Ai assistant method and device

Abstract

An artificial intelligence device (AID) is disclosed that is configured for a specific, predetermined use case and is operable without access to a remote server during normal operation. The AID comprises a processor, a first memory storing at least one AI decision matrix comprising trained neural network weight values, a database storing response templates and vocabulary data, one or more microphones, and a user interface including a touchscreen and speaker. The processor applies the AI decision matrices to user input signals to generate analysis results and constructs responses by querying the database. The AI decision matrices are derived through a supervised training process. The AID may include a self-erase routine triggered by tamper detection, timer expiration, or remote command. A system and methods for operating and training the AID are also disclosed.

Description

Al ASSISTANT METHOD AND DEVICECROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority benefit to U.S. Pat. App. No. 63 / 790,827, filed 18 April 2025, which is incorporated by reference in its entirety.FIELD

[0002] The embodiments relate generally to a limited artificial intelligence provided for specific tasks that can be housed locally.BACKGROUND

[0003] Computer software has evolved to the point that it can mimic intelligence, often referred to as artificial intelligence, Al. Current research is focused on the development of Al not actually focused on its uses. What is needed is a use case scenario driven development of the Al, allowing the Al developed to be more focused and arguably more limitative so that the Al can be located locally without remote access required.SUMMARY

[0004] The present disclosure relates to an artificial intelligence device (AID) configured to perform a specific, predetermined use case using an Al software system that is stored and executed locally, without the necessity of transmitting user input signals, Al decision matrix data, or Al analysis results to a remote server during normal operation. The AID comprises a processor, a first memory storing at least one Al decision matrix comprising trained neural network weight values, a database storing response templates and associated vocabulary data, at least one microphone, and a user interface including a touchscreen and speaker. The processor receives user input signals derived from microphone signals or touchscreen input, applies the Al decision matrices to generate an Al analysis result, and constructs an AID response by querying the database using the Al analysis result as a query key. The Al decision matrices are derived through a supervised training process in which temporary decision matrices are iteratively adjusted until their outputs match preferred responses within a predetermined accuracy threshold.

[0005] In some embodiments, the AID includes a voice recognition routine comprising signal conditioning circuitry and speech recognition algorithms configured to apply noise reduction filtering and feature extraction to microphone signals, identify voice commands, words, or sentences, and generate conditioned acoustic signals for input to the Al analysis routine. The AID may process multimodal inputs including acoustic sensor data, optical sensor data from one or more cameras, and miscellaneous sensor data, each processed through a respective Al decision matrix and combined using a multimodal fusion algorithm. The AID supports multiple languages by storing compressed language packs comprising acousticdecision matrix weights, vocabulary databases, and phonetic mapping tables, and can automatically detect and load the appropriate language pack based on acoustic features of user speech.

[0006] In further embodiments, the AID includes a self-erase routine configured to overwrite Al decision matrix values with dummy values and reformat the first memory upon detection of a tamper condition, receipt of a delete message from a remote server, or expiration of a predetermined timer initiated upon first activation. A plurality of AIDs may operate cooperatively within a shared communications network, sharing sensor data and coordination information while each maintaining local Al processing capability. Methods for operating the AID and for training the Al decision matrices for deployment in the AID are also disclosed, together with a non-transitory computer-readable medium storing instructions for performing the disclosed operations.BRIEF DESCRIPTION OF THE FIGURES

[0007] The embodiment and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0008] FIGs 1A, IB, and 1C are schematics of the electronic components of a nonlimiting example of an Al assistant Device (AID);

[0009] FIG. 2 and FIG. 3 are block diagrams of an AID and personal communication device that can contain elements of an AID, according to some non-limiting embodiments herein;

[0010] FIG. 4 is a schematic diagram of a system for utilizing AIDs over networks according to an embodiment of the present disclosure;

[0011] FIG. 5 is a schematic diagram of a machine in the form of a computer system which a set of instructions, when executed, may cause the machine to perform any one or more of the methodologies or operations of the systems and methods for utilizing an AID according to embodiments of the present disclosure;

[0012] FIG. 6 illustrates an outline of a non-limiting list of the parts of an AID system;

[0013] FIG. 7 illustrates an outline of the Al software system used in an AID system;

[0014] FIG. 8A, FIG. 8B, FIG. 8C, FIG. 8D, FIG. 8E, and FIG. 8F are directed to various nonlimiting examples of uses of the AID system;

[0015] FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, FIG. 9E, and FIG. 9F are directed to various nonlimiting examples of uses of the AID system;

[0016] FIG. 10 illustrates an outline of generating training data;

[0017] FIG. 11 illustrates generating the decision matrices used in the Al model(s);

[0018] FIG. 12 illustrates a generic artificial neural network (ANN);

[0019] FIG. 13 illustrates a perceptron;

[0020] FIG. 14 illustrates a convolutional artificial neural network (CNN);

[0021] FIG. 15 illustrates an ANN with interneuron relationships;

[0022] FIG. 16 illustrates an ANN with interneuron relationships;

[0023] FIG. 17 illustrates an ANN displayed in a ring type relationship between layers;

[0024] FIG. 18 illustrates a deep neural network;

[0025] FIG. 19 illustrates a non-limiting example of a medical use of an AID 100;

[0026] FIG. 20 illustrates a non-limiting example of a mechanics repair use of an AID 100;

[0027] FIG. 21 illustrates a non-limiting example of a veterinarian use of AIDs; and

[0028] FIG. 22 illustrates a non-limiting example of a pharmacy use of an AID.NON LIMITING SAMPLE OF REFERENCE NUMBERS

[0029] 1 - device, that incorporates at least one embodiment of a device that can incorporate a localized Al (Artificial Intelligence).

[0030] 2 - power source, that supplies power to the device 1 , for example a battery, wireless / wired power, solar power, motion generated power, fuel cells, mini radioactive power systems, and other power systems as known by one of ordinary skill in wearable power systems.

[0031] 3A- voice control systems, for example, microphone detection systems, sensor detection and other methods of voice detection as known by those of ordinary skill in voice detection;

[0032] 3B- gesture control systems, for example using camera detection, ultrasonic detection systems, motion detection, touch sensor, an external gesture detection, and other gesture detection and control systems as known by those of ordinary skill in gesture detection and gesture control;

[0033] 3C- user interface systems, for example buttons, voice control, touch screen interfaces, gesture detection, and other possible user interfaces as known by those of ordinary skill in the art of user interfaces in controlling wearables (e.g., phones, watches, laptops, rings, electronic jewelry, tablets);

[0034] 4- processing systems, for example Snapdragon Wear 4100 and 4100+, Apple S8, Exynos W920, MediaTek Dimensity 9000+, MT2625, nRF2840, i.MX RT1060, Ambiq Apollo4 and the like;

[0035] 5- sensor systems, for example accelerometer, gyroscope, optical PPG, heart rate sensor, GPS, barometer, ambient light sensor, infrared sensor, temperature, SpO2 Sensor, pulse oximeter, electrocardiogram, ECG (Electrocardiogram) or EKG sensor, galvanic skin response (GSR) sensor, magnetometer (compass), motion sensors, blood pressure systems (e.g., PPG-based), UV (Ultraviolet Radiation) sensor, Thermometer, Hydration sensor, Pedometer, gas sensor, radiation dosage sensor, microphones, touch sensors and screens, quantum magnetometry sensor and any other sensor as would be known by one of ordinary skill in developing devices to sense input signals (e.g., electromagnetic, acoustic, temperature, moisture, gravitational, particles, quantum entanglement);

[0036] 5B- brain control systems including sensors such as Electroencephalography (EEG) sensors, SQUIDs (Superconducting Quantum Interference Devices) and other Magnetoencephalography (MEG) sensors, miniaturized Functional MRI (mfMRI), micro wired EcoG (Electrocorticography) and Intracortical and other sensors and associated control systems as known by one of ordinary skill in brain control systems;

[0037] 6A, 6B and 6C wireless communication systems;

[0038] 7A- memory, for example RAM (Radom Access Memory), SRAM (Static RAM), DRAM (Dynamic RAM), LPDDRs (Low-Power DRAM), Flash Memory, EEPROM, Cache Memory, FRAM (Ferroelectric RAM), MRAM (Magnetoresistive RAM), combinations and any other data storage system as know by one of ordinary skill in electronic memory storage;

[0039] 7B- recording system, wherein the recording system can store any type of measured data (e.g., acoustic, image);

[0040] 7C- databases, for example Natural Language Processing (NLP) Databases, CoNLL-2003, GloVe, Kaggle Datasets, UCI Machine Learning Repository, ImageNet, COCO, Open Images, CIFAR-10 / CIFAR-100, MNIST, ADE20K, LibriSpeech, VoxCeleb, UrbanSound8K, TIMIT, UCI Time Series Dataset, PhysioNet, MIMIC-III Database, OpenAI Gym, DeepMind Lab, TCIA, CARLA, and other databases as known by one of ordinary skill in the use of databases in Al software;

[0041] 7D- analysis system, for example any type of signal analysis and / or Al processing systems as known by those of ordinary skill in developing software based analysis systems;

[0042] 7E- server systems, for example, Intel Xeon Processors, Intel Xeon Platinum 8480, Intel Xeon E-2400, AMD EPVC Processors, AWS Graviton 3, Ampere Altra, Apple M2 Ultra, IBM Power 10, NVDIA Grace CPU, Google TPU v4 and the like;

[0043] 8- user interface systems, for example graphic user interfaces, touch screens, systems using touch sensors, voice control systems, gesture detection systems, optical systems, systems using various sensors to detect inputs from a user, and other user interface systems as known by one of ordinary skill in wearable user interface systems;

[0044] 9 A- payment systems, for example encryption software, software for remote communication to banking info and interaction, software payment applications such as Vemno, software systems that allow the device to pay as if it were a wallet (e.g., Apple Pay, Starbucks App for paying from phone) or other payment devices and software as known by one of ordinary skill in wearable payment systems;

[0045] 9B- application systems, for examples software systems developed specifically for particular uses (e.g., auto, medical, exercise, consumer, social media, military, legal, medical);

[0046] 11 A- Artificial Intelligence (Al) software system is any system comprising executable instructions and Al decision matrices having trained neural network weight values, wherein the executable instructions implement at least one artificial neural network (ANN) algorithm;

[0047] 100 -Al device is any device including an Al software system and contains some form of Artificial Neural Network implemented through Al decision matrices having trained neural network weight values;

[0048] 110 touch screen for example capacitive, including self-capacitance, resistive formats, OLED, AMOLED, LCD, Nextion NX4024T032;

[0049] 115 touchscreen support sensor / chips, that can generate a touchscreen signal for example Synaptics T100, FocalTech FTx06 series, Cypress TrueTouch CYBC, Microchip MTCH6102, Atmel QTouch series, Texas Instruments TSC2003, Microchip AR1020, STMicroelectrics STM32+LcdTouch combo, Snaptics S3718 / S3308, FocalTech FT5x06 / FT6206, Cypress TrueTouch CYBC, Goodix GT911 / GT1151, FT6206 / FT6236, XPT2046, ADS7843 / STMPE610, NXP MPR121, AD7147 / AD7146 and other circuits and controller as known by one of ordinary skill in using touchscreen screen in wearables;

[0050] 120 speaker vent;

[0051] 130 microphone, for example electret condenser microphones, micro-electro-mechanical systems, dynamic microphones, piezoelectric microphones, other microphones as know by one of ordinary skill in the use of wearable microphones;

[0052] 135 microphone port;

[0053] 140 acoustic deflector, for example a surface designed to reflect or refract sound waves to a primary designed direction;

[0054] 150 speaker, for example small MEMs speakers, or other speakers as know by one of ordinary skill that develops speakers for wearables and small devices;

[0055] 160 DSP and / or processor;

[0056] 170 memory and / or support electrical components;

[0057] 180 battery electrodes;

[0058] 190 battery, for example ActionHeat 9300mAh, ororo 4800mAh Mini Wearable Battery, Conformal Wearable Battery 148Wh, Tactical Cell Wearable Battery, MChaos 5000mAh Wearable Power Bank, KIQ iPhone 3227mAh battery, KIQ Samsung Galaxy A54 Battery 4905mAh, and other batteries as known by one of ordinary skill in fabricating wearable batteries ; and

[0059] 195 housing.DETAILED DESCRIPTIONNON-LIMITING EXAMPLES OF TERMS

[0060] The terms and phrases used herein are not intended to be limiting but rather to provide an understandable description of the embodiments.

[0061] The terms "a" or "an", as used herein, are defined as one or more than one. The term "another", as used herein, is defined as at least a second or more. The terms "including" and / or "having" as used herein, are defined as comprising (i.e. open transition). The term "coupled" or "operatively coupled" as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items. Like numbers refer to like elements throughout. In the figures, tlie sizes of certain lines, layers, components, elements or features may be exaggerated for clarity.

[0062] It will be understood that when an element is referred to as being “on”, “attached” to, “connected” to, “coupled” with, “contacting”, etc., another element, it can be directly on, attached to, connected to, coupled with, a part of, or contacting the other element or intervening elements may also be present. In contrast, when an element is referred to as being, for example, “directly on”, “directly attached” to, “directly connected” to, “directly coupled” with or “directly contacting” another element, there are no intervening elements present. It will also be appreciated by those of skill in the art that references to a structure or feature that is disposed “adjacent” another feature may have portions that overlap or underlie the adjacent feature.

[0063] Spatially relative terms, such as “under”, “below”, “lower”, “over”, “upper” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of a device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is inverted, elements described as “under” or “beneath” other elements or features would then be oriented “over” the other elements or features. Thus, the exemplary term “under” can encompass both an orientation of “over” and “under”. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. Similarly, the terms “upwardly”, “downwardly”, “vertical”, “horizontal” and the like are used herein for the purpose of explanation only unless specifically indicated otherwise.

[0064] The term Al device or “Al device” includes any type of device that may be self-contained including an Al software system with Al decision matrices implementing artificial neural networks (ANN) and optionally accessed via various user interfaces voice or brain waves, without limitation. Such configurations include, but are not limited to, Al devices standalone devices, ear buds, headphones, headsets, hearing aids, watches, tablets, personal sound amplification products (PSAPS), and glasses.

[0065] As used herein, the term "self-contained" means that the Al software system, including all Al decision matrices required for the AID'S defined specific use case, is stored in and executed by local memory and processor resources of the specific Al device (AID) itself, without the necessity of transmitting user input signals, Al decision matrix data, or Al analysis results to a remote server during normal operation of the AID, such normal operation including the AID’S performance of its defined use case functions, including receiving user input signals, applying Al decision matrices to generate Al analysis results, querying the database to retrieve response data, and outputting AID responses to the user. An AID that is "self-contained" may optionally communicate via a wireless communication system for purposes such as delivering an alert, transmitting non- Al sensor data, receiving an authorized update, or executing a selferase routine, witfiout departing from the self-contained character of its Al processing capability.

[0066] The term “real-time” is used to describe a process of sensing, processing, or transmitting information in a time frame which is equal to or shorter than the minimum timescale at which the information is needed. For example, the real-time monitoring of pulse rate may result in a single average pulse-rate measurement every minute, averaged over 30 seconds, because an instantaneous pulse rate is often useless to the end user. Typically, averaged physiological and environmental information is more relevant than instantaneous changes. Thus, in the context of the present invention, signals may sometimes be processed over several seconds, or even minutes, in order to generate a “real-time” response.

[0067] The term “monitoring” refers to the act of measuring, quantifying, qualifying, estimating, sensing, calculating, interpolating, extrapolating, inferring, deducing, or any combination of these actions. More generally, “monitoring” refers to a way of getting information via one or more sensing elements. For example, “blood health monitoring” can include monitoring of blood gas levels, blood hydration, blood flow, and metabolite / electrolyte levels. In the embodiments herein, the term “physiological” is intended to be used broadly, covering both physical and psychological characteristics of or from the body of an organism. However, in some cases, the term “psychological” is called-out separately to emphasize aspects of physiology that are more related to brain activity and a state of being or mood rather than the activity of other organs, tissues, or cells. In this regard, multimodal monitoring can enhance the meaning or interpretation of psychological information, particularly with the analysis of voice, words, phrases and semantics in conjunction with other physiological measurements as further detailed below.

[0068] The term “health” refers generally to the quality or quantity of one or more physiological parameters with reference to an organism's functional abilities. Health can include both private and public information. In the private portion, health information is personalized for each subject that is stored. In the public portion, anonymous health is stored and is accessible by third parties. The private or public health information may also include environmental information or other data as well.

[0069] The term “ad hoc” refers generally to a wireless connection established for the duration of one session without the need for a base station. Instead, devices discover others within range to form a network. Bluetooth®, Low Energy Bluetooth, Zigbee, and Wi-Fi protocols are a few examples.

[0070] The term “processor” typically refers to logic circuitry that responds to and processes basic instructions that drive a computer or other electronic devices. The term processor has generally replaced the term central processing unit (CPU) and can further refer to a microprocessor, a digital signal processor, a programmable logic device, an application specific integrated circuit or ASIC or any number of other logic devices. The processor in a personal computer or embedded in small devices is often called a microprocessor.

[0071] The term “sensor” refers to a device that detects or measures a physical property and enables the recording, presentation or response to such detection or measurement using processor and optionally memory. A sensor and processor can take one form of information and convert such information into another form, typically having more usefulness than the original form. For example, a sensor may collect raw physiological or environmental data from various sensors and process this data into a meaningful assessment, such as pulse rate, blood pressure, or air quality using a processor. A “sensor” herein can also collect or harvest acoustical data for biometric analysis (by a processor) or for digital or analog voice communications. A “sensor” can include any one or more of a physiological sensor (e.g., blood pressure, heart beat, etc.), a biometric sensor (e.g., a heart signature, a fingerprint, etc.), an environmental sensor (e.g., temperature, particles, chemistry, etc.), a neurological sensor (e.g., brainwaves, EEG, etc.), or an acoustic sensor (e.g., sound pressure level, voice recognition, sound recognition, etc.) among others. A variety of microprocessors or other processors may be used herein. Although a single processor or sensor may be represented in the figures, it should be understood that the various processing and sensing functions can be performed by a number of processors and sensors operating cooperatively or a single processor and sensor arrangement that includes transceivers and numerous other functions as further described herein. Processor herein is meant to include, but not exclusively, central processing units (CPUs). Graphic processing units (GPUs), and Tensor processing units (TPUs).

[0072] The term “clinical study” refers broadly to the application of science to health, where “health” may refer to both physical health as well as mental or psychological health. The term “clinical study” and “clinical trial” are used interchangeably herein. As an example, the interaction between a therapy and health or physiology, such as a drug therapy, exercise / diet plan, physical regime, etc., can constitute a clinical study. As another example, the interaction between the health and the environmental exposure of individuals or groups can constitute a clinical study. In some cases a clinical study is performed by professionals in medicine or science. In other cases, a clinical study is performed by amateurs, computer programs, or individuals themselves, sometimes in the form of self-help.

[0073] The term “marketing” refers to the act of bringing together buyers and sellers, and the term “marketing study” refers to the study of the needs and wants of buyers and sellers and how the buyers and sellers can come together.

[0074] The term “health study” refers to monitoring the health of an organism and studying the data regardless of the method of study.

[0075] The term “wellness” generally refers to a healthy balance of the mind-body and spirit that results in an overall feeling of well-being, and / or the state of being healthy. The term “wellness study” refers to the study of the quality of health and wellbeing. In some cases a wellness study is performed by professionals in medicine or science. In other cases, a clinical study is performed by amateurs, computer programs, or individuals themselves, sometimes in the form of self-help.

[0076] The term “dieting plan” refers to a method of planning and / or regulating the intake of food or nutrients into the body. The term “exercise plan” refers to a method of planning or regulating physical activity. In many cases, a diet / exercise plan are used together to improve or reduce health. These plans can be operated by professionals, such as professional dieticians or physical trainers, or by amateurs. In some cases, these plans are regulated by computer programs or individuals themselves, sometimes in the form of self-help.

[0077] The term “health study” refers to studying health as in its raw form, without necessarily being concerned about interactions between health and other factors.

[0078] The term “sickness and / or disease” refers generally to aspects of a sickness, disease, or injury in an individual or group of individuals.

[0079] The term “environmental exposure” refers to any environmental occurrence (or energy) to which an individual or group of individuals is exposed. For example, exposure to solar energy, air pollution, water pollution, temperature, nuclear radiation, humidity, particles, water, etc. which may all constitute environmental exposure. A variety of relevant environmental energies are listed elsewhere herein.

[0080] The term “aggregated” refers to information that is stored and / or grouped. In some cases, these groupings can be based on personal or demographical information, such as grouping based on ethnicity, sex, income, personal preferences or the like. Aggregated information, particularly in social media contexts, in addition to the personal or demographic information can also include current or recent location info, current or recent activity info, as well as current or recent biometric, physiological, or environmental information. For example, a device can enable the sharing of current location (e.g., at the Guggenheim Museum in NYC, or a pharmacy in Colorado), current or recently listened to content (whether reproduced in the ear (e.g., listening to a streaming or downloaded Andrea Bocelli album) or heard via an ambient microphone in the field (e.g., at a Cold Play concert) and recent keywords from a conversation or exchange with a third party (e.g., “I’ll have a little of the red cab” or “Bartender, can I have a Sierra Nevada Pale Ale”or “Fill this prescription for Girl Scout Cookies or OG Kush for me dude”), and a cunent physiological measure (e.g., current heart rate or blood pressure) to create a possibly shared point of interest with another individual in a social network. Such AID systems can thus act as assistants to their user’s.

[0081] The term “multimodal” refers to monitoring of at least two different parameters such as sound pressure level and blood pressure or heart rate. Note, the different parameters can be related types of measurements such as heart rate and blood pressure, but they can also quite different capture or harvested from acoustic, biologic, neurologic, motion, or vision sensors as examples. In some embodiments, multimodal monitoring can enhance the interpretation and analysis relating to semantics. For example, the reading of motion, brainwaves, sound pressure level, blood pressure, or heart rate along with voice recognition analysis of spoken words can provide richer contextual meaning. Assuming baseline readings exist for an individual, multimodal readings can more clearly determine if an elevated heart beat or blood pressure reading is an indication of potential sleep disorder or heart disease within the context of a typical daily activity (e.g., sleeping, walking or sitting) or within the context of a less typical daily activity (e.g., sprinting to catch a bus or rigorously exercising). Multimodal analysis or processing can enhance the logical interpretation given to words. In other words, multimodal analysis or processing can improve a semantics engine that interprets the logic and meaning in spoken words.

[0082] The terms “health and environmental network” and “health and environmental monitoring system” are used interchangeably herein. The terms “monitoring system” and “network” may be used interchangeably, as well. The term “biofeedback” relates to measuring a subject's bodily processes such as blood pressure, heart rate, skin temperature, galvanic skin response (sweating), muscle tension, etc., and conveying such information to the subject in real-time in order to raise the subject's awareness and conscious control of the related physiological activities. Herein, biofeedback is synonymous with personal physiological monitoring, where biochemical processes and environmental occurrences may be integrated into information for one or more individuals. For example, monitoring hormone levels and air quality through the innovative sensor network described herein for the purpose of fracking, predicting, and / or controlling ovulation is also considered biofeedback. Biofeedback is also considered a technique used to learn to control bodily functions, such as heart rate. With biofeedback, the user can be connected to electrical sensors that help the user receive information (feedback) about their body (bio). This feedback helps the user focus on making subtle changes in their body, such as relaxing certain muscles, to achieve the desired results, such as reducing pain. In essence, biofeedback gives the user the power to use their thoughts to control their body, often to help with a health condition or physical performance. Biofeedback is often used as a relaxation technique.

[0083] The term “profile” relates to a summary of noteworthy characteristics and / or habits of an individual or group of individuals. These characteristics may be physiological (health-related), environmental, statistical, demographical, behavioral, and the like. Age, location, gender, sex, weight,ethnicity, and / or height may be included in a profile. The profile and the aforementioned characteristics and / or habits can be used in the context of social media and further information in the interactions within a social media network can be extracted to form a part of a profile as well Additionally, a profile may reference the buying and / or spending habits of an individual or group and can further include a credit rating. Profiles may be utilized in making predictions about an individual or group.

[0084] The term “support,” when used as a verb, means to assist and / or provide at least one method or outcome for something. For example, a method of supporting a therapy for something may refer to a method of assisting a therapeutic technique. In some cases, supporting a therapy may involve providing an entirely new method having a therapeutic outcome. As a more specific example, a noninvasive health and environmental monitor system / network may support a therapeutic drug study by noninvasively monitoring the real-time drug dosage in the body through multiwavelength pulse oximetry, monitoring core body temperature through thermal sensing of the tympanic membrane, and monitoring environments which may positively or negatively affect the quality of the drug therapy.

[0085] The terms "machine-readable medium," "machine-readable device," or "computer-readable device" shall accordingly be taken to include, but not be limited to: memory devices, solid-state memories such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories; magneto-optical or optical medium such as a disk or tape; or other self-contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. The "machine-readable medium," "machine-readable device," or "computer-readable device" may be non-transitory, and, in certain embodiments, may not include a wave or signal per se. Accordingly, the disclosure is considered to include any one or more of a machine-readable medium or a distribution medium, as listed herein and including art-recognized equivalents and successor media, in which the software implementations herein are stored.

[0086] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and / or clarity.NON-LIMITING EXAMPLES OF GENERAL USE CASES

[0087] The features of the embodiments, which are believed to be novel, are set forth with particularity in the appended claims. The embodiments may best be understood by reference to the following description, taken in conjunction with the accompanying drawings. The examples illustrated,however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. While the specification concludes with the claims defining the features of the invention that are regarded as novel, it is believed that the embodiments may be better understood from a consideration of the following description in conjunction with the drawings, figures, in which like reference numerals are carried forward.

[0088] At least one exemplary embodiment is directed to a specific Al device (AID) helper housed locally without the need of remote wireless access to a remote server. The AID is trained for a specific task and for example, included with shipping to help the user put together or use the product. For example, if one orders furniture online and the box arrives. In the box will be an inexpensive AID that has been trained to set up the furniture and answer questions and make suggestions about settling up and using the furniture. It can also respond to questions about how to return if there is an issue. In general, the AID is intended to have a limited life and scope. For example the AID that accompanies a self-assembly furniture set would not, in general, be able to answer questions of a medical nature. This allows the AID to be designed in limitative scope so that it can optionally be self-contained (e.g., no need for remote server access). In an alternative usage, when the AID has completed its task, the user can deposit the Al in a designated return station and get credit for their purchase. The AID can then be checked and refurbished for the next similar purchase. The AID can have many sensors whose data is passed into the localized Al software system, for example acoustic, and optical. For example the user can show the AID, via through an AID camera whether the user has done the assembling correctly. The AID can also be designed to self-erase if tampered with. For example suppose a foreign entity wishes to copy the AID and produce cheap knock offs, the housing can be designed such that when tapered with a computer instruction in memory is activated to detect critical software files associated with the localized Al. Additionally the AID can be constructed to receive a delete message from a remote server. The delete message can be linked to a timer that is activated by user when first using the AID or a stored start date in a database. For example, when a self-assembly furniture box is opened and an associated AID removed, once activated (e.g., on button pushed) a timer starts such that when a threshold time is reached a command is sent to AID processor that deletes critical files and reformats so that the critical software cannot be reconstructed. For example the matrix weights used in the neural net can be written with dummy values, stored and overwritten several times to overwrite the memory at particular memory pointer locations, then deleted so that the original matrix values cannot be reconstructed.

[0089] In some embodiments, the AID 100 includes error handling and recovery mechanisms for situations where user input cannot be reliably interpreted or when sensor data is ambiguous or corrupted. When the Al analysis routine 760 determines that confidence in interpreting a user input signal falls below a predetermined threshold (e.g., 70% confidence), the Al response generation routine 750 can generate aclarification request to the user, such as “I didn’t understand that, could you please repeat?” or “Can you show me again?” When sensor data from cameras 770A-770Z or miscellaneous sensors is corrupted or incomplete, the AID 100 can request that the user reposition the device or verify sensor obstructions. The AID 100 can maintain operation logs in database 600 to record error events and recovery actions, which can be used during refurbishment to identify systematic issues. In embodiments where the AID 100 experiences a critical error preventing normal operation, the device can enter a safe mode that provides basic functionality while alerting the user to the limitation.

[0090] In further embodiments relating to security features, the AID 100 can incorporate multiple tamper detection mechanisms. Physical tamper detection can include sensors that detect unauthorized opening of housing 195, such as light sensors that detect exposure of internal components or continuity circuits that break when the housing is opened. The processor 4 can continuously monitor these tamper detection sensors and initiate the self-erasure sequence upon detection of any tampering event. The selferasure sequence can include overwriting the Al decision matrices 700, 710, and 720 with random values, overwriting the first memory containing the Al software system with predetermined patterns multiple times, and clearing the database 600 of any stored user interaction data. In some embodiments, the AID 100 can include a hardware security system (HSS) 747 that stores encryption keys separately from main memory, wherein the HSS 747 is configured to destroy stored keys upon tamper detection, rendering any recovered data unreadable.

[0091] In certain embodiments, the AID 100 can include secure update mechanisms that allow improvement of the Al decision matrices without compromising the security features disclosed herein. An authorized service provider can connect the AID 100 to a secure update terminal during refurbishment at a designated return station, wherein updated decision matrices can be loaded into the first memory following authentication protocols. In addition any lessons learned during operation can also be conveyed back to a remote server and any information used to improve future versions of the AID 100. The update process can include cryptographic verification to ensure that only authorized updates are applied. In embodiments where the AID 100 is designed for extended deployment periods, the device can be configured to receive encrypted delta updates via wireless communication systems 6A-6C from authenticated servers, wherein the updates are applied only after successful integrity verification. The self-erasure capabilities remain active during update processes such that any tampering attempt triggers the security response regardless of update status.

[0092] In one alternative embodiment, the AID 100 is designed as a disposable or single-deployment device intended to operate for a predetermined period tied to the life cycle of a specific product or service. Upon first activation by the user, for example, by pressing the on button of an AID 100 included with a selfassembly product, a timer stored in the memory 7A is initiated. The timer is associated with a threshold duration sufficient for the user to complete the intended task (e.g., furniture assembly, applianceinstallation, product setup). When the threshold time is reached, the processor executes a self-erase routine that overwrites the Al decision matrices 700, 710, and 720 with dummy values, repeating the overwrite cycle a sufficient number of times to prevent reconstruction of the original matrix weights from residual memory state. The self-erase routine can alternatively be triggered by a delete message received from a remote server 660 via the transceiver 620, for example after the retailer or manufacturer confirms that the task window has expired or that the AID 100 has been reported as tampered with. In a further embodiment, the AID 100 housing 195 includes a tamper-detection mechanism configured to trigger the self-erase routine upon physical intrusion, such that unauthorized disassembly or reverse engineering of the locally stored Al model is prevented. The AID 100 can further be designed for return and refurbishment, wherein the self-erase routine preserves hardware integrity while eliminating the task-specific Al model, allowing the device to be reloaded with a new Al model for a subsequent deployment.

[0093] In various embodiments, the AID 100 can incorporate extended battery life mechanisms and low-power operating modes to support deployment contexts where power access is limited. The processor 4 can implement dynamic power management algorithms that reduce clock speed and disable non-essential subsystems during periods of user inactivity, transitioning from a full-power active mode to a low-power standby mode when no user input is detected for a configurable timeout period. In standby mode, the AID 100 can maintain minimal sensor monitoring, such as voice activity detection via a dedicated low-power audio codec, sufficient to detect a wake word or activation phrase that triggers return to full-power operation. The battery 190 can be supplemented or replaced by alternative power sources, including integrated solar cells positioned on exterior surfaces of the housing 195 to harvest ambient light energy, thermoelectric generators that convert body heat or enviromnental temperature differentials into electrical power, or kinetic energy harvesting mechanisms that capture energy from user motion or device handling. In embodiments designed for extended outdoor deployment, such as the agricultural monitoring AID or survival kit AID described elsewhere herein, the solar charging arrangement can include a foldable or detachable solar panel accessory that connects to the AID 100 via a charging port, enabling recharging in remote locations without access to electrical infrastructure. The power management circuitry can prioritize critical functions, such as emergency alerts and sensor monitoring, when battery reserves fall below a predetermined threshold, while temporarily disabling less essential features such as display brightness or speaker volume to extend operational duration.

[0094] In certain embodiments, the AID 100 can incorporate ruggedized housing configurations designed to withstand harsh environmental conditions and physical abuse encountered in demanding deployment contexts. The housing 195 can be constructed from impact-resistant materials such as reinforced polymers, aluminum alloys, or composite materials that meet or exceed military specification (MIL-STD-810) drop test requirements. Sealing arrangements, such as O-rings, gaskets, or ultrasonic welding of housing seams, can provide ingress protection against dust and water consistent with IP67 orIP68 ratings, enabling operation in dusty industrial environments, agricultural fields, or wet conditions including rain exposure and temporary submersion. The touch screen 110 can incorporate chemically strengthened glass or sapphire crystal cover materials resistant to scratching and cracking, while maintaining touch sensitivity when operated with wet fingers or work gloves. In embodiments designed for extreme temperature deployment, such as the construction site AID or survival kit AID described elsewhere herein, the housing 195 can incorporate thermal insulation and heating elements to maintain internal components within operational temperature ranges in cold environments, or heat dissipation features such as thermal vents or heat sinks for high-temperature environments. The microphones 630A-630Z and speakers 640A-640Z can be protected by hydrophobic mesh covers that permit acoustic transmission while blocking liquid and particulate ingress. Shock-absorbing internal mounting arrangements can isolate sensitive electronic components from mechanical shock and vibration, protecting the processor 4, memory 7A, and sensor connections from damage during drops or impacts.

[0095] The self-contained Al architecture of the disclosed AID provides a measurable technical improvement in response latency compared to cloud-based Al systems. In conventional cloud-based architectures, user input signals must traverse a wireless transmission path to a remote server, undergo Al processing on the server, and return via the wireless transmission path before a response can be presented to the user. This round-trip transmission introduces latency typically ranging from 200 milliseconds to several seconds depending on network conditions, creating perceptible delays that degrade user experience in interactive applications. The disclosed AID eliminates transmission latency by performing Al inference locally on the processor 4 using Al decision matrices stored in the first memory 7A. The disclosed architecture achieves end-to-end response latency from user input to AID response of less than 100 milliseconds for typical voice commands, representing a technical improvement over cloud-based systems.

[0096] The latency reduction achieved by local Al processing is particularly advantageous for use cases requiring real-time interaction, such as the medical emergency assistance illustrated in FIG. 9E where delayed CPR instructions could result in patient harm, or the industrial equipment maintenance illustrated in ‘|

[10198] where real-time vibration analysis enables predictive failure detection. The technical improvement in latency also enables deployment in environments where network connectivity is unavailable or unreliable, such as the agricultural monitoring illustrated in FIG. 9F or survival scenarios illustrated in FIG.8F.

[0097] In embodiments supporting multiple languages, the AID 100 can store and select among multiple language models within the limited local memory constraints of the device. The first memory storing the Al software system can allocate a base portion to a shared inference engine and common processing routines, while reserving a modular portion for language-specific Al decision matrices and vocabulary databases. Each supported language can be represented by a compressed language pack comprising acoustic decision matrix weights trained on native speaker data for that language, a vocabularydatabase containing response templates and domain-specific terminology, and phonetic mapping tables for the voice recognition routine 650. The language packs can be stored in a compressed format using quantization techniques that reduce neural network weight precision from 32-bit floating point to 8-bit or 4-bit integer representations, achieving memory reduction ratios of 4:1 to 8:1 while maintaining acceptable inference accuracy for the defined use case. Upon activation or upon detection of a language switch by the Al analysis routine 760, the processor 4 can load the appropriate language pack from compressed storage into working memory for active use. In embodiments where local memory capacity is insufficient to store all desired language packs simultaneously, the AID 100 can be configured with a primary language loaded at activation and one or more secondary languages available for on-demand loading from external storage media or via wireless download from an authorized server during a configuration session. The language detection algorithm within the Al analysis routine 760 can analyze acoustic features of user speech, such as phoneme distributions and prosodic patterns, to automatically identify the spoken language and trigger loading of the corresponding language pack without requiring explicit user selection.

[0098] The disclosed AID architecture achieves a specific technical improvement in memory utilization efficiency compared to conventional Al systems. In a conventional cloud-based Al assistant, the Al model may comprise billions of parameters stored as 32-bit floating point values, requiring gigabytes of memory that exceeds practical limits for portable devices. The disclosed embodiments address this technical limitation through a combination of (1) task-specific training that constrains the Al decision matrices to a defined vocabulary and response domain, reducing the required model size by orders of magnitude compared to general-purpose models; (2) neural network weight quantization that converts 32-bit floating point weights to 8-bit or 4-bit integer representations, achieving memory compression ratios of 4: 1 to 8: 1 while maintaining inference accuracy sufficient for the defined use case; and (3) modular language pack architecture that loads only the active language into working memory while storing additional languages in compressed form.

[0099] The memory allocation scheme of the disclosed AID partitions the first memory into a base portion storing shared inference engine code and a modular portion storing use-case-specific Al decision matrices and vocabulary data. This partitioning enables tire same hardware platform to be deployed across diverse use cases (e.g., furniture assembly, medical monitoring, equipment maintenance) by loading different Al decision matrices into the modular portion during manufacturing or refurbishment, without requiring hardware changes. The base inference engine implements fixed-point matrix multiplication optimized for the quantized weight format, achieving inference throughput that enables real-time response generation within the power budget of the battery 190.

[0100] In the following figures, Al devices will be illustrated and described for use in various scenarios. It is to be understood that embodiments of the present invention are not limited to those worn by humans or even in a biological context. Moreover, monitoring apparatus according to embodiments are notlimited to Al devices configured to be attached to or inserted within the ear, it is intended that the AID is a wearable or carriable device, unless attached to a stationary system, such as an ATM or museum exhibit. Monitoring apparatus according to embodiments herein may be worn on various parts of the body or even worn inside the body. Different monitoring units working across the body can be used as a system, integrating, sharing and providing feedback to a user or care providers. The feedback can be given to the user in many different forms including acoustically, haptically, visually via any number of sensors including via temperature sensors and neurological sensors to just name a few.

[0101] It should be understood that the embodiments herein can apply and be adapted for nonhuman usage for example for animals having vastly different anatomical structures than humans. For example, an Al device attached or inserted into or on a cow, a dog, or a horse (or other anatomical conduit) which may have vastly different shapes or architectures, but can certainly be adapted to worn by such biological systems. For example an AID system can be attached to a horses saddle and aid a rider via verbal interaction or wirelessly communication to an earphone in a riders ear, instructions, or trail information. The AID device can also be used to interact with a rider, for example to notify the rider about the animal’s health, for example monitoring a cow’s health, productivity or other parameters or a dog or a horse’s speed or sentiment (during training) using some of the objective techniques herein for deciphering or understanding semantics (for humans) can equally apply to animals. For example over time an AID device can determine particular animal mannerism and tell when a particular animal is grumpy, tired, hungry or in stress and communicate that to a user. Another example is an AID system placed in a stall of a horse to keep the horse company, playing music, talking, soothing the horse. The AID system can also control electronic, robotic, mechanical systems to aid the horse, for example a feeding mechanism.

[0102] In some circumstances an AID may need to have wireless access to convey and receive data. For example an AID may be Bluetooth-enabled. The AID may be configured to incorporate physiological and / or environmental sensors, according to some embodiments of the present invention.Bluet00th3.0, 4.0, or LE or nowBluet00thSmart are the intelligent, power-friendly versions of Bluetooth wireless technology, and be configured to convey biometric data to a remote site (e.g., Doctor). For a biometric AID many physiological and environmental sensors may be incorporated such as accelerometers, auscultatory sensors, pressure sensors, humidity sensors, color sensors, light intensity sensors, pulse oximetry sensors, pressure sensors, etc. Besides Bluetooth (a nonlimiting example of a wireless communication protocol) other types of communication protocols, such as ZigBee can be used in the embodiments herein to create personal area networks built from small, low-power digital radios. ZigBee is based on an IEEE 802.15.4 standard. Though its low power consumption limits transmission distances to 10-100 meters line-of-sight, depending on power output and environmental characteristics, ZigBee devices can transmit data over long distances by passing data through a mesh network of intermediate devices to reach more distant ones. ZigBee is typically used in low data rate applications that require long battery life and secure networking (ZigBee networks aresecured by 128 bit symmetric encryption keys.) ZigBee applications currently include wireless light switches, electrical meters with in-home-displays, traffic management systems, and other consumer and industrial equipment that requires short-range low-rate wireless data transfer. The technology defined by the ZigBee specification is intended to be simpler and less expensive than other wireless personal area networks (WPANs), such as Bluetooth or Wi-Fi. Thus, embodiments herein are intended to be used with or without a phone. Typical applications between a phone and a monitoring Al device would likely use Bluetooth while other applications that communicate between the Al device and a home appliance might use ZigBee or Bluetooth or other wireless communication protocols and methods as know by one of ordinary skill in wireless communication (e.g., data transfer). Thus although the AID can house an Al software system locally, a wireless communication can be used to query the AID, set the parameters, send and receive data.

[0103] The commercial application of an AID device for use in the health industry of the proposed platform encourages individual-driven health maintenance and promotes a healthier lifestyle through proper caloric intake and exercise. For example a Doctor might provide a patient an AID device to take with them, that would remind them when to take medication, monitor their health, feed data back to the Doctor or health institute, call emergency when there is a health issue, analyze the user for health cues (e.g., stroke indication) and other health issues of the patient and Doctor instructions.

[0104] Embodiments herein are also not limited to devices that communicate wirelessly. In some embodiments of the present invention, devices configured to monitor an individual's physiology and / or environment may be wired to a device that stores, processes, and / or transmits data. In some embodiments, this information may be stored on the Al device itself. In view of the above discussion, systems and methods for monitoring various physiological and environmental factors, as well as systems and methods for using this information for a plurality of useful purposes, are provided. According to some embodiments, real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices. Physiological and environmental data can be collected or acquired and wirelessly transmitted into a wireless network, where the data is stored and / or processed. This information is then used to support a variety of useful methods, such as sports training or monitoring, clinical trials, marketing studies, biofeedback, entertainment, ide nt i ty verification, authentication, purchase authorizations, and others. In a general sense, embodiments herein are primarily defined in either a device or method that uses three major pathways that include a first pathway for user interfaces and interactions, a second pathway for sensing, and a third pathway for analysis based on sensed data and optionally based on user interactions. Most embodiments contemplated herein include an auditory and / or visual front end that includes at least one microphone, at least one speaker and an optional display. In many embodiments, the auditory front end can include an ambient or external microphone as well as an ear canal microphone (e.g., if embedded in an earphone) and speaker for use in communicating with the user. In the disclosed embodiments, the Al software system HA and the hardware components of the AID 100 are specificallydesigned to operate interdependent^, such that the technical advantages of the disclosed architecture are not achievable by either the hardware or software alone. The Al decision matrices 700, 710, and 720 are formatted for efficient execution on the specific processor architecture of processor 4, using fixed-point arithmetic operations that match the processor's native instruction set. The memory allocation scheme of the first memory 7A is organized to align Al decision matrix data with cache line boundaries of the processor 4, reducing memory access latency during inference operations. The sensor interfaces of sensors 5 are configured to produce data in formats directly consumable by the Al analysis routine 760, eliminating data transformation overhead. This hardware-software co-design produces a technical system whose performance characteristics exceed what would be achievable by implementing the disclosed Al algorithms on generic computing hardware or by using the disclosed hardware with generic Al software.Further Al Device (AID) Non-Limiting Embodiments

[0105] FIGs 1 A to 1C illustrate various parts of a non-limiting embodiment of an AID system. The general idea is that every facet of life, for example operating an ATM can have a small Al dedicated to that function that a person can interact with and ask questions. For example, if you order furniture that has to be assembled, then a dedicated AID trained on that specific task can be included with the shipping so that when you put together the furniture you can activate and interact with that AID. Asking it questions and showing it where you are (using its camera to view what you’ve done). The user focused AID facilitates a size of AID software that can be housed locally without the need, although an option, of communicating with a remote server. The AID 100 can tell you what to do next, keep track of where you are incase you have to come back later, and tell you what you’ve done wrong and how to redo it. Hence the particular components of an AID system 100 can vary, but the basic idea is that the AID 100 is limited in its scope to a very specific task. FIG. 1 A illustrates an example of an AID system 100. It can include a touch screen 110 that a user can use to interact with the device (e.g., turn on / off). The user can also interact verbally; hence the system can have microphones (e.g., 130) with pickup through microphone vents 135. The AID system 100 can also communicate acoustically, hence can have speakers 150. Some configurations can include an acoustic deflector 140 which directs the acoustic energy to speaker vents 120. The AID 100 can include a DSP / processor 160, for example TMS320 TI family, Intel Core 19, AMD Ryzen 9, Intel Xeon, NVIDIA RTX 40990, AMD MI300, NXP i.MX 8M, and other processors as known by those of ordinary skill in the art of Al processing. The DSP / processor 160 can include support memory and components 170, for example Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), Double Data Rate Synchronous Dynamic RAM (DDR SDRAM), Graphics Double Data Rate Synchronous Dynamic RAM (GDDR SDRAM), Flash Memory, and other memory system known by those of ordinary skill. The AID 100 can be powered by a battery 190 (e.g., batteries used in wearables) with electrodes 180. Additional power sources can also be used such as solar power cells, USB power cord. A bottom housing195 can also be included. A speaker 150 can direct acoustic energy that can be deflected to an acoustic reflector 140 to a speaker vent 120. FIG. IB illustrates an example of an AID system 100 assembled. FIG.1C shows a touch screen support sensor 115 supporting the touchscreen 110. The touch screen support sensor 115 is operatively connected to the processor 160 and is configured to detect user touch inputs on the touch screen 110, convert the detected touch inputs into a touchscreen input signal, and transmit the touchscreen input signal to the processor 160 for interpretation by the Al software system.

[0106] In some embodiments, a system or device 1 as illustrated in FIG. 2, can be part of an integrated miniaturized Al device (or other body worn or embedded device) that includes all or a portion of the components shown. In the version illustrated in FIG. 2, an Al software system or where the Al software system is incorporated into the components such as the processing unit 4 (e.g., processor, logic circuit, Digital Signal Processor -DSP) and memory 7A, can be housed remotely, whereas the non-limiting embodiment shown in FIG. 3 the Al software system 11 A is built into the wearable or stationary AID as a separate subsystem. In other embodiments, a first portion of the components shown comprise part of a system working with an Al device having a remaining portion that operates cooperatively with the first portion. Thus, in some embodiments, a fully integrated system or device 1 can include an AID software embedded into Memory 7A, the device can have a power source 2 (such as button cell battery, a rechargeable battery, or other power source) and one or more processors 4 that can process a number of sensor channels, using the sensor data via the AID software, process sensor data, convert signals to and from digital and analog and perform appropriate filtering. In some embodiments, the processor 4 is formed from one or more digital signal processors (DSPs). The device can include one or more sensors 5 operationally coupled to the processor 4 also referred to herein as the circuit processing unit. Data from the sensors 5 can be sent to the processor 4 directly or wirelessly using appropriate wireless communication circuitry 6A and communication protocols such as Bluetooth, WiFi, NFC, RF, and Optical such as infrared for example. The sensors 5 can constitute biometric, physiological, environmental, acoustical, or neurological among other classes of sensors. In some embodiments, the sensors 5 can be embedded or formed on or within wearable devices. Such sensors 5 can include non-invasive contactless sensors that have electrodes for EEGs, ECGs, transdermal sensors, temperature sensors, transducers, microphones, optical sensors, motion sensors or other biometric, neurological, or physiological sensors that can monitor brainwaves, heartbeats, breathing rates, vascular signatures, pulse oximetry, blood flow, skin resistance, glucose levels, and temperature among many other parameters. The sensor(s) 5 can also be environmental including, but not limited to, ambient microphones, temperature sensors, humidity sensors, barometric pressure sensors, radiation sensors, volatile chemical sensors, particle detection sensors, or other chemical sensors. The sensors 5 can be directly coupled to the processor 4 or wirelessly coupled via a wireless communication system 6A. Also note that many of the components shown can be wirelessly coupled to each other and not necessarily limited to the wired connections shown.

[0107] Some AID 100 embodiments incorporate methods of user control, for example an acoustical voice controlled (using an ambient microphone or an ear canal microphone for example), but AID can be a multimodal device that can be controlled by not only voice using a speech or voice recognition engine 3A (which can be local or remote), but by other user inputs such as gesture control 3B, or other user interfaces 3C can be used (e.g., external device keypad, camera, etc). The gesture control 3B, for example, can be a motion detector for detecting certain user movements (finger, head, foot, jaw, etc.) or a capacitive or touch screen sensor for detecting predetermined user patterns detected on or in close proximity to the sensor. The user interface 3C can be a display on a phone or a pair of virtual reality (VR) or augmented reality (AR) “glasses” or other pair of glasses for detecting a wink or blink of one or both eyes. The user interface 3C can also include external input devices such as touch screens or keypads on mobile devices operatively coupled to the device 1. The gesture control 3B can be local to the Al device or remote (such as on a phone). As an Al device, the output can be part of a user interface 8 that will vary greatly based on the application 9B (which will be described in further detail below). The user interface 8 can be primary acoustic for example if a user speaks, the voice can be converted into a voice command. A response can also be converted into speech for example via text to speech conversion, or some form of sonification that provides some form of non-speech audio to convey information or perceptualize data. Of course, other parts of the user interface 8 can be visual or tactile using a screen, LEDs and / or haptic device as examples. Similarly, the outputs can primarily be acoustic and visual, but other outputs can be provided.

[0108] In one embodiment, the User Interface 8 can use what is known as “sonification” to enable way finding to provide users an auditory means of direction finding. For example and analogous to a Geiger counter, the user interface 8 can provide a series of beeps or clicks or other sound that increase in frequency as a user follows a correct path towards a predetermined destination. Straying away from the path will provide beeps, clicks or other sounds that will then slowdown in frequency. In one example, the wayfinding function can provide an alert and steer a user left and right with appropriate beeps or other sonification. The sounds can vary in intensity, volume, frequency, and direction to assist a user with way finding to a particular destination. Differences or variations using one or two sensors (e.g., microphones) can also be exploited. In Bi-aural type of delivered acoustic information, Head-related transfer function (HRTF) cues can be provided. A HRTF is a response that characterizes how an ear receives a sound from a point in space where a pair of HRTFs for two ears can be used to synthesize a binaural sound that seems to come from a particular point in space. Humans have just two ears, but can locate sounds in three dimensions in terms of range (distance), in terms of direction above and below, in front and to the rear, as well as to either side. This is possible because the brain, inner ear and the external ears (pinna) work together to make inferences about location. This ability to localize sound sources may have developed in humans and ancestors as an evolutionary necessity, since the eyes can only see a fraction of the world around a viewer, and vision is hampered in darkness, while the ability to localize a soundsource works in all directions, to varying accuracy, regardless of the surrounding light. Some consumer home entertainment products designed to reproduce surround sound from stereo (two-speaker) headphones use HRTFs and similarly, such directional simulation can be used with Al devices to provide a wayfinding function.

[0109] In some embodiments, the processor 4 is coupled (either directly or wirelessly via wireless communication circuitry 6B) to memory 7A which can be local to the device 1 or remote to the device (but part of the system). The memory 7A can store acoustic information, raw or processed sensor data, or other information as desired. The memory 7A can receive the data directly from the processor 4 or via wireless communications 6B. In some embodiments, the data or acoustic information is recorded (7B) in a circular buffer or other storage device for later retrieval. In some embodiments, the acoustic information or other data is stored at a local or a remote database 7C. In some embodiments, the acoustic information or other data is analyzed by an analysis routine 7D (either with or without recording 7B) and done either locally or remotely. Note that recording 7B is not limited to acoustic recording but can include data representing any type of sense, for example visual data can be recorded, as can biometric data. The output of the analysis routine can be stored at the database 7C or provided as an output to the user or other interested part (e.g., user’s physician, a third party payment processor). Note that storage of information can vary greatly based on the particular type of information obtained. In the case of acoustic information, such information can be stored in a circular buffer, while biometric and other data may be stored in a different form of memory (either local 7, 7A, 7C, or remote 7E). In some embodiments, captured or harvested data can be sent to remote storage such as storage in “the cloud” when battery and other conditions are optimum (such as during sleep).

[0110] In some embodiments, the Al device or monitoring device can be used in various commercial scenarios, some of which require user verification. For example when a new bank account is opened, an AID 100 may be shipped or provided at the bank for the user that is tied to the user’s biometrics and directly linked to a financial account. Thus the user can interact with the AID 100, once a user’s ID is verified, to give the AID 100 vocal commands wherein the AID 100 then pays bills or interacts with its linked financial accounts, for example buy / sell stocks. One or more of the sensors 5 used in the AID 100 can be used to create a unique or highly non-duplicative signature sufficient for authentication, verification or identification. Some human biometric signatures can be quite unique and be used by themselves or in conjunction with other techniques to corroborate certain information. For example, a heartbeat or heart signature can be used for biometric verification. An individual’s heart signature under certain contexts (under certain stimuli as when listening to a certain tone while standing or sitting) may have certain characteristics that are considered sufficiently unique. The heart signature can also be used in conjunction with other verification schemes such as pin numbers, predetermined gestures, fingerprints, or voice recognition to provide a more robust, verifiable and secure system. In some embodiments, biometricinformation can be used to readily distinguish one or more speakers from a group of known speakers such as in a teleconference call or a videoconference call.

[0111] In some embodiments, the Al device can be part of a payment system 9A that works in conjunction with the one or more sensors 5. In some embodiments, the payment system 9A can operate cooperatively with a wireless communication system 6B such as a 1-3 meter Near Field Communication (NFC) system, Bluetooth wireless system, WiFi system, or cellular system. In one embodiment, a very short range wireless system uses an NFC signal to confirm possession of the device in conjunction with other sensor information that can provide corroboration of identification, authorization, or authentication of the user for a transaction. In some embodiments, the system will not fully operate using an NFC system due to distance limitations and therefore another wireless communication protocol can be used.

[0112] In one embodiment, the sensor 5 can include a Snapdragon Sense ID 3D fingerprint technology by Qualcomm or other designed to boost personal security, usability and integration over touchbased fingerprint technologies. The new authentication platform can utilize Qualcomm’s SecureMSM technology and the FIDO (Fast Identity Online) Alliance Universal Authentication Framework (UAF) specification to remove the need for passwords or to remember multiple account usernames and passwords. As a result, in the future, users will be able to login to any website which supports FIDO through using their device and a partnering browser plug-in which can be stored in memory 7A or elsewhere. The Qualcomm fingerprint scanner technology is able to penetrate different levels of skin, detecting 3D details including ridges and sweat pores, which is an element touch-based biometrics do not possess. Of course, in a multimodal embodiment, other sensor data can be used to corroborate identification, authorization or authentication and gesture control can further be used to provide a level of identification, authorization or authentication. Of course, in many instances, 3D fingerprint technology may be burdensome and considered “over-engineering” where a simple acoustic or biometric point of entry is adequate and more than sufficient. For example, after an initial login, subsequent logins can merely use voice recognition as a means of accessing a device. If further security and verification is desired for a commercial transaction for example, then other sensors as the 3D fingerprint technology can be used.

[0113] In some embodiments, an external portion of the Al device (e.g., an end cap) can include a fingerprint sensor and / or gesture control sensor to detect a fingerprint and / or gesture. Other sensors and analysis can correlate other parameters to confirm that user fits a predetermined or historical profile within a predetermined threshold. For example, a resting heart rate can typically be within a given range for a given amount of detected motion. In another example, a predetermined brainwave pattern in reaction to a predetermined stimulus (e.g., music, sound pattern, visual presentation, tactile stimulation, etc.) can also be found be within a given range for a particular person. In yet another example, sound pressure levels (SPL) of a user’s voice and / or of an ambient sound can be measured in particular contexts (e.g., in a particular store or at a particular venue as determined by GPS or a beacon signal) to verify and corroborate additionalinformation alleged by the user. For example, a person conducting a transaction at a known venue having a particular background noise characteristic (e.g., periodic tones or announcements or Music playing in the background at known SPL levels measured from a point of sale) commonly frequented by the user of the monitoring device can provide added confirmation that a particular transaction is occurring in a location by the user. In another context, if a registered user at home (with minimal background noise) is conducting a transaction and speaking with a customer service representative regarding the transaction, the user may typically speak at a particular volume or SPL indicative that the registered user is the actual person claiming to make the transaction. A multimodal profile can be built and stored for an individual to sufficiently corroborate or correlate the information to that individual. The correlation and accuracy becomes stronger over time as more sensor data is obtained as the user utilizes the device 1 and a historical profile is essentially built. Thus, a very robust payment system 9A can be implemented that can allow for mobile commerce with the use of the Al device alone or in conjunction with a mobile device such as a cellular phone. Of course, information can be stored or retained remotely in server or database and work cooperatively with the device 1. In other applications, the pay system can operate with almost any type of commerce.

[0114] Referring to FIG. 3, a device 1, substantially similar to the device 1 of FIG. 2 is shown with further details in some respects and less details in other respects. For simplicity, local or remote memory, local or remote databases, and features for recording can all be represented by the storage device 7 which can be coupled to an analysis routine 7D. As before, the device can be powered by a power source 2. The device 1 can include one or more processors 4 that can process a number of acoustic channels and process such channels for situational awareness and / or for keyword or sound pattern recognition, as well as daily speech the user speaks, coughs, sneezes, etc. The processor / s) 4 can provide for hearing loss correction and prevention, process sensor data, convert signals to and from digital and analog and perform appropriate filtering as needed. In some embodiments, the processor 4 is formed from one or more digital signal processors (DSPs). The device can include one or more sensors 5 operationally coupled to the processor 4. The sensors can be biometric and / or environmental. Such environmental sensors can sense one or more among light, radioactivity, electromagnetism, chemicals, odors, or particles. The sensors can also detect physiological changes or metabolic changes. In some embodiments, the sensors can include electrodes or contactless sensors and provide for neurological readings including brainwaves. The sensors can also include transducers or microphones for sensing acoustic information. Other sensors can detect motion and can include one or more of a GPS device, an accelerometer, a gyroscope, a beacon sensor, or NFC device. One or more sensors can be used to sense emotional aspects such as stress or other affective attributes. In a multimodal, multisensory embodiment, a combination of sensors can be used to make emotional or mental state assessments or other anticipatory determinations.

[0115] User interfaces can be used alone or in combination with the aforementioned sensors to also more accurately make emotional or mental state assessments or other anticipatory determinations. A voice control routine 3A can include one or more of an ambient microphone, an ear canal microphone or other external microphones (e.g., from a phone, lap top, or other external source) to control the functionality of the device 1 to provide a myriad of control functions such as retrieving search results (e.g., for information, directions) or to conduct transactions (e.g., ordering, confirming an order, making a purchase, canceling a purchase, etc.), or to activate other functions either locally or remotely (e.g., turn on a light, open a garage door). Through appropriate mixing of the signal from the ear canal microphone and the ambient microphone, such mixing technique can provide for a more intelligible voice substantially free of ambient noise that is more recognizable by voice recognition engines such as SIRI by Apple, Google Now by Google, or Cortana by Microsoft. Note that sensors such as accelerometers, infrared detector can be used to aid in voice recognition.

[0116] The voice control interface 3A can be used alone or optionally with other interfaces that provide for gesture control 3B. Alternatively, the gesture control interface(s) 3B can be used by themselves. The gesture control interface(s) 3B can be local or remote and can be embodied in many different forms or technologies. For example, a gesture control interface can use radio frequency, acoustic, optical, capacitive, or ultrasonic sensing. The gesture control interface can also be switch-based using a foot switch or toe switch. An optical or camera sensor or other sensor can also allow for control based on winks, blinks, eye movement tracking, mandibular movement, swallowing, or a suck-blow reflex as examples.

[0117] The processor 4 can also interface with various devices or control mechanisms within the ecosystem of the device 1. For example, the device can include various valves that control the flow of fluids or acoustic sound waves. More specifically, in one example the device 1 can include a shutter or “aural iris” in the form of an electro active polymer that controls a level or an opening size that controls the amount of acoustic sound that passes through to the user’s ear canal. In another example, the processor 4 can control a level of battery charging to optimize charging time or optimize battery life in consideration of other factors such as temperature or safety in view of the rechargeable battery technology used.

[0118] A brain control interface (BCI) 5B can be incorporated in the embodiments to allow for control of local or remote functions including, but not limited to prosthetic devices. In some embodiments, electrodes or contactless sensors as part of an Al device can pick up brainwaves or perform an EEG reading that can be used to control the functionality of the Al device itself or the functionality of external devices. The BCI 5B can operate cooperatively with other user interfaces (8A or 3C) to provide a user with adequate control and feedback. In some embodiments, the Al device and electrodes or contactless sensors can be used in Evoked Potential Tests. Evoked potential tests measure the brain's response to stimuli that are delivered through sight, hearing, or touch. These sensory stimuli evoke minute electrical potentials thattravel along nerves to the brain, and can be recorded typically with patch-like sensors (electrodes) that are attached to the scalp and skin over various peripheral sensory nerves, but in these embodiments, the contactless sensors in the Al device can be used instead. The signals obtained by the contactless sensors are transmitted to a computer, where they are typically amplified, averaged, and displayed. There are 3 major types of evoked potential tests including: 1) Visual evoked potentials, which are produced by exposing the eye to a reversible checkerboard pattern or strobe light flash, help to detect vision impairment caused by optic nerve damage, particularly from multiple sclerosis; 2) Brainstem auditory evoked potentials, generated by delivering clicks to the ear, which are used to identify the source of hearing loss and help to differentiate between damage to the acoustic nerve and damage to auditory pathways within the brainstem; and 3) Somatosensory evoked potentials, produced by electrically stimulating a peripheral sensory nerve or a nerve responsible for sensation in an area of the body which can be used to diagnose peripheral nerve damage and locate brain and spinal cord lesions The purpose of the Evoked Potential Tests include assessing the function of the nervous system, aiding in the diagnosis of nervous system lesions and abnormalities, monitoring the progression or treatment of degenerative nerve diseases such as multiple sclerosis, monitoring brain activity and nerve signals during brain or spine surgery, or in patients who are under general anesthesia, and assessing brain function in a patient who is in a coma. In some embodiments, particular brainwave measurements (whether resulting from Evoked Potential stimuli or not) can be correlated to particular thoughts and selections to train a user to eventually consciously make selections merely by using brainwaves. For example, if a user is given a selection among A. Apple B. Banana and C. Cherry, a correlation of brainwave patterns and a particular selection can be developed or profiled and then subsequently used in the future to determine and match when a particular user merely thinks of a particular selection such as “C. Cherry”. The more distinctively a particular pattern correlates to a particular selection, the more reliable the use of this technique as a user input.

[0119] The user interfaces, types of control, and / or sensors may likely depend on the type of application 9B. In a mobile application, a mobile phone microphone(s), keypad, touchscreen, camera, or GPS or motion sensor can be utilized to provide a number of the contemplated functions. In a vehicular enviromnent, a number of the functions can be coordinated with a car dash and stereo system and data available from a vehicle. In an exercise, medical, or health context, a number of sensors can monitor one or more among, heartbeat, blood flow, blood oxygenation, pulse oximetry, temperature, glucose, sweat, electrolytes, lactate, pH, brainwave, EEG, ECG or other physiological, or biometric data. Biometric data can also be used to confirm a patient’s identity in a hospital or other medical facility to reduce or avoid medical record errors and mix-ups. In a social networking environment, users in a social network can detect each other’s presence, interests, and vital statistics to spur on athletic competition, commerce or other social goals or motivations. In a military or professional context, various sensors and controls disclosed herein can offer a discrete and nearly invisible or imperceptible way of monitoring and communicating thatcan extend the “eyes and ears” of an organization to each individual using an Al device as described above. In a commercial context, a short-range communication technology such as NFC or beacons can be used with other biometric or gesture information to provide for a more robust and secure commercial transactional system. In a call center context or other professional context, the Al device could incorporate a biosensor that measures emotional excitement by measuring physiological responses. The physiological responses can include skin conductance or Galvanic Skin Response, temperature and motion.

[0120] In yet other aspects, some embodiments can monitor a person’s sleep quality, mood, or assess and provide a more robust anticipatory device using a semantics acoustic engine with other sensors. The semantic engine can be part of the processing unit 4 or part of the analysis routine 7D that can be performed locally at the device 1 or remotely as part of an overall system. If done remotely at a remote server, the system 1 can include a server (or cloud) that includes algorithms for analysis of gathered sensor data and profile information for a particular user. In contrast to other schemes, the embodiments herein can perform semantic analysis based on all biometrics, audio, and metadata (speaker ID, etc.) in combination and also in a much “cleaner” environments within a sealed EAC sealed by a proprietary balloon that is immune to many of the detriments in other schemes used to attempt to seal an EAC. Depending on the resources available at a particular time such as processing power, semantic analysis applications, or battery life, the semantic analysis would be best performed locally within a monitoring Al device itself, or within a cellular phone operationally coupled to the Al device, or within a remote server or cloud or a combination thereof.

[0121] In other embodiments a user might wish to have suggestive advertising, thus a method of delivering targeted advertising to a person includes collecting physiological, acoustic, words, and / or environmental information from the person, selecting an advertisement for delivery to the person based upon the collected physiological and / or environmental information, and delivering the selected advertisement to the person. The physiological and / or environmental information is collected via the AID monitoring sensors associated with the person and that includes at least one physiological sensor and / or environmental sensor, as described above. The received physiological and / or environmental, acoustic or neurological information is analyzed to identify a physiological condition, health, level of safety of the person and / or environmental condition in a vicinity of the person, and an advertisement is selected for a product or service related to an identified physiological and / or environmental condition. The selected feedback, recommendations or advertisement can be delivered via any of various channels including, but not limited to, short messaging service (SMS), video, graphic, acoustic, hepatic, text, email, (whether on a smartphone, computer, VR glass, body worn device, or otherwise) or postal mail, television, radio, newspaper, magazine, the internet, and outdoor advertising.

[0122] In some embodiments, “anticipatory services” based on the acquired information can be based on monitoring a user and user interactions for a previous predetermined period of time. The monitored or acquired information can be customized and for example can be set for monitoring the past 60 seconds, thatpast hour(s)or the past month(s). It could be based on trends or frequency (repetitiveness) of data such as detecting the saying of “I love you” to your significant other.

[0123] In some embodiments, a method, system and device for supporting interpersonal relationships can include an Al device that uses a short-range communication system such as Near Field Communication (NFC), Bluetooth, or WiFi signals to initiate or facilitate communication among potential partners based on any number of various parameters. The parameters can be selected among any one or more among preexisting profiles, biometric or physiological data (current, near current, or historical data), or environmental data. The communication can be initiated based on a match reflecting common interests or a relative match based on meeting certain thresholds of certain data parameters. For example, a sudden elevated heartbeat, blood pressure, or certain brainwave activity may be used as a trigger for initiating communication.Alternatively, the match can enable the user of the Al device and the corresponding partner of interest to affirmatively enable communication between the parries in response to a notice of such match rather than having the communication being automatic. The feedback could include various methods and information including acoustic wayfinding information, which could enable two individuals previously unknown to each other to meet, based on a common interest, or other data points as described herein.

[0124] A physiological sensor (5 A) can be any compact sensor for monitoring the physiological functioning of the body, such as, but not limited to, sensors for monitoring: heart rate, pulse rate, breathing rate, blood flow, heartbeat signatures, cardio-pulmonary health, organ health, metabolism, electrolyte type and concentration, physical activity, caloric intake, caloric metabolism, metabolomics, physical and psychological stress levels and stress level indicators, physiological and psychological response to therapy, drug dosage and activity (drug dosimetry), physiological drug reactions, drug chemistry in the body, biochemistry, position & balance, body strain, neurological functioning, brain activity, brain waves, blood pressure, cranial pressure, hydration level, auscultatory information, auscultatory signals associated with pregnancy, physiological response to infection, skin and core body temperature, eye muscle movement, blood volume, inhaled and exhaled breath volume, physical exertion, exhaled breath physical and chemical composition, the presence, identity, and concentration of viruses & bacteria, foreign matter in the body, internal toxins, heavy metals in the body, anxiety, fertility, ovulation, sex hormones, psychological mood, sleep patterns, hunger & thirst, hormone type and concentration, cholesterol, lipids, blood panel, bone density, body fat density, muscle density, organ and body weight, reflex response, sexual arousal, mental and physical alertness, sleepiness, auscultatory information, response to external stimuli, swallowing volume, swallowing rate, sickness, voice characteristics, tone, pitch, and volume of the voice, vital signs, head tilt, allergic reactions, inflammation response, auto-immune response, mutagenic response, DNA, proteins, protein levels in die blood, body hydration, water content of the blood, pheromones, internal body sounds, digestive system functioning, cellular regeneration response, healing response, stem cell regeneration response, and the like. Vital signs can include pulse rate, breathing rate, blood pressure, pulsesignature, body temperature, hydration level, skin temperature, and the like. A physiological sensor may include an impedance plethysmograph for measuring changes in volume within an organ or body (usually resulting from fluctuations in the amount of blood or air it contains). For example, the wearable monitoring device 1 may include an impedance plethysmograph to monitor blood pressure in real-time. Note that one or more of these physiological sensors can be incorporated within or on the expandable element or balloon.

[0125] An external energy sensor (5A), serving primarily as an environmental sensor, can be any compact sensor for monitoring the external environment in the vicinity of the body, such as, but not limited to, sensors for monitoring: climate, humidity, temperature, pressure, barometric pressure, pollution, automobile exhaust, soot density, airborne particle density, airborne particle size, airborne particle shape, airborne particle identity, volatile organic chemicals (VOCs), hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), carcinogens, toxins, electromagnetic energy (optical radiation, X-rays, gamma rays, microwave radiation, terahertz radiation, ultraviolet radiation, infrared radiation, radio waves, and the like), EMF energy, atomic energy (alpha particles, beta-particles, gamma rays, and the like), gravity, light properties (such as intensity, frequency, flicker, and phase), ozone, carbon monoxide, greenhouse gases, CO2, nitrous oxide, sulfides, airborne pollution, foreign material in the air, biological particles (viruses, bacteria, and toxins), signatures from chemical weapons, wind, air turbulence, sound and acoustical energy (both human audible and inaudible), ambient noise, ultrasonic energy, noise pollution, human voices, animal sounds, diseases expelled from others, the exhaled breath and breath constituents of others, toxins from others, bacteria & viruses from others, pheromones from others, industrial and transportation sounds, allergens, animal hair, pollen, exhaust from engines, vapors & fumes, fuel, signatures for mineral deposits or oil deposits, snow, rain, thermal energy, hot surfaces, hot gases, solar energy, hail, ice, vibrations, traffic, the number of people in a vicinity of the user, the number of people encountered throughout the day, other Al device users in the vicinity of the Al device user, coughing and sneezing sounds from people in the vicinity of the user, loudness and pitch from those speaking in the vicinity of the user, and the like.

[0126] The signal processor 4 may be configured to process information from a physiological sensor and / or an environmental sensor or other sensors to assess aging rate. Physiological sensors configured to assess aging rate may include pulse rate sensors, blood pressure sensors, activity sensors, and psychosocial stress sensors. Environmental sensors configured to assess aging rate may include UV sensors and pollution sensors.

[0127] The signal processor 4 provides a means of converting the digital or analog signals from the sensors into data that can be fransmitted wirelessly by the transmitter 6A-C. The signal processor 4 may be composed of, for example, signal conditioners, amplifiers, filters, digital-to-analog and analog-to-digital converters, digital encoders, modulators, mixers, multiplexers, transistors, various switches, microprocessors, or the like. For personal communication, the signal processor 4 processes signals received by a wireless communication receiver into signals that can be heard or viewed by the user. The receivedsignals may also contain protocol information for linking various telemetric components together, and this protocol information can also be processed by the signal processor 4 or alternatively by a remote processor or server (not shown).

[0128] The signal processor 4 may utilize one or more compression / decompression algorithms (CODECs) used in digital media for processing data. The communication circuitry (6A-C) can be comprised of one or transmitters that can be a variety of compact electromagnetic transmitters. A standard compact antenna can be used in the standard Bluetooth headset protocol, but any kind of electromagnetic antenna suitable for transmitting at human-safe electromagnetic frequencies may be utilized. The communication circuitry (6A-C) can also include a communication receiver that can also include an antenna. In some embodiments, the receiving antenna and the transmitting antenna are physically the same. The receiver / transmitter can be, for example, a non-line-of-sight (NLOS) optical scatter transmission system. These systems typically use short-wave (blue or UV) optical radiation or “solar blind” (deep-UV) radiation in order to promote optical scatter, but IR wavelengths can also be used.

[0129] In some embodiments, the transmitter / receiver also referred to as communication circuitry (6A-C) is configured to transmit signals from tire signal processor 4 to a remote terminal following a predetermined time interval. For example, the transmitter may delay transmission until a certain amount of detection time has elapsed, until a certain amount of processing time has elapsed, etc. In some cases, the transmitter / receiver is configured to transmit signals to the remote terminal dependent on information sensed by the sensors (5A). For example, if an unstable pulse rate is sensed, a warning message may be sent to a remote terminal to communicate a need for help at a particular location as determined by a GPS device.

[0130] According to some embodiments, the power source 2 is a portable rechargeable lithiumpolymer or zinc-air battery Additionally, piezoelectric devices or microelectromechanical systems (MEMS) can be used to collect and store energy from body movements, electromagnetic energy, and other forms of energy in the environment or from the user himself. A thermoelectric or thermovoltaic device can be used to supply some degree of power from thermal energy or temperature gradients. In some embodiments, a cranking or winding mechanism can be used to store mechanical energy for electrical conversion or to convert mechanical energy into electrical energy that can be used immediately or stored for later. Further note that the power source 2 can be incorporated or be part of the inflatable element or balloon 21.Biocompatible battery chemistry can be used within the balloon for biological applications and other battery chemistries can be used when non-biological applications are considered.

[0131] The communication circuitry is used for, but not limited to: processing or generating an audible sound, images, and / or videos from information received via the receiver (from a cell phone, computer, network, database, or the like) and / or processing or generating an electrical signal from an audible sound from the user such that the electrical signal can be transmitted telemetrically via the transmitter. For example, in standard Bluetooth protocol, communication electronics are used to convert anaudible conversation into an electrical signal for telemetric conversation; communication electronics are also used to convert a digitized telemetric conversation into an audible conversation for the Al device user. Additionally, the communication circuitry can be used to store, process, or play analog or digital information from music, radio shows, videos, or other audible entertainment and to communicate this information to an Al device user. In many cases, this information includes information received by the receiver. In many cases, the analog or digital information is not stored in the communication circuitry but, rather, is stored in a portable telecommunication device such as a cell phone. In such case, the communication circuitry is used for converting the analog or digital information into audible sound for the Al device user. The communication circuitry may contain at least one microphone, speaker, signal processor, and digital memory. In some embodiments, the communication circuitry may apply at least one CODEC for encoding or decoding information. The communication circuitry may utilize non-audible forms of communication with the user, such as visual, physical, or mental (i.e., brainwaves or neural stimulation) communication with the user.

[0132] Furthermore, the signal processor 4 can be composed of several components located throughout the device. As used herein, the terms “Al software system,” “Al analysis routine,” “Al response generation routine,” and “voice recognition routine” each refer to executable instructions stored in memory and executed by a processor. The Al software system comprises a plurality of Al decision matrices having trained neural network weight values, together with executable instructions that implement the Al analysis routine and the Al response generation routine. The Al decision matrices are data structures containing numerical weight values derived through a training process, as described with respect to FIG. 11. The executable instructions, when executed by the processor, cause the processor to perform the algorithmic steps described herein, including applying multimodal fusion algorithms to decision matrix outputs, querying databases, and generating responses.

[0133] As a non-limiting example, if a user is in a polluted environment, such as air filled with VOCs, the communication circuitry may notify the user to move to a new environment. As another example, if one or more of the physiological and environmental sensors (5A) of the wearable monitoring device 1 pick up a high particulate density in the environment, with an elevation in core body temperature, and a change in voice pitch occurring simultaneously (or near-simultaneously) within a common timeframe, the communication circuitry may alert the user that he / she may be having an allergic response. As a further example, the user can use the communication circuitry to execute biofeedback for willfully controlling blood pressure, breathing rate, body temperature, pulse rate, and the like. The communication circuitry may utilize audible or visible alerts if the user is meeting their physiological targets or exceeding safe physiological limits. Alerting a user by physical or electrical force, such as the sense of touch or tingling from an electric pulse or vibration, can also be utilized. Thus, although communication by audible means isoften utilized, the communication circuitry can alert, signify, or communicate with the user through sound, light, electrical actuation, and physical actuation.

[0134] As a second example of this biofeedback method, basic vital signs collected by the physiological sensors 5A and processed by the signal processor 4 can be presented to tire monitoring device user audibly, through the communication circuitry. For example, the user may be able to listen to his / her breathing rate, pulse rate, and the like. Additionally, an entertaining or aggravating sound or song can be used to alert the user to favorable or unfavorable personal health and environmental factors occurring in real-time. This technique may be applied towards education, such as positive or negative feedback for educational games, learning games, or games of deception (e.g., poker, etc.).

[0135] In some embodiments, a telecommunication device 6D transmits data in only one direction or particular directions. For example, in one embodiment, the portable telecommunication device 6D can receive telemetric information from the sensor unit 1 but is not configured to send out signals to a transmission system. For example, charts relating health and environment, as well as real-time biofeedback and the like, can be displayed on a cell phone, media player, PDA, laptop, or other device (6D). The telecommunication device 6D may also contain physiological and environmental sensors itself, such as sensors for blood pressure, pulse rate, air quality, pulse-oximetry, and the like. Additionally, the telecommunication device 6D can communicate with the wearable sensor unit 1 to transfer commands, activate or deactivate sensors, communicate with the user, and the like.

[0136] In some embodiments, the portable telecommunication device 6D sends / receives wireless information directly to / from a transmission system for transmission to a database (such as personal database and / or anonymous database on a server 7E or 7C) for storage, analysis, and retrieval of data. The style of transmission system may depend largely on the location of the database. For example, if the database is located in a local computer, the wireless information from the telecommunication device 6D can be sent directly to the local computer. This computer may be connected with the Internet, allowing access to the database from the web. However, the database is more typically located far away from the user and telecommunication circuitry. In this case, the wireless signal from the telecommunication device 6D can be sent to a reception tower and routed through a base station (not shown). This information can then be sent to a database through the Internet. A variety of other transmission protocols can be applied for connection between the telecommunication device 6D and the databases 7E.

[0137] The personal and anonymous databases 7E represent databases that may or may not be located on the same computer. A difference between these two databases is not the physical location of the database but rather the type of information available on each database. For example, the anonymous database, containing aggregated health and environmental data from multiple indistinct monitoring device users, can be public and accessible through the Internet by various users. In contrast, the personal database contains health and environmental data that is personalized for each monitoring device user, including personalizedinformation such as name, birth date, address, and the like. Users can log-in to their personalized information in the personal database through an interactive user interface and compare this information with information from multiple users in the anonymous database via a graphical user interface, etc. In some cases, the wearable sensor unit 1 or 20 or portable telecommunication device 6D may additionally communicate information not directly related to health and environment, such as physical location, personal information, proximity to various locations or properties, etc., to either database.

[0138] In another example, the physiological sensors can be used to perform transdermal ethanol detection to prevent drunk driving if integrated into an ignition interlock system for example. However, experimental data from previous research has shown significant time delays between alcohol ingestion and detection at the skin which makes real time estimation of blood alcohol concentration via skin measurement difficult. A computational model that predicts the lag time between peak blood and skin alcohol concentrations can enable better accuracy. Accounting for how the lag time varies with ethanol dose, body weight and metabolic rate can possibly improve transdermal alcohol sensing for detecting driver B AC for different segments of the population and levels of intoxication.

[0139] Notwithstanding the current inaccuracies in trying to measure BAC in real time, transdermal measurements can prove useful as a dichotomous test to sense if the driver has been consuming alcohol. Additionally, an easy way to circumvent a transdermal measurement would be to block direct skin contact with the sensor. An intoxicated driver wearing gloves could potentially prevent the sensors for detecting any ethanol on their skin at all. A secondary sensor system would be required to ensure that the measurement is being made at the surface of the skin. An Al device having such a sensor can serve as such a measurement device.

[0140] A data storage component may include various algorithms, without limitation. In some embodiments, at least one algorithm is configured to focus processing resources on the extraction of physiological and / or environmental information from the various environmental and / or physiological sensors. Algorithms may be modified and / or uploaded wirelessly via a transmitter.

[0141] Physiological and / or environmental information collected from sensors 5 or 5A in an Al device may be corrupted by the motion artifacts of a subject. As a specific example, when measuring pulse rate in a subject via photoplethysmography while the subject is walking, optical scatter associated with footstep-related skin vibrations may be misinterpreted as coming from a pulse. This problem can be especially difficult where footstep rates are on the order of normal human pulse rates. By measuring body motion in real-time via one or more accelerometers inside the wearable monitor, sampled pulse rate data can be processed to subtract, reduce, or eliminate signals associated with footsteps. In some cases, the processor 4 may simply send a command to ignore the sampling and / or logging of pulse rate when body motion is detected. In this way, average pulse rate estimate is not convoluted with footstep information. In other cases, the processor 4 may correct for body motion in real time through dynamic feedback from theaforementioned accelerometer. A variety of other body motion sensors, such as acoustic sensors for monitoring footstep sounds and MEMS motion sensors, can also be used to monitor footsteps and correct physiological and / or environmental data for motion artifacts.

[0142] According to some embodiments of the present invention, a method of delivering targeted advertising to a person includes collecting physiological and / or environmental information from the person, selecting an advertisement for delivery to the person based upon the collected physiological and / or environmental information, and delivering the selected advertisement to the person. Collecting information includes receiving physiological and / or environmental information from a monitoring device associated with the person. Selecting an advertisement includes analyzing the received physiological and / or environmental information to identify a physiological condition of the person and / or environmental condition in a vicinity of the person, and selecting an advertisement for a product or service related to an identified physiological and / or environmental condition. Delivery of a selected advertisement can be via any of many different channels including, but not limited to, email, postal mail, television, radio, newspaper, magazine, the internet, and outdoor advertising.

[0143] Electrodes within or about an Al device can also be utilized to monitor blood gases diffused through the skin, giving an indication of blood gas metabolism. For example, a compact Severinghaus electrode can be incorporated within an Al device for the real-rime monitoring of CO2 levels in the blood, for example, through an earlobe connector, a sensor region of an Al device fitting, or along or about an ear support. These Severinghaus -type electrodes can also be used to monitor other blood gases besides CO2, such as oxygen and nitrogen.

[0144] In some embodiments, the EEG electrodes within the EAC can be used to monitor or measure a level of sleep quality. In other aspects, the EEG can be used to monitor apnea-induced hypoxemia or sleep apnea based measurements of electrocortical activity. Sleep apnea is a potentially serious sleep disorder in which breathing repeatedly stops and starts. Sleep apnea is sometimes diagnosed if one snores loudly and they feel tired even after a full night's sleep. Although snoring is not definitive, it is considered a soft marker of the condition. There are two main types of sleep apnea. One type is known as Obstructive sleep apnea which is the more common form that occurs when throat muscles relax. The second type is Central sleep apnea, which occurs when your brain doesn't send proper signals to the muscles that control breathing. Treatment is necessary to avoid heart problems and other complications.

[0145] In some embodiments, an Al device can be used to monitor individuals with obstructive sleep apnea syndrome (OSAS). The EEG recordings can be timed with respiratory events or other monitored vitals. In some embodiments, the Al device can be used to monitor REM or non-REM sleep which is used in diagnosing sleep apnea. The Al device can further monitor other aspects for further correlation or correspondence to suspected disorders. For example, a microphone or other transducer can monitor for sounds such as snoring or another sensor can monitor for restlessness or other motion. Thus, a single Aldevice (or a combination in a pair of Al devices) can serve to detect various diseases, disorders, or conditions by monitoring EEG that looks at neurological data in combination with other data gathered from sound or motion detectors or sensors. If a particular profile of sensing data meets a threshold indicative of one condition or another, the device can further include a reporting mechanism that provides notice (and optionally the actual data) to an appropriate physician such as a pulmonologist, neurologist or internist. Further note that in some embodiments, the physical profile monitoring device in the form of an Al device should not extending from the concha bowl to avoid dislodging by a pillow or other movement during sleep or rest since minimal or no physical contact should be made with the Al device during such analysis.

[0146] In some embodiments, by using the ear as the location for electrodes (in a wireless Al device) used in electroencephalography (EEG), a patient or physician can avoid having to shave a patient’s scalp and further avoid attaching many electrodes and cables. With sufficient battery life, the device can be used to monitor patients over extended periods of time and enable monitoring of patients having regularly recurring problems such as seizures or microsleep. The placement of the EEG electrodes inside the EAC makes for a stable and consistent recording system that is substantially free of signal noise due to body movement. Such signal noise can further be detected and filtered as discussed elsewhere herein. Using the ear also ensures that the electrodes are essentially placed in the same spot providing for greater consistency and reliability between different readings. If an Al device is to be situated within a channel, a balloon can be used for stability. The balloon when placed appropriately, essentially registers with the channel (e.g., ear’s anatomy) and locks in place. Furthermore, if the Al device isolates an ambient environment from an internal environment, a balloon can provide an added isolation from ambient noise that other devices fail to provide when using microphones as part of an overall sensing scheme.

[0147] In some embodiments, the monitoring of EEG and / or one or more other physiological parameter can assist in the assessment or treatment or analysis of one or more of behavior studies, estimation of auditory attention (feedback to hearing aids), fatigue monitoring, vigilance or alertness monitoring, hypoglycemia, Alzheimer’s, Dementia, Schizophrenia, Neurodegenerative diseases, Epilepsies, Brain Computer Interface (BCI), Sleep monitoring, Objective Hearing Threshold (HTL) estimation, antidepressants, Parkinson disease, Attention Deficit Hyperactivity Disorder (ADHD), Stress monitoring, or objective assessment or monitoring of the effect and efficacy of treatment or rehabilitation. Note that the HTL estimation can be used for the fitting of hearing ais or for tracking a hear loss characteristic. The BCI can be used as a user interface enabling conscious and unconscious control. In one example, using the embodiments herein as a fatigue monitor can enable a user to monitor neurological changes and can provide feedback in the form of a sound, haptic output, shock, etc. in order to stimulate or wake up someone who may be getting drowsy during a long drive. In another example, the monitoring device operating as a hypoglycemic monitor can monitor high or low blood sugar levels and provide an acoustic stimuli regarding such status to enable the user to take corrective action.

[0148] A person's body motion and head position can also be monitored by integrating a motion sensor into an Al device. Two such compact motion sensors include gyroscopes and accelerometers, typically mechanical or optical in origin. In some embodiments, an accelerometer may be composed of one or more microelectromechanical systems (MEMS) devices. In some embodiments, an accelerometer can measure acceleration or position in two or more axes. When the head is moved, a motion sensor detects the displaced motion from the origin. A head position monitor can be used to sense convulsions or seizures and relay this information wirelessly to a recording device. Similarly, head position monitoring may serve as a feedback mechanism for exercise and athletic training where head positioning with respect to the body is important. Additionally, the head position monitoring can be used to monitor when someone has fallen down or is not moving. The monitoring of body motion or head position can also be used to activate functions on the device 1 or other devices operational coupled to the device 1. Further note that the accelerometers or other motion detectors can also be used as part of a Voice Activity Detector or VAD. In some embodiments, detection of a fall can be based on the measurements made by an accelerometer (and the direction of the movement). Such a system could provide a user with access to emergency services and further provide feedback (acoustic) to the user.

[0149] Body temperature, including core and skin temperature, can be monitored in real-time by integrating compact infrared sensors into an Al device, according to some embodiments of the present invention. Infrared sensors are generally composed of thermoelectric / pyroelectric materials or semiconductor devices, such as photodiodes or photoconductors. Thermistors, thermocouples, and other temperature-dependent transducers can also be incorporated for monitoring body temperature. These sensors can be very compact and thus can be integrated throughout an Al device. In some embodiments, these sensors may be mounted along the backside of an Al device body where the Al device connects with the ear canal. Temperature sensors aimed at the tympanic membrane may be more accurate than sensors aimed in other directions. A combination of temperature sensors and use of temperature differentials can also provide more accurate readings and significant or useful data.

[0150] In some embodiments, a pedometer can be integrated into an Al device to measure the number of steps walked during a day. Pedometers that can be integrated into an Al device include, but are not limited to, mechanical pedometers (usually implementing a metallic ball or spring), microelectromechanical systems (MEMS) pedometers, inertial sensor pedometers, accelerometer-based pedometers, accelerometry, gyroscopic pedometers, and the like.

[0151] In some embodiments, a pedometer for an Al device employs an acoustic sensor for monitoring the characteristic sounds of footsteps channeled along the ear canal. For example, an acoustic sensor can be integrated into an Al device housing along the backside thereof and / or within an Al device fitting thereof. The sounds generated from footsteps can be detected and analyzed with a signal processor using a noise cancellation or signal extraction approach to identify footstep sounds in the midst ofconvoluting physiological noise. In this embodiment, digitized electrical signals from footstep sounds from outside the body are compared with digitized electrical signals from footstep sounds traveling through the body (and ear canal), and only the spectral features associated with both types of digitized signals are amplified. This provides a new signal that contains cleaner information about footsteps.

[0152] Breathing characteristics can also be monitored in a manner similar to that of acoustic pedometry (described above) via auscultatory signal extraction. In some embodiments, an acoustic sensor in an Al device is used to sense sounds associated with breathing. Signal processing algorithms are then used to extract breathing sounds from other sounds and noise. This information is processed into a breathing monitor, capable of monitoring, for example, the intensity, volume, and speed of breathing. Another method of monitoring breathing is to employ pressure transducers into an Al device. Changes in pressure inside or near the ear associated with breathing can be measured directly and, through signal processing, translated into a breathing monitor. Similarly, optical reflection sensors can be used to monitor pressure in or near the ear by monitoring physical changes in the skin or tissues in response to breathing. For monitoring the physical changes of the tympanic membrane in response to breathing, and hence ascertaining breathing rate, an optical signal extraction approach may be employed. At least one color sensor, or colorimetric sensor, can be employed to monitor changes in color associated with breathing and other health factors.

[0153] It should be noted that some embodiments of the present invention incorporate health sensors that do not employ chemical or biological reagents for monitoring various health factors. This is because such sensors have traditionally required larger instrumentation (not suitable for portability) and / or disposable samplers (not acceptable to most end users). However, sensors employing chemical or biological reagents may be incorporated into Al devices, according to some embodiments. For example, the diffusion of analyte through the skin can be monitored electrically or optically by selective binding to enzymes or antibodies contained in the health sensors integrated into an Al device. In some cases, iontophoresis, agitation, heat, or osmosis may be required to pull ions from the skin or blood into the sensor region for monitoring health factors. In some cases, these analytes may be tagged with markers for electromagnetic, electrical, nuclear, or magnetic detection.

[0154] Caloric intake, physical activity, and metabolism can be monitored using a core temperature sensor, an accelerometer, a sound extraction methodology, a pulse oximeter, a hydration sensor, and the like. These sensors can be used individually or in unison to assess overall caloric metabolism and physical activity for purposes such as diet monitoring, exercise monitoring, athletic training, and the like. For example, a sound extraction methodology can be used to extract sounds associated with swallowing, and this can give an indication of total food volume consumed. Additionally, a core temperature sensor, such as a thermopile, a pyroelectric sensor, a thermoelectric sensor, or a thermistor, or a tympanic membrane extraction technique, can be used to assess metabolism. In one case, the core temperature is compared withthe outdoor temperature, and an estimate of the heat loss from the body is made, which is related to metabolism.

[0155] Environmental temperature can be monitored, for example, by thermistor, thermocouple, diode junction drop reference, or the like. Electrical temperature measurement techniques are well known to those skilled in the art, and are of suitable size and power consumption that they can be integrated into a wireless Al device without significant impact on the size or functionality of the wireless Al device.

[0156] Environmental noise can be monitored, for example, by transducer, microphone, or the like. Monitoring of environmental noise preferably includes, but is not limited to, instantaneous intensity, spectral frequency, repetition frequency, peak intensity, commonly in units of decibels, and cumulative noise level exposures, commonly in units of decibel-hours. This environmental noise may or may not include noise generated by a person wearing an Al device. Sound made by a person wearing an Al device may be filtered out, for example, using analog or digital noise cancellation techniques, by directional microphone head shaping, or the like. The environmental noise sensor may or may not be the same sensor as that used for the intended purpose of wireless communication. In some embodiments, the environmental noise sensor is a separate sensor having broader audible detection range of noise level and frequency, at the possible sacrifice of audio quality.

[0157] Environmental smog includes VOC's, formaldehyde, alkenes, nitric oxide, PAH's, sulfur dioxide, carbon monoxide, olefins, aromatic compounds, xylene compounds, and the like. Monitoring of the aforementioned smog components can be performed using Al devices and other wearable apparatus, according to some embodiments of the present invention, and in a variety of methods. All smog components may be monitored. Alternatively, single smog components or combinations of smog components may be monitored. Photoionization detectors (PID's) may be used to provide continuous monitoring and instantaneous readings. Other methods of detecting smog components according to embodiments of the present invention include, but are not limited to, electrocatalytic, photocatalytic, photoelectrocatalytic, calorimefric, spectroscopic or chemical reaction methods. Examples of monitoring techniques using the aforementioned methods may include, but are not limited to, IR laser absorption spectroscopy, difference frequency generation laser spectroscopy, porous silicon optical microcavities, surface plasmon resonance, absorptive polymers, absorptive dielectrics, and calorimetric sensors. For example, absorptive polymer capacitors inductors, or other absorptive polymer-based electronics can be incorporated into an Al device (e.g., 5 or 5A, FIG. 2) according to embodiments. These polymers change size or elecfrical or optical properties in response to analyte(s) from the environment (such as those described above). The electrical signal from these absorptive polymer electronic sensors can be correlated with the type and intensity of environmental analyte. Other techniques or combinations of techniques may also be employed to monitor smog components. For example, a smog component may be monitored in addition to a reference, such as oxygen, nitrogen, hydrogen, or the like. Simultaneous monitoring of smogcomponents with a reference analyte of known concentration allows for calibration of the estimated concentration of the smog component with respect to the reference analyte within the vicinity of an Al device user.

[0158] In some embodiments, environmental air particles can be monitored with a flow cell and a particle counter, particle sizer, particle identifier, or other particulate matter sensor incorporated as part of an Al device or externally attached to an Al device. Non-limiting examples of particles include oil, metal shavings, dust, smoke, ash, mold, or other biological contaminates such as pollen. In some embodiments of the present invention, a sensor for monitoring particle size and concentration is an optical particle counter. A light source is used (e.g., a laser or a laser diode), to illuminate a stream of air flow. However, a directional LED beam, generated by a resonant cavity LED (RCLED), a specially lensed LED, or an intense LED point source, can also be used for particle detection. The optical detector which is off-axis from the light beam measures the amount of light scattered from a single particle by refraction and diffraction. Both the size and the number of particles can be measured at the same time. The size of the monitored particle is estimated by the intensity of the scattered light. Additionally, particles can be detected by ionization detection, as with a commercial ionization smoke detector. In this case, a low-level nuclear radiation source, such as americium-241, may be used to ionize particles in the air between two electrodes, and the total ionized charge is detected between the electrodes. As a further example, piezoelectric crystals and piezoelectric resonator devices can be used to monitor particles in that particles reaching the piezoelectric surface change the mass and hence frequency of electromechanical resonance, and this can be correlated with particle mass. If the resonators are coated with selective coatings, certain types of particles can attach preferentially to the resonator, facilitating the identification of certain types of particles in the air near a person wearing an Al device. In some embodiments, these resonators are solid state electrical devices, such as MEMS devices, thin film bulk acoustic resonators (FBARs), surface-acoustic wave (SAW) devices, or the like. These compact solid state components may be arrayed, each arrayed element having a different selective coating, for monitoring various types of particles. To the extent that the sensors can be mounted or embedded on or within an expandable element or balloon, the additional isolation provided by such balloon, particularly isolation within the EAC, only serves to provide for a more efficient and elegant solution to other solutions that include corrupted data or must be compensated for such corrupted data.

[0159] In some embodiments of the present invention, environmental air pressure or barometric pressure can be monitored by a barometer. Non-limiting examples of barometric pressure measurement include hydrostatic columns using mercury, water, or the like, foil-based or semiconductor-based strain gauge, pressure transducers, or the like. In some embodiments of the present invention, semiconductorbased strain gauges are utilized. A strain gauge may utilize a piezoresisti ve material that gives an electrical response that is indicative of the amount of deflection or strain due to atmospheric pressure. Atmospheric pressure shows a diurnal cycle caused by global atmospheric tides. Environmental atmospheric pressure isof interest for prediction of weather and climate changes. Environmental pressure may also be used in conjunction with other sensing elements, such as temperature and humidity to calculate other environmental factors, such as dew point, air pressure can also be measured by a compact MEMS device composed of a microscale diaphragm, where the diaphragm is displaced under differential pressure and this strain is monitored by the piezoelectric or piezoresisti ve effect.

[0160] In some embodiments of the present invention, environmental humidity, relative humidity, and dew point can be monitored by measuring capacitance, resistivity or thermal conductivity of materials exposed to the air, or by spectroscopy changes in the air itself. Resistive humidity sensors measure the change in electrical impedance of a hygroscopic medium such as a conductive polymer, salt, or treated substrate. Capacitive humidity sensors utilize incremental change in the dielectric constant of a dielectric, which is nearly directly proportional to the relative humidity of the surrounding environment. Thermal humidity sensors measure the absolute humidity by quantifying the difference between the thermal conductivity of dry air and that of air containing water vapor. Humidity data can be stored along with pressure monitor data, and a simple algorithm can be used to extrapolate the dew point. In some embodiments of the present invention, monitoring humidity is performed via spectroscopy. The absorption of light by water molecules in air is well known to those skilled in the art. The amount of absorption at known wavelengths is indicative of the humidity or relative humidity. Humidity may be monitored with a spectroscopic method that is compatible with the smog monitoring spectroscopic method described above.

[0161] When environmental factors such as the aforementioned are monitored continuously in realtime, a user's total exposure level to an environmental factor can be recorded. When a representative volume of air a user has been exposed to is monitored or estimated, the volumetric concentration of the analytes can be calculated or estimated. In order to estimate the volume of air a person wearing an Al device has been exposed to, a pedometer or accelerometer or air flow sensor can also be integrated into an Al device. Pedometers and accelerometers can be integrated into an Al device via mechanical sensors (usually implementing a mechanical-electrical switch), MEMS devices, and / or gyroscopic technologies. The technologies required for these types of pedometers and accelerators are well known to those skilled in the art. The incorporated pedometer or accelerometer (or more than one pedometer or accelerometer) is used to gage the distance a person has traveled, for use in the estimation of the volume of air to which a person has been exposed, and the subsequent estimate of the volumetric concentration of monitored analytes.

[0162] The health and environmental sensors utilized with Al devices and other wearable monitoring apparatus, according to embodiments of the present invention, can operate through a user-selectable switch on an Al device. However, health and environmental sensors can also be run automatically and independently of the person wearing the apparatus. In other embodiments, the person may control health and environmental monitoring through a device wirelessly coupled to an Al device, such as a portabletelecommunication device. For example, health and environmental sensors in or about an Al device can be controlled wirelessly through, for example, a cell phone, laptop, or personal digital assistant (PDA).

[0163] In some embodiments, the Al device can include a multicolor indicator such as a multicolor light emitting diode (LED) to provide status information. In some embodiments, a very small (e.g., ,5mm or even smaller) tri-color LED can be mounted in an end cap of the Al device. In some embodiments, the LED would only be "on" meaning a color light would appear when the Al device is out of the ear, such as on the charging station or while in your pocket. The tri-colors can indicate operational status of the device and provide information such status information involving battery charging, battery life, connectivity (e.g., WiFi or Bluetooth status), noise level, or intelligibility for example.

[0164] In some embodiments, the Al device can include biometric or physiological sensors for confirmation for identification, authentication, and / or secure payment transactions. The data gathered from the sensors can be used to identify an individual among an existing group of known or registered individuals. In some embodiments, the data can be used to authenticate an individual for additional functions such as granting additional access to information or enabling transactions or payments from an existing account associated with the individual or authorized for use by the individual.

[0165] The various sensors in the Al device can be used to build a profile of the user. Some sensor data may be more reliable and consistent than others, but generally one or more sensors for detecting motion, heart rate, voice, fingerprint, or even a tell-tale brainwave signal or profile that may be consistently and uniquely repeated in response to a particular stimulus can be used to confirm or authorize payment in a payment system.

[0166] In some embodiments, the Al device can include a fingerprint detector or a gesture detector that can detect a particular predetermined pattern. A fingerprint detector can be used on an external portion of an Al device to authenticate an individual and then authorize the individual to perform one or more functions of the AID 100, such as initiating a payment transaction via the payment system 9A, accessing privileged information stored in the database 7C, or enabling a restricted operational mode of the AID 100. In some embodiments, successful fingerprint authentication can be combined with one or more additional authentication signals, such as a voice recognition signal generated by the first microphone, a heart rate signature from physiological sensors 5, or a predetermined gesture detected by the gesture control circuitry 3B, to provide a multi-factor authentication scheme consistent with the robust biometric payment system described herein.

[0167] Various microphones can be used for example Menis Digital and Analog microphones, e.g. Knowles SiSonic Microphones, model SPH0641LM4H-1, model TO-30043-000 and other microphones that can be used in earphones or phones, Knowles model RAB-32063, model TWFK-30017-000 and other types of speakers. Varins processors and DSP can be used for example, and DSP PCB board (e.g., CSRchips, Wolfson chips, and any other DSP chip that can process audio input that can be used in earphones or phones).

[0168] An AID 100 can be housed in many devices for example devices that can be used in or around a user’s ear (e.g., ear wear). Some ear wear exemplary embodiments can be directed to or can be operatively used on various passive eartips for hearing protection or electronic wired or wireless Al device devices (e.g., hearing aids, ear monitors, headphones, ear terminal, behind the ear devices or other acoustic devices as known by one of ordinary skill, and equivalents). For example, the Al devices can have one or more transducers (e.g. ambient sound microphone (ASM), ear canal microphone (ECM), ear canal receiver (ECR / SPKR)) for monitoring / providing sound. In all the examples illustrated and discussed herein, any specific values should be interpreted to be illustrative only and non-limiting. Tims, other examples of the exemplary embodiments could have different values.

[0169] As shown in Figure 4, a system 400 and methods for utilizing AID systems (415) are disclosed. The system 400 may be configured to support, but is not limited to supporting, data and content services, audio processing applications and services, audio output and / or input applications and services, applications and services for transmitting and receiving audio content, authentication applications and services, computing applications and services, cloud computing services, internet services, satellite services, telephone services, software as a service (SaaS) applications, platform-as-a-service (PaaS) applications, gaming applications and services, social media applications and services, productivity applications and services, voice-over-internet protocol (VoIP) applications and services, speech-to-text translation applications and services, interactive voice applications and services, mobile applications and services, and any other computing applications and services. The system may include a first user 401, who may utilize a first user device 402 to access data, content, and applications, or to perform a variety of other tasks and functions. As an example, the first user 401 may utilize first user device 402 to access an application (e.g. a browser or a mobile application) executing on the first user device 402 that may be utilized to access web pages, data, and content associated with the system 400. In certain embodiments, the first user 401 may be any type of user that may potentially desire to listen to audio content, such as from, but not limited to, a music playlist accessible via the first user device 402, a telephone call that the first user 401 is participating in, audio content occurring in an environment in proximity to the first user 401, any other type of audio content, or a combination thereof. For example, the first user 401 may be an individual that may be participating in a telephone call with another user, such as second user 420.

[0170] The first user device 402 utilized by the first user 401 may include a memory 403 that includes instructions, and a processor 404 that executes the instructions from the memory 403 to perform the various operations that are performed by the first user device 402. In certain embodiments, the processor 404 may be hardware, software, or a combination thereof. The first user device 402 may also include an interface 405 (e.g., screen, monitor, graphical user interface, etc.) that may enable the first user 401 to interact withvarious applications executing on the first user device 402, to interact with various applications executing within the system 400, and to interact with the system 400 itself. In certain embodiments, the first user device 402 may include any number of transducers, such as, but not limited to, microphones, speakers, any type of audio-based transducer, any type of transducer, or a combination thereof. In certain embodiments, the first user device 402 may be a computer, a laptop, a tablet device, a phablet, a server, a mobile device, a smartphone, a smart watch, and / or any other type of computing device. Illustratively, the first user device 402 is shown as a mobile device in Figure 4. The first user device 402 may also include a global positioning system (GPS), which may include a GPS receiver and any other necessary components for enabling GPS functionality, accelerometers, gyroscopes, sensors, and any other componentry suitable for a mobile device.

[0171] In addition to using first user device 402, the first user 401 may also utilize and / or have access to a second user device 406 and a third user device 410. As with first user device 402, the first user 401 may utilize the second and third user devices 406, 410 to transmit signals to access various online services and content. The second user device 406 may include a memory 407 that includes instructions, and a processor 408 that executes the instructions from the memory 407 to perform the various operations that are performed by the second user device 406. In certain embodiments, the processor 408 may be hardware, software, or a combination thereof. The second user device 406 may also include an interface 409 that may enable the first user 401 to interact with various applications executing on the second user device 406 and to interact with the system 400. In certain embodiments, the second user device 406 may include any number of transducers, such as, but not limited to, microphones, speakers, any type of audio-based transducer, any type of transducer, or a combination thereof. In certain embodiments, the second user AID 406 may be and / or may include a computer, any type of sensor, a laptop, a set-top-box, a tablet device, a phablet, a server, a mobile device, a smartphone, a smart watch, and / or any other type of computing device.Illustratively, the second user device 402 is shown as a smart watch device in Figure 4.

[0172] The third user AID 410 may include a memory 411 that includes instructions, and a processor 412 that executes the instructions from the memory 411 to perform the various operations that are performed by tire third user AID 410. In certain embodiments, the processor 412 may be hardware, software, or a combination thereof. The third user AID 410 may also include an interface 413 that may enable the first user 401 to interact with various applications executing on the second user device 406 and to interact with the system 400. In certain embodiments, the third user AID 410 may include any number of transducers, such as, but not limited to, microphones, speakers, any type of audio-based transducer, any type of transducer, or a combination thereof. In certain embodiments, the third user AID 410 may be and / or may include a computer, any type of sensor, a laptop, a set-top-box, a tablet device, a phablet, a server, a mobile device, a smartphone, a smart watch, and / or any other type of computing device. Illustratively, the third user AID 410 is shown as a smart watch device in Figure 4.

[0173] The first, second, and / or third user AID 402, 406, 410 may belong to and / or form a communications network 416. In certain embodiments, the communications network 416 may be a local, mesh, or other network that facilitates communications among the first, second, and / or third user AID 402, 406, 410 and / or any other devices, programs, and / or networks of system 400 or outside system 400. In certain embodiments, the communications network 416 may be formed between the first, second, and third user AID 402, 406, 410 through the use of any type of wireless or other protocol and / or technology. For example, the first, second, and third user AID 402, 406, 410 may communicate with one another in the communications network 416, such as by utilizing Bluetooth Low Energy (BLE), classic Bluetooth, ZigBee, cellular, NFC, Wi-Fi, Z-Wave, ANT+, IEEE 802.15.4, IEEE 802.22, ISAlOOa, infrared, ISM band, RFID, UWB, Wireless HD, Wireless USB, any other protocol and / or wireless technology, satellite, fiber, or any combination thereof. Notably, the communications network 416 may be configured to communicatively link with and / or communicate with any other network of the system 400 and / or outside the system 400.

[0174] The system 400 may also include an AID 415, which the first user 401 may utilize to hear and / or audition audio content, transmit audio content, receive audio content, experience any type of content, process audio content, adjust audio content, store audio content, perform any type of operation with respect to audio content, or a combination thereof. The AID 415 may be an Al device, a hearing aid, an ear monitor, an ear terminal, a behind-the-ear device, any type of acoustic device, or a combination thereof. The AID 415 may include any type of component utilized for any type of Al device. In certain embodiments, the AID 415 may include any number of ambient sound microphones that may be configured to capture and / or measure ambient sounds and / or audio content occurring in an environment that the AID 415 is present in and / or is proximate to. In certain embodiments, the ambient sound microphones may be placed at a location or locations on the AID 415 that are conducive to capturing and measuring ambient sounds occurring in the environment. For example, the ambient sound microphones may be positioned in proximity to a distal end (e.g., the end of the AID 415 that is not inserted into the first user's 401 ear) of the AID 415 such that the ambient sound microphones are in an optimal position to capture ambient or other sounds occurring in the environment. In certain embodiments, the AID 415 may include any number of ear canal microphones, which may be configured to capture and / or measure sounds occurring in an ear canal of the first user 401 or other user wearing the AID 415. In certain embodiments, the ear canal microphones may be positioned in proximity to a proximal end (e.g. the end of the AID 415 that is inserted into the first user's 401 ear) of the AID 415 such that sounds occurring in the ear canal of the first user 401 may be captured more readily.

[0175] The AID system 415 may also include any number of transceivers, which may be configured transmit signals to and / or receive signals from any of the devices in the system 400. In certain embodiments, a transceiver of the AID system 415 may facilitate wireless connections and / or transmissionsbetween the AID system 415 and any device in the system 400, such as, but not limited to, the first user AID 402, the second user AID 406, the third user AID 410, the fourth user AID 421, the fifth user AID 425, the AID system 430, the servers 440, 445, 450, 460, and the database 455. The AID system 415 may also include any number of memories for storing content and / or instructions, processors that execute the instructions from the memories to perform the operations for the AID system 415, and / or any type integrated circuit for facilitating the operation of the AID system 415. In certain embodiments, the processors may comprise, hardware, software, or a combination of hardware and software. The AID 415 may also include one or more ear canal receivers, which may be speakers for outputting sound into the ear canal of the first user 401. The ear canal receivers may output sounds obtained via the ear canal microphones, ambient sound microphones, any of the devices in the system 400, from a storage device of the AID system 415, or any combination thereof.

[0176] The ear canal receivers, ear canal microphones, transceivers, memories, processors, integrated circuits, and / or ear canal receivers may be affixed to an electronics package that includes a flexible electronics board. The AID system 415 may include an electronics packaging housing that may house the ambient sound microphones, ear canal microphones, ear canal receivers (i.e. speakers), electronics supporting the functionality of the microphones and / or receivers, transceivers for receiving and / or transmitting signals, power sources (e.g. batteries and the like), any circuitry facilitating the operation of the AID system 415, or any combination thereof. The electronics package including the flexible electronics board may be housed within the electronics packaging housing to form an electronics packaging unit. The AID system 415 may further include an earphone housing, which may include receptacles, openings, and / or keyed recesses for connecting the earphone housing to the electonics packaging housing and / or the electronics package. For example, nozzles of the electronics packaging housing may be inserted into one or more keyed recesses of the earphone housing so as to connect and secure the earphone housing to the electronics packaging housing. When the earphone housing is connected to the electronics packaging housing, the combination of the earphone housing and the electronics packaging housing may form the AID system 415. The AID system 415 may further include a cap for securing the electronics packaging housing, the earphone housing, and the electronics package together to form the AID system 415.

[0177] In addition to the first user 401, the system 400 may include a second user 420, who may utilize a fourth user AID 421 to access data, content, and applications, or to perform a variety of other tasks and functions. Much like the first user 401 , the second user 420 may be may be any type of user that may potentially desire to listen to audio content, such as from, but not limited to, a storage device of the fourth user AID 421, a telephone call that the second user 420 is participating in, audio content occurring in an environment in proximity to the second user 420, any other type of audio content, or a combination thereof. For example, the second user 420 may be an individual that may be listening to songs stored in a playlist that resides on the fourth user AID 421. Also, much like the first user 401, the second user 420 may utilizefourth user AID 421 to access an application (e.g. a browser or a mobile application) executing on the fourth user AID 421 that may be utilized to access web pages, data, and content associated with the system 400. The fourth user AID 421 may include a memory 422 that includes instructions, and a processor 423 that executes the instructions from the memory 422 to perform the various operations that are performed by the fourth user AID 421. In certain embodiments, the processor 423 may be hardware, software, or a combination thereof. The fourth user AID 421 may also include an interface 424 (e.g., a screen, a monitor, a graphical user interface, etc.) that may enable the second user 420 to interact with various applications executing on the fourth user AID 421, to interact with various applications executing in the system 400, and to interact with the system 400. In certain embodiments, the fourth user AID 421 may include any number of transducers, such as, but not limited to, microphones, speakers, any type of audio-based transducer, any type of transducer, or a combination thereof. In certain embodiments, the fourth user AID 421 may be a computer, a laptop, a tablet device, a phablet, a server, a mobile device, a smartphone, a smart watch, and / or any other type of computing device. Illustratively, the fourth user AID 421 may be a computing device in Figure 4. The fourth user AID 421 may also include any of the componentry described for first user AID 402, the second user device 406, and / or the third user device 410. In certain embodiments, the fourth user device 421 may also include a global positioning system (GPS), which may include a GPS receiver and any other necessary components for enabling GPS functionality, accelerometers, gyroscopes, sensors, and any other componentry suitable for a computing device.

[0178] In addition to using fourth user device 421, the second user 420 may also utilize and / or have access to a fifth user AID 425. As with fourth user device 421, the second user 420 may utilize the fourth and fifth user devices 421, 425 to transmit signals to access various online services and content. The fifth user device 425 may include a memory 426 that includes instructions, and a processor 427 that executes the instructions from the memory 426 to perform the various operations that are performed by the fifth user AID 425. In certain embodiments, the processor 427 may be hardware, software, or a combination thereof. The fifth user AID 425 may also include an interface 428 that may enable the second user 420 to interact with various applications executing on the fifth user AID 425 and to interact with the system 400. In certain embodiments, the fifth user AID 425 may include any number of transducers, such as, but not limited to, microphones, speakers, any type of audio-based transducer, any type of transducer, or a combination thereof. In certain embodiments, the fifth user AID 425 may be and / or may include a computer, any type of sensor, a laptop, a set-top-box, a tablet device, a phablet, a server, a mobile device, a smartphone, a smart watch, and / or any other type of computing device. Illustratively, the fifth user AID 425 is shown as a tablet device in Figure 4.

[0179] The fourth and fifth user AIDs 421, 425 may belong to and / or form a communications network 431. In certain embodiments, the communications network 431 may be a local, mesh, or other network that facilitates communications between the fourth and fifth user AIDs 421, 425, and / or any other devices,programs, and / or networks of system 400 or outside system 400. In certain embodiments, the communications network 431 may be formed between the fourth and fifth user AIDs 421, 425 through the use of any type of wireless or other protocol and / or technology. For example, the fourth and fifth user AIDs 421, 425 may communicate with one another in the communications network 416, such as by utilizing BLE, classic Bluetooth, ZigBee, cellular, NFC, Wi-Fi, Z-Wave, ANT+, IEEE 802.15.4, IEEE 802.22, ISAlOOa, infrared, ISM band, RFID, UWB, Wireless HD, Wireless USB, any other protocol and / or wireless technology, satellite, fiber, or any combination thereof. Notably, the communications network 431 may be configured to communicatively link with and / or communicate with any other network of the system 400 and / or outside the system 400.

[0180] Much like first user 401, the second user 420 may have his or her own AID 430. The AID 430 may be utilized by the second user 420 to hear and / or audition audio content, transmit audio content, receive audio content, experience any type of content, process audio content, adjust audio content, store audio content, perform any type of operation with respect to audio content, or a combination thereof. The AID 430 may be an Al device, a hearing aid, an ear monitor, an ear terminal, a behind-the-ear device, any type of acoustic device, or a combination thereof. The AID 430 may include any type of component utilized for any type of Al device, and may include any of the features, functionality and / or components described and / or usable with AID 415. For example, AID 430 may include any number of transceivers, ear canal microphones, ambient sound microphones, processors, memories, housings, eartips, foam tips, flanges, any other component, or any combination thereof.

[0181] In certain embodiments, the first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AID systems 415, 430 may have any number of software applications and / or application services stored and / or accessible thereon. For example, the first and second user AIDs 402, 411 may include applications for processing audio content, applications for playing, editing, transmitting, and / or receiving audio content, streaming media applications, speech-to-text translation applications, cloud-based applications, search engine applications, natural language processing applications, database applications, algorithmic applications, phone-based applications, product-ordering applications, business applications, e-commerce applications, media streaming applications, content-based applications, database applications, gaming applications, internet-based applications, browser applications, mobile applications, service-based applications, productivity applications, video applications, music applications, social media applications, presentation applications, any other type of applications, any types of application services, or a combination thereof. In certain embodiments, the software applications and services may include one or more graphical user interfaces so as to enable the first and second users 401, 420 to readily interact with the software applications. The software applications and services may also be utilized by the first and second users 401, 420 to interact with any device in the system 400, any network in the system 400 (e.g. communications networks 416, 431, 435), or any combination thereof. For example, the software applications executing onthe first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AID systems 415, 430 may be applications for receiving data, applications for storing data, applications for auditioning, editing, storing and / or processing audio content, applications for receiving demographic and preference information, applications for transforming data, applications for executing mathematical algorithms, applications for generating and transmitting electronic messages, applications for generating and transmitting various types of content, any other type of applications, or a combination thereof. In certain embodiments, the first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AIDs 415, 430 may include associated telephone numbers, internet protocol addresses, device identities, or any other identifiers to uniquely identify the first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AIDs 415, 430 and / or the first and second users 401, 420. In certain embodiments, location information corresponding to the first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AIDs 415, 430 may be obtained based on the internet protocol addresses, by receiving a signal from the first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AIDs 415, 430 or based on profile information corresponding to the first, second, third, fourth, and / or fifth user AIDs 402, 406, 410, 421, 425 and / or AID systems 415, 430.

[0182] The system 400 may also include a communications network 435. The communications network 435 may be under the control of a service provider, the first and / or second users 401, 420, any other designated user, or a combination thereof. The communications network 435 of the system 400 may be configured to link each of the devices in the system 400 to one another. For example, the communications network 435 may be utilized by the first user AID 402 to connect with other devices within or outside communications network 435. Additionally, the communications network 435 may be configured to transmit, generate, and receive any information and data traversing the system 400. In certain embodiments, the communications network 435 may include any number of servers, databases, or other componentry. The communications network 435 may also include and be connected to a mesh network, a local network, a cloud-computing network, an IMS network, a VoIP network, a security network, a VoLTE network, a wireless network, an Ethernet network, a satellite network, a broadband network, a cellular network, a private network, a cable network, the Internet, an internet protocol network, MPLS network, a content distribution network, any network, or any combination thereof. Illustratively, servers 440, 445, and 450 are shown as being included within communications network 435. In certain embodiments, the communications network 435 may be part of a single autonomous system that is located in a particular geographic region or be part of multiple autonomous systems that span several geographic regions.

[0183] Notably, the functionality of the system 400 may be supported and executed by using any combination of the servers 440, 445, 450, and 460. The servers 440, 445, and 450 may reside in communications network 435, however, in certain embodiments, the servers 440, 445, 450 may reside outside communications network 435. The servers 440, 445, and 450 may provide and serve as a serverservice that performs the various operations and functions provided by the system 400. In certain embodiments, the server 440 may include a memory 441 that includes instructions, and a processor 442 that executes the instructions from the memory 441 to perform various operations that are performed by the server 440. The processor 442 may be hardware, software, or a combination thereof. Similarly, the server 445 may include a memory 446 that includes instructions, and a processor 447 that executes the instructions from the memory 446 to perform the various operations that are performed by the server 445. Furthermore, the server 450 may include a memory 451 that includes instructions, and a processor 452 that executes the instructions from the memory 451 to perform the various operations that are performed by the server 450. The server 460 may include a memory 461 that includes instructions, and a processor 462 that executes the instructions from the memory 461 to perform various operations that are performed by the server 460. including coordinating communications between the AIDs 415, 430 and other devices in the system 400. In certain embodiments, the servers 440, 445, 450, and 460 may be network servers, routers, gateways, switches, media distribution hubs, signal transfer points, service control points, service switching points, firewalls, routers, edge devices, nodes, computers, mobile devices, or any other suitable computing device, or any combination thereof. In certain embodiments, the servers 440, 445, 450 may be communicatively linked to the communications network 435, the communications network 416, the communications network 431, any network, any device in the system 400, any program in the system 400, or any combination thereof.

[0184] The database 455 of the system 400 may be utilized to store and relay information that traverses the system 400, cache content that traverses the system 400, store data about each of the devices in the system 400 and perform any other typical functions of a database. In certain embodiments, the database 455 may be connected to or reside within the communications network 435, the communications network 416, the communications network 431, any other network, or a combination thereof. In certain embodiments, the database 455 may serve as a central repository for any information associated with any of the devices and information associated with the system 400. Furthermore, the database 455 may include a processor and memory or be connected to a processor and memory to perform the various operation associated with the database 455. In certain embodiments, the database 455 may be connected to the AIDs 415, 430, the servers 440, 445, 450, 460, the first user AID 402, the second user AID 406, the third user AID 410, the fourth user device 4 1, the fifth user AID 425, any devices in the system 400, any other device, any network, or any combination thereof.

[0185] The database 455 may also store information and metadata obtained from the system 400, store metadata and other information associated with the first and second users 401, 420, store user profiles associated with the first and second users 401, 420, store device profiles associated with any device in the system 400, store communications traversing the system 400, store user preferences, store information associated with any device or signal in the system 400, store information relating to patterns of usagerelating to the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425, store audio content associated with the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or AIDs 415, 430, store audio content and / or information associated with the audio content that is captured by the ambient sound microphones, store audio content and / or information associated with audio content that is captured by ear canal microphones, store any information obtained from any of the networks in the system 400, store audio content and / or information associated with audio content that is outputted by ear canal receivers of the system 400, store any information and / or signals transmitted and / or received by transceivers of the system 400, store any device and / or capability specifications relating to the AIDs 415, 430, store historical data associated with the first and second users 401, 415, store information relating to the size (e.g. depth, height, width, curvatures, etc.) and / or shape of the first and / or second user's 401, 420 ear canals and / or ears, store information identifying and or describing any eartip utilized with the AIDs 401, 415, store device characteristics for any of the devices in the system 400, store information relating to any devices associated with the first and second users 401, 420, store any information associated with the AIDs 415, 430, store log on sequences and / or authentication information for accessing any of the devices of the system 400, store information associated with the communications networks 416, 431, store any information generated and / or processed by the system 400, store any of the information disclosed for any of the operations and functions disclosed for the system 400 herewith, store any information traversing the system 400, or any combination thereof. Furthermore, the database 455 may be configured to process queries sent to it by any device in the system 400.

[0186] The system 400 may also include a software application, which may be configured to perform and support the operative functions of the system 400, such as the operative functions of the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or the AIDs 415, 430. In certain embodiments, the application may be a website, a mobile application, a software application, or a combination thereof, which may be made accessible to users utilizing one or more computing devices, such as the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or the AIDs 415, 430. The application of the system 400 may be accessible via an internet connection established with a browser program or other application executing on the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or the AIDs 415, 430, a mobile application executing on the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or the AIDs 415, 430, or through other suitable means.Additionally, the application may allow users and computing devices to create accounts with the application and sign-in to the created accounts with authenticating username and password log-in combinations. The application may include a custom graphical user interface that the first user 401 or second user 420 may interact with by utilizing a browser executing on the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or the AIDs 415, 430. In certain embodiments, the software application mayexecute directly as an installed program on the first, second, third, fourth, and fifth user AIDs 402, 406, 410, 421, 425 and / or the AIDs 415, 430.Computing System for Facilitating the Operation and Functionality of the System

[0187] Referring now also to Figure 5, at least a portion of the methodologies and techniques described with respect to the exemplary embodiments of the AID system 400 can incorporate a machine, such as, but not limited to, computer system 500, or other computing device within which a set of instructions, when executed, may cause the machine to perform any one or more of the methodologies or functions discussed above. The machine may be configured to facilitate various operations conducted by the system 400. For example, the machine may be configured to, but is not limited to, assist the system 400 by providing processing power to assist with processing loads experienced in the system 400, by providing storage capacity for storing instructions or data traversing the system 400, by providing functionality and / or programs for facilitating the operative functionality of the AIDs 415, 430, and / or the first, second, third, fourth, and fifth user devices 402, 406, 410, 421, 425 and / or the AIDs 415, 430, by providing functionality and / or programs for facilitating operation of any of the components of the AIDs 415, 430 (e.g. ear canal receivers, transceivers, ear canal microphones, ambient sound microphones, or by assisting with any other operations conducted by or within the system 400.

[0188] In some embodiments, the machine may operate as a standalone device. In some embodiments, the machine may be connected (e.g., using communications network 435, the communications network 416, the communications network 431, another network, or a combination thereof) to and assist with operations performed by other machines and systems, such as, but not limited to, the first user device 402, the second user device 411, the third user device 410, the fourth user device 421, the fifth user device 425, the AID 415, the AID 430, the server 440, the server 450, the database 455, the server 460, or any combination thereof. The machine may be connected with any component in the system 400. In a networked deployment, the machine may operate in the capacity of a server or a client user machine in a server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet PC, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0189] The computer system 500 may include a processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)), a main memory 504 and a static memory 506, which communicate with each other via a bus 508. The computer system 500 may further include a video display unit 510, whichmay be, but is not limited to, a liquid crystal display (LCD), a flat panel, a solid state display, or a cathode ray tube (CRT). The computer system 500 may include an input device 512, such as, but not limited to, a keyboard, a cursor control device 514, such as, but not limited to, a mouse, a disk drive unit 516, a signal generation device 518, such as, but not limited to, a speaker or remote control . and a network interface device 520.

[0190] The disk drive unit 516 may include a machine-readable medium 522 on which is stored one or more sets of instructions 524, such as, but not limited to, software embodying any one or more of the methodologies or functions described herein, including those methods illustrated above. The instructions 524 may also reside, completely or at least partially, within the main memory 504, the static memory 506, or within the processor 502, or a combination thereof, during execution thereof by the computer system 500. The main memory 504 and the processor 502 also may constitute machine-readable media.

[0191] Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays and other hardware devices can likewise be constructed to implement the methods described herein. Applications that may include the apparatus and systems of various embodiments broadly include a variety of electronic and computer systems. Some embodiments implement functions in two or more specific interconnected hardware components or devices with related control and data signals communicated between and through the components, or as portions of an application-specific integrated circuit. Thus, the example system is applicable to software, firmware, and hardware implementations.

[0192] In accordance with various embodiments of the present disclosure, the methods described herein are intended for operation as software programs running on a computer processor. Furthermore, software implementations can include, but not limited to, distributed processing or component / object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.

[0193] The present disclosure contemplates a machine-readable medium 522 containing instructions 524 so that a device connected to the communications network 435, the communications network 416, the communications network 431, another network, or a combination thereof, can send or receive voice, video or data, and communicate over the communications network 435, the communications network 416, the communications network 431, another network, or a combination thereof, using the instructions. The instructions 524 may further be transmitted or received over the communications network 435, another network, or a combination thereof, via the network interface device 520.

[0194] While the machine-readable medium 522 is shown in an example embodiment to be a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one ormore sets of instructions. The term "machine-readable medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the present disclosure.

[0195] FIG. 6 illustrates an outline of tire parts of an AI-Device (AID) 100 system. In a non-limiting exemplary embodiment a user interacts verbally with the AID 100, the interaction picked up by microphones 630A to 630Z which generate microphone signals. A user can also use alternative user interface systems (e.g., touch screen). The microphone signals are analyzed by the voice recognition routine 650 to condition the microphone signals (conditioned acoustic signals) to identify voice commands, words, sentences. The conditioned acoustic signals and / or other sensor signals are interpreted by the Al software system 11 A, which can use or save information from or to a database 600 or other data structure, where a response and / or interaction with the user (output signal) can be sent to speakers (640A to 640Z) or to other user interfaces (e.g. touchscreen). The Al software system HA can include its own processor or / and interact with a separate processor 610. The Al software system 11 A can also be purely software comprising executable instructions and Al decision matrices. The Al software system 11 A can optionally interact with a remote server 660 via a transceiver 620, however the Al software system 11 A should include at least a limited local (no remote sever) capability and at least one ANN implemented through Al decision matrices used by its own processor. The voice recognition routine 650 can utilize microphone data as well as other sensor data to detect, interpret, determine voice command and enact a response. Examples of voice recognition routines can be found U.S. Patent No. 10631087, the contents of which are incorporated by reference, U.S. Patent No. 11570393, U.S. Patent No. 10515641, U.S. Patent No. 11323771 and U.S. Patent No. 12014117. The Al software system HA can include any of the pails shown in device 1 , as discussed above with regards to Figures 1 and 2. The Al software system 11 A must include executable instructions that access at least one ANN (e.g., 1200, 1300, 1400, 1500, 1600, 1700, 1800), for example as discussed with regards to Figures 12, 13, 14, 15, 16, 17 and 18.

[0196] The voice recognition routine 650 operates as a voice recognition processor comprising signal conditioning circuitry and speech recognition algorithms embodied as executable instructions. The signal conditioning circuitry receives the raw microphone signals from microphones 630A-630Z and applies noise reduction filtering, gain normalization, and feature extraction to generate conditioned acoustic signals suitable for speech recognition processing. The speech recognition algorithms, embodied as executable instructions executed by a processor, can include acoustic model processing using hidden Markov models or neural network classifiers trained on speech patterns, language model processing to identify word sequences, and command recognition logic to map identified words and sentences to predefined voice commands stored in the database 600. The voice recognition processor can operate in parallel with other sensor processing, enabling multimodal input interpretation wherein acoustic commands are processed concurrently with optical or other sensor inputs.

[0197] FIG. 7 illustrates an outline of the Al software system 11 A used in an AID 100 system. The Al software system 11 A comprises executable instructions and a plurality of Al decision matrices. First data is acquired from microphones 630A to acquire acoustic sensor data 705. Then optional cameras 770A to 770Z generate optical sensor data 715 that can also optionally be input into the Al software system 11A. Additionally other sensors can generate miscellaneous sensor data 725 that can be used by the Al software system 11 A. Each input mode (acoustic, optical, misc. sensor) has been used previously during training to derive the respective decision matrices (e.g., neural net weight values) 700, 710, and 720. Each of these decision matrices are data structures containing trained neural network weight values, which can also include a computed weighted combination of the neural network weight values, set to act upon inputs, such as derived words (acoustic), visual images (optical), and sensor data (such as infrared to determine hand positions) to determine responses to the inputs. The results from the decision matrices are sent / used by the Al analysis routine 760 which can generate a result, which analyzes the decision matrix results and sends the information to the Al response generation routine 750 which can generate a result, which then constructs a response which is sent to the speakers 640A to 640Z or to displays or some other type of user interface. There are many types of camera devices can be used for example, Apple iPhone 15 Pro Camera System, Samsung galaxy S24 Ultra Camera System, Sony IMX989, Sony IMX766, ISOCELL HP2, LYT-901, Sony IMX462, OmniVision OV9281, Sony IMX487, Hamamatsu S 13371 Series, Vishay VEML6075, MicaSense RedEdge-MX, Parrot Sequoia, IMEC Snapscan VNIR, Sony IMX990, and the like as known by one of ordinary skill in the design and fabrication of wearable cameras.

[0198] In further detail, the Al analysis routine 760 operates as an analysis processor comprising executable instructions that, when executed by the processor, receive the outputs from the Al decision matrices 700, 710, and 720 and apply a multimodal fusion algorithm to generate the Al analysis result. The multimodal fusion algorithm can include weighted combination of the decision matrix outputs, wherein the weights are determined during the training process described with respect to FIG. 11. For acoustic inputs processed through decision matrices 700, the analysis processor can apply a confidence scoring algorithm that compares the decision matrix output against stored patterns to determine a confidence level. When the confidence level exceeds a predetermined threshold (e.g., 70%, 80%, or 90% depending on the use case), the analysis processor generates the Al analysis result by selecting the highest-confidence interpretation. When multiple input modalities are available (e.g., acoustic from decision matrices 700 and optical from decision matrices 710), the analysis processor can apply cross-modal verification, wherein the acoustic interpretation is confirmed or refined based on the optical interpretation, improving overall accuracy.

[0199] The Al response generation routine 750 operates as a response generator comprising executable instructions that, when executed by the processor, receive the Al analysis result from the analysis processor and construct an output signal for delivery to the user. The response generator queries the database 600 using the Al analysis result as a query key, retrieving response templates, vocabulary items,and contextual information appropriate for the identified user input. For use cases requiring natural language responses, the response generator can apply a template-based response generation algorithm, wherein the retrieved template is populated with context-specific information derived from the Al analysis result. For use cases requiring visual responses, the response generator can retrieve image data or instructional diagrams from the database 600 and format them for display on the touch screen 110. The response generator can also apply language selection logic, wherein the language of the response is matched to the language detected by the Al analysis routine 760 as described herein.

[0200] In some embodiments, the Al response generation routine 750 can itself incorporate one or more artificial neural networks (ANNs) to improve the quality, contextual relevance, and adaptability of generated responses beyond what is achievable through template lookup alone. In such embodiments, the Al response generation routine 750 can include a response generation ANN having an input layer, one or more hidden neuron layers, and an output layer, wherein the input layer receives as input the Al analysis result generated by the Al analysis routine 760 together with contextual data retrieved from the database 600, such as prior interaction history, user preference data, and use-case-specific vocabulary. The output layer of the response generation ANN generates a response vector that is used to construct the output signal delivered to the user via the speakers 640A-640Z, the touch screen 110, or another user interface. The response generation ANN can be implemented using any of the ANN architectures described with respect to FIGs. 12-18, including the generic ANN 1200, the convolutional ANN 1400, and the deep neural network 1800.

[0201] In some embodiments, the response generation ANN can include an artificial neuron layer in which at least one neuron's value depends partially upon the values of other neurons within tire same layer (interneuron dependency), consistent with the architectures described with respect to FIGs. 15 and 16. For example, in a natural language response generation context, the value of a response neuron corresponding to a particular word or phrase selection can depend not only on the Al analysis result inputs but also on the values of neighboring neurons in the same hidden layer corresponding to adjacent words or phrases, enabling the response generation ANN to account for grammatical coherence and contextual consistency across the generated response. This interneuron dependency within the response generation ANN can be expressed analogously to the formulation of Equations (5) through (8) described herein, wherein the weight values W connecting neurons within a response generation hidden layer are derived through the same training process described with respect to FIG. 11 , using training data pairs comprising known Al analysis results and their corresponding correct or preferred response outputs.

[0202] In some embodiments, the response generation ANN can include a first artificial neuron layer whose neurons depend partially upon values of groups of neurons in a second artificial neuron layer, consistent with the ring-type ANN architecture described with respect to FIG. 17. In a response generation context, this grouped-layer dependency enables the response generation ANN to incorporate higher-orderrelationships among response candidates, for example recognizing that a response phrase appropriate in a first context is inappropriate in a related but distinct context, by weighting groups of candidate neurons in a prior layer rather than individual neurons. The specific configuration of interneuron dependencies in the response generation ANN, including which neurons within a hidden layer depend on which other neurons and the associated weight values, is derived during the training process described with respect to FIG. 11 using training data specific to the defined use case of the AID 100.

[0203] In some embodiments, the response generation ANN and the Al decision matrices 700, 710, 720 are trained together as an end-to-end system, wherein the training process described with respect to FIG. 11 is extended such that the temporary matrices 1100, 1110, 1120 and a temporary response generation ANN are jointly adjusted until the combined end-to-end output, from raw sensor input through analysis to final response, matches the preferred response output within a predetermined accuracy threshold. In other embodiments, the response generation ANN is trained separately using training data pairs comprising Al analysis results generated by the trained decision matrices and corresponding preferred response outputs, allowing the response generation ANN to be updated or replaced independently of the Al decision matrices 700, 710, and 720. The response generation ANN weight values, once trained, are stored in the first memory together with the Al decision matrices 700, 710, and 720 as part of the Al software system 11 A, consistent with the self-contained operation described herein.

[0204] FIG. 8A, FIG. 8B, FIG. 8C, FIG. 8D, FIG. 8E, and FIG. 8F are directed various non-limiting examples of uses of the AID system. For example, constructing furniture (FIG. 8A, 8B), car mechanics (FIG. 8C, 8D), cooking (FIG. 8E), and survival skills (FIG. 8F). One non-limiting example of an AID 100 developed for a specific usage is for mechanical assembly. For example if a user orders furniture (Figs. 8A and 8B) to be delivered that requires assembly, an AID 100 can accompany the box. The AID 100 can be activated by the user, for example by pressing an on button. The AID 100 can then activate whereupon the user can then start interacting with the AID 100. The AID can use it’s display to walk the user through the steps of assembly and vocalize the steps and any questions as if there were an expert to which the user can ask any questions related to the function of assembly. An AID 100 can be associated with any function, for example if a person is working along a pipeline, a carried AID 100 can respond to questions from the person regarding aspects of pipeline maintenance and repair. The AID 100 can respond to the persons request for equipment , for example by sending a request to a repair shop to send someone with the required parts. The person can also position the AID 100 camera to parts of the pipeline so that the AID can analyze the problem and suggest solutions to the person. Additional non-limiting use cases are shown in FIGs. 8C, 8D, 8E and 8F. For example FIGs 8C and 8D illustrate using AID 100 for car repair, where each AID 100 can be trained for specific models. Hie AID 100 can be modified to easily fit where a user can place it so that it can view what the user is doing, for example magnetic base with the AID 100 magnetically attached to some metal part, or grips so that the AID 100 can be attached to some structure nearby. FIG. 8E showsthe use of an AID 100 that was ordered to help a user cook. For example a chef might ask which herbs could make a sauce more tart and what should be the amount to add. Another example would be if a domestic partner wished to cook cookies, a bakery AID 100 can interact with the domestic partner either conversationally to cook the cookies and the AID 100 can be left to monitor the cooking. Another example is an AID 100 designed to accompany a survival kit as shown in FIG. 8F. The survival AID 100 can instruct the user how to make a fire, can monitor the environment when the user is sleeping, view plants to determine which are edible, instruct the user how to make shelters, clean water and any other survival related instruction, including viewing the stars and providing navigation. Other use cases can utilized specialized AID 100 and the discussion herein are not intended to be limitative.

[0205] FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, FIG. 9E, and FIG. 9F are directed various non-limiting examples of uses of the AID 100 system. For example, using a bank ATM (FIG. 9A). In this use case a bank customer can interact with e AID 100 designed to interact with bank customers, for example questions on deposits, moving a customer’s money between accounts, after identification verification. In sensitive bank transactions the AID 100 can monitor the environment and determine if the bank customer is being coerced, and in such a case notify authorities. Another example is a ticket counter (FIG. 9B). The user can request verbally which tickets they want from the AID 100 and the details such as seating. The AID 100 can be designed to recognize various languages and respond accordingly. Another non-limiting use case is an AID 100 designed for a museum (FIG. 9C). For example an AID 100 in a museum can explain a certain exhibits to visitors. The AID 100 can listen to the visitors, determine the language used and respond in kind. In another use case an AID 100 can be designed to act as an emergency pilot providing instructions to someone and even take semi contol of emergency piloting aiding a user similar to a co-pilot. Further use cases are shown in FIG. 9E and FIG. 9F. For example in FIG. 9E and emergency AID 100 can instruct a user on proper CPR, can be used to diagnose a problem and provide possible emergency solutions, can answer basic medical facts and other emergency procedures such as applying emergency medicines and where to apply. A last non-limiting AID 100 use case shown (FIG. 9F) is developed to provide farming advice and analysis. For example a user can show the plant to the AID 100 and it can determine if it is suffering from some issue, such as lack of water, pests and suggest solutions. It can also provide advice on planting rimes, methods of planting and other farming type issues.

[0206] In embodiments supporting multiple languages, the AID 100 can process user input signals in various languages and respond in kind without requiring remote translation services. The voice recognition routine 650 can include language detection algorithms within the Al analysis routine 760 that identify the language spoken by the user based on acoustic characteristics processed through the acoustic decision matrices 700. Once a language is identified, the Al response generation routine 750 can generate responses using language-specific response templates and vocabulary stored in database 600. The AID 100 can support seamless language switching, such that a first user speaking English receives responses in English,while a subsequent user speaking Spanish receives responses in Spanish. In some embodiments, the AID 100 can support mixed-language interactions where the user may switch between languages during a single session, with the Al analysis routine 760 continuously monitoring for language changes and adapting responses accordingly. For use cases such as the museum guide (FIG. 9C) or ticket counter (FIG. 9B), multilingual capability enables a single AID 100 to serve diverse user populations.

[0207] In various embodiments, the AID 100 can coordinate with one or more additional AIDs or external systems to provide enhanced functionality. For example, in a manufacturing facility, multiple industrial maintenance AIDs can share diagnostic data through wireless communication systems 6A-6C to identify patterns indicating systemic equipment issues affecting multiple machines. In a retail environment, multiple retail AIDs stationed at different locations can share inventory status information stored in their respective databases 600 to provide customers with accurate product availability across a store. The coordination protocol can operate locally within a defined network perimeter without requiring remote server access, wherein each AID 100 maintains its self-contained Al processing capability while optionally sharing specific data types with peer devices. Coordination messages can be encrypted using keys stored in the respective AIDs to prevent unauthorized interception or injection of false data.

[0208] In accordance with at least one embodiment, an AID 100 can be configured for educational tutoring applications. For example, a mathematics tutorial AID 100 can be included with educational materials purchased by a student or educational institution. The AID 100 can be trained on specific curriculum content, such as algebra, geometry, or calculus, using the training methodology described with respect to FIG. 10 and FIG. 11. The educational AID 100 can receive spoken questions from the student via microphones 630A-630Z, process the acoustic sensor data 705 through the Al decision matrices 700, and generate step-by-step explanations delivered through speakers 640A-640Z. The camera 770A can capture images of handwritten work shown by the student, with the optical sensor data 715 processed through decision matrices 710 to identify errors and suggest corrections. The Al analysis routine 760 can track the student’s progress through a curriculum database 600, identifying areas requiring additional practice. The limited scope of the educational AID, focused on a specific subject or grade level, allows the Al software system to be housed locally without requiring remote server access.

[0209] In at least one embodiment, an AID 100 can accompany a home appliance, such as a washing machine, dishwasher, or HVAC system, to assist with installation and ongoing troubleshooting. The appliance manufacturer can include the AID 100 in the product packaging, wherein the AID 100 is trained specifically for that appliance model. During installation, the user can activate the AID 100 by pressing an activation button, whereupon the AID 100 can guide the user through connection procedures including water line hookups, electrical connections, and ventilation requirements. The user can position the AID 100 camera to show installation progress, and the Al analysis routine 760 can determine whether connections are properly made using the optical decision matrices 710. For ongoing troubleshooting, the AID 100 caninterpret user descriptions of problems (e.g., “it’s making a grinding noise”) through acoustic analysis 705 and suggest diagnostic steps. The miscellaneous sensor data 725 can include accelerometer readings to detect abnormal vibrations when the AID 100 is placed on the appliance. Error codes displayed on the appliance can be shown to the AID 100 camera for interpretation and guidance.

[0210] In accordance with at least one embodiment, an AID 100 can be configured for industrial equipment maintenance, such as maintenance of CNC machines, conveyor systems, or manufacturing robots. The AID 100 can be deployed with specific equipment or carried by maintenance personnel. The AID 100 can include specialized sensors 5 for detecting environmental conditions relevant to equipment health, such as temperature sensors, vibration sensors, and acoustic sensors for detecting anomalous sounds indicative of bearing wear or motor issues. Tire Al analysis routine 760 can correlate multiple sensor inputs using multimodal processing to identify potential failures before they occur. The maintenance personnel can verbally describe symptoms or show specific components to the AID 100 camera, and the Al response generation routine 750 can provide step-by-step maintenance procedures, required replacement parts, and safety warnings. The AID 100 can also request additional information from the user, such as “Please show me the serial number plate” or “Please describe the sound you heard when the error occurred.” The limited scope of the industrial maintenance AID, focused on specific equipment types, enables local operation without remote server access.

[0211] In at least one embodiment, an AID 100 can be configured for retail point-of-sale environments to assist customers with product selection, price inquiries, and checkout procedures. The retail AID 100 can be stationed at product displays or checkout areas. The AID 100 can include multilingual voice recognition capabilities, wherein the voice recognition routine 650 detects the language spoken by the customer and responds accordingly. For example, a customer speaking Spanish can receive product information in Spanish, while a subsequent customer speaking Mandarin can receive information in Mandarin, all without requiring remote server translation services. The camera 770A can scan product barcodes or recognize products visually through the optical decision matrices 710, providing pricing, availability, and compatibility information. Payment systems 9 A integrated with the AID 100 can process transactions using NFC, Bluetooth, or other wireless communication systems 6A-6C.

[0212] In accordance with at least one embodiment, an AID 100 can be adapted for animal applications, such as pet care and training assistance. The pet care AID 100 can be trained on specific animal species and breeds to provide owners with guidance on feeding schedules, health monitoring, and behavioral training. The AID 100 can be attached to a pet’s living area, such as a kennel or stable, or worn by the pet owner during training sessions. The Al analysis routine 760 can process acoustic sensor data 705 to interpret animal vocalizations and environmental sounds, determining whether an animal is in distress, hungry, or exhibiting specific behaviors. Over time, the AID 100 can learn particular animal mannerisms and communicate health status to the owner. For training applications, the AID 100 can provide real-timefeedback to the owner on training techniques, correct timing for commands, and recognition of successful behavioral responses. The miscellaneous sensor data 725 from motion sensors can detect the animal’s movements and posture during training exercises.

[0213] In at least one embodiment, a healthcare provider can provide a patient with an AID 100 configured for elderly care and medication management. The AID 100 can be trained on a specific patient’s medication schedule, health monitoring requirements, and emergency protocols. The AID 100 can remind the patient when to take medication via acoustic output through speakers 640A-640Z, and can use optical sensor data 715 from camera 770A to verify that the correct medication is being taken. Physiological sensors 5 can monitor vital signs such as heart rate, blood pressure, and temperature, with the Al analysis routine 760 analyzing the sensor data for health anomalies. In emergency situations, such as detection of a fall through accelerometer data or detection of stroke indicators through voice analysis and physiological monitoring, the AID 100 can automatically initiate emergency protocols, including contacting emergency services through wireless communication systems 6A-6C. The AID 100 can also provide voice-guided CPR instructions in accordance with the emergency medical use case illustrated in FIG. 9E.

[0214] In accordance with at least one embodiment, an AID 100 can be configured for construction site safety monitoring and guidance. The construction safety AID 100 can be carried by workers or stationed at hazardous work areas to provide real-time safety information and equipment operation guidance. Environmental sensors 5 A can monitor air quality including VOCs, dust particles, and toxic gases, alerting workers to hazardous conditions through acoustic warnings. The Al analysis routine 760 can process multiple environmental sensor inputs to assess overall safety conditions and recommend appropriate personal protective equipment. The AID 100 can provide voice-guided instruction for operating heavy equipment, similar to the car repair use case illustrated in FIGS. 8C and 8D, wherein workers can ask questions about equipment operation and show the AID 100 camera specific components for identification and guidance. The limited scope of the construction safety AID, focused on specific equipment types or site conditions, enables local operation without remote server access.

[0215] In at least one embodiment, an AID 100 can be configured for language learning and real-time translation assistance. The language learning AID 100 can be designed for travelers or students learning a specific foreign language. The voice recognition routine 650 can capture spoken phrases in one language, process them through acoustic decision matrices 700 trained for language recognition, and provide translations, pronunciation corrections, and contextual usage examples through speakers 640A-640Z. The Al analysis routine 760 can adapt to the user’s proficiency level, providing simplified explanations for beginners or advanced grammatical corrections for more experienced users. The touch screen 110 can display written translations and visual aids, while the camera 770A can capture text from signs, menus, or documents for optical character recognition and translation using the optical decision matrices 710. Theself-contained nature of the language AID 100 enables operation in areas without internet connectivity, such as remote travel destinations.

[0216] In one non-limiting embodiment, an AID 100 is configured and trained for use in a medical adherence and patient monitoring context. For example, a physician may provide a patient with an AID 100 that has been trained specifically to manage and monitor a defined therapeutic regimen for a particular patient or padent class (e.g., post-operative cardiac patients, diabetic patients). Upon activation, the AID 100 can interact with the patient vocally, remind the patient when to take specific medications, and confirm via a voice response or touchscreen input 110 that the medication has been taken. The AID 100 can further utilize one or more physiological sensors 5 operatively connected to the processor 4, such as a pulse oximeter, ECG sensor, galvanic skin response sensor, or blood pressure sensor (e.g., PPG-based), to monitor the patient’s vitals in real time and compare the acquired sensor data against baseline values stored in the database 7C. Where monitored values deviate from a predetermined threshold, the Al analysis routine 760 can flag the deviation and the Al response generation routine 750 can instruct the AID 100 to alert the patient, a designated caregiver, or an emergency contact via the wireless communication system 6A (e.g., Bluetooth, cellular). In embodiments where the AID 100 operates without contact with a remote server, the decision matrices 700, 720 are pre-trained using clinically validated sensor data and response protocols for the relevant condition, sufficient to support the AID 100’s defined clinical scope. The Al software system HA can also monitor acoustic indicators of patient condition, for example changes in voice pitch, tremor, or coughing patterns, using the microphone 130, and can correlate such acoustic data with physiological sensor data to generate a multimodal health assessment. In one embodiment, if the Al analysis routine 760 detects acoustic and physiological signatures consistent with a medical emergency (e.g., labored breathing paired with low SpO2), the Al response generation routine 750 generates an emergency alert and provides the patient with verbal first-response instructions while simultaneously transmitting sensor data to a designated medical contact via the transceiver 620.

[0217] In another non-limiting embodiment, an AID 100 is configured for use by a field technician performing inspection or maintenance of industrial equipment, such as electrical panels, HVAC systems, pressure vessels, or manufacturing machinery. The AID 100 can be trained for a specific equipment model or class, with the Al decision matrices 700, 710, 720 trained on acoustic signatures of nominal and faultstate equipment operation, optical images of correctly and incorrectly configured components, and sensor data from relevant environmental and process parameters (e.g., temperature, vibration, electromagnetic emissions). The technician can carry or mount the AID 100 near the equipment under inspection, for example, using a magnetic base or adjustable clamp as described with respect to FIG. 8D, and interact with the AID 100 vocally via the voice recognition routine 650 to ask questions such as which components require inspection, what the correct torque specification is for a given fastener, or whether a particular reading is within tolerance. The AID 100 can use the camera of sensor 5 to analyze images of theequipment and, using the optical decision matrix 710, compare the captured images against trained reference images to identify visual anomalies such as corrosion, misalignment, or improper component installation. The Al response generation routine 750 can communicate findings to the technician acoustically via the speaker 150, and can generate a step-by-step inspection report stored in the local memory 7A for later retrieval. In embodiments where the AID 100 is deployed within a regulated environment (e.g., nuclear, chemical), the AID 100 can further incorporate environmental sensors 5 configured to detect radiation levels, volatile organic compounds (VOCs), or particulate matter, and alert the technician when sensor readings approach hazardous thresholds, consistent with the environmental sensing capabilities described with respect to FIG. 3.

[0218] In yet another non-limiting embodiment, an AID 100 is developed for use as an educational or skills-training assistant specific to a particular subject area, skill level, or curriculum. For example, an AID 100 trained for language learning can be included with a language-learning product or shipped to a student enrolled in a particular course. Upon activation, the AID 100 can interact with the student through a combination of the touchscreen 110, microphone 130, and speaker 150 to deliver instructional content, pose practice questions, and evaluate student responses. The Al analysis routine 760 can process the student’s spoken responses using the acoustic decision matrix 700 to assess pronunciation accuracy, vocabulary usage, and grammatical correctness relative to training data derived from native speaker inputs. The Al response generation routine 750 can then provide real-time feedback, for example, confirming a correct response, identifying a mispronunciation, or suggesting a corrected phrasing, and can track student progress by storing performance metrics in the local memory 7A. In one embodiment, the AID 100 is trained for a specific subject area (e.g., basic arithmetic, elementary chemistry, or a particular foreign language at a defined proficiency level), making the Al software system 11 A sufficiently limited in scope to operate locally without remote server access. The AID 100 can adaptively adjust the difficulty level of instructional content based on accumulated performance data stored in the database 7C, consistent with the anticipatory services described elsewhere herein. In another embodiment, the AID 100 is included with a physical educational kit (e.g., a science experiment kit, a robotics kit, a music instrument learning kit), and is trained to guide a user through the specific activities associated with that kit, analogous to the furniture assembly embodiment described with respect to FIGs. 8 A and 8B.

[0219] In one non-limiting embodiment, an AID 100 is configured as a stationary customer service and point-of-sale assistant for a retail environment. The AID 100 can be mounted at a retail counter, kiosk, or self-service station, analogous to the ATM embodiment described with respect to FIG. 9 A. The AID 100 can be trained specifically for the product catalog, return policies, promotional offerings, and transaction procedures of the associated retailer. A customer can interact with the AID 100 vocally via the voice recognition routine 650 to inquire about product availability, request product recommendations based on stated preferences, or initiate a purchase or return transaction. The Al analysis routine 760 processes thecustomer’s spoken input using the acoustic decision matrix 700 to identify product queries, commands, and transaction intents, and the Al response generation routine 750 accesses the product and pricing database 7C to generate an accurate and contextually appropriate response. In embodiments incorporating a payment system 9 A, the AID 100 can facilitate payment transactions using NFC, Bluetooth, or other short-range wireless communication 6B, and can use one or more biometric sensors 5 to authenticate the customer prior to completing the transaction, consistent with the payment and biometric authentication capabilities described elsewhere herein. The AID 100 can further use ambient microphone data and sensor data to detect whether a customer appears to be in distress or under duress during a transaction, and if so, can notify store personnel or security consistent with the coercion-detection capability described with respect to FIG. 9A.

[0220] In another non-limiting embodiment, an AID 100 is trained for use as a language translation and wayfinding assistant in a transportation hub, tourist attraction, or international venue. Consistent with the multilingual capability referenced with respect to FIG. 9B, the AID 100 can be trained on a defined set of language pairs and on the specific vocabulary, signage, and spatial layout of the deployment venue (e.g., an international airport, a train station, or a convention center). A user can address the AID 100 in their native language, and the Al analysis routine 760 can process the acoustic input using the acoustic decision matrix 700 to identify the language being spoken and interpret the user’s request. The Al response generation routine 750 can generate a response in the user’s language and can simultaneously display translated text or visual directions on the touchscreen 110. In embodiments incorporating sonifi cation, the user interface 8 can provide wayfinding guidance using directional audio cues, including Head-related Transfer Function (HRTF) cues, to acoustically guide a user toward a desired location within the venue, consistent with the sonification and wayfinding functionality described elsewhere herein. The camera of sensor 5 can also be used to capture and interpret signage or maps visible in the user’s environment, with the optical decision matrix 710 trained to recognize venue-specific visual elements and translate or contextualize them for the user.

[0221] In yet another non-limiting embodiment, an AID 100 is configured for veterinary or animal husbandry applications, consistent with the non-human use cases described elsewhere herein. For example, an AID 100 can be trained specifically for the health monitoring, behavioral assessment, and care management of a particular animal species or breed (e.g., equine, bovine, canine). The AID 100 can be attached to an animal enclosure, saddle, collar, or other mounting structure in proximity to the animal, and can use the microphone 130 and camera of sensor 5 to continuously monitor the animal’s acoustic signatures (e.g., vocalizations, breathing patterns, heart sounds) and visual behavior. The acoustic decision matrix 700, trained on recordings of healthy and distressed vocalizations and physiological sounds for the relevant species, can enable the Al analysis routine 760 to detect deviations from baseline that may indicate illness, injury, stress, or abnormal behavior. Consistent with the multimodal monitoring described elsewhere herein, additional physiological sensors 5 (e.g., infrared temperature sensor, accelerometer foractivity and gait monitoring) can provide sensor data processed through the miscellaneous sensor decision matrix 720 to further enrich the Al software system 11 A’s assessment. The Al response generation routine 750 can alert a caretaker acoustically or via a wireless communication 6A to a companion device (e.g., a smartphone) when a health or behavioral threshold is exceeded, and can provide the caretaker with a verbal summary of the detected condition and suggested remedial actions. In embodiments where the AID 100 is deployed in a stable or barn, the AID 100 can additionally interface with automated environmental control systems (e.g., feeding mechanisms, ventilation controls) via the device control circuitry 3D to autonomously adjust the animal’s environment consistent with detected needs, for example activating a feeding mechanism in response to detected hunger vocalizations.

[0222] In at least one embodiment, an AID 100 can be configured to self contained and configured for lifetime learning. For example a child might be provided an AID 100 in the form of a teddy bear, programmed for topics age appropriate. As time progresses, the AID 100 can be designed to self learn. Thus as the child asks questions to which the AID 100 is not designed for, it can seek answers to the questions and update its decision matrices to incorporate the new information, learning as it exists along side it’ s user. It can also query authorized remote servers for specific updates to incorporate new age appropriate scope. Thus, the AID 100 becomes a life long companion for the child as they live life, helping answer school questions, aiding in learning letters and such. Additionally the original AID 100 as it changes over time (i.e. evolving AID 100) can be transferred to other platforms, for example from the teddy bear to a watch worn by the child. The original AID 100 will be different than the evolved AID 100 which incorporates the life time experiences the AID 100 experiences while a companion with the child.

[0223] In another non-limiting embodiment, an AID 100 is configured for legal compliance and regulatory guidance in specific professional contexts. For example, a compliance AID 100 can accompany regulated equipment or be provided to workers in industries subject to specific regulatory frameworks (e.g., food service, hazardous materials handling, healthcare). The AID 100 can be trained on relevant regulatory requirements, standard operating procedures, and compliance checklists specific to the deployment context. A food service worker can query the AID 100 regarding proper food storage temperatures, and the AID 100 can use temperature sensors included in sensors 5 and the miscellaneous sensor decision matrices 720 to verify that refrigeration units are within compliance thresholds. The camera 770A can visually inspect food preparation areas, with the optical decision matrices 710 trained to identify sanitation violations or improper storage conditions. The AID 100 can generate compliance reports stored in local memory 7A and provide real-time verbal reminders through speakers 640A-640Z regarding required documentation or inspection schedules. The database 600 can store regulatory citation information and penalty schedules, allowing the Al response generation routine 750 to provide contextually appropriate guidance.

[0224] In accordance with at least one embodiment, an AID 100 is configured as a fitness coaching and athletic training assistant. The AID 100 can be provided with athletic training equipment, gymmemberships, or sporting goods purchases, trained specifically on exercise form, workout routines, and physiological monitoring for particular sports or fitness goals. The camera 770A and optical decision matrices 710 can assess the user’s exercise form by comparing captured movements against trained reference images of proper technique, alerting the user through speakers 640A-640Z when form deviates from optimal biomechanics. Physiological sensors 5, such as heart rate sensors, pulse oximeters, and accelerometers, can provide real-time biometric data processed through the miscellaneous sensor decision matrices 720 to monitor workout intensity, recovery state, and metabolic indicators. The Al analysis routine 760 can correlate multiple sensor inputs using the multimodal fusion algorithm to assess overall training load and recommend adjustments. The Al response generation routine 750 can provide motivational feedback, count repetitions, time intervals, and adapt workout recommendations based on accumulated performance data stored in database 600.

[0225] In at least one embodiment, an AID 100 is configured for pharmaceutical inventory management and medication dispensing guidance in healthcare settings. The AID 100 can be stationed at pharmacy counters, medication dispensing stations, or integrated into automated medication cabinets. The camera 770A and optical decision matrices 710 can be trained to recognize medication packaging, pill shapes and colors, and barcode or QR code information to verify correct medication selection. The voice recognition routine 650 can process pharmacist or nurse queries regarding drug interactions, dosing guidelines, and contraindications, with the Al analysis routine 760 querying a pharmaceutical database stored in database 600 to generate responses. The AID 100 can provide verbal confirmation through speakers 640A-640Z that the selected medication matches the prescription order, reducing dispensing errors. The touch screen 110 can display visual verification information including patient photographs, allergies, and medication administration schedules. The self-contained nature of the pharmaceutical AID ensures reliable operation even during network outages that might otherwise compromise patient safety.

[0226] In another non-limiting embodiment, an AID 100 serves as a central coordinator for smart home ecosystems, trained specifically on the user’s installed appliances, home automation systems, and personal preferences. Unlike general-purpose voice assistants requiring cloud connectivity, the smart home AID 100 can operate entirely locally, with device control circuitry 3D enabling direct communication with compatible smart home devices via wireless communication protocols such as ZigBee, Z-Wave, or Bluetooth. The acoustic decision matrices 700 can be trained to recognize voice commands specific to the user’s installed devices and customized naming conventions. The Al analysis routine 760 can process contextual information from environmental sensors 5, such as ambient light sensors, temperature sensors, and motion detectors, to anticipate user needs and automate responses without explicit commands. The AID 100 can coordinate scheduled operations, energy management routines, and security monitoring, storing automation rules and user preferences in database 600. The optical decision matrices 710 can be trained torecognize authorized household members via the camera 770A, enabling personalized responses and security features.

[0227] FIG. 10 illustrates generating training data. An example of training the AID of the railroad ticketing counter is shown in FIG. 10. Multiple users take turns using the train ticket machine.Microphones 1007, cameras 1009 and other sensors 1011, record input from the users 1041, including questions, buttons pushed, ticket machine image displays, and infrared to see if a user is really present is stored in the memory in a generating training data system 1000. A processor 1023 and / or a user provides the correct response and stores the data in response to the input data. The input data is then stored in an accepted format as training acoustic sensor data 1005, training optical sensor data 1015, and training sensor data 1025. These training data sets 1005, 1015, 1025 can be used later to adjust Al decision matrices, by training temporary decision matrices 1100, 1110, and 1120 associated with various data input types (e.g., acoustic, image, various other sensor data).

[0228] In various embodiments, the training data used to generate the Al decision matrices 700, 710, and 720 can be collected through controlled user interaction sessions. For a given use case, multiple users representing diverse demographics, accents, physical characteristics, and usage patterns can interact with prototype AID systems under supervised conditions. Quality control procedures can ensure that training data meets predetermined accuracy standards before incorporation into the training datasets 1005, 1015, and 1025. Training data can be validated by comparing recorded inputs against expert-verified correct interpretations. In some embodiments, edge cases and unusual inputs can be specifically collected to improve the robustness of the decision matrices. The training process can employ data augmentation techniques, such as varying acoustic noise levels in training acoustic sensor data 1005 or applying lighting variations to training optical sensor data 1015, to improve generalization performance in real-world deployment scenarios.

[0229] FIG. 11 illustrates generating the decision matrices used in the Al model. Many types of models, such as computer vision models, NLP models, Hybrid models, machine learning models, deep learning models and generative models can be used. Techniques such as support vector machines, random forests, decision trees, linear regression, can be used to cluster and classify data. Neural networks using multiple nodes (layers of matrices) can be used to arrive at a decision matrix that when applied to input data provides the most likely relevant response. For example, training acoustic sensor data 1005 can be fed into the decision matrix system that determines a temporary Al decision matrix (multiple neural networks) matrices associated with acoustic data input 1100 and compared (1130) to the preferred response to see if the output of 1100 matches the preferred output. If it doesn’t (false 1160) then the Al decision matrices associated with acoustic input data is adjusted until the output is the preferred output (true 1140) in which case the temporary Al decision matrices for acoustic input data is set to the Al decision matrices for acoustic data 700, used in the actual operation of AID systems. Likewise, optical sensor training data 1015can be fed into the decision matrix system that determines a temporary Al decision matrix (multiple neural networks) matrices associated with optical data input 1110 and compared (1130) to the preferred response to see if the output of 1110 matches the preferred output. If it doesn’t (false 1160) then the Al decision matrices associated with acoustic input data is adjusted until the output is the preferred output (true 1140) in which case the temporary Al decision matrices for acoustic input data is set to the Al decision matrices for optical data 710, used in the actual operation of AID systems. Likewise, miscellaneous sensor training data 1025 can be fed into the decision matrix system that determines a temporary Al decision matrix (multiple neural networks) matrices associated with sensor data input 1120 and compared (1130) to the preferred response to see if the output of 1120 matches the preferred output. If it doesn’t (false 1160) then the Al decision matrices associated with acoustic input data is adjusted until the output is the preferred output (true 1140) in which case the temporary Al decision matrices for acoustic input data is set to the Al decision matrices for miscellaneous sensor data 720, used in the actual operation of AID systems. The temporary matrices 1100, 1110, and 1120 are provided values that result in outputs that are then compared to correct outputs 1130, and if false 1160, 1165, 1167 adjust the matrix elements. When the temporary matrices 1100, 1110, and 1120 provide correct responses within a threshold (e.g., 95% correct responses) the temporary matrices 1100, 1110, and 1120 can then be used to set the values for decision matrices 700, 710 and 720.

[0230] In another alternative embodiment, and consistent with the multi-user networked system described with respect to FIG. 4, a plurality of AID 100 devices can be configured to operate cooperatively within a shared communications network (e.g., 416, 431, or 435). In one non-limiting example applicable to a large physical space such as a manufacturing floor, hospital ward, or event venue, a first AID 100 handling acoustic input at one location and a second AID 100 equipped with optical sensors at another location can exchange sensor data via the wireless communication systems 6A-6C, with aggregated multimodal inputs routed through a shared Al software system 11 A either locally residing on one of the AID devices or distributed across several devices in the network. Each AID 100 in the network can maintain its own local decision matrices 700, 710, 720 as a baseline capability, while also being capable of pooling sensor data and Al inference results with peer AIDs to generate a richer, multi-perspective analysis than any single AID 100 could generate alone. In one embodiment, one AID 100 in the network acts as a coordinator, receiving inference results from peer AIDs and running the Al analysis routine 760 on the combined inputs to generate a unified response delivered to the appropriate user. In an alternative configuration, the decision matrices of multiple AIDs in the network can be synchronized, such that training data acquired during the operation of one AID 100 (e.g., from new user interactions as described with respect to FIG. 10) can be used to update the temporary training matrices 1100, 1110, 1120 of peer AIDs in the network, consistent with the training pipeline described with respect to FIG. 11.

[0231] Figure 12 illustrates a generic artificial neural network (ANN) 1200. In general, an input vector having values 1201, 1203, and 1205, is fed into a decision matrix composed of neurons (e.g., 1211,1213, 1215, 1219 and 1219) and relationships (e.g., al, bl, cl, dl, a2, b2, c2, d2, a3, b3, c3, d4) between inputs and neurons calculates an output. There are many forms the relationships can take, for example the value in neuron 1211, V1211, can be calculated as:

[0232] (1) Vi2ii=Xn=iwin * (j-nput)n+ baseln

[0233] The value 1211 can also be adjusted, for example by using a threshold, T1211 :

[0234] (2) 71211= 0 if * (input)n+ baselnf) < T1211; or

[0235] (3) 71211=77I2II if (2=i(win * (input)„ + baseln)) > T1211

[0236] Wherein the base values can also lie outside the summation andis a weighting value or function.

[0237] Note that the input values can be actual values or values associated with characteristics (e.g., blue, square shape, amplitude). Additionally, the characteristics can be associated with number values, for example the color blue can be assigned a value 1, red a value of 2 and so on and then these values can make up the input vector. Additionally certain shapes can be assigned a value such as a circle a value of 1 , triangle a value of 3, a square a value of 4 and so on.

[0238] Where VV1211 = can be the value calculated in eqn (1) or some function of V1211.

[0239] Figure 13 illustrates a perceptron 1300, one optional element of a neural network. The output is typically binary 0 or 1. Multiple inputs 1301, 1303, and 1305 are connected, al, a2, and a3 to neuron 1311, for example where the inputs are summed 1311 A. An activation function 131 IB can be applied to the summed 1311 A (e.g., threshold comparison) to determine whether to output a value of 0 or 1 or any other value. The formulaic relationship can take many forms, for example;

[0240] (4) result =(Sn=i(wln* (input)n+ baseln)) x factivation* ^nput)n)')

[0241] Note that a further restriction can be applied such that if the result is >0 then output (e.g., xl) = result. Alternatively no further restriction is applied and output = result. The activation function 131 IB for a generic perceptron 1300 can take many forms, for example it can be a heaviside step function, a semi linear form, sigmoid, fermi functions, or another specialized function for a particular usage.

[0242] Figure 14 illustrates a convolutional artificial neural network (CNN). Additionally relationship vectors can be used to analyze inputs in parallel examining the context and relationship between “words’ in a sentence, useful in large language models and aid in image detection. Note that another ANN called a transformer neural network (TNN), is useful in large language models (LLM). A TNN can look at all the words in a sentence in parallel and determine the context and relationship between words, and normally consists of an encoder that reads the text and a decoder that generates a meaningful response. In TNN a token (e.g., word, subword, or character) is turned into a vector, with positional information added. TNN includes a self-attention portion that lets each token “look at” all other tokens to determine which are important for understanding each individual token. Traditionally three token vectors,often referred to a query (Q), Key (K), and Value (V), are used to derive an Attention vector, which essentially lets one determine how much you should look at the other token (e.g., words) when interpreting the token you are looking at. The formula for an attention vector can be expressed as:

[0243] (5) Attention^, K, V) fsoftmax

[0244] Wherein the softmax function is a normalization probability function that takes a vector of real numbers, turns it into a probability between 0 and 1 where all the values add up to 1. Wherein “dk” is the dimension of key vectors used for scaling., where “T” refers to the transpose. TNNs typically use multiple attention vectors with each focused on different relationships (e.g., syntax, semantic meaning). After developing the Attention vectors, a TNN often uses a feed forward layer to add non-linearity. Additionally, each sublayer of neurons (e.g., hidden layers) are often normalized to aid in computation.

[0245] The CNN displayed in figure 14, takes an input 1400 and strips out features to examine , using them as new inputs 1430 (e.g., 1401, 1403, 1405). For example, for image analysis, instead of values on a pixel by pixel basis being the input, certain features can be extracted instead such as color patches, shapes, pattern and other features. Note that various images with various frequency ranges can be used as input 1400, then features extracted. An example is an image in the visual spectrum, where the features are extracted to use as new input values vector 1430 (e.g., 1401 , 1403, 1405). A second image might be in infrared, where temperature values and patches can be used as features. This can be useful in minimizing identification errors when identifying animals. For example, is the image a picture of a cat or an actual cat? The infrared image would determine the difference. With respect to Figure 14, although only one hidden layer 1440 is shown with neurons (e.g., 1411, 1413, 1415, 1417, 1419) multiple layers can be used. The features / new inputs 1430 (e.g., 1401, 1403, 1405) can be input to neuron layers. For example, input 1401 can feed it’s values, features to neuron 1411 (e.g. via al), to neuron 1413 (e.g., via bl), to neuron 1415 (e.g., cl), to neuron 1417 (e.g., dl). Likewise, the other neurons (e.g., 1413, 1415, 1417) can likewise be fed the new inputs 1403 and 1405, via a2, b2, c2, d2, a3, b3, c3, d3). An optional base neuron, 1419 can hold abase value of the hidden layer to feed in determining the output vector, with elements 1421, 1423. The output vector 1450 having elements 1421, 1423, can be determined by input values from hidden layer neurons 1411, 1413, 1415, 1417 and 1419, via connections xl, yl, x2, y2, x3, y3, x4, y4, x5 and y5. Note that ANN, CNN, and TNN are non-limiting examples of Al Decision Matrices.

[0246] Figure 15 illustrates an ANN 1500 where the neurons (e.g., 1511, 1513, 1515, 1517, 1519) can have relationship (e.g., ml, m2, m3, m4) to each other within a neuron layer (e.g., 1540). As with a generic ANN, there is an input vector 1530, hidden neuron layer(s) 1540, and an output vector 1550. Input vector 1530 (e.g., with inputs 1501, 1503, 1505), which can include any type of input such as numerical, phonetic, functions, colors, sensor data, and manipulated data. Note that although the hidden layer 1540 in Figure 15 does not show an isolated basis neuron (e.g., 1419), one or more can be optionally part of the hidden layer1540. In addition, the neurons (e.g., 1511, 1513, 1515, 1517, 1519) in hidden layer 1540 can have values that depend (e.g., ml, m2, m3, m4) on other neurons (e.g., 1511, 1513, 1515, 1517, and 1519) in the layer in which the neuron rests. Taking this inner neuron dependency into account, for example a value of a neuron 1511 (e.g., m=l in formula below) can optionally be expressed as:

[0247] (5) V 151 l=Sn=iwin * (jnput)n+ Xm=2wim * (neurori)m+ baseln

[0248] The values (e.g., 1521, 1523) of the output vector 1550, can then be determined by combination of values (e.g., numerical, phonetic, logical) of the previous hidden layer 1540. The output can be numerical, phonetic, logical, phrased, or in any other format designed for. The output values (e.g., V1521, V1523) can be non-limiting expressed as:

[0249] (6) V1521= w*5519171519-I- w* / 17F1517+ w^5315!71515+ W*52131Z1513-I- w^11V1511

[00250] (7) V1523=w1y5519V1519+ w^17V1517+ w£1571515+ w^13V1513+ w^irV1511

[0251] The values V1521 and V1523 can be further processed before generating final output values, for example compared to a threshold., matched to stored values corresponding to phonetic words, phrases, letters, and other outputs.

[0252] Where there are ‘k’ inputs, and ‘m’ neurons in the particular layer examined.

[0253] Figure 16 illustrates an ANN 1600 with more relationship between neurons and combinations of neurons. Similar to ANN 1500 an interneuron dependency can also be used within a layer. For example the value of neuron 1611 (V 1611) can be dependent upon previous layer or input values 1601, 1603, and 1605. Note that although all input values are shown to add to the value of V 1611 , not all the values of the input need to contribute to V 1611. For example V 1611 may only depend upon V 1601 and V 1605 , thus the weight associated with value VI 603 would be 0.0. Within a neuron layer having neurons 1611, 1613 and 1615, there can be interneuron dependency based not only on single neighboring neurons (e.g., V1611 dependency on V1613), but also remote neurons (e.g., V1611 dependency on V1613, V1615). The expression for the value of the neuron V1611 can be expressed as:

[00254] (8) V1611=^=1w1„ * (input)“n+ w^P3* V1613+ w]1gL155* V1615+*** +w1n / 1< * V1617-

[0255] The value of neuron 1611, V1611, can be used to determine outputs V 1621 and V 1623 of outputs 1621 and 1623 respectively.

[0256] Figure 17 illustrates an ANN 1700 displayed in a ring type relationship between layers, where not only are there relationships between previous neurons, but groups of previous neurons as well. As shown there is an input layer 1750 that contains various inputs (e.g., 1701, 1703, 1705, 1707) that can have associated values V1701, V1703, V1705, and V1707. The input layer (vector) 1750 can contribute to the value(s) in neuron 1711. Neuron 1711 is part of the next layer 1760, which in Figure 17, contains neurons 1711, 1713, 1715, and 1717. The neuron 1721, in an additional layer, can be dependent on neurons in layer 1760 or a subset thereof, and / or a subset of neurons in layer 1760. Not only can the value of neuron 1721,V1721, be dependent upon each or a subset of neurons in layer 1760, V1721 can be dependent upon groupings of neuron values in layer 1760. For example V 1721 can be expressed as:

[00257] (9) V1721=Xn=l(wln *V) +w1712 * ^1712 +vv1714 * ^1714 + * ^1716

[0258] Figure 18 illustrates a deep neural network 1800 that contains multiple layers of relationships. An input vector, or matrix (e.g., image) having inputs 1801, 1803, and 1805 links to a first hidden neural layer (hidden layer 1) having neurons 1811, 1813, 1815, 1817. A base neuron having value of VI 819 can also be optionally associated with Hidden Layer 1. Subsequent hidden neural layers (e.g. Hidden Layer 2) include neurons (e.g., 1821, 1823) that depend on neurons from previous layers (e.g., 1811, 1813, 1815, 1817, and 1819) having associated values V 1811 , V1813, V1815, V1817 and V1819. Many further layers can also be included and ultimately linked to output neuron 1813 having an output value V1831. Note that a modified deep neural network can also include interneural dependencies as discussed in Figure 15, 16 and 17.

[0259] FIG. 19 illustrates a non-limiting example of a medical use of an AID 100. A doctor 1900 can provide 1910 a patient 1930 with a device 1920 having an AID that will help the patient 1930. For example the device 1920 can remind the patient 1930 to take their medicine 1940 or remind them that they already took their medicine 1940. The device 1920 can measure and record data, advise the patient 1930 when a health issue presents itself, and communicate with any emergency entity, providing health data , location and the current situation. For example if the patient 1930 has a stroke, the AID device 1920 can determine from acoustic analysis, or image analysis that a stroke has likely occurred. The device 1920 can communicate with the user to take some emergency medicine if they are able to understand, for example aspirin, notify the emergency services. The patient 1930 can also interact with device 1920, asking questions related to the device’s 1920 scope. The Device can also serve as a virtual real time medical analysis, where the doctor can tap into the data stream and see through the cameras and microphones of device 1920.

[0260] FIG. 20 illustrates a non-limiting example of a mechanics repair use of an AID 100. In the non-limiting example illustrated a maintenance worker 2000 selects the AID 2020 related to the particular device to repair leaving other specialized AID 2010. In the example shown the maintenance worker 2000 selected 2005 the AID 2020 related to a tractor 2040 that he must repair. There could be other AID devices, 2010, related to other vehicles or systems, for example a helicopter 2030, that the maintenance worker 2000 is responsible for keeping in working order. The maintenance worker 2000 can place the AID 2020 near the tractor 2040, while he is working. He can let the AID 2020 listen and see what is wrong with the tractor2040 and provide suggestions to the worker 2000 to try and diagnose and isolate the issue. Additionally, the device 2020 can link with the electronics of the tractor 2040 to also aid in diagnosis.

[0261] FIG. 21 illustrates a non-limiting example of a veterinarian 2100 use of AIDs. The AIDs can be specialized for particular animals or procedures. For example the vet 2100 can select specialized AIDs by species, for example AID 2110 for cats 2150, AID 2120 for chickens 2170, and AID 2130 for dogs 2170. In the example shown, the vet 2100 selects 2135 the AID 2130 associated with a cat 2150. The AID 2130 can be used as a tool to examine the cat 2150, providing suggestions for the Doctor to check to isolate any potential issues and which treatments to provide.

[0262] FIG. 22 illustrates a non-limiting example of a pharmacy use of an AID 2220. In the example illustrated, a pharmacist 2200 can utilize an AID 2220 to obtain details of all medicines 2210 and dosages suggested for a particular patient 2230 based upon accessible medical information. The Pharmacists 2200 can then select 2235 the medicine 2210 for the patient 2230. The AID 2220 can keep up with medicine conflicts in a patient’s record and notify the pharmacist 2200.

[0263] The illustrations of arrangements described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Other arrangements may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Figures are also merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

[0264] Thus, although specific arrangements have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific arrangement shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments and arrangements of the invention. Combinations of the above arrangements, and other arrangements not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description. Therefore, it is intended that the disclosure not be limited to the particular arrangement(s) disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments and arrangements falling within the scope of the appended claims.

[0265] The foregoing is provided for purposes of illustrating, explaining, and describing embodiments of this invention. Modifications and adaptations to these embodiments will be apparent to those skilled in the art and may be made without departing from the scope or spirit of this invention. Upon reviewing the aforementioned embodiments, it would be evident to an artisan with ordinary skill in the art that saidembodiments can be modified, reduced, or enhanced without departing from the scope and spirit of the claims described below.

Claims

CLAIMSI claim:

1. An artificial intelligence device (AID) comprising:a microphone configured to generate a microphone signal;a user interface configured to receive user input;a first memory that stores an Al software system comprising executable instructions and at least one Al decision matrix, wherein the executable instructions implement an Al analysis algorithm and an Al response generation algorithm;a second memory that stores instructions;a processor operatively connected to the microphone, operatively connected to the user interface, operatively connected to the first memory, operatively connected to the second memory, wherein the processor is configured to execute the instructions to perform operations, the operations comprising: receiving the microphone signal;generating a user input signal based at least in part on the microphone signal or the user input; interpreting the user input signal using the Al analysis algorithm to generate an Al analysis result;generating an AID response based on the Al analysis result using the Al response generation algorithm; andoutputting the AID response to a user.

2. An artificial intelligence device (AID) comprising:a microphone configured to generate a microphone signal;a touch screen configured to generate a touchscreen input signal;a sensor configured to generate a sensor signal;a first memory storing at least one Al decision matrix comprising neural network weight values trained to perform a predetermined single-domain task without access to a remote server during normal operation;a second memory storing operational instructions;a database storing response template data and associated vocabulary data; anda processor operatively connected to the microphone, the touch screen, the sensor, the first memory, the second memory, and the database, wherein the processor is configured to execute the operational instructions to perform operations comprising:receiving the microphone signal;receiving the touchscreen input signal;generating a user input signal using the touchscreen input signal or the microphone signal; applying the at least one Al decision matrix to the user input signal by computing a weightedcombination of the neural network weight values stored in the first memory to generate an Al analysis result;querying the database using the Al analysis result as a query key to retrieve the response template data;constructing an AID response by populating the retrieved response template data with contextspecific information derived from the Al analysis result; andoutputring the AID response to the user via the touch screen or a speaker.

3. An artificial intelligence device (AID) comprising:a microphone configured to generate a microphone signal;a touch screen configured to generate a touchscreen input signal;a sensor configured to generate a sensor signal;a first memory that stores an Al software system comprising one or more Al decision matrices having trained neural network weight values, an Al analysis routine embodied as executable instructions configured to apply a fusion algorithm to outputs of the one or more Al decision matrices when more than one Al decision matrix is present, and an Al response generation routine embodied as executable instructions configured to query a database, wherein the Al analysis routine is designed for a task without access to a remote server during normal operation and is configured to process one or more modalities of input data;a second memory that stores instructions;the database configured to be accessed by the Al response generation routine; anda processor, operatively connected to the microphone, operatively connected to the touch screen, operatively connected to the sensor, where the processor is operatively connected to the first memory, where the processor is operatively connected to the second memory, where the processor is configured to execute the instructions to perform operations, the operations comprising:receiving the microphone signal;receiving the touchscreen input signal;generating a user input signal using the touchscreen input signal or the microphone signal;interpreting the user input signal by executing the Al analysis routine to apply the multimodal fusion algorithm to at least one Al decision matrix output, thereby generating an Al analysis result; sending the Al analysis result to the Al response generation routine, wherein execution of the Al response generation routine causes the processor to query the database and generate an AID response; and sending the AID response to the user using either the touch screen or a speaker.

4. The AID according to claim 3, wherein the Al response generation routine includes at least one ANN.

5. The AID according to claim 3, wherein the Al response generation routine includes an ANN that includes an artificial neuron layer whose artificial neurons depend partially upon values of other artificial neurons within the artificial neuron layer.

6. The AID according to claim 5, wherein at least one artificial neuron within the artificial neuron layer is dependent upon a group of two or more values of artificial neurons within the layer.

7. The AID according to claim 3, wherein the Al response generation routine includes an ANN that includes a first artificial neuron layer whose artificial neurons depend partially upon values of groups of artificial neurons in a second artificial neuron layer.

8. The AID according to claim 7, wherein at least one artificial neuron within the first artificial neuron layer depends partially upon values of other artificial neurons within the first artificial neuron layer.

9. The AID according to claim 3, wherein the AID does not transmit Al decision matrix data or Al analysis results to a remote server during normal operation.

10. The AID according to claim 9, wherein the sensor is a camera that generates a camera signal.

11. The AID according to claim 10, wherein the user input signal is generated using both the microphone signal and the camera signal.

12. The AID according to claim 11, wherein the operations further include:sending a request to the user for more information.

13. The AID according to claim 3, wherein the Al software system further includes a voice recognition routine embodied as executable instructions, the voice recognition routine operatively connected to the microphone, wherein the voice recognition routine comprises signal conditioning instructions configured to apply noise reduction filtering and feature extraction to the microphone signal, and speech recognition instructions configured to identify at least one of a voice command, a word, or a sentence, and to generate a conditioned acoustic signal therefrom, and wherein the conditioned acoustic signal constitutes or contributes to the user input signal.

14. The AID according to claim 3, wherein the Al software system includes:a first Al decision matrix comprising trained acoustic neural network weight values, the first Al decision matrix configured to process acoustic sensor data derived from the microphone signal to generate a first decision matrix result;a second Al decision matrix comprising trained optical neural network weight values, the second Al decision matrix configured to process optical sensor data derived from a camera signal generated by a camera operatively connected to the processor to generate a second decision matrix result;a third Al decision matrix comprising trained sensor neural network weight values, the third Al decision matrix configured to process sensor data derived from the sensor signal to generate a third decision matrix result;wherein the Al analysis routine is configured to analyze at least one of the first decision matrix result, the second decision matrix result, and the third decision matrix result to generate the Al analysis result.

15. The AID according to claim 14, wherein the first Al decision matrix, the second Al decision matrix, and the third Al decision matrix are each derived through a training process comprising:feeding training data of a respective input type into a respective temporary decision matrix; comparing an output of the temporary decision matrix against a preferred response; adjusting elements of the temporary decision matrix until the output matches the preferred response within a predetermined accuracy threshold; andsetting the adjusted temporary decision matrix as the respective operational Al decision matrix.

16. The AID according to claim 1 , wherein the first memory further stores a self-erase routine embodied as executable instructions, and wherein the processor is further configured to execute the self-erase routine that includes overwriting Al decision matrix values with dummy values and reformatting the first memory.

17. The AID according to claim 16, wherein the processor is further configured to execute the self-erase routine upon detection of at least one triggering condition selected from the group consisting of:a tamper condition indicative of unauthorized physical access to a housing of the AID; receipt of a delete message from a remote server via a wireless communication system of the AID; andexpiration of a predetermined timer initiated upon first activation of the AID by a user.

18. An artificial intelligence device (AID) comprising:a microphone configured to generate a microphone signal;a touch screen configured to generate a touchscreen input signal;a sensor configured to generate a sensor signal;a first memory that stores an Al software system comprising executable instructions and aplurality of Al decision matrices, wherein the executable instructions implement an Al analysis algorithm configured to apply multimodal fusion to outputs of the Al decision matrices and an Al response generation algorithm configured to query a database, wherein the Al analysis algorithm is designed for a specific use case;a second memory that stores instructions;the database configured to be accessed by the Al response generation algorithm; anda processor, operatively connected to the microphone, operatively connected to the touch screen, operatively connected to the sensor, where the processor is operatively connected to the first memory, where the processor is operatively connected to the second memory, where the processor is configured to execute the instructions to perform operations, the operations comprising:receiving the microphone signal;receiving the touchscreen input signal;generating a user input signal using the touchscreen input signal or the microphone signal; interpreting the user input signal using the Al analysis algorithm to apply multimodal fusion to at least one Al decision matrix output, thereby generating an Al analysis result;sending the Al analysis result to the Al response generation algorithm, wherein the Al response generation algorithm queries the database to retrieve response data and generates an AID response therefrom; andsending the AID response to the user using either the touch screen or a speaker.

19. The AID according to claim 18, wherein the Al response generation algorithm includes at least one ANN.

20. The AID according to claim 18, wherein the Al response generation algorithm includes an ANN that includes an artificial neuron layer whose artificial neurons depend partially upon values of other artificial neurons within the artificial neuron layer.

21. The AID according to claim 20, wherein at least one artificial neuron within the artificial neuron layer is dependent upon a group of two or more values of artificial neurons within the layer.

22. The AID according to claim 18, wherein the Al response generation algorithm includes an ANN that includes a first artificial neuron layer whose artificial neurons depend partially upon values of groups of artificial neurons in a second artificial neuron layer.

23. The AID according to claim 22, wherein at least one artificial neuron within the first artificial neuronlayer depends partially upon values of other artificial neurons within the first artificial neuron layer.

24. The AID according to claim 18, wherein the AID does not transmit Al decision matrix data or Al analysis results to a remote server during normal operation.

25. The AID according to claim 24, wherein the sensor is a camera that generates a camera signal.

26. The AID according to claim 25, wherein the user input signal is generated using both the microphone signal and the camera signal.

27. The AID according to claim 26, wherein the operations further include:sending a request to the user for more information.

28. The AID according to claim 18, wherein the Al software system further includes a voice recognition algorithm comprising signal conditioning logic configmed to apply noise reduction filtering and feature extraction to the microphone signal, and speech recognition logic configured to identify at least one of a voice command, a word, or a sentence, and to generate a conditioned acoustic signal therefrom, and wherein the conditioned acoustic signal constitutes or contributes to the user input signal.

29. The AID according to claim 18, wherein the Al software system includes:a first Al decision matrix configured to process acoustic sensor data derived from the microphone signal to generate a first decision matrix result;a second Al decision matrix configmed to process optical sensor data derived from a camera signal generated by a camera operatively connected to the processor to generate a second decision matrix result;a third Al decision matrix configured to process sensor data derived from the sensor signal to generate a third decision matrix result;wherein the Al analysis algorithm is configured to analyze at least one of the first decision matrix result, the second decision matrix result, and the third decision matrix result to generate the Al analysis result.

30. The AID according to claim 29, wherein the first Al decision matrix, the second Al decision matrix, and the third Al decision matrix are each derived through a training process comprising:feeding training data of a respective input type into a respective temporary decision matrix; comparing an output of the temporary decision matrix against a preferred response;adjusting elements of the temporary decision matrix until the output matches the preferred response within a predetermined accuracy threshold; andsetting the adjusted temporary decision matrix as the respective operational Al decision matrix.

31. The AID according to claim 30, wherein the first memory further stores a self-erase algorithm, and wherein the processor is further configured to execute the self-erase algorithm that includes overwriting Al decision matrix values with dummy values and reformatting the first memory.

32. The AID according to claim 31 , wherein the processor is further configured to execute the self-erase algorithm upon detection of at least one triggering condition selected from the group consisting of:a tamper condition indicative of unauthorized physical access to a housing of the AID; receipt of a delete message from a remote server via a wireless communication system of the AID; andexpiration of a predetermined timer initiated upon first activation of the AID by a user.

33. A system for providing task-specific artificial intelligence assistance, the system comprising: a first artificial intelligence device (AID) comprising:a microphone configured to generate a microphone signal;a processor;a first memory storing an Al software system comprising at least one Al decision matrix having trained neural network weight values and executable instructions implementing an Al analysis routine;a transceiver configured for wireless communication;a second AID comprising a processor, a memory storing an Al software system, and a transceiver;a communications network communicatively coupling the first AID and the second AID; wherein the first AID and the second AID are each configured to process user input signals locally using their respective Al software systems without requiring transmission of Al decision matrix data to a remote server, and wherein the first AID and the second AID are configured to share coordination data via the communications network.

34. The system according to claim 33, further comprising a user device communicatively coupled to the communications network, wherein the user device is configured to transmit control signals to at least one of the first AID or the second AID.

35. The system according to claim 33, wherein the coordination data comprises at least one of diagnosticdata, inventory status information, or sensor data aggregated from the first AID and the second AID.

36. The system according to claim 33, wherein the communications network comprises at least one of Bluetooth, ZigBee, WiFi, NFC, or cellular communication protocols.

37. The system according to claim 33, further comprising a server communicatively coupled to the communications network, wherein the server is configured to transmit encrypted delta updates to at least one of the first AID or the second AID.

38. The system according to claim 33, wherein each of the first AID and the second AID is trained for a specific use case selected from the group consisting of: furniture assembly assistance, appliance troubleshooting, industrial equipment maintenance, educational tutoring, retail customer service, medical adherence monitoring, and language translation.

39. The system according to claim 33, wherein the coordination data is encrypted using keys stored in the respective AIDs to prevent unauthorized interception.

40. A method for providing task-specific artificial intelligence assistance using an artificial intelligence device (AID), the method comprising:receiving, by a processor of the AID, a microphone signal from a microphone;generating a user input signal based at least in part on the microphone signal;interpreting the user input signal by applying at least one Al decision matrix stored in a first memory of the AID, the Al decision matrix comprising trained neural network weight values, to generate an Al analysis result;querying a database using the Al analysis result to retrieve response data; andoutputting an AID response to a user based on the retrieved response data;wherein the interpreting step is performed locally by the processor without transmitting the user input signal to a remote server.

41. The method according to claim 40, further comprising:receiving optical sensor data from a camera of the AID;applying an optical decision matrix to the optical sensor data to generate an optical analysis result; andapplying a multimodal fusion algorithm to the Al analysis result and the optical analysis result to generate a combined analysis result.

42. The method according to claim 40, further comprising:detecting a language of the user input signal based on acoustic characteristics; and generating the AID response in the detected language using language-specific response templates stored in the database.

43. The method according to claim 40, further comprising:monitoring for a tamper condition indicative of unauthorized physical access to a housing of the AID; andupon detection of the tamper condition, executing a self-erase routine that overwrites Al decision matrix values with dummy values.

44. The method according to claim 40, further comprising:initiating a timer upon first activation of the AID;monitoring for expiration of a threshold duration associated with the timer; andupon expiration, executing a self-erase routine that prevents reconstruction of the Al decision matrix values.

45. The method according to claim 40, further comprising:receiving sensor data from at least one physiological sensor or environmental sensor; applying a miscellaneous sensor decision matrix to the sensor data to generate a sensor analysis result; andincorporating the sensor analysis result into the Al analysis result.

46. The method according to claim 40, wherein the interpreting step comprises:applying an acoustic decision matrix to acoustic sensor data derived from the microphone signal to generate a first decision matrix result;applying an optical decision matrix to optical sensor data derived from a camera signal to generate a second decision matrix result; andanalyzing the first decision matrix result and the second decision matrix result using a multimodal fusion algorithm.

47. The method according to claim 40, further comprising:determining that a confidence level for interpreting fire user input signal falls below a predetermined threshold; andgenerating a clarification request to the user.

48. A non-transitory computer-readable medium storing instructions that, when executed by a processor of an artificial intelligence device (AID), cause the processor to perform operations comprising:receiving a user input signal generated from at least one of a microphone signal or a touchscreen input signal;applying at least one Al decision matrix comprising trained neural network weight values to the user input signal to generate an Al analysis result, wherein the Al decision matrix is trained for a specific use case;querying a database using the Al analysis result to retrieve response template data; and constructing an AID response by populating the retrieved response template data with contextspecific information derived from the Al analysis result.

49. The non-transitory computer-readable medium according to claim 48, wherein the operations further comprise:detecting a language spoken by a user based on acoustic characteristics of the user input signal; andselecting language-specific response templates based on the detected language.

50. The non-transitory computer-readable medium according to claim 48, wherein the operations further comprise:receiving optical sensor data from a camera;applying an optical decision matrix to the optical sensor data; andapplying a multimodal fusion algorithm to outputs of the Al decision matrix and the optical decision matrix.

51. The non-transitory computer-readable medium according to claim 48, wherein the operations further comprise:monitoring sensor data from at least one tamper detection sensor; andinitiating a self-erase routine upon detection of a tamper condition, wherein the self-erase routine overwrites the Al decision matrix values.

52. The non-transitory computer-readable medium according to claim 48, wherein the operations further comprise:loading a compressed language pack from storage into working memory, wherein the compressedlanguage pack comprises quantized neural network weight values; andapplying the loaded language pack to process the user input signal.

53. A method for training an artificial intelligence device (AID) for a specific use case, the method comprising:collecting training data from multiple user interaction sessions, the training data comprising at least one of training acoustic sensor data, training optical sensor data, or training sensor data;feeding the training data into a temporary decision matrix;comparing an output of the temporary decision matrix against a preferred response; adjusting elements of the temporary decision matrix until the output matches the preferred response within a predetermined accuracy threshold; andsetting the adjusted temporary decision matrix as an operational Al decision matrix for deployment in the AID.

54. The method according to claim 53, wherein the collecting step comprises:recording user interactions with a prototype AID system under supervised conditions; validating the recorded interactions by comparing against expert-verified correct interpretations; andapplying data augmentation techniques to improve generalization performance.

55. The method according to claim 53, wherein the training data comprises training acoustic sensor data, training optical sensor data, and training miscellaneous sensor data, and wherein the method further comprises generating a separate Al decision matrix for each type of training data.

56. The AID according to claim 1 , further comprising:power management circuitry configured to transition the AID between a full-power active mode and a low-power standby mode based on detection of user inactivity;wherein in the low-power standby mode, the AID maintains voice activity detection sufficient to detect a wake word that triggers return to full-power operation.

57. The AID according to claim 1, further comprising at least one alternative power source selected from the group consisting of:integrated solar cells positioned on exterior surfaces of a housing;thermoelectric generators configured to convert temperature differentials into electrical power; andkinetic energy harvesting mechanisms configured to capture energy from device handling.

58. The AID according to claim 1, wherein a housing of the AID comprises:impact-resistant materials meeting military specification (MIL-STD-810) drop test requirements; andsealing arrangements providing ingress protection against dust and water consistent with IP67 or IP68 ratings.

59. The AID according to claim 58, wherein the housing further comprises hydrophobic mesh covers protecting microphones and speakers while permitting acoustic transmission.

60. The AID according to claim 1, wherein the first memory stores a plurality of compressed language packs, each language pack comprising:acoustic decision matrix weights trained on native speaker data for a respective language; a vocabulary database containing response templates and domain-specific terminology; and phonetic mapping tables for voice recognition;wherein the language packs are stored using quantization techniques that reduce neural network weight precision.

61. The AID according to claim 60, wherein the processor is configured to:detect a language spoken by a user based on acoustic features comprising phoneme distributions and prosodic patterns; andautomatically load a corresponding language pack from compressed storage into working memory.

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