height, medical history, biofluid-based data history, genetic / genomic history, prescription history). By running one or more simulations utilizing at least a portion of the actual animal data, a medical professional or other administrator (e.g., a predictive system) can generate one or more artificial data sets based on which one or more predictive indicators can be derived.The predictive indicator may, for example, provide the medical personnel and the patient with the likelihood of an occurrence (e.g., % chance that the patient may suffer a stroke within the next 6 months based on an analysis of the animal data and other data, and % chance that the patient will experience symptom p within the next 30 days if they do not take steps x, y, z based on one or more characteristics of the patient), and recommended actions to mitigate the risk (e.g., reduce stress by walking n minutes per day and continue to eat certain foods to keep blood pressure low). In an improvement, the inference system may be programmed to provide one or more alerts based on one or more measurements related to the predictive indicator, the computing assets, the animal data, and / or one or more derivatives thereof. For example, an alert may be based on the subject achieving a maximum heart rate, reaching a predefined "energy level" that requires an alert, or the system detecting an irregularity in the ECG data. In this example, detection of such anomalies from the subject may be performed utilizing past ECG information collected from the subject by the system, and utilizing one or more objects that share one or more characteristics with the subject (e.g., age, weight, height, medical condition, and the like). The system may be operable to detect anomalies in any given subject by using the past data as a baseline, and further to provide insights related to any changes in morphology, or any other relevant findings. In another refinement, a medical professional or the system may take one or more actions based on the predictive indicators.For example, the system may be operable to generate and provide a predictive indicator that indicates a particular patient with diabetes and a wearable sensor (e.g., insulin pump) is relatively likely to experience a severe bodily response (e.g., going into a coma) based on a biological fluid measurement (e.g., low blood glucose level), and if the data is communicated at least in part to the system by the sensor, or if insulin is not administered and a severe bodily response will be experienced within the next n hours, to communicate directly with the insulin pump and provide one or more commands to administer insulin to the body via the insulin pump, or to generate a nutrition plan based on the predictive indicator to adjust blood glucose levels. Additional details regarding biological data tracking systems utilizing web browser-based applications are disclosed in U.S. Patent Application No. 16 / 274,701, filed February 13, 2019, and International Application No. PCT / US20 / 18063, filed February 13, 2020, the entire disclosures of which are incorporated herein by reference.

[0081] Another method for creating a simulated data set involves rerunning previously acquired data through one or more simulations to create one or more new data sets. For example, if the computing system uses a statistical model or a neural network such as a long short-term memory (LSTM) and the user desires to create an artificial data set for athlete A using at least a portion of athlete A's heart rate data (e.g., characteristics of athlete A's heart rate data for various scenarios or heart rate per minute) to be incorporated into a simulation (e.g., a simulation game such as a video game), the inference subsystem can be trained using athlete A's actual heart rate data to generate an artificial data set based on at least a portion of athlete A's actual heart rate data that can be incorporated into the simulation game. The simulation game can feature, for example, one or more biological metrics (e.g., heart rate, foot speed, swing speed) for athlete A that are derived from at least a portion of athlete A's actual animal data and / or one or more derivatives thereof. Simulated data for the virtual subject (e.g., simulated biological metrics) may be based on historical data collected from real-world subjects, or may be displayed in real-time or near real-time for the virtual subject based on real-world or near real-time data provided by the real-world subject to the inference system and converted by the inference system into a new artificial data set. In a variation, one or more simulated established metrics may be transformed such that they are applicable to the simulation, although they may differ from the real-world data (e.g., heart rate data may be converted to a color in a game, or virtual currency may be associated with maintaining or exceeding a heart rate within a particular zone).Advantageously, predictive indicators can be made available to the user within the simulation game (e.g., an "energy level" bar can indicate when a virtual object is tired, predicting when an object in the game will run out of energy, or predicting when a user needs to take an action based on the likelihood that an event will occur). In an improvement, predictive indicators can be made available for purchase within the game (or associated with a price). In some cases, predictive indicators may need to be adjusted or modified to fit one or more parameters of the simulation and to integrate data within the simulation. This method can also be used to examine probabilities for various outcomes. For example, the probability of a particular outcome can be determined using existing data (e.g., previously collected or acquired) about athlete A and the system's ability to create one or more new data sets by running one or more simulations. In an improvement, outcomes can be predicted or probabilities can be created by running one or more simulations while incorporating forward-looking data (e.g., artificially generated data) within another simulation or simulations. For example, future heart rate data or biofluid-based data for a person in a given scenario may be generated based on the collected data, and the artificial heart rate data or biofluid-based data is utilized in one or more further simulations to predict another future outcome (e.g., an outcome will or will not occur based on the future heart rate data and / or one or more biofluid-based measurements).

[0082] In a variation for creating one or more simulated data sets, existing data with one or more randomized variables are re-run through one or more simulations, thereby creating a new data set not previously seen by the system. Using this method, and using the example inputs described above, one or more predictive indicators can be generated. For example, if a prediction system has a data set for a particular individual (e.g., an athlete) and a particular event (e.g., a match played by the athlete), the prediction system may have the ability to recreate and / or modify one or more variables in the data set (e.g., altitude, on-court temperature, humidity) and re-run the one or more events through one or more simulations, thereby generating a simulated data output of interest. For example, in the context of tennis, an acquirer may request heart rate data for Player A at the one-hour mark when the temperature is above 95 degrees throughout the entire two-hour match. The system may have one or more heart rate data sets at different temperatures (e.g., 85, 91, 94), the inputs described above for Player A in similar conditions, and other similar and non-similar players in similar and non-similar conditions. Since no heart rate data has yet been collected for Player A above 95 degrees, the system may generate the data by running one or more simulations, which may then be used in one or more further simulations. In another example, the acquirer may request a predictive index that provides the likelihood of Player A winning a match. In an improvement, the system may also be programmed to create or recreate one or more new data sets by combining non-similar data sets. For example, a user may request one hour of heart rate data for Player A when temperatures are above 95 degrees throughout a two-hour match in a particular tournament, where one or more characteristics, such as altitude, may affect performance.Although this data has not yet been collected in its entirety, different datasets may contain the requested data (e.g., one or more datasets from Player A characterizing heart rate, one or more datasets from Player A characterizing playing tennis in temperatures above 95 degrees, one or more datasets with requested characteristics such as altitude at the requested tournament). The system may identify these requested parameters within and between datasets, and may create one or more new artificial datasets (which may be, for example, predictive indicators, computing assets, or artificial animal data) that meet the user's requirements based on these non-similar datasets by running one or more simulations. In an alternative embodiment, the non-similar datasets used to create or recreate the one or more new datasets may feature one or more different objects that share at least one common characteristic with the subject of interest (which may include, for example, age range, weight range, height range, gender, similar or dissimilar biological characteristics, and the like). Using the above example, heart rate data may be utilized for player A, but the system may utilize another data set or data sets from players b, c, d selected based on their relevance to the desired data set (e.g., some or all of the players may exhibit similar heart rate patterns as player A, some or all of the players may have similar biofluid derived measurements as player A, some or all of the players may have data sets characterized as playing tennis in temperatures above 95 degrees among the data sets collected by the system). These one or more data sets may serve as inputs into one or more simulations to more accurately predict player A's heart rate under the desired conditions.

[0083] Yet another method of simulated data is to create an artificial data set of general nature (e.g., lacking shared biological parameters). In a variant, one or more randomized data sets are created, with one or more variables selected by the system rather than by the acquirer. This may be particularly useful, for example, when an insurance company is looking for a specific data set (e.g., 1,000,000 smokers) among a random sample (e.g., age and medical history are unspecified and can be randomly selected by the system), or when a betting company is trying to create one or more new markets (e.g., prop bets) for an event that did not exist (e.g., prop bets on simulated outcomes of video games). In an improvement, one or more artificial data sets are created based on a predetermined number of individuals selected by a given user of the system. In another improvement, one or more artificial data sets are created from a predetermined number of individuals randomly selected by the inference system.

[0084] Additional details related to systems for generating simulated animal data and models are disclosed in U.S. Patent Application No. 62 / 897,064, filed September 6, 2019, the entire disclosure of which is incorporated herein by reference. The present invention is not limited to the type of statistical model or models employed or the type of artificial intelligence techniques (e.g., machine learning models, deep learning techniques) employed. As the present invention is not limited by any particular application for using simulated data, such data can be used as a baseline or input for testing and / or modifying and / or revising one or more sensors, algorithms, outputs, and / or hypotheses. Moreover, data generated from one or more simulations can be used for a wide range of use cases, including as a control set to identify issues / patterns in the real data, or as input to further simulations, or as input to an artificial intelligence or machine learning model in the role of a test set or training set or set with identifiable patterns, etc. Using this artificial data, simulation scenarios can be performed, and use cases can range from training to performance improvement and the like. For example, an artificial data set created based on actual animal data of a particular athlete can be modified using a guessing system to introduce one or more deviations into the data corresponding to characteristics such as fatigue or rapid heart rate changes. Using this modified data, one or more simulations can be performed to see how an individual (e.g., an athlete, a soldier, a patient) would perform under stressful circumstances or under certain environmental conditions (e.g., high altitude, high temperature on the court), for example. This can be particularly useful in fitness applications, insurance applications, and the like.In the case of humans (e.g., athletes) or other animals, as the system establishes patterns between biological metrics (e.g., heart rate, respiration, positional data, biomechanical data) and the likelihood of something happening (e.g., winning a particular match, maintaining biological function at a particular or specified level), the inference system can calculate one or more probabilities for certain conditional scenarios (e.g., "what if" scenarios and likely outcomes). As an example, a system for creating artificial data can be operable to run multiple simulations in real time or near real time for any given event (e.g., a tennis match) that may be taking place live at any given time, using n data inputs in one or more simulations. Based on the results of those simulations, the system can assign probabilities to a given outcome occurring. For example, if the desired analysis is "will player A's HR reach 200 in the current match?", the system can create a probability of this outcome occurring by running one or more simulations that may include any number of scenarios (e.g., player B wins the first set, player A begins to feel stress, fatigue, and muscle stiffness in a particular part of his body, and the temperature and humidity rise by n degrees during the match, affecting player A). Such n simulation scenarios are possible, and further simulation scenarios may be created on the fly (i.e., dynamically) via the ML / AI engine of the inference system, for example, based on past similar matches. After the simulations are run and the outputs are collected and analyzed, the system may be configured to provide one or more probabilities associated with the outcome under consideration. In a variant, more than one simulation may be run during the course of any event, resulting in different outputs (e.g., probabilities) based on the change in one or more inputs or factors (e.g., time).For example, a system that runs one or more simulations to provide Player A with an n% probability of winning a match may run one or more future simulations at future times (e.g., 10 seconds after the first simulation, 5 minutes after the first simulation, 1 hour after the first simulation, etc.) which may provide different probabilities (e.g., a simulation run 30 seconds after the first simulation using "score" and "stress" as part of one or more inputs may revise Player A's probability of winning the match to 52% because Player A has lost a match in that time period and has a higher than normal stress level, and because this has been shown to cause poor performance in previous matches).

[0085] In another embodiment, the inference system may be utilized as a tool to test, establish, and / or verify the accuracy, consistency, and / or reliability of a sensor or connected device. Multiple sensors each producing a similarly labeled output (e.g., heart rate) may use different components (e.g., hardware, algorithms) to derive their output (e.g., heart rate sensors from different manufacturers, or different heart rate sensors from the same manufacturer utilizing different data collection methods or algorithms to produce the "same" output). This means, for example, that an output such as heart rate from one device may not be the same as compared to heart rate from another device. The inference system's ability to bypass native applications, aggregate data, and perform processing on the data, including normalizing and / or synchronizing the data, allows the user to perform a relatively "apples to apples" comparison, if desired, comparing the output of each sensor with the corresponding hardware / firmware and algorithm(s) that derive each output (e.g., raw data, processed data), while providing context for the data (e.g., the activity at which the data was collected), and eliminating other variables (e.g., transmission-related, software-related) that may affect the output. Testing and comparing each sensor or connected device's hardware or algorithm(s) or output unbiasedly (e.g., against specified criteria) ensures quantifiable results that are decoupled from the particular component being evaluated. The ability to obtain quantified results for each sensor type and its corresponding component allows the user to select a particular sensor and / or algorithm for all participants in a given group based on any given requirement or use case (e.g., gambling, evaluating or calculating probabilities, creating or acquiring a product, or risk mitigation use cases). For example, one sensor manufacturer may offer sensors that are more suitable for use with a particular use case compared to other sensor manufacturers.The ability to select components (e.g., sensor type, corresponding algorithms) eliminates key sensor-related variables that are typically found when using different or sub-components (e.g., different sensors that obtain the "same" output, or different algorithms). This methodology also ensures the ability to isolate one or more other variables (e.g., differences between one or more individual subjects). This methodology ensures the reliability of the data by users, and provides organizations creating market or gambling strategies, probability assessments, product, recommendations, or strategies for risk mitigation or prevention with a quantifiable method to select the sensor or sensors that fit their requirements.

[0086] FIG. 3 is a basic high-level overview of a guessing system applied to events including sports betting. The sports guessing system 60 is an example of the system described in the above description in connection with FIG. 1 and FIG. 2. The sports guessing system 60 includes one or more source sensors 62 that collect animal data from one or more target individuals participating in a sporting activity at a sports venue 64. Also, although a tennis venue is illustrated in FIG. 3, the design is applicable to any venue, including any sports venue, and to any number of industries. Examples of such sports venues include, but are not limited to, baseball venues, football venues, basketball venues, soccer venues, ice hockey venues, track and field venues, open racing venues or open race courses including cycling or triathlons or auto racing, volleyball venues, horse racing venues, dog racing venues, and the like. In an improvement, the venue 64 can be a non-sporting venue, including a gym, a home, a fitness studio, a manufacturing plant, a hospital, a construction site, and the like. As mentioned above, the animal data can be transmitted electronically via a wireless or wired connection. The transmission subsystem 76 transmits the animal data to the computing subsystem 66. As described above, the transmission subsystem 76 and / or one or more components thereof may be part of the computing subsystem 66, may be external to the computing subsystem, or may be integrated with one or more source sensors. The computing subsystem 66 receives the animal data. As described above, at least a portion of the animal data is transformed by the computing subsystem 66 and / or the one or more source sensors 62 into at least one computing asset assigned to a selected individual of interest or to a group of selected individuals of interest. The computing subsystem 66 is operable to transform the at least one computing asset into a predictive indicator.Transformation of the animal data into either computational assets or predictive indicators can occur via the analytical feature 84. The analytical feature 84 can be one or more analytical subsystems, tools, and the like, that are part of the computing subsystem 66, or that are separate from the computing subsystem and operated by an entity operating the inference system, or that are separate from the computing subsystem and operated by an entity (e.g., a third party) different from the entity operating the inference system, or that analyze the data and provide at least a portion of the data and / or one or more derivatives thereof back to the computing subsystem (e.g., a third party). The analytical feature 84 may transform such data by utilizing one or more statistical models and / or artificial intelligence techniques. In a refinement, the analytical feature 84 may be operable to create or modify or enhance one or more products from at least a portion of the output information and further provide the one or more products to one or more users. In another refinement, the analytical feature 84 may be operable to provide at least a portion of the output information to one or more users.

[0087] Still referring to FIG. 3 , the computing subsystem 66 is operable to provide (e.g., present, distribute, make available, and the like) the predictive indicators, at least one computing asset, at least a portion of the animal data, one or more derivatives of the animal data, and / or combinations thereof (collectively referred to as “output information”) to one or more users via the data distribution feature 72. The computing subsystem 66 is operable to provide the data to one or more persons, including those directly involved in the event, to one or more individuals, or to one or more systems (e.g., platforms, applications). In a sports context, this can include coaches and medical personnel 78, as well as analysts, administrators, players, trainers, nutritionists, and other relevant persons. Advantageously, the output information can be provided in real time or near real time. In an improvement, output information from the computing subsystem 66 may be used to develop a betting evaluation product or probability evaluation product, or such output information may be provided to another entity to develop a betting development product or probability evaluation product, as illustrated by product feature 86. Products may include probability-based products, risk mitigation products, animal monitoring applications (e.g., human performance monitoring applications), recommendation products, betting incentive products, betting information products, new betting types, and the like. Product feature 86 may be one or more product subsystems, tools, and the like, which may be part of the computing subsystem 66, or may be separate from the computing subsystem and operated by the entity operating the guessing system, or may be separate from the computing subsystem and operated by a different entity (e.g., a third party) than the entity operating the guessing system, or may be operated by an entity (e.g., a third party) that creates one or more products or systems (e.g., applications) and provides the one or more products back to the computing subsystem for distribution.In another refinement, one or more product subsystems may be operable to provide one or more products and / or at least a portion of the output information to one or more users. Finally, Fig. 3 illustrates a revenue adjustment feature 90 that may distribute compensation to one or more stakeholders for contributions in the creation, collection, modification, enhancement, analysis, provision, distribution, and / or production of the animal data or for contributions in the operation of the inference system or any component thereof. In a refinement, the transmission subsystem 76, analysis feature 84, product feature 86, distribution feature 72, distribution endpoint 78, revenue adjustment feature 90, or any combination thereof, may be part of the computing subsystem 66.

[0088] As shown in FIG. 3, the computing system 66 executes the inference program. When executed, the inference program is defined by an integration layer, a transmission layer, and a data management layer. With respect to the integration layer, a user or administrator of one or more sensors can enable the system to collect information from one or more sensors in one of two ways: (1) the system communicates directly with the sensor, thereby bypassing the native system associated with the sensor, or (2) the system collects data into the system's database by communicating via an API or other mechanism with a cloud or native system associated with the sensor, or with other systems that store sensor data. Direct communication with the sensor is achieved by creating new code to communicate with the sensor, or by the sensor manufacturer writing code to function in the system. The system may create a standard for communication with the system that multiple sensor manufacturers can follow. Communication between the system and the sensor may be two-way communication, where the system may receive data and transmit one or more commands to the sensor. For example, the system may transmit one or more commands to one or more sensors to change one or more functions of the sensor (e.g., change gain or power mode or sampling rate, start / stop streaming, update firmware). In some cases, the sensor may have multiple sensors within the device (e.g., accelerometer, gyroscope, ECG, etc.) that may be controlled by the system. This includes turning one or more sensors on or off, and increasing or decreasing sampling frequency or sensitivity gain. Advantageously, the system's ability to directly communicate with one or more sensors also allows for real-time or near real-time collection of sensor data from the sensor to the system. The system may have the ability to control any number of sensors, any number of functions, and may have the ability to stream any number of sensors on any number of individuals of interest via a single system.

[0089] With regard to the transmission layer, a by-product of the system communicating directly with the sensors is that the system is operable to stretch the sensor's transmission signal for real-time or near real-time communication, thereby increasing the communication distance between the sensor and the system, amplifying the receiving connection, and extending the range limit of the sensor or sensors and transmission protocol or protocols. This can be accomplished by utilizing a transmission system that allows the system to communicate with and utilize any low-power or standard transmission hardware present in the sensor itself (e.g., Bluetooth, BLE, Zigbee, WIFI, cellular, Ant+, and the like). Another by-product of the system communicating directly with the sensors is that a single transmission system can synchronize real-time or near real-time streaming communication for multiple sensors that are in direct communication with the system, and act on the data itself to transmit the data somewhere or store the data for later use. This can be done for a single individual or multiple individuals. The transmission system can be configured in a variety of ways, can have a variety of form factors, can be located in a variety of locations, can use one or more transmission / communication protocols or networks (e.g., Bluetooth, ZigBee, WIFI, cellular networks, and the like), can be utilized in a variety of environments, and can have functionality in addition to simply transmitting data from the sensors to the system (e.g., summarizing or synthesizing or analyzing the data based on the requirements of the use case). Advantageously, the system communicates directly with the sensors via the transmission system, and allows for real-time or near real-time streaming, especially in harsh environments where potential interference from other communications or radio frequencies may be an issue.

[0090] With respect to the data management layer, the data management layer manages all data (including its derivatives), its properties, its relevance (e.g., to whom / what the data is related to), and functions associated with the data (e.g., normalization, synchronization, distribution, etc.). The sensor data input to the system is in either raw data (no manipulation of the data) or processed data (manipulated) structure. The system may contain one or more algorithms or other logic that deploy data noise filtering, data recovery techniques, and / or extraction or prediction techniques, which may extract relevant "good" sensor data from among all collected sensor data (both "good" and "bad"), or may create artificial "good" values ​​if at least some of the sensor data is "bad". The system may be programmed to simultaneously communicate with one or more sensors on either one object or multiple objects, and may have the ability to de-duplicate those sensors to transmit enough information for the recipient to reconstruct the origin of the data and the wearer of the sensor. For the purpose of clarity, this means providing metadata to the system receiving the data to identify the characteristics of the data, e.g., a given data set belongs to timestamp A, sensor B, and object C. In addition, the system may have the ability to associate one or more sensors to one or more users. After being received by the computing subsystem, the sensor data is either transmitted to the system cloud or left locally on the system's server, depending on the required request. The sensor data introduced into the system is tagged by the system with information related to the user (e.g., metadata) or information related to the sensor characteristics, including timestamp, sensor type, and sensor settings, along with one or more other characteristics in the system. For example, the sensor data may be assigned to a particular user.The sensor data may also be assigned to a particular event in which a user is participating (e.g., a person is playing basketball in game X of league Y in season Z) or to a general class of activity that a data acquirer may be interested in acquiring (e.g., group cycling data). The system may synchronize one or more timestamps with other data sources (e.g., a timestamp associated with the game clock of the official time of a basketball game, a timestamp associated with the score, etc.). The system may be schema-less and may be designed to ingest any type of data, classifying the data by one or more characteristics including data type (e.g., ECG, EMG) and data structure. The system may perform one or more further transformations on the sensor data as it is introduced into the system, including normalization, timestamping, aggregation, storage, manipulation, denoising, enhancement, reduction, analysis, anonymization, synthesis, replication, summary, production, and / or synchronization. This ensures consistency across separate data sets. These processes may be performed in real-time, near real-time, or non-real-time depending on the use case and user requirements. Given the large influx of data streaming or provided from one or more sensors, which may be substantial, the system may also utilize a data management process that may include a hybrid approach of unstructured and structured data schemas and formats. Additionally, synchronization of all incoming data may use a specific schema suitable for real-time or near real-time data transfer, reducing latency and providing error checking, as well as providing a security layer with the ability to encrypt portions of or entire data packets.The system monitors, receives, and logs all requests for sensor data by communicating directly with other systems, and provides organizations seeking access to sensor data with the ability to make one or more specific requests for the data required for a use case. For example, a request may be to obtain a specific individual's heart rate in real time, once per second, over a 10 minute period. The system may also associate these requests with one or more user groups / classes.

[0091] Another aspect of an effective inference system is the use of animal data in commerce, including one or more promotions (e.g., advertisements, engagements) related to one or more animal data-derived products or services created and / or provided (e.g., offered, distributed, made available, and the like) by the system or one or more third parties engaged to one or more users. For example, the animal data may be used directly or indirectly within a promotion on a web page or other digital platform to encourage a user to click through to a web page (e.g., a third party's web page) or other digital destination that directly or indirectly utilizes the animal data. In the case of web services, one way to accomplish this is to use an inline frame (Iframe), which can be an HTML document embedded within another HTML document on a website. An Iframe can be used to insert content from another source, such as an advertisement or engagement (e.g., gambling opportunities, informative literature), into a web page. In some cases, an Iframe or widget is utilized to increase the user's time on a web page or other digital destination that features display ads that refresh at a specified time period (e.g., every 15 seconds) and target the user to click through to another destination, possibly a third party site, to offer (e.g., sell) a service, product, or benefit to the user in exchange for compensation. In addition, increased time on the page typically increases the user's engagement, which can lead to repeat visits to the site and increased click-throughs. There are other ways to provide services within a third party widget (e.g., JavaScript), and the invention is not limited by these other methodologies used. FIG. 4 provides an example of an advertisement that may be displayed within an Iframe. In addition to FIG. 4, FIGS. 5A-5G provide examples of certain types of promotions for animal data obtained by the inference system described above.It should be noted that the present invention is not limited to the type of display device utilized to display the promotions, and may include one or more monitors, mobile devices, smart watches, or within smart glasses or eyewear on which the promotions may be visualized. Although these particular examples of promotions are rendered within a web page to communicate in a visual format, it should be noted that the promotions may be communicated in other manners, including in audio or voice format (e.g., verbal communication regarding an advertisement). In another embodiment, other digital platforms that utilize at least a portion of the animal data for advertising purposes or for user engagement (e.g., gambling) purposes include virtual reality and augmented reality systems.

[0092] In the case of a user (e.g., fan) engagement system, such as an augmented reality system or virtual reality system, the guessing system can provide a media audience (e.g., a person watching a live event, such as a sporting event) with the ability to understand and interact with the animal data. This may be part of an in-venue or at-home experience, for example. The animal data can be utilized within the fan engagement system to place one or more bets (e.g., a user can see the energy level of a sports player within the fan engagement system as the player plays in a match and the user places a bet on the match via the fan engagement system at any given time during the match, such as when the player appears tired), or can be utilized within the fan engagement system to enable one or more bets to be placed outside of the fan engagement system (e.g., a user sees the energy level of a player through the augmented reality system and the user places a bet on the match on the user's mobile device), or can provide information to inspire a bettor (e.g., a user sees a player's predictive metrics through the augmented reality system and decides to place a bet). The animal data may also have other visuals (e.g., sponsorship branding) associated with it within the fan engagement system to further leverage the value of the animal data within the fan engagement system.

[0093] For a guessing system to provide animal data for a fan engagement system such as an augmented reality system for gambling, the system may first use recognition and tracking of objects around a specified area (e.g., in a sports context, around the playing field, including stadiums and fields with known boundaries and fixed objects). The system may then create a list of known identified scenes and tracking information and may have the ability to update this information as needed. The system may acquire known imagery datasets available to help fill in gaps in this list. Using sports as an example (although not limited to sports), the AR system may use 3D tracking for players and ancillary objects (e.g., tracking the movement of the ball). Augmented objects may be placed such that the visualization is relevant to the play based on the position of the players relative to the playing field and relative to other players. Additional data from sensors such as location-based data (GPS), orientation sensors, accelerometers, etc. may be used to fine-tune the placement of players, and other data points such as altitude and latitude may also be incorporated into the calculation of the 3D model. The system may also search for features in the environment around the fixed known objects and attempt to recognize and replace the associated virtual objects in the overlay display by tracking their changes relative to some fixed points. The system optimizes the data transmitted to the computing device (e.g., a mobile device) to perform real-time or near real-time rendering. The system renders complex data sets and performs all 3D calculations by using system resources via a terrestrial, airborne, or cloud-based system. The augmented objects may include one or more types of animal data (e.g., including simulated data) or one or more derivatives from animal data that provide information related to one or more objects.The augmented reality system may also include a terminal for a user to place bets, evaluate or calculate odds, view predictions or possibilities, and / or mitigate or prevent risks. In an improvement, the augmented reality system may also provide recommendations and / or actions to take. The terminal and / or user's ability to place bets, evaluate or calculate odds, view predictions or possibilities, mitigate risks, and / or perform actions may be controlled via various mechanisms, including but not limited to audio control (e.g., voice control), physical cues (e.g., head movements, eye movements, or hand gestures), neurological cues, controls found in AR hardware, or by a localized device (e.g., a mobile phone).

[0094] In the case of a betting system, the guessing system offers many novel opportunities with respect to providing dynamic betting that may be at least partially supported by one or more statistical models and / or artificial intelligence techniques. One or more new prop bets may be dynamically created based on user interaction with the animal data. For example, in a sports context, if one or more users frequently use (e.g., view the animal data on an application) animal data that features one or more similar characteristics (e.g., the heart rate of player A, or the heart rates of all guards of team B, when heart rate is used as an example of animal data that users frequently use), the system may dynamically create a prop bet and target those one or more specific users (e.g., a bet may be that player A's maximum heart rate will exceed 180 bpm during the match, or that the average heart rate of all guards of team B will be below 150 bpm during the last five minutes of the fourth quarter). Bets and products related to wagering and probability assessments (including risk mitigation / prevention products, recommendation products, etc.) can be created utilizing any type of animal data and / or one or more derivatives thereof. The system may also calculate odds, assign odds, modify odds, enhance odds, or utilize odds created internally or by one or more third parties to accompany these bets. Personalized bets may also be created dynamically based on one or more user interactions with the animal data.For example, if a particular user frequently uses animal data associated with one or more parameters or characteristics (e.g., User A frequently uses historical heart rate data for the second set across all tennis matches), a prop bet may be created by the system or by a third party system interacting with that particular user utilizing at least a portion of that animal data as part of the bet (e.g., a bet may be created that allows a user to bet on whether Player A's heart rate will exceed 180 bpm in the second set of the first match, or whether Player B's heart rate will exceed 186 bpm in the second set of the seventh match). The system may also calculate odds, assign odds, modify odds, enhance odds, or utilize odds created internally or by one or more third parties to accompany one or more of these bets.

[0095] Dynamic pricing may also be implemented based on user interaction with the animal data. For example, if there are one or more users who frequently use animal data that share at least one characteristic (e.g., player A's heart rate, where heart rate is used as an example of animal data that users frequently use), the system may dynamically provide a better pricing model (in terms of products) and / or product for that particular user or users around the supplemental animal data related to the one or more characteristics that the user is interested in. For example, instead of paying $10 for access to player A's winning percentage when his heart rate is over 180 beats per minute, the system may adjust and charge $5.

[0096] "Pop-up" betting may also be offered, including web ad betting or betting within fan engagement applications (e.g., web applications, mobile applications for smart devices, mobile applications for virtual reality systems, mobile applications for augmented reality systems). Figure 6 provides an example of a pop-up that may be displayed or a betting system that may be embedded when a user is streaming a sporting event and the application invites the user to place one or more bets or acquire one or more products. In an improvement, the pop-up may be displayed as an overlay display feature on any given medium (e.g., as a transparent display on any given medium). In-ad gambling, e.g., an IFrame, may feature: (1) at least one odds provided directly or indirectly based on the animal data, (2) at least one bet type or other product provided directly or indirectly based on the animal data, (3) at least one opportunity to place a bet, (4) at least one opportunity to obtain at least a portion of the animal data and / or one or more derivatives thereof, (5) at least one probability or prediction associated with an outcome, (6) at least one recommendation to take an action, (7) at least one risk mitigation or risk prevention strategy, and / or (8) at least one promotion related to the use of the animal data. For example, a user may see an advertisement within a web page about life insurance. An insurance company may create one or more predictive indicators that are utilized to assess the premium for any given life insurance policy for any given object (or group of objects) by asking an individual to upload or provide access to at least a portion of their animal data, thereby allowing the insurance company to provide tailored or adjustable, real-time or near real-time quotes for insurance to the individual based at least in part on the animal data.In another embodiment, shown in FIG. 5G, the one or more bets or engagements may feature a web page or application for any particular object, group of objects, and the like (e.g., in a sports context, a particular athlete, group, team, league, federation, or organization). When clicking on a link (e.g., a news article link), for example, to view more content related to the object or group of objects (e.g., a particular person, team, league), the system may offer one or more bets or products related to that object (e.g., a particular player) or group of objects within the website (e.g., via an IFrame). In an improvement, the act of scrolling the control mouse over the names of one or more objects (e.g., player names, team names) within an article or content displayed via a web page or application, or by touching a particular area of ​​the content on the screen (e.g., player names, team names) with a finger or other gesture, may trigger the system to present one or more bets or products related to the scrolled or touched area (e.g., when the control mouse is hovered over the name of player A, bets on player A are displayed). In a further improvement, the bets or products may also be triggered to be presented by using audio control (e.g., voice activation) or eye movement control or sensing devices. In another embodiment, a "pop-up" bet or product may be manifested within a digital destination (e.g., a web page) targeted to the particular content the user is reading. For example, if a user is on a web page and reading about a particular team, one or more bets related to that team may pop up, the ability to place one or more bets may be made available within the digital destination, or the user may be linked to another digital destination (e.g., another web page) to place a bet.These examples are not limited to sports, but may be applicable to various industries that may utilize predictive indicators, computing assets, at least a portion of the animal data, and / or one or more derivatives thereof, including healthcare, insurance, health, fitness, transportation, and the like. In another refinement, the virtual assistant may inform the user (e.g., text, audio, email) of one or more bets, bet types, products, recommendations, predictions, and / or types of data (e.g., predictive indicators) that may be of interest to the user or that may be available to the user based on one or more parameters set by the user (e.g., the user only wants to know about bets or products related to player D of team A related to his "energy level" in the fourth quarter of a home game), or based on content that the user is interested in and that may be determined by the system or entered by the user. In another refinement, the bets may appear in the virtual reality or augmented reality system together with or integrated with other content. In yet another refinement, the animal data and / or associated bets or products are time sensitive, and the system denies the ability to place a bet (or obtain a product) based on one or more time conditions (e.g., a web page being idle for more than 30 seconds) or places an expiration date on the bet or product. In these scenarios, the animal data may be rendered (e.g., via a display) in either a continuous, intermittent, or static manner. In the case of continuous or intermittent rendering, the content may be updated in real time or near real time.Upon click-through to a third party site, the publisher (e.g., the site or platform featuring the advertisement) and the data provider, which may consist of one or more parties including a sensor company, a platform company, an analytics company, the individual from whom the data was obtained, or the owner of the data, may participate in revenue sharing regarding revenue that may be generated from the click-through and / or user interaction with the data (e.g., a bet, if the digital destination is programmed to allow betting within the advertisement). If the user interacts with the advertisement to the point of purchase, the data provider may participate in further revenue sharing opportunities with the third party site (e.g., a mobile app).

[0097] As mentioned above, the use of prop bets stimulates both betting volume and revenue. Examples include 10-minute markets used by betting companies, such as "3+ corners in the next 10 minutes" or "Will team A score in the next 10 minutes?" These prop bets create new betting products / opportunities that differentiate bookmakers by offering more diverse offerings, thus ensuring customer retention and retention. Animal data can be used as a driver for new and innovative bet types, allowing bookmakers to use the animal data to create prop bets that bettors have never seen before. Some examples based on heart rate (these provide just a few examples of potential bet types that the system may offer using any type of animal data and / or one or more derivatives thereof): Player A's average heart rate in the wth match, beats / min. Player A's maximum heart rate in the wth match, beats / min. The average heart rate, beats per minute (BPM), of player A in the zth game of the wth match is greater than the past average heart rate of player A in the zth game of the wth match. Player A's maximum heart rate (maximum BPM) in game z of match w (e.g., game 4 of the second set) is greater than Player A's previous maximum heart rates in all games z played to date (e.g., all fourth games in all second sets played); Player A's average heart rate (BPM) in game x will be greater than Player A's historical average heart rate in all of his past games. Player A's maximum heart rate (max BPM) in the xth match will be greater than Player A's previous maximum heart rates in all previous matches. Athlete A's maximum heart rate (max BPM) is greater than Athlete B's for the first n points of the xth set. Player A's average "efficiency" in the xth game (e.g., a calculation that may be established by looking at how close Player A's heart rate is at any given time compared to Player A's maximum heart rate; one way to calculate this calculation may be to divide the average heart rate by the established maximum heart rate) is greater than Player B's average "efficiency" in the xth game; The maximum "efficiency" of player A in the yth set (a calculation which may be established, for example, by dividing the maximum heart rate by an established maximum heart rate) is greater than the maximum "efficiency" of player B in the yth set; The average "efficiency" of Player A in game t (a calculation that may be established, for example, by dividing the average heart rate by an established maximum heart rate) is greater than the average "efficiency" of Player B in game t; The maximum "efficiency" of player A in game t (a calculation that may be established, for example, by dividing the maximum heart rate by an established maximum heart rate) is greater than the maximum "efficiency" of player B in game t; Player A has a faster recovery rate (e.g., a calculation that can be established by comparing the efficiency of the players) between game x and game y than player B; Player A's resting heart rate (e.g., a calculation that may be established by obtaining the BPM at rest) does not fall below a predetermined BPM between the xth game and the yth game; may include.

[0098] 7-10 illustrate the functionality of the guessing system of FIG. 1 that may be deployed within a web page or within a window for a dedicated program or other application (e.g., a smart device app). The term "window" is used to refer to a web page and / or to a window for a program or computing device (e.g., a smart device, smartphone, tablet, virtual reality headset, augmented reality headset, etc.) application. FIG. 7 is an example of a home page for a wagering application. The wagering application may be owned / operated by an entity that operates the guessing system (the "guessing operator"), or may be owned / operated by a third party that receives output information from the guessing system. The home page window 100 includes an event selection section 102 that allows a user to select an event (e.g., a sport) on which to wager. The event selection section 104 lists a subset of one or more bets that may be made (featuring "best real-time odds" in this example). It should be noted that section 104 presents only one example of potential bets that the system may offer. The current best odds section 106 lists the best original odds offered by any third party betting system ("betting entity") accepting bets on the outcome of a particular event (e.g., including prop bets). The odds section 106 may be updated (e.g., refreshed) as new odds information is entered into the system. In a refinement, the betting entity is the operator of the guessing system. In another refinement, the betting entity is part of the guessing system. In this example of FIG. 7, the best odds offered on the third party site for "James Smith to win" (current best odds 106) are 3 / 1, so this particular bet, which the system determined to be a better odd with a high indicator of success, is highlighted as the bet that had the "best real-time odds."The "human data indicators" section 108, which may be derived from any animal, but is not limited to humans, provides predictive indicators derived from animal data as described above (e.g., data from one or more individuals, or data from one or more groups of individuals, such as a team). The "human data indicators" section 108 is meant to represent one way to convey predictive insights that may provide a bettor with information that may improve their understanding of the probability of an outcome occurring and the likelihood of successfully winning any given bet, and / or provide the bettor with confidence to place one or more bets. In the example shown in FIG. 7, the "human data indicators" box 108 represents the percentage of likely outcomes occurring (e.g., a 74% chance of winning the bet based on the current animal data, which may include both real-time and non-real-time data). This may be derived from any type of animal data and / or one or more derivatives thereof (e.g., heart rate, biofluid data, location-based data such as how far and where a player moves for each point, outcome data (e.g., points won or lost), historical data, and the like). Advantageously, the human data indicator 108 may be updated in real-time or near real-time as new data is collected and analyzed. In FIG. 7, the predictive indicator is the % probability of winning a bet, with a higher number indicating a greater likelihood of a more favorable gambling outcome in section 104 occurring.

[0099] Still referring to FIG. 7, given that the estimation system may be programmed to generate one or more outcome probabilities or predictions, the estimation system may be operable to utilize at least a portion of the output information for any given outcome to derive real-time or near real-time odds. The "new counter wager" section 110 provides one or more revised odds that the estimation system is willing to offer to a bettor based on use of the output information. In one example, a "new counter wager" refers to odds offered for a bet / market that is not supported by a predictive indicator (e.g., human data indicator) to win the bet. Given the underlying notion that no data may be able to predict an outcome with 100% certainty, a bettor may choose to ignore the data (e.g., predictive indicator) and place a bet using the odds created or adjusted by the system (i.e., in this example, a new counter wager), which may incorporate at least a portion of the output information in creating or adjusting the odds. Often, these odds will be more favorable to a bettor willing to bet against the predictive indicator. "New counter wager" is meant to indicate one way of providing such new odds. The odds 110 may be provided by the entity operating the guessing system, or may be provided by one or more third parties that receive output or odds information from the guessing system, or may be provided by one or more third parties that receive output or odds information from other third parties. The full odds section 112 is a control component that displays a full odds web page or other information related to the animal data.

[0100] If the speculation operator also operates a wagering subsystem (either separately or as part of the speculation system), the speculation operator may create or adjust its own odds, place one or more bets, and / or facilitate one or more transactions. However, an alternative scenario is illustrated in FIG. 8. As a system for collecting and storing animal data and other information, the speculation operator may provide one or more users (e.g., bettors) with third party wagering odds information (e.g., third party wagering odds information) for any selected event along with animal data derived from one or more individuals or groups of individuals (or one or more other animals) featured in the selected event. Advantageously, a user may have access to information related to one or more animals or groups of animals (e.g., individual athletes, one or more teams of athletes) featured in the selected event prior to placing a bet, thereby providing the bettor with various types of animal data information as well as one or more probabilities and / or predictions and / or possibilities associated with the selected event. Characteristically, the data may be provided in real-time or near real-time. In betting window 120 of FIG. 8, the guessing operator aggregates odds information from third party bookmakers and provides odds from relevant bookmakers to the user as shown in section 122. Odds may be displayed in multiple ways and may be provided for any given bet and may be adjusted in real-time or near real-time. Section 122 allows the user to select one or more bets that may feature real-time odds adjustments. Advantageously, upon selection of a bet in section 122, the user may set a bet amount (e.g., the amount the user intends to bet) using component 123 while placing a bet that may be attributed to a third party betting entity by selecting control component 125. Characteristically, the guessing system is operable to provide output information to one or more users.Bets may be placed by one or more users at any given time when bets are being offered. The types of data that the system may provide may include, for example, animal data or derivatives, recommendations on what to bet on, the likelihood of outcomes occurring, or predictions related to any given outcome, and may include both animal and non-animal data. In some cases, the guess operator may provide unique odds 124 at which the guess operator may undertake one or more bets. The odds 124 are novel counter wagering odds 110 from FIG. 7 that take into account at least a portion of the output information to understand the likelihood of any given outcome occurring. In most cases, outcome-based data is generated by running one or more simulations of an event. The guess operator will generate the odds 124 based on the guess operator's belief in any given outcome, and the odds 124 will typically reflect a more favorable position for bettors who wish to bet on the opposite view to the outcome or outcomes that the guess operator believes will occur. In an improvement, the odds 124 are updated in real-time or near real-time as new output information or other data is collected and / or as new odds are generated by running one or more new simulations (the odds may or may not include updated output information, but may include other information, such as traditional statistical data, such as points won / lost or other non-animal data). Typically, these odds are more favorable than the odds offered by one or more other bookmakers. In another improvement, the guess operator is the aggregator of betting odds, such that a bettor may view all the various odds on a single page. After collecting the odds information, the guess operator may supplement the odds information with animal data information, so that a bettor can find the most favorable betting opportunities based on the data information provided by the system. Advantageously, providing bettors with valuable information 126, such as animal data (including computing assets and predictive indicators), at the right time may increase the frequency of betting and provide confidence to any bettor.This data may be provided to the bettor in a variety of manners, including on an ad-hoc basis (e.g., the bettor may acquire or purchase data by data type), on a bet volume basis (e.g., the more bets or the more money spent on bets, the more animal data information the bettor has access to), or on a subscription basis (e.g., the bettor may pay a fee, such as on a per bet or per month basis, for access to the animal data and / or one or more derivatives thereof, which may include computing assets and predictive indicators). The animal data may also facilitate one or more impulsive bets by allowing the bettor to form stronger opinions about probabilistic outcomes and eliminate indecision, thereby giving the bettor the confidence needed to place another bet. Some examples are listed in section 130, which displays additional insights derived from the animal data that provide the bettor with further information related to any given object and / or any given bet. In an improvement, probability-based data may be provided to the guessing system by a third party (e.g., a probability evaluation system). The animal data may also provide bookmakers with the confidence to dynamically adjust odds, including in real-time or near real-time, which may increase the amount of bets placed.

[0101] Animal data, and particularly human data, may be sold in several ways. Users may select which animal data they wish to consume (e.g., view) in real-time (or near real-time) and which non-real-time data they wish to consume based on the particular information they are interested in. The system may be operable so that users may obtain (e.g., purchase) data on a subscription basis, an ad-hoc basis, a per sport, a per transaction (e.g., per API call), an animal data type basis, or other basis. Given that users have the ability to place one or more bets, setting one or more wagers, on third party sites (thereby generating more revenue for the third party) via the guess operator's platform, the entity operating the guess system may retain at least a portion of each bet placed on the third party site.

[0102] Still referring to FIG. 8 , the predictive indicator 128 (e.g., percentage of “% chance of winning bet”) may dynamically change based on one or more different factors. In one example, the predictive indicator 128 may change based on the type of animal data the user purchases from the system, or based on the type of subscription the user has that allows the user to access the animal data, or more simply based on the type of animal data that is captured and utilized by the guessing system. The frequency with which the predictive indicator changes may also change based on many factors, including the type of subscription or data package the bettor purchased (e.g., display and update every 5 seconds based on a new simulation run every 5 seconds, or display and update every 5 minutes based on a new simulation run every 5 minutes), the number of simulations, the frequency with which the guessing system takes one or more actions on the output information, and the like. Depending on the purchase, the output information may not be displayed as a number (e.g., a percentage). The output information may be shown in many ways, including a graph, a color (i.e., green may mean full power, red may mean high fatigue and low energy), or other indicators. The output information may also be communicated to the user in a variety of manners, including visually (as described above, it may be presented and overlaid on the athletes or teams by being integrated into virtual or augmented reality), verbally (e.g., a virtual assistant may provide audio related to the information and whether to place a bet), or physically (e.g., a user may have a smartwatch that vibrates to provide notifications when it receives notifications related to the data).

[0103] In a variant, one or more users may communicate to the computing subsystem or the betting system or the probability evaluation system (1) placing a bet, mitigating a risk, evaluating a probability, using a prediction, acquiring or consuming a product, or providing a recommendation, (2) one or more users (e.g., gamblers) as a result of winning one or more bets, acquiring or consuming one or more products, mitigating one or more risks, evaluating one or more probabilities, using one or more predictions, providing one or more recommendations, or taking one or more actions, or (3) placing one or more bets or performing one or more actions. In exchange for providing at least a portion of the consideration derived from one or more users (e.g., bookmakers, betting insurers, insurance companies) for which a product is being bet on, one or more risks are being mitigated, one or more probabilities are being evaluated, one or more products are being acquired or consumed, one or more predictions or recommendations are being provided, or one or more actions are being taken, the computing subsystem or betting system or probability evaluation system provides at least a portion of the predictive indicators, computing assets, animal data, and / or one or more derivatives thereof to one or more users of the data (e.g., bettors, bookmakers, insurance providers). For example, the computing subsystem may provide a bettor with the ability to obtain predictive indicators, computing assets, animal data, and / or one or more derivatives thereof associated with any given bet placed in exchange for the computing subsystem receiving a portion of the winnings if the bettor wins the bet (e.g., the computing subsystem receiving 1% of the winnings of a bet placed by the bettor after obtaining the predictive indicator).In another example, the computing subsystem may provide the bookmaker with the ability to view predictive indicators (e.g., human data indicators) or other animal data or one or more derivatives thereof associated with bets being offered in exchange for the computing subsystem receiving a winning percentage (e.g., the computing subsystem receiving n% of bets that the bookmaker wins based on viewing predictive indicators or other odds created based at least in part on the animal data). In another example, an insurance company (e.g., automobile insurance, health insurance, life insurance) may obtain predictive indicators (e.g., human data indicators) or other animal data to determine the likelihood of a happening (e.g., getting into a car accident, having a heart attack or stroke, experiencing a medical condition or event) for one or more individuals based on one or more animal data characteristics (e.g., physiological characteristics, age, medical condition) for the individuals, and may adjust the insurance premiums paid by the individuals, in which case the computing subsystem or betting system or probability assessment system receives compensation (e.g., via a portion of the monthly or annual premiums paid by the individuals). A life insurance company may, for example, desire to obtain a predictive index to predict the lifespan of a person with certain characteristics (e.g., age, weight, medical condition) indicative of a certain biological characteristic. The computing subsystem may obtain a portion of the consideration paid by one or more individuals to the insurance company in exchange for providing the predictive index, which may be accessible to the insurance company at any given time and may be updated in real-time or near real-time. The predictive index or other animal data may be generated by running one or more simulations. In an improvement, a user (e.g., a subject) may have their premiums adjusted by choosing to provide at least a portion of the animal data and / or one or more derivatives thereof to the insurance company, in which case the computing subsystem receives a consideration (e.g., a portion of the predicted savings) from the user.

[0104] In an improvement, a user of the data (e.g., a bettor, bookmaker, insurance provider) may have the ability to determine the consideration (e.g., monetary value or percentage) that the computing subsystem or betting system or probability evaluator system will receive in exchange for viewing the predictive indicators or other animal data or derivatives thereof (e.g., odds of an event occurring), with the computing subsystem or betting system or probability evaluator system having the ability to accept, reject or modify the consideration. For example, a bettor may wish to obtain the predictive indicators, computing assets or other animal data or one or more derivatives thereof for a user-specified consideration (e.g., a user may wish to be provided with 1% of winnings, or $20 for a $1000 bet placed). The computing subsystem or betting system or probability evaluator system may be programmed to accept, reject or modify an offer of access to the predictive indicators, computing assets and / or other animal data or derivatives thereof made by a bettor or bookmaker.

[0105] As mentioned above, with respect to virtual wagering, such as virtual horse racing or other games, a speculation operator may use one or more statistical models or artificial intelligence techniques to create simulated animal data based at least in part on actual animal data that users may engage to understand the likely outcome of any given bet. Using the simulated animal data for virtual wagering may also create new prop bets / markets for one or more virtual events.

[0106] Advantageously, the inference system utilizing the animal data information can be configured in a number of different ways. Examples of such configurations include:

[0107] (1) A gambling subsystem in which one or more users / bettors can specify bets, and the gambling subsystem or one or more third parties can create one or more odds on the bets.

[0108] (2) A gambling subsystem in which one or more users / bettors may specify bets and create one or more odds, and the gambling subsystem or one or more third parties may select whether to accept the one or more odds and place the one or more bets.

[0109] (3) A gambling subsystem in which one or more users / bettors may specify bets and create one or more odds, and an opportunity to acquire the odds and the right to place one or more bets is presented via an auction or via a bidding opportunity or via a marketplace.

[0110] (4) A gambling subsystem whereby one or more users / bettors may place bets, whereby the gambling subsystem or one or more third parties may create one or more odds, and whereby one or more users / bettors may accept one or more odds from the gambling subsystem or one or more third parties.

[0111] Or (5) a gambling subsystem in which one or more users / bettors may specify bets and / or create one or more odds, and the gambling subsystem or one or more third parties may guarantee bets and / or assume payment risk to the bettors based on the odds created by the gambling subsystem or by the one or more third parties or by the users / bettors.

[0112] Advantageously, the purchase or acquisition of additional data 130 in FIG. 8 may adjust the accuracy or precision of the human data indicator, or the frequency with which the human data indicator is updated or rendered. Control components 134 and 136 in FIG. 8 allow the user to scroll down to view more information if more data is purchased. FIG. 9 provides examples of types of bets that may be offered. Betting section 140 provides examples of bets, particularly proposition bets that may be offered to one or more users, including bets based on output information. Activation of control component 142 in FIG. 10 causes window 140 in FIG. 9 to be displayed. For any single type of animal data, bets can be detailed and granular, allowing the wagering subsystem to create a vast number of bets from the data. For example, using heart rate as an example, window 140 may display the following bet types:

[0113] Higher maximum heart rate in the first game of the first set.

[0114] Higher maximum heart rate in the second game of the first set.

[0115] Higher maximum heart rate in the third game of the first set.

[0116] Higher maximum heart rate in the fourth game of the second set.

[0117] Higher average maximum heart rate in the first set.

[0118] Higher average maximum heart rate in the second set.

[0119] Achieve a maximum heart rate above 170 for the first two games of the first set.

[0120] Achieve a maximum heart rate above 200 for the first four games of the second set.

[0121] Stay within 10% of your maximum heart rate for more than 3 minutes in the first set.

[0122] In the second set, stay within 10% of your maximum heart rate for more than 5 minutes.

[0123] Your resting heart rate should not drop below 100 bpm between the first and second sets.

[0124] Between the second and third sets, your resting heart rate must not drop below 115 bpm.

[0125] FIG. 10 presents an example where a user selects a new market (e.g., a prop bet or other bet) via the control component 142 of the window 120. In the example of FIG. 10, the user selects to place a bet on which player will have the "higher maximum heart rate in the fourth game of the second set." In this scenario, after the user selects the market, the guessing system displays odds provided by one or more betting entities, as well as odds provided by the guess operator and derived using at least a portion of the output information. The guessing system then provides at least a portion of the animal data as part of one or more data points (e.g., a predictive indicator) so that the user can see one or more predictions or forecasts or recommendations (e.g., what the chances of winning a bet are at any given time based on the collected animal data). For example, in FIG. 10, the guessing system 120 provides a predictive indicator that John Doe has an 86% chance of having the higher heart rate in this game. This may be based on one or more data points from the user's search and acquisition (e.g., purchase) 126. In this illustrated example, the "historical maximum heart rate", "average of the historical maximum heart rate in the second set of each match", "historical maximum heart rate in the fourth game of the second set of each match", and / or "historical maximum heart rate when playing" a particular opponent are used. In an improvement, multiple predictive indicators may be generated and displayed simultaneously. A bettor may also search and obtain additional information about each player to give the bettor more confidence in placing a bet. Considering the predictive indicators of the guessing entity labeled as "human data indicators", a bettor may be offered more favorable odds if the bettor wishes to bet on the "loser" predicted by the system.

[0126] FIG. 11 illustrates the functionality of the inference system of FIGS. 1, 2, and 3 that may deploy a dedicated health program or other health application for the subject of interest, for multiple subjects of interest, or for one or more groups of multiple subjects of interest within a web page or window. The health system 150 includes one or more outputs along with other animal data related to the subject of interest or subjects. The health system 150 may include a window 151 that may be a still image of the subject of interest or a video (e.g., live or delayed depending on the use case) of the subject of interest. In a variant, the subject of interest or subjects may be able to see another animal or animals (e.g., humans) within the same window (e.g., a patient sees a doctor or medical professional within a telemedicine platform, or a client sees a rehabilitation professional within the same window within a rehabilitation program). Additional features may also be displayed (e.g., whether a video camera is connected to the system, whether the camera is turned on, etc.). The system may be operable to detect whether a sensor is connected to the system (which may be displayed as a connected notification component 152) or disconnected from the system (which may be displayed as a connected notification component 154). The animal information with real-time or near real-time output may display one or more predictions or probabilities or possibilities along with information that may be calculated, computed, derived, extracted, extrapolated, modified, enhanced, estimated, evaluated, inferred, estimated, established, determined, observed, communicated, or otherwise acted upon. Section 156 provides one or more predictive indicators derived from the animal data, and section 158 provides one or more recommendations based on the predictions and probabilities established by the system. Trend section 155 may provide real-time or predefined time trends associated with live and historical biological signals and measurements along with other animal data. Additional fields may be added based on the animal data.Advantageously, the system may be programmed to identify one or more critical alerts 160 that require attention of one or more objects and / or one or more users of the system (e.g., medical professionals using the system to monitor the objects of interest) based on one or more outputs. The critical alerts may be set by predefined thresholds by an individual or administrator to alert one or more users of a potential problem associated with one or more signals or one or more measurements (e.g., if the probability of something happening is greater than n%, it is communicated as a critical alert). Characteristically, the system may be configured to identify known biologically relevant problems from one or more individuals of interest or groups of individuals of interest by correlating the data sets using one or more artificial intelligence techniques, as well as to identify biologically relevant problems based on the collected data by identifying hidden patterns in one or more data sets. This may include finding entirely new patterns in the data that have not previously been correlated with known problems, or may include finding new patterns in one or more data sets that may identify new problems.

[0127] Although exemplary embodiments have been described above, it is not intended that these embodiments describe all possible forms of the present invention. Rather, the terms used herein are for purposes of illustration rather than limitation, and it will be understood that various modifications may be made without departing from the spirit and scope of the present invention. Moreover, features of the various embodiments may be combined together to form further embodiments of the present invention.

Claims

1. 1. An inference system, comprising: one or more source sensors that collect electronically transmitted animal data from one or more target individuals engaging in a sporting activity at a sporting venue, the one or more source sensors providing one or more electrical signals, the one or more source sensors including at least one optical or translational sensor providing biomechanical data including angular velocity, joint path, gait description, number of steps, position data, or acceleration in various directions that can characterize the movement of the target individuals, the animal data being data obtained from the animals or data generated directly or indirectly by the animals and that can be converted into a form that can be transmitted to a server or other computing device; a computing subsystem that receives the animal data, wherein at least a portion of the animal data is transformed by the computing subsystem or by the one or more source sensors into at least one computing asset assigned to a selected individual of interest or to a group of selected individuals of interest, the computing asset being one or more numerical values, values, metrics, measurements, insights, graphs, charts, or plots derived from at least a portion of the animal data or one or more derivatives thereof, the computing subsystem being configured to extract or derive the at least one computing asset from one or more of the electrical signals, the one or more source sensors or the computing subsystem being operable to transform the at least one computing asset into a predictive indicator, the predictive indicator being a calculated computing asset and metric derived from at least a portion of the animal data or one or more derivatives thereof, and the computing subsystem being further operable to provide the predictive indicator, the at least one computing asset, and / or at least a portion of the animal data to one or more users; a gambling system configured to accept one or more bets; a transmission subsystem for transmitting the animal data to the computing subsystem; The predictive indicators enable one or more users to at least one of: decide whether to place a bet, determine the probability of an outcome occurring for an event, modify previously determined probabilities for an event, and form a strategy in a market in which an individual is formed to place a bet or take an action, wherein the predictive indicators are derived from one or more statistical models or one or more artificial intelligence techniques.

2. The inference system of claim 1 , wherein the one or more source sensors include at least one biosensor.

3. The inference system of claim 1 , wherein the at least one computing asset comprises one or more values, a plurality of values, a metric, an insight, a graph, a chart, or a plot derived from at least a portion of the animal data.

4. 4. The inference system of claim 3, wherein the at least one computing asset comprises one or more signals or one or more measurements including elements selected from the group consisting of ambient temperature data, humidity data, barometric pressure data, altitude data, wind speed data, nutritional data, family history data, psychological data, and statistical data.

5. The system of claim 1 , wherein the predictive index is a computational asset calculated from at least a portion of the animal data.

6. 6. The system of claim 5, wherein the predictive indicator comprises one or more signals or one or more measurements including elements selected from the group consisting of ambient temperature data, humidity data, barometric pressure data, altitude data, wind speed data, nutritional data, family history data, psychological data, and statistical data.

7. The prediction system according to claim 5 , wherein the predictive indicator is made up of a plurality of predictive indicators.

8. 6. The method of claim 5, wherein at least some of the predictive indicators are at least partially derived from or at least partially related to a group consisting of a target individual, a target plurality of individuals, a target group consisting of a target plurality of individuals, or a target plurality of groups including a target plurality of individuals.

9. The inference system of claim 5 , wherein the predictive index is a composite calculated from two or more signals or two or more measurements from the one or more source sensors.

10. 6. The system of claim 5, wherein the predictive indicator is calculated from the at least one computing asset including biological data selected from the group consisting of facial recognition data, eye tracking data, blood flow data, blood volume data, blood pressure data, biofluid data, body composition data, biochemical composition data, biochemical structure data, pulse data, oxygenation data, core body temperature data, skin temperature data, galvanic skin response data, sweat data, location data, geolocation data, audio data, hydration data, cardiac-based data, neurological data, genetic data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic data, thoracic electrical bioimpedance data, or combinations thereof.

11. At least some of the predictive indicators may be used in (1) a market for making or underwriting one or more bets; (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 or probabilities or possibilities; (4) to develop one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; or (7) to use in one or more simulators.

6. The inference system of claim 5, wherein the inference system is used, directly or indirectly, as one or more measurements utilized in a simulation, calculation or analysis, (8) as part of one or more simulations the output of which directly or indirectly involves one or more users, (9) to recommend one or more actions, (10) as one or more core components or one or more supplements to one or more consumption media, (11) in one or more promotions, or (12) any combination thereof.

12. One or more outputs from the computing subsystem are used to: (1) provide a market for placing or underwriting one or more bets; (2) to underwrite one or more bets; (3) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; (5) to develop one or more strategies; (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) to generate one or more revenue streams; 10. The inference system of claim 1, wherein the inference system is used directly or indirectly as one or more signals or one or more measurements utilized in one or more simulations, calculations or analyses, (9) as part of one or more simulations whose output directly or indirectly involves one or more users, (10) to recommend one or more actions, (11) as one or more core components or one or more supplements to one or more consumer media, (12) in one or more promotions, or (13) as a combination thereof.

13. 13. The inference system of claim 12, wherein the one or more direct uses or the indirect uses by the computing subsystem are at least partially dynamic and based on one or more user interactions with the one or more outputs from the computing subsystem.

14. 13. The speculation system of claim 12, wherein the market or wager includes at least one of a proposition bet, a spread bet, a line bet, a future bet, a parlay bet, a round robin bet, a handicap bet, an over / under bet, a full cover bet, or a teaser bet.

15. The inference system of claim 12 , wherein the one or more outputs from the computing subsystem are dynamically created, modified or augmented by the computing subsystem.

16. 16. The inference system of claim 15, wherein the creation or modification or enhancement of the one or more outputs is based at least in part on or derived from one or more user interactions with the predictive indicator and / or the at least one computing asset and / or the animal data.

17. At least a portion of the dynamically created, modified, or enhanced one or more outputs may be used to: (1) provide or underwrite one or more bets; (2) create, modify, enhance, acquire, offer, or distribute one or more products; (3) evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; (4) develop one or more strategies; (5) take one or more actions; (6) mitigate or prevent one or more risks; or (7) provide one or more systems.

16. The inference system of claim 15, wherein the inference system is utilized, directly or indirectly, as one or more signals or one or more measurements utilized in a simulation, calculation, or analysis, (8) as part of one or more simulations the output of which directly or indirectly involves one or more users, (9) to recommend one or more actions, (10) as one or more core components or one or more supplements to one or more consumer media, (11) in one or more promotions, or (12) as a combination thereof.

18. The inference system of claim 1 , wherein the computing subsystem provides one or more data outputs to one or more systems.

19. 20. The method of claim 18, wherein the predictive indicators are generated, modified or enhanced by the one or more systems.

20. The one or more systems may use at least a portion of the one or more data outputs: (1) as a market for placing or underwriting one or more bets; (2) to underwrite one or more bets; (3) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; (5) to develop one or more strategies; (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) to develop one or more 20. The inference system of claim 18, operable to directly or indirectly utilize the inference data as one or more signals or one or more measurements utilized in one or more simulations, calculations or analyses, (9) as part of one or more simulations the output of which directly or indirectly involves one or more users, (10) to recommend one or more actions, (11) as one or more core components or one or more supplements to one or more consumption media, (12) in one or more promotions, or (13) as a combination thereof.

21. 20. The inference system of claim 18, wherein the computing subsystem provides the same or substantially similar output or outputs to multiple users.

22. 20. The inference system of claim 18, wherein one or more data outputs of the computing subsystem are synchronized with one or more consumption media.

23. The inference system of claim 1 , wherein the computing subsystem is operable to receive animal data groups from a single individual of interest or from multiple individual of interest.

24. 2. The inference system of claim 1, wherein the computing subsystem is operable to collect information from the one or more source sensors by communicating directly to the one or more source sensors, to an associated cloud, or to a native application associated with the one or more source sensors.

25. 25. The inference system of claim 24, wherein the computing subsystem is operable to manage the one or more source sensors and one or more data streams from the one or more source sensors by at least one characteristic from the group consisting of tissue, sensor type, sensor parameter, data type, data quality, timestamp, location, activity, individual of interest, grouping of individuals of interest, and data measurement.

26. 2. The inference system of claim 1, wherein the computing subsystem is operable to simultaneously communicate to multiple source sensors on one or more of the individuals of interest, or to one or more source sensors on multiple individuals of interest.

27. The inference system of claim 1 , wherein the transmission subsystem enables the one or more source sensors to wirelessly transmit data in real-time or near real-time.

28. The inference system of claim 1 , wherein the transmission subsystem communicates with the one or more source sensors utilizing one or more transmission protocols.

29. The inference system of claim 1 , wherein the computing subsystem synchronizes communication with one or more data signals or one or more measurements from a plurality of sensors in communication with the computing subsystem.

30. The estimation system of claim 1 , wherein the transmission subsystem includes a transmitter and a receiver, or a combination thereof.

31. 2. The inference system of claim 1, wherein the transmission subsystem includes an on-body or airborne transceiver that optionally functions as another sensor on or above one or more of the individuals of interest, the on-body or airborne transceiver operable to communicate with one or more other sensors on one or more of the individuals of interest.

32. 2. The system of claim 1, wherein the animal data is synchronized, time-stamped, and tagged with information related to the one or more target individuals from which the animal data was collected and with information related to the one or more source sensors and including at least one characteristic of the one or more source sensors.

33. The inference system of claim 1 , wherein the animal data includes metadata identifying one or more characteristics related to the animal data and the one or more source sensors.

34. 2. The inference system of claim 1, wherein the computing subsystem or the gaming system performs one or more actions on the animal data selected from the group consisting of normalizing, time stamping, aggregating, tagging, storing, manipulating, denoising, productizing, enhancing, organizing, visualizing, analyzing, summarizing, replicating, synthesizing, anonymizing, synchronizing, or distributing the animal data.

35. 2. The guessing system of claim 1, wherein the computing subsystem or the gambling system (1) monitors, receives, and records at least one request for the predictive indicators and / or the at least one computing asset and / or the animal data by directly communicating with one or more systems, (2) provides the ability to make one or more requests for data to one or more users requesting access to the predictive indicators and / or the at least one computing asset and / or the animal data, and (3) is operable to transmit and / or receive data.

36. 2. The guessing system of claim 1, wherein the computing subsystem or the betting system associates at least one request for the predictive indicator and / or the at least one computing asset and / or the animal data with at least one user or with a group of users or with a class of users.

37. 2. The guessing system of claim 1, wherein the computing subsystem or the wagering system is operable to allow one or more users to select the animal data and / or the at least one computing asset and / or at least one characteristic for which the predictive index is provided.

38. 2. The estimation system of claim 1, wherein the computing subsystem or the wagering system generates simulated data derived from at least a portion of the predictive indicator, the at least one computing asset, and / or the animal data relating to the one or more target individuals or a group of the multiple target individuals.

39. 40. The system of claim 38, wherein the simulated data is generated using artificial intelligence techniques.

40. 40. The inference system of claim 39, wherein the artificial intelligence techniques include one or more trained neural networks.

41. The computing subsystem or the wagering system may use at least a portion of the simulated data: (1) as a market to place or underwrite one or more bets; (2) to underwrite one or more bets; (3) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions or probabilities or possibilities; (5) to develop one or more strategies; (6) to take one or more actions; or (7) to mitigate or prevent one or more risks.

40. The inference system of claim 38, wherein the inference system is used, directly or indirectly, to: (8) as one or more signals or one or more measurements utilized in one or more simulations, calculations, or analyses to prevent, (9) as part of one or more simulations the output of which directly or indirectly involves one or more users; (10) to recommend one or more actions; (11) as one or more core components or one or more supplements to one or more consumer media; (12) in one or more promotions; or (13) as a combination thereof.

42. 39. The estimation system of claim 38, wherein the computing subsystem or the wagering system applies at least a portion of the simulated data directly or indirectly to create, enhance or modify the predictive index and / or the at least one computing asset and / or the animal data.

43. The created or enhanced or modified predictive indicator and / or at least a portion of the at least one computing asset and / or the animal data may be used (1) as a market for placing or underwriting one or more bets; (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 or probabilities or possibilities; (4) to develop one or more strategies; (5) to take one or more actions; or (6) to mitigate or prevent one or more risks. (7) as one or more signals or one or more measurements utilized in one or more simulations, calculations or analyses; (8) as part of one or more simulations the output of which directly or indirectly involves one or more users; (9) to recommend one or more actions; (10) as one or more core components or one or more supplements to one or more consumer media; (11) in one or more promotions; or (12) as a combination thereof.

44. The system of claim 1 , wherein the animal data is grouped into one or more categories, each category having an associated computational asset or value.

45. 2. The inference system of claim 1, wherein when transmitting the predictive indicator and / or the at least one computing asset and / or the animal data to another source, the computing subsystem records one or more characteristics of the predictive indicator and / or the at least one computing asset and / or the animal data provided as part of the one or more distributions thereof.

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