Method for translating animal data into a language understandable by ai models

Abstract

An animal data language system includes an animal language computing system configured to execute one or more animal data language models to infer discriminative information describing input animal data requiring analysis, the one or more animal data language models applying a biodata language derived from animal data, the biodata language including patterns of data that operate as one or more words, characters, symbols, signs, phrases, and / or units of digital information, the biodata language including a lexicon and syntax rules for the lexicon, wherein the animal language computing system is trained to receive the input animal data and to output the discriminative information in the biodata language.

Description

A SYSTEM AND METHOD FOR TRANSLATING ANIMAL DATA INTO A LANGUAGE UNDERSTANDABLE BY Al MODELS TO COMMUNICATE WITH AND ABOUT THEANIMAL BODYCROSS-REFERENCE TO RELATED APPLICATIONS[0001 J This application claims the benefit of U.S. provisional application Serial No.63 / 472,660 filed June 13, 2023, the disclosure of which is hereby incorporated in its entirety by reference herein.TECHNICAL FIELD

[0002] In at least one aspect, the present invention relates to a system and method for translating data coming from the human body into a language understandable by Al models such that the system can comprehend such data and communicate back to a user or other systems with insights, predictions, and suggestions.BACKGROUND

[0003] As more and more data is being collected from the human body using various means, such as on-body sensors, sensing systems, and the like, it is becoming increasingly clear that such data has value beyond the metrics shown such as heart rate, blood pressure, and the like. Such human data not only tells us about the current state of human body but it also contains signs and symptoms for what is about to come or in what state certain body functions and organs are.

[0004] Accordingly, there is a need for improved methods for characterizing animal data, and in particular, human data acquired from sensors and other systems.SUMMARY

[0005] In at least one aspect, a computer-implemented animal data language system is provided. The computer-implemented animal data language system includes an animal language computing system configured to execute one or more animal data language models to infer discriminative information describing input animal data requiring analysis, the one or more animal data language models applying a biodata language derived from animal data, the biodata languageincluding features (e.g., patterns) of data that operate as one or more words, characters, symbols, signs, phrases, and / or units of digital information, the biodata language including a lexicon and syntax rules for the lexicon. Characteristically, one or more animal data language models are trained to receive the input animal data and to output the discriminative information in the biodata language.

[0006] In another aspect, an interface in electrical communication with the animal language computing system for providing the animal data to the animal language computing system is provided.

[0007] In another aspect, the present invention provides a system that leverages one or more artificial intelligence-based large language models that include but are not limited to, generative pretrained transformers (GPT) models as well as a new biological data language (i.e., “biodata” language).|0008] In another aspect, a computer-implemented animal data language system is provided. The animal data language system can leverage one or more artificial intelligence-based large language models that include but are not limited to generative pre-trained transformers (GPT) models as well as a new “biodata” language.

[0009] In another aspect, one or more models are trained with large sets (e.g., large quantities / volumes) of biological data (e.g., including physiological data).

[0010] In another aspect, one or more models are trained with large sets of animal data (e.g., including biological data) and reference data (e.g., baseline data) about or related to the one or more individuals, which can include any animal data used as a reference to classify, categorize, or evaluate (e.g., compare, analyze) other animal data, as well as to derive information from other data.

[0011] In another aspect, one or more models are trained with large sets of animal data (e.g., including biological data) which includes reference data about or related to the one or more individuals and / or their associated animal data-based parameters (e.g., physiological parameters), which can include any animal data used as a reference to classify, categorize, or evaluate (e.g., compare, analyze) other animal data, as well as to derive information from other data.

[0012] In another aspect, the present invention provides a system that leverages GenAI models and techniques that includes, but is not limited to the use of Large Language Models (LLMs), DeepNeural Networks, Transformer Models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Model, and the like, as well as a new “bio-data” language.|0013] In another aspect, the above modelling techniques can be applied to prompt for new “biodata” language outputs that provide recommendations, analysis, reports, discriminative information, and the like.

[0014] In at least one aspect, the GenAI modelling techniques expand and extend the “biodata” language and its outputs as well as include audio and video content.

[0015] In another aspect, a new and unique “biodata” language can be applied to infer discriminative downstream tasks that include, but are not limited to, relation extraction, question answering, classification, recommendations, text generation, translation, suggestions, and the like.10016] In another aspect, the “biodata” language enables more efficient (e.g., faster, more comprehensive) identification, evaluation, and communication of inferred discriminative information in animal data (or its derivatives thereof), as well as more accurate and timelier (e.g., faster) insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, or recommendations related to one or more outcomes for one or more current or future events or sub-events associated with the one or more subjects based on inferred discriminative information, at least in part.

[0017] In another aspect, the “biodata” language optimizes biological-based communication between one or more Al-based models.

[0018] In another aspect, one or more models are trained with large sets of animal data and associated metadata (e.g., contextual data associated with the animal data).

[0019] In another aspect, the biodata language plays a central role in streamlining one or more Al-based actions (e.g., processes) with or related to animal data and bringing interoperability to one or more systems work together to create one or more cross-platform and / or system-agnostic solutions.

[0020] In another aspect, an animal data language training system includes a preprocessing module configured to receive input training animal data, perform noise reduction on the input training animal data, optionally normalize the input training animal data to a standard scale, and extractrelevant features from the input training animal data based on one or more physiological -based events. The system also includes a training module configured to train animal data language models (e.g., transformer networks, hybrid CNN-RNN, and the like) by applying supervised learning with labeled physiological data and to fine-tune the animal data language models by applying reinforcement learning with expert feedback. Additionally, the system includes a validation module configured to validate the performance of the animal data language models by applying performance metrics with a validation scheme that includes accuracy, precision, recall, and / or Fl score to form validated animal language models. Furthermore, the system includes an animal data processing and inference module configured to receive the validated animal language models and input inference animal data from at least one sensor monitoring physiological parameters of a subject. The input inference animal data (and the input training data) include physiological signals. This module is further configured to execute one or more animal data language models to infer discriminative information from the input animal data. The animal data language models apply a biodata language derived from animal data (e.g., the input inference animal data and / or the input training animal data). The biodata language includes a lexicon of one or more predefined units representing one or more physiological states and syntax rules for combining the one or more predefined units into one or more meaningful phrases, and to extract spatial features from the physiological signals.

[0021] In another aspect, an animal data language system includes an input interface module for receiving input animal data and a preprocessing module configured to perform noise reduction on the input animal data. The system also includes an animal language computing module in communication with the preprocessing module, which is configured to execute one or more animal data language models to infer discriminative information describing input animal data requiring analysis. The one or more animal data language models apply a biodata language derived from animal data, including patterns of data that operate as words and phrases, as well as a lexicon and syntax rules for the lexicon. The one or more animal data language models are trained to receive the input animal data and output the discriminative information in the biodata language. Furthermore, the system includes a communication module in communication with the animal language computing module, which is configured to transmit and receive data between the system and external devices or networks.

[0022] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, furtheraspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.BRIEF DESCRIPTION OF THE DRAWINGS|0023] For a further understanding of the nature, objects, and advantages of the present disclosure, reference should be made to the following detailed description, read in conjunction with the following drawings, wherein like reference numerals denote like elements and wherein:

[0024] FIGURE 1 A. Schematic of a computer-implemented animal data language system.

[0025] FIGURE IB. Schematic of a computing device that can implement the methods of the computer-implemented animal data language system.

[0026] FIGURE 2A. Schematic of an animal data language system.

[0027] FIGURE 2B. Schematic of an animal data language system.

[0028] FIGURE 3A. Schematic of an animal data language training system.

[0029] FIGURE 3B. Schematic of an animal data language training system.DETAILED DESCRIPTION

[0030] Reference will now be made in detail to presently preferred embodiments and methods of the present invention, which constitute the best modes of practicing the invention presently known to the inventors. The Figures are not necessarily to scale. However, it is to be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. Therefore, specific details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for any aspect of the invention and / or as a representative basis for teaching one skilled in the art to variously employ the present invention.

[0031] It is also to be understood that this invention is not limited to the specific embodiments and methods described below, as specific components and / or conditions may, of course, vary. Furthermore, the terminology used herein is used only for the purpose of describing particular embodiments of the present invention and is not intended to be limiting in any way.

[0032] It must also be noted that, as used in the specification and the appended claims, the singular form "a," "an," and "the" comprise plural referents unless the context clearly indicates otherwise. For example, reference to a component in the singular is intended to comprise a plurality of components.

[0033] 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 elements or method steps.

[0034] The phrase “consisting of’ excludes any element, step, or ingredient not specified in the claim. When this phrase appears in a clause of the body of a claim, rather than immediately following the preamble, it limits only the element set forth in that clause; other elements are not excluded from the claim as a whole.

[0035] The phrase “consisting essentially of’ limits the scope of a claim to the specified materials or steps, plus those that do not materially affect the basic and novel character! stic(s) of the claimed subject matter.

[0036] With respect to the terms “comprising,” “consisting of,” and “consisting essentially of,” where one of these three terms is used herein, the presently disclosed and claimed subject matter can include the use of either of the other two terms.

[0037] The term “using” is synonymous with “by applying” or “applying.”

[0038] It should also be appreciated that integer ranges explicitly include all intervening integers. For example, the integer range 1-10 explicitly includes 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. Similarly, the range 1 to 100 includes 1, 2, 3, 4. . . . 97, 98, 99, 100. Similarly, when any range is called for, intervening numbers that are increments of the difference between the upper limit and the lower limit divided by 10 can be taken as alternative upper or lower limits. For example, if the range is 1.1. to 2.1 the following numbers 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0 can be selected as lower or upper limits.

[0039] When referring to a numerical quantity, in a refinement, the term “less than” includes a lower non-included limit that is 5 percent of the number indicated after “less than.” A lower non-includes limit means that the numerical quantity being described is greater than the value indicated as a lower non-included limited. For example, “less than 20” includes a lower non-included limit of 1 in a refinement. Therefore, this refinement of “less than 20” includes a range between 1 and 20. In another refinement, the term “less than” includes a lower non-included limit that is, in increasing order of preference, 20 percent, 10 percent, 5 percent, 1 percent, or 0 percent of the number indicated after “less than.”

[0040] The term “in communication” means that objects exchange information or data. When the modules described below are implemented in an ASIC, the modules are in electrical communication. When the modules are implemented in software, “in communication means” that a module provides input data to another module

[0041] The term “electrical communication” or “in electrical communication” means that an electrical signal is either directly or indirectly sent from an originating electronic device to a receiving electrical device. Indirect electrical communication can involve processing of the electrical signal, including but not limited to, filtering of the signal, amplification of the signal, rectification of the signal, modulation of the signal, attenuation of the signal, adding of the signal with another signal, subtracting the signal from another signal, subtracting another signal from the signal, and the like. Electrical communication can be accomplished with wired components, wirelessly connected components, or a combination thereof. When the modules described below are implemented in an ASIC, the modules are in electrical communication. When the modules are applied in software, “in communication means” that a module provides input data to another module.

[0042] With respect to electrical devices, the term “connected to” means that the electrical components referred to as connected to are in electrical communication. In a refinement, “connected to” means that the electrical components referred to as connected to are directly wired to each other. In another refinement, “connected to” means that the electrical components communicate wirelessly or by a combination of wired and wirelessly connected components. In another refinement, “connected to” means that one or more additional electrical components are interposed between the electrical components referred to as connected to with an electrical signal from an originating component being processed (e.g., filtered, amplified, modulated, rectified, attenuated, summed, subtracted, etc.) before being received to the component connected thereto.

[0043] 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 “plurality” as a subset. In a refinement, “one or more” includes “two or more.” In another refinement, “at least one of’ means any combination of the components indicated, including a combination of all the components indicated.

[0044] The terms "configured to” or “operable to" mean that the processing circuitry (e.g., a computer or computing device) is configured or adapted to perform one or more of the actions set forth herein, by software configuration and / or hardware configuration. The terms "configured to” and “operable to” can be used interchangeably.

[0045] It should be appreciated that when a device, and in particular, a computing device is described as performing a list of actions or configured to perform a list of actions, the device can perform any one of the actions or any combination of the actions. Similarly, when an item is described by a list of item choices (e.g., whereby each, a subset, or all of the one or more choices can be selected), the item can be any one of the item choices or any combination of the item choices.

[0046] The term “derivative” wherein referring to data means that the data is mathematically transformed to produce the derivative as an output. In a refinement, a mathematic function receives the data as input and outputs the derivative as an output.

[0047] The term “or its one or more derivatives” can be interchangeable with “and its one or more derivatives” depending on the use case and is not intended to be limiting in any way.

[0048] It should be understood that “input animal data” or “input data” can be input inference animal data or input training animal data, depending on the context. Typically, the components that output the discriminative information can receive input inference animal data or input training animal data to output the discriminative information. In contrast, animal data used for training will always formally be input training animal data.

[0049] The term “substantially,” “generally,” or “about” may be used herein to describe disclosed or claimed embodiments. The term “substantially” may modify a value or relative characteristic disclosed or claimed in the present disclosure. In such instances, “substantially” may signify that the value or relative characteristic it modifies is within ± 0%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5% or 10% of the value or relative characteristic.

[0050] The processes, methods, or algorithms disclosed herein can be deliverable to / implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components.

[0051] The term “codified language” refers to a language that has been systematically organized and documented through the establishment of explicit rules, conventions, and standards. It includes the creation of a formalized structure and set of guidelines for the language's vocabulary, grammar, syntax, pronunciation, and usage.

[0053] The terms “subject” and “individual” are synonymous, and interchangeable, and can refer to a human or other animal, including, but not limited to, birds, reptiles, amphibians, and fish, as well as all mammals including, but not limited to, primates (particularly higher primates), horses, sheep, dogs, rodents, pigs, cats, rabbits, bulls, cows, and the like. The one or more subjects or individuals can be, for example, humans participating in athletic training or competition, horses racing on a race track, humans playing a video game, humans monitoring their personal health or having their personal health monitored, humans providing their animal data to a third party (e.g., insurance system, health system, animal data-based monetization system), humans participating in a research or clinical study, cows or other animals grazing, humans participating in a fitness class, and the like. A subject or individual can also be a derivative of a human or other animal (e.g., lab-generated organism derived at least in part from a human or other animal), one or more individual components, elements, or processes of a human or other animal (e.g., cells, proteins, biological fluids, amino acid sequences, tissues, hairs, limbs) that make up the human or other animal, one or more digital representations that share at least one characteristic with a human or other animal (e.g., data set representing a human thatshares at least one characteristic with a human representation in digital form - such as sex, age, biological function as examples - but is not generated from any human that exists in the physical world; a simulated individual or digital individual that is based on, at least in part, a real-world human or other animal, such as a digital representation of an individual or avatar in a virtual environment, augmented or mixed reality environment, or simulation such as a video game or metaverse, or a representation of an individual featured in synthetic media), or one or more artificial creations that share one or more characteristics with a human or other animal (e.g., lab-grown human brain cells that produce an electrical signal similar to that of human brain cells). In a refinement, the subject or individual can be one or more programmable computing devices such as a machine (e.g., robot, autonomous vehicle, mechanical arm) or network of machines that share at least one biological -based 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 in part, artificial in nature (e.g., data from artificial intelligence-derived activity that mimics biological brain activity; biomechanical movement data derived a programmable machine that mimics, at least in part, biomechanical movement of an animal).

[0054] The term “animal data” refers to any data obtainable from, or generated directly or indirectly by, a subject that can be transformed into a form that can be transmitted to a server or other computing device. Typically, the animal data is transmitted electronically via a wired or wireless connection, or a combination thereof. Animal data includes, but is not limited to, any subject-derived data, including any signals, values, measurements, and / or readings (e.g., metrics) and / or other information (e g., including units of digital information), that can be obtained or measured either directly or indirectly via one or more sensors (e.g., which can include sensing equipment and / or other sensing systems), and in particular, biological sensors (i.e., biosensors) that capture biological data, as well as its one or more derivatives. Animal data also includes any biological phenomena capable of being captured from a subject and converted to electrical signals that can be captured by one or more sensors (e.g., including biological data). Animal data also includes descriptive data related to a subject (e.g., name, age, height, eye color, gender, anatomical information, other characteristics related to the subject), auditory data related to a subject (including audio information related to one or more biological signals or readings, voice data, and the like), visually-captured data related to a subject (e.g., image, likeness, video or other media featuring the subject, observable information related to the subject), neurologically-generated data (e.g., brain signals from neurons), evaluative data related to a subject (e.g., skills of a subject), data that can be manually or automatically entered / inputted orgathered related to a subject (e.g., medical history, social habits, feelings of a subject, mental health data, financial information, social media activity, virtual activity, subjective data, and the like), and the like (e.g., other attributes / characteristics of the individual). The term “animal data” can be meant to include one or more types of animal data. It can include animal data in both its raw and / or processed form. In a refinement, the term “animal data” is inclusive of any derivative of animal data, including one or more computed assets (e.g., heart rate derived from ECG readings), insights, predictions (e.g., which can include one or more probabilities, possibilities, forecasts, assessments, and the like in some variations), artificial data (e.g., synthetic data generated based upon animal data; simulated animal data in a virtual environment, video game, or other simulation derived from the digital representation of the subject), and other derivative forms such as features (e.g., in this context, biological-based identifiers, rhythms, symbols, patterns, trends, frequencies, occurrences, scores, summaries, thresholds, measurements, signs, symptoms, digital signatures, outliers, abnormalities, anomalies, words, phrases, graphs, charts, plots, visual representations, characteristics, attributes, states, structures, variations, relationships, outcomes, scenarios, and the like). In another refinement, animal data includes one or more inputs (e.g., signals, readings, other data) from one or more non-animal data sources. In another refinement, animal data includes one or more attributes related to the subject or the animal data. In another refinement, animal data includes at least a portion of non-animal data that provides contextual information related to the animal data. In another refinement, animal data includes any metadata gathered or associated with the animal data. In another refinement, animal data includes reference animal data, which refers to any animal data used as a reference or baseline (e.g., a base for measurement) to classify, categorize, or evaluate (e.g., compare, analyze) other animal data, as well as to derive information from other data. In another refinement, animal data includes at least a portion of simulated data. In another refinement, animal data is inclusive of simulated data. In some variations, animal data can be inclusive of animal data and its derivatives in the biodata language.10055] The term “artificial data” refers to artificially-created data that is derived from, based on, or generated using, at least in part, animal data or one or more derivatives thereof. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and can include one or more inputs (e.g., signals, readings, other data) from one or more non- animal data sources as one or more inputs. In a refinement, artificial data includes any artificially- created data that shares at least one biological function with a human or another animal (e.g., artificially-created vision data, artificially-created movement data). It is inclusive of “synthetic data,”which can be any production data applicable to a given situation that is not obtained by direct measurement. Synthetic data can be created by statistically modeling original data and then using the one or more models to generate new data values that reproduce at least one of the original data's statistical properties. In a refinement, the term “artificial data” is inclusive of any derivative of artificial data. For the purposes of the presently disclosed and claimed subject matter, the terms “simulated data” and “synthetic data” are synonymous and used interchangeably with “artificial data” (and vice versa), and a reference to any one of the terms should not be interpreted as limiting but rather as encompassing all possible meanings of all the terms. In a refinement, the term “artificial data” is inclusive of the term “artificial animal data.”

[0056] The term “reference data” refers to data or other information used as a reference or baseline to classify, categorize, compare, evaluate, analyze, and / or value other data, as well as to derive information from other data. The term “reference data” is inclusive of the term “reference animal data,” which is animal data used as a reference or baseline (e.g., a base for measurement) to classify, categorize, compare, evaluate, analyze, and / or value other animal data, as well as to derive information from other data. Reference data can include any available, accessible, or gathered data, including any type of animal data and / or non-animal data and associated metadata, either directly or indirectly related to (or derived from) the one or more targeted subjects (e.g., including associated medical / health conditions, biological responses, and the like) use cases (e.g., including associated data collection plans, schedules, data requirements such as requirements to fulfill one or more data collection, analysis or distribution requirements, obligations, targets, and the like implemented by the system using one or more sensors and with one or more specified operating parameters), or events associated with the one or more targeted subjects that enables one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof, related to one or more outcomes, or execution (e.g., fulfillment) of one or more requirements or targets for one or more use cases, for one or more current or future events or subevents to be calculated, computed, derived, extracted, extrapolated, quantified, simulated, created, modified, assigned, enhanced, estimated, inferred, evaluated, established, determined, converted, deduced, observed, communicated, or actioned upon. Reference data can be gathered from any number of subjects (e.g., one, tens, hundreds, thousands, millions, billions, and the like) and data sources (e.g., data that can be gathered from sensors or computing devices, manually inputted, artificially created, derived from one or more actions, and the like). It can be structured (e.g., created, curated, transformed,modified) in a way to facilitate one or more evaluations (e.g., comparisons) of (or between) data sets as well as derivatives of data sets utilizing and / or applying the biodata language, at least in part, and / or one or more other languages. Reference data can also be categorized and associated with one or more profiles (e.g., type of individual, characteristics associated with one or more individuals, type of biological response, type of sensor(s), type of operating parameters associated with each or subset - with “subset” including all sensors in some variations - of the one or more sensors, the type of data generated from each or subset of the one or more sensors with the associated one or more operating parameters in light of the associated contextual data, type of target use case / requirements, type of medical condition, type of event (e g., medical / health event), target monetary value, and the like) and tagged in order to make the datasets searchable and accessible. In this regard, the system can utilize reference data to create or modify (e.g., including update) one or more digital records (e.g., which can include the system creating or customizing one or more profiles based upon the one or more requirements or targets) which can include categorized and searchable reference data information related to one or more individuals (e.g., including one or more characteristics related to the individual and / or subsets of individuals that share one or more characteristics / attributes), reference data information related to the one or more computing devices collecting data from the one or more sensors or other computing devices (e.g., type of computing device, specifications related to the computing device, actions taken by the computing device, and the like), reference data information related to the type of data generated from each or subset of the one or more sensors or systems and their associated one or more operating parameters, reference data information related to one or more characteristics of the data generated (e.g., quality, volume, and the like), reference data information related to the associated contextual data (e.g., activity the data was collection in, conditions, reference data information related to the subject, monetary or non-monetary value(s) if applicable, and the like), reference data information related to the state of the system (e.g., what processing occurred during data collection, analysis, or distribution; how much is free storage space or empty space did the system have during data collection; what actions was the system taking any given time during data collection; what RAM or processor does the system have; what type of computing device(s) is the system comprised of; and the like), reference data information related to other hardware, software, and / or firmware information gathered by the system and related to the data collected from the one or more sensors or other computing devices (e.g., including any algorithms used to transform or action upon the data), reference data information related to one or more use cases, and the like. Reference data canalso include any previously collected animal data and non-animal data (e.g., historical animal data, baseline animal data for one or more individuals or group of individuals, other baseline data), including derivatives of animal data and its associated contextual data, which can include other animal data, non-animal data, or a combination thereof, previously collected animal data derived from one or more sensors, and non-sensor based animal data. In another refinement, reference data includes at least a portion of non-animal data (e g., including non-animal contextual data to provide more context to the animal data). In another refinement, reference data includes at least a portion of simulated data. In another refinement, reference data includes metadata gathered or associated with the animal data, the subject, the one or more sensors and / or data collection systems, one or more computing devices associated with the one or more sensors and / or data collection systems, one or more computing devices associated with the system, the one or more use cases, or a combination thereof. The metadata associated with the animal data, subject, the one or more sensors and / or data collection systems, one or more computing devices associated with the one or more sensors, one or more computing devices associated with system, the one or more use cases, or combination thereof can include sensor type, sensor configurations (e.g., sensor operating parameters including sampling rate, units of measure, recorded frequency such as how often data is stored per second, storage rate, and the like), ancillary information related to data collection (e.g., for an infusion pump, information like flow rate, delivery rate, starting rate, starting volume, drug calculations, alerts, and the like; note that the types of information can vary based on the type of sensor being used such as anesthesia systems, blood pressure-based systems, capnometer systems, EEG monitors, PTM monitors, polysomnography monitors, EMG Monitors, fetal monitors, Holter monitors, infusion pumps, IOM systems, irrigation pumps, multi-metric patient monitors, vital sign monitors, pulse oximetry systems, spirometer systems, respiration flow systems, stress test systems, thermometers, tourniquet systems, vascular therapy systems, ventilator systems, and the like; note that this is not an exhaustive list of systems and monitors, and the invention can be applied to any system or monitor that utilizes one or more sensors), data type (e.g., including raw or processed data; high sampling vs low sampling data; type of data; requisite data type based on use case), placement of sensor, body composition of the subject (e.g., including impediments or other characteristics that can impact data collection or one or more characteristics of data, such as quality), bodily condition of the subject, one or more medical / health conditions of or related to the subject (e.g., including one or more medical / health conditions of other individuals that share one or more characteristics with the subject), outcome-related data (e.g.,outcome of the treatment; life outcome) health information of or related to the subject (e.g., including health deterioration or health improvement data, particularly over time), biological response data (e.g., activity the subject is engaged in while collecting the animal data; bodily response or biological phenomenon capable of being converted to electrical signals that can be captured by one or more sensors including a biological state; a medical / health event), environmental conditions (e.g. if the data was collected in a dangerous condition, rare or desired condition; the environment in which the data was collected in; and the like), quality of data (e.g., a rating or other indices applied to the data, completeness of a data set, noise levels within a data set, whether data is missing), size of the data set (e.g., size or volume of the required data set; size of the data set as to not exceed certain storage thresholds), rules or restrictions related to the data (e.g., any permissions or restrictions related of the data based upon one or more pre-existing agreements or preferences established by the data owner or administrator), one or more values associated with the data (e.g., monetary values; non-monetary values; range of values), one or more use cases associated with the data, one or more characteristics of the data, characteristics of the one or more computing devices (e.g., specifications) collecting the data, the type of software or firmware associated with the animal data collecting computing device or other computing device in communication with the collecting computing device collecting the data, state of the system that gathers the animal data, characteristics related to the transmission subsystem used to collect the data, and the like. Characteristically, the system can be configured to modify (e.g., update, enhance) reference data (e.g., including the one or more digital records, tags, and the like) and information associated with reference data as new information is gathered by the system. In another refinement, reference data can include previously collected animal data for a targeted individual. In another refinement, reference data can include data that is not derived directly or indirectly from the targeted individual or the one or more sensors but shares at least one attribute (e.g., characteristic) with the one or more targeted individuals, their biological responses (e.g., the activity the subject is undertaking, bodily response or biological phenomenon capable of being converted to electrical signals that can be captured by one or more sensors including a biological state; a medical / health event such as a heart attack or stroke), their one or more medical / health conditions or potential medical / health conditions based upon one or more shared characteristics with one or more other individuals, or the one or more sensors. In another refinement, reference data can include identifiable, de-identified (e.g., pseudonymized), semi-anonymous, or anonymous data tagged with metadata. In another refinement, reference data includes data derived from the one or more biological responsesderived from anonymized, semi -anonymized, or de-identified (e.g., pseudonymized) sources. In another refinement, reference data can be categorized or grouped together, to form one or more units of such data (e.g., including one or more digital assets that can be distributed for consideration). In another refinement, reference data can be dynamically created, modified, or enhanced with one or more additions, changes, or removal of non-functioning data (e.g., data that the system will remove or stop using). In another refinement, at least a portion of the reference data can be weighted based upon one or more characteristics of (or related to) the one or more sensors (e.g., reference animal data from sensors that produce average quality data may have a lower weighted score than reference animal data from sensors that produce high quality data), the one or more individuals or groups of individuals, the contextual data associated with the animal data (e.g., other animal data, non-animal data), the use case (e.g., the value of the use case based upon the potential monetary return), or a combination thereof. In another refinement, the system can be operable to conduct one or more data audits on reference data. For example, the system may recall reference data originating from one or more sensors based upon one or more sensor characteristics (e.g., a faulty data gathering functionality within the one or more sensors could cause the system to recall and remove the data from the reference animal data database), or may change one or more tags or characteristics of reference data based upon new information (e.g., a new disease identified based upon people with certain characteristics can modify the characteristics - type, volume, duration, etc. - of data the system collects, including the sensors used and the operating parameters created or modified). In another refinement, reference data include variable information related to animal data (e.g., the administration of one or more substances in areas such as infusion therapy that can impact animal data readings; administration of stimuli or other stimulation that can impact animal data readings).

[0057] In a refinement, reference data includes contextual data associated with the animal data, the contextual data including one or more associated monetary values (e.g., pricing value(s) or other information) and / or non-monetary values (e.g., the equivalent value of the animal data in the context of one or more goods, services, and the like) of the collected data based upon the metadata associated with the animal data (e.g., the type of sensor used to collect the animal data, sensor settings, type of algorithms used, and the like). In this example, the system can be configured to learn what sensors, sensor parameters, animal data characteristics, and subject characteristics are associated with any given price point or value for the data, enabling the system to recommend one or more sensor parameters based upon the creation or modification of one or more monetary targets or thresholds. Inanother refinement, reference data can include animal data, its one or more derivatives, its associated contextual data, or a combination thereof, in the biodata language. In another refinement, reference data is applied (used) as input training animal data.

[0058] The one or more sensors used to collect animal data, and in particular human data, include one or more biological sensors (also referred to as biosensors). Biosensors collect biosignals, which in the context of the present embodiment are any signals or properties in, or derived from, animals that can be continuously, continually, intermittently, or periodically (e.g., point-in-time) measured, monitored, observed, calculated, computed, or interpreted, including both electrical and non-electrical signals, measurements, and artificially-generated information. A biosensor can gather biological data (including readings and signals, both in raw and / or manipulated / processed form) such as one or more of physiological data, biometric data, chemical data, biomechanical data, genetic data, genomic data, glycomic data, location data, or other biological data (i.e., other animal data) from one or more targeted individuals. For example, some biosensors may measure, or provide information that can be converted into or derived from, biological data such as eye tracking & recognition data (e.g., pupillary response, movement, pupil diameter, iris recognition, retina scan, eye vein recognition, EOG-related data), blood flow data and / or blood volume data (e.g., PPG data, pulse transit time, pulse arrival time), biological fluid data (e.g., analysis derived from blood, urine, saliva, sweat, cerebrospinal fluid), body composition data (e.g., bioelectrical impedance analysis, weight-based data including weight, body mass index, body fat data, bone mass data, protein data, basal metabolic rate, fat-free body weight, subcutaneous fat data, visceral fat data, body water data, metabolic age (e.g., biological age), skeletal muscle data, muscle mass data), pulse data, oxygenation data (e.g., SpO2), core body temperature data, galvanic skin response data, skin temperature data, perspiration data (e.g., rate, composition), blood pressure data (e.g., systolic, diastolic, MAP), glucose data (e.g., fluid balance VO, glycogen usage), 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, QRS, QT, R-R intervals, echocardiogram data, thoracic electrical bioimpedance data, transthoracic electrical bioimpedance data), neurological data and other neurological-related data (e.g., EEG-related data), genetic-related data (e.g., performance enhancing polymorphisms (PEPs) such as ACTN3, ACE, ADRB2, AMPD1, BDKRB2, APOE, and others), genomic-related data, skeletal data, muscle data (e.g., EMG-related data including surface EMG, amplitude, adenosine triphosphate (ATP) data, muscle fiber types, muscle contraction velocity, muscleelasticity, soft-tissue strength), respiratory data (e.g., respiratory rate, respiratory pattern, inspiration / expiration ratio, tidal volume, spirometry data), and the like. Some biosensors may detect biological data such as biomechanical data which may include, for example, angular velocity, joint paths, kinetic or kinematic loads, gait description, step count, reaction time, or position or accelerations in various directions from which a subject’s movements can be characterized (e.g., including volumetric data). Some biosensors may gather biological data such as location and positional data (e.g., GPS, ultra-wideband RFID-based data; posture data), facial recognition data, posterior profiling data, audio data (e.g., audio signals derived from one or more biological functions; voice data; hearing data), kinesthetic data (e.g., physical pressure captured from a sensor located at the bottom of a shoe or sock), other biometric authentication data (e.g., fingerprint data, hand geometry data, voice recognition data, keystroke dynamics data - including usage patterns on computing devices such as mobile phones, signature recognition data, ear acoustic authentication data, eye vein recognition data, finger vein recognition data, footprint and foot dynamics data, body odor recognition data, palm print recognition data, palm vein recognition data, skin reflection data, thermography recognition data, speaker recognition data, gait recognition data, lip motion data), or auditory data (e.g., speech / voice data, sounds made by the subject; emotion captured derived from verbal tone or words used; sounds captured from one or more biological phenomena occurring within the body or externally from the body) related to the one or more targeted individuals. Some biological sensors may be image or videobased and collect, provide and / or analyze video or other visual data (e.g., still or moving images, including video, MRIs, computed tomography scans, ultrasounds, echocardiograms, X-rays) upon which biological data can be detected, measured, monitored, observed, extrapolated, calculated, or computed (e.g., biomechanical movements or location-based information derived from video data, a fracture detected based on an X-Ray, or stress or a disease of a subject observed based on video or image-based visual analysis of a subject; observable animal data such as facial movements, bodily movements or a wince which can indicate pain or fatigue). Some biosensors may derive information from biological fluids such as blood (e.g., venous, capillary), saliva, urine, sweat, and the like including (but not limited to) triglyceride levels, red blood cell count, white blood cell count, adrenocorticotropic hormone levels, hematocrit levels, platelet count, ABO / Rh blood typing, blood urea nitrogen levels, calcium levels, carbon dioxide levels, chloride levels, creatinine levels, glucose levels, hemoglobin Ale levels, lactate levels, sodium levels, potassium levels, bilirubin levels, alkaline phosphatase (ALP) levels, alanine transaminase (ALT) levels, and aspartate aminotransferase (AST)levels, albumin levels, total protein levels, prostate-specific antigen (PSA) levels, microalbuminuria levels, immunoglobulin A levels, folate levels, cortisol levels, amylase levels, lipase levels, gastrin levels, bicarbonate levels, iron levels, magnesium levels, uric acid levels, folic acid levels, vitamin B- 12 levels, and the like. In a variation, some biosensors may collect biochemical data including acetylcholine data, dopamine data, norepinephrine data, serotonin data, GABA data, glutamate data, hormonal data, and the like. In addition to biological data related to one or more targeted individuals, some biosensors may measure non-biological data (e.g., ambient temperature data, humidity data, elevation data, barometric pressure data, and the like). In a refinement, one or more sensors provide biological data that include one or more calculations, computations, measurements, predictions, probabilities, possibilities, combinations, estimations, evaluations, inferences, determinations, deductions, observations, projections, recommendations, comparisons, assessments, or forecasts that are derived, at least in part, from animal data. In another refinement, the one or more biosensors are capable of providing at least a portion of artificial data. In another refinement, the one or more biosensors are capable of providing 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, or the like). In another refinement, the one or more sensors is a biosensor that gathers physiological, biometric, chemical, biomechanical, location, environmental, genetic, genomic, glycomic, or other biological data from one or more targeted individuals. In another refinement, one or more biosensors collect image / imagery data and / or video data (e.g., one or more images of the subject, one or more videos of the subject, or a combination thereof) via one or more image-based sensors (e.g., including optical sensors that capture static imagery or video). In some variations, the one or more image-based sensors are also operable to gather other animal data (e.g., audio data). In another refinement, the one or more biosensors collect at least a portion of non-animal data.

[0059] The term “computing device” refers generally to any device that can perform at least one function, including communicating with another computing device. In a refinement, a computing device includes a central processing unit that can execute program steps and memory for storing data and a program code.

[0060] When a computing device is described as performing an action or method step, it is understood that the one or more computing devices are operable and / or configured to perform the action or method step typically by executing one or more lines of source code. The one or more actionsor method steps can be encoded onto non-transitory memory (e.g., hard drives, optical drive, flash drives, and the like).|0061] It should be appreciated that when a device, and in particular, a computing device is described as performing a list of actions or configured to perform a list of actions, the device can perform any one of the actions or any combination of the actions.

[0062] The term "server" refers to any computer or computing device (including, but not limited to, desktop computer, notebook computer, laptop computer, mainframe, mobile phone, smart phone, smart watch, smart contact lens, head-mountable or attached unit such as smart-glasses, headsets such as augmented reality headsets, virtual reality headsets, mixed reality headsets, and the like, hearables, augmented reality devices, virtual reality devices, mixed reality devices, unmanned aerial vehicles, manned aerial vehicles, and the like), distributed system, blade, gateway, switch, processing device, or a combination thereof adapted to perform the methods and functions set forth herein.

[0063] It should also be appreciated that any method step described herein can be performed by a computer or any computing device. Although collecting computing devices, receiving computing devices, and generating computing devices are described, the method steps can be performed by any computing device or system having the design of Figure IB. Moreover, any task described as being performed by a particular computing device can be performed by any other specific computing device described herein or by any computing device in general.

[0064] The term “electronic communication” means that an electrical signal is either directly or indirectly sent from an originating electronic device to a receiving electronic device. Indirect electronic communication can involve processing of the electrical signal, including but not limited to, filtering of the signal, amplification of the signal, rectification of the signal, modulation of the signal, attenuation of the signal, adding of the signal with another signal, subtracting the signal from another signal, subtracting another signal from the signal, and the like. Electronic communication can be accomplished with wired components, wirelessly-connected components, or a combination thereof.

[0065] For the purposes of this invention, any reference to the collection or gathering of animal data from one or more source sensors from a subject includes gathering the animal data from one ormore computing devices associated with the one or more source sensors (e g., a cloud server or other computing device associated with the one or more source sensors where the data is gathered, stored and / or accessible). Additionally, the terms “gathering” and “collecting” can be used interchangeably, and reference to any one of the terms should not be interpreted as limiting but rather as encompassing all possible meanings of both terms. In a refinement, the terms “gathering” and “collecting” can be used interchangeably with the term “receiving” (and vice versa), and reference to any one of the terms should not be interpreted as limiting but rather as encompassing all possible meanings of all the terms.

[0066] The term “or a combination thereof’ can mean any subset of possibilities or all possibilities. In a refinement, “or a combination thereof’ includes both “or combinations thereof’ and “and combinations thereof’ and vice versa.

[0067] The terms “use,” “uses,” or “used” when referring to actions taken by a computing system mean that the item being “used” is received as an input for a calculation performed by the computing system to provide an indicated output.

[0068] In some variations, when a computing device performs an action of accessing information, it is also performing an action of selecting that information.

[0069] In some variations, when a computing device performs an action “automatically,” it may also be configured to perform the action dynamically with little or no user input or interaction.

[0070] The term “model” or “deep learning model” refers to a mathematical representation of a neural network architecture that can process and analyze data to make predictions or generate outputs. The term “model” and “deep learning model” are used interchangeably.

[0071] The term “language model” refers to one or more models, and in particular Al-based models, trained to understand and generate human language. In particular, a language model is trained to predict the likelihood of a sequence of words or characters given the context of the preceding words or characters. Language models capture the statistical patterns, grammar, and semantic relationships in a language. In a refinement, a language model is trained to understand and generate one or more animal languages. In another refinement, a language model is trained to predict the likelihood of a sequence of words, characters, symbols, signs, phrases, and / or units of digital information given the context of the preceding words, characters, symbols, signs, phrases, and / or units of digital information.

[0072] The term “animal data language model” refers to one or more models, which can include one or more Al-based models, that are trained to understand, identify (e.g., including observe), and generate features (e.g., in this context, patterns, identifiers, rhythms, trends, occurrences, frequencies, signs, symptoms, digital biological signatures, outliers, abnormalities, anomalies, structures, states, variations, relationships, outcomes, scenarios, and / or other attributes or characteristics) in animal data based upon an evaluation of units of information derived from data. The features can be used to create words, characters, symbols, signs, phrases, and / or units of digital information from the features. In this context, the words, characters, symbols, signs, phrases, and / or units of digital information are any symbolic representation of the features or sequences of features in the animal data which can include words, characters, symbols, signs, phrases, and / or units of digital information in a codified language such as English. The language model is designed to predict the likelihood of a sequence pattern given preceding patterns (e.g., the likelihood of a sequence of words or phrases given the context of the preceding words and phrases.) Animal data language models capture the statistical patterns, grammar, and semantic relationships present in a biodata language. The concept of “animal data language model” is analogous the normal definition of a language model.

[0073] The term “biodata language” refers to a system and language of communication used to convey one or more features (e.g., in this context, animal data-based patterns, identifiers, rhythms, trends, occurrences, frequencies, signs, symptoms, digital signatures, outliers, abnormalities, anomalies, states, structures, variations, relationships, outcomes, scenarios, and / or other attributes or characteristics) in animal data. The one or more words, characters, symbols, signs, phrases, and / or units of digital information are any symbolic representation of the one or more features or sequences of features in the animal data which can include words, characters, symbols, signs, phrases, and / or units of digital information in a codified language such as English. In a refinement, the biodata language applies non-linear orthography principles to communicate and / or convey meaning. In another refinement, the biodata language is not static and can evolve over time. In another refinement, the biodata language can support transformations from one or more other languages and direct translation to (and from) other languages. In another refinement, the biodata language can facilitate hooks for transfer of instructions and data to and from other languages. In another refinement, abiodata language can include a plurality of languages (e.g., including a plurality of Al-based languages) that comprise the biodata language. In another refinement, the animal data language system is configured to transform portions of the inferred discriminative information and / or its derivatives thereof in thebiodata language into another one or more languages (e.g., human languages, other Al-based languages).|0074] In a refinement, the biodata language includes animal data and the one or more features (e.g., in this context, animal data-based patterns, identifiers, rhythms, trends, frequencies, occurrences, signs, symptoms, digital signatures, outliers, abnormalities, anomalies, structures, variations, relationships, outcomes, scenarios, and / or other attributes or characteristics) derived from the animal data that enables more efficient identification, evaluation, and / or communication of one or more biological responses (e.g., one or more biological functions or occurrences that happen with the human body), medical / health conditions, medical / health events, or a combination thereof, associated with (e.g., related to, derived from, of) one or more targeted subjects, as well the creation (e.g., generation), modification, enhancement, communication, or a combination thereof, of as one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, and / or recommendations related to the one or more targeted subjects and / or one or more outcomes for one or more current or future events or sub-events (e.g., biological-based events or subevents) associated with the one or more targeted subjects. In another refinement, the biodata language is comprised of the animal data and the one or more features (e.g., in this context, biologicalbased patterns, identifiers, rhythms, trends, frequencies, occurrences, signs, symptoms, digital signatures, outliers, abnormalities, anomalies, structures, variations, relationships, outcomes, scenarios, and / or other attributes or characteristics) created by the computer-implemented animal data language system (e g., the one or more animal data language models) and derived from the animal data, at least in part, that can enable more efficient and targeted access to, and / or gathering of, other data (which can include other animal data, reference data, contextual data, non-animal data, and the like), as well as identification, evaluation (e.g., including observation), and / or communication of one or more biological responses (e.g., one or more biological functions that happen with the human body, such as bodily response or a biological phenomenon or phenomena capable of being converted to electrical signals and / or into the biodata language that can be captured by one or more sensors, at least in part, including a biological state - such as stress - or activities in the body, one or more heart beats or accelerated breathing rate; anomalies in biological patterns or rhythms; an injury; an activity such as standing up and down, jumping, moving side to side; and the like), medical / health conditions (e.g., including any disease, illness or injury; any pathologic, mental or psychological condition or disorder; non-pathologic conditions that normally receive medical treatment), medical (i.e., health) events (e.g.,a heart attack, stroke), medical conditions, medical (i.e., health) events, or a combination thereof, associated with (e.g., related to, derived from, of) one or more targeted subjects, as well as the creation (e.g., generation), modification, enhancement, communication, or a combination thereof, one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, and / or recommendations related to the one or more targeted subjects and / or one or more outcomes for one or more current or future events or sub-events associated with the one or more targeted subjects. In another refinement, the biodata language is derived from the one or more features derived from the animal data, at least in part, that can enable more efficient and targeted access to, and / or gathering of, other data (which can include other animal data, reference data, contextual data, non-animal data, and the like), as well as identification, evaluation, and / or communication of one or more biological responses, medical conditions, medical / health events, or a combination thereof, associated with (e.g., related to, derived from, of) one or more targeted subjects, as well as the creation (e.g., generation), modification, enhancement, communication, or a combination thereof, of one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, and / or recommendations related to the one or more targeted subjects and / or one or more outcomes for one or more current or future biological-based events or sub-events.

[0075] In a refinement, the one or more features (including a combination of features) indicate one or more biological responses, medical conditions, medical / health events, or a combination thereof. In another refinement, the one or more features can be one or more exhibited biological responses, medical (health) conditions, medical (health) events, or a combination thereof. In another refinement, a pattern be representative of, and / or include, one or more features (e.g., in this context, one or more identifiers, occurrences, frequencies, rhythms, trends, signs, symptoms, digital signatures, outliers, abnormalities, anomalies, structures, variations, relationships, outcomes, scenarios, states, and / or other attributes or characteristics).

[0076] In a refinement, any portion of the identification, evaluation, and / or communication of one or more biological responses, medical (health) conditions, medical (health) events, or a combination thereof, can occur in the biodata language, one or more other languages, or a combination thereof, as well as the creation (e.g., generation), modification, enhancement, communication, or a combination thereof, of as one or more insights, metrics, forecasts, predictions, probabilities,assessments, possibilities, projections, determinations, summaries, recommendations, or a combination thereof, related to the one or more targeted subjects and / or one or more outcomes for one or more current or future events or sub-events associated with the one or more subjects (or groups of targeted subjects).

[0077] In some variations, an identification can include a partial identification, non- identi fi cation, and / or a match. In another refinement, identification can be characterized by at least one of: a percentage match, possibility, probability, prediction, confidence indicator (e.g., degree of confidence), score (e.g., accuracy score, precision score, and the like), or likelihood (e.g., 78% likelihood that the targeted individual is a specific known subject). Characteristically, an identification can include a positive identification (e.g., 100% match), partial positive identification, meaning the identification is not absolute (e.g., n % match that is less than 100%), or non-identification (e.g., the system verifies that the targeted subject does not have a specific medical condition). In a refinement, the term “identification” includes the term “recognition” and vice versa. In another refinement, an identification and / or recognition via the biodata language enables the animal data language model to create, modify, enhance, and / or communicate one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries or recommendations.

[0078] In some variations, an event can be comprised of one or more sub-events that feature a definable (e.g., definitive), quantifiable, observable, and / or measurable outcome (e.g., in the context of sports, tennis shots or points can all be sub-events within a tennis match, set, or game with the outcome being whether the individual made or missed the shot or subset of shots, won or lost the point / game / set / match, or the like; in some variations, sub-events can be events and vice versa depending on the context). In a refinement, an event in one instance can be classified a sub-event in another instance (and vice versa) depending on the context or the event. The invention can also be applied to any event and / or sub-event where there is a quantifiable, definable, observable, and / or measurable outcome (e.g., health applications).

[0079] In a refinement, the system is configured to identify and / or communicate one or more sub-responses within the one or more biological responses (e.g., a degree of a response, such as walking slow vs walking fast). In another refinement, a condition can have one or more sub-conditions. For example, type 1 diabetes and type 2 diabetes can be sub-conditions of diabetes; STEMI, NSTEMI, coronary spasm, and / or unstable angina can be sub-conditions of a heart attack.

[0080] The term “Fl score” refers is a measure of predictive performance. It is used to measure a test's accuracy. It considers both the precision and the recall of the test to compute the score. The Fl score is the harmonic mean of precision and recall, giving equal weight to both metrics.

[0081] In another aspect, one or more models are trained with large amounts of animal data and associated metadata (e.g., contextual information associated with the animal data).

[0082] In another aspect, a new and unique “biodata” language can be applied to infer discriminative downstream tasks that include but are not limited to relation extraction, question answering, classification, recommendations, text generation, translation, suggestions and the like.

[0083] The term “ transformer network” refers to a type of deep learning architecture designed to handle sequential data by employing self-attention mechanisms. The architecture includes an encoder and a decoder. The encoder and decoder each include multiple layers of self-attention and feed-forward neural networks. The self-attention mechanism enables the model to weigh the importance of different parts of the input data dynamically, capturing long-range dependencies and contextual relationships.

[0084] The term “trained transformer network” refers to a transformer architecture that has completed the training process, during which it has learned to perform specific tasks by adjusting its parameters based on input data and feedback. This training allows the network to make accurate predictions or generate meaningful outputs based on new, unseen data. The training process typically involves supervised learning with labeled data and can also include reinforcement learning for fine- tuning. Once trained, the transformer network is equipped to handle tasks such as natural language processing, time-series analysis, or physiological data interpretation with high accuracy.

[0085] Abbreviations:

[0086] Al" means Artificial Intelligence.

[0087] AR" means Augmented Reality.

[0088] ASICs" means Application Specific Integrated Circuits.|0089] ATP" means Adenosine Triphosphate.

[0090] "AUC-ROC" means Area Under the Curve - Receiver Operating Characteristic.

[0091] "BD" means Blu-ray Disc.

[0092] "CD" means Compact Disc.

[0093] "CNN" means Convolutional Neural Network.

[0094] CPU" means Central Processing Unit.

[0095] "ECG" means Electrocardiogram.

[0096] EEG" means Electroencephalogram.

[0097] "EOG" means Electrooculography.

[0098] EMG" means Electromyography.

[0099] "FPGA" means Field-Programmable Gate Array.

[0100] "GABA" means Gamma-Aminobutyric Acid.[01011 "GPT" means Generative Pre-trained Transformer.

[0102] GPU" means Graphics Processing Unit.

[0103] "GPS" means Global Positioning System.

[0104] "FIR" means Heart Rate.

[0105] "HRV" means Heart Rate Variability.

[0106] ICU" means Intensive Care Unit.

[0107] "VO" means Input / Output.

[0108] "MAP" means Mean Arterial Pressure.

[0109] MRIs" means Magnetic Resonance Imaging.

[0110] "MPU" means Microprocessing Unit.

[0111] PEPs" means Performance Enhancing Polymorphisms.

[0112] "PSA" means Prostate-Specific Antigen.

[0113] "PPG" means Photoplethysmography.

[0114] "RAM" means Random Access Memory.

[0115] "RNN" means Recurrent Neural Network.

[0116] "ROM" means Read-Only Memory.

[0117] "SpO2" means Peripheral Oxygen Saturation.

[0118] VR" means Virtual Reality.

[0119] Referring to Figures lA and IB, a computer-implemented animal datalanguage system is schematically illustrated. Computer-implemented animal data language system 10 includes an animal language computing system 12 configured to execute one or more animal data language models to infer discriminative information describing input animal data (i.e., input inference animal data) requiring analysis. Typically, the animal language computing system 12 includes one or more computing devices. In one refinement, animal language computing system 12 includes a computer 14 as depicted in Figure IB. In this context, a generic computer can be a high-performance server, or an edge device (e.g., a laptop or desktop computer). In a refinement, animal language computing system 12 includes a graphics processing unit 16. In a refinement, animal language computing system 12 includes a tensor processing unit 18. In a variation, the computing system can include an interface 20 in electrical communication with animal language computing system 12 for collecting animal data. Interface 20 can be a data logger or collector which is a specialized device for collecting and storing data over a period of time. Interface 20 can also be another computing device of the general design of Figure 1 A. Interface 20 can also be an Internet of Things device. In a refinement, interface 20 is a data collector that is an unmanned aerial vehicle (e.g., drone) that can relay animal data to animal language computing system 12. In a further refinement, interface 20 is a network interface card. In another refinement, one or more unmanned aerial vehicles (e.g., drones) comprise the animal languagecomputing system 12, at least in part. In another refinement, animal language computing system 12 can be further configured to operate as one or more sensors to gather animal data and metadata (e.g., contextual data).

[0120] Still referring to Figures 1A and IB, the one or more animal data language models apply (e.g., utilize) a biodata language derived from animal data. The biodata language includes one or more features (e.g., patterns) of data that operate as characters, symbols, signs, phrases, and / or units of information (e.g., digital information) that are useful to describe how the biodata language works. Characteristically, the biodata language includes a lexicon and syntax rules for the lexicon. Advantageously, the animal language computing system, and in particular, the one or more animal data language models, is trained to receive the input animal data (e.g., input inference animal data) and to output the discriminative information in the biodata language. In a refinement, the patterns of data include contextual meaning. In this regard, a comprehensive vocabulary is constructed around rules derived from scenarios and surrounding contexts. In a refinement, the one or more characters, symbols, signs, phrases, and / or units of information are comprised of one or more morphemes.

[0121] In another aspect, computer-implemented animal data language system 10 can include at least one sensor 221in electrical communication with the animal language computing system 12, where z is an integer label running from 1 to the total number of sensors. The at least one sensor is configured to generate the animal data from target individuals 241which is collected by one or more data acquirers 261, which is a hardware device that collects data 281from the sensors. In a refinement, data 281can be received by interface 20 described above.

[0122] The one or more data acquirers 261can be local to target individuals 241or remotely communicating, for example, via cloud 30.

[0123] Figure IB provides a block diagram of a computing system or computing device that can be used in animal language computing system 12. In particular, each of the computing devices set forth above can be of the design depicted in Figure IB. Computing device 40 includes a processing unit 42 that executes the computer-readable instructions for computer-implemented animal data language system 10. Computer processing unit 42 can include one or more central processing units (CPU) or microprocessing units (MPU). Computer device 40 also includes RAM 44 or ROM 46 that can have instructions encoded thereon for the computer-implemented animal data language system 10methods which are executed by computer processing unit 42. In some variations, computing device 48 is configured to display a user interface on display device 56.|0124] Still referring to Figure IB, computer device 40 can also include a secondary storage device 48, such as a hard drive. Input / output interface 50 allows interaction of computing device 40 with an input device 52 such as a keyboard and mouse, external storage 54 (e.g., DVDs and CDROMs), and a display device 56 (e.g., a monitor). Network adapter 58 is used to connect to other computing devices via a network, a cloud, the Internet, and the like. Computer processing unit 42, the RAM 44, the ROM 46, the secondary storage device 48, and input / output interface 50 are in electrical communication with (e.g., connected to) bus 60. During operation, computer device 40 reads computer-executable instructions (e.g., one or more programs) recorded on a non-transitory computer- readable storage medium which can be secondary storage device 48 and or external storage 54. Computer processing unit 42 executes these reads computer-executable instructions for the computer- implemented animal data language system 10 methods that are executed by computer processing unit 42. Specific examples of non-transitory computer-readable storage medium for which executable instructions for the computer-implemented animal data language system 10 are encoded onto include but are not limited to, a hard disk, RAM, ROM, an optical disk (e.g., compact disc, DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like.|0125] In another aspect, enhanced processing power is advantageous for implementing the computer-implemented animal data language system on computing device 40 or any computing device herein. Upgrading to a multi-core, high-performance CPU can handle the intensive computations required for running complex animal data language models. Integrating a powerful GPU accelerates the training and inference of deep learning models, especially for tasks involving Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Additionally, incorporating Tensor Processing Units (TPUs) provides specialized hardware acceleration for neural network computations, improving the speed and efficiency of model training and inference. Utilizing Field- Programmable Gate Arrays (FPGAs) allows for custom neural network processing, offering reconfigurable hardware optimized for specific algorithms. Moreover, increased memory capacity is essential to manage large datasets and models without performance bottlenecks. Expanding the system’s RAM ensures it can handle extensive data processing needs. Enhancing the GPU’s VRAM supports high-resolution input animal data processing (e.g., input inference animal data or inputtraining animal data) . Furthermore, upgrading to high-speed Solid State Drives (SSDs) reduces data access times and improves the overall speed of data handling, storage, and retrieval. Using NVMe SSDs for even faster data transfer rates is crucial for managing the large volumes of data generated by sensors and processed by the system.

[0126] In another aspect, computing device 40 or any computing device herein includes improved data acquisition and interface capabilities significantly enhance system performance. Integrating high-precision sensors for monitoring various forms of animal data, including one or more physiological parameters such as heart rate, blood pressure, respiratory rate, and activity levels, is vital. Adding data acquisition cards facilitates the high-speed collection and digitization of analog signals from various sensors. Utilizing high-speed Network Interface Cards (NICs) ensures robust and fast data communication between the system and external devices or networks. Additionally, integrating Bluetooth and Wi-Fi modules provides wireless connectivity to sensors and other peripheral devices.

[0127] In another aspect, computing device 40 or any computing device herein includes specialized modules for preprocessing that are critical for real-time data handling. Implementing Digital Signal Processors (DSPs) enables real-time noise reduction, normalization, and feature extraction from input animal data (e.g., input inference animal data and / or input training animal data). Developing custom Application-Specific Integrated Circuits (ASICs) handles specific preprocessing tasks, such as filtering and data segmentation, more efficiently. Moreover, enhancing display and user interface capabilities improves the system's usability. Using high-resolution monitors to display detailed visualizations of the processed data, dashboards, and alerts is beneficial. Implementing touchscreen interfaces allows intuitive user interaction with the system.

[0128] In another aspect, computing device 40 or any computing device herein ensures a robust power supply and efficient cooling systems are essential for maintaining optimal performance. A reliable power supply unit (PSU) capable of handling the increased power demands of enhanced hardware components is necessary. Upgrading cooling systems, including advanced fans, heat sinks, and liquid cooling solutions, help maintain optimal operating temperatures and prevent overheating. These hardware modifications collectively enhance the performance and efficiency of the computer- implemented animal data language system, enabling it to process complex data in real-time, provide accurate insights, and ensure reliability in various applications.

[0129] In another aspect, the animal language computing system is configured to recognize predefined words, characters, symbols, signs, phrases, and / or units of digital information of the biodata language in the input animal data (e.g., input inference animal data) and to organize an output as the discriminative information. In a refinement, an animal language computing system uses (e.g., applies) reference data, at least in part, as training data to recognize the predefined words, characters, symbols, signs, phrases, and / or units of digital information. In another refinement, the biodata language is used to output human-understandable speech, text, or other communication mediums (e.g., colors, vibrations).

[0130] In another aspect, the input animal data (e.g., input inference animal data and / or input training animal data) is human data. In a refinement, the input animal data (e.g., input inference animal data and / or input training animal data) includes biological data.

[0131] In another aspect, the discriminative information includes extraction, question answering, classification, recommendations, text generation, translation, or any combination thereof.

[0132] In another aspect, the one or more animal data language models include a model implemented by one or more pre-trained neural networks. In particular, the one or more animal data language models can include a transformer network and, in particular, a generative pre-trained transformer model. In a refinement, the one or more animal data language models are trained with animal data (e.g., biological data). In a refinement, the one or more animal data language models are trained on the biodata language by applying supervised learning whereby human-crafted specialized encoding is provided to one or more animal data language models to enable learning of vocabulary and grammatical structures for relationship extraction between data and outcomes. In a further refinement, the one or more animal data language models are fine-tuned by a reinforcement learning step where model responses to questions, tasks, relationships, recommendations, and inference are graded by humans into a reward system and fed back into the one or more animal data language models. Alternately, the one or more animal data language models can include a hybrid CNN-RNN model, and in particular, a trained hybrid CNN-RNN model.

[0133] In a refinement, the one or more animal data language models are trained with a combination of animal data and non-animal data. In another refinement, the one or more animal data language models are trained with animal data, contextual data (which may be comprised of animaldata, non-animal data, or a combination thereof), reference data, (which may be comprised of animal data, non-animal data, or a combination thereof), or a combination thereof. In a refinement, reference data includes reference contextual data. In another refinement, at least a portion of contextual data can be categorized as reference data and vice versa.

[0134] The computer-implemented animal data language system can be configured to gather contextual data and reference data from sources including, but not limited to, one or more sensors, one or more programs operating via one or more computing devices, other databases, and the like. Contextual data can include any set of data that describes and provides information about other data, including data that provides context for other data (e.g., the activity a targeted individual is engaged in while the animal data is collected, the outcome of the activity the targeted subject is engaged in, animal data to provide context for other animal data). Contextual data can be animal data, non-animal data, or a combination thereof. In a refinement, contextual data can be applied (used) as metadata for input inference animal data.

[0135] In a refinement, contextual data can be characterized as metadata associated with the animal data, or other information gathered, and vice versa (i.e., metadata can be characterized as contextual data). In many variations, animal data collected by the computer-implemented animal data language system can include or have attached thereto metadata, which can include one or more characteristics directly or indirectly related to the animal data, including characteristics related to the one or more sensors (e.g., identity of the sensor, sensor type, sensor brand, sensing type, sensor model, firmware information, sensor positioning on or related to a subject, sensor operating parameters, sensor configurations, sensor properties, sampling rate, mode of operation, data range, gain, battery life, shelf life / number of times the sensor has been used, timestamps, and the like), characteristics of the one or more targeted individuals, origination of the animal data (e.g., event, activity, or situation in which the animal data was collected, duration of data collection period, quality of data, when the data was collected), type of animal data, source computing device of the animal data, data format, algorithms used, quality of the animal data, quality of data, size / volume / quantity of the data, latency information / requirements, speed at which the animal data is provided, environmental condition, bodily condition, and the like. Metadata can also be associated with the animal data after it is collected. Metadata can include non-animal data, animal data, or a combination thereof. Metadata can also include one or more attributes directly or indirectly related to the one or more targeted individuals.Metadata can also provide information that directs the system in its access, creation, or modification of reference data. In a refinement, the computer-implemented animal data language system can be configured to transform metadata into a language compatible with the biodata language, at least in part, to create, modify, and / or enhance the biodata language and / or its one or more derivatives (e.g., features). In another refinement, the computer-implemented animal data language system can be configured to transform metadata into the biodata language, at least in part, and / or use metadata in another one or more languages with the biodata language to create, modify, and / or enhance one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, or recommendations related to one or more outcomes for one or more current or future biological -based events or sub-events. In another refinement, the computer- implemented animal data language system can be configured to transform metadata into the biodata language, at least in part.

[0136] In a refinement, contextual data is metadata (and vice versa) associated with the animal data, the one or more targeted subjects, the one or more sensors (e.g., including one or more components associated with the one or more sensors), the one or more events associated with the one or more targeted subjects, or a combination thereof. In another refinement, contextual data is data derived from one or more Artificial Intelligence techniques that provides context to other data. In another refinement, contextual data includes one or more terms (e.g., user preferences, rules, conditions, permissions, conditions, rights, and the like) associated with the animal data (e.g., one or more uses of the animal data) established by the data owner / provider, data acquirer, one or more previous agreements associated with animal data (e.g., including current or future animal data being collected, with one or more terms for the current or future animal data accessible via one or more digital records), or a combination thereof.

[0137] Examples of contextual data can include a targeted individual’s one or more characteristics / attributes such as name, age, weight, height, birth date, race, eye color, skin color, hair color (if any), country of origin, country of birth (if different), area of origin, ethnicity, current residence, addresses, phone number, reference identification (e.g., social security number, national ID number, digital identification), gender of the targeted individual from which the animal data originated, data quality assessment, information (e.g., animal data) gathered from medication history, medical history, medical records, health records, genetic-derived data, genomic-derived data (e.g.,including information related to one or more medical conditions, traits, health risks, inherited conditions, drug responses, DNA sequences, protein sequences, and structures), biological fluid- derived data (e.g., blood type), drug / prescription records, allergies, family history, health history (including mental health history), manually-inputted personal data, physical shape (e.g. body shape), historical personal data, training regimen, nutritional history / nutrition regime (e.g., what foods are ingested, timing / quantity of ingestion, food allergies), one or more preferences associated with collection, transformation, distribution and / or use of the animal data (e.g., terms, conditions, permissions, restrictions, requirements, requests, rights, and the like associated with their animal data by the individual, data acquirer, other data owner, licensee / licensor, administrator, or the like), and the like. The targeted individual’s one or more attributes can also include one or more activities the targeted individual is engaged in while the animal data is collected, one or more associated groups (e.g., if the individual is part of a sports team, or assigned to a classification based on one or more medical conditions), one or more habits (e.g., tobacco use, alcohol consumption, exercise habits, nutritional diet, the like), education records, criminal records, financial information (e.g., bank records, such as bank account instructions, checking account numbers, savings account numbers, credit score, net worth, transactional data), social data (e.g., social media accounts, social media history, social media content, records, internet search data, social media profiles, metaverse profiles, metaverse activities / history), employment history, marital history, relatives or kin history (in the case the targeted subject has one or more children parents, siblings, and the like), relatives or kin medical history, relatives or kin health history, manually inputted personal data (e.g., one or more locations where a targeted individual has lived, emotional feelings, mental health data, preferences), historical personal data, and / or any other individual-generated data (e.g., including data about or related to the individual). In a refinement, one or more characteristics / attributes associated with another one or more subjects can be associated with one or more targeted individuals as metadata. For example, in the event the targeted individual has children, the subject’s (i.e., child’s) health condition can be associated with the one or more targeted individuals as a characteristic associated with the one or more targeted individuals’ data (e.g., if the child is sick, the parent can be under considerable stress or have deteriorating mental health which may impact their animal data). In another example, the one or more characteristics / attributes of the targeted individual’s avatar or representation in a digital environment, video game, or other simulation (e.g., including their actions, experiences, conditions, preferences, habits, and the like) can be associated with the targeted individual as metadata and can be included aspart of the targeted individual’s animal data. In a refinement, animal data is inclusive of the targeted individual’s one or more characteristics / attributes (i.e., the one or more characteristics / attributes can be categorized as animal data). In another refinement, at least a portion of gathered data can be classified as both animal data and metadata. In another refinement, the system may associate metadata with one or more types of animal data prior to its collection (e.g., the system may collect one or more attributes related to the targeted individual prior to the system collecting animal data and associate the one or more attributes in the targeted individual’s profile to the one or more types of animal data prior to its collection).

[0138] In the context of a sporting event, contextual data in can include, but are not limited to, event data such as traditional sports statistics collected during an event (e.g., any given outcome data, including game score, set score, match score, individual quarter score, halftime score, final score, points, rebounds, assists, shots, goals, pass accuracy, touchdowns, minutes played, and other similar traditional statistics), in-game data (e g., whether the player is on-court vs off-court, whether the player is playing offense vs defense, whether the player has the ball vs not having the ball, the player’s location on the court / field at any given time, specific on-court / field movements at any given time, who the player is guarding on defense, who is guarding the player on offense, ball speed, ball location, exit velocity, spin rate, launch angle), streaks (e.g., consecutive points won vs lost; consecutive matches won vs lost; consecutive shots made vs missed), competition (e.g., men, women, other), round of competition (e.g., quarterfinal, finals), matchup (e.g., player A vs. player B; team A vs team B), opponent information, type of event (e g., exhibition vs real competition), date, time, location (e.g., specific court, arena, field, and the like), crowd size, crowd noise levels, prize money amount, number of years associated with the event (e.g., number of years a player has been playing within a specific league or with a specific team), ranking or standing / seeding, the type of sport, level of sport (professional vs amateur), career statistics (e.g., in the case of individual athletes in racquet sports as an example, number of: tournaments played, titles, matches played, matches won, matches lost, games played, games won, games lost, sets, sets won, sets lost, points played, points won, points lost, retirements, and the like), points won vs. points played, games (e.g., sets) won vs. games played, matches won vs. matches played, any given round rate (e.g., finals win / loss rate or semi-finals win / loss rate; number of times a player makes any given round in any given tournament (e.g., number of times a player makes the semifinals in any given tournament can be on a yearly or career basis), title win rate (e.g., how many times the player has won this year or any given year or over a career; how manytimes a player has won that particular tournament), match retirement history, court surface (e.g., hard court vs clay court), and the like. Contextual data can also include information such as historical animal data / reference animal data (e.g., outcomes that happened which are cross referenced with what was happening with the athlete’s body and factors surrounding it such as their heart rate and HRV data, body temperature data, distance covered / run data for a given point / game / match, positional data, biological fluid readings, hydration levels, muscle fatigue data, respiration rate data, any relevant baseline data, an athlete’s biological data sets against any given team, who the player guarded in any given game, who guarded the player in any given game, the player’s biological readings guarding any given player, the player’s biological readings being guarded by any given player, minutes played, court / ground surface, the player’s biological readings playing against any given offense or defense, minutes played, on-court locations and movements for any given game, other in-game data), comparative data to similar and dissimilar players in similar and dissimilar situations (e g., other player stats when guarding or being guarded by a specific player, playing against a specific team) injury data (e.g., including injury history), recovery data (e g., sleep data, rehabilitation data), training data (e.g., how the player performed in training in the days or weeks leading up to a game), nutrition data, a player’s self-assessment data (e.g., how they are feeling physically, mentally, or emotionally), nutritional data, mental health data, and the like. It can also include information such as country of origin, height, weight, dominant hand or handedness (e.g., right hand dominant vs left hand dominant), residence, equipment manufacturer, coach, race, nationality, habits, activities, genomic information, genetic information, medical history, family history, medication history, and the like. Contextual information can also be scenario-specific. For example, in the sport of tennis, contextual information can be related to when a player is winning 2-0 or 2-1 in sets or losing 1-2 or 0-2 in sets, or the time of day the player is playing, or the specific weather conditions the game is played in. Contextual information can also be related to head-to-head matchups. In the sport of squash, for example, head- to-head information can be related to the number of head-to-head matches, games, the number of times a player has been in a specific scenario vs the other player (e.g., in terms of game score: 3-0, 3-1, 3-2, 2-3, 1-3, 0-3, 2-0, 2-1, 1-2, 0-2, or retired). Contextual information can also include how that player has performed in that particular tournament (e.g., matches played, matches won, games played, games won / lost, sets played, sets won / lost, court time per match, total court time, previous scores and opponents, and the like). Characteristically, the system can be configured to evaluate a single type of data or a plurality of data (e.g., data types, data sets) simultaneously. For example, in the context of asport like tennis, the system may evaluate multiple sources of data and data types simultaneously utilizing one or more Artificial Intelligence techniques such as sensor-based animal data readings (e.g., positional data, location data, distance run, physiological data readings, biological fluid data readings, biomechanical movement data), non-animal data sensor data (e.g., humidity, elevation, and temperature for current conditions; humidity, elevation, and temperature for previous match conditions), length of points, player positioning on court, opponent, opponent’s performance in specific environmental conditions, winning percentage against opponent, winning % against opponent in similar environmental conditions, current match statistics, historical match statistics based on performance trends in the match, head-to-head win / loss ratio, previous win / loss record, ranking, a player’s performance in the tournament in previous years, a player’s performance on court surface (e.g., grass, hard court, clay), length of a player’s previous matches, current match status of a tennis player (e.g., athlete A is in Game 2 of Set 2 and is losing 4-2) and their historical data in the context of the current match status (e.g., all of athlete A match results when athlete A is in Game 2 of Set 2 and is losing 4-2, first serve percentage in second sets after playing n number of minutes, unforced errors percentage on the backhand side after hitting three n topspin backhands), and the like, which can occur in conjunction with contextual data such as video data (e.g., one or more optical cameras generating one or more video feeds of the event which feature the one or more individuals) and other information (e.g., contextual data such as timing & scoring data and other statistical information). In a refinement, any contextual data related to an event (either directly or indirectly) can be categorized as event data for (or associated with) the event. In another refinement, contextual data is inclusive of event data. In another refinement, event data is comprised of any contextual data associated either directly or indirectly with the event. In another refinement, event data includes at least a portion of contextual data.

[0139] It should be appreciated that such examples of contextual data, including contextual data in the context of a sports competition / event, are merely exemplary and not exhaustive, and similar types of information can be collected for all sports and events. In the context of non-sporting events, similar types of contextual data and methodologies can be utilized. In a refinement, contextual data in the context of non-sports related events can also include outcome-related information that may or may not provide context to other data. In a refinement, the creation, modification, and / or enhancement of the one or more features induces the one or more animal data language models to automatically initiate one or more actions to create, modify, access, or a combination thereof, additional data (e g., animaldata, reference data, contextual data, non-animal data, or a combination thereof). In another refinement, the computer-implemented animal data language system can be configured to transform at least a portion of the contextual data, reference data, or a combination thereof, into the format compatible for use or application with the biodata language. In another refinement, the computer- implemented animal data language system can be configured to read from, or translate to, one or more other languages.

[0140] In another aspect, the biodata language is derived by executing one or more languageidentifying artificial intelligence algorithms on a language-identifying computing system, the language-identifying artificial intelligence algorithms being configured to determine the lexicon and the syntax rules for the lexicon of the biodata language.

[0141] In another aspect, the biodata language is generated by a language-identifying artificial intelligence algorithm. For example, one or more language-identifying artificial intelligence algorithms can include a component having a transformer network and, in particular, a generative pretrained transformer. In a refinement, the one or more language-identifying artificial intelligence algorithms is a machine learning algorithm that can identify patterns. The machine learning algorithms can be supervised learning algorithms (e.g., neural networks, random forests, support vector machines, and decision trees) or unsupervised learning algorithms (e.g., clustering algorithms such as k-means or hierarchical clustering and dimensionality reduction techniques (e.g., principal component analysis).10142] In another aspect, a language-identifying computer algorithm determines the lexicon and the syntax rules of the biodata language by identifying one or more patterns in the input animal data (e.g., input inference animal data and / or input training animal data), identifying segments of data within these patterns, and separating the segments into pattern units. It then generates codified vocabulary units from these pattern units, ensuring that each vocabulary unit represents a word or phrase in the lexicon. Optionally, each word is defined as a part of speech within the biodata language. The algorithm adds the syntax rules associated with these words or phrases to complete the biodata language. Finally, it applies these syntax rules to translate the biodata language into human- understandable speech, text, and / or other communication medium (e.g., codes, graphs, charts, plots, colors, sounds, vibrations, and the like), making the physiological information accessible and actionable.

[0143] In another aspect, the one or more language-identifying artificial intelligence algorithms is a deep learning algorithm. In another refinement, the one or more language-identifying artificial intelligence algorithms include an association rule mining algorithm. Association rule mining is a technique for finding relationships and patterns in large datasets. In another refinement, the one or more language-identifying artificial intelligence algorithms includes a time series analysis algorithm. Time series analysis algorithms identify patterns in sequential data. In time series analysis the order is important. Much of the data collected from sensors will provide a value versus time so that the patterns identified will also be a function of time.

[0144] In another aspect, elements of the biodata language are developed to be specific to a predetermined domain with a complete syntactical structure, vocabulary, and corresponding semantics. Moreover, the biodata language can incorporate a set of rules that govern relationships between various combinations of data patterns and consequential occurrence of outcomes. In particular, the biodata language is applied to describe biological data (e.g., physiological data), including patterns, structures, variations, relationships, and outcomes. In a refinement, the biodata language is developed as a means to communicate physiological data-based outcomes between a user and the animal language computing system.

[0145] In another aspect, the biodata language is trained such that reverse training is applied to a subset of the one or more animal data language models to generate a set of human data based on a given condition or set of conditions.

[0146] In another aspect, the animal language computing system is further configured to translate human language described outcomes, relationships, and scenarios to the biodata language.

[0147] In another aspect, the animal language computing system is further configured to map the biodata language to raw biological data and its one or more features (e.g., data patterns, occurrences, and frequencies, and the like). In a refinement, the animal language computing system is further configured to map the biodata language to other information (e.g., reference data, contextual data, other animal or non-animal data) related to the one or more subjects, or group of subjects.

[0148] In another aspect, the discriminative information includes a description of one or more physiological states.

[0149] The computer-implemented animal data language system 10 can be used to identify and / or characterize a number of physiological states. The following examples are not exhaustive. The physiological state can indicate that a subject is under stress. The physiological state can indicate that a subj ect is sleeping or resting. The physiological state can indicate that a subj ect is engaged in physical exertion. The physiological state can indicate that a subject is fatigued. The physiological state can indicate that a subject is prone to injury. The physiological state can indicate that a subject has an elevated heart rate. The physiological state can indicate that a subject has elevated blood pressure. The physiological state can indicate that a subject has an elevated amount of perspiration. The physiological state can indicate the overall health of a subject. The physiological state can indicate whether a subject is experiencing one or more biological-based episodes or events (e.g., medical or health episodes / events such as a heart attack or stroke). The physiological state can indicate the degree to which a subject is optimizing their physiological performance (e.g., physical performance, mental performance) or performing at their optimal physiological functionality.

[0150] In a refinement, the discriminative information that includes a description of a physiological state derived from computer-implemented animal data language system 10 can indicate the subject is exhibiting, has exhibited, or will exhibit, one or more biological responses. In another refinement, the discriminative information that includes a description of a physiological state derived from computer-implemented animal data language system 10 can indicate the subject is experiencing, has experienced, or will experience, one or more medical / health events or episodes. In another refinement, the discriminative information that includes a description of a physiological state derived from computer-implemented animal data language system 10 can indicate the subject is experiencing, has experienced, or will experience, one or more medical / health conditions. In another refinement, the discriminative information that includes a description of a physiological state derived from computer- implemented animal data language system 10 can enable the one or more animal data language models to generate, modify, and / or enhance one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, or recommendations related to one or more outcomes for one or more current or future biological-based events or sub-events associated with the one or more subjects.

[0151] In another aspect, at least a portion of the biodata language is generated in, or modified into, a digital format of communication or other communication medium or representation that isreadable, interpretable, understandable, executable, actionable, or a combination thereof, by a plurality of computing devices and / or programs. In another refinement, at least a portion of the biodata language is generated in, or modified into, a digital format of communication or other communication medium or representation not readable, interpretable, understandable, executable, or actionable by animals (e.g., humans). In another refinement, the one or more language-identifying artificial intelligence algorithms can be configured to automatically create, modify, or assess, the one or more features (e.g., patterns) of data that operate as words and phrases.

[0152] In another aspect, the discriminative information in the biodata language derived from the captured animal data from the at least one sensor informs the system about the current biological state of a subject, the discriminative information being further utilized by the computer-implemented animal data language system, at least in part, as training data to obtain feedback from the system, the system being further configured to communicate the feedback to the one or more sensors in the form of one or more commands that provide one or more instructions to the one or more sensors to take one or more actions. In another refinement, the feedback is translated into one or more alerts provided to one or more other computing devices (e.g., an alert that an adverse biological -based event for one or more targeted individuals is likely). In another refinement, the system provides one or more actions, instructions, recommendations, predictions, probabilities, possibilities, forecasts, assessments, summaries, or the like related to the subject based upon the description of their physiological state (e.g., what to do to reduce stress based upon the current readings and the subject’s baseline).

[0153] In another aspect, the discriminative information includes one or more features.

[0154] In another aspect, the discriminative information includes one or more commands, actions, instructions, recommendations, predictions, probabilities, possibilities, forecasts, assessments, summaries related to one or more biological-based occurrences for one or more targeted subjects. In a refinement, the discriminative information induces the computing device to create, modify, assess, access, or a combination thereof, one or more commands (e g., sensor commands, computing devices commands, and the like), actions, instructions, recommendations, predictions, probabilities, possibilities, forecasts, assessments, summaries, data, and the like. In another refinement, the one or more commands are generated to create, modify, assess, access, or a combination thereof, one or more sensor parameters or settings. In another refinement, the creation or modification of the one or more sensor commands, the one or more computing device commands, ora combination thereof, induces the one or more sensors to interact with the animal body either directly or indirectly (e.g., the sensor is configured to automatically engage with the animal body). In another refinement, the discriminative information induces the computing device to create, modify, assess, access, or a combination thereof, one or more commands that provide one or more instructions to the one or more sensors, and transmit the one or more commands to the one or more sensors, the one or more commands including at least one of: (1) selecting and enabling (e.g., activating) the one or more sensors to provide animal data to one or more computing devices; (2) selecting and stopping (e.g., deactivating) the one or more source sensors from providing animal data to one or more computing devices; (3) creating, modifying, setting, or a combination thereof, one or more sensor parameters for each of the one or more sensors which change one or more actions taken by the one or more sensors or one or more computing devices in communication with the one or more sensors; or (4) a combination thereof. In another refinement, the animal language computing system is configured to take one or more actions based upon the discriminative information, the one or more actions including: (i) intelligently gathering animal data from the one or more sensors either directly (e.g., directly from to the one or more sensors) or indirectly (e.g., (e.g., via another one or more sensors; via another one or more computing devices in communication with the one or more sensors); (ii) creating, modifying, and / or accessing one or more sensor commands that provide one or more instructions to the one or more sensors, one or more computing devices in communication with the one or more sensors (e.g., directly or indirectly), or a combination thereof, to perform one or more actions; and / or (iii) intelligently transmitting the one or more commands either directly or indirectly to the one or more sensors, the one or more computing devices in communication with the one or more sensors, or a combination thereof. In another refinement, the animal language computing system is configured to take one or more actions (e.g., dynamically) based upon the discriminative information, wherein the animal data language system is configured to automatically initiate the one or more computing devices associated with the animal language computing system and gathering animal data from the one or more sensors to create, modify, and / or access (e.g., dynamically) one or more commands that provide one or more instructions to the one or more sensors (e.g., to take one or more actions, one or more computing devices in communication with the one or more sensors, or a combination thereof, and transmit the one or more commands to the one or more sensors, one or more computing devices in communication with the one or more sensors, or a combination thereof, the one or more commands including at least one of: (1) selecting and enabling (e.g., activating) the one or more sensors to provideanimal data to one or more computing devices; (2) selecting and enabling a computing device gathering animal data from the one or more sensors to provide animal data to one or more computing devices; (3) selecting and stopping (e.g., deactivating) the one or more sensors from providing animal data to one or more computing devices; (4) selecting and stopping a computing device gathering animal data from the one or more sensors from providing animal data to one or more computing devices; (5) creating, modifying, setting, or a combination thereof, one or more sensor operating parameters (settings) for each, a subset, or all of the one or more sensors which change one or more actions taken by the one or more sensors or one or more computing devices in communication with the one or more sensors; (6) creating, modifying, setting, or a combination thereof, one or more operating parameters for each, a subset, or all of the one or more computing devices in communication with the one or more sensors which can change one or more actions taken by the one or more computing devices, the one or more sensors, one or more computing devices in communication with the one or more sensors, or a combination thereof; or (7) a combination thereof. Additional details related to a system and method for intelligently selecting sensors and their associated operating parameters are disclosed in PCT Application No. PCT / US22 / 491666 filed November 15, 2022; the entire disclosure of which is hereby incorporated by reference.10155] In another aspect, the animal data language system includes several modules that can be implemented in hardware or software. Referring to Figure 2A, animal data language system 70 includes an input interface module 72 (e.g., input inference animal data) for receiving input animal data (e g., input inference animal data). In a refinement, the input interface module 72 is configured to communicate with sensors that monitor physiological parameters such as heart rate, blood pressure, respiratory rate, and activity levels. Animal data language system 70 also includes a preprocessing module 74 configured to perform noise reduction on the input animal data (e.g., input inference animal data). An animal language computing module 76 (also referred to as animal data processing and inference model 76), in communication with the preprocessing module 74, is configured to execute one or more animal data language models to infer discriminative information describing input animal data (e.g., input inference animal data) requiring analysis. In a refinement, the animal data language system 70 receives input animal data (e.g., input inference animal data) such as ECG readings, heart rate variability, and other physiological signals. The one or more animal data language models apply a biodata language derived from animal data. The biodata language includes patterns of data that operate as words and phrases, as well as a lexicon and syntax rules for the lexicon. The animallanguage computing module 76 is trained to receive the input animal data (e.g., input inference animal data) and to output the discriminative information in the biodata language. In a refinement, an animal language computing module 76 is configured to recognize predefined words and phrases of the biodata language in the input animal data (e.g., input inference animal data). It organizes the output as the discriminative information. In another refinement, the animal data language system 70 is configured to generate alerts based on a translated biodata language, enabling timely interventions. System 70 also includes a communication module 78 in communication with the animal language computing module, which is configured to transmit and receive data between the system and external devices or networks. In a refinement, the animal data language system 70 includes a user interface module 80. Module 80 is configured to present the inferred discriminative information in the biodata language to a user in a human-understandable format, such as visual dashboards, charts, or graphs. It also provides audio or haptic feedback based on the inferred discriminative information. In a refinement, the discriminative information includes detected patterns indicative of one or more health conditions, stress levels, or performance metrics.

[0156] In another aspect, the animal data language system 70 includes a training module 82 in communication with the preprocessing module 74 and / or animal language computing module 76. This module is configured to train the animal data language models by applying supervised learning with labeled animal data (e.g., physiological data), and in some variations reference data and / or contextual data, and to fine-tune the animal data language models by applying reinforcement learning with expert feedback. In a refinement, the animal data language system 70 includes a validation module 84 in communication with the training module configured to validate the performance of the animal data language models by applying a validation scheme (e.g., k-fold cross-validation) and performance metrics, including accuracy, precision, recall, and Fl score. In a further refinement, the animal data language system 70 includes continuous learning module 86 configured to update the animal data language models with new data and feedback to improve accuracy and adaptability.

[0157] In a refinement, the animal language computing system, and in particular the one or more animal data language models, are configured to take one or more actions, or is restricted from taking one or more actions, or a combination thereof, with regards to accessing the animal data and / or its one or more derivatives for model training purposes based upon one or more terms (e.g., permissions, restrictions, conditions) and / or other forms of consent provided by animal data creator(e.g., the targeted individual from whom the animal data is derived from), animal data rights holder (e.g., the animal data owner, which may be different than the animal data creator in some cases; a licensor or licensee with one or more rights to the animal data), authorized animal data manager or representative (e.g., lawyer, agent, an Al representing the individual, an entity), authorized administrator of animal data, one or more legal statutes (e.g., legal statutes that provide one or more rights to the individual; restrictions placed upon one or more entities / Al systems that collect, process, and / or use data), or a similar subject, entity, or Al that has one or more rights to create or modify one or more terms related to the acquisition, distribution, and / or use of the animal data. In another refinement, the one or more consents are attached to the animal data as metadata, wherein the animal language computing system reads the metadata which instructs the one or more animal data language models to take one or more actions, or restricts the one or more animal data language models from taking one or more actions, or a combination thereof, with the animal data and / or its one or more derivatives.

[0158] Referring to Figure 2B, an Application-Specific Integrated Circuit (ASIC) designed for the animal data language system includes various specialized modules, each optimized for the specific functions described in the claims. These modules ensure efficient processing, accurate data interpretation, and reliable communication within the system.|0159] The input interface module 72 of the ASIC is responsible for receiving input animal data (e g., input inference animal data) from various sensors. This module includes input ports 90 in electrical communication with the sensors. Analog-to-digital converters (ADCs) 92 digitize the analog signals from sensors such as ECG monitors, heart rate variability sensors, and other physiological signal detectors. It also integrates signal conditioning circuits 94 to prepare the raw data for further processing. Output port 96 provides data to other modules.

[0160] The preprocessing module 74 within the ASIC performs noise reduction on the input animal data (e.g., input inference animal data). Input port 98 receives input from module 72. This module includes digital signal processors (DSPs) 100 designed to filter out noise and artifacts from the physiological signals. It also features normalization units 102 to standardize the input data (e.g., input inference animal data) to a consistent scale and feature extraction circuits to identify and isolate relevant patterns within the data. Output port 104 provides data to other modules such as module 76 or 82.

[0161] The animal language computing module 76 is a component of the ASIC, tasked with executing one or more animal data language models to infer discriminative information. Input port 106 receives input from module 74 or any of the other modules. This module incorporates neural network accelerators 108, such as Tensor Processing Units (TPUs) or dedicated hardware 110 for Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These accelerators apply the biodata language, which includes patterns of data that function as words and phrases, along with a lexicon and syntax rules. The module is designed to efficiently process the input data (e.g., input inference animal data) and generate the discriminative information, which is then outputted in the biodata language. Output port 112 provides data to other modules.

[0162] The communication module 78 facilitates data transmission between the ASIC and external devices or networks. It includes high-speed network interface cards (NICs) 114 and wireless communication modules such as Bluetooth and Wi-Fi transceivers. This module ensures that the processed data and inferred information can be shared with other systems and devices in real-time.

[0163] In another aspect, the ASIC includes a user interface module 80 designed to present the inferred discriminative information in a human-understandable format. Input port 116 receives input from module 76 or any of the other modules. This module features graphical processing units (GPUs) 118 to render visual dashboards, charts, and graphs. It can also include audio and haptic feedback circuits 120 to provide multi-sensory alerts and notifications to the user based on the analyzed data. Output port 122 provides data to an output device.

[0164] The training module 82 within the ASIC is responsible for training the animal data language models by applying a transformer network. After training, the transformer network is referred to as a trained transformer network. Input port 126 receives input from module 74, module 76, or any of the other modules. This module includes additional transformer network accelerators 128 and memory units 130 to handle the large datasets required for supervised learning with labeled physiological data. It also integrates reinforcement learning circuits 132 to fine-tune the models based on expert feedback and new data. Output port 134 provides data to modules 76, 84, or 86. Alternatively, the training module 82 trains a hybrid CNN-RNN model. In this variation, input port 126 receives input from module 74, module 76, or any of the other modules. This module includes additional neural network accelerators 128 and memory units 130 to handle the large datasets required for supervised learning with labeled physiological data. It also integrates reinforcement learningcircuits 132 to fine-tune the models based on expert feedback and new data. Output port 134 provides data to module 76, 84, or 86.|0165] The validation module 84 ensures the accuracy and reliability of the animal data language models. Input port 136 receives input from module 82. This module includes hardware 138 for performing a validation scheme (e.g., k-fold cross-validation) and calculating performance metrics such as accuracy, precision, recall, and Fl score. It works in conjunction with the training module to continuously validate and update the models. Output port 142 provides data to modules 76 or 86.10166] The continuous learning module 86 updates the animal data language models with new data and feedback, ensuring the models remain accurate over time. Module 86 includes data input interfaces 146 configured to continuously receive new data and feedback. In a refinement, hardware 148 for continuous learning algorithms, such as online gradient descent, is configured to allow realtime model updates. Model update logic 150 is configured to continuously adjust model weights as new data and feedback are processed, maintaining the system's adaptability. Output port 152 provides data to module 76, module 80, or module 78.10167] In another aspect, the ASIC includes components to recognize predefined words and phrases in the biodata language. This functionality is integrated within the animal language computing module, which uses pattern recognition algorithms to identify and organize the output as discriminative information.

[0168] The ASIC is also equipped to handle various input animal data types, such as ECG readings, heart rate variability, and other physiological signals. This versatility is enabled by the flexible design of the input interface and preprocessing modules, which can adapt to different sensor inputs and data formats.

[0169] In another aspect, the ASIC generates alerts based on the translated biodata language, enabling timely interventions. The communication and user interface modules work together to deliver these alerts to the user in a clear and actionable manner.

[0170] Overall, the ASIC for the animal data language system is a highly specialized and efficient hardware solution, incorporating advanced components to ensure real-time processing, accurate data interpretation, and effective communication of health insights. This comprehensivedesign supports various applications, from healthcare monitoring to athletic performance optimization and veterinary care10171] In another aspect, the animal data language models include a model implemented by one or more pre-trained neural networks. In a refinement, the biodata language is derived by executing one or more language-identifying artificial intelligence algorithms on a language-identifying computing system, the language-identifying artificial intelligence algorithms being configured to determine the lexicon and the syntax rules for the lexicon of the biodata language.10172] In another aspect, training the animal language computing system involves several fundamental steps, including data collection, preprocessing, model selection, training, and validation. These steps ensure that the system is robust, accurate, and capable of translating animal data such as physiological data into one or more actionable insights. Data is collected from various sources, including biosensors that monitor physiological parameters such as heart rate, respiratory rate, blood pressure, oxygen saturation, ECG readings, blood glucose levels, and body temperature. Additionally, existing medical and other animal data databases (e.g., physiological databases) with annotated data (e.g., contextual data, which provides the context related to the animal data, as well as clinical trials and studies where one or more types of animal data are monitored (such as physiological parameters), serve as valuable data sources. The collected data can encompass forms of animal data including physiological signals, activity data (like step count and movement patterns), biometric data, genetic data, and biological fluid data (e g., hormone levels).

[0173] The collected data undergoes extensive preprocessing to ensure its quality and usability. Noise reduction techniques, such as filtering, are applied to remove artifacts from physiological signals. Data normalization is performed to standardize the scale across different data sources. Feature extraction involves segmenting continuous data into meaningful intervals based on one or more physiological events (e.g., identifying QRS complexes in an ECG) and selecting relevant features like heart rate variability (HRV) and peak values. Data is also labeled, either manually by experts or through automated algorithms, to indicate relevant physiological states or conditions.

[0174] The selection of appropriate models is critical for effective data analysis. Transformer networks, and in particular, generative pre-trained transformers (GPT) can be utilized for their capability to handle sequential data and generate meaningful text from input data (e.g., input inferenceanimal data and / or input training animal data). In a variation, transformer networks are employed to process time-series data and extract features from complex physiological signals. Transformer architectures are considered to leverage the strengths of different neural network types by combining attention mechanisms with powerful feature extraction capabilities. Encoder-decoder architectures are implemented to translate raw data into the biodata language. The transformer network plays a crucial role in the animal language computing system by processing and interpreting physiological data, which is then translated into the biodata language used by the one or more Al models. The data processing and feature extraction phase utilizes transformer networks to handle raw physiological data collected from sensors, such as ECG signals, blood pressure readings, and other biometric data. The transformer network applies self-attention mechanisms to extract significant features from this data. For example, in ECG signals, the transformer can identify features like QRS complexes, which are critical for detecting heartbeats and other cardiac events. The output of this process is a sequence of high-level feature representations that encapsulate the essential characteristics of the input data (e.g., input inference animal data and / or input training animal data). Following this, the sequence analysis phase continues to employ transformer networks to analyze the temporal sequence of features extracted. Transformers are particularly adept at recognizing patterns over time, which is crucial for understanding the dynamic nature of various types of animal data such as physiological data. They process the sequence of feature representations, maintaining a comprehensive understanding of previous inputs to capture temporal dependencies and contextual information. This capability is vital for detecting patterns such as arrhythmias in ECG signals or trends in blood pressure changes. The transformer network then produces a sequence of context-aware representations that encapsulate both spatial and temporal information from the input data (e.g., input inference animal data and / or input training animal data).

[0175] In a variation, recurrent neural networks (RNNs) can be employed to process timeseries data, while convolutional neural networks (CNNs) can be used for feature extraction from complex physiological signals. Hybrid models, such as CNN-RNN combinations, are considered to leverage the strengths of different neural network types. Encoder-decoder architectures can be implemented to translate raw data into the biodata language. The CNN-RNN model can play a crucial role in the animal language computing system by processing and interpreting physiological data, which is then translated into the biodata language used by the one or more Al models. The data processing and feature extraction phase utilizes Convolutional Neural Networks (CNN) to handle rawphysiological data collected from sensors, such as ECG signals, blood pressure readings, and other biometric data. The CNN applies convolutional layers to extract significant spatial features from this data. For example, in ECG signals, the CNN can identify features like QRS complexes, which are critical for detecting heartbeats and other cardiac events. The output of this process is a sequence of high-level feature maps that encapsulate the essential characteristics of the input data (e.g., input inference animal data and / or input training animal data). Following this, the sequence analysis phase employs Recurrent Neural Networks (RNN) to analyze the temporal sequence of features extracted by the CNN. RNNs are particularly adept at recognizing patterns over time, which is crucial for understanding the dynamic nature of various types of animal data such as physiological data. They process the sequence of feature maps, maintaining a memory of previous inputs to capture temporal dependencies and contextual information. This capability is vital for detecting patterns such as arrhythmias in ECG signals or trends in blood pressure changes. The RNN then produces a sequence of context-aware representations that encapsulate both spatial and temporal information from the input data (e.g., input inference animal data and / or input training animal data).

[0176] Training can involve one or more supervised learning and reinforcement learning techniques. In a refinement, training involves one or more unsupervised learning techniques. In another refinement, training involves one or more deep learning techniques. In supervised learning, labeled animal data (e.g., physiological data) is applied to train the models. Loss functions such as cross-entropy loss for classification tasks and mean squared error for regression tasks are defined. Optimization algorithms like Adam or RMSprop are employed to enhance training efficiency. Reinforcement learning incorporates a reward system where model responses are graded by human experts, and this feedback is applied to fine-tune the model. Continuous improvement is achieved by incorporating this feedback into the training process.

[0177] To ensure the model's performance and generalizability, a validation scheme (e.g., k- fold cross-validation) is applied, and validation metrics such as accuracy, precision, recall, Fl score, and AUC-ROC are employed. The final model is tested on a separate test dataset to evaluate its real- world applicability. Real-world scenarios are simulated to test the model’s robustness. One or more continuous learning mechanisms (e.g., methods, techniques) are implemented to regularly update the model with new data, improving accuracy and adaptability to new patterns. A feedback loop is established where the system learns from its interactions and refines its predictions over time.

[0178] In practice, the training of the animal language computing system can be illustrated with the following example. Data is collected from biosensors, such as ECG data from 1000 patients over one year, and from existing databases with annotated heart rate variability data.

[0179] The data undergoes preprocessing, including noise reduction by applying a band-pass fdter, normalization of heart rate data to a range of 0 to 1, and feature extraction to identify QRS complexes and calculate HRV. A transformer network is selected for its architecture, which includes self-attention mechanisms for feature extraction and analysis of sequential ECG data. In supervised learning, labeled data indicating normal and abnormal heart rhythms is used, with cross-entropy loss for classification and the Adam optimizer for training. Reinforcement learning involves a reward system where cardiologists review and grade model predictions, and this feedback is used to fine-tune the model. Validation is performed by applying 5-fold cross-validation, evaluating the model with precision, recall, and Fl score metrics. The model is then tested on a separate dataset of a select number of patients (e.g., 200 patients). Continuous learning mechanisms are incorporated to regularly update the model with new data and feedback, ensuring ongoing improvement and adaptation to new patterns. In the transformer network, the self-attention mechanism captures dependencies across the entire sequence of ECG data, allowing it to effectively identify complex patterns and relationships in the heart rhythm data. This mechanism helps the model to focus on relevant features at different times, improving the accuracy of QRS complex identification and HRV calculation. Through the use of transformer networks, the system can achieve high performance in both feature extraction and sequential data analysis, providing robust and accurate predictions of heart rhythms and other physiological patterns. Alternatively, a hybrid CNN-RNN can be used in this example

[0180] In another aspect, the training process includes several modules that can be implemented in hardware or software. It should be appreciated that all of the modules are in communication with each other or any subset thereof. Referring to Figure 3 A, the training system 160 includes a preprocessing module 162 that receives input training animal data. Preprocessing module 162 is configured to perform noise reduction on the input training animal data, optionally normalize the input training animal data to a standard scale, and extract relevant features from the input animal data based on one or more physiological events. Additionally, training system 160 includes a training module 164 in communication training module 164 configured to train the animal data language models by applying supervised learning with labeled physiological data and to fine-tune the animaldata language models by applying reinforcement learning with expert feedback. Furthermore, training system 160 includes validation module 166 configured by the computer to validate the performance of the animal data language models by applying a validation scheme (e.g., k-fold cross-validation) and performance metrics, including accuracy, precision, recall, and Fl score to provide validated animal data language models.

[0181] Referring to Figure 3A, the training system 160 optionally includes animal data processing and inference module 168. Animal data processing and inference module 168 is configured to receive input inference animal data from at least one sensor monitoring physiological parameters of a subject. Animal data processing and inference module 168 is also configured to receive the validated animal data language models. In a refinement, the input inference animal data includes physiological signals. Animal data processing and inference module 168 is further configured to execute the one or more validated animal data language models to infer discriminative information from the input inference animal data. The validated animal data language models apply a biodata language derived from the animal data. The biodata language include a lexicon of predefined units representing one or more physiological-based states (e.g., physiological states) and syntax rules for combining the predefined units into meaningful phrases. In a refinement, animal data processing and inference module 168 processes the input inference animal data by applying a transformer network architecture. The transformer network applies self-attention mechanisms to capture both spatial and temporal features from the physiological signals. This allows the model to identify complex patterns and dependencies within the data, providing a comprehensive analysis of the physiological states. Advantageously, the transformer network's self-attention mechanism can simultaneously extract spatial features and analyze temporal sequences, identifying patterns indicative of physiological states. This approach leverages the transformer network's ability to handle long-range dependencies and intricate relationships within the input data, leading to more accurate and robust inferences about the subject's physiological conditions.

[0182] In another aspect, the training system 160 includes a communications module 170 configured to provide the biodata language to a user. The interface is further configured to communicate the inferred discriminative information in the biodata language to a user in a human- understandable format.

[0183] In another aspect, the system further comprises a continuous learning module 172 configured to update the animal data language models with new data and feedback to improve accuracy and adaptability. The preprocessing module is further configured to segment the input animal data (e.g., input inference animal data and / or input training animal data) into meaningful intervals based on identified physiological events. These meaningful intervals include identified QRS complexes in ECG signals, peaks in heart rate data, or other significant physiological markers.

[0184] In another aspect, the animal data processing and inference module 168 is further configured to identify and classify physiological anomalies based on the extracted patterns from the input inference animal data and to generate alerts or notifications when specific physiological anomalies are detected. The training module is further configured to incorporate one or more additional animal data sources, including genetic data, biometric data, and activity data, to enhance the accuracy of the animal data language models. In a refinement, the training module can be further configured to incorporate one or more additional data sources which include contextual data, reference data, or a combination thereof, to enhance the accuracy of the animal data language models.

[0185] In another aspect, the validation module 166 is further configured to perform real-time or near real-time validation of the animal data language models during live monitoring of the subject and to adjust model parameters dynamically based on validation results. The continuous learning module 172 is further configured to implement a feedback loop where user interactions and corrections are used to refine the animal data language models and to periodically retrain the models with accumulated new data and feedback.

[0186] In another aspect, the communications module 170 is configured to display the inferred discriminative information in the biodata language through visual dashboards, charts, or graphs, and to provide audio or haptic feedback based on the inferred discriminative information. In a refinement, communications module 170 can be configured to provide neural feedback based on the inferred discriminative information. The input training animal data and / or the input inference animal data includes multi-modal physiological data captured from different types of sensors, such as ECG sensors, blood pressure monitors, and motion sensors. The animal data processing and inference module is configured to integrate and correlate data from the different types of sensors to enhance the accuracy of the inferred discriminative information.

[0187] In another aspect, the animal data language models in animal data processing and inference module 168 are further configured to support predictive analytics, providing forecasts on potential physiological states or events based on historical data patterns. This comprehensive system ensures accurate, real-time translation of complex animal physiological data into actionable insights, leveraging advanced Al and machine learning techniques to enhance health monitoring and decisionmaking.

[0188] In another aspect, the validation module 166 is further configured to perform real-time or near real-time validation of the animal data language models during live monitoring of the subject and to adjust model parameters dynamically based on validation results

[0189] Referring to Figure 3B, an application-specific integrated circuit (ASIC) is designed to implement a computer-implemented animal data language system. In this variation, the modules described above are implemented in hardware. The ASIC comprises several key modules to perform specific tasks. The ASIC includes a preprocessing module 162 configured to perform noise reduction on the input animal data (e.g., input inference animal data and / or input training animal data), optionally normalize the input animal data (e.g., input inference animal data and / or input training animal data) to a standard scale, and extract relevant features from the input animal data (e.g., input inference animal data and / or input training animal data) based on one or more physiological events. The ASIC also contains a training module 164 configured to train the animal data language models by applying supervised learning with labeled physiological data and fine-tune the models by applying reinforcement learning with expert feedback. Additionally, there is a validation module 166 configured to validate the performance of the animal data language models by applying a validation scheme (e.g., k-fold cross-validation) and performance metrics including accuracy, precision, recall, and Fl score.

[0190] Still referring to Figure 3B, training system 160 includes an animal data processing and inference module 168 configured to receive input inference animal data (or input training animal data) from at least one sensor monitoring the one or more physiological parameters of a subject. Animal data processing and inference module 168 executes one or more animal data language models to infer discriminative information from the input inference animal data (or input training animal data). The models apply a biodata language derived from the animal data, which includes a lexicon of predefined units representing the one or more physiological states and syntax rules for combining these units intomeaningful phrases. Animal data processing and inference module 168 processes the input inference animal data (or input training animal data) by applying a transformer network architecture. The transformer network utilizes self-attention mechanisms to capture both spatial and temporal features from the physiological signals. This approach enables the model to identify complex patterns and dependencies within the data, providing a comprehensive analysis of the physiological states. The transformer network's self-attention mechanism allows it to effectively extract spatial features and analyze temporal sequences, identifying one or more patterns indicative of physiological states. This capability leverages the transformer's ability to handle long-range dependencies and intricate relationships within the input inference animal data (or input training animal data). Animal data processing and inference module 168 then outputs the discriminative information in the biodata language. Alternatively, animal data processing and inference module 168 processes the input inference animal data (or input training animal data) by applying a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model architecture, where the CNN extracts spatial features from the physiological signals, and the RNN analyzes temporal sequences of these features to identify patterns indicative of physiological states. Animal data processing and inference module 168 then outputs the discriminative information in the biodata language.|0191] Still referring to Figure 3B, a continuous learning module 98 is included to update the animal data language models with new data and feedback, thereby improving accuracy and adaptability over time. Lastly, a communications module 170 in electrical communication with the animal data processing and inference module 168 is configured to provide the input animal data (e.g., input inference animal data and / or input training animal data) to the animal data processing and inference module 168 and to communicate the inferred discriminative information in the biodata language to a user in a human -understandable format. By integrating these modules, the ASIC efficiently performs the required tasks of the computer-implemented animal data language system, ensuring high performance, low power consumption, and real-time processing capabilities.

[0192] Implementing each of these modules in an ASIC involves designing dedicated hardware circuits optimized for the specific tasks that each module performs. Preprocessing module 162 handles noise reduction, normalization, and feature extraction from the input animal data (e.g., input inference animal data and / or input training animal data). Input port 176 received input from sensors. Digital filters 178, such as low-pass and high-pass filters, are implemented to remove noisefrom the input signals. In a refinement, arithmetic units 180 are configured to perform normalization operations, adjusting scaling and offset, as necessary. In a refinement, custom hardware circuits 182 are configured to segment the data and extract relevant features, such as peak detection and heart rate variability (HRV) calculations. Output ports 184 provide input to training module 164.[0193J Still referring to Figure 3B, training module 164 trains the transformer network-based animal data language models by applying supervised learning and fine-tunes them with reinforcement learning. Dedicated input interfaces 196 load training data into the system, typically from preprocessing module 162. In a refinement, specialized hardware units 188 perform gradient descent calculations necessary for training the transformer network. Weight update logic 190 adjusts model weights based on the calculated gradients. For reinforcement learning, circuits 192 implements reward systems and feedback loops to refine the transformer network further. Output ports 194 provide input to validation module 166. Alternatively, training module 164 trains the hybrid CNN-RNN.10194] Still referring to Figure 3B, the animal data language models' validation is managed by the validation module 166. Input port 196 receives input from training module 164. Validation module 166 includes logic 198 configured for performing k-fold cross-validation or another validation algorithm, dividing the data into training and validation sets. In a refinement, dedicated circuits 200 are responsible for computing performance metrics, such as accuracy, precision, recall, and Fl score. Control logic 202 is configured to manage the validation processes and adjust model parameters based on the results, ensuring the models are dependable and accurate. Output ports 204 provide input to communications module 170 and / or animal data processing and inference module 168.

[0195] Still referring to Figure 3B, the animal data processing and inference module 168 is responsible for executing animal data language models to infer discriminative information from input animal data (e.g., input inference animal data and / or input training animal data). Animal data processing and inference module 168 includes dedicated input ports and buffers 206 designed to receive data from sensors efficiently or from sensors through preprocessing module 162. In a refinement, specialized hardware accelerators 208 are integrated and configured to perform transformer network computations, utilizing self-attention mechanisms to capture both spatial and temporal features from the physiological data. On-chip memory 210, such as SRAM, stores intermediate data, model weights, and activations. In a refinement, the control logic 212 is configured to manage the data flow and coordinate operations within the transformer network processes, whileoutput ports 214 are designed to send the inferred discriminative information to subsequent modules.Alternatively, training module 164 trains the hybrid CNN-RNN.|0196] Still referring to Figure 3B, communications module 170 manages communication between the animal data processing and inference module 162 and external systems. Communications module 170 includes feature data communication ports 216 for efficient data transfer between sensors and the ASIC. In a refinement, data formatting units 218 are configured to convert discriminative information into human -understandable formats, such as visual or audio signals. Control logic 220 is configured to synchronize data flow between internal modules and external interfaces through output ports 222, ensuring seamless integration and communication.

[0197] Still referring to Figure 3B, continuous learning module 172 updates the animal data language models with new data and feedback, ensuring the models remain accurate over time. Continuous learning module 172 includes data input interfaces 226 configured to continuously receive new data and feedback. In a refinement, hardware 228 for continuous learning algorithms, such as online gradient descent, is configured to allow real-time model updates. Model update logic 230 is configured to continuously adjust model weights as new data and feedback are processed, maintaining the system's adaptability. In a refinement, output ports 232 provide input to communication module 170 and / or animal data processing and inference module 168.

[0198] When designing these modules for an ASIC as set forth above, several general considerations must be addressed. Power efficiency is crucial, particularly for portable or battery- operated devices, requiring each module to be optimized for low power consumption. Area efficiency is also important to minimize the silicon area used by each module, reducing manufacturing costs. Performance optimization ensures that data processing speeds meet real-time requirements, and scalability is essential for accommodating future upgrades or increased data loads. By implementing each module as described in Figures 2 and 3, the ASIC can efficiently perform the tasks required by the computer-implemented animal data language system, ensuring high performance, low power consumption, and real-time processing capabilities.

[0199] Creating an ASIC for the computer-implemented animal data language system and training system involves several detailed phases, starting with the design. The requirements and specifications for the ASIC, which include modules for animal data processing and inference,preprocessing, training, validation, and continuous learning, are meticulously defined. The architecture design follows, with detailed circuit designs described by applying hardware description languages like VHDL or Verilog. Extensive simulations and verifications ensure the design meets the functional and performance criteria. Once verified, the physical layout is created through floor planning, component placement, and routing, ensuring the design adheres to manufacturing rules and accurately reflects the schematic design.

[0200] In the fabrication phase, the ASIC design is transferred onto silicon wafers using photolithography, followed by etching, deposition, and doping to create the circuit’s structure. Each module of the ASIC, including those for noise reduction, data normalization, feature extraction, model training, and inference using transformer network architectures, is fabricated on the wafer. Wafer testing is performed to ensure each chip functions correctly, followed by dicing the wafer into individual chips. These chips are then packaged for protection and ease of integration into electronic systems, with final testing to ensure they operate correctly under various conditions. Quality control measures throughout the process monitor for defects and ensure high yields.

[0201] Mass production scales up the fabrication process to meet demand, integrating the ASICs into final products, often mounted on PCBs with other components. System-level testing verifies overall functionality, ensuring the ASIC accurately translates animal data into biodata language using predefined lexicons and syntax rules. The final products are deployed to customers, with ongoing maintenance and support provided, including firmware updates and design revisions if needed. This thorough process ensures the ASIC is reliable, efficient, and specifically tailored to the animal data language system's requirements, enhancing health monitoring and decision-making capabilities in various applications.10202] The process of developing a biodata language begins with the collection and analysis of physiological data. This data is gathered from various sources, including sensors that monitor heart rate, blood pressure, respiratory rate, oxygen saturation, ECG patterns, and other relevant physiological parameters. The collected data is then analyzed to identify key physiological states and conditions that are critical for monitoring health, such as normal, elevated, and low states for different physiological parameters. Next, a lexicon is developed by defining discrete units that represent specific physiological states. Each unit corresponds to a measurable condition or state, such as normal heart rate (HR Norm), elevated heart rate (HR Elev), or arrhythmia detected (ECG Arrhythmia). It isessential to standardize the terminology used for the lexicon to facilitate clear communication and interpretation. The establishment of syntax rules follows the development of the lexicon. These rules include simple descriptions for physiological states using the defined lexicon units, such as "HR Norm" for a normal heart rate. Conditional statements are created to describe actions to be taken based on specific physiological conditions, for example, "IF HR Elev THEN Alert HighHR," which means that if the heart rate is elevated, an alert for high heart rate should be generated. Additionally, rules for combining multiple physiological states into a single phrase are defined, such as "BP High AND HR Elev," which indicates both high blood pressure and elevated heart rate. More complex conditions involving multiple criteria and resulting actions are also developed, for instance, "IF HR Elev AND RR High THEN Alert Stress," meaning that if both heart rate and respiratory rate are elevated, a stress alert should be generated. Furthermore, severity levels for physiological states are established, like "Stress_Low," "Stress_Med," and "Stress_High," along with rules for expressing trends and changes over time in physiological data, such as "Trend HR INCREASE" to indicate an increasing heart rate trend.

[0203] Validation and testing of the defined lexicon and syntax rules with real physiological data ensure they accurately represent the states and conditions observed in the data. This step involves iterative refinement based on feedback from medical experts and real-world applications to improve accuracy and clarity. The validation and testing of the defined lexicon and syntax rules with real physiological data are critical steps to ensure they accurately represent the states and conditions observed in the data. Initially, the system's defined lexicon, which includes predefined units representing various physiological states, and the syntax rules, which dictate how these units are combined into meaningful phrases, are put to the test using actual physiological data collected from sensors monitoring different parameters, such as heart rate, blood pressure, and respiratory rate. This collected data serves as the benchmark for evaluating the system's accuracy and functionality. The validation process involves feeding this real -world physiological data into the system to see how well the defined lexicon and syntax rules capture and represent the physiological states. The system processes the input data using its transform network (or hybrid CNN-RNN) model architecture, extracting spatial and temporal features, and translating these into the biodata language. The output is then compared to known conditions and expert annotations to determine the accuracy of the system's inferences. To refine the system iteratively, feedback from medical experts plays a crucial role. These experts review the system's outputs, providing insights into any discrepancies or inaccuracies theyobserve. For instance, if the system incorrectly identifies a normal heart rate as elevated or fails to recognize an arrhythmia pattern accurately, the experts highlight these issues. This feedback is then used to adjust the lexicon and syntax rules, ensuring they more precisely reflect the physiological conditions. Moreover, real-world applications offer practical insights into the system's performance. By deploying the system in controlled environments, such as clinical settings or research studies, developers can observe how it functions in practice. These applications reveal any limitations or areas needing improvement that may not be evident in a lab setting. For example, the system might need adjustments to handle noisy data or varying data quality from different sensors. The iterative refinement process continues by incorporating these expert feedback and real-world observations. The lexicon and syntax rules are modified, and the Al models are retrained with the updated definitions and rules. This cycle of validation, feedback, and refinement repeats until the system consistently produces accurate and reliable outputs. Throughout this process, performance metrics such as accuracy, precision, recall, and Fl score are tracked to quantify improvements. These metrics provide a measurable way to assess the system's progress and identify remaining gaps. As the system evolves, it becomes better at translating complex physiological data into the biodata language, ultimately achieving a high level of accuracy and clarity in representing physiological states and conditions. This thorough validation and testing ensure that the biodata language is robust, reliable, and ready for real- world health monitoring and decision-making applications.

[0204] Once validated, the biodata language is integrated with artificial intelligence models, such as the transformer network (or hybrid CNN-RNN), which are trained to use the biodata language for processing and interpreting physiological data. These Al models are configured to infer discriminative information from the input physiological data and translate it into the biodata language using the predefined lexicon and syntax rules.

[0205] Finally, the system is implemented and applied in real-world scenarios. This involves deploying the biodata language in a computer-implemented system that monitors physiological data in real-time and developing an interface that communicates the inferred discriminative information to users in a human-understandable format, such as visual dashboards, alerts, or reports. Through this comprehensive process, the biodata language facilitates the translation of complex physiological data into meaningful and actionable insights, enhancing health monitoring and decision-making.

[0206] The following provides a specific illustration of the biodata language, including a lexicon of predefined units representing physiological states and syntax rules for combining these units into meaningful phrases.

[0207] Biodata language illustration 1:

[0208] Lexicon:Heart Rate (HR):HR_Norm: Normal heart rateHR Elev: Elevated heart rateHR Low: Low heart rateBlood Pressure (BP):BP Norm: Normal blood pressureBP High: High blood pressureBP Low: Low blood pressureRespiratory Rate (RR):RR_Norm: Normal respiratory rateRR High: High respiratory rateRR Low: Low respiratory rateOxygen Saturation (SpO2):SpO2_Norm: Normal oxygen saturationSpO2_Low: Low oxygen saturationECG Patterns:ECG_Normal: Normal ECG patternECG Arrhythmia: Arrhythmia detectedECG ST Elev: ST segment elevationStress Indicators:Stress_Low: Low stress levelStress_Med: Medium stress levelStress_High: High stress levelFatigue Indicators:Fatigue Low: Low fatigueFatigue High: High fatigue

[0209] Syntax Rules:1. Simple Descriptions:[Physiological State] + "_Norm"Example: "HR_Norm" (Normal heart rate)2. Conditional States:"IF" + [Condition] + "THEN" + [Action]Example: "IF HR Elev THEN Alert HighHR" (If elevated heart rate, then alert high heart rate)3. Combinatory Phrases:[Physiological State] + "AND" + [Another Physiological State]Example: "BP High AND HR Elev" (High blood pressure and elevated heart rate)4. Complex Conditions:"IF" + [Condition] + "AND" + [Condition] + "THEN" + [Action]Example: "IF HR Elev AND RR High THEN Alert Stress" (If elevated heart rate and high respiratory rate, then alert stress)5. Severity Levels:[Physiological State] + "_Low" + "|" + "_Med" + "|" + "_High"Example: "Stress_High" (High stress level)6. Temporal Changes:"Trend" + [Physiological State] + "INCREASE|DECREASE"Example: "Trend HR INCREASE" (Increasing trend in heart rate)

[0210] Examples of Phrases in the Biodata Language:Simple Condition:"HR Elev"Meaning: The heart rate is elevated.Combined Condition:"BP High AND HR Elev"Meaning: The blood pressure is high, and the heart rate is elevated.Actionable Alert:"IF HR Elev THEN Alert HighHR"Meaning: If the heart rate is elevated, then generate an alert for high heart rate.Complex Condition with Action:"IF HR Elev AND RR High THEN Alert Stress"Meaning: If both the heart rate is elevated and the respiratory rate is high, then generate an alert for stress.Trend Analysis:"Trend HR INCREASE"Meaning: There is an increasing trend in heart rate.Severity Level:"Stress_High"Meaning: The stress level is high.10211] Biodata language illustration 2:

[0212] LexiconHeart Rate (HR):HR Norm: Normal heart rateHR Elev: Elevated heart rateHR Low: Low heart rateBlood Pressure (BP):BP_Norm: Normal blood pressureBP High: High blood pressureBP Low: Low blood pressureRespiratory Rate (RR):RR_Norm: Normal respiratory rateRR High: High respiratory rateRR Low: Low respiratory rateOxygen Saturation (SpO2):SpO2_Norm: Normal oxygen saturationSpO2_Low: Low oxygen saturationECG Patterns:ECG_Normal: Normal ECG patternECG Arrhythmia: Arrhythmia detected ECG ST Elev: ST segment elevationStress Indicators:Stress_Low: Low stress levelStress_Med: Medium stress levelStress_High: High stress levelFatigue Indicators:Fatigue Low: Low fatigueFatigue High: High fatiguePatterns in Data:Pattem Spike: Sudden spike in physiological data Pattern Drop: Sudden drop in physiological data Pattem Rhythm: Regular rhythmic pattern in data Pattem Anomaly: Irregular or unusual pattern detected Pattern Trendlncrease: Increasing trend in data Pattem TrendDecrease: Decreasing trend in dataDate and Time (DT):DT Current: Current date and timeDT Past: Past date and timeSyntax RulesSimple Descriptions:[Physiological State] + "_Norm"Example: "HR_Norm" (Normal heart rate)Conditional States:"IF" + [Condition] + "THEN" + [Action]Example: "IF HR Elev THEN Alert HighHR" (If elevated heart rate, then alert high heart rate)Combinatory Phrases:[Physiological State] + "AND" + [Another Physiological State]Example: "BP High AND HR Elev" (High blood pressure and elevated heart rate)Complex Conditions:"IF" + [Condition] + "AND" + [Condition] + "THEN" + [Action]Example: "IF HR Elev AND RR High THEN Alert Stress" (If elevated heart rate and high respiratory rate, then alert stress)Severity Levels:[Physiological State] + "_Low" + "|" + "_Med" + "|" + "_High"Example: "Stress_High" (High stress level)Temporal Changes:"Trend" + [Physiological State] + "INCREASE|DECREASE"Example: "Trend HR INCREASE" (Increasing trend in heart rate)Date and Time Representation:"DT" + [Specific Date / Time]Example: "DT_20240609_1200" (Date and Time: June 9, 2024, at 12:00 PM)Examples of Phrases in the Biodata LanguageSimple Condition:"HR Elev"Meaning: The heart rate is elevated.Combined Condition:"BP High AND HR Elev"Meaning: The blood pressure is high, and the heart rate is elevated.Actionable Alert:"IF HR Elev THEN Alert HighHR"Meaning: If the heart rate is elevated, then generate an alert for high heart rate.Complex Condition with Action:"IF HR Elev AND RR High THEN Alert Stress"Meaning: If both the heart rate and respiratory rate are elevated, then generate an alert for stress.Trend Analysis:"Trend HR INCREASE'Meaning: There is an increasing trend in heart rate.Severity Level:"Stress_High"Meaning: The stress level is high.Pattern Recognition:"ECG_Var"Meaning: Variability detected in ECG patterns.Date and Time Context:"DT_20240609_1200"Meaning: The date and time is June 9, 2024, at 12:00 PM.Sleep Pattern Description:"Sleep Disrupt"Meaning: The sleep pattern is disrupted.Activity Level Description:"Act High"Meaning: The activity level is high.

[0213] It should be appreciated that the lexicon for the biodata language can include any representation of animal data and / or features derived therefrom, including but not limited to patterns, identifiers, rhythms, trends, signs, symptoms, digital biological signatures, outliers, abnormalities, anomalies, and / or other attributes or characteristics. In a refinement, it should be appreciated that the lexicon for the biodata language can include any representation of animal data and / or features derived therefrom, including but not limited to biological-based patterns, identifiers, rhythms, trends, signs, symptoms, digital signatures, outliers, abnormalities, anomalies, and / or other attributes or characteristics.|0214] In another aspect, healthcare facilities can utilize the animal data language system to evaluate (e.g., collect data from), manage, and / or monitor patients in various settings (e.g., in person, virtual), such as hospitals, clinics, human performance centers, rehabilitation centers, telehealth platforms, employment facilities (e.g., team facilities in sports), home care, remote care, and other laboratory and non-lab oratory settings. Patients utilize (e.g., are fitted with, wear, allow or enable data capture from) one or more sensors or other data capturing systems / programs that can track animaldata-based parameters (e.g., physiological parameters) such as ECG signals, blood pressure, and other vital signs. The CNN component of the system processes this data, identifying key features (e.g., in this context, animal data-based identifiers, rhythms, patterns, trends, frequencies, occurrences, signs, symptoms, digital biological signatures, outliers, abnormalities, anomalies, structures, variations, relationships, outcomes, scenarios, states, and / or other characteristics) from the animal data and its corresponding contextual data. The RNN analyzes these features over time, detecting patterns, trends, frequencies, occurrences, abnormalities, anomalies, signs, symptoms, rhythms, digital biological signatures, structures, variations, relationships, outcomes, scenarios, states, and / or other identifiers or characteristics indicative of one or more potential health irregularities, one or more health regularities, or a combination thereof. The animal data language models apply the biodata language to identify and communicate these features in a format that provides one or more alerts of the one or more findings to healthcare providers. In a refinement, the animal data language model translates these findings into biodata language, generating alerts for healthcare providers. These alerts enable timely medical interventions, improving patient outcomes across a wide range of medical / health conditions. Characteristically, the one or more identifications of the one or more features can occur in real-time or near real-time. In a variation, the biodata language can be configured to modify the one or more features, or create one or more new features, based upon new data entering the system (e.g., animal data, other contextual data) and / or further processing of existing data.

[0215] In another aspect, athletes and fitness enthusiasts can apply the animal data language system during training sessions, live competition, and / or daily activities. The system collects data from wearable sensors and other data collection systems, including heart rate monitors and motion capture devices. The CNN extracts features related to physical activity, while the RNN tracks performance trends over time. The animal data language model applies the biodata language to identify and communicate these features in a digital format that provides one or more actionable insights, such as recommendations for training adjustments and recovery strategies. Coaches and individuals receive these insights, helping to optimize performance and reduce the risk of injury across various sports and fitness routines. In a refinement, the computer-implemented animal data language system is applied by athletes and fitness enthusiasts during training sessions, live competition, and / or daily activities to provide one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, and / or recommendations for performance optimization, injury prevention, and / or outcome management (e.g., ensure that the athlete or group of athletesachieve the desired outcome, such as score the next goal or win the competition). In a refinement, one or more sportsbooks, oddsmakers, bettors, or a combination thereof, can utilize the one or more insights to (1) create, modify, enhance, or evaluate one or more odds; (2) create, modify, enhance, or evaluate one or more bets; (3) as a market upon which one or more bets are placed or accepted; (4) formulate one or more strategies; (5) create, modify, enhance, acquire, offer, or distribute one or more products; or (6) mitigate, prevent, or take one or more risks.

[0216] In a refinement, one or more functions such as processing (e.g., identifying, normalizing, aggregating, transforming, tagging, manipulating, denoising, enhancing, augmenting, organizing, categorizing, analyzing, summarizing, replicating, synthesizing, anonymizing, synchronizing, translating, measuring, visualizing, characterizing, or a combination thereof) data occurs using the biodata language itself, meaning the language can be configured to enable computing devices to execute any combination of or all of organize data, denoise data, normalize data, and the like in the biodata language itself. Characteristically, this means that the biodata language itself is not only a mode of communication between computing systems and / or devices, but the language is the means upon which one or more functions such as processing occurs (e.g., normalizing data occurs in the biodata language; enhancing data occurs in this biodata language, and the like). In some variations, functions such as processing can occur in two or more languages. For example, one or more functions can occur in one language (e.g., collected animal data can be normalized using an available computing language) while other functions (e.g., similar, dissimilar) can occur using the biodata language (e.g., the biodata language can translate the data from the available computing language to the biodata language where it is further organized).10217] In another aspect, healthcare facilities utilize the animal data language system to evaluate, monitor, and / or manage patients (or a combination thereof) in various settings, (e.g., in person, virtual), such as hospitals, clinics, human performance centers, rehabilitation centers, telehealth platforms, employment facilities (e.g., team facilities in sports), home care, remote care, and other laboratory and non-laboratory settings. Patients utilize (e.g., are fitted with, wear, allow or enable data capture from) one or more sensors or other data capturing systems / programs that track animal data-based parameters (e.g., physiological parameters) such as ECG signals, blood pressure, and other vital signs. The transformer network component of the system processes this data, identifying key features (e.g., in this context, animal data-based identifiers, rhythms, patterns, trends, frequences,occurrences, signs, symptoms, digital biological signatures, outliers, abnormalities, anomalies, structures, variations, relationships, outcomes, scenarios, states, and / or other characteristics) from the animal data and its corresponding contextual data. The transformer network analyzes these features in real-time and overtime, detecting patterns, trends, frequencies, occurrences, abnormalities, anomalies, signs, symptoms, rhythms, digital biological signatures, and / or other identifiers or characteristics indicative of one or more potential health irregularities, one or more health regularities, or a combination thereof. The animal data language models apply the biodata language to identify and communicate these features in a format that provides one or more alerts of the one or more findings to healthcare providers. In a refinement, the one or more findings can include one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof. In another refinement, the animal data language model translates these findings into biodata language, generating alerts for healthcare providers. These alerts enable timely medical interventions, improving patient outcomes across a wide range of medical / health conditions. Characteristically, the one or more identifications of the one or more features occur in real-time or near real-time. In a variation, the biodata language is configured to modify the one or more features or create one or more new features based upon new data entering the system (e.g., animal data, other contextual data) and / or further processing of existing data.

[0218] In another aspect, athletes and fitness enthusiasts can apply the animal data language system during training sessions, live competition, and / or daily activities. The system collects data from wearable sensors and other data collection systems, including heart rate monitors and motion capture devices. The transformer network extracts features related to physical activity and tracks performance trends over time. The animal data language model applies the biodata language to identify and communicate these features in a digital format that provides one or more actionable insights, such as recommendations for training adjustments and recovery strategies. In a refinement, the one or more insights can include one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof. Coaches and individuals receive these insights, helping to optimize performance and reduce the risk of injury across various sports and fitness routines. In a refinement, one or more sportsbooks, oddsmakers, bettors, or a combination thereof, can utilize the one or more insights to (1) create, modify, enhance, or evaluate one or more odds; (2) create, modify, enhance, or evaluate one ormore bets; (3) as a market upon which one or more bets are placed or accepted; (4) formulate one or more strategies; (5) create, modify, enhance, acquire, offer, or distribute one or more products; or (6) mitigate, prevent, or take one or more risks. In another refinement, the computer-implemented animal data language system is applied to sports betting to aid one or more sportsbooks, oddsmakers, bettors, or a combination thereof, in the creation, modification, enhancement, and / or placement of a bet by providing one or more insights upon which (1) one or more odds are created, modified, enhanced, and / or evaluated; (2) one or more bets are created, modified, enhanced, evaluated, or placed; (3) one or bets are placed or accepted based upon the one or more insights becoming one or more markets (e.g., subject matter for the bet, at least in part); (4) one or more strategies are formulated; (5) one or more products are created, modified, enhanced, acquired, offered, and / or distributed; (6) one or more risks are mitigated, prevented, or taken; or (7) a combination thereof.|0219] In a refinement, the animal data language system can be configured to create, modify, enhance, assign, or a combination thereof, one or more monetary (and / or non-monetary) values to one or more features (or subset of features) for one or more subjects (including one or more groups of subjects), as well as one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, or recommendations derived from the discriminative information, at least in part, and related to one or more outcomes for one or more current or future events or sub-events associated with the one or more subjects. For example, the one or more features may enable early detection of a medical event. If the individual experienced the medical event, the cost to the individual would have been X. Instead, the cost to treat the individual to prevent the medical event from happening is Y. The system can create one or more values to assign to the one or more features based on one or more variables (e.g., which may include probability of the medical event happening; the likelihood the medical event would have been discovered prior to it happening without the one or more features; the impact on quality of life on the individual based upon the medical event happening; and the like). The one or more variables can be a tunable parameter. Such value(s) can be utilized, for example, for insurance billing or other healthcare purposes. In another example, the system can create (e g., dynamically and / or in real-time or near real-time) and assign one or more monetary values to a prediction related to the outcome of a competition in a given sport that it provides to sportsbooks based upon one or more variables (e.g., betting volume on a given bet; projected profit margins). The one or more variables can be a tunable parameter. Characteristically, a plurality of monetary values can be created for the same prediction based upon one or more variables (e g., thestate / country in which the prediction is being used; how the prediction is being used; the betting volume of the acquirer utilizing the prediction; and the like).|0220] In a refinement, one or more functions such as processing (e.g., identifying, normalizing, aggregating, transforming, tagging, manipulating, denoising, enhancing, augmenting, organizing, categorizing, analyzing, summarizing, replicating, synthesizing, anonymizing, synchronizing, translating, measuring, visualizing, characterizing, or a combination thereof) data occurs using the biodata language itself, meaning the language is configured to enable computing devices to execute any combination of or all of organize data, denoise data, normalize data, and the like in the biodata language itself. Characteristically, this means that the biodata language itself is not only a mode of communication between computing systems and / or devices but also the means upon which one or more functions such as processing occur (e.g., normalizing data occurs in the biodata language; enhancing data occurs in this biodata language, and the like). In some variations, functions such as processing occur in two or more languages. For example, one or more functions occur in one language (e.g., collected animal data is normalized using an available computing language) while other functions (e.g., similar, dissimilar) occur using the biodata language (e.g., the biodata language translates the data from the available computing language to the biodata language where it is further organized).|0221] In another aspect, veterinary practices and animal care facilities implement the animal data language system to evaluate, monitor, and / or manage (or a combination thereof) the health of one or more animals, including pets, livestock, and wildlife. Animals are equipped with, or utilize, one or more sensors or sensing systems that track a variety of animal data, which can include physiological and behavioral data. The transformer network processes this data to detect anomalies and analyze trends over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format (e.g., codified language) that provides the findings via one or more generated reports for veterinarians and caregivers. These reports, which can feature one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof, help in the early or current detection, prevention, and / or treatment (e.g., which can include one or more recommendations) of one or more health issues or potential health issues, ensuring the well-being of animals in diverse settings.

[0222] In another aspect, organizations and individuals integrate the animal data language system to evaluate, monitor and / or manage mental health and stress levels. Wearable devices measure physiological indicators such as galvanic skin response, heart rate, and cortisol levels. The transformer network extracts features related to stress and tracks these features over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that provides personalized feedback, assessments, and / or recommendations for stress management techniques. This helps individuals and wellness coordinators to implement effective strategies to reduce stress and improve mental well-being.

[0223] In another aspect, a military defense firm and / or government unit integrate the animal data language system to evaluate, monitor and / or manage the physical and mental health of its soldiers and / or combatants to ensure successful execution of a mission. One or more sensors (e.g., wearable sensors, optical sensors via one or more unmanned aerial vehicles) capture animal data such as physiological indicators that include facial recognition data, heart rate, heart rate variability, skin temperature, blood pressure, and other information from gathered animal data and metadata. The transformer network extracts features related to stress and fatigue and tracks these features over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format understandable to humans (e.g., English language) that provides one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof, related to a desired outcome (e.g., ensuring soldiers have the mental and physical capacity to implement effective strategies to execute the mission).

[0224] In another aspect, a financial trading firm utilizes the animal data language system to evaluate, monitor, and / or manage (or a combination thereof) its stock traders prior, during, and after trading hours (e.g., including in office, at home, and in other settings) to ensure traders are executing one or more trades with optimal mental and emotional clarity and confidence. Traders utilize (e.g., are fitted with, wear, allow or enable data capture from) one or more sensors or other data capturing systems / programs that track animal data-based parameters (e.g., physiological parameters) such as ECG signals, blood pressure, and other vital signs, with a particular focus on real-time animal data collection during live trading hours. The transformer network component of the system processes this data, identifying key features (e.g., in this context, animal data-based identifiers, rhythms, patterns,trends, frequences, occurrences, signs, symptoms, digital biological signatures, outliers, abnormalities, anomalies, structures, variations, relationships, outcomes, scenarios, states, and / or other characteristics) from the animal data and its corresponding contextual data. The transformer network analyzes these features in real-time and over time, detecting patterns, trends, frequences, occurrences, abnormalities, anomalies, signs, symptoms, rhythms, digital biological signatures, and / or other identifiers or characteristics indicative of one or more potential issues related to mental and / or physical stress for any particular trade. The animal data language models apply the biodata language to identify and communicate these features a format that provides one or more alerts of the one or more findings to their supervisor / management (which may be a human, an artificial intelligence, or a combination thereof) in real-time or near real-time prior to the execution of any particular trade to prevent (or enable) the trader to execute the one or more trades. In a refinement, the system (or an associated program) can be configured to automatically prevent (or enable) a trader from executing a trade based on the one or more insights and / or alerts. In another refinement, the computer-implemented animal data language system is applied to financial trading to aid in the execution of a stock trade by providing one or more insights related to one or more physiological-based parameters of the one or more traders.

[0225] In another aspect, companies can apply the animal data language system to enhance user experiences in human-machine interactions, such as virtual reality (VR), augmented reality (AR) applications, and / or mixed reality applications. Users wear sensors that monitor physiological responses like heart rate, pupil dilation, and skin conductance. The transformer network processes this data to identify features related to engagement and stress and analyzes these features in real-time and over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that adjusts the interaction environment dynamically. This ensures a more immersive and enjoyable experience for users across various applications.

[0226] In another aspect, individuals can apply the animal data language system to track, assess, and / or improve their health and wellness. Wearable sensors, other sensing systems, and / or other computer programs monitor sleep patterns, activity levels, and dietary intake. The transformer network extracts features from gathered data and analyzes trends overtime. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that offers personalized advice for one or more lifestyle changes. The one or more one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities,projections, determinations, recommendations, or a combination thereof, can help individuals improve their sleep quality, physical activity, health metrics, and overall well-being.|0227] In another aspect, research teams conduct clinical studies applying the animal data language system to monitor participants' animal data-based parameters (e.g., physiological parameters). Participants use (e.g., wear) sensors and utilize other sensing systems that track animal data-based metrics such as heart rate variability, blood pressure, and glucose levels, as well as various forms of biometric data (e.g., facial recognition). The transformer network processes this data to extract key features and analyze changes over the study period. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that generates one or more comprehensive reports which can include one or more forecasts, predictions, probabilities, assessments, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof. These reports support researchers in drawing meaningful conclusions and publishing their findings.

[0228] In another aspect, companies implement the animal data language system to monitor workers' health and safety in various environments. Workers wear sensors that track core body temperature, heart rate, and hydration levels. The transformer network extracts features related to health risks and analyzes these features in real-time and / or over time (e.g., over the course of the workday). The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that generates one or more alerts for supervisors or other responsible management. These alerts help prevent potential health issues by recommending timely interventions, such as hydration and rest breaks.

[0229] In another aspect, health insurance companies can apply the animal data language system to assess the health risks of policyholders. One or more sensors (e.g., wearable sensors, other sensing and / or data gathering systems) collect animal data that can include heart rate, blood pressure, cholesterol levels, and activity levels. The transformer network processes this data to identify health risk features and analyzes trends over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format (e.g., codified language) that generates risk assessments. The one or more risk assessments can include one or more forecasts, predictions, probabilities, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof. The one or more assessments helpinsurance companies tailor one or more premiums and / or offer one or more incentives for healthy behavior(s), reducing overall health risks.|0230] In another aspect, assisted living facilities integrate the animal data language system to evaluate, manage, and / or monitor the health of elderly residents. Residents utilize (e.g., wear, enable data capture with) sensors that track animal data including vital signs and physical activity. The transformer network extracts features from the data and analyzes changes over time. The animal data language model applies the biodata language to process and communicate the one or more features in one or more digital formats (e.g., another one or more codified languages) that generates one or more alerts for caregivers. The one or more alerts can include one or more forecasts, predictions, probabilities, summaries, comparisons, evaluations, possibilities, projections, determinations, recommendations, or a combination thereof. These alerts recommend interventions such as physical therapy assessments, helping ensure the safety and well-being of residents.10231] In another aspect, educational institutions integrate the animal data language system into their learning environments to evaluate, manage, and / or monitor students' engagement and stress levels. Students utilize (e.g., wear, enable data capture with) sensors and other data capturing devices and systems that track heart rate, EEG readings, and galvanic skin response during classes. The transformer network processes this data to identify features related to cognitive activity and stress and analyzes trends over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that provides insights for teachers. These insights help teachers adapt their teaching strategies, improving student engagement and learning outcomes.

[0232] In another aspect, sports clinics can apply the animal data language system to aid in the rehabilitation of injured athletes. Sensors evaluate, manage, and / or monitor muscle activity, joint angles, range of motion, and pain levels during recovery exercises. The transformer network processes this data to extract relevant features and analyzes recovery progress over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that provides feedback for rehabilitation exercises. This helps sports therapists modify rehabilitation plans, promoting effective recovery.

[0233] In another aspect, educational institutions can integrate the animal data language system into their learning environments to evaluate, manage, and / or monitor students' engagement and stress levels. Students wear devices that track heart rate, EEG readings, and galvanic skin response during classes. The CNN processes this data to identify features related to cognitive activity and stress, while the RNN analyzes trends over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that provides insights for teachers. These insights help teachers adapt their teaching strategies, improving student engagement and learning outcomes.

[0234] In another aspect, educational institutions integrate the animal data language system into their learning environments to evaluate, manage, and / or monitor students' engagement and stress levels. Students wear devices that track heart rate, EEG readings, and galvanic skin response during classes. The transformer network processes this data to identify features related to cognitive activity and stress and analyzes trends over time. The animal data language model applies the biodata language to process and communicate the one or more features in a digital format that provides insights for teachers. These insights help teachers adapt their teaching strategies, improving student engagement and learning outcomes.

[0235] In another aspect, a language around Human Data can be created by following these steps: In step 1, gather data from at least one sensor, such as a heart rate monitor. In step 2, identify patterns within the collected data, such as QRS complexes in ECG readings. In step 3, create segments of data separated as units. These segments serve as the basic building blocks for the codified vocabulary, each representing a discrete physiological event or measurement. In step 4, generate a codified vocabulary where each unit represents a word or phrase in a lexicon. In step 5, define each word as a part of speech in the codified language. In step 6, add rules around the words to complete the codified language. In step 7, applying these rules, translate the codified language elements into human-understandable speech or another codified language. This translation enables the data to be communicated effectively, making the physiological information accessible and actionable.

[0236] The following examples illustrate the various embodiments of the present invention. Those skilled in the art will recognize many variations that are within the spirit of the present invention and scope of the claims, including variations within many applications that include, but are not limited to, sports media systems (e.g., for displaying the collected data in broadcasts or streaming of content),sports wagering (e.g., betting) systems, insurance provider systems, telehealth systems, health analytics systems, risk analytics systems (e.g., insurance, finance), performance analytics systems, health and wellness monitoring systems (e.g., including systems to monitor viral infections, electronic medical record systems, electronic health records systems, corporate wellness systems, and the like), research systems, fitness systems, military systems, hospital systems, animal data monetization systems (e.g., animal data marketplace or exchange to buy, sell, and trade animal data and / or its or more derivatives), pharmaceutical systems, emergency response systems, financial systems (e.g., to monitor, evaluate and / or manage traders), video game systems, simulation systems, and the like.

[0237] Example 1:

[0238] Data shows units of high HR occurring over a short period of time and respiration rate indicates shortness of breath. If these two patterns are seen together over a short period of time, then this artifact can be codified into the biodata language as “high stress” plus “logical AND” plus “low respiration.” This is, in turn, translated into a digital format (e.g., a codified language) readable by one or more computing devices and / or humans to represent “I have stress, and I cannot breathe.” In this scenario, stress, respiration, and other predefined units or patterns of data represent nouns / pronouns, while “high,” “low,” etc., will be adjectives. Their occurrence together is defined with language constructs such as logical ANDs, ORs, or other conditions, predicates, or relationships inverse or converse, etc.

[0239] Example 2:|0240] A hospital utilizes the animal data language system to evaluate, monitor, and / or manage (or a combination thereof) patients in the intensive care unit (ICU). A patient with a history of cardiac issues is fitted with an ECG sensor and a blood pressure monitor. The transformer network component of the system processes the ECG data, identifying key features such as QRS complexes and ST segments. The transformer network then analyzes these features in real-time and / or over time, detecting patterns indicative of potential arrhythmias. The animal data language model applies the biodata language to process the data (e.g., identifying one or more patterns or other features within the singular or aggregated data sets) and communicates the one or more animal data-based features in a codified language, providing such findings via one or more generated alerts: "Patient showing signsof ventricular arrhythmia, immediate intervention required." This alert is sent to the attending physician, who promptly administers the necessary treatment, potentially saving the patient's life.|0241] Example 3:|0242] An elite athlete uses the animal data language system during training sessions, in live competition, and / or other daily activities. The system collects data from wearable sensors, including heart rate monitors, motion capture devices, and other data-capturing systems. The transformer network extracts features related to muscle activity and joint movements and tracks performance trends over time. The animal data language model applies the biodata language to process this data and communicates the one or more animal data-based features in a digital format (e.g., codified language), the animal data language model is further configured to transform such biodata languagebased features and findings into actionable insights in another codified language, such as: "Increased muscle fatigue detected, recommend reducing training intensity and focusing on recovery exercises." The coach receives these recommendations and adjusts the athlete's training regimen and / or the athlete’s playing time in the live competition accordingly, optimizing performance and reducing injury risk.

[0243] Example 4:

[0244] A dairy farm implements the animal data language system to monitor the health of its livestock. Cows are equipped with and / or associated with one or more sensors that track body temperature, heart rate, environmental data, and activity levels. The transformer network processes this data to detect anomalies and analyzes trends over time. The animal data language model applies the biodata language to process collected data and other relevant information (e.g., baseline information related to cows; contextual data) and communicates the one or more animal data-based features in a codified language, the animal data language model being further configured to generate a report in the codified language: "Cow #1234 showing elevated body temperature and decreased activity, possible onset of mastitis." The farmer receives this alert and promptly consults a veterinarian, who confirms the diagnosis and initiates treatment, ensuring the cow's health and productivity. In a refinement, the animal data language model translates the findings into biodata language, generating the report.

[0245] Example 5:

[0246] A corporate wellness program integrates the animal data language system to monitor employees' stress levels. Employees wear devices that measure galvanic skin response, heart rate, and cortisol levels. The transformer network extracts features related to stress indicators and tracks these features over time. The animal data language model applies the biodata language to process gathered data (e.g., identifying one or more features in the data) and communicates the one or more animal data-based features in one or more digital formats (e.g., codified languages), the one or more digital formats providing personalized feedback: "High stress levels detected during afternoon meetings, suggest scheduling relaxation breaks and mindfulness exercises." The wellness coordinator receives this feedback and implements changes in the work schedule, helping to reduce employee stress and improve productivity. In a refinement, the animal data language model translates the data into biodata language, providing personalized feedback.

[0247] Example 6:

[0248] A virtual reality (VR) or augmented reality (AR) or mixed reality gaming company uses the animal data language system to enhance user experience. Players wear sensors that monitor physiological responses such as heart rate, pupil dilation, and skin conductance, as well as leverage other sensors or sensing systems that capture other animal data-based and non-animal data-based information. The transformer network processes this data to identify features related to engagement and stress and analyzes these features in real-time. The animal data language model applies the biodata language to process collected data and communicates the one or more animal data-based features and any derivatives thereof in a codified language, the animal data language model is further configured to adjust the game environment: "Increased stress detected, reducing difficulty level to maintain engagement." The game adapts dynamically, providing a more immersive and enjoyable experience for the player.

[0249] Example ?:|0250] An individual uses the animal data language system to track their health and wellness. Wearable sensors monitor sleep patterns, activity levels, and dietary intake. The transformer network extracts features from this data and analyzes trends over time. The animal data language model appliesthe biodata language to process collected data and communicates the one or more animal data-based features and any derivatives thereof in one or more digital formats, which include offering personalized advice: "Insufficient REM sleep detected, recommend adjusting bedtime routine and reducing caffeine intake." The individual receives these recommendations through a mobile app and makes the suggested lifestyle changes, leading to improved sleep quality and overall well-being.

[0251] Example s:

[0252] A research team conducts a clinical study on the effects of a new medication using the animal data language system. Participants wear sensors that monitor various physiological parameters, such as heart rate variability, blood pressure, and glucose levels. The transformer network processes this data to extract key features and analyzes changes over the study period. The animal data language model applies the biodata language to process gathered data (e.g., identifying one or more features in the data) and other information (e.g., baseline information, other contextual data) and communicates the one or more animal data-based features in one or more other digital formats (e.g., one or more other codified languages), the one or more communications including a generated comprehensive report: "Participants show significant reduction in blood pressure and improved glucose control, indicating positive effects of the medication." The researchers use this report to support their findings and publish the results in a scientific journal.

[0253] Example 9:

[0254] A construction company implements the animal data language system to monitor workers' health and safety. Workers utilize sensors that track core body temperature, heart rate, environmental conditions, and hydration levels. The transformer network extracts features related to heat stress and fatigue and analyzes these features over the course of the workday. The animal data language model applies the biodata language to process gathered data and other information (e.g., baseline information, other contextual data) and communicates the one or more animal data-based features via one or more digital formats, the one or more digital formats, including one or more alerts in a codified language: "Worker #567 showing signs of heat stress, recommend immediate hydration and rest. " The site supervisor (or other management) receives these alerts and ensures the worker takes a break, preventing potential heat-related illnesses.

[0255] Example 10:

[0256] A health insurance company uses the animal data language system to assess the health risks of policyholders. Wearable sensors and other sensing systems collect data on heart rate, blood pressure, cholesterol levels, activity levels, and other animal data. The transformer network processes this data to identify features indicative of health risks and analyzes trends over time. The animal data language model applies the biodata language to process gathered data and other information (e.g., baseline information about or related to the individual; other contextual data) and communicates the one or more animal data-based features via one or more digital formats, the one or more digital formats including one or more generated risk assessments: "Policyholder showing elevated blood pressure and high cholesterol, recommend lifestyle changes to reduce risk." The insurance company uses this information to tailor premiums and offer incentives for healthy behaviors.

[0257] Example 11:

[0258] An assisted living facility integrates the animal data language system to monitor the health of its residents. Elderly individuals wear sensors that track vital signs and physical activity. The transformer network extracts features from the data and analyzes changes over time. The animal data language model applies the biodata language to process gathered data and other information (e.g., baseline information about or related to the individual; other contextual data) and communicates the one or more animal data-based features via one or more digital formats, the one or more digital formats including one or more generated alerts: "Resident #45 showing reduced mobility and increased fall risk, recommend physical therapy assessment." The facility staff receives these alerts and arranges for the resident to undergo a physical therapy evaluation, ensuring their safety and well-being.

[0259] Example 12:

[0260] An educational institution integrates the animal data language system into its learning environment to monitor students' engagement and stress levels. Students wear devices that track heart rate, EEG readings, and galvanic skin response during classes. The transformer network processes this data to identify features related to cognitive activity and stress and analyzes trends over time. The animal data language model applies the biodata language to process gathered data and other information (e.g., baseline information about or related to the individual; other contextual data) andcommunicates the one or more animal data-based features via one or more digital formats that provide insights: "Increased stress and decreased focus detected during math lessons, recommend interactive teaching methods." Teachers receive this feedback and adapt their teaching strategies, improving student engagement and learning outcomes.

[0261] Example 13:

[0262] A hospital system utilizes the animal data language system to evaluate patients as part of a medical and / or health-based animal evaluation (e.g., primary opinion, second opinion, third opinion, fourth opinion, and so on), consultation, or similar function conducted by a medical professional (e.g., doctor, nurse), an artificial intelligence (e.g., which in some variations may operate as a medical professional), or a combination thereof. A patient with a history of cardiac issues is fitted with an ECG sensor and a blood pressure monitor (sensor) and is monitored. The CNN component of the system processes the ECG data, identifying key features such as QRS complexes and ST segments. The RNN then analyzes these features overtime, detecting patterns indicative of potential arrhythmias. The animal data language model applies the biodata language to process the data (e.g., identifying one or more patterns or other features within the singular or aggregated data sets) and communicates the one or more animal data-based features in a codified language, providing such findings via one or more generated alerts: "Patient showing signs of ventricular arrhythmia, immediate intervention required." This alert is sent to the attending physician, who promptly administers the necessary treatment, potentially saving the patient's life.

[0263] Example 14:

[0264] An elite athlete uses the animal data language system during training sessions, in live competition, and / or daily activities (e.g., which can include rest / sleep). The system collects data from wearable sensors, including heart rate monitors, motion capture devices, and other data-capturing systems. The CNN extracts features related to muscle activity and joint movements, while the RNN tracks performance trends over time. The animal data language model applies the biodata language to process this data and communicates the one or more animal data-based features in a digital format (e.g., codified language), the animal data language model being further configured to transform such biodata language-based features and findings into actionable insights in another codified language, such as: "Increased muscle fatigue detected, recommend reducing training intensity and focusing onrecovery exercises." The coach receives these recommendations and adjusts the athlete's training regimen or amount of playing time in the live competition accordingly (e.g., if healthy and / or not fatigued, the coach will put the player back in the game; if fatigued and / or not healthy, the coach will take the player out of the game), optimizing performance and reducing injury risk.

[0265] Example 15:

[0266] A dairy farm implements the animal data language system to monitor the health of its livestock. Cows are equipped with and / or associated with one or more sensors that track body temperature, heart rate, environmental data, and activity levels. The CNN processes this data to detect anomalies, while the RNN analyzes trends over time. The animal data language model applies the biodata language to process collected data and other relevant information (e.g., baseline information related to cows; contextual data) and communicates the one or more animal data-based features in a codified language, the animal data language model being further configured to generate a report in the codified language: "Cow #1234 showing elevated body temperature and decreased activity, possible onset of mastitis." The farmer receives this alert and promptly consults a veterinarian, who confirms the diagnosis and initiates treatment, ensuring the cow's health and productivity. In a refinement, the animal data language model translates the findings into biodata language, generating the report.

[0267] Example 16:

[0268] A corporate wellness program integrates the animal data language system to monitor employees' stress levels. Employees wear devices that measure galvanic skin response, heart rate, and cortisol levels. The CNN extracts features related to stress indicators, while the RNN tracks these features over time. The animal data language model applies the biodata language to process gathered data (e.g., identifying one or more features in the data) and communicates the one or more animal data-based features in one or more digital formats (e.g., codified languages), the one or more digital formats providing personalized feedback: "High stress levels detected during afternoon meetings, suggest scheduling relaxation breaks and mindfulness exercises." The wellness coordinator receives this feedback and implements changes in the work schedule, helping to reduce employee stress and improve productivity. In a refinement, the animal data language model translates the data into biodata language, providing personalized feedback.

[0269] Example 17:

[0270] In this example, the process of training the computer-implemented system to accurately translate animal data into a biodata language is described, focusing on the steps and methods involved in training the animal data language models. A research hospital collects physiological data from 1 ,000 patients over a period of one year. Each patient is equipped with multiple sensors, including ECG monitors, blood pressure cuffs, and wearable devices that track heart rate, respiratory rate, and body temperature. The collected data encompasses various physiological signals, such as ECG waveforms, blood pressure readings, and heart rate variability (HRV).

[0271] The preprocessing module performs several tasks on the collected data to ensure its quality and usability. First, noise reduction techniques, such as filters, are applied to the ECG signals to remove artifacts and noise, ensuring clean and accurate data. Next, the physiological data is normalized to a standard scale, facilitating consistent analysis across different data sources. Additionally, the module segments the ECG signals to identify QRS complexes and calculates HRV. Relevant features, such as peak values, mean values, and variability metrics, are extracted from the data.

[0272] The training module then selects a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model architecture for the animal data language models. The CNN is chosen for its ability to extract spatial features from complex physiological signals, while the RNN is selected for its capability to analyze temporal sequences and identify patterns over time. The training module applies supervised learning and employs labeled physiological data to train the animal data language models. The labeled data includes annotations from medical experts indicating normal and abnormal physiological states, such as healthy heart rhythms and arrhythmias. The models are trained to recognize these states by minimizing loss functions, such as cross-entropy loss, for classification tasks. To further refine the models, reinforcement learning is incorporated into the training process. Expert feedback is used to grade the model's predictions, and this feedback is fed back into the system to fine-tune the models. This process involves a reward system where correct predictions are positively reinforced, and incorrect ones are penalized. Throughout the training process, the validation module continuously evaluates the performance of the animal data language models. K-fold cross-validation is employed to ensure the models generalize well to new data. Performance metrics, including accuracy, precision, recall, and Fl score, are calculated to assess the models' effectiveness. Finally, acontinuous learning mechanism is implemented to update the animal data language models with new data and feedback. This ensures the models remain accurate and adaptable to new patterns in physiological data. As new data is collected and processed, the system periodically retrains the models, incorporating the latest information to improve their predictive capabilities.

[0273] Through this comprehensive training process, the computer-implemented system becomes proficient in translating animal data into a biodata language, providing actionable and interpretable insights derived from raw and / or manipulated (e.g., processed) physiological data.

[0274] Example 18:

[0275] In this example, the process of training the computer-implemented system to accurately translate animal data into a biodata language is described, focusing on the steps and methods involved in training the animal data language models. A research hospital collects physiological data from 1,000 patients over a period of one year. Each patient is equipped with multiple sensors, including ECG monitors, blood pressure cuffs, and wearable devices that track heart rate, respiratory rate, and body temperature. The collected data encompasses various physiological signals, such as ECG waveforms, blood pressure readings, and heart rate variability (HRV).

[0276] The preprocessing module performs several tasks on the collected data to ensure its quality and usability. First, noise reduction techniques, such as fdters, are applied to the ECG signals to remove artifacts and noise, ensuring clean and accurate data. Next, the physiological data is normalized to a standard scale, facilitating consistent analysis across different data sources. Additionally, the module segments the ECG signals to identify QRS complexes and calculates HRV. Relevant features, such as peak values, mean values, and variability metrics, are extracted from the data.

[0277] The training module then selects a transformer network architecture for the animal data language models. The transformer network is chosen for its ability to process complex physiological signals and capture long-range dependencies in the data. The training module applies supervised learning and employs labeled physiological data to train the animal data language models. The labeled data includes annotations from medical experts indicating normal and abnormal physiological states,such as healthy heart rhythms and arrhythmias. The models are trained to recognize these states by minimizing loss functions, such as cross-entropy loss, for classification tasks.|0278] To further refine the models, reinforcement learning is incorporated into the training process. Expert feedback is used to grade the model's predictions, and this feedback is fed back into the system to fine-tune the models. This process involves a reward system where correct predictions are positively reinforced, and incorrect ones are penalized. Throughout the training process, the validation module continuously evaluates the performance of the animal data language models. K-fold cross-validation is employed to ensure the models generalize well to new data. Performance metrics, including accuracy, precision, recall, and Fl score, are calculated to assess the models' effectiveness.

[0279] Finally, a continuous learning mechanism is implemented to update the animal data language models with new data and feedback. This ensures the models remain accurate and adaptable to new patterns in physiological data. As new data is collected and processed, the system periodically retrains the models, incorporating the latest information to improve their predictive capabilities.

[0280] Through this comprehensive training process, the computer-implemented system becomes proficient in translating animal data into a biodata language, providing actionable and interpretable insights derived from raw (and / or manipulated / processed) animal (e.g., physiological) data.

[0281] While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the invention. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the invention.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented animal data language system comprises: an animal language computing system configured to execute one or more animal data language models to infer discriminative information describing input animal data requiring analysis, the one or more animal data language models applying a biodata language derived from animal data, the biodata language including patterns of data that operate as words, characters, symbols, phrases, and / or units of digital information, the biodata language including a lexicon and syntax rules for the lexicon, wherein the one or more animal data language models are trained to receive the input animal data and to output the discriminative information in the biodata language; and an interface in electrical communication with the animal language computing system for providing the input animal data to the animal language computing system.

2. The computer-implemented animal data language system of claim 1, wherein the animal language computing system is configured to recognize predefined words, characters, symbols, phrases, and / or units of digital information of the biodata language in the input animal data and to organize an output as the discriminative information.

3. The computer-implemented animal data language system of claim 1, wherein the biodata language is applied to output human-understandable speech or text.

4. The computer-implemented animal data language system of claim 1, wherein the input animal data is human data.

5. The computer-implemented animal data language system of claim 1, wherein the input animal data includes biological data.

6. The computer-implemented animal data language system of claim 1, wherein the one or more animal data language models are trained by a training process applying a transformer network.

7. The computer-implemented animal data language system of claim 6, wherein the training process includes supervised learning with labeled physiological data and reinforcement learning with expert feedback.

8. The computer-implemented animal data language system of claim 1, wherein the input animal data includes ECG readings, heart rate variability, and other physiological signals.

9. The computer-implemented animal data language system of claim 1, wherein the animal language computing system is configured with continuous learning mechanisms to update the one or more animal data language models with new data and feedback.

10. The computer-implemented animal data language system of claim 1, wherein the animal language computing system generates alerts based on a translated biodata language, enabling timely interventions.

11. The computer-implemented animal data language system of claim 1, wherein the interface is configured to communicate with sensors that monitor physiological parameters such as heart rate, blood pressure, respiratory rate, and activity levels.

12. The computer-implemented animal data language system of claim 1, wherein the discriminative information includes detected patterns indicative of one or more health conditions, stress levels, or performance metrics.

13. The computer-implemented animal data language system of claim 1, further comprising a feedback mechanism to continuously update and improve the animal data language models based on new data and user feedback.

14. The computer-implemented animal data language system of claim 1, wherein the biodata language includes one or more predefined units representing one or more physiological states, with rules for combining the one or more units into one or more meaningful phrases.

15. The computer-implemented animal data language system of claim 1, wherein the animal language computing system is configured to translate the biodata language into human- understandable speech or text.

16. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to evaluate, monitor, and / or manage patients in healthcare facilities, such as hospitals, clinics, performance centers, and home care settings, for early detection and / or prevention of one or more potential health issues.

17. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied by athletes and fitness enthusiasts during training sessions, live competition, and / or daily activities to provide one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, and / or recommendations for performance optimization, injury prevention, and / or outcome management.

18. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied in veterinary practices and animal care facilities to evaluate, monitor, and / or manage health of one or more animals, including pets, livestock, and wildlife, for early detection and treatment of one or more health issues.

19. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to evaluate, monitor, and / or manage mental health and stress levels in individuals by providing personalized feedback, assessments, and recommendations for stress management techniques.

20. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to enhance user experiences in human-machine interactions, such as virtual reality (VR), augmented reality (AR), and / or mixed reality applications, by adjusting an interaction environment dynamically based on physiological responses.

21. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to track, assess, and improve personal health and wellness by offering personalized advice for lifestyle changes based on monitored animal data.

22. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied in clinical studies to monitor participants' physiological parameters and generate comprehensive reports for researchers.

23. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to monitor workers' health and safety in various environments by generating alerts for supervisors to prevent potential health issues.

24. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied by health insurance companies to assess health risks of policyholders and provide tailored premiums and incentives for healthy behaviors.

25. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied in assisted living facilities to manage, evaluate, and / or monitor health of elderly residents and recommend interventions such as physical therapy assessments.

26. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied in sports clinics to aid in rehabilitation of injured athletes by providing feedback for rehabilitation exercises.

27. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to financial trading to aid in the execution of a stock trade by providing one or more insights related to one or more physiological- based parameters of one or more traders.

28. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied to sports betting to aid one or more sportsbooks, oddsmakers, bettors, or a combination thereof, in creation, modification, enhancement, and / or placement of a bet by providing one or more insights upon which (1) one or more odds are created, modified, enhanced, and / or evaluated; (2) one or more bets are created, modified, enhanced, evaluated, or placed; (3) one or bets are placed or accepted based upon the one or more insights becoming one or more markets (e.g., subject matter for the bet, at least in part); (4) one or more strategies are formulated; (5) one or more products are created, modified, enhanced, acquired, offered, and / or distributed; (6) one or more risks are mitigated, prevented, or taken; or (7) a combination thereof.

29. The computer-implemented animal data language system of claim 1, wherein the computer-implemented animal data language system is applied in educational institutions to monitor students' engagement and stress levels, providing insights for teachers to adapt their teaching strategies.

30. The computer-implemented animal data language system of claim 1, wherein the discriminative information includes extraction, question answering, classification, recommendations, text generation, translation, or any combination thereof.

31. The computer-implemented animal data language system of claim 1, wherein the one or more animal data language models include a model implemented by one or more pre-trained neural networks.

32. The computer-implemented animal data language system of claim 1, wherein the one or more animal data language models include a trained transformer network.

33. The computer-implemented animal data language system of claim 1, wherein the one or more animal data language models include a generative pre-trained transformer model.

34. The computer-implemented animal data language system of claim 1, wherein the one or more animal data language models include a trained hybrid CNN-RNN.

35. The computer-implemented animal data language system of claim 1, wherein the one or more animal data language models are trained with biological data.

36. The computer-implemented animal data language system of claim 32, wherein the one or more animal data language models are trained on the biodata language by applying supervised learning whereby human-crafted specialized encoding is provided to one or more animal data language models to enable learning of vocabulary and grammatical structures for relationship extraction between data and outcomes.

37. The computer-implemented animal data language system of claim 36, wherein the one or more animal data language models are fine-tuned by a reinforcement learning step where model responses to questions, tasks, relationships, recommendations, and inference are graded by humans into a reward system and fed back into the one or more animal data language models.

38. The computer-implemented animal data language system of claim 1, wherein the biodata language is derived by executing one or more language-identifying artificial intelligence algorithms on a language-identifying computing system, the language-identifying artificial intelligence algorithms being configured to determine the lexicon and the syntax rules for the lexicon of the biodata language.

39. The computer-implemented animal data language system of claim 1, wherein the biodata language is generated by one or more language-identifying artificial intelligence algorithms.

40. The computer-implemented animal data language system of claim 39, wherein the one or more language-identifying artificial intelligence algorithms includes a component having a generative pre-trained transformer.

41. The computer-implemented animal data language system of claim 39, wherein the one or more language-identifying artificial intelligence algorithms is a machine learning algorithm.

42. The computer-implemented animal data language system of claim 39, wherein a language-identifying computer algorithm determines the lexicon and the syntax rules of the biodata language by: identifying patterns in the input animal data; identifying segments of data in the patterns; separating the segments into pattern units; generating codified vocabulary units from the pattern units such that each vocabulary unit represents a word or phrase of the lexicon; optionally defining each word as a part of speech of the biodata language; adding the syntax rules associated with the words or phrases to complete the biodata language; and applying the syntax rules to translate the biodata language into human speech or text.

43. The computer-implemented animal data language system of claim 42, wherein the one or more language-identifying artificial intelligence algorithms is a deep learning algorithm.

44. The computer-implemented animal data language system of claim 42, wherein the one or more language-identifying artificial intelligence algorithms include an association rule mining algorithm.

45. The computer-implemented animal data language system of claim 42, wherein the one or more language-identifying artificial intelligence algorithms includes a time series analysis algorithm.

46. The computer-implemented animal data language system of claim 1, wherein elements of the biodata language are developed to be specific to a predetermined domain with a complete syntactical structure, vocabulary, and corresponding semantics.

47. The computer-implemented animal data language system of claim 1, wherein the biodata language incorporates a set of rules that govern relationships between various combinations of data patterns and consequential occurrence of outcomes.

48. The computer-implemented animal data language system of claim 1, wherein the biodata language is applied to describe biological data, including patterns, structures, variations, relationships, and outcomes.

49. The computer-implemented animal data language system of claim 1, wherein the biodata language is developed as a means to communicate physiological data-based outcomes between a user and the animal language system.

50. The computer-implemented animal data language system of claim 1, wherein the patterns of data include contextual meaning.

51. The computer-implemented animal data language system of claim 1, wherein a comprehensive vocabulary is constructed around rules derived from scenarios and surrounding context.

52. The computer-implemented animal data language system of claim 1, wherein the biodata language is trained such that reverse training is applied to a subset of the one or more animal data language models to generate a set of human data based on a given condition or set of conditions.

53. The computer-implemented animal data language system of claim 1, wherein the animal language computing system is further configured to translate human language described outcomes, relationships, and scenarios to the biodata language.

54. The computer-implemented animal data language system of claim 1, wherein the animal language computing system is further configured to map the biodata language to raw biological data and data patterns, occurrences, and frequencies.

55. The computer-implemented animal data language system of claim 1, wherein the discriminative information includes a description of a physiological state.

56. The computer-implemented animal data language system of claim 55, wherein the physiological state indicates that a subject is under stress.

57. The computer-implemented animal data language system of claim 55, wherein the physiological state indicates that a subject is sleeping or resting.

58. The computer-implemented animal data language system of claim 55, wherein the physiological state indicates that a subject is engaged in physical exertion.

59. The computer-implemented animal data language system of claim 55, wherein the physiological state indicates that a subject has an elevated heart rate.

60. The computer-implemented animal data language system of claim 55, wherein the physiological state indicates that a subject has an elevated blood pressure.

61. The computer-implemented animal data language system of claim 55, wherein the physiological state indicates that a subject has an elevated amount of perspiration.

62. The computer-implemented animal data language system of claim 55, further comprising at least one sensor in electrical communication with the animal language computing system, the at least one sensor configured to generate the animal data.

63. The computer-implemented animal data language system of claim 1, wherein the discriminative information indicates a subject is exhibiting, has exhibited, or will exhibit, one or more biological responses.

64. The computer-implemented animal data language system of claim 1, wherein the discriminative information indicates a subject is experiencing, has experienced, or will experience, one or more medical events or episodes.

65. The computer-implemented animal data language system of claim 1, wherein the discriminative information indicates a subject is experiencing, has experienced, or will experience, one or more medical conditions.

66. The computer-implemented animal data language system of claim 1, wherein the discriminative information enables the one or more animal data language models to generate, modify, and / or enhance one or more insights, metrics, forecasts, predictions, probabilities, assessments, possibilities, projections, determinations, summaries, or recommendations related to one or more outcomes for one or more current or future biological -based events or sub-events associated with one or more subjects.

67. An animal data language training system comprising: a preprocessing module configured to: receive input training animal data; perform noise reduction on the input training animal data; optionally normalize the input training animal data to a standard scale; and extract relevant features from the input training animal data based on one or more physiological -based events; a training module configured to: train animal data language models by applying supervised learning with labeled physiological data; and fine-tune the animal data language models by applying reinforcement learning with expert feedback; and a validation module configured to: validate the performance of the animal data language models and performance metrics with a validation scheme that includes accuracy, precision, recall, and / or Fl score to form validated animal language models; an animal data processing and inference module configured to: receive the validated animal language models;receive input inference animal data from at least one sensor monitoring physiological parameters of a subject, the input inference animal data including physiological signals; execute one or more animal data language models to infer discriminative information from the input inference animal data, wherein the animal data language models apply a biodata language derived from animal data, the biodata language comprising a lexicon of predefined units representing physiological states and syntax rules for combining the predefined units into meaningful phrases; and extract spatial features from the physiological signals.

68. The system of claim 67 wherein the performance of the animal data language models by applying k-fold cross-validation.

69. The system of claim 67 wherein the animal data processing and inference module is further configured to process the input inference animal data by applying a transformer network.

70. The system of claim 69 further comprises a communication module configured to provide the biodata language to a user.

71. The system of claim 70, wherein the communications module is further configured to communicate the inferred discriminative information in the biodata language to a user in a human- understandable format.

72. The system of claim 70, wherein the communications module is configured to: display the inferred discriminative information in the biodata language through visual dashboards, charts, or graphs; and provide audio or haptic feedback based on the inferred discriminative information.

73. The system of claim 67 further comprising a continuous learning module configured to update the animal data language models with new data and feedback to improve accuracy and adaptability.

74. The system of claim 73, wherein the continuous learning module is further configured to: implement a feedback loop where user interactions and corrections are used to refine the animal data language models; and periodically retrain the models with accumulated new data and feedback.

75. The system of claim 67, wherein the preprocessing module is further configured to segment the input training animal data into meaningful intervals based on identified physiological events.

76. The system of claim 75, wherein the meaningful intervals include identified QRS complexes in ECG signals, peaks in heart rate data, or other significant physiological markers.

77. The system of claim 67, wherein the animal data processing and inference module is further configured to: identify and classify physiological anomalies based on extracted patterns from the input inference animal data; and generate alerts or notifications when specific physiological anomalies are detected.

78. The system of claim 67, wherein the training module is further configured to incorporate one or more additional animal data sources, including genetic data, biometric data, and activity data, to enhance the accuracy of the animal data language models.

79. The system of claim 67, wherein the training module is further configured to incorporate one or more additional data sources which include contextual data, reference data, or a combination thereof, to enhance the accuracy of the animal data language models.

80. The system of claim 67, wherein the validation module is further configured to: perform real-time validation of the animal data language models during live monitoring of the subject; and adjust model parameters dynamically based on validation results.

81. The system of claim 67, wherein the input training animal data and / or the input inference animal data includes multi-modal physiological data captured from different types of sensors, such as ECG sensors, blood pressure monitors, and motion sensors.

82. The system of claim 77, wherein the animal data processing and inference module is configured to integrate and correlate data from different types of sensors to enhance the accuracy of the inferred discriminative information.

83. The system of claim 67, wherein the animal data language models are further configured to support predictive analytics, providing forecasts on potential physiological states or events based on historical data patterns.

84. An animal data language system comprises: an input interface module for receiving input animal data; a preprocessing module configured to perform noise reduction on the input animal data; an animal language computing module in communication with the preprocessing module configured to execute one or more animal data language models to infer discriminative information describing input animal data requiring analysis, the one or more animal data language models applying a biodata language derived from animal data, the biodata language including patterns of data that operate as words and phrases, the biodata language including a lexicon and syntax rules for the lexicon,wherein the one or more animal data language models are trained to receive the input animal data and to output the discriminative information in the biodata language; and a communication module in communication with the animal language computing module configured to transmit and receive data between the system and external devices or networks.

85. The animal data language system of claim 84 further comprising a user interface module configured to present the inferred discriminative information in the biodata language to a user in a human-understandable format, such as visual dashboards, charts, or graphs, and to provide audio or haptic feedback based on the inferred discriminative information.

86. The animal data language system of claim 84, wherein the animal language computing system is configured to recognize predefined words and phrases of the biodata language in the input animal data and to organize an output as the discriminative information.

87. The animal data language system of claim 84, wherein the input animal data includes ECG readings, heart rate variability, and other physiological signals.

88. The animal data language system of claim 84, wherein the animal language computing system generates alerts based on a translated biodata language, enabling timely interventions.

89. The animal data language system of claim 84, wherein the interface module is configured to communicate with sensors that monitor physiological parameters such as heart rate, blood pressure, respiratory rate, and activity levels.

90. The data language system of claim 84, wherein the discriminative information includes detected patterns indicative of one or more health conditions, stress levels, or performance metrics.

91. The animal data language system of claim 84, further comprising a training module in communication with the preprocessing module configured to train the animal data language models by applying supervised learning with labeled physiological data, and to fine-tune the animal data language models by applying reinforcement learning with expert feedback.

92. The animal data language system of claim 91, further comprising a validation module in communication with the training module configured to validate the performance of the animal datalanguage models by applying k-fold cross-validation and performance metrics including accuracy, precision, recall, and Fl score.

93. The animal data language system of claim 92, wherein the training module is configured to incorporate one or more additional animal data sources, contextual data sources, reference data sources, or a combination thereof, to enhance the accuracy of the animal data language models.

94. The animal data language system of claim 84, wherein the one or more animal data language models are trained on a transformer network.

95. The animal data language system of claim 84, wherein the one or more animal data language models are trained on a hybrid CNN-RNN.

96. The animal data language system of claim 84, wherein the animal data language models include a model implemented by one or more pre-trained neural networks.

97. The animal data language system of claim 84, wherein the biodata language is derived by executing one or more language-identifying artificial intelligence algorithms on a languageidentifying computing system, the language-identifying artificial intelligence algorithms being configured to determine the lexicon and the syntax rules for the lexicon of the biodata language.

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