The present invention introduces a universal AI-powered pet management platform that establishes an entirely new category of technology, transcending conventional pet training systems. This comprehensive system integrates a modular wearable pet device with interchangeable sensors, sophisticated AI processing capabilities, and diverse output modules to create a unified ecosystem for holistic pet care. Unlike traditional training devices focused solely on behavior modification, this platform simultaneously manages multiple domains including real-time health monitoring, environmental safety assessment, emotional well-being analysis, autonomous training, emergency response, and seamless integration with external systems. The platform's universal architecture enables dynamic adaptation across diverse applications from companion animals to service animals, wildlife monitoring, and specialized deployments. By leveraging advanced artificial intelligence models, multimodal communication pathways, and a universal API for third-party integration, the system creates an interconnected technological framework that fundamentally transforms the relationship between pets, technology, and human interaction, rendering isolated pet devices obsolete.

A novel pet finder and rescue tracker device is disclosed. The device is a microchip that can be fastened to a collar by way of a tag or pendant, incorporated into the pet's collar itself, implanted under the animal's skin like a standard microchip, or secured to a smart watch if used with a person. The pet finder and rescue tracker device comprises a body component comprising a battery, a microchip, a location tracking component, an audible component, at least one light, and a transmitter/transceiver. Typically, when the microchip/body component senses danger, it will light up and start flashing and making noise to aid those in search of the animal. The schematic on the app will also light up in the location of where the animal is, in order to aid in its rescue.

A computer-implemented method for using an image classifier to identify pet oral conditions and pet dermatological conditions is disclosed. The method includes receiving an indication from a user device to initiate a pet condition analysis process for a pet, collecting pet data corresponding to the pet, outputting a pet image prompt to a user interface of the user device, receiving pet image data via the user interface of the user device, wherein the pet image data includes oral image data of the pet or dermatological image data of the pet, inputting the pet image data and the pet data into a machine-learning model to identify a pet condition and a pet condition recommendation, based on the inputting, receiving the pet condition and the pet condition recommendation from the machine-learning model, and outputting the pet condition and the pet condition recommendation to the user interface of the user device.

The present disclosure proposes a solution wherein a smart vest worn on dogs and a related system including the vest, a sever and a user device are used for discerning the dogs' mood and anxiety levels, and alleviate their distress e.g., when left alone at home. The solution may provide emotional monitoring, multi-sensory interaction, remote control and monitoring, customizable responses, and holistic wellbeing for the dogs.

A smart cat scratcher and a system for implementing a reward protocol for a smart cat scratcher, and/or methods of manufacturing and/or using the smart cat scratcher and system. The smart cat scratcher is configured to automatically dispense rewards in response to detected scratching activity by a cat. The smart cat scratcher includes a scratching surface, one or more sensors configured to detect the scratching activity, a scratcher controller, and a reward dispenser. The scratcher controller is programmed with a reward protocol that includes a training protocol and a maintenance protocol. The training protocol includes multiple training levels, each with corresponding reward schedules and criteria for graduation. The maintenance protocol includes a partial reinforcement schedule for maintaining established scratching behavior. The smart cat scratcher is configured to communicate with a user terminal to receive input and provide status updates regarding the cat's interaction with the smart cat scratcher.

A method for predicting pet health conditions includes receiving activity data generated by at least one sensor device configured to detect activity of a pet, determining behavior data indicative of a plurality of behaviors of the pet based on the activity data, receiving a trained neural architecture for a representation model configured to facilitate health condition predictions and another neural architecture for a classification model configured to predict a health condition of the pet based at least in part on the behavior data, predicting, using the trained neural architecture, presence of the health condition of the pet, and causing a user device to display a notification including information identifying the health condition.

Provided is a robot including: communication circuitry; a driver; memory storing instructions; and a processor, wherein the instructions, when executed by the processor, cause the robot to: based on cleaning being started, transmit, to at least one accessory device through the communication circuitry, a first signal notifying of a start of cleaning and requesting a pet search, based on receiving search information regarding a pet obtained from the at least one accessory device, transmit, to the at least one accessory device through the communication circuitry, a second signal notifying of a luring position of the pet based on a cleaning position and the search information, and based on receiving a third signal indicating the pet was successfully lured to the luring position, control the driver to move the robot to the cleaning position and control the robot to start cleaning at the cleaning position.