Monetization of animal data

Abstract

To provide a system for monetization of animal data.SOLUTION: A system for monetization of animal data 14i includes a source 12 of animal data that can be transmitted electronically, the source including at least one sensor 18i. An intermediate server 22 receives and collects the animal data such that data to be collected has metadata attached thereto. The intermediate server includes a single computer server or a plurality of interacting computer servers. The metadata includes at least one of origin of the animal data and personal attributes of individuals from which the animal data is originated. The intermediate server provides requested animal data 24 to one or more data acquirers for consideration, the requested animal data including simulated animal data. The intermediate server also distributes at least a portion of the consideration to at least one stakeholder.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims priority to U.S. Provisional Application No. 62 / 834,131, filed on April 15, 2019, and U.S. Provisional Application No. 62 / 912,210, filed on October 8, 2019, the disclosures of which are hereby incorporated by reference in their entirety.

[0002] In at least one aspect, the present invention relates to a system for monetizing animal data.

Background Art

[0003] With the continuous facilitation of information access via the Internet, the way of doing business has substantially changed. Along with such an information explosion, sensor technology, especially biosensor technology, has also advanced. In particular, small biosensors for measuring electrocardiogram, blood flow, body temperature, sweating amount, or respiratory rate are now available. However, there is no unified service provider that collects, organizes, and collates the information collected from such biosensors for the purpose of monetizing it.

[0004] Therefore, there is a need for a system that collects, organizes, and further classifies sensor data from an individual or a group of individuals so that the data can be sold.

Summary of the Invention

[0005] In at least one aspect, a system for monetizing animal data is provided. The system includes a source of animal data that includes at least one sensor. The animal data can be transmitted electronically. Characteristically, the source of animal data includes at least one sensor. An intermediate server receives and collects the animal data such that metadata is added to the data being collected. The metadata includes at least one of the origin of the animal data or one or more personal attributes of one or more individuals from which the animal data originated. The intermediate server provides the requested animal data to one or more data acquirers for consideration. The intermediate server also distributes at least a portion of the consideration to at least one stakeholder. The intermediate server includes a single computer server or a plurality of interacting computer servers.

[0006] In another aspect, a system for monetizing animal data is provided. The system includes a source of animal data that can be transmitted electronically, and the source of animal data includes at least one sensor. An intermediate server receives and collects the animal data. The intermediate server also provides the requested animal data to data acquirers for consideration. Characteristically, at least a portion of the animal data requested or provided is simulated animal data. The intermediate server distributes at least a portion of the consideration to at least one stakeholder. The intermediate server includes a single computer server or a plurality of interacting computer servers.

[0007] In another aspect, the animal data used in a system for monetizing animal data is human data.

[0008] In another aspect, a system for monetizing animal data can provide another dimension for one or more users to interact with a sports event. In particular, the present invention may provide a new dimension to sports gambling, including events that include humans or other mammals (e.g., horse racing).

[0009] In yet another aspect, a system for monetizing animal data can provide data purchasers (e.g., individuals, pharmaceutical companies, insurance companies, healthcare companies, military organizations, research institutions) with the ability to obtain animal data for their specific use cases via an e-commerce website or platform such as a data marketplace.

Brief Description of the Drawings

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Best Mode for Carrying Out the Invention

[0011] Next, the presently preferred embodiments and methods of the present invention, which constitute the best mode for carrying out the present invention as presently recognized by the present inventors, will be described in detail. The drawings are not necessarily to scale. However, it should be understood that the disclosed embodiments are merely illustrative examples of the present invention that can be embodied in various alternative forms. Accordingly, the specific details disclosed herein should not be construed as limiting, but rather as merely representative bases for any aspect of the present invention and / or as representative bases for teaching those skilled in the art how the present invention can be variously employed.

[0012] Also, it should be understood that the present invention is not limited to the specific embodiments and methods described below, since certain components and / or certain conditions can naturally be changed. Further, the terms used herein are used only for the purpose of describing specific embodiments of the present invention and are not intended to limit in any way.

[0013] Also, it should 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 a singular component is intended to include a plurality of components.

[0014] The term "comprising" is synonymous with "including," "having," "containing," or "characterized by." These terms are inclusive and open-ended and do not exclude additional, unrecited components or method steps.

[0015] The expression "consisting of" excludes any element or step or component not specified in the claim. When this expression appears not immediately following the preamble of the claim but in a clause of the body of the claim, only the elements recited in that clause are limited and other elements are not excluded from the claim as a whole.

[0016] The expression "consisting essentially of" limits the scope of the claim to those things that do not materially affect one or more basic and novel characteristics of the claimed subject matter in addition to the specified materials or steps.

[0017] When a computing device is described as performing an act step or method step, it will be understood that the computing device is operable to perform the act step or method step typically by executing one or more lines of source code. The act step or method step can be encoded on a non-transitory memory (e.g., hard drive, optical drive, flash drive, and the like).

[0018] Regarding 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 can include the use of either of the other two terms.

[0019] 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 "a plurality" and "a number" as subsets.

[0020] Throughout this application, if publications are referenced, the disclosures of these publications are hereby incorporated by reference in their entirety into this application to more fully describe the state of the art relevant to the present invention.

[0021] 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) configured to execute the methods and functions described herein, a distributed system, a blade, a gateway, a switch, a processing device, or a combination thereof.

[0022] The term "computing device" generally refers to any device capable of performing at least one function, including communication with another computing device. In an improvement, 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.

[0023] The processes or methods or algorithms disclosed herein can be distributed to a computing device or controller or computer that may include any existing programmable electronic control unit or dedicated electronic control unit, or can be implemented by such a computing device or controller or computer. Similarly, the processes or methods or algorithms can be stored in many forms including, but not limited to, information permanently stored on a non-rewritable storage medium such as a ROM device as data and instructions executable by a controller or computer, and information variably stored on a rewritable storage medium such as floppy disks, magnetic tapes, CDs, RAM devices, other magnetic and optical media, shared or dedicated cloud computing resources, etc. Also, the processes or methods or algorithms can be implemented within a software-executable object. Alternatively, the processes or methods or algorithms can be embodied in whole or in part using appropriate hardware components such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), state machines, controllers, or other hardware components or devices, or a combination of hardware components, software components, and firmware components.

[0024] 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 can be, for example, a human participating in athletic training or competition, a horse racing on a track, a human playing a video game, a human monitoring their own personal health status, a human providing data to a third party, a human participating in a research or clinical study, or a human participating in a fitness class. A subject or individual can also be a derivative of a human or other animal (e.g., a laboratory-generated organism that is at least partially derived from a human or other animal), or one or more individual components or members or processes that make up a human or other animal (e.g., cells, proteins, biological fluids, amino acid sequences, tissues, hair, limbs), or one or more artificial creations that share one or more characteristics with a human or other animal (e.g., laboratory-cultured human brain cells that generate electrical signals similar to those of human brain cells). In the improvement, a subject or individual can be a machine (e.g., a robot, an autonomous vehicle, a robotic arm) or a network of machines that can be programmed 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 can be derived, which can be at least partially and essentially artificial (e.g., data from activities derived from artificial intelligence that mimics biological brain activity).

[0025] The term "animal data" refers to any data obtained from an object or any data generated directly or indirectly by an object, which can be converted into a form that can be transmitted (e.g., wireless transmission or wired connection) to a server or other computing device. Animal data includes any data that can be obtained from one or more sensors or sensing devices / sensing systems, particularly biological sensors (biosensors). Also, animal data can include descriptive data, auditory data, visually acquired data, neurologically generated data (e.g., brain signals from neurons), data that can be manually input in relation to the object (e.g., medical history, social habits, emotions related to the object), and data that includes at least a part of the animal data. In an improvement, the term "animal data" includes any derivative of the animal data. In another improvement, animal data includes at least a part of the simulated data. In yet another improvement, the animal data includes the simulated data.

[0026] The term "artificial data" refers to artificially created data that is at least partially derived from actual animal data or one or more derivatives thereof, or that is generated using at least partially actual animal data or one or more derivatives thereof. Artificial data can be created by performing one or more simulations using 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 (e.g., artificially created visual data, artificially created movement data) that shares at least one biological function with a human or other animal. This includes "synthetic data", which can be any production data that is applicable to a given situation and is not obtained by direct measurement. Synthetic data can be created by statistically modeling the original data and using that model to generate new data values that reproduce at least one of the statistical characteristics of the original data. For the 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 any reference to any one of these terms should not be construed as limiting, but rather should be construed as encompassing all possible meanings of all of the terms.

[0027] The term "insight" refers to one or more descriptors that can be assigned to a subject individual and that describe the state or status of the subject individual. 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 a plurality of numerical values, and may be expressed as a probability or as a similar odds-based metric. Also, an insight may be characterized by one or more evaluation criteria, measurements, insights, graphs, charts, plots, or metrics related to a predefined performance (e.g., something visual such as color, or something physical such as vibration).

[0028] Explanation of abbreviations:

[0029] "AFE" means analog front end.

[0030] Referring to FIG. 1, a schematic of a system for monetizing animal data is presented. The monetization system 10 includes a source 12 of animal data 14 that can be transmitted electronically. i Characteristically, the source 12 of animal data includes at least one sensor 18. i The subject individual 16 i is the object from which the corresponding animal data 14 i is collected. Label i is simply an integer label from 1 to i max associated with each subject individual, where i max is the total number of individuals that can be from 1 to thousands or more. In this context, animal data refers to data related to the body of an object that is at least partially obtained from one or more sensors, particularly from biological sensors (biosensors). In many useful applications, the object is a human (e.g., a sports athlete) and the animal data is human data.

[0031] Biological sensors (biosensors) collect biological signals. In the context of this embodiment, biological signals are any signals or characteristics in or arising from an object that can be continuously or intermittently measured, monitored, observed, calculated, computed, input, or interpreted, including electrical signals, non-electrical signals, measurement values, and artificially generated information. Biological sensors can collect biological data 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 being targeted. For example, some biosensors can measure biological data such as eye-tracking data (e.g., pupil response, movement, EOG-related data), blood flow / volume data (e.g., PPG data, pulse transit time, pulse arrival time), biofluid data (e.g., analysis derived from blood, urine, saliva, sweat, cerebrospinal fluid), body composition data (e.g., BMI, body fat percentage, protein / muscle), biochemical composition data, biochemical structure data, pulse data, oxygenation data (e.g., SpO2), core body temperature data, skin temperature data, skin electrical response data, sweating data (e.g., rate, composition), blood pressure data (e.g., systolic, diastolic, MAP), hydration data (e.g., fluid balance I / O), heart-based data (e.g., heart rate, average HR, HR range, heart rate variability, HRV time domain, HRV frequency domain, autonomic tone, ECG-related data including PR interval, QRS interval, QT interval, RR interval), nerve-related data (e.g., EEG-related data), gene-related data, genome-related data, skeletal data, muscle data (e.g., EMG-related data including surface EMG and amplitude), respiratory data (e.g., respiratory rate, respiratory pattern, inhalation / exhalation ratio, tidal volume, vital capacity measurement data), thoracic electrical bioimpedance data, or combinations thereof, or provide information that can be converted into such biological data, or provide information that can be derived from such biological data. Some biosensors can detect biological data such as biomechanical data that may include, for example, angular velocity, joint paths, gait descriptions, step counts, or position or acceleration in various directions that can characterize the movement of the object being targeted.Some biosensors may collect biological data such as location data and location information data (e.g., GPS-based data, RFID-based data, posture data), face recognition data, kinesthetic data (e.g., physical pressure obtained from sensors installed on the soles of shoes), or audio / auditory data, etc., related to one or more individuals 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 images or videos including videos, MRIs, computed tomography scans, ultrasounds, X-rays), and based on this, biological data can be detected, measured, monitored, observed, extrapolated, calculated, or computed (e.g., biomechanical movements, positions, X-ray-based fractures, or stress or diseases based on video-based or image-based visual analysis of objects). Some biosensors can derive information including triglyceride levels, red blood cell counts, white blood cell counts, adrenocorticotropic hormone levels, hematocrit values, platelet counts, ABO / Rh blood types, blood urea nitrogen levels, calcium levels, carbon dioxide levels, chloride levels, creatinine levels, glucose levels, hemoglobin A1c levels, lactate values, sodium levels, potassium levels, bilirubin levels, alkaline phosphatase (ALP) 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 values, folic acid levels, vitamin B-12 levels, and the like, from biological fluids such as blood (e.g., veins, capillaries), saliva, urine, sweat, and the like. In addition to biological data related to one or more individuals of interest, some biosensors may measure environmental conditions such as ambient temperature and humidity, elevation, atmospheric pressure, etc.In the improvement, one or more sensors provide biological data including one or more calculations, operations, predictions, estimations, evaluations, inferences, estimations, decisions, incorporations, observations, or anticipations that are at least partially derived from biosensor data. In another improvement, 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 elevation data).

[0032] In a variant, at least one sensor 18 i collects or derives at least one 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, skin electrical response data, sweating data, position data, position information data, audio data, biomechanical data, hydration data, heart-based data, neurological data, genetic data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic data, thoracic electrical bioimpedance data, ambient temperature data, humidity data, air pressure data, elevation data, or a combination thereof.

[0033] At least one sensor 18 iis, and / or one or more of its appendages, with respect to the subject, including the subject's body or eyeball or vital organ or muscle or hair or vein or biological fluid or blood vessel or tissue or skeletal system, fixed to, in contact with, transmitting one or more electronic communications related to the subject, transmitting one or more electronic communications derived from the subject, embedded within the subject, placed or implanted within the subject, ingested by the subject, integrated to form at least a part of the subject, in direct contact with the individual forming the subject or in contact with the individual forming the subject via one or more intermediate members or in direct communication with the individual forming the subject or in communication with the individual forming the subject via one or more intermediate members, integrated into, integrated as a part of, fixed to, or embedded within a fabric or cloth or clothing or material or equipment or object or device. For example, a saliva sensor fixed to a tooth, or fixed to a dental arch, or fixed to an instrument in contact with one or more teeth, a sensor for extracting DNA information derived from the biological fluid or hair of the subject, a wearable sensor (e.g., a sensor wearable on the human body), a sensor fixed to or implanted within the brain of the subject and capable of detecting brain signals from neurons, a sensor for tracking one or more biological functions by being ingested by an individual, a sensor attached to or integrated into a machine (e.g., a robot) sharing at least one characteristic with an animal (e.g., a robotic arm having the ability to perform one or more tasks similar to those of a human, a robot having information processing capabilities similar to those of 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 the subject.Other examples include sensors attached to the skin via an adhesive, sensors integrated within a watch or headset, sensors integrated or embedded within a shirt or jersey, sensors integrated within a steering wheel, sensors integrated or embedded within a video game controller, sensors integrated within a basketball that contacts the hand of a subject, sensors integrated within a hockey stick or puck that intermittently contacts an intermediate member held by a subject (e.g., a hockey stick), sensors integrated or embedded within one or more handles or grips of a fitness machine (e.g., a treadmill, a bicycle, a bench press), sensors integrated within a robot (e.g., a robotic arm) controlled by a subject individual, sensors integrated or embedded within a shoe that can contact a subject individual via an intermediate member in the form of a sock or via an adhesive tape wrapped around the ankle of the subject individual, and the like. In another improvement, one or more sensors may be woven, embedded, integrated with, or fixed to a floor covering or ground surface (e.g., an artificial turf field, a basketball court, a soccer field, a manufacturing line floor, or an assembly line floor), a seat / chair, a helmet, a bed, or an object that contacts a subject directly or via one or more intermediate members (e.g., the subject contacts a sensor within a seat via a gap in clothing). In another improvement, the sensor and / or one or more of its accessories may contact particles or objects derived from the body of a subject (e.g., tissue from an organ, hair from a subject), and one or more sensors may derive or provide information from such particles or objects that can be calculated or converted into biological data. In yet another improvement, 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, calculated, or computed.In yet another refinement, one or more of the sensors may be light-based and may use infrared technology (e.g., a temperature or heat sensor) to calculate the temperature of the individual or the relative heat of different parts of the individual.

[0034] In the variant shown in FIG. 1, at least one sensor 18 i However, each individual 16 i Animal Data 14 i The intermediate server 22 collects the animal data 14 such that the collected data is accompanied by personalized metadata, which may include one or more characteristics of the animal data and / or the origin of the animal data and / or sensor data (e.g., type, operating parameters, etc.). i The source 12 receives and collects the animal data 14 from the intermediate server 22. The metadata may also include any data set that describes and provides information about other data, including data that provides context for the other data (e.g., activities in which the individual of interest is engaged while the animal data is being collected). Other information may be added to the metadata or associated with the animal data at the time of collection of the animal data, including one or more attributes of the individual from which the animal data originated, or other attributes related to the sensor or data (e.g., name, height, age, weight, data quality rating, etc.). In an improvement, the source 12 may provide the animal data 14 to the intermediate server 22. i a computing device 20 that mediates the transmission of the data, i.e., collects and transmits the data to an intermediate server 22; i For example, the computing device 20 i The computing device 20 may be a smartphone, a smartwatch, or a computer. i can be any computing device. Typically, the computing device 20 iAlthough not an essential requirement of the present invention, it is local to the individual being targeted. Referring still to FIG. 1, the intermediate server 22 provides the requested animal data 24 to the data acquirer 26 for consideration (e.g., payment, reward, something of value whether essentially monetary or not). As used herein, the terms “data purchaser,” “data acquirer,” and “purchaser” are synonymous. In some variations, the intermediate server 22 provides raw data or processed data, analyzed data, combined data, visualized data, simulated data, and / or a report or summary regarding the data. Moreover, the intermediate server 22 can provide data analysis and other services related to the data (e.g., visualization, reporting, summarization) that can be provided by one or more parties for acquisition (e.g., purchase).

[0035] In an improvement, the intermediate server 22 synchronizes and tags the animal data with one or more properties (e.g., characteristics) related to the source of the animal data. Examples of such properties related to the source of the animal data include, but are not limited to, timestamps, sensor types, and sensor settings (e.g., operating mode, sampling rate, gain). The intermediate server 22 can also synchronize the animal data with one or more sensor characteristics, individual attributes, and data types being collected. The intermediate server 22 distributes at least a portion of the consideration to at least one stakeholder 30. The one or more stakeholders can be the user who generated the data, the owner of the data, the data collection company, the authorized agent, the sensor company, the analysis company, the application company, the data visualization company, the intermediate server company operating the intermediate server, or another entity (e.g., typically something that provides value to any of the above stakeholders or data acquirers). In the improvement, the consideration is distributed according to a revenue sharing protocol having one or more adjustable parameters that determine the consideration or a portion thereof received by each stakeholder (as shown in FIG. 17).

[0036] It will be understood that the intermediate server 22 may include a single computer server or a plurality of interacting computer servers. In this regard, the intermediate server 22 can monitor, receive, and further record all requests regarding animal data to be purchased based on one or more use cases or requirements by communicating with other systems. Moreover, the intermediate server 22 can, by communicating with one or more other systems, monitor, receive, and record all requests regarding animal data, and further utilize one or more parameters established by metadata, one or more search parameters, or one or more other characteristics related to an output consisting of a sensor or data type or an individual or a group of individuals or an output of a plurality of individuals forming a target, to be operable to provide one or more data acquirers with the ability to search for and request animal data and / or one or more derivatives thereof.

[0037] In a variant, the intermediate server 22 is, as indicated by the communication link 34 with the sensor 18 i or, as shown by the computing device 20 iCommunicate directly with the source of animal data, as indicated by communication link 36. In the improvement, intermediate server 22 communicates with the source 12 of animal data via cloud 40 or a local server. Cloud 40 can be the Internet, a public cloud, a private cloud utilized by the organization operating intermediate server 22, a localized or networked server / storage, a localized storage device (e.g., an n - terabyte external hard drive, or a media storage card), or a distributed network of computing devices. Typically, the source 12 of animal data transmits the animal data wirelessly. However, the animal data may be transmitted using a wired connection. In the improvement, the source 12 of animal data transmits the animal data to intermediate server 22 via a hardware transmission subsystem. The hardware system can include one or more receivers, transmitters, transceivers, and / or support components (e.g., dongles) that utilize a single antenna or multiple antennas (which may be configured as part of a mesh network).

[0038] As described above, the individualized metadata includes the origin of the animal data and one or more attributes of the subject individual. Examples of one or more attributes of such a subject individual can include, but are not limited to, age, weight, height, date of birth, race, reference identifier (e.g., social security number, national ID number, digital identifier), country of origin, region of origin, ethnicity, current place of residence, and gender, with respect to the individual from whom the animal data originated. In an improvement, the attributes of the subject individual can include information collected from medical history, medical records, gene-derived data, genome-derived data (e.g., information related to one or more medical conditions, traits, health risks, genetic diseases, drug reactions, DNA sequences, protein sequences and structures), biofluid-derived data (e.g., blood type), drug / prescription records, family history, health history, manually entered personal data, past personal data, and the like. When the subject is a human, one or more attributes of the subject individual can include one or more activities the subject individual was involved in while the animal data was being collected, one or more related groups, one or more social habits (e.g., tobacco use, alcohol consumption, and the like), educational records, criminal records, social data (e.g., social media records, internet search data), employment history, and / or manually entered personal data (e.g., one or more places the subject individual lived, feelings). It will be understood that various components of the animal data can be anonymized or de-identified. De-identification includes removing identifying information of an individual to protect the privacy of the individual. In the context of the present invention, anonymization and de-identification are considered synonymous.

[0039] In one variation, the animal data is from a single individual of interest. Such individualized animal data can be a single dataset resulting from one or more sensors (e.g., creating a single dataset using a sensor that collects only heart rate or neurological activity, creating a single dataset consisting of both heart rate and neurological activity using two separate sensors that collect heart rate and neurological activity), or multiple datasets resulting from a single sensor (e.g., creating multiple heart rate datasets using a sensor that collects only heart rate, creating one or more heart rate datasets and one or more sEMG datasets using a sensor that collects both heart rate and sEMG data), or multiple datasets resulting from multiple sensors (e.g., creating multiple datasets from the collected data using one sensor that collects heart rate and another sensor that collects glucose data). In an improvement, a single dataset may include multiple data types and / or multiple objects, and the creation of multiple datasets may be based on only a single individual and a single data type. In another variation, the data of the individual of interest is combined with one or more datasets from one or more other individuals, and either the one or more datasets or the individuals share at least one or more similar characteristics, and are provided to the data acquirer as a collection of animal data. In this regard, the intermediate server can add a dataset representing the specific criteria the data acquirer is looking for. As an example, within the age range of 25 to 35-year-old males, the system can, if desired, provide data of 25 to 30-year-old males and data of 30 to 35-year-old males in a ratio of 60 to 40. In an improvement, the data acquirer defines the criteria for making individuals or datasets similar to each other. For example, the data acquirer may request DNA data samples or biofluid data samples from individuals who exhibit a specific genetic trait but may differ in other respects (e.g., age, weight, height). In some variations, composite data is created from multiple data types collected from one or more sensors.Classifications (e.g., groups) can be created (e.g., to simplify the search process for data acquirers and provide more exposure to any given data provider), and the classifications may be based on the data collection process, practice, or relevance, rather than on the characteristics of the individuals. For example, a group may be created based on individuals who use a specific sensor in a specific setting and further collect ECG sensor data or PPG sensor data according to a specific data collection method. In another example, a group may be created for people who have previously experienced a heart attack. It will be understood that any single characteristic related to animal data (e.g., including any characteristic related to the data, one or more sensors, and one or more individuals being targeted) can be associated or assigned to one or more groups / classifications or tags. Moreover, one or more classifications or tags related to animal data contribute to the creation or adjustment of the relevant value regarding the animal data. Examples of classifications or tags include evaluation criterion classifications (e.g., characteristics of an object obtained by one or more sensors that can be assigned numerical values such as heart rate and water replenishment amount), personal classifications of individuals (e.g., age, weight, height, medical history), classifications of individuals' insights (e.g., "stress," "energy level," the possibility of one or more results occurring), sensor classifications (e.g., sensor type, sensor brand, sampling rate, other sensor settings), classifications of data properties (e.g., raw data or processed data), classifications of data quality (e.g., good data, bad data based on defined criteria), classifications of data timeliness (e.g., data provided within milliseconds, data provided within hours), classifications of data context (e.g., NBA finals games, NBA preseason games), classifications of data ranges (e.g., providing a range regarding the data such as bilirubin levels from 0.2 mg / dL to 1.2 mg / dL), and the like. In another variation, some classifications of data may have greater value than other classifications. For example, heart rate data for people aged 25 to 34 from sensor X may be less valuable than glucose data for people aged 25 to 34 from sensor Y.Differences in value can be due to various reasons, including the rarity of the data type (e.g., on average, glucose data may be more difficult to collect than heart rate data and thus may not be readily available or collectible), the data quality due to any given sensor (e.g., one sensor may provide higher-quality data than another), one or more individuals from which the data originates compared to any other given individual (e.g., the data of one individual may be of higher value compared to the data of another individual), the type of data (e.g., raw AFE data from which ECG data can be derived from sensor X and from a group of multiple individuals with specific ethnic characteristics may be of higher value compared to only ECG data derived from the same group of multiple individuals with the same ethnic characteristics from the same sensor X, considering that the AFE data enables deriving insights other than ECG, including surface electromyogram data), the derived use cases related to the data (e.g., glucose data may also be of higher value as it can be used to derive fluid replenishment, which may be a data type that is more difficult to collect than heart rate-based data), the amount or volume of data (e.g., daily heart rate data over a year from 100 people aged 45 to 54 may be of higher value compared to daily heart rate data over a month from the same 100 people aged 45 to 54).

[0040] In another variant, the collected animal data is assigned to a classification (e.g., group) having a corresponding value that can be determined by the system. It will be understood that one or more classifications can have a predetermined value, an evolving value or a dynamic value, or both. For example, a group of data may increase in value when data is added to the group, or when more data within the group becomes available, or when the demand for data from that particular group increases, and may decrease in value when time has passed since the data was created, or when the relevance of the data decreases, or when the demand for data from that particular group decreases. In another improvement, one or more classifications may be dynamically changed by creating or modifying one or more new classifications based on the requirements of one or more purchasers or based on the input of new information or sources into the system. For example, a new type of sensor may be developed, the sensor may be updated by new firmware providing new settings and functions for the sensor, and one or more new data types (e.g., data types derived from biological fluids) may be introduced into the system, whereby data acquirers can search for and / or acquire the data, from which data providers can create new opportunities for value creation. In another improvement, one or more artificial intelligence technologies (e.g., machine learning, deep learning technologies) may be utilized to dynamically assign one or more classifications and / or groups and / or values to one or more data sets.

[0041] In yet another variant, one or more data quality assessments for animal data may be provided to the data acquirer or other interested parties, either as part of the metadata or separately. The data quality assessment provides the suitability of the animal data for fulfilling the purpose in a given context. The factors considered in determining data quality include: (1) accuracy (or validity, correctness) which occurs when the recorded values conform to the range of actual or known values; (2) timeliness which occurs when the recorded values are within the time requirements of duration and latency and are not stale; (3) data consistency (or reliability, or lack of contradiction with other data values) which occurs when the representation of the data values is the same in all cases; and (4) data completeness which occurs when all values for a particular variable are recorded (and further, to determine if the data is insufficient or unusable). Other factors affecting the data quality assessment include, but are not limited to, conformance or compliance to a standard format, evaluation of user feedback, and reproducibility of the data. Data quality can be evaluated or certified in multiple ways, including by one or more experts, by one or more programs described considering one or more of the above factors to evaluate the data based on predetermined quality management parameters, and the like. Such an evaluation can include a predetermined or dynamic data quality scale. In an improvement, the evaluation and / or certification may be created or adjusted using one or more artificial intelligence techniques considering one or more factors.

[0042] Advantageously, value is typically associated with animal data. This value is used for acquisition, purchase, sale, transaction, licensing, lease, advertising, evaluation, standardization, certification, investigation, distribution, or brokering of acquisition or purchase or sale or transaction or licensing or lease or distribution, with respect to individually identified or anonymized animal data. The value can be essentially monetary or non-monetary. The value created for any animal data is uniquely assigned to that animal data. Often, the value is assigned and / or adjusted by the data provider, or the data owner, or one or more other administrators of the data. However, the value may be assigned and / or adjusted by an intermediate server or a third party. In an improvement, the relevant value is dynamically assigned and / or adjusted. For example, a particular dataset for which a value is assigned at a particular point in time may be assigned a different value at another point in time, which means that the value of the data can change based on one or more factors (e.g., timeliness of the data. As an example, in the case of professional golfers, their heart rate data may be of higher value to sports bettors when putting on the 18th green in the final round with a tournament victory at stake compared to when putting on the 4th green in the first round). The intermediate server can be programmed to dynamically assign and / or adjust any given value for any data based on various factors, classifications, and tags created by the system. In a variant, the same set of animal data may have one or more different relevant values. For example, the acquirer of the data, the way the data is used, the period of use, one or more markets in which the data is used (e.g., whether the data is used in a single market or overall), the time frame in which the data is used (e.g., whether the data is used in real-time or at a later date), and the like, are all relevant considerations in assigning different values to the same data and are also considerations in dynamically assigning and adjusting the value.In another variant, one or more values are created or adjusted by at least partially inputting reference evaluation data (e.g., price setting data) from one or more sources (e.g., historical price values derived from a monetization system, third-party sources that evaluated similar data or similar attributes) into one or more models that establish one or more values for one or more data types sold by the monetization system. For example, price setting data regarding the heart rate of player X in league Y of sport Z may be established by the monetization system by referring to at least a portion of the statistical data price setting of player X in league Y of sport Z from one or more third parties as an input to a price setting model that establishes one or more values for the data, or by referring to historical price values regarding player X (or individuals similar to player X) within the monetization system and regarding their similar data. In an improvement, the reference evaluation data provided may be from one or more heterogeneous data sets. For example, even though the monetization system is dynamically establishing the price setting of hydration data regarding player X in league Y of sport Z, if there is no price setting of hydration data in a certain sector (e.g., sports), the monetization system may refer to other sectors or other use cases to establish the price setting (e.g., how insurance or fitness-related use cases are pricing hydration compared to the acquired evaluation criteria such as heart rate, and how other evaluation criteria such as muscle activity level, heart rate, or location data are priced in sports and how values are derived based on a series of information). When the sale of data sets evaluated based on other use cases continues, the value may be dynamically adjusted based on demand, scarcity, or other factors. One or more artificial intelligence techniques or statistical models may be utilized to create such value.

[0043] In some variations, the system may be operable to monitor the life cycle of any given transaction regarding an individual's data, including where the data was transmitted (e.g., via the intermediate server 22) and how, when, and where the data was used. Using a technology such as blockchain, a data provider or an authorized user can view a conflict history tree regarding that individual's data starting from the time the data was collected by the system. The system may be operable to monitor animal data and all transactions related to that data, including details related to any given transaction. This may include confirmation that the data was collected in the manner proposed by the subject, details regarding how the data was used, details regarding where the data was transmitted, any restrictions associated with the data (e.g., ensuring that the use of the data, including any derivative works created, is free and there are no future potential claims), consideration related to the data, and the like. It may also include enforcement of various types of rights (e.g., exclusive rights per region or data type) and the like granted to the acquirer when the data is distributed. In an improvement, the system may have a function to enforce data restrictions or usage within the blockchain ecosystem. For example, if a party is granted a 15 - minute license regarding data, the system can ensure that the licensor cannot use or transfer that data within the blockchain ecosystem when the license expires.

[0044] In another variant, when one or more data sets derived from the same animal data are distributed to and used by multiple parties, it may be important for the data acquirer to know how the data has been used previously and the conditions associated with such use. In such cases, by utilizing a technology such as blockchain, the monetization system may provide functions (e.g., services) related to the rights transfer list of the data, thereby ensuring that the data acquirer can acquire and utilize the animal data after understanding how, when, and where the data can be used. This can be important to ensure that the use of the data is free and there are no future claims. The rights transfer list is an official ownership record for any given property such as the data of the object. In another variant, the monetization system may function as a unified registry or a unified system that provides one or more records for each type of distributed data and for each associated use. In yet another variant, the data distribution service of the monetization system may also include insurance-related data services (e.g., title insurance related to the use of the data and to derivatives created from the distributed data).

[0045] In another variant, when the acquirer requests a data type or data set that does not exist within the intermediate server 22, the intermediate server 22 may transmit a request to one or more current users of the system to create one or more desired data sets or to obtain data from one or more third parties. Alternatively, if raw (e.g., unprocessed) data for creating the requested data exists within the intermediate server 22, the intermediate server may process that raw data to create the data requested by the acquirer (e.g., perform one or more acts on the data, including operations, analysis, and the like). For example, if the system has AFE data derived from sensors placed on the chest and the request is for ECG data, the system may convert the AFE data to ECG data to satisfy the request. To create the requested data, the intermediate server 22 may use one or more developed tools (e.g., tools created by a monetization system or by the system operator), or may incorporate one or more third-party tools housed internally, or may transmit the data (e.g., raw data) to one or more third-party analysis systems, in which case the intermediate server receives the data requested by the acquirer before distributing it to the acquirer. When transmitting the data to the acquirer, the intermediate server records the characteristics of the data provided as part of the transaction. These characteristics of the data include at least one of one or more sources of the animal data, a timestamp, specific individual attributes, one or more types of sensors used, sensor properties, sensor parameters, sensor sampling rate, classification, data format, data type, algorithms used, data quality, and the rate at which the data was provided (e.g., latency).

[0046] In another variant, the monetization system 10 provides an alternative to an actual dataset (e.g., one generated by a user or data provider). For example, if the acquirer has one or more requirements that prevent the acquisition (e.g., purchase) of user-generated data (e.g., if the required data cannot be acquired within the required time frame), or if the acquirer cannot afford the acquisition cost for one or more existing animal datasets (e.g., if the purchase price is too high), or if the use case requested by the acquirer results in one or more datasets that cannot be found or acquired within the system, or if the acquirer cannot purchase a subset of the required actual animal dataset, the monetization system 10 may provide the option to purchase artificially generated data (e.g., artificial sensor data) that is created (e.g., generated) and / or derived from and / or based on at least a portion of the actual animal data (e.g., actual sensor data) and / or one or more derivatives that can be generated by the monetization system 10 via one or more simulations that conform to one or more parameters (e.g., requirements) set by the data acquirer. In this regard, the one or more parameters selected by the data acquirer determine the range of relevant actual animal data that can be used as one or more inputs to ensure that the artificial data is generated and / or the generated artificial output meets the requirements desired by the acquirer. For example, a pharmaceutical company or research institution may desire to acquire 10,000 two-hour ECG datasets from at least 10,000 unique males who are 24 to 25 years old, weigh between 175 and 185 pounds (79.4 kg to 83.9 kg), smoke 10 to 20 cigarettes per week, drink at least one alcoholic beverage two to three days per week, have a specific blood type, exhibit levels from biological fluids, and have a family medical history of diabetes and stroke.The monetization system may only have 500 data sets from 500 unique males that meet the minimum requirements of a particular search. Therefore, the monetization system can artificially create the remaining 9,500 data sets regarding 9,500 simulated males to meet the requirements of the pharmaceutical company. The monetization system may randomly generate artificial data sets (e.g., artificial ECG data sets) based on 500 sets of actual animal data using the required parameters. The new one or more artificial data sets may be created by applying one or more artificial intelligence techniques that analyze previously acquired data sets that match some or all of the characteristics required by the acquirer. One or more artificial intelligence techniques (e.g., one or more trained neural networks, machine learning models) can recognize the patterns of the actual data sets and can be trained by the data collected to understand the biology and related profiles of animals (e.g., humans), and can be further trained by the data collected to understand the effects of one or more parameters (e.g., variables, other characteristics) regarding the biology and related profiles of animals, and further, artificial data can be created taking into account one or more parameters selected by the acquirer so as to match or conform to the minimum requirements of the purchaser. In an improvement, the simulated animal data is at least partially generated from the actual animal data collected. In another improvement, one or more statistical models are used. Additional details related to the system for generating simulated animal data and models, as well as examples of ways in which one or more trained neural networks can be utilized within the monetization system, are disclosed in U.S. Patent Application No. 62 / 897,064, filed on September 6, 2019, the entire disclosure of which is incorporated herein by reference and is applicable to any reference to artificial data within this specification.One or more artificial datasets can be created based on various criteria, including a single individual, a group consisting of one or more individuals having one or more similar characteristics, a random selection of one or more individuals within a group where one or more characteristics are defined, a random selection of one or more characteristics within a group where one or more individuals are defined, a defined selection of one or more individuals within a group where one or more characteristics are defined, or a defined selection of one or more characteristics within a group where one or more individuals are defined. Typically, one or more artificial datasets created through one or more simulations and derived from at least a portion of actual animal data share at least one characteristic with the actual animal data. Based on the purchaser's requirements, the monetization system can ensure reproducibility in creating the dataset by separating a single variable or multiple variables to maintain the data in both relevance and randomness. Additionally, the actual data underlying the simulation and / or one or more of its derivatives may be purchased individually, or may be packaged as part of the acquisition of simulated data, or may be used at least partially as a baseline for creating artificial data. If an organization requests simulated data, one or more individuals whose data was used in one or more simulations (e.g., for training one or more neural networks) may receive consideration, at least in part.

[0047] In addition to generating new datasets, the creation of simulated data may also be utilized to augment previously collected actual datasets. For example, a system that has access to a specific amount of dataset (e.g., in-game data over 10 hours, 100 hours, 1000 hours, or more for sports player A) regarding any given activity, including different types of data and metadata (e.g., in the context of sports such as tennis, temperature on the court, humidity, average heart rate, oxygenation data, data from biological fluids, distance traveled, swing speed, energy level, shot power, point length, positioning on the court, opponent, opponent's performance under specific environmental conditions, win rate, opponent, win rate % against the opponent under similar environmental conditions, current match statistics, past match statistics based on performance trends in the match, date, timestamp, points won / lost, score) can use one or more artificial intelligence techniques to expand the dataset by reproducing at least a portion of an event (e.g., a match) that a given sports player may not have played, and / or generate artificial data regarding sports player A within the reproduced event (e.g., sports player A played a 2-hour tennis match and heart rate data for that case was acquired, but the user is asking for heart rate data at the 3-hour mark of a match that has not yet been played but will be played in the future. Thus, the monetization system can create data by performing one or more simulations.). More specifically, by using one or more of these datasets to train one or more neural networks, one may come to understand the biological functions of sports player A, as well as how one or more variables affect any given biological function. By further training the neural network, one can understand what one or more results will occur based on one or more biological functions and based on the influence of one or more variables, thereby enabling correlation analysis and causal analysis.After a neural network in a monetization system has been trained to understand information such as one or more biological functions of sports player A in any given scenario including the current scenario, one or more biological functions exhibited by sports player A, and / or based on one or more current variables, one or more results that occurred previously in any given scenario including the current scenario, one or more biological functions of sports players similar and dissimilar to sports player A in any given scenario including scenarios similar to the current scenario, one or more other variables that can affect one or more biological functions of sports player A in any given scenario including scenarios similar to the current scenario, one or more variables that can affect one or more biological functions of other sports players similar and dissimilar to sports player A in any given scenario including scenarios similar to the current scenario, one or more results that occurred previously in any given scenario including scenarios similar to the current scenario based on one or more biological functions exhibited by sports players similar and dissimilar to sports player A and / or based on one or more variables, etc., the data acquirer may, for example, request the execution of one or more simulations to expand the current dataset using artificially generated data (e.g., sports player A has played for 2 hours and various biological data including heart rate have been acquired. The acquirer requests heart rate data at the 3-hour mark under the same game conditions. Therefore, the system may create data based on the previously collected data by executing one or more simulations.), or may predict the results that occur for any given activity (e.g., based on looking only at the data of sports player A, it may be possible to predict the likelihood that sports player A will win the final set against opposing sports player B).In an improvement, one or more neural networks may be trained using one or more animals (e.g., sports players) within a team or within a group or competing against each other, and by training one or more neural networks using one or more data sets from each animal, one or more results (e.g., whether sports player A will win a match against sports player B) may be predicted more accurately. In this example, by performing one or more simulations, artificial sensor data may first be generated based on actual sensor data, and then by utilizing at least a portion of the generated artificial sensor data in one or more further simulations, the likelihood of any given result may be determined.

[0048] In another example, an airline may wish to determine whether to extend a pilot's retirement age, and a hospital may wish to determine whether a given surgeon should continue to perform surgeries beyond a certain age. By performing one or more simulations, the airline or hospital can facilitate an analysis that enables the airline or hospital to generate one or more artificial datasets that extend the current one or more datasets collected by the system, and thereby perform one or more actions that can determine probabilities and / or reduce risks. In the example of the airline, even if any given n-year-old pilot (e.g., 65 years old) for whom data has been collected by the system exhibits certain biological characteristics that may include either physiological or biomechanical characteristics, the question of whether to allow continued flight beyond a certain age may arise. More specifically, rather than imposing a mandatory work stoppage (e.g., retirement) based on metrics such as a person's age, determining a pilot's biological "fitness" and predicting future biological fitness can be in the best interests of the airline, as the pilot's experience can lead to a safer overall flight experience and / or enable flights on more routes to expand the business. Thus, the system may utilize the collected data (e.g., heart / ECG data, age, weight, habits, medical history, body fluid levels) for any given pilot and perform one or more simulations with various selected parameters (e.g., during sleep, during flight) to generate one or more artificial datasets (e.g., by expanding the dataset collected for the pilot and creating artificial sensor data to check the heart activity of future pilots aged 66 to 80 years and determine the biological "fitness" and "flight fitness" according to the pilot's age).In the case of a hospital, the question can be whether to allow any given surgeon to continue performing surgeries beyond a certain age, even if that surgeon exhibits specific characteristics that may include either physiological or biomechanical characteristics, and the advantage can be that the experience of the surgeon, which can lead to saving more lives, can be utilized.

[0049] In an improvement, the simulation can provide one or more probabilities or predictions related to what results will occur in the future. For example, if an airline desires to know the likelihood that any given pilot exhibiting certain physiological characteristics will have a heart attack during a flight, one or more simulations utilizing at least a portion of the pilot's animal data can be run, and by using the output, the probability of the event occurring can be determined, or a prediction related to a future event can be made. In another example, if an insurance company desires to know the likelihood that any given person having certain characteristics (e.g., age, weight, height, genetic makeup, medical condition) will experience one or more physical illnesses (e.g., stroke, diabetes, virus) within a given period (e.g., 24 months), one or more simulations utilizing at least a portion of the actual animal data can be run while using these characteristics as one or more inputs, and by using the output, the probability of the event occurring can be determined. In another example, if a pharmaceutical company desires to better understand the probability that an existing pharmaceutical will have a specific effect on one or more individuals having certain characteristics, the monetization system can determine the probability of the event occurring by running a plurality of simulations (e.g., 10, 100, 10000, or more simulations). In yet another example, if a team desires to know the likelihood that athlete A of a sports team will make the next shot based on exhibiting certain physiological characteristics and also based on other collected data, one or more simulations utilizing at least a portion of athlete A's animal data can be run, and by using the output, the probability of the event occurring can be determined.

[0050] In a variation for creating one or more simulated data sets, existing data with one or more randomized variables is re-run through one or more simulations, thereby creating new data sets that the system has not seen before. This method can be used to investigate one or more probabilities associated with one or more results. For example, if a monetization system has a data set regarding a particular individual (e.g., a sports player) and a particular event (e.g., a game in which the sports player played), the system may have the ability to recreate and / or modify one or more variables within the data set (e.g., elevation, temperature on the court, humidity) and re-run one or more events through one or more simulations, thereby generating simulated data outputs for a particular scenario. For example, in the context of tennis, the acquirer may desire the heart rate data of Player A at the one-hour mark when the temperature is 95 degrees or more over the course of a two-hour game. The system may have one or more heart rate data sets at different temperatures (e.g., 85, 91, 94), the above-described inputs regarding Player A under similar conditions, and the above-described inputs regarding other similar and dissimilar players under similar and dissimilar conditions. Since the heart rate data regarding Player A at 95 degrees or more has not yet been collected, the system can create the data by running one or more simulations and then utilize that data in one or more further simulations. In another example, the acquirer may desire the probability that Player A wins the game. In an improvement, the system may also be programmed to create or recreate one or more new data sets by combining dissimilar data sets. For example, the acquirer may desire data regarding the heart rate of Player A at the one-hour mark when the temperature is over 95 degrees over the course of a two-hour game in a particular tournament, in which case one or more characteristics such as elevation may affect performance.Although this data has not yet been fully collected, different datasets can contain the required data (e.g., one or more datasets featuring heart rate from Player A, one or more datasets from Player A playing tennis at temperatures over 95°F (35°C), one or more datasets with the required features such as elevation in the required tournament). The system may identify these required parameters within and between datasets and create one or more new artificial datasets that meet the acquirer's requirements based on these dissimilar datasets by performing one or more simulations. In a variant, the dissimilar datasets used to create or recreate one or more new datasets may feature one or more different objects that share at least one common characteristic with the subject individual (which can include, for example, age range, weight range, height range, gender, similar or dissimilar biological characteristics, and the like). Using the above example, although heart rate data can be utilized regarding Player A, the system may utilize one or more other datasets from Players b, c, d selected based on their relevance to the desired dataset (e.g., some or all players may exhibit a heart rate pattern similar to Player A, some or all players may have measurements from biological fluids similar to Player A, some or all players may have a dataset featuring playing tennis at temperatures over 95 degrees in the datasets collected by the system). These one or more datasets may function as inputs into one or more simulations to more accurately predict Player A's heart rate under the desired conditions.

[0051] In another method for simulated data, a randomized data set is created and one or more variables are selected by the system rather than the acquirer. This can be particularly useful, for example, when an insurance company is looking for a particular data set (e.g., 1,000,000 smokers) from a random sample (e.g., where neither age nor medical history is defined and can be randomly selected by the system). In an improvement, one or more artificial data sets are created from a predetermined number of individuals randomly selected by the system.

[0052] In another example, data that is at least partially derived from actual animal data may be obtained as part of a video game or game-based system, or may be utilized within a video game or game-based system. The video game or game-based system may be played within various consoles and systems provided, including conventional PC games (e.g., Nintendo, Sony PlayStation), portable games, virtual reality, augmented reality, mixed reality, and extended reality. The video game or game-based data may be derived from one or more simulations and / or may be artificially created based on at least a portion of the animal data, and can be associated with one or more characters (e.g., animals) that are incorporated as part of the game. The character may be based on an actually existing animal (e.g., an actual soccer player may have a character depicting themselves in a soccer video game), or may be artificially created and may be based on one or more characteristics of one or more actual animals or may share such one or more characteristics (e.g., a soccer player within the game may share a jersey number, jersey color, or biological characteristics, in the same manner as a human soccer player). The system may allow a user of the video game or game-based system to purchase the data, or may allow the purchase of a game that utilizes at least a portion of the actual data within the game. In an improvement, the animal data purchased within the game may be artificial data that can be generated via one or more simulations. This data may be utilized, for example, as an indicator regarding events within the game. For example, a gamer may have the ability to play against a simulated version of an actual sports player within a game that utilizes "real-world data" that may include the actual sports player's biological data in the real world or one or more derivatives thereof. This may mean, for example, that "energy level" data of an actual sports player collected over a long period of time is integrated within the game.In one specific example, the "energy level" of a sports player within a video game may be adjusted based on, or may be affected by, real-world data collected by an actual sports player, depending on the length of a match within the video game or the distance run by a sports player simulated within the video game. Real-world data can show how a sports player's fatigue level increases based on the distance run or any given length of a match. This data may also be utilized to obtain advantages within the game, such as the ability to run faster, jump higher, have a longer energy lifespan, or hit a ball farther, etc. FIG. 19 shows one example of a video game where a user can purchase a type of artificially generated animal data (e.g., "energy level") based at least in part on actual animal data, thereby providing an advantage to the user of the video game. In another example, in-game artificial data derived from or sharing at least one characteristic with animal data can also provide one or more special forces, derived from one or more simulations, to one or more objects within the game. In another improvement, one or more individuals providing at least a portion of animal data and / or one or more of its derivatives to a video game or a game-based system may receive consideration in exchange for providing such data. For example, a certain tennis player may provide their biological data to a video game company so that game users can play as or against a virtual representation of that tennis player. In this scenario, the user may pay a fee to the video game company for access to the data or its derivatives (e.g., artificial data generated based at least in part on actual animal data), and a portion of that fee may be paid to the tennis player.Alternatively, the video game company may pay a licensing fee to the sports player or offer another consideration (e.g., a percentage of the game's sales or a percentage of the sales of data-related products) for using the data within the game. In another example, the video game company may enable one or more bets / wagers to be placed on the game itself (e.g., between the user and the star tennis player) or on in-game proposition bets (e.g., microbets based on various aspects within the game). In an improvement, one or more prop bets are based on at least a portion of the animal data and / or one or more of its derivatives (including simulated data). In this situation, the user and / or the star tennis player may receive a portion of the consideration from each bet placed, and / or from the total number of bets, and / or from one or more products created and / or offered and / or sold based on at least a portion of the data.

[0053] Although the present invention is not limited to a specific application for using simulated data, such data can be used as a baseline or input for testing and / or changing and / or modifying sensors and / or algorithms and / or various hypotheses. This artificial data can be used for execution for various simulation scenarios from training to performance improvement. The potential reason for using artificial data based on actual data is that artificial data can significantly reduce costs compared to actual data. While one or more specific rights may be associated with actual data, artificial data based on the patterns or knowledge of actual data may not be (or may be limited) associated with rights and can therefore be obtained (e.g., purchased) at a much lower cost. Moreover, data generated from one or more simulations can be used in a wide range of use cases, including as a control set for identifying problems / patterns in actual data, as an input for further simulations, or as an input to an artificial intelligence or machine learning model in the role of a test set or training set or a set with identifiable patterns. For example, by using this system to modify a dataset created based on actual data from a specific individual, a deviation corresponding to characteristics such as fatigue or a rapid change in heart rate can be introduced into the data. Using this modified data, simulations can be performed to see how an individual performs, for example, in a stressful situation or under specific environmental conditions (e.g., high altitude, high temperature on a court). Such simulations can be particularly useful in fitness applications, insurance applications, and the like. In the case of humans (e.g., sports players) or other animals, the system may establish a pattern between biological evaluation criteria (e.g., heart rate, respiration, position data, biomechanical data) and the likelihood of an event occurring (e.g., winning a specific game).In this situation, the monetization system can calculate probabilities for specific conditional scenarios (e.g., "hypothetical" scenarios and likely outcomes).

[0054] As described above, the intermediate server receives animal data in raw form or in a processed form. In this regard, the intermediate server can perform one or more actions on the animal data. For example, the intermediate server can normalize the animal data, associate a timestamp with the animal data, aggregate the animal data, apply tags to the animal data, store the animal data, manipulate the animal data, remove noise from the animal data, enhance the animal data, sort the animal data, analyze the animal data, synthesize the animal data, replicate the animal data, summarize the animal data, anonymize the animal data, visualize the animal data, synchronize the animal data, display the animal data, distribute the animal data, keep a ledger regarding the animal data, and perform at least one action selected from these combinations to handle the animal data.

[0055] In another embodiment, the system can be utilized as a tool to test, establish, and / or verify the accuracy, consistency, and / or reliability of sensors or connected devices. Similarly, multiple sensors that each generate a labeled output (e.g., heart rate) may use different components (e.g., hardware, algorithms) to derive their outputs. This means that, for example, the output from one device, such as a heart rate, may not be the same when compared to the heart rate from another device. Due to the system's ability to bypass native applications and perform operations on the data, including data normalization and / or synchronization, the user has the ability to relatively compare "like with like" as needed, compare the output of each sensor with the corresponding hardware / firmware and one or more algorithms that derive each output (e.g., raw data, processed data), and, in so doing, provide the context regarding the data (e.g., the activity during which the data was collected) and further eliminate other variables that may affect the output (e.g., transmission-related, software-related). By fairly testing and comparing the hardware or one or more algorithms or outputs of each sensor or connected device (e.g., against specified criteria), quantifiable results are ensured. The ability to obtain quantified results regarding each sensor type and its corresponding components enables the user to select specific sensors and / or algorithms for a given group of participants based on any given requirement or use case (e.g., activity), and eliminates the major variables associated with the sensors as typically seen when using different hardware components or subordinate hardware components (e.g., different sensors that obtain the "same" output) or different algorithms. This process removes potential variables that can affect the results and ensures the reliability of the data by the user. Similarly, it provides a quantitative approach for a data acquirer to select one or more sensors and / or assign a premium value to any given output. Also, the system enables assigning a premium value to any given output.

[0056] Another aspect of the monetization system is the recovery of consideration for animal data. When transmitting animal data to a user, the intermediate server monitors and / or records the recovery of consideration for the provided animal data. The recovery of consideration may occur simultaneously with the transaction or at a later time. In an improvement, the recovery may occur before transmitting any data to the acquirer. Advantageously, the animal data can be provided on a marketplace for such sale or acquisition of animal data or on other media. Typically, the data acquirer (e.g., purchaser) purchases or acquires at the price or value set by the data provider. The marketplace can register data from any type of individual with various characteristics (e.g., age, height, weight, hair color, eye color, skin color, etc.), regardless of the presence of pre-existing diseases (e.g., diabetes, hypertension, kidney disease), from any location (e.g., on Earth, in space), using any type of sensor for data collection for any imaginable activity. In an improvement, the monetization system may define the data types required in the marketplace based on potential demand determined from items such as search results by the data acquirer, and may encourage the data provider to provide specific data for which the data provider receives a fee after the sale of the data. In another improvement, the data acquirer can define criteria regarding one or more individuals, one or more locations, one or more sensors, one or more activities, and whether video of one or more activities is required, and can set the price of the data for the data provider to accept or reject. The marketplace enables the data acquirer to collect data from the data providers whose applications have been accepted in real time or within a deadline set by the data acquirer. For example, if a sensor manufacturer desires to collect data from n individuals and also desires those individuals to follow specific instructions (e.g., activities or movements), the sensor manufacturer can present to each individual what to do (e.g., live or on a delay basis) by initiating a video conference.Advantageously, this process may enable the data acquirer to utilize the artificial intelligence and machine learning capabilities of the monetization system to determine whether the data being collected by each individual is actually actionable data, rather than having the data acquirer wait until the entire dataset is collected. For example, if the sensor manufacturer does not require data in real time and there is no need to explain the data collection method, individual data providers can collect the data at their own time within the deadline and upload it through the monetization system. The marketplace also incorporates a feedback mechanism that allows the data acquirer to evaluate, for example, the quality of each individual's data collection, their compliance, reliability, timeliness, and attention to returning sensor, hardware, and other attributes. Some components of the feedback evaluation are driven by the monetization system as needed, such as the timeliness of data submission.

[0057] In a variant, the data acquirer can set a price or value for the animal data or can place one or more bids to acquire the animal data. In another variant, the monetization system at least partially determines the value of the animal data based on one or more variables (e.g., time, demand, scarcity, sensor from which the data is derived, quantity). In a further refinement, the data acquirer can place one or more requests / bids regarding data from one or more subjects that have or used one or more characteristics (e.g., specific individual attributes, type of data, type of sensor used) requested by the data acquirer. The data acquirer may or may not know the identity of one or more subjects in response to the request. In another refinement, the data provider can bid on the data acquirer's data request.

[0058] Figures 2 to 17 illustrate the functions of the monetization system of FIG. 1 that can be deployed within a web page or within a window for a dedicated program or computing device (e.g., a smart device) application. FIG. 2 illustrates a window 100 through which a user (e.g., a data acquirer, a data provider) can interact with the above monetization system. The term "window" is used to refer to a web page and / or a window for a program or computing device (e.g., a phone, a tablet, etc.) application. Window 100 includes a control component 102 selected by the user to identify as a data provider, or a control component 104 selected by the user to identify as a data acquirer. Each of the control components 102, 104 is illustrated as a "button". With respect to each of the control components shown in FIGS. 2 to 17, it will be understood that control components such as selection boxes, drop-down lists, buttons, and the like can be used interchangeably. In an improvement, one or more control components may be replaced with one or more linguistic or neurological or physical or other communication cues, including communicating commands using a voice-activated assistant, or communicating commands by physical gestures (e.g., finger swipes, or eye movements), or communicating commands neurologically (e.g., a computing device such as a brain-computer interface may acquire one or more brain signals of an object from neurons, analyze the one or more brain signals, and further convert the one or more brain signals into commands that are relayed to an output device to perform a desired action. Acquisition of brain signals can be performed via many different mechanisms, including one or more sensors that can be implanted in the brain of the object.). This can also be applied to components such as login authentication information required to access the monetization system.The data provider and the data purchaser can each independently be an individual (e.g., a person) or a group (e.g., an administrator of a company, organization, or group) representing one or more individuals, or one or more individuals or groups. Window 100 also includes a selection box 106 where the user can select non-live data (e.g., previously collected data), or a selection box 108 where the user can select live data. Live data includes data collected in real-time or near real-time, or data collected within a time frame where data collection is made available during an activity / event or while the occurrence of the activity / event is still ongoing. In an improvement, upon selection of box 108, the user may also be able to search for and obtain at least a portion of the non-live data.

[0059] Figure 3A illustrates the window presented to the data provider after the selection of the control component 102 in Figure 2. Prior to Figure 3A, login authentication information may be presented. Window 110 is an initial settings page for an individual. Window 110 includes a section 112 where the creator or administrator / manager (e.g., user) of the data can enter various individual attributes of the object. In the case of a human, this includes age, height, personal history, social habits, and the like. One or more fields provided by the system can be added by the user (e.g., data provider) if the data acquirer wants to provide additional information to create a more targeted search (e.g., blood type). One or more photos or visual representations of the user may also be uploaded and made available via button 127. Window 110 also includes a section 114 for entering medical history information, a section 115 for entering medication history, and a section 116 for entering family history. The exemplary fields only present a sample list of potential input parameters. Also, more detailed data including an individual's history (e.g., surgeries, fractures, abuse, other illnesses), genetic information / genomic information related to the individual (e.g., one or more data sets related to an individual's DNA sequence, protein sequence and structure, RNA sequence and structure, gene expression profile, gene-gene interaction, DNA-protein interaction, DNA methylation profile) and the like, and other types of personal information may be included or uploaded. The user may also upload additional personal information such as biospecimen data, which can be collected using one or more sensors and may include information derived from blood (e.g., venous, capillary), saliva, urine, and the like. One or more data types collected can be made into one or more searchable parameters created by the system.In the improvement, one or more types of biological fluid data may be combined into one or more groups, including groups related to one or more tests or panels (e.g., complete blood count, comprehensive metabolic panel, renal function panel, electrolyte panel, basic metabolic panel, hepatitis panel, and the like) and test categories (e.g., information related to estradiol level, prolactin level, progesterone level, DHEA-sulfate level, and follicle-stimulating hormone level may be classified as part of female reproductive health tests), thereby enabling more efficient search and data acquisition parameters. This may be useful, for example, when the acquirer is interested in examining one or more biological components or functions (e.g., the health of the liver and kidneys) across one or more subjects using the same data input. In another improvement, the monetization system may be operable to enable one or more search functions (e.g., including the creation of one or more groups) based on variations within the data. For example, the acquirer may have the ability to search for individuals who exhibit variations or ranges within a particular biological characteristic (e.g., individuals with a blood glucose level less than 100 mg / dL, a potassium level between 5.1 mEq / L and 6.0 mEq / L, a red blood cell count between 4.9 million and 5.8 million per microliter of blood for males, and the like). Similar to other collected animal data, biological fluid information may be information that the acquirer is interested in obtaining as complementary information related to the dataset (e.g., a person obtaining heart-based data may desire to use data related to biological fluids from an individual as a parameter, such as an acquirer who requests ECG data from an individual with a low white blood cell count or red blood cell count) or as the data itself (e.g., raw or processed information collected from one or more sensors and derived from biological fluids as one or more datasets). In another improvement, the user may upload artificial data (e.g., computer vision data) that shares at least one characteristic with actual biological animal data.

[0060] Note that FIG. 3A merely shows a sample of potential personal parameters that the system can provide, and that at least some of them can be adjustable parameters and can be added by the system as one or more searchable parameters. The control component 119 provides one or more recommended groups for the user to participate in based on the information provided to the monetization system (e.g., individual information, sensor information, activity information, data information). Finally, the control component 118 can be used to search for one or more terms (e.g., group name, characteristics of one or more individuals or sensors, activities where sensor data was collected) to associate the data presented within the window 110 with previously created groups, while the control component 120 is used to create new groups. In an improvement, one or more groups are automatically assigned or associated with an individual profile by the system based on the input data. FIG. 3B shows a list 122 of tags 124 created in relation to the selection and input data within the window 110. As shown in FIG. 3B, a tag is created by the system for each input characteristic (the column on the right). These tags may be exactly matching based on the data input (e.g., "male" if the subject is male), or these tags may be created based on inferences or created classifications so that the data acquirer can more easily search for data based on the desired parameters. For example, if the user is a smoker who smokes 20 to 40 cigarettes per week, the monetization system may create a tag called "social smoker" inferred based on the number of cigarettes smoked per week (and the monetization system's determination that smoking 20 to 40 cigarettes is considered social). Tags may also be created retroactively or dynamically based on a request from the data acquirer or based on some other consideration (e.g., a request based on an increase in the number of searches may result in new tags being created for previously collected data).The user can also add themselves to a group or create a group for which additional tags will be created for an individual. These groups can represent many different link characteristics or metrics. For example, the group can be a team to which an individual belongs. The group can be two or more people who utilize a specific process and methodology, whereby data can be collected more accurately (which can be considered of higher value than other data collection processes and methodologies). The association with the latter exemplary group can mean that one or more data sets associated with this group are of higher value to the data acquirer if the data acquirer attempts to obtain data using the specific process and methodology of that group. In an improvement, one or more associations (e.g., tags, groups) may be automatically assigned by the system to any individual or any data set by utilizing one or more artificial intelligence techniques. In another improvement, the monetization system may be programmed to deny the user's ability to assign one or more groups to any given user.

[0061] Figure 4 illustrates a window for providing sensor information. The window 110 in Figure 3A includes a control component 126 labeled "My Sensors" at the top. By activating the control component 126, a page 130 showing the user's active sensors 132 (e.g., sensors used for data collection) is displayed, enabling the user to view the sensor settings / parameters 134. In some cases, by enabling the monetization platform to communicate directly with one or more sensors, the user will have the ability to change the settings regarding one or more sensors within the platform. With the control component 133, one or more new sensors for collecting data from the user can be added. The addition of sensors can be done in several ways. For example, by clicking on the control component 133, the monetization system may be programmed to perform one or more actions including scanning and / or detecting and / or adding and / or pairing one or more new sensors and may include assigning one or more new sensors to an individual. However, the present invention is not limited by the manner in which the device is added.

[0062] Figure 5 illustrates a window for a user to manage data, including one or more sensors used to obtain the data within Figure 5, relevant evaluation criteria collected by a monetization system via the one or more sensors, metadata related to the collected data, one or more data types that may be made sellable, and the ability of a user to set a price for any data type from any selected sensor or dataset. By activating control component 136 labeled as "My Data" in Figure 3A, a window 140 is displayed showing the active sensors and the relevant evaluation criteria collected by those sensors. If the user is an administrator of multiple users, the user who is the administrator has the ability to select information for display related to the one or more users being managed. In an improvement, window 140 may also include data from sensors that are not active, and this may also be made sellable. Figure 5 also shows additional data 141 that may be made sellable. Data 141 can include data derived from sensors and obtained by the monetization system, or can include data uploaded via element 127 and made available for acquisition by a data provider. Window 140 also shows data records 142 collected by relevant data characteristics including IDs, timestamps, sensor settings, and the like. The user can also create an acquisition cost (e.g., price) that the user charges for the data based on one or more parameters including the sensors and data types. In an improvement, the user can create a data acquisition cost based on any parameter including time, the activity during which the data was collected (e.g., the cost for the user to participate in a particular activity can increase the cost of the data), and the like. The user can set parameters within window 140. The consideration may be established by the user via component 135. In an improvement, component 135 can include one or more fields that enable the user to set a value based on more detailed information (e.g., create value by activity).For example, even if the user uses the same sensor, a certain activity (e.g., participating in yoga for one hour) may establish a higher value compared to another activity (e.g., sleeping). The user can also choose whether to make their data available with identification information or anonymously. After establishing a fee 135 for the selected data and selecting the control component 129, the acquisition conditions 131 established by the user are displayed. The acquisition conditions established by the user can be adjusted or edited at any time by selecting the control component 137. In the improvement, the user can also have the ability to attach one or more additional conditions to the data in order to add value to the data. For example, if the user has a video regarding the activity at the time the data was collected, the user can upload the video and associate it with any specific data set by clicking on the left selection component 144 (e.g., selection box) and then clicking on the control component 146 labeled "Media Upload". Similarly, one or more photos regarding one or more sensors on the user's body, or other media related to the data, may also be uploaded. If the environment in which the data was collected (e.g., humidity, temperature, elevation), or other conditions that may affect the data (e.g., for a specific optical sensor, skin color / tattoo) are known, that information may also be added, and in that case, the system is made operable to identify one or more common characteristics (e.g., timestamp, location) between the collected data sets, thereby allowing the data sets to be linked to each other. In the improvement, social data or other forms of data related to the user or user group that can provide context or value regarding the collected sensor data may be uploaded. In another improvement, a premium may be applied to one or more data sets based on one or more tags related to the data and that can be dynamically assigned by the system.For example, if an individual's heart rate data is associated with a particular sports league, or if an individual is associated with a particular group that collects data using a process that enables more accurate data collection, the system may assign a premium value to one or more of the requested data sets. The assignment of the premium value may be done dynamically based on one or more factors (e.g., a new group for which a premium value is assigned to a data set is created later, and the demand for the data set increases over time, such that a data set that initially did not have a premium value comes to have a premium value). In some cases, the premium may be viewable by the user within region 131. In other cases, the premium may not be viewable by the user (e.g., if the premium is not assigned to the user, or if the premium is assigned dynamically at a later date). Another improvement is that two or more premiums may be applied or associated with any given data set. The multiple premiums may be associated with a given data set within region 131 based on one or more tags or considerations created or determined by the system, and the multiple premiums may occur simultaneously or at different times (e.g., the premiums may be assigned at a later date based on dynamic factors including increased demand at a later date, and based on tags that are created dynamically or automatically at a later date and with which a premium value is associated).

[0063] Figure 6 shows a window that provides additional details related to any given collected dataset and the ability to modify one or more aspects of any given dataset. If the user desires a more detailed view of the data, by activating the control component 148 in Figure 5, the window 150 in Figure 6 is displayed. If the user is an administrator of multiple users, the user who is the administrator has the ability to select information for the display related to one or more managed users and other characteristics related to one or more managed users or data. Figure 6 shows the details of the data collected by individual data providers (e.g., users). It will be understood that the window 150 lists the type of sensor, the location of the sensor, the sampling rate, the activity of the measurement object, the sensor output, and the quality assessment. Note that Figure 6 only shows a sample of the potential information that the system can provide, and that all of them are adjustable parameters. In some cases, the system may be programmed to add additional information (e.g., metadata, notes) related to the sensor or the collected data after the data is input into the system via a component 152 that may be made available as part of any given data acquisition. In addition, the system may be programmed to identify one or more details related to metadata that can be edited by the user or an administrator (e.g., a data administrator). For example, the administrator may have the ability to edit or add a specific type of descriptive information (e.g., activity) via an activation component 154. This ability may be deleted or added depending on the user or the dataset, or may be blocked or made effective by a monetization system based on the provided metadata. Moreover, if the user desires to further classify the data and tag the data, the user has the ability to assign additional group tags to a specific dataset or to receive recommended group tags from the monetization system.In an improvement, the monetization system may be programmed to deny a user's ability to assign one or more groups to any given dataset (e.g., if the user does not conform to a profile, or if the data collected does not meet one or more group requirements as determined by the monetization system or an administrator). The monetization system may also automatically assign tags to data without requiring any input from the data provider. For example, the monetization system may be made operable to identify groups of data collected together at the same time and under the same conditions by looking at metadata.

[0064] FIG. 7 is a summary page regarding the consideration collected by the system on behalf of a user (in this example, John Doe). By activating control component 125 labeled as “My Wallet” in FIG. 3A, a window 160 is displayed that presents a summary page showing the fees collected for any individual data provider. The total purchase price, which may include one or more premium values set by the system based on one or more tags associated with the data for each dataset, may differ from the fees collected because the received consideration or total purchase price may be distributed to one or more additional parties (e.g., a sensor manufacturer, an analytics company). As described on summary page 160, multiple stakeholders, including individual providers / creators of the data or group administrators, may claim some form of revenue for any single transaction. This page merely shows the fees received by each data provider. Additionally, it will be understood that an individual may sell the same dataset to multiple users at different purchase prices and at different times. The monetization system may also provide the purchaser with the ability to purchase the data exclusively, or may set custom parameters or restrictions regarding the purchaser's specific use (e.g., territorial rights, usage rights).

[0065] Figure 8 shows a scenario where a data acquirer requests non-live data (e.g., a historical dataset). Data acquirers for both live and non-live data can be represented by a wide profile including financial trading companies, sports teams, sports broadcasters, sports gambling-related organizations, local government bodies (e.g., police, fire departments), hospitals, healthcare companies, insurance organizations, manufacturing companies, airlines, transportation companies, pharmaceutical companies, military organizations, government agencies, automobile companies, communication companies, food and beverage organizations, ICT organizations, elderly care organizations, construction companies, research institutions, oil and gas companies, personal health companies, analytics organizations, other technology companies, individuals, and the like. When the data acquirer selects the control component 104 indicating that the user is a data acquirer and the selection box 106 within the window 100 of FIG. 2 indicating an interest in purchasing non-live data, the search window 180 shown in FIG. 8 is displayed, and prior to this, a request for login authentication information to identify one or more acquirers may be made. The data acquirer can select one or more data types to acquire from the search window 180. Note that FIG. 8 only shows a sample of potential search parameters that the system can provide, and that all of them are adjustable parameters. The parameters can be initially registered based on data collected by the monetization system, and such data can include information provided by the user in FIG. 3A, information provided by one or more sensors, information uploaded by the user, information derived from any of the collected information, and the like. The system can render initial data types for acquisition, but the data acquirer may have the ability to add one or more data types. Characteristically, two or more data types can be selected and searched simultaneously, whereby the data acquirer can acquire multiple types of data from each individual user. After selecting one or more data types, the data acquirer can add or select one or more parameters related to the profile of one or more individuals in whom the data acquirer is interested in data acquisition.Each search can be performed based on the preferences of the acquirer for anonymized data or identifiable data (e.g., data that can be associated with a particular individual or group). By clicking on the identifiable data, the acquirer may be able to select all the data collected from any selected user, or may be able to search for data sets within any user profile or group. As an example, this can be advantageous for an insurance company that may be interested in collecting all sensor data regarding a particular individual or group of individuals (e.g., a particular family, a soccer team, a control group for a particular disease). In an improvement, the acquirer may be able to access both anonymized search results and user-identifiable search results within the same search. For example, a user who may wish to view anonymized data regarding any given parameter may subsequently have the ability to view, via component 184, which identifiable individuals may be included within that search. In another improvement, animal data collected by the system is included as one or more profile search parameters regarding one or more individuals of interest. For example, the acquirer may wish to obtain n ECG data sets from individuals whose maximum heart rate exceeded 180 beats per minute while performing any given activity (e.g., yoga) over any given period (e.g., minutes). In such a case, the system can be made operable such that the data acquirer can add one or more fields that enable selection of one or more animal data-related search parameters.

[0066] Based on each parameter selected in FIG. 8, tags are created, whereby the monetization system can determine and find one or more individuals or datasets that match a given search criterion, as well as the data type desired by the acquirer (e.g., simulated data). When individual tags are created, the system may render the number of results of the search criterion, which may include the number of users that match the criterion and the number of available datasets. After the quantity of the initial search results is provided, the search can be narrowed, the data can be further filtered, additional tags can be created, and more defined search results can be rendered. For example, the monetization platform can be further programmed to search for and identify individuals in a desired pool of multiple individuals in which datasets characterized by one or more specific characteristics (e.g., activities, sensors used) were collected. Characteristically, at least a portion of the selected data may be simulated data. The data acquirer may select simulated data for any number of reasons, including cost (e.g., simulated data can be inexpensive), quantity (e.g., the acquirer may be able to obtain more datasets consisting of simulated data), acquisition time (e.g., it may be faster to obtain a simulated dataset compared to an actual dataset), and the like. The control component 181 labeled as "Next" is activated after the search criterion is specified and the system meets the requirements of the data acquirer. In an improvement, an option to purchase machine-generated artificial animal data may be provided to the acquirer. For example, the acquirer may desire to obtain computer vision data to train an artificial intelligence model for autonomous driving.

[0067] In some cases, the data acquirer performs a search based on the user's assignment of themselves to one or more groups. The groups may have a particular value based on the value provided by the group (e.g., a group with an impeccable data collection method, and thus, the purchaser desires to purchase data only from people associated with that group), or based on the characteristics of the group (e.g., a group with a particular medical condition, a group composed of teams, a group characterized by people taller than a particular height, a fitness class instructed by a particular instructor). In an improvement, groups can be created to show that data from multiple users is consistent and / or similar in one or more respects (e.g., the data was acquired at the same time, in the same location, and under the same conditions). Grouping may also be dynamically created by a monetization system based on one or more characteristics of the sensor data, or based on metadata related to the data (e.g., the metadata may indicate that all data was collected as part of a basketball game, or as part of a group yoga class, or as part of a data collection sleep study). The grouping or other tags may also have one or more premium values assigned to one or more data sets by the system. In a further improvement, the monetization system may have a feedback mechanism for evaluating each user who provides data with respect to a number of criteria including, but not limited to, the collection process, the willingness to provide video or images of the data collection period, the willingness and extent to follow instructions, the willingness to participate in video-led research sessions, and the like.

[0068] FIG. 9 shows a purchase window 190 that is displayed after a data acquirer locates and selects one or more datasets derived from the profile of one or more individuals of interest. The price or value proposal is created by the system based on one or more factors, including the number of requested datasets, the price or associated cost that each data provider charges for that dataset, the conditions associated with acquisition (e.g., exclusive, non-exclusive), and / or a premium that the system sets for one or more datasets. Note that one or more additional factors may be included within FIG. 9 to more precisely adjust the acquisition cost. This can include usage conditions (e.g., type of license, how the data can be used, when the data can be used, where the data can be used), requirements related to contractual terms (e.g., intellectual property rights related to the data), and the like. If there are multiple data providers in a position to offer one or more of the requested datasets, the monetization system may surface the best option based on the preferences of one or more data acquirers (e.g., highlight the least expensive option for the data acquirer). In an improvement, the monetization system may offer an auxiliary product or service or other value offering as part of the transaction. For example, the monetization system may offer the ability to purchase or acquire timestamped videos during the data collection period in addition to the acquired data, whereby the data acquirer can view the user's behavior during the data collection period. In another improvement, the system may offer the acquirer the ability to preview the videos and / or apply one or more artificial intelligence or machine learning techniques to determine the quality of the videos (e.g., acceptable videos, otherwise) and the usability for the acquirer (e.g., the data acquirer may desire that the data provider always face the front of the camera, and the artificial intelligence technique may allow the monetization program to identify whether the video meets this requirement).In a variant, monetization may enhance the video or add value to the video by applying one or more technologies, thereby creating an opportunity for the monetization system to sell at a higher price. In another improvement, the purchaser may have the ability to define the quality and / or usability of the video by selecting one or more parameters within the system. After the purchase is made by activating the control component 192, the monetization system may provide one or more opportunities for higher-value sales (for example, having an analysis tool or other analysis tools applied to the purchased data). One or more opportunities for higher-value sales (for example, analysis tools) may be housed within the system, created internally or by a third party, or transmitted to another system (for example, a third-party analytics company). One or more processes related to higher-value sales, performing one or more additional steps based on higher-value sales (for example, analyzing data within the system), and / or, if necessary, transmitting data to another destination (for example, an analytics company) as part of a higher-value sale and retrieving the data for distribution to the data acquirer can be done within the monetization platform.

[0069] Figure 10 shows a window 200 that includes a section 202 that enables a data acquirer to set prices for data sets and additional data-related offerings. In this scenario, the data acquirer activates a control component 194 labeled "Set Price" in Figure 9, thereby enabling the acquirer to set the purchase price for the data set it has requested (e.g., the collection of the requested data). The data acquirer can also set the purchase price for auxiliary services or add-ons related to the data set, such as a timestamped video related to the data collection, as shown in Figure 10. When the data acquirer selects the control component 204, the monetization system determines what the cost per data set (including any auxiliary services, if requested) is and notifies the data provider of the price presented for the data. The data provider will have a specified period (e.g., n hours or n days) in which to accept or reject the offer. The specified period is an adjustable parameter set by the acquirer or the system, and the acceptance or rejection of the offer may occur within the system or via a third-party system communicating with the monetization system (e.g., an email application, a mobile platform). The system may have customizable default settings for data providers who do not reply or communicate directly or indirectly with the monetization system (e.g., the offer may be automatically accepted or rejected), or for data providers who desire a minimum price for the data (e.g., the monetization system will automatically accept the offer as long as the acquisition offer is above the minimum price set by the data provider). The system may also choose to reject the offer based on a premium that the system would hold for the requested data set (e.g., the premium that the system would hold as part of the data set may be too low for the system to accept).

[0070] In the improvement, the data acquirer may request a completely new dataset from individuals with specific characteristics, and may also request that those individuals follow specific instructions (e.g., when to collect, how to collect the data, what activities to perform). To find such individuals, the data acquirer may post an "advertisement" describing the specific characteristics, requirements and instructions, as well as the fees paid to the data acquirer, within the monetization system. When the data acquirer selects specific characteristics of the individuals, the monetization system will display the number of individuals that match within the monetization system. For the matching individuals, a notice will be sent and they will be given the opportunity to accept the data acquirer's offer. A useful example of this type of mechanism is a sensor company that wants to collect data on sensors to increase the sample size and desires testing and tuning of the sensor hardware, algorithms, and software.

[0071] Figures 11 and 12 show examples of the display of a web page or window when one or more desired data sets have been selected but the requested one or more data sets are initially unavailable. For example, as shown in Figure 11, a potential acquirer (e.g., a purchaser) may use the search window 210 to search for data sets and find that the data sets meeting the search criteria are unavailable or unavailable in the quantity the purchaser is looking for. As part of that search, the user has the ability to select and add simulated data, including the number of simulated data sets requested, via the activation component 183, whereby it should be noted that the system can create one or more artificial data sets to meet any given request. In an improvement, the user will have the ability to select any combination of simulated data and, if available, user data collected, for acquisition by the data acquirer. In another improvement, the value of the simulated data may be adjusted based on one or more variables (e.g., the amount of data used, data quality). For example, a large amount of data used to train one or more neural networks in a simulation or more accurate and precise data may increase the value of the generated simulated data. If the number of data sets or the number of users is less than what is required in the data acquirer's search and the data acquirer does not wish to satisfy the request with simulated data, after the search criteria are specified, by activating the control component 182 labeled "Request Data", the window shown in Figure 12 is displayed. If there are no readily available data sets or if there are fewer than the desired number of data sets, contact is made with one or more individuals who match one or more of the parameters requested by the data acquirer, and it is determined whether data can be collected in a manner that matches the one or more requested parameters in exchange for a fee (e.g., a fee per data set or a fee for all data sets collected).In the improvement, when the monetization system can obtain data from one or more third parties and / or derive the required data from the collected data by cooperating with one or more analytics companies, it can create the required data, and / or create one or more analytics tools internally to derive the required data from the collected data, and / or create artificial data to satisfy one or more requests of the data requester for one or more data sets.

[0072] FIG. 13 presents an example of a display window 230 that a data provider would see to notify of the opportunity to create data according to the exact specifications and parameters of the data requester and receive consideration therefor.

[0073] Figure 14 shows a scenario where a data acquirer requests live data. To select live data, the data acquirer activates control component 104 and selection box 108 labeled as "Live Data" in window 100 of Figure 2. After login authentication information for identifying the data acquirer is provided, window 240 of Figure 14 appears and additional information regarding the dataset is displayed. First, at the top of the screen, purchase 242 of trend products that the platform can offer is displayed. For example, in the context of sports betting, such a trend purchase can be "Purchase the next 10 minutes of athlete A's heart rate" or "Purchase the last 0.5 miles (800m) of horse A's respiratory rate in the 3rd race". In an improvement, one or more offerings can be transmitted to a third party by a monetization system for display (e.g., within a sports betting platform or a game-based system). If the acquirer is looking for customized data or one or more specific types of data, the acquirer can select one or more parameters (e.g., date) and check which activities are available, such as in customization section 244. Then, the user can narrow down the search to obtain very specific data (e.g., real-time heart rate data of a specific sports athlete in the last 5 minutes of the 4th quarter) or very broad data (e.g., real-time heart rate data of a specific sports athlete over the entire season). In an improvement, the monetization system can be configured to enable more detailed data searches. For example, the data acquirer may wish to purchase an alert regarding all instances where the target person's heart rate exceeds n times per minute (e.g., 190 bpm) in a given game, or may wish to request an alert when the average heart rate of the target person in any given quarter exceeds n times per minute (e.g., 190 bpm), or may wish to obtain data related to the average "energy level" of team n in the 4th quarter of the past 3 games against team y.Note that FIG. 14 only shows samples of search parameters that the system can provide (and that all of these are adjustable parameters), and that the acquirer is able to access past data and other non-live data. The data acquirer can define the parameters necessary for its own use cases, as shown in Section 246. These adjustable parameters (e.g., data usage method, frequency at which data is transmitted to the acquirer, and the like) can affect the cost to the acquirer.

[0074] After defining the parameters in FIG. 14, the data acquirer activates the control component 248 labeled as "Next" to display the window 250 of FIG. 15. FIG. 15 presents a window 250 showing one or more right options related to potential acquisition (e.g., purchase). For example, if a purchaser desires the heart rate data of a reality show participant, the data purchaser may have the ability to define rights (e.g., license) related to the acquisition, including defining the usage period, the regions where the purchased data can be used (e.g., linear TV, digital), and the like. Note that FIG. 15 only shows a sample of the parameters that the system can provide, and all of these are adjustable parameters. Advantageously, the pricing model can be customized. For example, if the acquirer selects a specific delivery method (e.g., an API as in section 256), the user or administrator may have the ability to customize the method of allocating the consideration to one or more stakeholders. For example, the fee may be paid not as a flat acquisition fee, but per API call as shown in section 258, or per data transfer. In this example, if the acquirer requires one API call per second and desires real-time heart rate data for a person over a 10-minute period, the monetization system will prompt 600 API calls and bill the acquirer for each call. The purchaser may also have the ability to execute one or more data simulations and purchase the simulated data output. In any given scenario, this can be useful, for example, if the purchaser is interested in predicting the likelihood of the results, or if the purchaser is interested in having the system generate a prediction. For example, if the purchaser is interested in understanding the probability that a basketball player's heart rate will exceed 190 times per minute in the 4th quarter of the Xth game, one or more simulations can be purchased and executed to create simulated data and provide the desired probability output.In the improvement, the simulation system, and related fields, can be configured to utilize at least a portion of animal data, simulated data, or a combination thereof, to examine one or more potential outcomes. For example, if a purchaser is interested in understanding the probability or likelihood that Player B will win a match against Player C by utilizing at least a portion of animal data (e.g., real-time heart rate, respiratory rate, position data, biomechanical data), one or more simulations can be executed to create simulated data (e.g., predicting how Player B's animal data will appear during a match against Player C), and this can be used in one or more further simulations to generate a desired purchasable output (i.e., the likelihood that Player B will win the match). In another example, if an insurance company desires to know the likelihood that a subject with certain characteristics will develop a medical condition (e.g., a heart attack) within a specified period (e.g., within the next six months), the simulation system can identify individuals and datasets within the monetization system that share an individual and one or more characteristics (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, ECG data history, heart rate history, blood pressure history, genome / gene history, biofluid-derived data history), and by performing one or more simulations, a desired purchasable result can be determined. Note that the system can be made operable to perform any number of simulations across any number of objects. After the purchaser has determined their requirements, in window 250, the cost is displayed, along with control components 252, 254 labeled "Buy Now" to complete the purchase. In the improvement, the acquisition cost for any simulated data can be dynamically adjusted (e.g., increased) based on the opportunity for one or more neural networks to generate a more accurate output (e.g., provided by better data or higher-quality data or a larger amount of more relevant data).In this scenario, the value of the generated data may increase as the simulation becomes "smarter" and more accurate. In another improvement, window 250 may include the ability for a data acquirer to purchase one or more simulations that utilize at least a portion of the actual animal data and / or one or more derivatives thereof to convert the actual animal data into artificial animal data for use within a video game or game-based system (e.g., a fitness game). In yet another improvement, the monetization system may provide the ability to obtain at least a portion of the simulated data via a third-party display (e.g., within a video game, an insurance application, a healthcare application).

[0075] FIG. 16 presents a diagram showing an example of how revenue can be distributed from a single transaction. Record 260 indicates that a transaction has occurred and been recorded. Transaction record 262 displays one or more stakeholders that can be part of the revenue transaction based on the value added by each party. Each stakeholder is assigned a corresponding percentage that the stakeholder receives, depending on the degree of contribution to the value of the data sale, and this percentage may vary under many different scenarios, including per transaction, per user, per requested data, and per purchaser. The percentages are adjustable parameters and may be automatically assigned by the system or manually assigned by one or more administrators.

[0076] FIG. 17 presents a diagram of window 290 showing an example of an administrator window for adding or removing stakeholders and the percentage of consideration transmitted to each stakeholder for each transaction that can be part of any revenue transaction. The percentages are adjustable parameters, and in certain use cases (e.g., a live professional basketball game), there may be a need to periodically change the stakeholders and percentages at any given time. In an improvement, one or more of the percentages are created or adjusted by one or more artificial intelligence techniques.

[0077] As shown in FIG. 1, the intermediate server 22 executes a monetization program. When executed, the monetization program is defined by an integration layer, a transmission layer, and a data management layer. Regarding the integration layer, a user or administrator of one or more sensors can: (1) bypass the native system associated with the sensor by the monetization system communicating directly with the sensor, or (2) the monetization system communicates with a cloud or native system associated with the sensor or with another system storing sensor data via an API or other mechanism, enabling the monetization system to collect data into the monetization system's database in one of two ways. Direct communication with the sensor is achieved by the monetization system creating new code for communicating with the sensor or by the sensor manufacturer writing code for functioning in the monetization system. The monetization system may create a standard that multiple sensor manufacturers can follow for communication with the monetization system. The ability of the monetization system to communicate directly with the sensor may be two-way communication, which means that the monetization system may have the ability to transmit one or more commands to the sensor. By transmitting commands from the monetization system to the sensor, one or more functions of the sensor may be changed (e.g., changing gain or sampling rate, firmware update). In some cases, the sensor may have multiple sensors within a device (e.g., accelerometer, gyroscope, ECG) that can be controlled by the monetization system. This includes turning one or more sensors on or off and increasing or decreasing frequency or gain. Advantageously, the system's ability to communicate directly with one or more sensors also enables real-time or near real-time collection of sensor data from the sensor to the monetization system. The monetization system may have the ability to control any number of sensors and any number of functions and may have the ability to stream any number of sensors via a single system.

[0078] The transport layer manages direct communication with one or more sensors or one or more communications with one or more clouds. With respect to direct communication with sensors, the by-product can use a single hardware transmission system to (1) synchronize communication and real-time or near-real-time streaming with respect to a plurality of sensors that are directly communicating with the monetization system, and (2) act on the data itself, where the data can be transmitted somewhere or stored for later use. The hardware transmission system can be configured in various ways, can adopt various form factors, can use various communication protocols (e.g., Bluetooth, Zigbee, WIFI, cellular network), and further has functions in addition to simply transmitting data from the sensors to the system. Advantageously, by the monetization system communicating directly with the sensors, real-time or near-real-time streaming is made possible in a harsh environment where potential interference from other communications or radio frequencies can be a problem.

[0079] Regarding the data management layer, the sensor data input to the monetization system is in the structure of either raw data (data that has not been manipulated) or processed data (data that has been manipulated). The monetization system may accommodate one or more algorithms or other logic that deploy data noise filtering, data recovery techniques, and extraction or prediction techniques, thereby extracting relevant "good" sensor data from all the collected sensor data (both "good" and "bad"), or creating artificial "good" values if at least some of the sensor data is "bad". The system may be programmed to communicate simultaneously with multiple sensors on either one object or multiple objects, and may also have the ability to disambiguate those sensors in order to transmit sufficient information for the recipient to reconstruct the source of the data and who is wearing which sensor. For clarity purposes, this means providing metadata to the system receiving the data from the system to identify the characteristics of the data, e.g., a given data set belongs to timestamp A, sensor B, and object C.

[0080] After being received by the computing subsystem, the sensor data is either transmitted to the monetization system cloud or left locally on an intermediate server, depending on the required requests. The sensor data introduced into the monetization system is synchronized and tagged by the system, along with one or more other characteristics within the monetization system, with information related to the user (e.g., metadata), or with information related to sensor characteristics including timestamps, sensor types, and sensor settings. For example, the sensor data may be assigned to a specific user. Also, the sensor data may be assigned to a specific event in which the user is participating (e.g., a person is playing basketball in game X), or to a general class of activities in which the purchaser of the data may be interested (e.g., group cycling data). The monetization system may synchronize the timestamp with other non-human data sources (e.g., the timestamp related to the official time game clock in a basketball game, the timestamp related to a score, etc.). The monetization system may be schema-less and may be designed to ingest any type of data, and the data will be classified by characteristics including data type (e.g., ECG, EMG) and data structure. After the sensor data is introduced into the system, the monetization system may perform one or more further actions on the sensor data, including normalization, timestamping, aggregation, storage, manipulation, noise removal, enhancement, sorting, analysis, anonymization, synthesis, replication, summarization, productization, and / or synchronization. This ensures consistency across separate data sets. These processes may be performed in real-time or non-real-time, depending on the use case of the data recipient and user requirements. Considering that a large amount of data is streaming in from one or more sensors, and considering that this can be a significant amount, the monetization system may also utilize a data management process that may include a hybrid approach of the schema and format of unstructured and structured data.In addition, the synchronization of all input data may use a specific schema suitable for real-time or near real-time data transfer, thereby reducing latency, providing error checking, and further providing a security layer with the ability to encrypt some or all of the data packets. The monetization system directly communicates with other systems to monitor, receive, and record all requests for sensor data, and to make specific requests for the data required for the use case to the organization requesting access to the sensor data. For example, a request may be to obtain the heart rate of a specific individual in real time at a rate of once per second for 10 minutes. The monetization system may also associate these requests with specific users or specific user groups / user classes.

[0081] Another aspect of an effective monetization system is the advertising of products and services provided (e.g., created, presented) by the system. Animal data may be used directly or indirectly within an advertisement, engagement, or sales promotion on a web page or other digital platform (e.g., within a virtual reality or augmented reality system) for the purpose of prompting a user to click through to a third-party web page or other digital destination that directly or indirectly utilizes the animal data. One way to achieve this within a web page is to use an inline frame (Iframe), which can be an HTML document embedded within another HTML document on a website. By using an Iframe, content such as an advertisement from another source can be inserted into the web page. In some cases, the Iframe or widget is used for engagement purposes to increase the user's stay time on the page, which can be useful if the page has display ads that are updated at specified intervals (e.g., every 15 seconds), and to click through the user to another destination, typically a third-party site, and in exchange for consideration, provide (e.g., sell) a service, product, or benefit to the user. Additionally, an increased stay time on the page typically leads to a higher level of user engagement and can result in repeat visits to the site. There are other ways to provide services within third-party widgets (e.g., JavaScript), and the present invention is not limited by these other methodologies used. FIG. 18 presents a flowchart showing users (blocks 270, 272, 274) interacting with a web publisher site having an advertisement of animal data. In a particular type of advertisement, as shown in block 276, a potential data acquirer clicks through the web advertisement. In that case, the revenue from the data purchase can be shared between the web publisher (block 278) and the stakeholders (blocks 280 and 282) described above.For example, an insurance company may offer a sales promotion where the insurance premium is reduced, or a quote for insurance, or an offer to enroll in insurance at a specific price, to one or more users within a pre-specified range (e.g., age, weight, height, social habits, medical history, genetic / genomic information). When a user clicks through to a third-party site and provides animal data, the monetization system may enable the insurance company to perform one or more actions (e.g., perform one or more simulations to determine the probability that a person will have a heart attack within the next three years based on age, weight, height, social habits, medical history, the animal data collected, and other relevant information). In this example, based on one or more simulations and the one or more probabilities generated, the insurance company may decide to offer a benefit (e.g., a specified insurance premium rate, an offer to reduce the insurance premium) to one or more users based on the likelihood that one or more outcomes will occur. When a benefit is accepted, the monetization system may enable one or more stakeholders to receive a portion of the consideration (e.g., an analytics company or data management company that provided a report or performed one or more simulations), and the consideration may be derived from the revenue generated from new users (e.g., a portion of the insurance premium paid by the user) or from the consideration provided by the insurance company (e.g., the insurance company makes a payment to the monetization system for one or more services that may include data collection and the performance of one or more simulations). In an improvement, the insurance premium may be increased based on at least a portion of the animal data, and in that case, the monetization system may receive at least a portion of the increase. In another improvement, one or more users may request that one or more simulations be performed based on at least a portion of their animal data in order to provide information to a third party (e.g., an insurance company) for the purpose of receiving a benefit (e.g., adjusting the insurance premium or receiving another benefit).Consideration from such one or more simulations may be distributed to one or more stakeholders.

[0082] Advantageously, the products or services provided by the system may be utilized for game-based media provision (e.g., augmented reality, virtual reality). For example, animal data may be integrated as part of an augmented reality system that enables fans to watch a sports event live while data (e.g., heart rate, "energy level", location-based data, biomechanical data) is overlaid as part of the viewing experience. By the user consenting to the system using such data, the user and / or other stakeholders may receive consideration in exchange for the use of the data. For the monetization system to provide animal data to a fan engagement system such as an augmented reality system, the system may first use object recognition and tracking around a designated area (e.g., in the context of sports, around the play area 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 obtain available known image datasets to assist in filling in gaps within this list. Using sports as an example (although not limited to sports), the AR system may use 3D tracking for players and associated objects (e.g., tracking the movement of a ball). Based on the position of the player relative to the arena and other players, virtual objects may be placed such that the visualization is relevant to the play. Additional data from sensors such as location-based data (GPS), direction sensors, accelerometers, etc. may be used to fine-tune the placement of the player, and other data points such as elevation and latitude may be incorporated into the calculation of the 3D model. The system may also explore features in the environment around fixed known objects and attempt to recognize and replace relevant virtual objects within the overlay by tracking changes to those objects relative to several fixed points. The system optimizes the data transmitted to the mobile device, thereby performing rendering in real-time or near real-time.The system performs all 3D calculations by rendering complex data sets by using system resources via a ground-based system, an airborne system, or a cloud-based system. The extended object may include one or more types of animal data (e.g., including simulated data) that provide information related to one or more objects, or one or more derivatives from the animal data. The augmented reality system may also include a terminal for further engagement with the data (e.g., for placing bets). The terminal and / or the user's ability to engage with the data may be controlled via various mechanisms including, but not limited to, audio control (e.g., voice control), physical cues (e.g., head movement, eye movement, or hand gestures), neural cues, controls found within the AR hardware, or by a localized device (e.g., a mobile phone).

[0083] Although exemplary embodiments have been described above, it is not intended that these embodiments illustrate all possible forms of the invention. Rather, the expressions used herein are expressions for explanation rather than limitation, and it will be understood that various changes can be made without departing from the spirit and scope of the invention. Additionally, further embodiments of the invention may be formed by combining the characteristic points in various embodiments.

Claims

1. A system for monetizing animal data, comprising: a source of animal data that can be electronically transmitted and includes at least one sensor; and an intermediate server that receives and collects the animal data such that metadata is added to the collected data, the metadata including at least one of the origin of the animal data or the personal attributes of the individual from which the animal data originated, the intermediate server providing the requested animal data to one or more data acquirers for consideration, the intermediate server distributing at least a portion of the consideration to at least one stakeholder, the intermediate server including a single computer server or a plurality of interacting computer servers.

2. The system according to claim 1, wherein the animal data is human data.

3. The system according to claim 1, wherein the animal data is assigned to one or more classifications including an evaluation criterion classification, an insight classification, a personal classification, a sensor classification, a data property classification, a data timeliness classification, or a data context classification.

4. The system according to claim 3, wherein the one or more classifications associated with the animal data contribute to the creation or adjustment of the associated value regarding the animal data.

5. The data quality assessment of the animal data is provided to one or more stakeholders as part of the metadata or separately, the data quality assessment including one or more factors selected from the group consisting of accuracy, timeliness, data consistency, and data completeness.

6. The at least one sensor or one or more of its accessories are fixed to, or in contact with, or transmit one or more electronic communications related to, or derived from, the body or eyeball or vital organ or muscle or hair or vein or blood or biological fluid or blood vessel or tissue or skeletal system of the subject, or are implanted in the subject individual, or are indwelled or implanted in the subject individual, or are ingested by the subject individual, or are integrated to constitute at least a part of the subject individual, or are in direct contact with the subject individual or in contact with the subject individual via one or more intermediate members or communicate directly with the subject individual or communicate with the subject individual via one or more intermediate members and are integrated as a part of a fabric or cloth or clothing or material or equipment or object or device, or are fixed to them, or are embedded in them, the system according to claim 1.

7. The sensor is a biosensor that collects physiological data, biometric data, chemical data, biomechanical data, position data, environmental data, genetic data, genomic data, or other biological data from one or more subject individuals, the system according to claim 1.

8. The at least one sensor collects or derives at least one of face 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, skin electrical response data, sweating data, position data, position information data, audio data, biomechanical data, hydration data, heart-based data, neurological data, gene data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic data, thoracic electrical bioimpedance data, ambient temperature data, humidity data, air pressure data, altitude data, or a combination of these, the system according to claim 1.

9. The system according to claim 1, wherein the animal data includes one or more data sets resulting from one or more sensors from one or more individuals that are the subject.

10. The system according to claim 1, wherein the data of the individual that is the subject is combined with one or more data sets from one or more individuals that are the subject and share at least one similar characteristic, and is provided to the data acquirer as the collection of animal data.

11. The system according to claim 1, wherein one or more of the personal attributes include at least one component selected from the group consisting of name, weight, age, height, date of birth, gender, country of origin, region of origin, race, reference identifier, one or more social habits, ethnicity, one or more medical conditions, one or more places where the individual that is the subject lived, current place of residence, one or more activities in which the individual that is the subject was involved while the animal data was being collected, one or more related groups, information collected from medical records, social habits, social data, family history, past personal data, educational records, criminal records, employment history, medication history, social media records, data derived from biological fluids, data derived from genes, data derived from genomes, manually entered personal data, or combinations thereof.

12. The system according to claim 1, wherein the intermediate server communicates with the source of the animal data directly or via the cloud or via a local server.

13. The system according to claim 1, wherein the source of the animal data transmits the animal data to the intermediate server wirelessly or using a wired connection.

14. The system according to claim 1, wherein the source of the animal data transmits the animal data to the intermediate server using a hardware transmission system.

15. The system according to claim 1, wherein the intermediate server receives the animal data in raw form or in a processed form.

16. The intermediate server processes the animal data by performing one or more actions selected from the group consisting of normalizing the animal data, associating a timestamp with the animal data, aggregating the animal data, applying tags to the animal data, storing the animal data, manipulating the animal data, removing noise from the animal data, enhancing the animal data, sorting the animal data, analyzing the animal data, anonymizing the animal data, visualizing the animal data, synthesizing the animal data, summarizing the animal data, synchronizing the animal data, replicating the animal data, displaying the animal data, distributing the animal data, commercializing the animal data, accounting for the animal data, and combinations thereof. The system according to claim 15.

17. The system according to claim 16, wherein a value is assigned to the animal data or the value is adjusted based on the one or more actions as a related value based on the one or more actions.

18. The related value is used for at least one of acquisition, purchase, sale, transaction, licensing, lease, advertising, evaluation, standardization, certification, investigation, distribution, or mediation of acquisition or purchase or sale or transaction or licensing or lease or distribution with respect to animal data where an individual is identified or anonymized. The system according to claim 17.

19. The intermediate server monitors, receives, and records all requests regarding animal data by communicating with one or more other systems, and further uses at least one parameter established by the metadata, or one or more search parameters, or one or more other characteristics related to the sensor or data type or the individual or group of individuals or output being targeted, to provide one or more data acquirers with the ability to make one or more requests regarding animal data. The system according to claim 1.

20. When transmitting the animal data to another source, the intermediate server records one or more characteristics of the animal data provided as part of a transaction, and the one or more characteristics of the animal data include at least one of a source of the animal data, a timestamp, personal attributes, a type of sensor used, a property of the sensor, a parameter of the sensor, a sampling rate of the sensor, classification, a data format, a type of data, an algorithm used, a quality of the animal data, or a speed at which the animal data is provided. The system according to claim 1.

21. When transmitting the animal data to the one or more data acquirers, the intermediate server monitors and records the collection of consideration for the distributed animal data. The system according to claim 1.

22. The animal data is provided on at least one of an e-commerce website or platform. The system according to claim 1.

23. A data acquirer sets a price for the animal data or bids on the animal data. The system according to claim 1.

24. Based on one or more tags created by the system, or based on one or more characteristics of the animal data, or based on one or more personal attributes regarding one or more individuals targeted, a premium value is assigned to at least a part of the animal data. The system according to claim 1.

25. The at least one stakeholder is selected from the group consisting of a user who created the animal data, an owner of the data, a manager of the data, a data collection company, an authorized agent, a sensor company, an analysis company, an application company, a data visualization company, or an intermediate server company that operates the intermediate server. The system according to claim 1.

26. A system for monetizing animal data, A source of animal data that can be electronically transmitted and includes at least one sensor, and a source of animal data An intermediate server that receives and collects the animal data, wherein the intermediate server provides the requested animal data to one or more data acquirers for consideration, at least a part of the animal data being simulated animal data, the intermediate server distributing at least a part of the consideration to at least one stakeholder, and the intermediate server including a single computer server or a plurality of interacting computer servers, a system including the intermediate server.

27. The system according to claim 26, wherein the simulated animal data is at least partially generated from the collected actual animal data.

28. The system according to claim 26, wherein the simulated animal data is provided to potential data acquirers with at least one parameter randomly generated.

29. The system according to claim 26, wherein the simulated animal data is generated by one or more artificial intelligence techniques.

30. The system according to claim 26, wherein the simulated animal data is generated from one or more trained neural networks.

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