Automated communication training system, method and apparatus

Abstract

An intelligent assistant device for animals and humans, featuring one or more of sensors, a speaker, input and output devices, and buttons that deliver customizable, association-reinforcing acoustic language cues based on the animal's behavior and environment. The device evaluates the animal's attentional state and identifies a likely object of attention using a range of sensors. The device then emits a related acoustic language cue that is appropriate to the animal's behavioral condition, learning state, and learning goals, creating an association in the animal that exists independent of the presentation of the acoustic cue, and thereby teaching the animal the label of words they attend to in their environment.

Description

Docket No.: 15238.58 AUTOMATED COMMUNICATION TRAINING SYSTEM, METHOD AND APPARATUS FIELD OF THE INVENTION

[0001] The present invention pertains to animal, human, and Artificial Intelligence (“AI”) communication and monitoring systems. In one aspect, it pertains to an intelligent collar with integrated sensors and a speaker designed to deliver reinforcing linguistic associations for domestic animals, such as dogs and cats, with or without a connected system. In other aspects, it pertains to an automated assistant able to assist the user, teach language and other skills to the other, and / or enable interaction by or with the user. BACKGROUND OF THE INVENTION

[0002] Recent advancements in interspecies communication, exemplified by FluentPet's Sound Buttons (see, e.g., United States Patent No.11,445,704 and related patent family members), have showcased the potential for domestic animals to learn and communicate using human language. This breakthrough has paved the way for enhanced communication and understanding between humans and their animals. However, existing methods demand considerable effort and consistency from humans to teach their animals, resulting in limited widespread adoption and effectiveness of such tools. Moreover, current methods lack adaptability, efficiency, and comprehensive monitoring capabilities, necessitating the development of a more automated, advanced, and user-friendly solution. Furthermore, important real world communications, such as commands in language foreign to the dog, or external signals such as a stop light, are not within the ambit of skills existing art allows animals to understand. SUMMARY OF THE INVENTION

[0003] The present invention aims to provide an intelligent assistant for animals (including humans, except where the context clearly requires otherwise), such as dogs and cats, featuring one or more of sensors (which may be integrated or operably connected), a speaker, various input and output devices, and buttons that deliver customizable, association-reinforcing acoustic language cues basedDocket No.: 15238.58 on the animal's behavior and environment. The device evaluates the animal’s attentional state and identifies a likely object of attention through the use of a range of sensors. The device then emits a related acoustic language cue that is selected to be appropriate to the animal’s behavioral condition, learning state, and learning goals. In so doing, the device creates an association in the animal that exists independent of the presentation of the acoustic cue, teaching the animal the label of words they attend to in their environment.

[0004] It is to be understood that both the foregoing general description and the following detailed description are exemplary, but not restrictive, of the invention. A more complete understanding of the Automated Communication Training System, Method And Apparatus disclosed herein will be afforded to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] FIG. 1 is an overall system architecture diagram of an animal communication and monitoring system according to an embodiment of the invention.

[0006] FIG.2 is a perspective external view of a collar / wearable device according to an embodiment of the invention.

[0007] FIG. 3 is a perspective view of the internal components of the collar / wearable device of FIG.2.

[0008] FIG.4 is a sensor array diagram for the collar / wearable device of FIG.2 according to an embodiment of the invention.

[0009] FIG.5 shows a user interface, including mobile app screens for an animal communication and monitoring system according to an embodiment of the invention.

[0010] FIG.6A - 6C is a flow chart of a method of training for an enhanced animal communication and learning system according to an embodiment of the invention.

[0011] FIG.7 is an animal learning progress visualization diagram according to an embodiment of the invention.

[0012] FIG. 8 is a multi-species communication diagram according to an embodiment of the invention.

[0013] FIG.9 is a second multi-species communication diagram according to an embodiment of the invention.

[0014] FIG. 10 is a haptic feedback mechanism diagram according to an embodiment of the invention.Docket No.: 15238.58

[0015] FIG. 11 is a smart home integrations diagram for enhanced animal communication and learning according to an embodiment of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Reference will now be made in detail to the preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with the preferred embodiments, it will be understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications, and equivalents that may be included within the spirit and scope of the invention. Furthermore, in the following detailed description of the present invention, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will readily be apparent to one skilled in the art that the present invention may be practiced without these specific details.

[0017] While the device is described in many places herein as a collar, it should be understood that (unless the context clearly requires otherwise) it may be another device mounted or otherwise on or near the animal and capable of providing cues, acoustic, visual, smell, vibratory, haptic, or otherwise, that the animal can perceive. In one aspect, the device may be capable of travel independent of the animal, such as a drone. In other cases the device may be affixed to and / or integrated with a leash, mounted on clothing, a pendant or other jewelry, a watch, glasses, or other appropriate place. In other cases, the device may be, in whole or part, a network or combination of ambient sensors, such as security cameras. In some cases, the cues are made difficult or impossible for other animals, or some category of them (such as humans) to perceive.

[0018] One example is to make a cue sound at a frequency humans cannot perceive; another is to make a visual cue at a wavelength humans cannot perceive. Another example may be to make a sound at a volume and a proximity to the animal that is perceptible to the animal but not to some or all living creatures nearby. The collar can operate independently or in conjunction with a connected system, employing computer vision or other recognition means to identify the animal's actions and adapt acoustic language cues accordingly. Additionally, theDocket No.: 15238.58 invention may incorporate behavior, activity, location, and monitoring of other aspects of the animal’s condition, whether or not in conjunction with categorization. The invention introduces novel and non-obvious improvements over existing art devices, such as those offered by Fitbark® and Fi®. The system offers a more accessible and efficient way to teach animals acoustic language and nonlinguistic associations, reducing the effort and consistency required while simultaneously enabling a richer and more effective learning experience for the pet.

[0019] In another aspect, the data gathered may be provided, in whole or in part, to an artificial intelligence training module and / or an artificial intelligence output module. The AI module may interpret motions, sounds, and other data output by the animal and learn to predict (in the case of the training module) or predict (in the case of the output module) actions that may follow and / or needs of the animal.

[0020] Turning to the collar, it may include: a. A speaker for emitting language cues and word sounds. b. Buttons or a receiver for incoming data such as data relating to pressing buttons on another device, corresponding to button presses by the pet or human. c. A receiver for incoming data originating from another device. d. Integrated sensors, such as accelerometers, strain gauges, temperature, radio frequency, gyroscopes, GPS and LPS, heart rate, heart rate variability, galvanic skin response, EKG / EEG, one or more cameras and / or microphones, forward looking infrared sensors, microexpression and micromovement sensors, and blood pressure, to monitor the animal’s behavior, activity, condition, and location independently of a connected system. e. Integrated sensors, such as accelerometers, strain gauges, temperature, radio frequency, gyroscopes, GPS and LPS, heart rate, heart rate variability, galvanic skin response, EKG / EEG, one or more cameras and / or microphones, microexpression and micromovement sensors, and bloodDocket No.: 15238.58 pressure, to monitor the animal’s behavior, activity, condition, and location in conjunction with a connected system. f. A wireless receiver for receiving language cues from an optional connected system. g. A power source, such as a rechargeable or non-rechargeable battery, inductive charging receiver, solar panel, or kinetic energy capture device, for supplying power to the integrated components. h. A controller to modulate the type, frequency and volume of acoustic language cues based on the animal's state of learning, preferences, and sensor data. i. Optional buttons that, when pressed by the animal or human, trigger the collar speaker to emit corresponding word sounds. j. Optional lights or signaling devices to support the acoustic language cues emitted by the speaker. k. A means to secure the device to the pet, ensuring a comfortable fit. In the case of a collar this could include an adjustable strap. In situations where the device is not physically connected to the animal, such as a drone hovering near the animal, the means to secure the device to the animal might include visual or electromagnetic cues to permit the device to maintain an appropriate proximity to the pet. l. One or more haptic feedback devices or features. m. A speaker, including without limitation one that may emit a clicking sound. n. A solenoid or other electromechanical device capable of producing sound, such as a clicking sound, and / or a sensation of clicking or movement. o. A light emitting device, optionally set to emit light at frequencies and / or for time periods not perceptible to humans. p. A near infrared sensor. q. A forward looking infrared sensor. r. Any one or more of the SENSOR LIST, as that term is defined below.Docket No.: 15238.58

[0021] Whether or not in conjunction with an optional connected system, the collar can interact with: a. A plurality of cameras, microphones, and / or sensors, strategically placed throughout the home and / or outdoor areas and / or mobile, to monitor the animal's behavior and environment. Optionally, potential privacy concerns may be addressed through (a) user-configurable settings and data encryption; (b) use of an image recognition system obviating the need to save any images; (c) other means of privacy protection, such as the facial recognition defeating system described in the United States patent family that includes U.S. Pat. No.9,721,144; and / or (d) a combination of the foregoing. b. A central processing unit configured to analyze the data collected by the integrated collar sensors, cameras, microphones, and / or external sensors, identifying patterns and events that trigger corresponding language cues. c. A wireless communication module to transmit the identified language cues and data to the animal collar's speaker and processing unit.

[0022] The collar's learning / communication / reinforcements can be modulated based on a range of different evaluations of the animal’s state of learning. This may be accomplished by adjusting the controller's settings, analyzing the animal’s responses to the cues (e.g., through collar sensor data or cameras), and / or receiving input from the animal's owner through a user interface, such as a mobile app, dedicated remote control, voice control system, or other data communication, generation, and / or transmission modality.

[0023] By providing an adaptive animal communication and monitoring system with association-reinforcing acoustic language cues and integrated sensors, the present invention has the potential to automate and accelerate the process of teaching language to animals, significantly enhancing the quality of life for both animals and their owners, fostering deeper connections and understanding between species. In one aspect, the system would develop (via machine learning and / or programming) continuous updates to recognition algorithms. Furthermore, the reliability of the collar may be improved by makingDocket No.: 15238.58 multiple power sources available. In one aspect, a generator powered by animal movement may be utilized. Future improvements may include refining the algorithms for greater accuracy, expanding the range of acoustic language cues, and integrating additional features and sensors to further enhance the animal's learning and monitoring experience.

[0024] Additional embodiments and extensions of the invention may include: a. Incorporating voice recognition technology, allowing the animal to respond to specific voice commands from the owner, as well as enabling the owner to provide real-time feedback on the animal's behavior. b. Integrating haptic feedback mechanisms within the collar, providing animals with tactile reinforcement in addition to the linguistic cues, enhancing their learning experience and creating a multi-modal training system. c. Integrating scent feedback as a potential reinforcer or to provide a contextual cue. d. Incorporating a machine learning system that continuously adapts to the animal's learning progress and preferences, creating personalized training programs that optimize the animal's learning experience. e. Using a large language model (LLM), other generative AI, or other machine learning system to interpret the intentions of and more precisely specify the requests produced by the human involved. Using an LLM or artificial general intelligence to synthesize data generated by the animal and use it in the pursuit of a different goals. f. Expanding the system to include communication with other smart devices, such as automated feeders, toys, and pet doors, to create a fully connected and interactive environment that promotes the animal's learning and overall well-being. g. Introducing a social networking and progress sharing feature in an accompanying mobile app, allowing pet owners to share their animals' progress, exchange training tips, and connect with other users for support and community-building.Docket No.: 15238.58

[0025] By incorporating some or all of these additional embodiments and extensions, the adaptive animal communication and monitoring smart collar with reinforcing language cues and integrated sensors provides a comprehensive, versatile, and user-friendly solution for enhancing interspecies communication and understanding, as well as improving the overall well-being of animals and their owners.

[0026] Turning to uses that are immediately useful to humans (while in most cases also being useful to animals), there are additional uses for these inventions.

[0027] In one aspect, for those with language comprehension difficulties, such as those with brain injury, the system may be utilized to ease understanding. For example, we imagine a device in or operably connected to a speaker in or near somebody’s ear. The device has a microphone capable of hearing words and translating them to a simpler form. For example, the sentence “the restaurant has two specials today, the first is a grilled halibut with risotto and vegetables; the second is filet mignon with au jus and horseradish with mashed potatoes and root vegetables” may be “translated” as “you can order fish or steak”. For a less severe case, the translation may be more complex.

[0028] In another aspect, for those with aphasia, such as Anomic aphasia, the system may be utilized to ease speech. For example, we imagine a device in or operably connected to a speaker in or near somebody’s ear and a camera equipped with image recognition capability. The person may look at a thing and the camera would identify the name of the thing (or person) and provide it via the speaker. For cases where the aphasia extends beyond difficulty with word recall, the name may be “spoken” via a speaker, optionally after being said to the user via the in-ear speaker and receiving clearance from the user to speak it. Such clearance may be provided verbally, through muscle movement such as blinking an eye or tilting the head, or otherwise.

[0029] In one aspect, it may simplify inputs on a scale of 1 to 100. These correspond approximately to the percentage of meaning that may be lost in translation. For example, if set to 25, it would simplify the text and is permitted to remove data until it reaches a floor of 25% if need be, to simplify the input. For example, the phrase "today's menu includes chicken, turkey, beef, lamb, shrimp, scallops, halibut and salmon", with a setting of 50, it may be simplified as "you canDocket No.: 15238.58 order poultry, red meat, shellfish, or regular fish". If set to 25, it may be simplified as "you can order meat or seafood".

[0030] This graduation in complexity can further be applied to language learning in the animal, with acoustic language cues increasing in detail and complexity as system evaluates that the animal’s competence has increased.

[0031] We now walk through an exemplary application of one of the aspects of the invention. This is intended as a non-limiting example. In this case, the user is “Gary”. Gary wants to learn Latin. A camera is utilized to detect objects or events. In one aspect, the camera may be mounted in a way that tracks face direction, such as glasses. In another aspect, the camera may track (either by identifying relevant pixels within a field of view or by moving the direction and / or focal point of the camera) Gary’s gaze. In one aspect, an eye-directed camera may be utilized to determine gaze. As Gary goes through his day, the camera identifies objects or events (i.e. “running”, “cat”, “dog”) and translates them to Latin then “speaks” the Latin word through an ear-worn (or otherwise audible) speaker. In another aspect, the world may additionally or alternatively be shown on a heads-up display, VR or AR glasses. This method and apparatus allow for passive learning of vocabulary. In one aspect, the automated voice used to provide the words is unique in characteristic or characteristics, such as being high pitched, a whisper, low pitched, or otherwise distinguishable from “normal” ambient sounds. In this way, Gary will quickly develop the ability to ignore the voice when it would be too distracting. In another aspect, the system may prioritize certain words and terms. For example, if Gary wants to learn French in preparation for a trip to Montreal, it may translate the names of foods, modes of transportation, restrooms, and other words that a tourist may wish to know. If Gary wants to learn French in preparation for a semester abroad, it may verbalize a different set of words. Once a term is learned, the system may be configured to eliminate that term from translations and / or substitute a new term.

[0032] In another aspect, the device may be used to correct errors and / or to suggest additional terms. By look-up in a database, use of programmatic rules, use of a large language model such as Chat-GPT, or otherwise, the system may assist Gary with his everyday interactions. For example, if Gary says, “the other day me and Koko went on a walk”, the system may say (preferably via a private headphone-type sound emitter) “incorrect grammar, it is Koko and I went on aDocket No.: 15238.58 walk”. In another aspect, it may identify a pause and suggest one or more next words or phrases. For example, Gary may say “the other day I went walking with” and pause. The system may access a database, such as a database of images or events, and identify that the other day he walked with Koko and with Jane. It may then suggest “you walked with Koko once and Jane twice”. In one aspect, the system may also be triggered by “pause” words such as “um” or “you know” or “like”.

[0033] In another aspect, the invention may be configured to provide an acoustic language cue when the animal is looking at or otherwise paying attention to semantic buttons. For example, when the learner looks at the button for “play”, the collar emits the word sound “play” to the animal. After an association is created, the observer looks at or otherwise attends to an object. In one aspect, it could only provide this language cue sporadically, at random intervals, in the process monitoring to detect whether the association was learned or not.

[0034] In another aspect, the system may use classical conditioning to create a novel word association in a dog. For example, to create an appropriate association for the word “outdoor”, the system may emit the word sound “outdoor” when it detects that the dog is looking toward the door. By gradually reducing the volume of the sound emitted, it can preserve the internal association created, and teach other associations that might be coincident. For example, the sound might first be emitted at volume 10, then 9, then 8, and eventually down to 0.

[0035] In another aspect, the system can determine the level of association learned by evaluating the level of “surprise” given the presentation of an acoustic language cue. In one example, the system evaluates the dynamics of the animal’s body movement, such as a head tilt, to estimate how expected or unexpected the acoustic cue was. For example, the head tilt may start sooner, last longer, or achieve a higher rotational velocity, when an acoustic language cue is more novel or otherwise unexpected. This can be accomplished through the use of one or more sensors, including without limitation those on the SENSOR LIST. In another example, electroencephalography (EEG) may be used to measure surprise evoked by a word sound that the animals are presented with acoustically. EEG signals have a property whereby they behave differently when stimuli are expected versus when they are not. For example the “N400” is an evoked response potential (ERP) visible as a negativity in the EEG occurringDocket No.: 15238.58 approximately 400 ms after onset of a surprising stimulus. The magnitude of the N400, or of other aspects of the ERP, can be used to evaluate how well a term has been learned: when an environmental temporal contingency is well learned it should be expected. When it is not well-learned, it should be surprising. For example, if a dog looks toward the outdoors and then hears “outdoor”, if this association was learned it won’t be surprising and will not appear as an N400 in the EEG. The ERP, and other sudden changes in the animal’s condition, can be used to identify and / or to flag an error in the system in its production of an unexpected word or phrase. In another aspect, other biological data may be utilized, such as blood pressure, heart rate, changes in heart rate, breathing rate, breathing depth, pulse oximetry, galvanic skin response, rate of panting, temperature, movement and movement sensors, rate of blinking, micro expressions, and other indicators. When we discuss ERP, it should be understood that one or more of these biological measurements may be utilized in addition to, or instead of, ERP.

[0036] If training Snoopy, for example, every time he walks to the front door, the device might say “outside”. As the magnitude of the surprise signal in the ERP reduces, the system can begin to infer that he has learned the word. Because a constant flow of words may be confusing or counterproductive to the animal, an ERP may be utilized to determine when to move on to other words, when to reduce the frequency of reinforcing the words, etc.

[0037] In another aspect, an intermediate language may be created that would transcend or partially transcend communication gaps between humans and animals understanding multiple languages. For example, a dog raised by Hebrew speakers in Israel would know to sit when hearing the term “Shev”, which is “sit” in Hebrew. However, an English-speaking family visiting from the United States would use the term “sit”, which the dog would not understand. The system may translate unilingual, polylingual, or omnilingual sets of languages into a simple language understandable to the animal (potentially including other humans). For example, “sit” might be “gr gr”, a sound that the dog and humans can make. The collar may make that sound in response to hearing “shev” or “sit”, simultaneously teaching the dog and the humans. In one aspect, a mobile device may be utilized to provide the sound to the humans. By creating a universal core of sounds that dogs understand, interaction with dogs would become far more reliable. In someDocket No.: 15238.58 implementations, it is desirable that the sounds be capable of production by more than one species of animal, such as cat and dog or human and dog.

[0038] In another aspect, the system may reward the animal using a system that substantially improves on traditional clicker training. In clicker training, a dog learns that clicks are positive reinforcement. In this system, some reinforcing sound or feeling, such as a solenoid closing or a click, may be provided when the dog does something correctly. For example, if the dog pees outside on the grass, the system may say “good pee grass” and click to reward the animal.

[0039] An overall teaching approach is another aspect of the inventions. They may be trained based on other sources of attentional cues, such as other contexts to label, and / or not even something they’re attending to. For example, if Snoopy walks into a room, the collar names the room by saying, for example, “living room”. When they sit it may say sit. In addition, the system could utilize sensors to determine certain smells. For example, it could identify the smell of smoke and reward the dog with clicks when they bark. This could be utilized for working dogs, such as drug dogs, as a method of signaling that they have alerted to something.

[0040] In one aspect, the sounds produced for a non-human animal may be raised in pitch and / or reduced in volume to something beyond the hearing capability of humans or less noticeable to humans. For example, few people would want a device to constantly say the name of a room every time the dog walks in. By increasing the pitch above 16 kHz in a system designed to teach children, the children would hear the sound but the adults would not. By contrast to humans, where the upper cutoff for hearing in children is approximately 20 kHz, the upper cutoff for dogs is approximately 60 kHz. By saying “living room” at 25 kHz, the dog would hear the cue but humans would not.

[0041] Definitions:

[0042] “Animal” shall mean an animal, including humans. For further clarity, and not by way of limitation, the term “animal” refers to a eukaryotic organism of the kingdom Animalia. This definition includes, but is not limited to, companion animals such as dogs, cats, birds, and other animals, as well as livestock and working animals such as goats, cows and other animals.Docket No.: 15238.58

[0043] “Collar” shall mean one or more devices mounted or otherwise on or near an animal and capable of providing cues, acoustic, smell, vibratory, haptic, or otherwise, that the animal can perceive.

[0044] “Pet” shall mean an Animal.

[0045] “SENSOR LIST” shall mean one or more of: Electrocardiogram (ECG / EKG) Sensors: Measure the electrical activity of the heart; Pulse Oximeters: Non-invasively monitor the oxygen saturation of a subject's blood; Blood Pressure Sensors: Monitor blood pressure continuously; Respiratory Rate Sensors: Measure breathing rate; Glucose Sensors: Continuously monitor blood sugar levels; Body Temperature Sensors: Monitor core body temperature continuously; Galvanic Skin Response Sensors: Measure the electrical conductance of the skin, which varies with its moisture level; Electroencephalography (EEG) Sensors: Detect electrical activity in the brain; Heart Rate Monitor: Track the heart rate in beats per minute; Gait Sensors: Analyze the way a person or animal walks or runs; Sleep Trackers: Monitor sleep quality and duration; Hydration Sensors: Assess the hydration level of the skin; Accelerometers: Measure body movement and orientation; Gyroscopes: Measure rotational rate or orientation; Pressure Sensors: Monitor interface pressures (like foot pressure distribution); Pacemaker Sensors: Used in devices that stimulate heart muscle contractions; Muscle Movement (EMG) Sensors: Detect muscle activity; pH Sensors: Monitor pH level of bodily fluids; Ultrasonic Sensors: Use sound waves to visualize organs within the body; Ingestible Sensors: Collect health data from within the body; Pacinian Corpuscle-Inspired Sensors: Detect mechanical pressure and vibrations; Proximity Sensors: Track the presence of nearby objects without physical contact; Intracranial Pressure Sensors: Monitor pressure inside the skull; Microphone Sensors (Phonocardiography): Capture heart sounds; Wearable Sweat Sensors: Analyze components of sweat for metabolic data; Lactate Sensors: Monitor lactate levels in sweat during exercise; Biopotential Electrodes: Measure electrical signals from the muscles or neurons; Tactile Sensors: Measure information arising from physical interaction with its environment; Drug Delivery Sensors: Embedded in devices that deliver drugs at controlled rates; Strain Gauge Sensors: Measure force, pressure, and stress by converting them into electrical resistance; SpO2 Sensors: Measure oxygen saturation in the blood; Alcohol Sensors: Measure alcohol concentration in the blood; Spirometry Sensors: Measure lung capacityDocket No.: 15238.58 and breathing patterns; Magnetoencephalography (MEG) Sensors: Map brain activity by recording magnetic fields produced by neural currents; Posture Sensors: Monitor and correct body posture; UV Exposure Sensors: Measure exposure to ultraviolet (UV) radiation; Impedance Cardiography (ICG) Sensors: Measure cardiovascular function and fluid status; Cuffless Blood Pressure Sensors: Non-invasive blood pressure monitoring; Visual Field Sensors: Analyze the field of view, used in ophthalmology; Ambient Light Sensors: Measure light intensity for applications such as controlling the brightness of displays based on the surrounding light conditions; Fetal Doppler Monitor: Used to hear the fetal heartbeat; Intraocular Pressure Sensor: Measures pressure inside the eye to check for glaucoma; Osmosis Sensor: Measures osmotic pressure in cells; Drug Level Sensor: Monitors levels of specific drugs in the body; Skin Elasticity Sensor: Used in studies of aging and hydration; Perfusion Index Sensor: Measures blood flow through the peripheral tissues; Blood Gas Sensor: Measures oxygen, carbon dioxide, and pH levels in blood; Piezoelectric Sensor: Measures changes in pressure, acceleration, temperature, strain, or force by converting them to an electrical charge; Activity Sensor: Monitors overall physical activity; Fall Detection Sensor: Identifies when a person has fallen down; Capnography Sensor: Measures the concentration of carbon dioxide in the respiratory gases; Bone Growth Sensor: Monitors the progress of bone growth in orthopedic treatments; Movement Sensors (Passive Infrared, Ultrasonic, Microwave): Detect movement for various health-related applications; Hemoglobin Sensor: Monitors levels of hemoglobin in blood; Cell Count Sensor: Monitors counts of specific types of cells in the body; Flow Sensor: Measures the flow rate of fluids like blood or respiratory gases; Cholesterol Sensor: Measures cholesterol levels in blood; Blood Coagulation Sensor: Monitors blood clotting function; Insulin Sensor: Monitors levels of insulin in the body; Caloric Burn Sensor: Estimates calories burned through activity; Bone Density Sensor: Monitors bone mineral density; Lipid Sensor: Monitors levels of specific lipids or fat molecules in the body; Bilirubin Sensor: Monitors levels of bilirubin in the body; Inflammation Sensor: Monitors signs of inflammation in the body; Prostate Specific Antigen (PSA) Sensor: Monitors levels of PSA in the body; Hematocrit Sensor: Measures the proportion of blood volume that is occupied by red blood cells; Conductivity Sensor: Measures the ability of a solution to conduct an electric current; ImmunologicalDocket No.: 15238.58 Sensor: Detects presence of specific immune responses; Ketone Sensor: Monitors levels of ketones in the body; Radiosensitivity Sensor: Monitors the degree of response to radiation exposure; Radon Sensor: Detects radon, a radioactive gas; Ionizing Radiation Sensor: Detects exposure to ionizing radiation; Bacteria Sensor: Detects specific bacteria; Virus Sensor: Detects specific viruses; Allergen Sensor: Detects specific allergens; Neurotransmitter Sensor: Monitors levels of specific neurotransmitters; Biomarker Sensor: Detects specific biological markers of disease or health; Melatonin Sensor: Monitors levels of melatonin in the body; Cortisol Sensor: Monitors levels of cortisol in the body; Force Sensor: Measures the force applied on it; Foot Strike Sensor: Measures the impact forces on the foot during locomotion; Vibration Sensor: Measures the levels of vibration; Gastrointestinal Tract Sensor: Monitors various aspects of gut health and function; Throat Microphone (Contact Microphone): Detects speech through vibrations in the throat; Anemia Sensor: Checks for signs of anemia; ultraviolet light sensor; A speaker for emitting acoustic language cues and word sounds; Buttons or a receiver for incoming data such as data relating to pressing buttons on another device, corresponding to button presses by the pet or human; A receiver for incoming data originating from another device; Integrated sensors, such as accelerometers, strain gauges, temperature, radio frequency, gyroscopes, GPS and LPS, heart rate, heart rate variability, galvanic skin response, EKG / EEG, one or more cameras and / or microphones, microexpression and micromovement sensors, and blood pressure, to monitor the animal’s behavior, activity, condition, and location independently of a connected system; Integrated sensors, such as accelerometers, strain gauges, temperature, radio frequency, gyroscopes, GPS and LPS, heart rate, heart rate variability, galvanic skin response, EKG / EEG, one or more cameras and / or microphones, microexpression and micromovement sensors, and blood pressure, to monitor the animal’s behavior, activity, condition, and location in conjunction with a connected system; A wireless receiver for receiving acoustic language cues from an optional connected system; A power source, such as a rechargeable battery or inductive charging receiver, for supplying power to the integrated components; A controller to modulate the type, frequency and volume of acoustic language cues based on the animal's state of learning, preferences, and sensor data; Optional buttons that, when pressed by the pet or human, trigger the collar speaker to emit corresponding word sounds;Docket No.: 15238.58 Optional lights or signaling devices to support the acoustic language cues emitted by the speaker; A means to secure the device to the pet, ensuring a comfortable fit. In the case of a collar this would be an adjustable strap; One or more haptic feedback devices or features; A speaker to emit a clicking sound; A solenoid or other electromechanical device capable of producing sound, such as a clicking sound, and / or a sensation of clicking or movement; A light emitting device, optionally set to emit light at frequencies not perceptible to humans; A near infrared sensor; A forward looking infrared sensor.

[0046] “Animal condition”:

[0047] As used herein, the term “animal condition” or “animal's condition” is broadly defined to include any behavior, movement, postural position, change in postural position, or any physiological or physical characteristics or change in physiological or physical characteristics of an animal.

[0048] Animal condition may reflect the state of the animal with respect to its physical characteristics, for example the animal's location (or the location or change in location of a particular part or parts of the animal) with respect to a defined space, the animal's orientation (or the orientation or change in orientation of a particular part or parts of the animal) in that space, likewise its position or its orientation (or the position or orientation or change in position or orientation of a particular part or parts of the animal) relative to a specific object, for example, pressing a button (with a paw or nose), pressing a lever, touching a touchpad, licking a lickometer, rolling a wheel or ball, flipping a switch.

[0049] Animal condition may refer to the posture or change in posture of the animal, including movement of the whole or part or parts of the animal. Some examples include: sitting, standing, walking, running (including different types of gaits), turning, laying down, laying on stomach, lying on side, lying on back, standing on two legs, standing on four legs, holding one paw up, pressing a button, touching a touchpad, moving a lever, holding tail up, holding tail down, wagging tail. Animal condition also includes facial postures or changes in facial posture, including the position and movement of the mouth, lips, tongue, ears, eyes, eyelids, eyebrows, cheeks, nose, or whiskers.

[0050] Animal condition may also refer to physiological characteristics or changes in physiological characteristics such as level of satiation or hunger, levelDocket No.: 15238.58 of thirst, the need to urinate or defecate, temperature, rate of breathing, level of certain compounds in the bloodstream, lymph, ocular fluids (tears), or cerebrospinal fluid, the physiological activity of the nervous system or a part or parts of the nervous system. Animal condition may also refer broadly to the physiological characteristics of the animal with respect to health or sickness.

[0051] Animal condition may also include inferred affective characteristics, for example: level of arousal or excitement, level of interest, boredom, happiness, sadness, frustration, anger, level of motivation, humor, jealousy, shame, guilt.

[0052] Animal condition may also be a combination of one or more of the above-mentioned types of characteristics, and may be describable by a commonly recognized behavior such as: wagging its tail, turning, moving in a particular direction by, for example, running or walking or rolling or sliding, chasing its tail. The behaviors may be vocalizations or audible behaviors, for example: barking, singing, whining, crying, growling, purring, sniffing, breathing, coughing, sneezing. The behaviors may be finer movements, for example, chewing, biting, licking, sniffing, turning head, tilting head. The behaviors might be linked in some way to the animal's physiology, for example: urinating, defecating, shedding fur or hair, scratching, scraping, rubbing, or grooming.

[0053] The animal's condition may also refer to something that is sensed directly by sensors, deduced by combinations of information from one or more sensors, or it may be inferred via information from zero or more sensors using indirect methods. For example, if it was known that the animal had last eaten at a particular time, say 8 AM, then it might be inferred that the animal would be less satiated at 4 PM than it was at 9 AM. Exemplary Automated Communication Training System, Method and Apparatus

[0054] Referring now to FIG. 1, therein is shown an overall system architecture for an animal communication and monitoring system according to an embodiment of the invention. The system architecture comprises: 1. The Central Component of the Animal Communication and Monitoring Device is a Smart Collar Device 103. Sub-components within this rectangle are:Docket No.: 15238.58 • Speaker 116 • Controller 117 • Sensor Array 118 • Power Source 119 • Wireless Transceiver 120 • Optional Buttons 121 • Haptic Feedback Mechanism 122 • EEG Sensor 123 • Scent Feedback Mechanism 124 • Heart Rate Variability (HRV) Monitor 125 • Blood Pressure Monitor 126 • Galvanic Skin Response (GSR) Monitor 127 2. Connected System 105 Sub-components within the cloud are: • Central Processing Unit 129 • Machine Learning Module 130 • Data Storage 131 • Wireless Communication Module 132 • Intermediate Language Module 133 • AI / LLM Component 134 • Privacy Protection Module 135 • Attention Assessment Module 136 • Surprise Evaluation Module 137 3. Environmental Sensors 104 comprise: • Cameras 141 • Microphones 142 • Motion Sensors 143 • Proximity Sensors 144 • Smart Home Devices 145 • Scent Sensors 146 4. User Interface102 comprises: • Smartphone with Mobile App 109 • Computer with Web Interface 110Docket No.: 15238.58 • Social Networking Feature 111 5. Animal 106 • A second animal silhouette with a collar 108 • A bidirectional arrow between the two animals 114 6. Data Flow Arrows: • Bidirectional arrow between Collar and Connected System 128 • Arrows from Environmental Sensors to Connected System 147 • Arrows from Environmental Sensors to Collar 148 • Bidirectional arrows between User Interfaces and Connected System 112 • Bidirectional arrows between User Interfaces and Collar 113 7. External Factors • Sun icon 151 • Calendar icon 152 • Location pin 153 • Weather icon 154 8. Learning and Communication Outputs 115 • Acoustic Cues (musical note icon) • Haptic Feedback (vibration waves icon) • Visual Cues (light bulb icon) • Scent Cues (scent icon) 9. Attention and Learning Assessment • Eye direction arrow (representing gaze tracking) • Head direction arrow (representing head orientation) • Surprise indicator (exclamation mark icon) • Learning progress bar (partially filled progress bar icon) 10. Notes • Explanations of key features and optional components • The system has the ability to modulate cue probability and volume based on learning assessmentDocket No.: 15238.58

[0055] Referring now to FIG. 2 therein is shown an external view of a collar / wearable device according to an embodiment of the invention. The figure shows a side view of a dog 201 wearing the smart collar device 200, wherein the dog is depicted in a neutral standing position, facing left. The collar is prominently displayed around the dog's neck. An enlarged view of the collar 200 is shown to the right, detailing its components, which comprise: 1. Collar 202: • Made of a flexible, durable material (e.g., nylon or leather) • Width: Approximately 1-1.5 inches (e.g., 2.5-3.8 cm) • Color: A neutral tone (e.g., black or dark gray) to contrast with added components • Adjustable strap with a secure buckle or clasp for proper fitting 2. Main Control Unit 207: • Located on the side of the collar, positioned for easy access • Dimensions: Approximately 2 x 1.5 x 0.5 inches (5 x 3.8 x 1.3 cm) • Streamlined, rounded edges for comfort • Waterproof casing with a matte finish 3. Speaker 212: • Positioned on the outer face of the main control unit • Small, weather-resistant speaker grille • Diameter: Approximately 0.5 inches (1.3 cm) 4. Microphone 210: • Located near the speaker • Small pinhole opening, barely visible • Protected by a water-resistant mesh 5. LED Indicator Lights 214: • Three small LED lights on the top edge of the main control unit • Colors: Green (power), Blue (connectivity), Red (alerts) • Diameter of each LED: Approximately 0.1 inches (2.5 mm) 6. Buttons 211:Docket No.: 15238.58 • Two small, tactile buttons on the side of the main control unit • Labeled with icons: Power symbol and a Wi-Fi symbol • Slightly raised for easy location by touch 7. External Sensors: • Small openings or transparent sections for various sensors • Includes: ^ Temperature sensor 215 ^ Light sensor 216 ^ GPS antenna 217 ^ Heart rate sensor 218 ^ Blood pressure sensor 221 8. Charging Port: • Located on the bottom edge of the main control unit • Water-resistant, covered by a small rubber flap • Universal USB-C type port 9. Haptic Feedback Module 205: • Small, cylindrical protrusion on the inner side of the collar • Positioned to make contact with the dog's neck • Diameter: Approximately 0.4 inches (1 cm) 10. EEG Sensors: • Three small, flat metal contacts on the inner side of the collar • Positioned to make contact with specific points on the dog's neck • Each sensor approximately 0.2 inches (5 mm) in diameter 11. Scent Emission Port: • Tiny, pinhole-sized opening near the main control unit • Located on the outer face of the collar • Barely visible to maintain aesthetics 12. Adjustment Mechanism 203: • Standard collar adjustment slots or a sliding mechanism • Allows for a range of sizes to fit different dog breeds 13. Safety Release 204: • Quick-release mechanism integrated into the buckleDocket No.: 15238.58 • Designed to detach under excessive force for the dog's safety 14. Identification Tag Attachment: • Small metal D-ring near the buckle • For attaching traditional identification tags 15. Texture and Finish: • The main control unit has a slightly textured surface for grip • The collar material has a soft, non-abrasive finish for comfort 16. Labeling: • Small, discreet logo of the device manufacturer • Serial number etched on the inner side of the main control unit 17. Scale Reference: • A small scale bar at the bottom of the figure • Shows the actual size of the device 18. Callout Labels: • Clear, legible labels pointing to each key component • Brief descriptions of each component's function 19. Color Scheme: • The main device in a neutral color (e.g., black, gray, or white) • Accent colors used sparingly to highlight key interactive elements 20. Galvanic Skin Response (GSR) Sensors: • Two small, flat metal contacts on the inner side of the collar • Positioned to make contact with the dog's skin • Each sensor approximately 0.2 inches (5 mm) in diameter 21. Camera 209: • Small, wide-angle camera lens on the outer face of the main control unit • Positioned to capture the dog's field of view • Diameter: Approximately 0.3 inches (7.5 mm) 22. Inertial Motion Unit (IMU) 213 (inside device): • Integrated within the main control unit (not visible externally)Docket No.: 15238.58

[0056] FIG. 3 shows the internal components of a collar / wearable device according to an embodiment of the invention, including an exploded view of the main control unit of the collar device. The components are arranged in a vertical stack, with connecting lines showing how they fit together. A scale bar is included to indicate the actual size of the components. 1. Outer Casing: • Two-piece design: top and bottom shells • Material: Durable, waterproof plastic or polymer • Dimensions: Approximately 2 x 1.5 x 0.5 inches (5 x 3.8 x 1.3 cm) • Features: Grooves and snap-fit connections for easy assembly • Gasket: Thin rubber seal between the two halves for water resistance 2. Main Circuit Board (PCB) 222: • Dimensions: Approximately 1.8 x 1.3 inches (4.5 x 3.3 cm) • Multi-layer design with surface-mount components • Color: typically green or blue • Key visible components: ^ Microprocessor (largest chip, centrally located) ^ Wireless communication module (near one edge) ^ Memory chips ^ Various smaller ICs for power management, sensor interfacing, etc. ^ AI / Machine Learning accelerator chip 3. Battery: • Type: Lithium-Ion or Lithium Polymer • Capacity: Approximately 500-1000 mAh • Dimensions: Custom-shaped to fit the available space • Connections: Power leads with a small connector to the PCB 4. Speaker: • Type: Miniature dynamic speaker • Diameter: Approximately 0.5 inches (1.3 cm)Docket No.: 15238.58 • Positioned directly behind the speaker grille in the casing • Connected to the PCB via thin wires 5. Microphone: • Type: MEMS (Micro-Electro-Mechanical Systems) microphone • Size: Approximately 3 x 4 x 1 mm • Located near the tiny opening in the casing • Soldered directly onto the PCB 6. Antenna: • Type: Flexible PCB antenna for GPS and Wi-Fi • Location: Integrated into the collar strap or along the edge of the PCB 7. Sensors Array: • Accelerometer / Gyroscope: Small IC, approximately 3 x 3 mm • Temperature Sensor: Tiny component near the casing edge • Heart Rate Sensor: Optical sensor on the inner side of the collar • EEG Sensors: Thin wires leading to the contact points on the collar's inner surface • Blood Pressure Sensor: Small pressure sensor connected to the inflatable cuff • Galvanic Skin Response (GSR) Sensor: Two small electrodes connected to the PCB 8. Haptic Feedback Module: • Type: Eccentric Rotating Mass (ERM) vibration motor • Size: Approximately 8 mm diameter, 3 mm thick • Connected to the PCB via spring contacts 9. LED Indicators: • Three small surface-mount LEDs on the PCB • Positioned to align with the transparent windows in the casing 10. Buttons: • Two tactile switch components on the PCBDocket No.: 15238.58 • Aligned with the external button locations on the casing • Small rubber or silicone button covers for water resistance 11. Charging Port 208: • USB-C connector soldered onto the PCB • Aligned with the port opening in the casing • Surrounding area reinforced for durability 12. Scent Emission System: • Miniature piezoelectric atomizer • Small reservoir for scent liquid • Micro-pump for precise scent release • Connected to the PCB via thin wires 13. Heat Dissipation: • Thin copper heat spreader under the main processor and AI chip • Thermal paste between the processors and heat spreader 14. Wiring and Connectors: • Flat Flexible Cables (FFCs) connecting different sections of the PCB • Small connectors for components like the battery and external sensors 15. Protective Coatings: • Conformal coating on the PCB for moisture resistance • EMI shielding on sensitive components 16. Attachment Points 223: • Small plastic or metal posts inside the casing for securing the PCB • Screw holes or snap-fit mechanisms for closing the casing 17. Collar Attachment: • Robust connection points where the main unit attaches to the collar strap • Potentially using strong adhesive or mechanical fasteners 18. Optional Expansion Slot: • Small connector or slot for potential future add-on modulesDocket No.: 15238.58 19. Assembly Guide: • Dotted lines or arrows showing how components stack together • Numbered sequence indicating the order of assembly 20. Camera Module: • Small wide-angle camera lens • Image sensor chip on the PCB • Connected to the main processor via a high-speed interface 21. Inertial Motion Unit (IMU): • Integrated circuit combining accelerometer, gyroscope, and magnetometer • Size: Approximately 3 x 3 x 1 mm • Soldered directly onto the PCB

[0057] FIG. 4 is a sensor layout and data flow diagram according to an embodiment of the invention showing a circular diagram with the collar device at the center. Sensors are arranged in concentric rings around the central collar image. Connecting lines show data flow between sensors and the central processing unit. 1. Central Collar Image 400: • A simplified top-down view of the collar device. • Dimensions approximately 1.5 inches (3.8 cm) wide. • Main control unit visible as a rectangular protrusion. 2. Inner Ring – Core Sensors: a. Accelerometer / Gyroscope 406: • 6-axis MEMS sensor • Measures motion and orientation b. Magnetometer 410: • 3-axis digital compass • Assists in determining direction c. EEG Sensors 415: • Three small, flat electrodes • Positioned to contact the animal's skin d. Heart Rate Variability (HRV) Monitor 405:Docket No.: 15238.58 • Optical sensor with LED and photodiode • Placed on the inner side of the collar 3. Middle Ring - Physiological and Behavioral Sensors: a. Blood Pressure Monitor 407: • Miniature pressure sensor with inflatable cuff b. Galvanic Skin Response (GSR) Sensor 411: • Two small electrodes • Measures skin conductance c. Temperature Sensor 414: • Miniature thermistor • Located near the skin contact area d. Inertial Motion Unit (IMU) 404: • Combines accelerometer, gyroscope, and magnetometer • For detailed motion and orientation tracking 4. Outer Ring - Environmental and Specialized Sensors: a. GPS Module 408: • Integrated chip with antenna 409 • For location tracking b. Microphone 412: • MEMS microphone • For audio input and environment sound analysis c. Camera 413: • Wide-angle lens • For visual input and object recognition d. Scent Sensor 403: • Electronic nose or gas sensor array • For detecting and analyzing odors 5. Attention Assessment Module: • Represented as a processing unit near the center • Data from multiple sensors (e.g., IMU, EEG, camera) is combined to assess the animal's attention 6. Surprise Evaluation Module: • Adjacent to the Attention Assessment ModuleDocket No.: 15238.58 • Processes sensor data to evaluate the animal's surprise response to stimuli 7. Data Flow Visualization: • Arrows showing data flow from each sensor to the central processing unit • Different colors used for different types of data (motion, physiological, environmental, attention-related) 8. AI / Machine Learning Processor: • Represented as a specialized processing unit • Sensor data is processed for learning assessment and cue generation 9. Wireless Communication: • Antenna icon near the GPS module • Capability to transmit sensor data wirelessly 10. Acoustic Cue Generation: • Speaker icon connected to the central processing unit • System has the ability to generate appropriate language cues 11. Haptic Feedback: • Vibration motor icon • For providing tactile feedback 12. Scent Emission System: • Small atomizer icon • For releasing scent cues 13. Animal Interaction: • Simplified outline of an animal's neck showing how physiological sensors make contact • Arrows indicating gaze direction and head orientation 14. Sensor Specifications Table: • Small table listing key specs for each sensor (sensitivity, range, power consumption) 15. Learning Assessment Visualization:Docket No.: 15238.58 • Small graph or indicator showing how the system evaluates learning progress 16. Cue Modulation Representation: • The system adjusts cue frequency and volume based on learning assessment

[0058] Referring now to FIG.5, therein is shown a user interface, including a series of smartphone screens, arranged in a grid (e.g., 3x3 or 4x3). Each screen is depicted as a high-resolution smartphone display, and screens are numbered for easy reference and connected with arrows to show user flow. 1. Home Screen 501: • Pet profile picture at the top center (e.g., circular, 120x120 pixels). • Pet's name below the picture in large font (e.g., 24pt). • Quick status indicators: Learning Progress, Attention Level, Recent Interactions. • Large, colorful buttons for main features: Monitor, Train, Communicate, Analytics. • Bottom navigation bar with icons for Home, Learning, Social, and Settings. 2. Learning Progress 502: • Circular progress ring showing overall learning advancement. • List of recently learned words / concepts with timestamps. • Daily interaction time and quality score. • "Set Learning Goals" button at the bottom. • Graph showing learning curve over time. 3. Attention Monitoring 503: • Heat map of the pet's environment showing areas of frequent attention. • Timeline of attention spans throughout the day. • List of objects / events that captured the pet's attention most. • Attention quality score with explanation. • Option to review and label unidentified attention targets.Docket No.: 15238.58 4. Communication Interface 504: • Large "Speak to Pet" button at the center. • Soundboard with learned words / phrases. • Voice modulation options: Normal, High Pitch, Low Pitch. • Recent communications log with pet's response indicators. • Toggle for "Auto-Translate Pet Behaviors". 5. Training Module 505: • List of available training programs: Basic Commands, Complex Concepts, Environmental Awareness. • Progress bar for each program. • "Start Training Session" button. • Recent achievements and milestones. • Customization options for acoustic cues and reinforcement schedules. 6. Analytics Dashboard 506: • Graphs showing correlation between attention, surprise levels, and learning progress. • EEG data visualization with highlights of significant events. • Heart rate variability and other physiological data trends. • AI-generated insights on learning patterns and suggestions. • Option to export data for further analysis. 7. Social Features 507: • Comparison of learning progress with other pets (anonymized). • Shared experiences and tips from other pet owners. • "Arrange Interactive Playdate" feature for socialization training. • Community challenges and group learning activities. • Share button for posting achievements to social media. 8. Alerts and Notifications 508: • List of recent alerts: Unusual behavior, Breakthrough learning moment, Stress detection. • Each alert has an icon, timestamp, and brief description.Docket No.: 15238.58 • Severity indicators: Low (blue), Medium (yellow), High (red). • "Manage Alert Settings" button at the bottom. • Option to set custom alerts for specific learned behaviors. 9. Settings and Preferences 509: • Profile settings: Pet info, Owner info, Vet info. • Device settings: Collar sensitivity, Update firmware, Battery status. • Learning preferences: Preferred cue types, learning pace, focus areas. • Data sharing and privacy settings. • Help and Support section. 10. Cue Customization 510: • Interface for recording custom voice commands. • Options to adjust acoustic cue volume and frequency. • Haptic feedback intensity settings. • Scent cue management (if applicable). • Preview and test custom cues. 11. Environmental Setup 511: • Floorplan view of the home / environment. • Option to mark areas for specific training focuses. • Set up virtual boundaries for location-based learning. • Integrate with smart home devices for enhanced interaction. • Customize environmental triggers for learning opportunities. 12. Color Scheme and Typography: • Use a color scheme and typography that aligns with the best practices for your chosen mobile platform (iOS or Android). • Ensure high contrast for readability. • Use consistent fonts and sizes across all screens. 13. Iconography: • Consistent, minimalist icon style throughout. • Each main feature has a unique, easily recognizable icon. • Icons should be clear and meaningful, representing core concepts like learning, attention, communication, etc.Docket No.: 15238.58 14. Data Visualization: • Graphs and charts use clear, colorful designs. • Elements allow users to zoom or get more details. 15. Accessibility Features: • High contrast mode toggle in settings. • Text size adjustment option. • Voice Over / Talk Back compatible elements. 16. Branding: • App logo in the top left of the home screen. • Consistent brand colors and styling across all screens. 17. Tutorial Overlay: • Shown on first launch, highlighting key features. • "Skip" and "Next" buttons for navigation. • Focus on explaining the unique aspects of the learning and communication system.

[0059] FIGS.6A – 6C is a flowchart of a method of training for an enhanced animal communication and monitoring system according to an embodiment of the invention. The flowchart of FIGS.6A – 6C is a vertical flowchart design, top to bottom and includes five main sections corresponding to the five steps of the process included in the embodiment. There are color coded elements for easy distinction between types of operations and the flowchart symbols adhere to ISO 5807 standards. 1. Start Point: • Oval shape labeled "Start” 601: Continuous Monitoring 602 2. Section 1: Attention Detection a. Diamond 603: "Animal paying attention to something?" • If No, loop back to start • If Yes, proceed to next step b. Rectangle 604: "Determine focus of attention" • Sub-process 605: "Analyze sensor data" ■ List sensors: cameras, microphones, proximity sensors, IMU, EEG, etc. c. Rectangle 606: "Classify type of stimulus"Docket No.: 15238.58 • Options: Visual, Acoustic, Scent, Tactile, Other 3. Section 2: Environmental Interpretation a. Rectangle 607: "Interpret environmental signals" b. Rectangle 608: "Identify canonical name of focus object / event" • Sub-process 609: "Query object recognition database" utilizing Object / Event Database 611 c. Diamond 610: "Confidence in identification above threshold?" • If No, return to start • If Yes, proceed to next step 4. Section 3: Acoustic Cue Generation a. Rectangle 612: "Retrieve corresponding word in target language" b. Diamond 613: "Should cue be provided?" (See Section 5 for inputs) • If No, return to start • If Yes, proceed c. Rectangle 615: “Determine cue characteristics” • Sub-process: “Set volume, pitch, and duration” d. Rectangle 616: "Generate acoustic cue," rectangle 614 “generate haptic feedback,” and / or rectangle 618, “Emit scent cue” e. Rectangle 617: "Emit cue through speaker" 5. Section 4: Learning Evaluation a. Rectangle 619: "Monitor immediate reaction" • Sub-process 620: "Analyze physiological signals" ■ List signals: EEG, heart rate variability, GSR, body language, etc. b. Diamond: "Signs of surprise detected?" • If Yes, rectangle 622, indicate likely new word • If No, rectangle 623, indicate likely known word c. Rectangle 624: "Evaluate degree of acknowledgment"Docket No.: 15238.58 d. Rectangle 626: "Update learning profile" utilizing Learning Profile Database 625 • Sub-process 627: "Adjust known / unknown word database" 6. Section 5: Adaptive Modulation a. Rectangle 628: "Calculate optimal cue probability" • Rectangle 630, “Inputs”: ■ Current learning rate ■ Recent cue frequency ■ Time of day ■ Animal's energy level ■ Previous surprise levels b. Rectangle 633: "Determine ideal cue characteristics" • Rectangle 634, “Inputs”: ■ Ambient noise level ■ Time of day ■ Animal's stress level ■ Learning stage for this word ■ Previous response to cue types c. Rectangle 635: "Adjust system parameters" • Rectangle 636: “Update cue probability” • Rectangle 637” “Update volume, pitch, and duration settings” d. Arrow back to Section 3 step b 7. Continuous Loop: • Arrow from Section 5 back to Start, indicating continuous process 8. Data Storage Integration: • Database cylinder symbols at relevant points: ■ After Section 2: 611 "Object / Event Database" ■ After Section 4: 625 "Learning Profile Database" ■ In Section 5: 632 "System Parameters Database" 9. External Input:Docket No.: 15238.58 • Cloud symbol: 631 "Owner Preferences" connecting to Section 5 • Cloud symbol: 629 "AI / Machine Learning Updates" connecting to Sections 2, 4, and 5 10. Alternative Cue Types: • Small branching path from Section 3, step d: ■ Rectangle 614: "Generate haptic feedback" ■ Rectangle: 615 "Emit scent cue" • These should be shown as optional paths

[0060] FIG. 7 shows an animal learning process visualization diagram according to an embodiment of the invention. 1. The Main Chart 707 represents the animal’s word acquisition over time • X-axis: Time (spans 6 months, labeled by month) • Y-axis: Cumulative Words Learned (scale from 0 to 100) • Primary line graph: Thick, solid line showing total words learned • Secondary line graphs: ^ Dashed line for nouns ^ Dotted line for verbs ^ Dash-dot line for adjectives • Small markers (e.g., dots) at each data point • Vertical gridlines are shown at each month mark • Horizontal gridlines are shown at every 10-word increment 2. Inset Charts: a. Top-left: Pie chart 702 showing distribution of word types (nouns, verbs, adjectives, others) b. Bottom-left 703: Bar chart showing top 5 most frequently recognized words 3. Learning Rate Indicator 708: • Right side of main chart • Vertical slider or gauge showing current learning rate • Scale from "Slow" at bottom to "Fast" at topDocket No.: 15238.58 • Marker or arrow indicating current rate • Small chart next to gauge showing rate changes over past week 4. Attention and Surprise Response Tracker 704: • Small line graph below main chart • Two lines: ^ Average attention level ^ Average surprise response (from EEG data) over time • Inverse relationship to word acquisition line expected for surprise response 5. Cue Emission Data 705: • Right side, below learning rate indicator • Three gauges: ^ Current cue frequency (cues per hour) ^ Average cue volume ^ Cue diversity (types of cues used) • Each gauge has a marker for current level and hatched area for optimal range 6. Notable Events Timeline 701: • Thin horizontal band above main chart • Marks for significant events: ^ Start of training ^ Milestones (e.g., 10th word learned, 25th word learned) ^ Changes in training protocol ^ Introduction of new cue types • Small icons or labels for each event 7. Statistical Summary 706: • Bottom-right corner • Text box with key stats: ^ Total words learned ^ Learning rate (words per week) ^ Most recent word learned ^ Strongest category (noun / verb / adjective)Docket No.: 15238.58 ^ Average attention span ^ Surprise response trend 8. Physiological Data Correlation: • Small chart showing correlation between learning rate and key physiological indicators (e.g., average heart rate variability, EEG patterns)

[0061] Referring now to FIG. 8, therein is shown a multi-species communication diagram, including a central area showing species interaction, side areas detailing translation processes / methods, and a bottom section illustrating an intermediate language concept. 1. Central Interaction Scene 816: • Depicts three species: Human, Dog, and Cat, in a triangle formation • Each species is wearing or near their respective communication devices • There are speech / thought bubbles above each to represent communication 2. Communication Devices: • Human: Smartphone or smart speaker • Dog: Smart collar (as described in previous figures) • Cat: Smaller version of the smart collar 3. Translation Flow Diagrams (Side Panels): a. Left panel: Human to Animal Translation • Flowchart showing: ^ Human speech input 802 ^ Speech recognition 804 ^ Language processing 806 ^ Translation to intermediate language 808 ^ Conversion to animal-appropriate cue (acoustic, haptic, or scent) 810 ^ Cue emission 812 b. Right panel: Animal to Human Translation • Flowchart showing:Docket No.: 15238.58 ^ Animal behavior / vocalization detection 828 ^ Sensor data analysis (including EEG, HRV, GSR) 830 ^ Behavior interpretation 832 ^ Translation to intermediate language 834 ^ Conversion to human language 836 ^ Output to human device 838 4. Intermediate Language Representation (Bottom Section): • Central circle 822 labeled "Intermediate Language" • Radiating lines connecting to smaller circles representing: ^ Human Languages (e.g., English, Spanish, Japanese) 824 ^ Dog Communication (e.g., Barks, Whines, Body Language) 818 ^ Cat Communication (e.g., Meows, Purrs, Tail Positions) 820 • Distinct patterns or line types are used for each species' connections 5. Sample Translations: • Show example translations near each species: ^ Human: "Sit" → Intermediate: "GR-GR" → Dog: Low- pitched sound ^ Dog: Tail wag + soft whine → Intermediate: "ZM-ZM" → Human: "Play?" ^ Cat: Paw gesture → Intermediate: "MR-MR" → Human: "Food" 6. Learning Process Integration: • Small inset diagram 814 showing: ^ Attention detection (from previous figures) ^ Cue generation (acoustic, haptic, scent) ^ Learning evaluation (surprise response, acknowledgment) • Arrows connect this to the main communication flow 7. Adaptive System Representation:Docket No.: 15238.58 • Circular arrows around each translation flow diagram • Label indicating "Continuous Adaptation" • Small chart showing improvement in translation accuracy over time 8. Cross-Species Understanding Metrics: • Bar graph 826 in a corner showing: ^ Human understanding of dog communication ^ Human understanding of cat communication ^ Dog-cat mutual understanding • Understanding percentage is labeled for each bar 9. Attention and Surprise Feedback Loop: • Small diagram showing how attention levels and surprise responses feed back into the translation system • Arrows indicating the flow of this information to improve accuracy 10. Environmental Context Integration: • Small diagram showing how environmental factors (e.g., location, time of day) influence the translation process

[0062] FIG. 9 shows a second multi-species communication diagram according to an embodiment of the invention, including three main sections (a) left section: physical setup, (b) center section: signal processing and analysis, and (c) right section: learning evaluation and feedback. 1. Left Section - Physical Setup: a. Perspective view 904 of a dog wearing the smart collar • The collar with embedded EEG sensors • EEG contact points on the dog's skin b. Enlarged view of an EEG sensor • Showing its components: electrode, amplifier, analog- to-digital converter c. Diagram of sensor placement • Top view 902 of the dog with optimal EEG sensor locations marked d. Additional physiological sensorsDocket No.: 15238.58 • Showing placement of HRV and GSR sensors on the collar 2. Center Section - Signal Processing and Analysis: a. Raw EEG Signal Display 906 • Show 2-3 seconds of multi-channel EEG waveforms • Each channel labeled (e.g., F3, F4, C3, C4) b. Signal Processing / Method Steps (flowchart): • Artifact Removal 908 ^ Common artifacts (e.g., muscle movement, eye blinks) • Frequency Decomposition 910 ^ Power spectra for different frequency bands (delta, theta, alpha, beta, gamma) • Event-Related Potential (ERP) Extraction 912 ^ N400 component highlighted c. Feature Extraction • Key features: amplitude, latency, power in specific frequency bands d. Integration of HRV and GSR data • These signals are processed alongside EEG 3. Right Section - Learning Evaluation and Feedback: a. ERP Analysis • Graph 924 showing N400 amplitude for known vs. unknown words • Threshold line for categorizing words as known / unknown b. Surprise Quantification • Scale from "Familiar" to "Surprising" with a sliding indicator c. Learning Progress Tracker • Small version of the learning curve from FIG.7 • How EEG, HRV, and GSR data points contribute to the curve are highlightedDocket No.: 15238.58 d. Feedback to Language Teaching System • Flowchart showing how multimodal assessment influences: ^ Word repetition frequency 914 ^ Introduction of new words 916 ^ Adjustment of teaching strategy 918 ^ Selection of cue types (acoustic, haptic, scent) 920 4. Integration with Language Teaching Method: • Inset diagram referencing FIG.6 • The "Learning Evaluation" and "Adaptive Modulation" steps are highlighted • How EEG, HRV, and GSR data feed into these processes is shown 5. Time Sequence Representation 922: • Horizontal timeline at the bottom of the drawing • Key events: cue presentation, physiological responses, learning assessment, teaching adjustment 6. Data Flow Arrows: a. Raw data flow b. Processed information flow c. Feedback and control signals 7. Quantitative Examples: • Sample data table: ^ Word presented ^ N400 amplitude ^ HRV change ^ GSR response ^ Surprise level ^ Learning status (known / unknown) 8. Attention Assessment Integration: • Small diagram showing how EEG data contributes to attention level estimationDocket No.: 15238.58 • Connection to the attention detection process from previous figures 9. Adaptive Cue Modulation: • Diagram showing how physiological data influences cue characteristics (volume, frequency, type) 10. Additional Details: • Small graph of typical N400 waveforms for known vs. unknown words • Simplified circuit diagram of the EEG, HRV, and GSR amplification and digitization process • Small illustration of neuron activity corresponding to EEG signals

[0063] Referring now to FIG. 10, therein is shown a haptic feedback mechanism diagram according to an embodiment of the invention divided into four main sections: (a) top left: exploded view of haptic component, (b) top right: integration with smart collar, (c) bottom left: haptic signal patterns, and (d) bottom right: haptic feedback in language learning process. 1. Top Left Section - Exploded View of Haptic Component: a. Eccentric Rotating Mass (ERM) Motor comprising • Internal weight and housing • Motor windings and terminals b. Linear Resonant Actuator (LRA) • Voice coil, spring, and magnetic mass c. Piezoelectric Actuator • Piezoelectric crystal structure d. Control Circuit Board • Microcontroller, driver IC, and connectors e. Protective Casing • Water-resistant enclosure with flexible contact surface 2. Top Right Section - Integration with Smart Collar: a. Perspective view of the smart collar 1014, 1016 • Placement of haptic component(s) • Wiring to main control unitDocket No.: 15238.58 b. Perspective view of dog with collar on dog's neck 1028 • Contact points of haptic component c. Enlarged view of skin contact area • Vibration transfers to the animal's skin d. Integration with other sensors (EEG, HRV, GSR) • Proximity and connections to haptic component 3. Bottom Left Section - Haptic Signal Patterns: a. Timing Diagram 1018 • X-axis: Time (in milliseconds) • Y-axis: Vibration intensity b. Sample Patterns: • Short pulse for attention-grabbing • Rhythmic pattern for positive reinforcement • Sustained vibration for urgent cues c. Frequency Spectrum • Graph showing vibration frequencies used • Optimal frequencies for dog perception d. Combination Patterns • Acoustic and haptic cues can be combined 4. Bottom Right Section - Haptic Feedback in Language Learning Process: a. Flowchart integrating with Figure 7 process: • Attention Detection (from Fig.7) 1020 • Cue Generation (acoustic + haptic) 1022 • Haptic Feedback Emission 1024 • Learning Evaluation (including EEG, HRV, GSR responses) 1026 • Adaptive Modulation of Haptic Signals 1028 b. Decision Tree: • When to use haptic feedback vs. acoustic only • How to combine acoustic and haptic cues c. Learning Reinforcement Diagram: • How haptic cues enhance memory formationDocket No.: 15238.58 • Multi-sensory association (sound + touch) 5. Integration with Acoustic Cues: • Timeline showing synchronization of acoustic and haptic cues • Diagram of how haptic intensity can emphasize acoustic volume 6. Power Management: • Small schematic showing power supply to haptic component • Graph of power consumption for different haptic patterns 7. Customization Options: • Parameters that can be adjusted: ^ Vibration intensity ^ Pattern duration ^ Frequency ^ Rhythm • These parameters adapt based on learning progress 8. Feedback Mechanism: • Sensors for detecting successful haptic cue delivery • Adjustment process based on animal's response (EEG, HRV, GSR data) 9. Safety Features: • Temperature monitoring to prevent overheating • Automatic shut-off for prolonged use • Gradual intensity increase feature 10. Attention and Surprise Response Integration: • Diagram showing how haptic feedback is modulated based on attention levels and surprise responses 11. Additional Details: • A small graph showing learning effectiveness with and without haptic feedback • Simplified block diagram of the haptic control system • An illustration of neural pathways activated by haptic stimulationDocket No.: 15238.58 • A diagram showing how haptic feedback integrates with the overall learning assessment system (EEG, HRV, GSR)

[0064] FIG. 11 shows a smart home integrations diagram for enhanced animal communication and learning according to an embodiment of the invention, including a cutaway view of a home with smart devices, a data flow diagram, a learning scenario flowchart, and a device interaction timeline. 1. Main Section - Smart Home Layout: a. Cutaway view of a two-story home 1100, showing: • Living room, kitchen, bedroom, home office, and backyard b. Animal (dog) wearing smart collar c. Smart devices throughout the home, including: • Smart speaker (living room) • Smart TV (living room) • Automated pet feeder (kitchen) • Smart pet door (leading to backyard) • Security cameras (various locations) • Smart lighting system • Environmental sensors (temperature, humidity, air quality) • Smart thermostat • Automated toy dispenser • Water fountain with flow sensor d. Central hub or router for device communication e. Smartphone or tablet showing control app interface 2. Data Flow Diagram: a. Central node: Smart collar 1102 b. Connecting to other nodes representing each smart device c. Arrows indicating bidirectional data flow d. Color-coded connections based on data type: • Blue: Command signals • Green: Sensor data • Red: Alert notificationsDocket No.: 15238.58 • Yellow: Learning-related information 3. Learning Scenario Flowchart: a. Start: Animal approaches smart feeder 1104 b. Feeder sensors detect presence1106 c. Collar receives signal from feeder 1108 d. Collar emits appropriate word sound 1110 (e.g., "Food" or "Eat") e. Animal's reaction monitored via collar sensors 1112 (e.g., EEG, HRV, GSR) f. Learning progress updated in central system 1114 g. Adapt future interactions based on response 1116 4. Device Interaction Timeline: a. Horizontal timeline showing a typical day b. Interactions between collar and other devices c. Highlight learning opportunities throughout the day d. Show how different devices contribute to varied vocabulary 5. Integration with Language Teaching Method: a. Small diagram referencing Figure 7 b. How smart home devices contribute to: • Attention Detection (Step 1) • Environmental Interpretation (Step 2) • Cue Generation (Step 3) c. Show how device data enhances: • Learning Evaluation (Step 4) • Adaptive Modulation (Step 5) 6. Key Features: a. Contextual Learning: • Different room contexts provide varied learning opportunities b. Multi-modal Interaction: • Demonstrate acoustic, visual, haptic, and scent cues from various devices c. Behavioral Triggers:Docket No.: 15238.58 • Animal actions activate different smart devices d. Safety and Comfort: • The system monitors and maintains optimal environment 7. Data Security and Privacy: • Encrypted communication • Data anonymization for non-essential information 8. Expandability and Customization: • An "expansion port" or area for adding future devices • A customization menu on the smartphone app interface 9. Energy Efficiency: • Optimized power consumption of the integrated system 10. Attention and Surprise Response Integration: • Show how smart home devices contribute to attention assessment and surprise detection • format for easy scaling and editing 11. Additional Details: • A table listing key smart devices and their primary functions in the learning process • A simplified network topology diagram showing how devices connect • An illustration of potential future integrations (e.g., smart garden for outdoor learning) • The system adapts to multi-pet households

[0065] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and its practical application, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular useDocket No.: 15238.58 contemplated. It is intended that the scope of the invention be defined by the components and elements described herein and their equivalents.

[0066] An animal communication and monitoring device, may comprise: a speaker for emitting acoustic cues or feedback; and a controller for modulating the frequency, type and volume of the acoustic cues based on the animal's state of learning and preferences.

[0067] The animal communication and monitoring device may further comprise: integrated smart sensors for monitoring the animal's behavior, activity, and location, wherein the integrated smart sensors comprise one or more of accelerometers, strain gauges, temperature, radio frequency, gyroscopes, EEG, EKG, body temperature, blood pressure, heart rate, heart rate variability, galvanic skin response, LPS, and GPS.

[0068] The animal communication and monitoring device may further comprise: a wireless receiver for receiving acoustic cues from a connected system.

[0069] The animal communication and monitoring device may further comprise: a plurality of buttons that, when actuated by the animal or a human, trigger the speaker to emit corresponding word sounds.

[0070] The animal communication and monitoring device may operate independently or in conjunction with a local or remotely connected system, the local or remotely connected system comprising: a plurality of cameras, microphones, or sensors placed throughout the home or outdoor areas; a central processing unit analyzing data collected by the integrated smart sensors, cameras, microphones, or sensors; a wireless communication module to transmit identified acoustic cues to the speaker, wherein the connected system includes user-configurable settings and data encryption.

[0071] The animal communication and monitoring device may further comprise: a mobile app receiving input from the animal's owner, providing real- time feedback, and adjusting the controller's settings based on the animal's learning progress and preferences.

[0072] The animal communication may further comprise: lights or signaling devices to support the acoustic cues emitted by the speaker.

[0073] The animal communication may further comprise: a voice recognition module recognizing specific voice commands from the animal’s owner andDocket No.: 15238.58 enabling the animal’s owner to provide real-time feedback on the animal's behavior.

[0074] The animal communication and monitoring device may further comprise: a haptic feedback mechanism providing tactile reinforcement in addition to the acoustic cues, enhancing the animal's learning experience.

[0075] The animal communication and monitoring device may communicate directly, or via an external controller, with other smart devices, the other smart devices comprising one or more of automated feeders, toys, and pet doors, thereby creating a local environment that provides behavioral enrichment, wherein the mobile app comprises a social networking and progress sharing feature for the animal’s owner to share the animal’s progress, exchange training tips, and connect with other users for support and community-building.

[0076] The animal communication and monitoring device may translate words in at least one human language into a language defined with sounds capable of reproduction by both a dog and the human.

[0077] In another embodiment, an animal communication and monitoring device, may comprise: means for securing the device to an animal; a speaker emitting acoustic cues or feedback; a controller modulating the frequency, type and volume of the acoustic cues based on the animal's state of learning and preferences; at least one sensor from the list of EEG, HRV monitor, blood pressure monitor, heart rate, inertial motion unit, breathing rate monitor, breathing depth monitor, pulse oximeter, galvanic skin response monitor, rate of panting monitor, and thermometer; and the device may modify output based at least in part on the feedback from the at least one sensor.

[0078] The animal communication and monitoring device may have at least one sensor that is an EFG sensor.

[0079] The animal communication and monitoring device may have at least one sensor that is a heart rate variability monitor.

[0080] The animal communication and monitoring may have at least one sensor that is a blood pressure monitor.

[0081] The animal communication and monitoring device may have at least one sensor that is a heart rate monitor. The animal communication and monitoring device may have at least one sensor that is a galvanic skin response monitor.Docket No.: 15238.58

[0082] All publications, patents, patent applications and other references cited in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication, patent, patent application or other reference was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. Citation of a reference herein shall not be construed as an admission that such is prior art to the present invention.

[0083] The detailed description set forth above is provided to aid those skilled in the art in practicing the present invention. However, the invention described and claimed herein is not to be limited in scope by the specific embodiments herein disclosed because these embodiments are intended as illustration of several aspects of the invention. Any equivalent embodiments are intended to be within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description, which do not depart from the spirit or scope of the present inventive discovery. Such modifications are also intended to fall within the scope of the appended claims.

Claims

Docket No.: 15238.58 CLAIMS What is claimed:

1. An animal communication and monitoring device, comprising: a speaker for emitting acoustic cues or feedback; and a controller for modulating the frequency, type and volume of the acoustic cues based on the animal's state of learning and preferences.

2. The animal communication and monitoring device of claim 1, further comprising: integrated smart sensors for monitoring the animal's behavior, activity, and location.

3. The animal communication and monitoring device of claim 2, wherein the integrated smart sensors comprise one or more of accelerometers, strain gauges, temperature, radio frequency, gyroscopes, EEG, EKG, body temperature, blood pressure, heart rate, heart rate variability, galvanic skin response, LPS, and GPS.

4. The animal communication and monitoring device of claim 1, further comprising: a wireless receiver for receiving acoustic cues from a connected system.

5. The animal communication and monitoring device of claim 4, further comprising: a plurality of buttons that, when actuated by the animal or a human, trigger the speaker to emit corresponding word sounds.

6. The animal communication and monitoring device of claim 2, operating independently or in conjunction with a local or remotely connected system, the local or remotely connected system comprising: a plurality of cameras, microphones, or sensors placed throughout the home or outdoor areas;Docket No.: 15238.58 a central processing unit analyzing data collected by the integrated smart sensors, cameras, microphones, or sensors; a wireless communication module to transmit identified acoustic cues to the speaker.

7. The animal communication and monitoring device of claim 6, wherein the connected system includes user-configurable settings and data encryption.

8. The animal communication and monitoring device of claim 2, further comprising: a mobile app receiving input from the animal's owner, providing real- time feedback, and adjusting the controller's settings based on the animal's learning progress and preferences.

9. The animal communication and monitoring device of claim 2, further comprising: lights or signaling devices to support the acoustic cues emitted by the speaker.

10. The animal communication and monitoring device of claim 2, further comprising: a voice recognition module recognizing specific voice commands from the animal’s owner and enabling the animal’s owner to provide real-time feedback on the animal's behavior.

11. The animal communication and monitoring device of claim 2, further comprising: a haptic feedback mechanism providing tactile reinforcement in addition to the acoustic cues, enhancing the animal's learning experience.

12. The animal communication and monitoring device of claim 2, communicating directly, or via an external controller, with other smart devices, the other smart devices comprising one or more of automatedDocket No.: 15238.58 feeders, toys, and pet doors, thereby creating a local environment that provides behavioral enrichment.

13. The animal communication and monitoring device of claim 8, wherein the mobile app comprises a social networking and progress sharing feature for the animal’s owner to share the animal’s progress, exchange training tips, and connect with other users for support and community-building.

14. The animal communication and monitoring device of claim 2, translating words in at least one human language into a language defined with sounds capable of reproduction by both a dog and the human.

15. An animal communication and monitoring device, comprising: means for securing the device to an animal; a speaker emitting acoustic cues or feedback; a controller modulating the frequency, type and volume of the acoustic cues based on the animal's state of learning and preferences; at least one sensor from the list of EEG, HRV monitor, blood pressure monitor, heart rate, inertial motion unit, breathing rate monitor, breathing depth monitor, pulse oximeter, galvanic skin response monitor, rate of panting monitor, and thermometer; the device modifying output based at least in part on the feedback from the at least one sensor.

16. The animal communication and monitoring device of claim 15, where the at least one sensor is EEG.

17. The animal communication and monitoring device of claim 15, where the at least one sensor is a heart rate variability monitor.

18. The animal communication and monitoring device of claim 15, where the at least one sensor is a blood pressure monitor.

19. The animal communication and monitoring device of claim 15, where the at least one sensor is a heart rate monitor.Docket No.: 15238.58 20. The animal communication and monitoring device of claim 15, where the at least one sensor is a galvanic skin response monitor.

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