System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting and storing pet information, a camera and a health device for acquiring video data and biological data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and detecting abnormality, and a means for transmitting a notification to a breeder on the basis of an analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The goal is to provide a system that allows pet owners to keep a detailed grasp of their pet's health condition and respond quickly, even when they are out or busy. Until now, many pet owners have tended to overlook changes in their pet's health and have faced the problem of not being able to provide appropriate care. As a result, it has been difficult to detect and prevent illness early, increasing the health risks for pets. [Means for solving the problem]

[0005] This invention provides a means for inputting and saving pet information, a camera for acquiring video data and biometric data, and a health device. Furthermore, by using a means for transmitting acquired data to a cloud server, it is possible to analyze the data on the cloud server and detect abnormalities. Furthermore, it is equipped with a means for sending notifications to the owner based on the analysis results. This allows for real-time monitoring of the pet's health status and prompt response as needed. The cloud server also has a function for generating personalized training methods and diet plans based on the pet's characteristics, thereby improving the pet's quality of life (QOL).

[0006] "Pet information" refers to basic data such as your pet's name, breed, age, and medical history.

[0007] "Camera" refers to a photographic device for capturing video data of pets.

[0008] A "health device" refers to a measuring device used to obtain a pet's biological data (heart rate, body temperature, activity level, etc.).

[0009] A "cloud server" refers to a server on the Internet for storing and analyzing data.

[0010] "Analyzing data" refers to the process of evaluating and determining the health of your pet based on the acquired video and biometric data.

[0011] "Detecting anomalies" refers to detecting an abnormal state from acquired and analyzed data.

[0012] "Sending a notification" refers to sending a message to the owner based on the analysis results or abnormality detection.

[0013] "Individualized training methods" refer to providing optimal exercise and behavioral guidance methods based on the characteristics of your pet.

[0014] An "individualized meal plan" refers to providing the optimal diet based on your pet's characteristics and health condition.

[0015] "QOL (Quality of Life)" refers to a standard that comprehensively evaluates a pet's quality of life and health condition. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The system of the present invention aims to monitor the health status of pets in real time, detect abnormalities, and notify owners. This system mainly includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, and a means for notifying owners based on the analysis results.

[0038] System Configuration

[0039] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[0040] 2. Device: The pet camera captures images of your pet, and the health device captures biometric data such as heart rate, body temperature, and activity level, which are then sent to a cloud server in real time.

[0041] 3. Server: Analyzes the received data using a data analysis tool to detect abnormalities. If an abnormality occurs, a notification is sent to the owner based on the analysis results.

[0042] The following will explain this with specific examples of system operation.

[0043] Specific example of system operation

[0044] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0045] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. Once the installation and attachment are complete, connect these devices to the dedicated app.

[0046] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and temperature.

[0047] 4. Terminal: The acquired video and biometric data is transmitted to a cloud server via Wi-Fi in real time.

[0048] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[0049] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[0050] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0051] 8. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner can consult a veterinarian.

[0052] Furthermore, the cloud server has the ability to generate individual training methods and diet plans based on the data, which can contribute not only to daily health management but also to improving the pet's quality of life (QOL).

[0053] As described above, the system of the present invention allows owners to monitor their pets' health conditions in real time and provide appropriate care, thereby reducing health risks and providing an optimal living environment for pets.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0057] Step 2:

[0058] User: Install the pet camera in a suitable location, such as the living room, and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[0059] Step 3:

[0060] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[0061] Step 4:

[0062] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[0063] Step 5:

[0064] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns from the video data and evaluates its health condition from the biometric data.

[0065] Step 6:

[0066] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[0067] Step 7:

[0068] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[0069] Step 8:

[0070] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0071] Step 9:

[0072] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0073] Step 10:

[0074] Server: Based on the data collected daily, AI automatically generates individual training methods and meal plans tailored to the pet's characteristics.

[0075] Step 11:

[0076] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[0077] Step 12:

[0078] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0079] In this way, the entire system works together to manage pet health in real time, helping owners respond appropriately.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] There is a growing need for systems that can monitor pet health in real time and quickly notify owners when abnormalities are detected. Conventional systems have struggled to quickly and accurately detect abnormalities and provide appropriate countermeasures. They also lack the ability to provide training methods and dietary plans that address individual pet needs.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes a means for inputting and saving pet information, a device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and identifying behavioral patterns, a means for evaluating health status and detecting abnormalities from the biometric data, a means for sending a notification of appropriate measures to the owner based on the analysis results, and a means for generating training methods and diet plans for the pet from the cloud server. This allows for accurate real-time monitoring of the pet's health status and prompt notification when an abnormality is detected. Furthermore, by providing training methods and diet plans tailored to the pet's individual needs, the quality of life (QOL) of the pet can be improved.

[0085] "Means for entering and saving pet information" refers to a function that allows users to enter basic information such as a pet's name, breed, age, and medical history through a dedicated app and save it in a database.

[0086] "Devices for acquiring video data and biometric data" refers to devices such as pet cameras and health devices that capture and collect video of pets and biometric data such as heart rate, body temperature, and activity level.

[0087] The "means for transmitting acquired data to a cloud server" is a communication function for transmitting video data and biometric data to a cloud server via the Internet in real time or periodically.

[0088] The "means for analyzing the data on the cloud server and identifying behavioral patterns" refers to algorithms and software for analyzing the video and biometric data received on the cloud server and identifying the pet's behavior (walking, resting, eating, etc.).

[0089] The "means for assessing health status and detecting abnormalities from biometric data" is a system that analyzes biometric data such as heart rate and body temperature on a cloud server to assess health status and detect abnormalities.

[0090] The "means for sending a notification of appropriate countermeasures to the owner based on the analysis results" is a function for sending a notification including information on countermeasures to the owner's dedicated app or messaging app when an abnormality is detected based on the analysis results.

[0091] "Means for generating training methods and meal plans for pets from the cloud server" refers to a function for generating and providing training methods and meal plans that meet the individual needs of pets based on data on the cloud server.

[0092] The system of the present invention aims to monitor the health status of pets in real time and notify the owner when an abnormality is detected. This system mainly includes the following components.

[0093] User input of pet information and device configuration

[0094] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information about their pet, such as its name, breed, age, and medical history. This information is stored in a database and processed on a cloud server. Users also install a pet camera in their living room and attach a health device to their pet's collar. By linking these devices with the dedicated app, data can be acquired and transmitted.

[0095] Device data acquisition and transmission

[0096] The pet camera captures video of the pet in real time, and the health device periodically measures biometric data such as heart rate, body temperature, and activity level. The captured video data and biometric data are transmitted to a cloud server in real time via Wi-Fi. The specific hardware used here includes a dedicated pet camera and health device.

[0097] Data analysis and anomaly detection by the server

[0098] The cloud server uses data analysis software such as Python and R to analyze the received video and biometric data. Image recognition algorithms are used to identify the pet's behavior (walking, resting, eating, etc.) from the video data, and statistical analysis is used to evaluate the pet's health status from the biometric data. This includes algorithms to detect abnormalities in heart rate and body temperature. If an abnormality is detected, a notification is generated based on the analysis results, containing a description of the abnormality and recommended measures.

[0099] Server-generated and sent notifications

[0100] The generated notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification will be sent saying, "Heart rate is high. Please consult a veterinarian." This notification allows the owner to take appropriate action immediately.

[0101] User Receipt and Response to Notices

[0102] Users can check the notifications in a dedicated app and take necessary measures to manage their pet's health. For example, if the heart rate is abnormally high, it is recommended that they consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user via notifications.

[0103] Specific examples of actions and prompts

[0104] Let's take the example of a pet owner using this system for their three-year-old dog. They enter their pet's information into a dedicated app and set up the camera and health device. The cloud server analyzes the pet's behavioral patterns and biological data in real time, and immediately sends a notification if an abnormality is detected. The pet owner can then take appropriate action.

[0105] Example prompt sentence:

[0106] Please explain the detailed processing steps of a system that monitors the health of pets in real-time and notifies the owner in case of anomalies, using specific terms and actions for each step.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1: User enters pet information and sets up device

[0109] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information such as their pet's name, breed, age, and medical history. The entered information is saved in a database. This information is the basic data used for analysis in subsequent steps and is processed on a cloud server. For example, a user may enter their pet's name as "Buddy" and its age as "3."

[0110] Step 2: Obtaining data via the device

[0111] A pet camera installed on a terminal captures video of the pet in real time. At the same time, a health device attached to the pet's collar periodically measures biometric data such as heart rate, body temperature, and activity level. The input data consists of video data and biometric data, and is acquired from each terminal. For example, the camera records video at a frame rate of 30 fps, and the health device records the heart rate as "90 bpm" every 30 seconds.

[0112] Step 3: Send data to cloud server

[0113] The video and biometric data captured by the device is transmitted to a cloud server in real time via Wi-Fi. The data is then sent to the cloud server's API endpoint via an Internet connection. This transmission process is continuous, with data updates in real time. For example, a camera device uploads a 30-second video clip to the cloud server.

[0114] Step 4: Data analysis by the server

[0115] The server analyzes the received data. Specifically, it uses data analysis software such as Python or R to analyze the video data and uses image recognition algorithms to identify the pet's behavior (walking, resting, eating, etc.). At the same time, it performs statistical analysis using biometric data to evaluate the pet's health. The input is the video data and biometric data sent to the cloud server, and the output is the behavioral patterns and health assessment results. For example, if the video analysis assigns a tag of "eating" and the heart rate exceeds "120 bpm," it will flag it as an "anomaly detected."

[0116] Step 5: Server detects anomalies and generates notifications

[0117] If an abnormality is detected based on the analysis results, the server generates a notification containing the details of the abnormality and recommended measures. This notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification stating "Heart rate is high. Please consult a veterinarian" is generated. The input is the result of data analysis, and the output is the notification message.

[0118] Step 6: User Receipt and Action

[0119] The user checks the notifications received through the dedicated app and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user may consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user through notifications. The input is the notification content and additional suggestions from the server, and the output is the pet owner's specific actions.

[0120] (Application example 1)

[0121] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] When raising a pet, it is important to monitor the pet's health in real time and quickly detect any suspicious movements or sounds and notify the owner. However, conventional systems have difficulty monitoring the pet's health and security simultaneously, requiring dual systems, which increases costs and management efforts. The present invention aims to solve this problem.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0124] In this invention, the server includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and detecting abnormalities, a means for sending a notification to the owner based on the analysis results, and a means for monitoring suspicious movements and sounds, detecting abnormalities, and notifying the owner. This makes it possible to simultaneously monitor not only the health status of the pet, but also security abnormalities and notify them in real time.

[0125] "Pet information" refers to basic attribute data including the pet's name, breed, age, medical history, etc.

[0126] "Video data" refers to real-time video data of a pet captured by a camera.

[0127] "Biometric data" refers to data that indicates your pet's health, such as heart rate, body temperature, and activity level.

[0128] A "camera" is a device for acquiring video data.

[0129] A "health device" is a device for acquiring biometric data from a pet.

[0130] A "cloud server" is a remote server that receives data over a network and performs analysis.

[0131] "Abnormal" refers to any unusual or suspicious changes in a pet's health or behavior.

[0132] "Notifications" are warnings or information sent to the owner based on the analysis results.

[0133] "Suspicious behavior" refers to unexpected or unusual behavior.

[0134] "Sound" refers to audio data acquired through a camera or microphone.

[0135] "Analysis means" refers to algorithms or software that analyze the acquired data and determine whether or not there are any abnormalities.

[0136] The following configuration and operation procedure are conceivable as an embodiment of the present invention.

[0137] System Configuration

[0138] Hardware and Software

[0139] 1. How to enter and save your pet's information:

[0140] A dedicated application installed on the owner's smartphone or other device.

[0141] 2. Cameras and health devices for capturing video and biometric data:

[0142] The camera device is a commercially available surveillance camera such as Nest Cam, which captures footage of your pet in real time.

[0143] Health devices are wearable devices like FitBark that continuously collect biometric data such as heart rate, body temperature, and activity levels.

[0144] 3. Cloud Server:

[0145] Use a cloud service such as AWS Lambda as a server to receive data, analyze it, and detect anomalies.

[0146] Data processing and calculation

[0147] Server-side processing

[0148] 1. Data Collection:

[0149] Collect real-time video and biometric data from pet cameras and health devices.

[0150] Obtain basic information about your pet (name, breed, age, medical history, etc.) from the smartphone app.

[0151] 2. Data transmission:

[0152] The collected data is sent to a cloud server via Wi-Fi.

[0153] 3. Data Analysis:

[0154] A generative AI model is used on a cloud server to analyze video data and biometric data, identifying pet behavior patterns (e.g., walking, resting, eating) from the video data and assessing health status from the biometric data.

[0155] Suspicious movements and sounds are also analyzed, and they are analyzed comprehensively.

[0156] 4. Anomaly detection:

[0157] Based on the analysis results, if there is anything abnormal about the pet's health or if suspicious movements or sounds are detected, it will be recognized as an abnormality.

[0158] 5. Notification Generation and Transmission:

[0159] If an abnormality is detected, the cloud server will notify the owner via a dedicated application or messaging app.

[0160] The notification will include details of the anomaly and recommended actions to take.

[0161] 6. Generate personalized training and meal plans:

[0162] Based on your pet's characteristics, the system generates optimal training and feeding plans and reflects them in a dedicated application.

[0163] Specific examples

[0164] When this system is running, it can detect the following situations, for example:

[0165] A higher than normal heart rate may mean your pet is overly stressed.

[0166] If a sudden rise in body temperature is recorded, a heatstroke warning will be displayed.

[0167] It picks up any unusual activity from the camera feed and notifies owners of any suspicious individuals entering or pets escaping.

[0168] Prompt Sentence Examples

[0169] Design a system to monitor a pet's health in real time. This system would measure the pet's heart rate, body temperature, and activity level using a health device, and capture video and audio using a camera. A mechanism must be in place to notify the pet owner if an abnormality is detected. The system should also use a cloud server to analyze the data and send notifications. Please explain the system configuration and provide a concrete example of how it works.

[0170] The above is an embodiment of the invention. This embodiment makes it possible to simultaneously monitor not only the health status of pets but also any security abnormalities, and to take any necessary measures promptly.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter and save basic information about their pet, such as its name, breed, age, and medical history. This information is then saved within the dedicated app.

[0174] Input: Basic information about your pet (name, breed, age, medical history, etc.)

[0175] Output: Basic information data of pets stored on the device

[0176] Step 2:

[0177] The user simply installs the pet camera in the living room and attaches the pet health device to the pet's collar. Once the installation and attachment are complete, the devices are linked to the dedicated app and are ready to use.

[0178] Input: Installing the pet camera, wearing the health device, and linking the device to the app

[0179] Output: Camera and health device linked to the app

[0180] Step 3:

[0181] The device receives real-time video data from the pet camera and periodically collects biometric data such as heart rate, body temperature, and activity level from the health device.

[0182] Input: Real-time video data from pet cameras, biometric data from health devices

[0183] Output: Acquired video data and biometric data

[0184] Step 4:

[0185] The device transmits the acquired video data and biometric data to a cloud server via Wi-Fi in real time.

[0186] Input: Acquired video data and biometric data

[0187] Output: Data sent to the cloud server

[0188] Step 5:

[0189] The server analyzes the received video and biometric data, uses a generative AI model to identify the pet's behavioral patterns (e.g., walking, resting, eating), and assesses its health status from the biometric data. It also analyzes any suspicious movements or sounds, and performs a comprehensive analysis.

[0190] Input: Video data and biometric data sent to the cloud server

[0191] Output: Evaluation results of behavioral patterns and health status, detection results of suspicious movements and sounds

[0192] Step 6:

[0193] If an abnormality is detected, the server generates a notification based on the analysis results to notify the owner of the abnormality, including details of the abnormality and recommended measures.

[0194] Input: Behavioral patterns and health status assessment results, suspicious movement and sound detection results

[0195] Output: Notification sent to the breeder

[0196] Step 7:

[0197] The server instantly sends the generated notifications to the owner via a dedicated application or messaging app.

[0198] Input: Generated notification

[0199] Output: Notification sent to the breeder

[0200] Step 8:

[0201] The user checks the notification and takes necessary measures to manage the health and safety of their pet, such as consulting a veterinarian if the heart rate is abnormally high, or contacting the police if a suspicious person is detected.

[0202] Input: Notification sent to breeder

[0203] Output: Appropriate action by the breeder

[0204] The above are the specific processing steps for carrying out the present invention, which enable real-time monitoring of pet health conditions and security abnormalities, and prompt response.

[0205] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0206] The system of the present invention not only monitors the health condition of pets in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on them. This system includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results by combining an emotion engine.

[0207] System Configuration

[0208] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[0209] 2. Device: The pet camera captures the pet's video, and the health device acquires biometric data such as heart rate, body temperature, and activity level, which are then transmitted to the cloud server in real time.

[0210] 3. Server: Analyzes the received data using a data analysis means and detects any abnormalities. If an abnormality occurs based on the generated analysis results, a notification is sent to the breeder.

[0211] 4. Server: The emotion engine recognizes emotions from the user's voice and text data and optimizes notification content, training plans, and meal plans.

[0212] Specific example of system operation

[0213] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0214] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. After installation and attachment are complete, link these devices with the dedicated app and check the device settings.

[0215] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and body temperature.

[0216] 4. Terminal: The acquired video data and biometric data are sent to the cloud server via Wi-Fi in real time.

[0217] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[0218] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[0219] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0220] 8. Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, it will reflect this in the notification and suggest appropriate ways to respond.

[0221] 9. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0222] 10. Server: Based on the data collected daily, the AI ​​automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts the way these plans are presented based on the user's emotional state.

[0223] 11. Server: The generated training methods and feeding plans are sent to the dedicated app and proposed to the owner. The implementation status of each proposal is tracked and the plan is adjusted as necessary.

[0224] 12. User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0225] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[0226] The processing flow will be explained below.

[0227] Step 1:

[0228] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0229] Step 2:

[0230] User: Install the pet camera in an appropriate location and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[0231] Step 3:

[0232] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[0233] Step 4:

[0234] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[0235] Step 5:

[0236] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video data and evaluates its health condition from the biometric data.

[0237] Step 6:

[0238] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[0239] Step 7:

[0240] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[0241] Step 8:

[0242] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0243] Step 9:

[0244] Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, the engine will reflect this in the notification and suggest appropriate ways to respond.

[0245] Step 10:

[0246] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0247] Step 11:

[0248] Server: Based on the data collected daily, AI automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts how these plans are presented based on the user's emotional state.

[0249] Step 12:

[0250] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[0251] Step 13:

[0252] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0253] Example 2

[0254] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0255] While conventional pet health management systems can monitor a pet's biological information and behavioral data and detect abnormalities, they lack the ability to recognize the user's emotional state and optimize notification content, or the ability to provide personalized training and diet plans. With such systems, users simply receive abnormality notifications, making it difficult to optimize their pet's health management.

[0256] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0257] In this invention, the server includes means for inputting and saving pet information, sensors and monitors for acquiring video data and biometric data, means for transmitting the acquired data to the server, means for analyzing the data on the server and detecting abnormalities, means for sending notifications to the caregiver based on the analysis results, emotion recognition means for optimizing the content of the notifications, and means for generating different training plans and meal plans depending on the situation. This makes it possible to monitor the health condition of the pet in real time and provide optimal notifications and personalized training plans and meal plans that take the user's emotional state into consideration.

[0258] "Means for entering and storing pet information" means an interface that allows a user to enter basic data about a pet (such as name, breed, age, medical history, etc.) and store it in a database or cloud.

[0259] "Sensors and monitors for acquiring video data and biometric data" refers to cameras that capture pet behavior and devices that measure pet biometric information (heart rate, body temperature, activity level, etc.).

[0260] The "means for transmitting acquired data to a server" refers to a function for transmitting data acquired from sensors and monitors to a cloud server using a communication means such as the Internet.

[0261] "Means for analyzing the data on the server and detecting abnormalities" refers to a method in which the cloud server processes the data received using an analytical algorithm and detects abnormalities in the pet's behavior and biological information.

[0262] "Means for sending a notification to the breeder based on the analysis results" refers to a function that notifies the user of the details and how to respond when an abnormality is detected through data analysis.

[0263] "Emotion recognition means for optimizing notification content" refers to an algorithm or system that analyzes the user's emotional state from their voice or text and adjusts the notification content based on the results.

[0264] "A means for generating different training plans and meal plans according to the situation" refers to a system or algorithm that automatically generates customized training methods and meal plans based on the behavioral data and biological information of a pet.

[0265] The system of the present invention not only monitors the health condition of a pet in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on the results. This system includes a means for inputting and saving pet information, sensors and monitors for acquiring video data and biological data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results, combining an emotion recognition means.

[0266] First, the user uses a dedicated app to input and save information about their pet. For example, they can use the app installed on their smartphone to input and save basic information about their pet, such as its name, breed, age, and medical history. This information is then stored on a cloud server.

[0267] Next, a camera is used to capture pet behavior and a health device is used to acquire biometric information. The pet camera captures video data of the pet in real time. The health device acquires biometric data such as the pet's heart rate, body temperature, and activity level. This data is sent to a cloud server via the Internet or Wi-Fi, and the sent data is encrypted and protected.

[0268] The cloud server analyzes the received video and biometric data to evaluate the pet's behavioral patterns and health status. Machine learning algorithms are used for data analysis. For example, the video data can identify behavioral patterns, such as whether the pet is walking, resting, or eating, and the biometric data can be used to evaluate whether the pet's heart rate is within a normal range.

[0269] If the anomaly detection method detects an abnormality in a pet's behavior or biometric data, it generates a notification containing the details and recommended measures. For example, it may generate a notification that reads, "Your heart rate is outside the normal range. Your body temperature is also elevated. Please refrain from walks and let your pet rest in a cool place. If necessary, consult a veterinarian." The generated notification is immediately sent to the user via a dedicated app or messaging app.

[0270] Furthermore, an emotion recognition system on the server analyzes the user's emotional state from their voice and text data. For example, if the user is feeling stressed, the notification content is optimized to take that emotional state into account. The notification may include customized suggestions such as "If you are concerned, contact your veterinarian immediately."

[0271] The user checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user is advised to immediately consult a veterinarian. In addition, the cloud server uses a generative AI model to automatically generate individual training methods and diet plans tailored to the pet's characteristics based on the data collected daily. Emotion recognition means adjusts the way these plans are presented based on the user's emotional state. Training methods and diet plans are suggested to the user through a dedicated app, which tracks the implementation of each suggestion and adjusts the plan as needed.

[0272] For example, the following prompt sentence is used:

[0273] User input:

[0274] Pet Name: Coco

[0275] Breed: French Bulldog

[0276] Age: 3 years old

[0277] Medical history: Allergies

[0278] Pet camera and health device setup complete.

[0279] Sending data to cloud server...

[0280] Start the analysis process on the server:

[0281] Identifying behavioral patterns from video data

[0282] Evaluating biological status from health device data

[0283] Anomaly detection:

[0284] If the heart rate is higher than normal, it will generate a notification saying "Pet's heart rate is outside of normal range"

[0285] Analyze user sentiment and optimize notifications

[0286] Final notice details:

[0287] "Your pet's heart rate is outside of the normal range. They also have an elevated body temperature. Please refrain from walks and allow them to rest in a cool place. Consult your veterinarian if necessary. If you are concerned, contact your veterinarian immediately."

[0288] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[0289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0290] Step 1:

[0291] User: The user installs the dedicated app on their smartphone, creates an account, and logs in. As input, they enter basic information about their pet, such as its name, breed, age, and medical history. This data is sent to the server and stored in the cloud. As output, they receive the saved pet information.

[0292] Step 2:

[0293] User: The user installs the pet camera in the living room and attaches the health device to the pet's collar. The user inputs the camera's installation location and the device's location information into the app. Based on this, the app connects with these devices and confirms the completion of the setup. The connection status between the camera and the device is obtained as an output.

[0294] Step 3:

[0295] Terminal: The pet camera captures the pet's video data in real time, and the health device obtains the pet's biometric data such as heart rate, body temperature, and activity level. The pet's movements and biometric information are captured by the camera and device as input. The video data and biometric data are obtained as output.

[0296] Step 4:

[0297] Terminal: The acquired video data and biometric data are sent to the cloud server via the Internet or Wi-Fi. The captured data is provided as input. The data is encrypted and transmitted. The output is the data stored on the cloud server.

[0298] Step 5:

[0299] Server: The cloud server analyzes the received video data and biometric data. The data stored on the server is fed as input to the analytical algorithm. The algorithm identifies the pet's behavioral patterns and evaluates its health status from the biometric data. The analysis results are provided as output.

[0300] Step 6:

[0301] Server: The anomaly detection means detects abnormalities in the pet's behavior and biometric data based on the analysis results. The data analysis results are provided as input. Based on this, a notification is generated containing the details of the abnormality and recommended measures. The output is an anomaly notification.

[0302] Step 7:

[0303] Server: The generated notification is sent to the user immediately via a dedicated app or messaging app. The abnormality notification is provided as input, allowing the user to instantly check the status of their pet. The notification sent to the user is obtained as output.

[0304] Step 8:

[0305] Server: The emotion recognition means analyzes the user's voice and text data to recognize the emotional state. The user's voice and text data are provided as input. The emotion engine analyzes this and evaluates the emotional state. The analysis result of the emotional state is obtained as output.

[0306] Step 9:

[0307] Server: The notification content is optimized based on the emotional state analysis results. The emotional state analysis results are provided as input. The notification generation algorithm regenerates the notification content according to the emotion. The optimized notification is obtained as output.

[0308] Step 10:

[0309] User: The user checks the notification and takes appropriate action to manage the health of the pet as needed. The input is the notification sent to the user. The user takes appropriate action based on the notification. The output is an improvement in the pet's health.

[0310] Step 11:

[0311] Server: Analyzes the data collected daily and generates training methods and diet plans tailored to the pet's characteristics. The accumulated data is provided as input. The generative AI model analyzes this and generates a customized plan. The output is the training method and diet plan.

[0312] Step 12:

[0313] Server: The generated training method and meal plan are proposed to the user through a dedicated app. The generated plan is provided as input. It is sent to the user, providing a specific action plan. The plan provided to the user is obtained as output.

[0314] Step 13:

[0315] User: The user checks the proposed training methods and diet plans through a dedicated app, selects the ones that apply, and implements them on their pet. The proposed plan is provided as input. The user manages their pet's health based on that plan. The implementation status and its effects are obtained as output.

[0316] In this way, each processing step works together to manage the pet's health condition in real time and generate optimal notifications and plans that take the user's emotions into consideration, thereby achieving more effective health management.

[0317] (Application example 2)

[0318] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0319] Systems already exist that monitor pet health in real time and detect and notify users of abnormalities. However, these systems lack the ability to optimize notification content based on the user's emotional state. As a result, they have been unable to fully reduce the psychological stress and burden on pet owners involved in managing their pet's health. Furthermore, training and feeding plans based on individual pet characteristics also fail to reflect the user's emotional state. This results in a lack of comprehensive support for both pets and owners.

[0320] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0321] In this invention, the server includes a means for analyzing the user's emotions and optimizing the notification content, an artificial intelligence means for generating new training plans and meal plans based on the pet's health condition, and a means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state, thereby making it possible to provide appropriate notifications and plans while monitoring the pet's health condition in real time and taking the caretaker's emotional state into consideration.

[0322] "Means for inputting and saving pet information" refers to a device or software that allows a user to input information such as the pet's name, breed, age, and health status, and save it in a database.

[0323] A "camera" is a device for capturing a pet's behavior and the surrounding environment as video data.

[0324] A "health device" is a device for collecting biometric data such as a pet's heart rate, body temperature, and activity level.

[0325] The "means for transmitting to a cloud server" refers to a device or software for transmitting acquired data to a remote cloud server via the Internet.

[0326] The "means for analyzing the data on the cloud server and detecting abnormalities" refers to software or algorithms that analyze the acquired data within the cloud server and diagnose whether there are any abnormalities in the pet's health condition.

[0327] The "means for sending a notification to the keeper based on the analysis results" refers to a messaging system or application that notifies the user of any abnormalities that are detected.

[0328] "Means for analyzing user emotions and optimizing notification content" refers to algorithms or software that analyze the user's voice and text data to determine their emotional state and send notifications in the most appropriate format accordingly.

[0329] "Artificial intelligence means for generating new training and diet plans based on the health status of pets" refers to AI models and algorithms that automatically generate optimal training methods and diet plans based on pet health data.

[0330] "Means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state" refers to an AI and analysis system that combines the pet's unique characteristics with the user's emotional state to customize a suitable training and meal plan.

[0331] This invention is a system that monitors the health status of pets in real time, detects abnormalities, and notifies owners. The system also analyzes the user's emotions and optimizes notification content and various plans based on those emotions. This provides comprehensive support for pet health management.

[0332] System configuration and functions

[0333] 1. Users

[0334] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information such as their pet's name, breed, age, and health condition. Users also install the pet camera in an appropriate location in their home or at a physical store (pet shop or pet hotel), and attach the health device to their pet's collar or other device.

[0335] 2. Terminal

[0336] The pet camera captures video data of your pet's behavior and surrounding environment, while the health device captures your pet's real-time biometric data, such as heart rate, body temperature, and activity level, which are then sent to a cloud server via the internet.

[0337] 3. Server

[0338] The server is in a cloud environment and has multiple analysis functions, including:

[0339] Data analysis and anomaly detection methods: Analyze your pet's video data and biometric data to detect anomalies. For this purpose, various algorithms and anomaly detection models are used.

[0340] Notification method: If an abnormality is detected, the user will be notified immediately via a dedicated smartphone app or messaging app.

[0341] Sentiment analysis engine: Analyzes the user's voice and text data to determine their emotional state. Emotion analysis uses emotion recognition algorithms and natural language processing technology.

[0342] Training and Feeding Plan Generation: AI models are available to generate personalized training and feeding plans based on your pet's health and characteristics, and the plan content and suggestions are automatically adjusted based on the user's emotional state.

[0343] Specific examples

[0344] For example, if a German Shepherd named Leo at a pet hotel exhibits an abnormal heart rate:

[0345] 1. The health device detects an abnormal heart rate.

[0346] 2. The data is sent to a cloud server in real time and any abnormalities are checked.

[0347] 3. The customer's (pet owner's) emotional data is sent to the server and analyzed by the emotion analysis engine.

[0348] 4. You will receive a notification saying, "Leo's heart rate is high. We recommend that you consult your veterinarian. You appear to be stressed and we also recommend that you take some time to relax."

[0349] Prompt Sentence Examples

[0350] "If a pet's heart rate is higher than normal, detect the abnormality and notify them. Also, take into account the customer's emotional state and suggest appropriate measures."

[0351] "Please create a program that analyzes pet health data and sends a notification if it detects any abnormalities. The content of the notification should also reflect the customer's emotional state."

[0352] In this way, the pet's health condition is monitored, and notifications and suggestions are given that take the user's emotional state into account, providing more effective and personal support.

[0353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0354] Step 1:

[0355] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information about their pet, such as its name, breed, age, and health condition. The entered data is sent from the smartphone app to a cloud server and stored in a database.

[0356] Input: Pet information

[0357] Output: Pet information stored on the cloud server

[0358] Step 2:

[0359] Users install the pet camera in an appropriate location in their home or a physical store (pet shop or pet hotel) and attach the health device to their pet's collar, etc. The camera and health device collect video data and biometric data (heart rate, body temperature, activity level, etc.) of their pet in real time.

[0360] Input: Installed cameras and health devices

[0361] Output: Real-time video and biometric data

[0362] Step 3:

[0363] The terminal transmits the video data and biometric data acquired from the camera and health device to a cloud server via the Internet.

[0364] Input: Video data and biometric data

[0365] Output: Data sent to the cloud server

[0366] Step 4:

[0367] The server, in a cloud environment, analyzes the received data. Machine learning algorithms are used to analyze the data and evaluate the pet's health and detect abnormalities. The algorithms analyze behavioral patterns from video data and identify abnormal patterns from biometric data.

[0368] Input: Data sent to the cloud server

[0369] Output: Health status assessment results and whether there are any abnormalities

[0370] Step 5:

[0371] If the cloud server detects an abnormality, it immediately notifies the user with details, which are sent to the user via a dedicated app or messaging app.

[0372] Input: Health status assessment results and whether there are any abnormalities

[0373] Output: User notification

[0374] Step 6:

[0375] The server receives the user's voice and text data and analyzes it with an emotion analysis engine. An emotion recognition algorithm is used to determine the user's emotional state (e.g., stress, relief, etc.).

[0376] Input: User voice and text data

[0377] Output: Evaluation result of the user's emotional state

[0378] Step 7:

[0379] The server generates new training and feeding plans based on the pet's health and the user's emotional state. The AI ​​model automatically generates individually optimized plans, taking into account the pet's characteristics and the user's emotions.

[0380] Input: Evaluation results of pet health status and evaluation results of user emotional status

[0381] Output: Personalized training and meal plans

[0382] Step 8:

[0383] The generated training and meal plans are sent from the cloud server to a dedicated app, where users can review the proposed plans, select the ones they want, and implement them on their pet.

[0384] Input: Personalized training and meal plans

[0385] Output: User informed of plan and execution

[0386] In this way, a system is realized that monitors the health condition of a pet in real time and provides notifications and suggests plans that take into account the user's emotional state.

[0387] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0388] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0390] [Second embodiment]

[0391] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0392] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0393] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0395] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0397] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0398] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0399] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0402] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0403] The system of the present invention aims to monitor the health status of pets in real time, detect abnormalities, and notify owners. This system mainly includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, and a means for notifying owners based on the analysis results.

[0404] System Configuration

[0405] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[0406] 2. Device: The pet camera captures images of your pet, and the health device captures biometric data such as heart rate, body temperature, and activity level, which are then sent to a cloud server in real time.

[0407] 3. Server: Analyzes the received data using a data analysis tool to detect abnormalities. If an abnormality occurs, a notification is sent to the owner based on the analysis results.

[0408] The following will explain this with specific examples of system operation.

[0409] Specific example of system operation

[0410] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0411] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. Once the installation and attachment are complete, connect these devices to the dedicated app.

[0412] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and temperature.

[0413] 4. Terminal: The acquired video and biometric data is transmitted to a cloud server via Wi-Fi in real time.

[0414] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[0415] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[0416] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0417] 8. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner can consult a veterinarian.

[0418] Furthermore, the cloud server has the ability to generate individual training methods and diet plans based on the data, which can contribute not only to daily health management but also to improving the pet's quality of life (QOL).

[0419] As described above, the system of the present invention allows owners to monitor their pets' health conditions in real time and provide appropriate care, thereby reducing health risks and providing an optimal living environment for pets.

[0420] The processing flow will be explained below.

[0421] Step 1:

[0422] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0423] Step 2:

[0424] User: Install the pet camera in a suitable location, such as the living room, and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[0425] Step 3:

[0426] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[0427] Step 4:

[0428] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[0429] Step 5:

[0430] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns from the video data and evaluates its health condition from the biometric data.

[0431] Step 6:

[0432] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[0433] Step 7:

[0434] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[0435] Step 8:

[0436] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0437] Step 9:

[0438] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0439] Step 10:

[0440] Server: Based on the data collected daily, AI automatically generates individual training methods and meal plans tailored to the pet's characteristics.

[0441] Step 11:

[0442] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[0443] Step 12:

[0444] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0445] In this way, the entire system works together to manage pet health in real time, helping owners respond appropriately.

[0446] Example 1

[0447] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0448] There is a growing need for systems that can monitor pet health in real time and quickly notify owners when abnormalities are detected. Conventional systems have struggled to quickly and accurately detect abnormalities and provide appropriate countermeasures. They also lack the ability to provide training methods and dietary plans that address individual pet needs.

[0449] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0450] In this invention, the server includes a means for inputting and saving pet information, a device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and identifying behavioral patterns, a means for evaluating health status and detecting abnormalities from the biometric data, a means for sending a notification of appropriate measures to the owner based on the analysis results, and a means for generating training methods and diet plans for the pet from the cloud server. This allows for accurate real-time monitoring of the pet's health status and prompt notification when an abnormality is detected. Furthermore, by providing training methods and diet plans tailored to the pet's individual needs, the quality of life (QOL) of the pet can be improved.

[0451] "Means for entering and saving pet information" refers to a function that allows users to enter basic information such as a pet's name, breed, age, and medical history through a dedicated app and save it in a database.

[0452] "Devices for acquiring video data and biometric data" refers to devices such as pet cameras and health devices that capture and collect video of pets and biometric data such as heart rate, body temperature, and activity level.

[0453] The "means for transmitting acquired data to a cloud server" is a communication function for transmitting video data and biometric data to a cloud server via the Internet in real time or periodically.

[0454] The "means for analyzing the data on the cloud server and identifying behavioral patterns" refers to algorithms and software for analyzing the video and biometric data received on the cloud server and identifying the pet's behavior (walking, resting, eating, etc.).

[0455] The "means for assessing health status and detecting abnormalities from biometric data" is a system that analyzes biometric data such as heart rate and body temperature on a cloud server to assess health status and detect abnormalities.

[0456] The "means for sending a notification of appropriate countermeasures to the owner based on the analysis results" is a function for sending a notification including information on countermeasures to the owner's dedicated app or messaging app when an abnormality is detected based on the analysis results.

[0457] "Means for generating training methods and meal plans for pets from the cloud server" refers to a function for generating and providing training methods and meal plans that meet the individual needs of pets based on data on the cloud server.

[0458] The system of the present invention aims to monitor the health status of pets in real time and notify the owner when an abnormality is detected. This system mainly includes the following components.

[0459] User input of pet information and device configuration

[0460] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information about their pet, such as its name, breed, age, and medical history. This information is stored in a database and processed on a cloud server. Users also install a pet camera in their living room and attach a health device to their pet's collar. By linking these devices with the dedicated app, data can be acquired and transmitted.

[0461] Device data acquisition and transmission

[0462] The pet camera captures video of the pet in real time, and the health device periodically measures biometric data such as heart rate, body temperature, and activity level. The captured video data and biometric data are transmitted to a cloud server in real time via Wi-Fi. The specific hardware used here includes a dedicated pet camera and health device.

[0463] Data analysis and anomaly detection by the server

[0464] The cloud server uses data analysis software such as Python and R to analyze the received video and biometric data. Image recognition algorithms are used to identify the pet's behavior (walking, resting, eating, etc.) from the video data, and statistical analysis is used to evaluate the pet's health status from the biometric data. This includes algorithms to detect abnormalities in heart rate and body temperature. If an abnormality is detected, a notification is generated based on the analysis results, containing a description of the abnormality and recommended measures.

[0465] Server-generated and sent notifications

[0466] The generated notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification will be sent saying, "Heart rate is high. Please consult a veterinarian." This notification allows the owner to take appropriate action immediately.

[0467] User Receipt and Response to Notices

[0468] Users can check the notifications in a dedicated app and take necessary measures to manage their pet's health. For example, if the heart rate is abnormally high, it is recommended that they consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user via notifications.

[0469] Specific examples of actions and prompts

[0470] Let's take the example of a pet owner using this system for their three-year-old dog. They enter their pet's information into a dedicated app and set up the camera and health device. The cloud server analyzes the pet's behavioral patterns and biological data in real time, and immediately sends a notification if an abnormality is detected. The pet owner can then take appropriate action.

[0471] Example prompt sentence:

[0472] Please explain the detailed processing steps of a system that monitors the health of pets in real-time and notifies the owner in case of anomalies, using specific terms and actions for each step.

[0473] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0474] Step 1: User enters pet information and sets up device

[0475] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information such as their pet's name, breed, age, and medical history. The entered information is saved in a database. This information is the basic data used for analysis in subsequent steps and is processed on a cloud server. For example, a user may enter their pet's name as "Buddy" and its age as "3."

[0476] Step 2: Obtaining data via the device

[0477] A pet camera installed on a terminal captures video of the pet in real time. At the same time, a health device attached to the pet's collar periodically measures biometric data such as heart rate, body temperature, and activity level. The input data consists of video data and biometric data, and is acquired from each terminal. For example, the camera records video at a frame rate of 30 fps, and the health device records the heart rate as "90 bpm" every 30 seconds.

[0478] Step 3: Send data to cloud server

[0479] The video and biometric data captured by the device is transmitted to a cloud server in real time via Wi-Fi. The data is then sent to the cloud server's API endpoint via an Internet connection. This transmission process is continuous, with data updates in real time. For example, a camera device uploads a 30-second video clip to the cloud server.

[0480] Step 4: Data analysis by the server

[0481] The server analyzes the received data. Specifically, it uses data analysis software such as Python or R to analyze the video data and uses image recognition algorithms to identify the pet's behavior (walking, resting, eating, etc.). At the same time, it performs statistical analysis using biometric data to evaluate the pet's health. The input is the video data and biometric data sent to the cloud server, and the output is the behavioral patterns and health assessment results. For example, if the video analysis assigns a tag of "eating" and the heart rate exceeds "120 bpm," it will flag it as an "anomaly detected."

[0482] Step 5: Server detects anomalies and generates notifications

[0483] If an abnormality is detected based on the analysis results, the server generates a notification containing the details of the abnormality and recommended measures. This notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification stating "Heart rate is high. Please consult a veterinarian" is generated. The input is the result of data analysis, and the output is the notification message.

[0484] Step 6: User Receipt and Action

[0485] The user checks the notifications received through the dedicated app and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user may consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user through notifications. The input is the notification content and additional suggestions from the server, and the output is the pet owner's specific actions.

[0486] (Application example 1)

[0487] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] When raising a pet, it is important to monitor the pet's health in real time and quickly detect any suspicious movements or sounds and notify the owner. However, conventional systems have difficulty monitoring the pet's health and security simultaneously, requiring dual systems, which increases costs and management efforts. The present invention aims to solve this problem.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0490] In this invention, the server includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and detecting abnormalities, a means for sending a notification to the owner based on the analysis results, and a means for monitoring suspicious movements and sounds, detecting abnormalities, and notifying the owner. This makes it possible to simultaneously monitor not only the health status of the pet, but also security abnormalities and notify them in real time.

[0491] "Pet information" refers to basic attribute data including the pet's name, breed, age, medical history, etc.

[0492] "Video data" refers to real-time video data of a pet captured by a camera.

[0493] "Biometric data" refers to data that indicates your pet's health, such as heart rate, body temperature, and activity level.

[0494] A "camera" is a device for acquiring video data.

[0495] A "health device" is a device for acquiring biometric data from a pet.

[0496] A "cloud server" is a remote server that receives data over a network and performs analysis.

[0497] "Abnormal" refers to any unusual or suspicious changes in a pet's health or behavior.

[0498] "Notifications" are warnings or information sent to the owner based on the analysis results.

[0499] "Suspicious behavior" refers to unexpected or unusual behavior.

[0500] "Sound" refers to audio data acquired through a camera or microphone.

[0501] "Analysis means" refers to algorithms or software that analyze the acquired data and determine whether or not there are any abnormalities.

[0502] The following configuration and operation procedure are conceivable as an embodiment of the present invention.

[0503] System Configuration

[0504] Hardware and Software

[0505] 1. How to enter and save your pet's information:

[0506] A dedicated application installed on the owner's smartphone or other device.

[0507] 2. Cameras and health devices for capturing video and biometric data:

[0508] The camera device is a commercially available surveillance camera such as Nest Cam, which captures footage of your pet in real time.

[0509] Health devices are wearable devices like FitBark that continuously collect biometric data such as heart rate, body temperature, and activity levels.

[0510] 3. Cloud Server:

[0511] Use a cloud service such as AWS Lambda as a server to receive data, analyze it, and detect anomalies.

[0512] Data processing and calculation

[0513] Server-side processing

[0514] 1. Data Collection:

[0515] Collect real-time video and biometric data from pet cameras and health devices.

[0516] Obtain basic information about your pet (name, breed, age, medical history, etc.) from the smartphone app.

[0517] 2. Data transmission:

[0518] The collected data is sent to a cloud server via Wi-Fi.

[0519] 3. Data Analysis:

[0520] A generative AI model is used on a cloud server to analyze video data and biometric data, identifying pet behavior patterns (e.g., walking, resting, eating) from the video data and assessing health status from the biometric data.

[0521] Suspicious movements and sounds are also analyzed, and they are analyzed comprehensively.

[0522] 4. Anomaly detection:

[0523] Based on the analysis results, if there is anything abnormal about the pet's health or if suspicious movements or sounds are detected, it will be recognized as an abnormality.

[0524] 5. Notification Generation and Transmission:

[0525] If an abnormality is detected, the cloud server will notify the owner via a dedicated application or messaging app.

[0526] The notification will include details of the anomaly and recommended actions to take.

[0527] 6. Generate personalized training and meal plans:

[0528] Based on your pet's characteristics, the system generates optimal training and feeding plans and reflects them in a dedicated application.

[0529] Specific examples

[0530] When this system is running, it can detect the following situations, for example:

[0531] A higher than normal heart rate may mean your pet is overly stressed.

[0532] If a sudden rise in body temperature is recorded, a heatstroke warning will be displayed.

[0533] It picks up any unusual activity from the camera feed and notifies owners of any suspicious individuals entering or pets escaping.

[0534] Prompt Sentence Examples

[0535] Design a system to monitor a pet's health in real time. This system would measure the pet's heart rate, body temperature, and activity level using a health device, and capture video and audio using a camera. A mechanism must be in place to notify the pet owner if an abnormality is detected. The system should also use a cloud server to analyze the data and send notifications. Please explain the system configuration and provide a concrete example of how it works.

[0536] The above is an embodiment of the invention. This embodiment makes it possible to simultaneously monitor not only the health status of pets but also any security abnormalities, and to take any necessary measures promptly.

[0537] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0538] Step 1:

[0539] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter and save basic information about their pet, such as its name, breed, age, and medical history. This information is then saved within the dedicated app.

[0540] Input: Basic information about your pet (name, breed, age, medical history, etc.)

[0541] Output: Basic information data of pets stored on the device

[0542] Step 2:

[0543] The user simply installs the pet camera in the living room and attaches the pet health device to the pet's collar. Once the installation and attachment are complete, the devices are linked to the dedicated app and are ready to use.

[0544] Input: Installing the pet camera, wearing the health device, and linking the device to the app

[0545] Output: Camera and health device linked to the app

[0546] Step 3:

[0547] The device receives real-time video data from the pet camera and periodically collects biometric data such as heart rate, body temperature, and activity level from the health device.

[0548] Input: Real-time video data from pet cameras, biometric data from health devices

[0549] Output: Acquired video data and biometric data

[0550] Step 4:

[0551] The device transmits the acquired video data and biometric data to a cloud server via Wi-Fi in real time.

[0552] Input: Acquired video data and biometric data

[0553] Output: Data sent to the cloud server

[0554] Step 5:

[0555] The server analyzes the received video and biometric data, uses a generative AI model to identify the pet's behavioral patterns (e.g., walking, resting, eating), and assesses its health status from the biometric data. It also analyzes any suspicious movements or sounds, and performs a comprehensive analysis.

[0556] Input: Video data and biometric data sent to the cloud server

[0557] Output: Evaluation results of behavioral patterns and health status, detection results of suspicious movements and sounds

[0558] Step 6:

[0559] If an abnormality is detected, the server generates a notification based on the analysis results to notify the owner of the abnormality, including details of the abnormality and recommended measures.

[0560] Input: Behavioral patterns and health status assessment results, suspicious movement and sound detection results

[0561] Output: Notification sent to the breeder

[0562] Step 7:

[0563] The server instantly sends the generated notifications to the owner via a dedicated application or messaging app.

[0564] Input: Generated notification

[0565] Output: Notification sent to the breeder

[0566] Step 8:

[0567] The user checks the notification and takes necessary measures to manage the health and safety of their pet, such as consulting a veterinarian if the heart rate is abnormally high, or contacting the police if a suspicious person is detected.

[0568] Input: Notification sent to breeder

[0569] Output: Appropriate action by the breeder

[0570] The above are the specific processing steps for carrying out the present invention, which enable real-time monitoring of pet health conditions and security abnormalities, and prompt response.

[0571] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0572] The system of the present invention not only monitors the health condition of pets in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on them. This system includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results by combining an emotion engine.

[0573] System Configuration

[0574] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[0575] 2. Device: The pet camera captures the pet's video, and the health device acquires biometric data such as heart rate, body temperature, and activity level, which are then transmitted to the cloud server in real time.

[0576] 3. Server: Analyzes the received data using a data analysis means and detects any abnormalities. If an abnormality occurs based on the generated analysis results, a notification is sent to the breeder.

[0577] 4. Server: The emotion engine recognizes emotions from the user's voice and text data and optimizes notification content, training plans, and meal plans.

[0578] Specific example of system operation

[0579] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0580] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. After installation and attachment are complete, link these devices with the dedicated app and check the device settings.

[0581] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and body temperature.

[0582] 4. Terminal: The acquired video data and biometric data are sent to the cloud server via Wi-Fi in real time.

[0583] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[0584] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[0585] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0586] 8. Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, it will reflect this in the notification and suggest appropriate ways to respond.

[0587] 9. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0588] 10. Server: Based on the data collected daily, the AI ​​automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts the way these plans are presented based on the user's emotional state.

[0589] 11. Server: The generated training methods and feeding plans are sent to the dedicated app and proposed to the owner. The implementation status of each proposal is tracked and the plan is adjusted as necessary.

[0590] 12. User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0591] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[0592] The processing flow will be explained below.

[0593] Step 1:

[0594] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0595] Step 2:

[0596] User: Install the pet camera in an appropriate location and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[0597] Step 3:

[0598] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[0599] Step 4:

[0600] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[0601] Step 5:

[0602] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video data and evaluates its health condition from the biometric data.

[0603] Step 6:

[0604] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[0605] Step 7:

[0606] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[0607] Step 8:

[0608] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0609] Step 9:

[0610] Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, the engine will reflect this in the notification and suggest appropriate ways to respond.

[0611] Step 10:

[0612] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0613] Step 11:

[0614] Server: Based on the data collected daily, AI automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts how these plans are presented based on the user's emotional state.

[0615] Step 12:

[0616] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[0617] Step 13:

[0618] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0619] Example 2

[0620] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0621] While conventional pet health management systems can monitor a pet's biological information and behavioral data and detect abnormalities, they lack the ability to recognize the user's emotional state and optimize notification content, or the ability to provide personalized training and diet plans. With such systems, users simply receive abnormality notifications, making it difficult to optimize their pet's health management.

[0622] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0623] In this invention, the server includes means for inputting and saving pet information, sensors and monitors for acquiring video data and biometric data, means for transmitting the acquired data to the server, means for analyzing the data on the server and detecting abnormalities, means for sending notifications to the caregiver based on the analysis results, emotion recognition means for optimizing the content of the notifications, and means for generating different training plans and meal plans depending on the situation. This makes it possible to monitor the health condition of the pet in real time and provide optimal notifications and personalized training plans and meal plans that take the user's emotional state into consideration.

[0624] "Means for entering and storing pet information" means an interface that allows a user to enter basic data about a pet (such as name, breed, age, medical history, etc.) and store it in a database or cloud.

[0625] "Sensors and monitors for acquiring video data and biometric data" refers to cameras that capture pet behavior and devices that measure pet biometric information (heart rate, body temperature, activity level, etc.).

[0626] The "means for transmitting acquired data to a server" refers to a function for transmitting data acquired from sensors and monitors to a cloud server using a communication means such as the Internet.

[0627] "Means for analyzing the data on the server and detecting abnormalities" refers to a method in which the cloud server processes the data received using an analytical algorithm and detects abnormalities in the pet's behavior and biological information.

[0628] "Means for sending a notification to the breeder based on the analysis results" refers to a function that notifies the user of the details and how to respond when an abnormality is detected through data analysis.

[0629] "Emotion recognition means for optimizing notification content" refers to an algorithm or system that analyzes the user's emotional state from their voice or text and adjusts the notification content based on the results.

[0630] "A means for generating different training plans and meal plans according to the situation" refers to a system or algorithm that automatically generates customized training methods and meal plans based on the behavioral data and biological information of a pet.

[0631] The system of the present invention not only monitors the health condition of a pet in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on the results. This system includes a means for inputting and saving pet information, sensors and monitors for acquiring video data and biological data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results, combining an emotion recognition means.

[0632] First, the user uses a dedicated app to input and save information about their pet. For example, they can use the app installed on their smartphone to input and save basic information about their pet, such as its name, breed, age, and medical history. This information is then stored on a cloud server.

[0633] Next, a camera is used to capture pet behavior and a health device is used to acquire biometric information. The pet camera captures video data of the pet in real time. The health device acquires biometric data such as the pet's heart rate, body temperature, and activity level. This data is sent to a cloud server via the Internet or Wi-Fi, and the sent data is encrypted and protected.

[0634] The cloud server analyzes the received video and biometric data to evaluate the pet's behavioral patterns and health status. Machine learning algorithms are used for data analysis. For example, the video data can identify behavioral patterns, such as whether the pet is walking, resting, or eating, and the biometric data can be used to evaluate whether the pet's heart rate is within a normal range.

[0635] If the anomaly detection method detects an abnormality in a pet's behavior or biometric data, it generates a notification containing the details and recommended measures. For example, it may generate a notification that reads, "Your heart rate is outside the normal range. Your body temperature is also elevated. Please refrain from walks and let your pet rest in a cool place. If necessary, consult a veterinarian." The generated notification is immediately sent to the user via a dedicated app or messaging app.

[0636] Furthermore, an emotion recognition system on the server analyzes the user's emotional state from their voice and text data. For example, if the user is feeling stressed, the notification content is optimized to take that emotional state into account. The notification may include customized suggestions such as "If you are concerned, contact your veterinarian immediately."

[0637] The user checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user is advised to immediately consult a veterinarian. In addition, the cloud server uses a generative AI model to automatically generate individual training methods and diet plans tailored to the pet's characteristics based on the data collected daily. Emotion recognition means adjusts the way these plans are presented based on the user's emotional state. Training methods and diet plans are suggested to the user through a dedicated app, which tracks the implementation of each suggestion and adjusts the plan as needed.

[0638] For example, the following prompt sentence is used:

[0639] User input:

[0640] Pet Name: Coco

[0641] Breed: French Bulldog

[0642] Age: 3 years old

[0643] Medical history: Allergies

[0644] Pet camera and health device setup complete.

[0645] Sending data to cloud server...

[0646] Start the analysis process on the server:

[0647] Identifying behavioral patterns from video data

[0648] Evaluating biological status from health device data

[0649] Anomaly detection:

[0650] If the heart rate is higher than normal, it will generate a notification saying "Pet's heart rate is outside of normal range"

[0651] Analyze user sentiment and optimize notifications

[0652] Final notice details:

[0653] "Your pet's heart rate is outside of the normal range. They also have an elevated body temperature. Please refrain from walks and allow them to rest in a cool place. Consult your veterinarian if necessary. If you are concerned, contact your veterinarian immediately."

[0654] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[0655] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0656] Step 1:

[0657] User: The user installs the dedicated app on their smartphone, creates an account, and logs in. As input, they enter basic information about their pet, such as its name, breed, age, and medical history. This data is sent to the server and stored in the cloud. As output, they receive the saved pet information.

[0658] Step 2:

[0659] User: The user installs the pet camera in the living room and attaches the health device to the pet's collar. The user inputs the camera's installation location and the device's location information into the app. Based on this, the app connects with these devices and confirms the completion of the setup. The connection status between the camera and the device is obtained as an output.

[0660] Step 3:

[0661] Terminal: The pet camera captures the pet's video data in real time, and the health device obtains the pet's biometric data such as heart rate, body temperature, and activity level. The pet's movements and biometric information are captured by the camera and device as input. The video data and biometric data are obtained as output.

[0662] Step 4:

[0663] Terminal: The acquired video data and biometric data are sent to the cloud server via the Internet or Wi-Fi. The captured data is provided as input. The data is encrypted and transmitted. The output is the data stored on the cloud server.

[0664] Step 5:

[0665] Server: The cloud server analyzes the received video data and biometric data. The data stored on the server is fed as input to the analytical algorithm. The algorithm identifies the pet's behavioral patterns and evaluates its health status from the biometric data. The analysis results are provided as output.

[0666] Step 6:

[0667] Server: The anomaly detection means detects abnormalities in the pet's behavior and biometric data based on the analysis results. The data analysis results are provided as input. Based on this, a notification is generated containing the details of the abnormality and recommended measures. The output is an anomaly notification.

[0668] Step 7:

[0669] Server: The generated notification is sent to the user immediately via a dedicated app or messaging app. The abnormality notification is provided as input, allowing the user to instantly check the status of their pet. The notification sent to the user is obtained as output.

[0670] Step 8:

[0671] Server: The emotion recognition means analyzes the user's voice and text data to recognize the emotional state. The user's voice and text data are provided as input. The emotion engine analyzes this and evaluates the emotional state. The analysis result of the emotional state is obtained as output.

[0672] Step 9:

[0673] Server: The notification content is optimized based on the emotional state analysis results. The emotional state analysis results are provided as input. The notification generation algorithm regenerates the notification content according to the emotion. The optimized notification is obtained as output.

[0674] Step 10:

[0675] User: The user checks the notification and takes appropriate action to manage the health of the pet as needed. The input is the notification sent to the user. The user takes appropriate action based on the notification. The output is an improvement in the pet's health.

[0676] Step 11:

[0677] Server: Analyzes the data collected daily and generates training methods and diet plans tailored to the pet's characteristics. The accumulated data is provided as input. The generative AI model analyzes this and generates a customized plan. The output is the training method and diet plan.

[0678] Step 12:

[0679] Server: The generated training method and meal plan are proposed to the user through a dedicated app. The generated plan is provided as input. It is sent to the user, providing a specific action plan. The plan provided to the user is obtained as output.

[0680] Step 13:

[0681] User: The user checks the proposed training methods and diet plans through a dedicated app, selects the ones that apply, and implements them on their pet. The proposed plan is provided as input. The user manages their pet's health based on that plan. The implementation status and its effects are obtained as output.

[0682] In this way, each processing step works together to manage the pet's health condition in real time and generate optimal notifications and plans that take the user's emotions into consideration, thereby achieving more effective health management.

[0683] (Application example 2)

[0684] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0685] Systems already exist that monitor pet health in real time and detect and notify users of abnormalities. However, these systems lack the ability to optimize notification content based on the user's emotional state. As a result, they have been unable to fully reduce the psychological stress and burden on pet owners involved in managing their pet's health. Furthermore, training and feeding plans based on individual pet characteristics also fail to reflect the user's emotional state. This results in a lack of comprehensive support for both pets and owners.

[0686] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0687] In this invention, the server includes a means for analyzing the user's emotions and optimizing the notification content, an artificial intelligence means for generating new training plans and meal plans based on the pet's health condition, and a means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state, thereby making it possible to provide appropriate notifications and plans while monitoring the pet's health condition in real time and taking the caretaker's emotional state into consideration.

[0688] "Means for inputting and saving pet information" refers to a device or software that allows a user to input information such as the pet's name, breed, age, and health status, and save it in a database.

[0689] A "camera" is a device for capturing a pet's behavior and the surrounding environment as video data.

[0690] A "health device" is a device for collecting biometric data such as a pet's heart rate, body temperature, and activity level.

[0691] The "means for transmitting to a cloud server" refers to a device or software for transmitting acquired data to a remote cloud server via the Internet.

[0692] The "means for analyzing the data on the cloud server and detecting abnormalities" refers to software or algorithms that analyze the acquired data within the cloud server and diagnose whether there are any abnormalities in the pet's health condition.

[0693] The "means for sending a notification to the keeper based on the analysis results" refers to a messaging system or application that notifies the user of any abnormalities that are detected.

[0694] "Means for analyzing user emotions and optimizing notification content" refers to algorithms or software that analyze the user's voice and text data to determine their emotional state and send notifications in the most appropriate format accordingly.

[0695] "Artificial intelligence means for generating new training and diet plans based on the health status of pets" refers to AI models and algorithms that automatically generate optimal training methods and diet plans based on pet health data.

[0696] "Means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state" refers to an AI and analysis system that combines the pet's unique characteristics with the user's emotional state to customize a suitable training and meal plan.

[0697] This invention is a system that monitors the health status of pets in real time, detects abnormalities, and notifies owners. The system also analyzes the user's emotions and optimizes notification content and various plans based on those emotions. This provides comprehensive support for pet health management.

[0698] System configuration and functions

[0699] 1. Users

[0700] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information such as their pet's name, breed, age, and health condition. Users also install the pet camera in an appropriate location in their home or at a physical store (pet shop or pet hotel), and attach the health device to their pet's collar or other device.

[0701] 2. Terminal

[0702] The pet camera captures video data of your pet's behavior and surrounding environment, while the health device captures your pet's real-time biometric data, such as heart rate, body temperature, and activity level, which are then sent to a cloud server via the internet.

[0703] 3. Server

[0704] The server is in a cloud environment and has multiple analysis functions, including:

[0705] Data analysis and anomaly detection methods: Analyze your pet's video data and biometric data to detect anomalies. For this purpose, various algorithms and anomaly detection models are used.

[0706] Notification method: If an abnormality is detected, the user will be notified immediately via a dedicated smartphone app or messaging app.

[0707] Sentiment analysis engine: Analyzes the user's voice and text data to determine their emotional state. Emotion analysis uses emotion recognition algorithms and natural language processing technology.

[0708] Training and Feeding Plan Generation: AI models are available to generate personalized training and feeding plans based on your pet's health and characteristics, and the plan content and suggestions are automatically adjusted based on the user's emotional state.

[0709] Specific examples

[0710] For example, if a German Shepherd named Leo at a pet hotel exhibits an abnormal heart rate:

[0711] 1. The health device detects an abnormal heart rate.

[0712] 2. The data is sent to a cloud server in real time and any abnormalities are checked.

[0713] 3. The customer's (pet owner's) emotional data is sent to the server and analyzed by the emotion analysis engine.

[0714] 4. You will receive a notification saying, "Leo's heart rate is high. We recommend that you consult your veterinarian. You appear to be stressed and we also recommend that you take some time to relax."

[0715] Prompt Sentence Examples

[0716] "If a pet's heart rate is higher than normal, detect the abnormality and notify them. Also, take into account the customer's emotional state and suggest appropriate measures."

[0717] "Please create a program that analyzes pet health data and sends a notification if it detects any abnormalities. The content of the notification should also reflect the customer's emotional state."

[0718] In this way, the pet's health condition is monitored, and notifications and suggestions are given that take the user's emotional state into account, providing more effective and personal support.

[0719] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0720] Step 1:

[0721] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information about their pet, such as its name, breed, age, and health condition. The entered data is sent from the smartphone app to a cloud server and stored in a database.

[0722] Input: Pet information

[0723] Output: Pet information stored on the cloud server

[0724] Step 2:

[0725] Users install the pet camera in an appropriate location in their home or a physical store (pet shop or pet hotel) and attach the health device to their pet's collar, etc. The camera and health device collect video data and biometric data (heart rate, body temperature, activity level, etc.) of their pet in real time.

[0726] Input: Installed cameras and health devices

[0727] Output: Real-time video and biometric data

[0728] Step 3:

[0729] The terminal transmits the video data and biometric data acquired from the camera and health device to a cloud server via the Internet.

[0730] Input: Video data and biometric data

[0731] Output: Data sent to the cloud server

[0732] Step 4:

[0733] The server, in a cloud environment, analyzes the received data. Machine learning algorithms are used to analyze the data and evaluate the pet's health and detect abnormalities. The algorithms analyze behavioral patterns from video data and identify abnormal patterns from biometric data.

[0734] Input: Data sent to the cloud server

[0735] Output: Health status assessment results and whether there are any abnormalities

[0736] Step 5:

[0737] If the cloud server detects an abnormality, it immediately notifies the user with details, which are sent to the user via a dedicated app or messaging app.

[0738] Input: Health status assessment results and whether there are any abnormalities

[0739] Output: User notification

[0740] Step 6:

[0741] The server receives the user's voice and text data and analyzes it with an emotion analysis engine. An emotion recognition algorithm is used to determine the user's emotional state (e.g., stress, relief, etc.).

[0742] Input: User voice and text data

[0743] Output: Evaluation result of the user's emotional state

[0744] Step 7:

[0745] The server generates new training and feeding plans based on the pet's health and the user's emotional state. The AI ​​model automatically generates individually optimized plans, taking into account the pet's characteristics and the user's emotions.

[0746] Input: Evaluation results of pet health status and evaluation results of user emotional status

[0747] Output: Personalized training and meal plans

[0748] Step 8:

[0749] The generated training and meal plans are sent from the cloud server to a dedicated app, where users can review the proposed plans, select the ones they want, and implement them on their pet.

[0750] Input: Personalized training and meal plans

[0751] Output: User informed of plan and execution

[0752] In this way, a system is realized that monitors the health condition of a pet in real time and provides notifications and suggests plans that take into account the user's emotional state.

[0753] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0754] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0756] [Third embodiment]

[0757] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0758] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0759] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0760] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0761] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0762] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0763] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0764] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0765] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0766] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0767] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0768] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0769] The system of the present invention aims to monitor the health status of pets in real time, detect abnormalities, and notify owners. This system mainly includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, and a means for notifying owners based on the analysis results.

[0770] System Configuration

[0771] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[0772] 2. Device: The pet camera captures images of your pet, and the health device captures biometric data such as heart rate, body temperature, and activity level, which are then sent to a cloud server in real time.

[0773] 3. Server: Analyzes the received data using a data analysis tool to detect abnormalities. If an abnormality occurs, a notification is sent to the owner based on the analysis results.

[0774] The following will explain this with specific examples of system operation.

[0775] Specific example of system operation

[0776] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0777] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. Once the installation and attachment are complete, connect these devices to the dedicated app.

[0778] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and temperature.

[0779] 4. Terminal: The acquired video and biometric data is transmitted to a cloud server via Wi-Fi in real time.

[0780] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[0781] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[0782] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0783] 8. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner can consult a veterinarian.

[0784] Furthermore, the cloud server has the ability to generate individual training methods and diet plans based on the data, which can contribute not only to daily health management but also to improving the pet's quality of life (QOL).

[0785] As described above, the system of the present invention allows owners to monitor their pets' health conditions in real time and provide appropriate care, thereby reducing health risks and providing an optimal living environment for pets.

[0786] The processing flow will be explained below.

[0787] Step 1:

[0788] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0789] Step 2:

[0790] User: Install the pet camera in a suitable location, such as the living room, and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[0791] Step 3:

[0792] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[0793] Step 4:

[0794] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[0795] Step 5:

[0796] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns from the video data and evaluates its health condition from the biometric data.

[0797] Step 6:

[0798] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[0799] Step 7:

[0800] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[0801] Step 8:

[0802] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0803] Step 9:

[0804] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0805] Step 10:

[0806] Server: Based on the data collected daily, AI automatically generates individual training methods and meal plans tailored to the pet's characteristics.

[0807] Step 11:

[0808] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[0809] Step 12:

[0810] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0811] In this way, the entire system works together to manage pet health in real time, helping owners respond appropriately.

[0812] Example 1

[0813] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0814] There is a growing need for systems that can monitor pet health in real time and quickly notify owners when abnormalities are detected. Conventional systems have struggled to quickly and accurately detect abnormalities and provide appropriate countermeasures. They also lack the ability to provide training methods and dietary plans that address individual pet needs.

[0815] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0816] In this invention, the server includes a means for inputting and saving pet information, a device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and identifying behavioral patterns, a means for evaluating health status and detecting abnormalities from the biometric data, a means for sending a notification of appropriate measures to the owner based on the analysis results, and a means for generating training methods and diet plans for the pet from the cloud server. This allows for accurate real-time monitoring of the pet's health status and prompt notification when an abnormality is detected. Furthermore, by providing training methods and diet plans tailored to the pet's individual needs, the quality of life (QOL) of the pet can be improved.

[0817] "Means for entering and saving pet information" refers to a function that allows users to enter basic information such as a pet's name, breed, age, and medical history through a dedicated app and save it in a database.

[0818] "Devices for acquiring video data and biometric data" refers to devices such as pet cameras and health devices that capture and collect video of pets and biometric data such as heart rate, body temperature, and activity level.

[0819] The "means for transmitting acquired data to a cloud server" is a communication function for transmitting video data and biometric data to a cloud server via the Internet in real time or periodically.

[0820] The "means for analyzing the data on the cloud server and identifying behavioral patterns" refers to algorithms and software for analyzing the video and biometric data received on the cloud server and identifying the pet's behavior (walking, resting, eating, etc.).

[0821] The "means for assessing health status and detecting abnormalities from biometric data" is a system that analyzes biometric data such as heart rate and body temperature on a cloud server to assess health status and detect abnormalities.

[0822] The "means for sending a notification of appropriate countermeasures to the owner based on the analysis results" is a function for sending a notification including information on countermeasures to the owner's dedicated app or messaging app when an abnormality is detected based on the analysis results.

[0823] "Means for generating training methods and meal plans for pets from the cloud server" refers to a function for generating and providing training methods and meal plans that meet the individual needs of pets based on data on the cloud server.

[0824] The system of the present invention aims to monitor the health status of pets in real time and notify the owner when an abnormality is detected. This system mainly includes the following components.

[0825] User input of pet information and device configuration

[0826] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information about their pet, such as its name, breed, age, and medical history. This information is stored in a database and processed on a cloud server. Users also install a pet camera in their living room and attach a health device to their pet's collar. By linking these devices with the dedicated app, data can be acquired and transmitted.

[0827] Device data acquisition and transmission

[0828] The pet camera captures video of the pet in real time, and the health device periodically measures biometric data such as heart rate, body temperature, and activity level. The captured video data and biometric data are transmitted to a cloud server in real time via Wi-Fi. The specific hardware used here includes a dedicated pet camera and health device.

[0829] Data analysis and anomaly detection by the server

[0830] The cloud server uses data analysis software such as Python and R to analyze the received video and biometric data. Image recognition algorithms are used to identify the pet's behavior (walking, resting, eating, etc.) from the video data, and statistical analysis is used to evaluate the pet's health status from the biometric data. This includes algorithms to detect abnormalities in heart rate and body temperature. If an abnormality is detected, a notification is generated based on the analysis results, containing a description of the abnormality and recommended measures.

[0831] Server-generated and sent notifications

[0832] The generated notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification will be sent saying, "Heart rate is high. Please consult a veterinarian." This notification allows the owner to take appropriate action immediately.

[0833] User Receipt and Response to Notices

[0834] Users can check the notifications in a dedicated app and take necessary measures to manage their pet's health. For example, if the heart rate is abnormally high, it is recommended that they consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user via notifications.

[0835] Specific examples of actions and prompts

[0836] Let's take the example of a pet owner using this system for their three-year-old dog. They enter their pet's information into a dedicated app and set up the camera and health device. The cloud server analyzes the pet's behavioral patterns and biological data in real time, and immediately sends a notification if an abnormality is detected. The pet owner can then take appropriate action.

[0837] Example prompt sentence:

[0838] Please explain the detailed processing steps of a system that monitors the health of pets in real-time and notifies the owner in case of anomalies, using specific terms and actions for each step.

[0839] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0840] Step 1: User enters pet information and sets up device

[0841] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information such as their pet's name, breed, age, and medical history. The entered information is saved in a database. This information is the basic data used for analysis in subsequent steps and is processed on a cloud server. For example, a user may enter their pet's name as "Buddy" and its age as "3."

[0842] Step 2: Obtaining data via the device

[0843] A pet camera installed on a terminal captures video of the pet in real time. At the same time, a health device attached to the pet's collar periodically measures biometric data such as heart rate, body temperature, and activity level. The input data consists of video data and biometric data, and is acquired from each terminal. For example, the camera records video at a frame rate of 30 fps, and the health device records the heart rate as "90 bpm" every 30 seconds.

[0844] Step 3: Send data to cloud server

[0845] The video and biometric data captured by the device is transmitted to a cloud server in real time via Wi-Fi. The data is then sent to the cloud server's API endpoint via an Internet connection. This transmission process is continuous, with data updates in real time. For example, a camera device uploads a 30-second video clip to the cloud server.

[0846] Step 4: Data analysis by the server

[0847] The server analyzes the received data. Specifically, it uses data analysis software such as Python or R to analyze the video data and uses image recognition algorithms to identify the pet's behavior (walking, resting, eating, etc.). At the same time, it performs statistical analysis using biometric data to evaluate the pet's health. The input is the video data and biometric data sent to the cloud server, and the output is the behavioral patterns and health assessment results. For example, if the video analysis assigns a tag of "eating" and the heart rate exceeds "120 bpm," it will flag it as an "anomaly detected."

[0848] Step 5: Server detects anomalies and generates notifications

[0849] If an abnormality is detected based on the analysis results, the server generates a notification containing the details of the abnormality and recommended measures. This notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification stating "Heart rate is high. Please consult a veterinarian" is generated. The input is the result of data analysis, and the output is the notification message.

[0850] Step 6: User Receipt and Action

[0851] The user checks the notifications received through the dedicated app and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user may consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user through notifications. The input is the notification content and additional suggestions from the server, and the output is the pet owner's specific actions.

[0852] (Application example 1)

[0853] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0854] When raising a pet, it is important to monitor the pet's health in real time and quickly detect any suspicious movements or sounds and notify the owner. However, conventional systems have difficulty monitoring the pet's health and security simultaneously, requiring dual systems, which increases costs and management efforts. The present invention aims to solve this problem.

[0855] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0856] In this invention, the server includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and detecting abnormalities, a means for sending a notification to the owner based on the analysis results, and a means for monitoring suspicious movements and sounds, detecting abnormalities, and notifying the owner. This makes it possible to simultaneously monitor not only the health status of the pet, but also security abnormalities and notify them in real time.

[0857] "Pet information" refers to basic attribute data including the pet's name, breed, age, medical history, etc.

[0858] "Video data" refers to real-time video data of a pet captured by a camera.

[0859] "Biometric data" refers to data that indicates your pet's health, such as heart rate, body temperature, and activity level.

[0860] A "camera" is a device for acquiring video data.

[0861] A "health device" is a device for acquiring biometric data from a pet.

[0862] A "cloud server" is a remote server that receives data over a network and performs analysis.

[0863] "Abnormal" refers to any unusual or suspicious changes in a pet's health or behavior.

[0864] "Notifications" are warnings or information sent to the owner based on the analysis results.

[0865] "Suspicious behavior" refers to unexpected or unusual behavior.

[0866] "Sound" refers to audio data acquired through a camera or microphone.

[0867] "Analysis means" refers to algorithms or software that analyze the acquired data and determine whether or not there are any abnormalities.

[0868] The following configuration and operation procedure are conceivable as an embodiment of the present invention.

[0869] System Configuration

[0870] Hardware and Software

[0871] 1. How to enter and save your pet's information:

[0872] A dedicated application installed on the owner's smartphone or other device.

[0873] 2. Cameras and health devices for capturing video and biometric data:

[0874] The camera device is a commercially available surveillance camera such as Nest Cam, which captures footage of your pet in real time.

[0875] Health devices are wearable devices like FitBark that continuously collect biometric data such as heart rate, body temperature, and activity levels.

[0876] 3. Cloud Server:

[0877] Use a cloud service such as AWS Lambda as a server to receive data, analyze it, and detect anomalies.

[0878] Data processing and calculation

[0879] Server-side processing

[0880] 1. Data Collection:

[0881] Collect real-time video and biometric data from pet cameras and health devices.

[0882] Obtain basic information about your pet (name, breed, age, medical history, etc.) from the smartphone app.

[0883] 2. Data transmission:

[0884] The collected data is sent to a cloud server via Wi-Fi.

[0885] 3. Data Analysis:

[0886] A generative AI model is used on a cloud server to analyze video data and biometric data, identifying pet behavior patterns (e.g., walking, resting, eating) from the video data and assessing health status from the biometric data.

[0887] Suspicious movements and sounds are also analyzed, and they are analyzed comprehensively.

[0888] 4. Anomaly detection:

[0889] Based on the analysis results, if there is anything abnormal about the pet's health or if suspicious movements or sounds are detected, it will be recognized as an abnormality.

[0890] 5. Notification Generation and Transmission:

[0891] If an abnormality is detected, the cloud server will notify the owner via a dedicated application or messaging app.

[0892] The notification will include details of the anomaly and recommended actions to take.

[0893] 6. Generate personalized training and meal plans:

[0894] Based on your pet's characteristics, the system generates optimal training and feeding plans and reflects them in a dedicated application.

[0895] Specific examples

[0896] When this system is running, it can detect the following situations, for example:

[0897] A higher than normal heart rate may mean your pet is overly stressed.

[0898] If a sudden rise in body temperature is recorded, a heatstroke warning will be displayed.

[0899] It picks up any unusual activity from the camera feed and notifies owners of any suspicious individuals entering or pets escaping.

[0900] Prompt Sentence Examples

[0901] Design a system to monitor a pet's health in real time. This system would measure the pet's heart rate, body temperature, and activity level using a health device, and capture video and audio using a camera. A mechanism must be in place to notify the pet owner if an abnormality is detected. The system should also use a cloud server to analyze the data and send notifications. Please explain the system configuration and provide a concrete example of how it works.

[0902] The above is an embodiment of the invention. This embodiment makes it possible to simultaneously monitor not only the health status of pets but also any security abnormalities, and to take any necessary measures promptly.

[0903] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0904] Step 1:

[0905] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter and save basic information about their pet, such as its name, breed, age, and medical history. This information is then saved within the dedicated app.

[0906] Input: Basic information about your pet (name, breed, age, medical history, etc.)

[0907] Output: Basic information data of pets stored on the device

[0908] Step 2:

[0909] The user simply installs the pet camera in the living room and attaches the pet health device to the pet's collar. Once the installation and attachment are complete, the devices are linked to the dedicated app and are ready to use.

[0910] Input: Installing the pet camera, wearing the health device, and linking the device to the app

[0911] Output: Camera and health device linked to the app

[0912] Step 3:

[0913] The device receives real-time video data from the pet camera and periodically collects biometric data such as heart rate, body temperature, and activity level from the health device.

[0914] Input: Real-time video data from pet cameras, biometric data from health devices

[0915] Output: Acquired video data and biometric data

[0916] Step 4:

[0917] The device transmits the acquired video data and biometric data to a cloud server via Wi-Fi in real time.

[0918] Input: Acquired video data and biometric data

[0919] Output: Data sent to the cloud server

[0920] Step 5:

[0921] The server analyzes the received video and biometric data, uses a generative AI model to identify the pet's behavioral patterns (e.g., walking, resting, eating), and assesses its health status from the biometric data. It also analyzes any suspicious movements or sounds, and performs a comprehensive analysis.

[0922] Input: Video data and biometric data sent to the cloud server

[0923] Output: Evaluation results of behavioral patterns and health status, detection results of suspicious movements and sounds

[0924] Step 6:

[0925] If an abnormality is detected, the server generates a notification based on the analysis results to notify the owner of the abnormality, including details of the abnormality and recommended measures.

[0926] Input: Behavioral patterns and health status assessment results, suspicious movement and sound detection results

[0927] Output: Notification sent to the breeder

[0928] Step 7:

[0929] The server instantly sends the generated notifications to the owner via a dedicated application or messaging app.

[0930] Input: Generated notification

[0931] Output: Notification sent to the breeder

[0932] Step 8:

[0933] The user checks the notification and takes necessary measures to manage the health and safety of their pet, such as consulting a veterinarian if the heart rate is abnormally high, or contacting the police if a suspicious person is detected.

[0934] Input: Notification sent to breeder

[0935] Output: Appropriate action by the breeder

[0936] The above are the specific processing steps for carrying out the present invention, which enable real-time monitoring of pet health conditions and security abnormalities, and prompt response.

[0937] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0938] The system of the present invention not only monitors the health condition of pets in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on them. This system includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results by combining an emotion engine.

[0939] System Configuration

[0940] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[0941] 2. Device: The pet camera captures the pet's video, and the health device acquires biometric data such as heart rate, body temperature, and activity level, which are then transmitted to the cloud server in real time.

[0942] 3. Server: Analyzes the received data using a data analysis means and detects any abnormalities. If an abnormality occurs based on the generated analysis results, a notification is sent to the breeder.

[0943] 4. Server: The emotion engine recognizes emotions from the user's voice and text data and optimizes notification content, training plans, and meal plans.

[0944] Specific example of system operation

[0945] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0946] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. After installation and attachment are complete, link these devices with the dedicated app and check the device settings.

[0947] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and body temperature.

[0948] 4. Terminal: The acquired video data and biometric data are sent to the cloud server via Wi-Fi in real time.

[0949] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[0950] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[0951] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0952] 8. Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, it will reflect this in the notification and suggest appropriate ways to respond.

[0953] 9. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0954] 10. Server: Based on the data collected daily, the AI ​​automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts the way these plans are presented based on the user's emotional state.

[0955] 11. Server: The generated training methods and feeding plans are sent to the dedicated app and proposed to the owner. The implementation status of each proposal is tracked and the plan is adjusted as necessary.

[0956] 12. User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0957] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[0958] The processing flow will be explained below.

[0959] Step 1:

[0960] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[0961] Step 2:

[0962] User: Install the pet camera in an appropriate location and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[0963] Step 3:

[0964] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[0965] Step 4:

[0966] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[0967] Step 5:

[0968] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video data and evaluates its health condition from the biometric data.

[0969] Step 6:

[0970] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[0971] Step 7:

[0972] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[0973] Step 8:

[0974] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[0975] Step 9:

[0976] Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, the engine will reflect this in the notification and suggest appropriate ways to respond.

[0977] Step 10:

[0978] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[0979] Step 11:

[0980] Server: Based on the data collected daily, AI automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts how these plans are presented based on the user's emotional state.

[0981] Step 12:

[0982] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[0983] Step 13:

[0984] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[0985] Example 2

[0986] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0987] While conventional pet health management systems can monitor a pet's biological information and behavioral data and detect abnormalities, they lack the ability to recognize the user's emotional state and optimize notification content, or the ability to provide personalized training and diet plans. With such systems, users simply receive abnormality notifications, making it difficult to optimize their pet's health management.

[0988] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0989] In this invention, the server includes means for inputting and saving pet information, sensors and monitors for acquiring video data and biometric data, means for transmitting the acquired data to the server, means for analyzing the data on the server and detecting abnormalities, means for sending notifications to the caregiver based on the analysis results, emotion recognition means for optimizing the content of the notifications, and means for generating different training plans and meal plans depending on the situation. This makes it possible to monitor the health condition of the pet in real time and provide optimal notifications and personalized training plans and meal plans that take the user's emotional state into consideration.

[0990] "Means for entering and storing pet information" means an interface that allows a user to enter basic data about a pet (such as name, breed, age, medical history, etc.) and store it in a database or cloud.

[0991] "Sensors and monitors for acquiring video data and biometric data" refers to cameras that capture pet behavior and devices that measure pet biometric information (heart rate, body temperature, activity level, etc.).

[0992] The "means for transmitting acquired data to a server" refers to a function for transmitting data acquired from sensors and monitors to a cloud server using a communication means such as the Internet.

[0993] "Means for analyzing the data on the server and detecting abnormalities" refers to a method in which the cloud server processes the data received using an analytical algorithm and detects abnormalities in the pet's behavior and biological information.

[0994] "Means for sending a notification to the breeder based on the analysis results" refers to a function that notifies the user of the details and how to respond when an abnormality is detected through data analysis.

[0995] "Emotion recognition means for optimizing notification content" refers to an algorithm or system that analyzes the user's emotional state from their voice or text and adjusts the notification content based on the results.

[0996] "A means for generating different training plans and meal plans according to the situation" refers to a system or algorithm that automatically generates customized training methods and meal plans based on the behavioral data and biological information of a pet.

[0997] The system of the present invention not only monitors the health condition of a pet in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on the results. This system includes a means for inputting and saving pet information, sensors and monitors for acquiring video data and biological data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results, combining an emotion recognition means.

[0998] First, the user uses a dedicated app to input and save information about their pet. For example, they can use the app installed on their smartphone to input and save basic information about their pet, such as its name, breed, age, and medical history. This information is then stored on a cloud server.

[0999] Next, a camera is used to capture pet behavior and a health device is used to acquire biometric information. The pet camera captures video data of the pet in real time. The health device acquires biometric data such as the pet's heart rate, body temperature, and activity level. This data is sent to a cloud server via the Internet or Wi-Fi, and the sent data is encrypted and protected.

[1000] The cloud server analyzes the received video and biometric data to evaluate the pet's behavioral patterns and health status. Machine learning algorithms are used for data analysis. For example, the video data can identify behavioral patterns, such as whether the pet is walking, resting, or eating, and the biometric data can be used to evaluate whether the pet's heart rate is within a normal range.

[1001] If the anomaly detection method detects an abnormality in a pet's behavior or biometric data, it generates a notification containing the details and recommended measures. For example, it may generate a notification that reads, "Your heart rate is outside the normal range. Your body temperature is also elevated. Please refrain from walks and let your pet rest in a cool place. If necessary, consult a veterinarian." The generated notification is immediately sent to the user via a dedicated app or messaging app.

[1002] Furthermore, an emotion recognition system on the server analyzes the user's emotional state from their voice and text data. For example, if the user is feeling stressed, the notification content is optimized to take that emotional state into account. The notification may include customized suggestions such as "If you are concerned, contact your veterinarian immediately."

[1003] The user checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user is advised to immediately consult a veterinarian. In addition, the cloud server uses a generative AI model to automatically generate individual training methods and diet plans tailored to the pet's characteristics based on the data collected daily. Emotion recognition means adjusts the way these plans are presented based on the user's emotional state. Training methods and diet plans are suggested to the user through a dedicated app, which tracks the implementation of each suggestion and adjusts the plan as needed.

[1004] For example, the following prompt sentence is used:

[1005] User input:

[1006] Pet Name: Coco

[1007] Breed: French Bulldog

[1008] Age: 3 years old

[1009] Medical history: Allergies

[1010] Pet camera and health device setup complete.

[1011] Sending data to cloud server...

[1012] Start the analysis process on the server:

[1013] Identifying behavioral patterns from video data

[1014] Evaluating biological status from health device data

[1015] Anomaly detection:

[1016] If the heart rate is higher than normal, it will generate a notification saying "Pet's heart rate is outside of normal range"

[1017] Analyze user sentiment and optimize notifications

[1018] Final notice details:

[1019] "Your pet's heart rate is outside of the normal range. They also have an elevated body temperature. Please refrain from walks and allow them to rest in a cool place. Consult your veterinarian if necessary. If you are concerned, contact your veterinarian immediately."

[1020] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[1021] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1022] Step 1:

[1023] User: The user installs the dedicated app on their smartphone, creates an account, and logs in. As input, they enter basic information about their pet, such as its name, breed, age, and medical history. This data is sent to the server and stored in the cloud. As output, they receive the saved pet information.

[1024] Step 2:

[1025] User: The user installs the pet camera in the living room and attaches the health device to the pet's collar. The user inputs the camera's installation location and the device's location information into the app. Based on this, the app connects with these devices and confirms the completion of the setup. The connection status between the camera and the device is obtained as an output.

[1026] Step 3:

[1027] Terminal: The pet camera captures the pet's video data in real time, and the health device obtains the pet's biometric data such as heart rate, body temperature, and activity level. The pet's movements and biometric information are captured by the camera and device as input. The video data and biometric data are obtained as output.

[1028] Step 4:

[1029] Terminal: The acquired video data and biometric data are sent to the cloud server via the Internet or Wi-Fi. The captured data is provided as input. The data is encrypted and transmitted. The output is the data stored on the cloud server.

[1030] Step 5:

[1031] Server: The cloud server analyzes the received video data and biometric data. The data stored on the server is fed as input to the analytical algorithm. The algorithm identifies the pet's behavioral patterns and evaluates its health status from the biometric data. The analysis results are provided as output.

[1032] Step 6:

[1033] Server: The anomaly detection means detects abnormalities in the pet's behavior and biometric data based on the analysis results. The data analysis results are provided as input. Based on this, a notification is generated containing the details of the abnormality and recommended measures. The output is an anomaly notification.

[1034] Step 7:

[1035] Server: The generated notification is sent to the user immediately via a dedicated app or messaging app. The abnormality notification is provided as input, allowing the user to instantly check the status of their pet. The notification sent to the user is obtained as output.

[1036] Step 8:

[1037] Server: The emotion recognition means analyzes the user's voice and text data to recognize the emotional state. The user's voice and text data are provided as input. The emotion engine analyzes this and evaluates the emotional state. The analysis result of the emotional state is obtained as output.

[1038] Step 9:

[1039] Server: The notification content is optimized based on the emotional state analysis results. The emotional state analysis results are provided as input. The notification generation algorithm regenerates the notification content according to the emotion. The optimized notification is obtained as output.

[1040] Step 10:

[1041] User: The user checks the notification and takes appropriate action to manage the health of the pet as needed. The input is the notification sent to the user. The user takes appropriate action based on the notification. The output is an improvement in the pet's health.

[1042] Step 11:

[1043] Server: Analyzes the data collected daily and generates training methods and diet plans tailored to the pet's characteristics. The accumulated data is provided as input. The generative AI model analyzes this and generates a customized plan. The output is the training method and diet plan.

[1044] Step 12:

[1045] Server: The generated training method and meal plan are proposed to the user through a dedicated app. The generated plan is provided as input. It is sent to the user, providing a specific action plan. The plan provided to the user is obtained as output.

[1046] Step 13:

[1047] User: The user checks the proposed training methods and diet plans through a dedicated app, selects the ones that apply, and implements them on their pet. The proposed plan is provided as input. The user manages their pet's health based on that plan. The implementation status and its effects are obtained as output.

[1048] In this way, each processing step works together to manage the pet's health condition in real time and generate optimal notifications and plans that take the user's emotions into consideration, thereby achieving more effective health management.

[1049] (Application example 2)

[1050] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Systems already exist that monitor pet health in real time and detect and notify users of abnormalities. However, these systems lack the ability to optimize notification content based on the user's emotional state. As a result, they have been unable to fully reduce the psychological stress and burden on pet owners involved in managing their pet's health. Furthermore, training and feeding plans based on individual pet characteristics also fail to reflect the user's emotional state. This results in a lack of comprehensive support for both pets and owners.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1053] In this invention, the server includes a means for analyzing the user's emotions and optimizing the notification content, an artificial intelligence means for generating new training plans and meal plans based on the pet's health condition, and a means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state, thereby making it possible to provide appropriate notifications and plans while monitoring the pet's health condition in real time and taking the caretaker's emotional state into consideration.

[1054] "Means for inputting and saving pet information" refers to a device or software that allows a user to input information such as the pet's name, breed, age, and health status, and save it in a database.

[1055] A "camera" is a device for capturing a pet's behavior and the surrounding environment as video data.

[1056] A "health device" is a device for collecting biometric data such as a pet's heart rate, body temperature, and activity level.

[1057] The "means for transmitting to a cloud server" refers to a device or software for transmitting acquired data to a remote cloud server via the Internet.

[1058] The "means for analyzing the data on the cloud server and detecting abnormalities" refers to software or algorithms that analyze the acquired data within the cloud server and diagnose whether there are any abnormalities in the pet's health condition.

[1059] The "means for sending a notification to the keeper based on the analysis results" refers to a messaging system or application that notifies the user of any abnormalities that are detected.

[1060] "Means for analyzing user emotions and optimizing notification content" refers to algorithms or software that analyze the user's voice and text data to determine their emotional state and send notifications in the most appropriate format accordingly.

[1061] "Artificial intelligence means for generating new training and diet plans based on the health status of pets" refers to AI models and algorithms that automatically generate optimal training methods and diet plans based on pet health data.

[1062] "Means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state" refers to an AI and analysis system that combines the pet's unique characteristics with the user's emotional state to customize a suitable training and meal plan.

[1063] This invention is a system that monitors the health status of pets in real time, detects abnormalities, and notifies owners. The system also analyzes the user's emotions and optimizes notification content and various plans based on those emotions. This provides comprehensive support for pet health management.

[1064] System configuration and functions

[1065] 1. Users

[1066] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information such as their pet's name, breed, age, and health condition. Users also install the pet camera in an appropriate location in their home or at a physical store (pet shop or pet hotel), and attach the health device to their pet's collar or other device.

[1067] 2. Terminal

[1068] The pet camera captures video data of your pet's behavior and surrounding environment, while the health device captures your pet's real-time biometric data, such as heart rate, body temperature, and activity level, which are then sent to a cloud server via the internet.

[1069] 3. Server

[1070] The server is in a cloud environment and has multiple analysis functions, including:

[1071] Data analysis and anomaly detection methods: Analyze your pet's video data and biometric data to detect anomalies. For this purpose, various algorithms and anomaly detection models are used.

[1072] Notification method: If an abnormality is detected, the user will be notified immediately via a dedicated smartphone app or messaging app.

[1073] Sentiment analysis engine: Analyzes the user's voice and text data to determine their emotional state. Emotion analysis uses emotion recognition algorithms and natural language processing technology.

[1074] Training and Feeding Plan Generation: AI models are available to generate personalized training and feeding plans based on your pet's health and characteristics, and the plan content and suggestions are automatically adjusted based on the user's emotional state.

[1075] Specific examples

[1076] For example, if a German Shepherd named Leo at a pet hotel exhibits an abnormal heart rate:

[1077] 1. The health device detects an abnormal heart rate.

[1078] 2. The data is sent to a cloud server in real time and any abnormalities are checked.

[1079] 3. The customer's (pet owner's) emotional data is sent to the server and analyzed by the emotion analysis engine.

[1080] 4. You will receive a notification saying, "Leo's heart rate is high. We recommend that you consult your veterinarian. You appear to be stressed and we also recommend that you take some time to relax."

[1081] Prompt Sentence Examples

[1082] "If a pet's heart rate is higher than normal, detect the abnormality and notify them. Also, take into account the customer's emotional state and suggest appropriate measures."

[1083] "Please create a program that analyzes pet health data and sends a notification if it detects any abnormalities. The content of the notification should also reflect the customer's emotional state."

[1084] In this way, the pet's health condition is monitored, and notifications and suggestions are given that take the user's emotional state into account, providing more effective and personal support.

[1085] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1086] Step 1:

[1087] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information about their pet, such as its name, breed, age, and health condition. The entered data is sent from the smartphone app to a cloud server and stored in a database.

[1088] Input: Pet information

[1089] Output: Pet information stored on the cloud server

[1090] Step 2:

[1091] Users install the pet camera in an appropriate location in their home or a physical store (pet shop or pet hotel) and attach the health device to their pet's collar, etc. The camera and health device collect video data and biometric data (heart rate, body temperature, activity level, etc.) of their pet in real time.

[1092] Input: Installed cameras and health devices

[1093] Output: Real-time video and biometric data

[1094] Step 3:

[1095] The terminal transmits the video data and biometric data acquired from the camera and health device to a cloud server via the Internet.

[1096] Input: Video data and biometric data

[1097] Output: Data sent to the cloud server

[1098] Step 4:

[1099] The server, in a cloud environment, analyzes the received data. Machine learning algorithms are used to analyze the data and evaluate the pet's health and detect abnormalities. The algorithms analyze behavioral patterns from video data and identify abnormal patterns from biometric data.

[1100] Input: Data sent to the cloud server

[1101] Output: Health status assessment results and whether there are any abnormalities

[1102] Step 5:

[1103] If the cloud server detects an abnormality, it immediately notifies the user with details, which are sent to the user via a dedicated app or messaging app.

[1104] Input: Health status assessment results and whether there are any abnormalities

[1105] Output: User notification

[1106] Step 6:

[1107] The server receives the user's voice and text data and analyzes it with an emotion analysis engine. An emotion recognition algorithm is used to determine the user's emotional state (e.g., stress, relief, etc.).

[1108] Input: User voice and text data

[1109] Output: Evaluation result of the user's emotional state

[1110] Step 7:

[1111] The server generates new training and feeding plans based on the pet's health and the user's emotional state. The AI ​​model automatically generates individually optimized plans, taking into account the pet's characteristics and the user's emotions.

[1112] Input: Evaluation results of pet health status and evaluation results of user emotional status

[1113] Output: Personalized training and meal plans

[1114] Step 8:

[1115] The generated training and meal plans are sent from the cloud server to a dedicated app, where users can review the proposed plans, select the ones they want, and implement them on their pet.

[1116] Input: Personalized training and meal plans

[1117] Output: User informed of plan and execution

[1118] In this way, a system is realized that monitors the health condition of a pet in real time and provides notifications and suggests plans that take into account the user's emotional state.

[1119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1121] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1122] [Fourth embodiment]

[1123] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1127] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1130] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1132] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1134] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1135] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1136] The system of the present invention aims to monitor the health status of pets in real time, detect abnormalities, and notify owners. This system mainly includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, and a means for notifying owners based on the analysis results.

[1137] System Configuration

[1138] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[1139] 2. Device: The pet camera captures images of your pet, and the health device captures biometric data such as heart rate, body temperature, and activity level, which are then sent to a cloud server in real time.

[1140] 3. Server: Analyzes the received data using a data analysis tool to detect abnormalities. If an abnormality occurs, a notification is sent to the owner based on the analysis results.

[1141] The following will explain this with specific examples of system operation.

[1142] Specific example of system operation

[1143] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[1144] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. Once the installation and attachment are complete, connect these devices to the dedicated app.

[1145] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and temperature.

[1146] 4. Terminal: The acquired video and biometric data is transmitted to a cloud server via Wi-Fi in real time.

[1147] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[1148] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[1149] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[1150] 8. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner can consult a veterinarian.

[1151] Furthermore, the cloud server has the ability to generate individual training methods and diet plans based on the data, which can contribute not only to daily health management but also to improving the pet's quality of life (QOL).

[1152] As described above, the system of the present invention allows owners to monitor their pets' health conditions in real time and provide appropriate care, thereby reducing health risks and providing an optimal living environment for pets.

[1153] The processing flow will be explained below.

[1154] Step 1:

[1155] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[1156] Step 2:

[1157] User: Install the pet camera in a suitable location, such as the living room, and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[1158] Step 3:

[1159] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[1160] Step 4:

[1161] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[1162] Step 5:

[1163] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns from the video data and evaluates its health condition from the biometric data.

[1164] Step 6:

[1165] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[1166] Step 7:

[1167] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[1168] Step 8:

[1169] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[1170] Step 9:

[1171] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[1172] Step 10:

[1173] Server: Based on the data collected daily, AI automatically generates individual training methods and meal plans tailored to the pet's characteristics.

[1174] Step 11:

[1175] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[1176] Step 12:

[1177] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[1178] In this way, the entire system works together to manage pet health in real time, helping owners respond appropriately.

[1179] Example 1

[1180] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1181] There is a growing need for systems that can monitor pet health in real time and quickly notify owners when abnormalities are detected. Conventional systems have struggled to quickly and accurately detect abnormalities and provide appropriate countermeasures. They also lack the ability to provide training methods and dietary plans that address individual pet needs.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1183] In this invention, the server includes a means for inputting and saving pet information, a device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and identifying behavioral patterns, a means for evaluating health status and detecting abnormalities from the biometric data, a means for sending a notification of appropriate measures to the owner based on the analysis results, and a means for generating training methods and diet plans for the pet from the cloud server. This allows for accurate real-time monitoring of the pet's health status and prompt notification when an abnormality is detected. Furthermore, by providing training methods and diet plans tailored to the pet's individual needs, the quality of life (QOL) of the pet can be improved.

[1184] "Means for entering and saving pet information" refers to a function that allows users to enter basic information such as a pet's name, breed, age, and medical history through a dedicated app and save it in a database.

[1185] "Devices for acquiring video data and biometric data" refers to devices such as pet cameras and health devices that capture and collect video of pets and biometric data such as heart rate, body temperature, and activity level.

[1186] The "means for transmitting acquired data to a cloud server" is a communication function for transmitting video data and biometric data to a cloud server via the Internet in real time or periodically.

[1187] The "means for analyzing the data on the cloud server and identifying behavioral patterns" refers to algorithms and software for analyzing the video and biometric data received on the cloud server and identifying the pet's behavior (walking, resting, eating, etc.).

[1188] The "means for assessing health status and detecting abnormalities from biometric data" is a system that analyzes biometric data such as heart rate and body temperature on a cloud server to assess health status and detect abnormalities.

[1189] The "means for sending a notification of appropriate countermeasures to the owner based on the analysis results" is a function for sending a notification including information on countermeasures to the owner's dedicated app or messaging app when an abnormality is detected based on the analysis results.

[1190] "Means for generating training methods and meal plans for pets from the cloud server" refers to a function for generating and providing training methods and meal plans that meet the individual needs of pets based on data on the cloud server.

[1191] The system of the present invention aims to monitor the health status of pets in real time and notify the owner when an abnormality is detected. This system mainly includes the following components.

[1192] User input of pet information and device configuration

[1193] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information about their pet, such as its name, breed, age, and medical history. This information is stored in a database and processed on a cloud server. Users also install a pet camera in their living room and attach a health device to their pet's collar. By linking these devices with the dedicated app, data can be acquired and transmitted.

[1194] Device data acquisition and transmission

[1195] The pet camera captures video of the pet in real time, and the health device periodically measures biometric data such as heart rate, body temperature, and activity level. The captured video data and biometric data are transmitted to a cloud server in real time via Wi-Fi. The specific hardware used here includes a dedicated pet camera and health device.

[1196] Data analysis and anomaly detection by the server

[1197] The cloud server uses data analysis software such as Python and R to analyze the received video and biometric data. Image recognition algorithms are used to identify the pet's behavior (walking, resting, eating, etc.) from the video data, and statistical analysis is used to evaluate the pet's health status from the biometric data. This includes algorithms to detect abnormalities in heart rate and body temperature. If an abnormality is detected, a notification is generated based on the analysis results, containing a description of the abnormality and recommended measures.

[1198] Server-generated and sent notifications

[1199] The generated notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification will be sent saying, "Heart rate is high. Please consult a veterinarian." This notification allows the owner to take appropriate action immediately.

[1200] User Receipt and Response to Notices

[1201] Users can check the notifications in a dedicated app and take necessary measures to manage their pet's health. For example, if the heart rate is abnormally high, it is recommended that they consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user via notifications.

[1202] Specific examples of actions and prompts

[1203] Let's take the example of a pet owner using this system for their three-year-old dog. They enter their pet's information into a dedicated app and set up the camera and health device. The cloud server analyzes the pet's behavioral patterns and biological data in real time, and immediately sends a notification if an abnormality is detected. The pet owner can then take appropriate action.

[1204] Example prompt sentence:

[1205] Please explain the detailed processing steps of a system that monitors the health of pets in real-time and notifies the owner in case of anomalies, using specific terms and actions for each step.

[1206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1207] Step 1: User enters pet information and sets up device

[1208] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter basic information such as their pet's name, breed, age, and medical history. The entered information is saved in a database. This information is the basic data used for analysis in subsequent steps and is processed on a cloud server. For example, a user may enter their pet's name as "Buddy" and its age as "3."

[1209] Step 2: Obtaining data via the device

[1210] A pet camera installed on a terminal captures video of the pet in real time. At the same time, a health device attached to the pet's collar periodically measures biometric data such as heart rate, body temperature, and activity level. The input data consists of video data and biometric data, and is acquired from each terminal. For example, the camera records video at a frame rate of 30 fps, and the health device records the heart rate as "90 bpm" every 30 seconds.

[1211] Step 3: Send data to cloud server

[1212] The video and biometric data captured by the device is transmitted to a cloud server in real time via Wi-Fi. The data is then sent to the cloud server's API endpoint via an Internet connection. This transmission process is continuous, with data updates in real time. For example, a camera device uploads a 30-second video clip to the cloud server.

[1213] Step 4: Data analysis by the server

[1214] The server analyzes the received data. Specifically, it uses data analysis software such as Python or R to analyze the video data and uses image recognition algorithms to identify the pet's behavior (walking, resting, eating, etc.). At the same time, it performs statistical analysis using biometric data to evaluate the pet's health. The input is the video data and biometric data sent to the cloud server, and the output is the behavioral patterns and health assessment results. For example, if the video analysis assigns a tag of "eating" and the heart rate exceeds "120 bpm," it will flag it as an "anomaly detected."

[1215] Step 5: Server detects anomalies and generates notifications

[1216] If an abnormality is detected based on the analysis results, the server generates a notification containing the details of the abnormality and recommended measures. This notification is immediately sent to the owner via a dedicated app or messaging app. For example, if the heart rate is higher than normal, a notification stating "Heart rate is high. Please consult a veterinarian" is generated. The input is the result of data analysis, and the output is the notification message.

[1217] Step 6: User Receipt and Action

[1218] The user checks the notifications received through the dedicated app and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user may consult a veterinarian. The cloud server also generates personalized training methods and diet plans based on the pet's characteristics, which are also provided to the user through notifications. The input is the notification content and additional suggestions from the server, and the output is the pet owner's specific actions.

[1219] (Application example 1)

[1220] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1221] When raising a pet, it is important to monitor the pet's health in real time and quickly detect any suspicious movements or sounds and notify the owner. However, conventional systems have difficulty monitoring the pet's health and security simultaneously, requiring dual systems, which increases costs and management efforts. The present invention aims to solve this problem.

[1222] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1223] In this invention, the server includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting the acquired data to a cloud server, a means for analyzing the data on the cloud server and detecting abnormalities, a means for sending a notification to the owner based on the analysis results, and a means for monitoring suspicious movements and sounds, detecting abnormalities, and notifying the owner. This makes it possible to simultaneously monitor not only the health status of the pet, but also security abnormalities and notify them in real time.

[1224] "Pet information" refers to basic attribute data including the pet's name, breed, age, medical history, etc.

[1225] "Video data" refers to real-time video data of a pet captured by a camera.

[1226] "Biometric data" refers to data that indicates your pet's health, such as heart rate, body temperature, and activity level.

[1227] A "camera" is a device for acquiring video data.

[1228] A "health device" is a device for acquiring biometric data from a pet.

[1229] A "cloud server" is a remote server that receives data over a network and performs analysis.

[1230] "Abnormal" refers to any unusual or suspicious changes in a pet's health or behavior.

[1231] "Notifications" are warnings or information sent to the owner based on the analysis results.

[1232] "Suspicious behavior" refers to unexpected or unusual behavior.

[1233] "Sound" refers to audio data acquired through a camera or microphone.

[1234] "Analysis means" refers to algorithms or software that analyze the acquired data and determine whether or not there are any abnormalities.

[1235] The following configuration and operation procedure are conceivable as an embodiment of the present invention.

[1236] System Configuration

[1237] Hardware and Software

[1238] 1. How to enter and save your pet's information:

[1239] A dedicated application installed on the owner's smartphone or other device.

[1240] 2. Cameras and health devices for capturing video and biometric data:

[1241] The camera device is a commercially available surveillance camera such as Nest Cam, which captures footage of your pet in real time.

[1242] Health devices are wearable devices like FitBark that continuously collect biometric data such as heart rate, body temperature, and activity levels.

[1243] 3. Cloud Server:

[1244] Use a cloud service such as AWS Lambda as a server to receive data, analyze it, and detect anomalies.

[1245] Data processing and calculation

[1246] Server-side processing

[1247] 1. Data Collection:

[1248] Collect real-time video and biometric data from pet cameras and health devices.

[1249] Obtain basic information about your pet (name, breed, age, medical history, etc.) from the smartphone app.

[1250] 2. Data transmission:

[1251] The collected data is sent to a cloud server via Wi-Fi.

[1252] 3. Data Analysis:

[1253] A generative AI model is used on a cloud server to analyze video data and biometric data, identifying pet behavior patterns (e.g., walking, resting, eating) from the video data and assessing health status from the biometric data.

[1254] Suspicious movements and sounds are also analyzed, and they are analyzed comprehensively.

[1255] 4. Anomaly detection:

[1256] Based on the analysis results, if there is anything abnormal about the pet's health or if suspicious movements or sounds are detected, it will be recognized as an abnormality.

[1257] 5. Notification Generation and Transmission:

[1258] If an abnormality is detected, the cloud server will notify the owner via a dedicated application or messaging app.

[1259] The notification will include details of the anomaly and recommended actions to take.

[1260] 6. Generate personalized training and meal plans:

[1261] Based on your pet's characteristics, the system generates optimal training and feeding plans and reflects them in a dedicated application.

[1262] Specific examples

[1263] When this system is running, it can detect the following situations, for example:

[1264] A higher than normal heart rate may mean your pet is overly stressed.

[1265] If a sudden rise in body temperature is recorded, a heatstroke warning will be displayed.

[1266] It picks up any unusual activity from the camera feed and notifies owners of any suspicious individuals entering or pets escaping.

[1267] Prompt Sentence Examples

[1268] Design a system to monitor a pet's health in real time. This system would measure the pet's heart rate, body temperature, and activity level using a health device, and capture video and audio using a camera. A mechanism must be in place to notify the pet owner if an abnormality is detected. The system should also use a cloud server to analyze the data and send notifications. Please explain the system configuration and provide a concrete example of how it works.

[1269] The above is an embodiment of the invention. This embodiment makes it possible to simultaneously monitor not only the health status of pets but also any security abnormalities, and to take any necessary measures promptly.

[1270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1271] Step 1:

[1272] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter and save basic information about their pet, such as its name, breed, age, and medical history. This information is then saved within the dedicated app.

[1273] Input: Basic information about your pet (name, breed, age, medical history, etc.)

[1274] Output: Basic information data of pets stored on the device

[1275] Step 2:

[1276] The user simply installs the pet camera in the living room and attaches the pet health device to the pet's collar. Once the installation and attachment are complete, the devices are linked to the dedicated app and are ready to use.

[1277] Input: Installing the pet camera, wearing the health device, and linking the device to the app

[1278] Output: Camera and health device linked to the app

[1279] Step 3:

[1280] The device receives real-time video data from the pet camera and periodically collects biometric data such as heart rate, body temperature, and activity level from the health device.

[1281] Input: Real-time video data from pet cameras, biometric data from health devices

[1282] Output: Acquired video data and biometric data

[1283] Step 4:

[1284] The device transmits the acquired video data and biometric data to a cloud server via Wi-Fi in real time.

[1285] Input: Acquired video data and biometric data

[1286] Output: Data sent to the cloud server

[1287] Step 5:

[1288] The server analyzes the received video and biometric data, uses a generative AI model to identify the pet's behavioral patterns (e.g., walking, resting, eating), and assesses its health status from the biometric data. It also analyzes any suspicious movements or sounds, and performs a comprehensive analysis.

[1289] Input: Video data and biometric data sent to the cloud server

[1290] Output: Evaluation results of behavioral patterns and health status, detection results of suspicious movements and sounds

[1291] Step 6:

[1292] If an abnormality is detected, the server generates a notification based on the analysis results to notify the owner of the abnormality, including details of the abnormality and recommended measures.

[1293] Input: Behavioral patterns and health status assessment results, suspicious movement and sound detection results

[1294] Output: Notification sent to the breeder

[1295] Step 7:

[1296] The server instantly sends the generated notifications to the owner via a dedicated application or messaging app.

[1297] Input: Generated notification

[1298] Output: Notification sent to the breeder

[1299] Step 8:

[1300] The user checks the notification and takes necessary measures to manage the health and safety of their pet, such as consulting a veterinarian if the heart rate is abnormally high, or contacting the police if a suspicious person is detected.

[1301] Input: Notification sent to breeder

[1302] Output: Appropriate action by the breeder

[1303] The above are the specific processing steps for carrying out the present invention, which enable real-time monitoring of pet health conditions and security abnormalities, and prompt response.

[1304] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1305] The system of the present invention not only monitors the health condition of pets in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on them. This system includes a means for inputting and saving pet information, a camera and health device for acquiring video data and biometric data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results by combining an emotion engine.

[1306] System Configuration

[1307] 1. User: Enter your pet's information into the dedicated app and set up the pet camera and health device.

[1308] 2. Device: The pet camera captures the pet's video, and the health device acquires biometric data such as heart rate, body temperature, and activity level, which are then transmitted to the cloud server in real time.

[1309] 3. Server: Analyzes the received data using a data analysis means and detects any abnormalities. If an abnormality occurs based on the generated analysis results, a notification is sent to the breeder.

[1310] 4. Server: The emotion engine recognizes emotions from the user's voice and text data and optimizes notification content, training plans, and meal plans.

[1311] Specific example of system operation

[1312] 1. User: Install the dedicated app on your smartphone, create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[1313] 2. User: Install the pet camera in the living room and attach the pet health device to the pet's collar. After installation and attachment are complete, link these devices with the dedicated app and check the device settings.

[1314] 3. Device: The pet camera captures real-time footage of your pet, and the health device periodically measures vital data such as heart rate and body temperature.

[1315] 4. Terminal: The acquired video data and biometric data are sent to the cloud server via Wi-Fi in real time.

[1316] 5. Server: The cloud server analyzes the received data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video and evaluates its health status from its biometric data.

[1317] 6. Server: Anomalies are detected, for example, if the heart rate is higher than normal or if there are abnormalities in the behavioral patterns. In this case, the anomaly detection method is activated and generates a notification containing the nature of the anomaly and recommended actions.

[1318] 7. Server: The generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[1319] 8. Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, it will reflect this in the notification and suggest appropriate ways to respond.

[1320] 9. User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[1321] 10. Server: Based on the data collected daily, the AI ​​automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts the way these plans are presented based on the user's emotional state.

[1322] 11. Server: The generated training methods and feeding plans are sent to the dedicated app and proposed to the owner. The implementation status of each proposal is tracked and the plan is adjusted as necessary.

[1323] 12. User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[1324] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[1325] The processing flow will be explained below.

[1326] Step 1:

[1327] User: Install the dedicated app on your device (smartphone, etc.), create an account and log in. Next, enter information such as your pet's name, breed, age, and medical history.

[1328] Step 2:

[1329] User: Install the pet camera in an appropriate location and attach the pet health device to the pet's collar or body. After installation is complete, connect these devices to the dedicated app and check the device settings.

[1330] Step 3:

[1331] Device: The pet camera captures images of your pet 24 / 7, while the health device periodically measures and stores vital data such as heart rate, body temperature, and activity level.

[1332] Step 4:

[1333] Terminal: Collected video and biometric data is sent to a cloud server via Wi-Fi in real time.

[1334] Step 5:

[1335] Server: The cloud server analyzes the received video data and biometric data using a data analysis tool. It identifies the pet's behavioral patterns (e.g., walking, resting, eating) from the video data and evaluates its health condition from the biometric data.

[1336] Step 6:

[1337] Server: Detects abnormalities based on the data. For example, if the heart rate is higher than normal or if there is something unusual in the pet's behavioral pattern, the anomaly detection method will be activated.

[1338] Step 7:

[1339] Server: When an abnormality is detected, a notification is generated containing the nature of the abnormality and recommended actions. The notification details the pet's current health status and the necessary actions.

[1340] Step 8:

[1341] Server: Generated notifications are sent to the owner immediately via a dedicated app or messaging app.

[1342] Step 9:

[1343] Server: The emotion engine analyzes the emotional state of the keeper from their voice and text messages. For example, if the keeper is feeling stressed, the engine will reflect this in the notification and suggest appropriate ways to respond.

[1344] Step 10:

[1345] User: The owner checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the owner may consult a veterinarian.

[1346] Step 11:

[1347] Server: Based on the data collected daily, AI automatically generates personalized training and feeding plans tailored to the pet's characteristics. The emotion engine adjusts how these plans are presented based on the user's emotional state.

[1348] Step 12:

[1349] Server: Sends generated training methods and diet plans to the app and makes suggestions to the owner. Tracks the implementation of each suggestion and adjusts the plan as needed.

[1350] Step 13:

[1351] User: Check the suggested training methods and meal plans through the dedicated app, select the ones that apply, and implement them on your pet.

[1352] Example 2

[1353] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1354] While conventional pet health management systems can monitor a pet's biological information and behavioral data and detect abnormalities, they lack the ability to recognize the user's emotional state and optimize notification content, or the ability to provide personalized training and diet plans. With such systems, users simply receive abnormality notifications, making it difficult to optimize their pet's health management.

[1355] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1356] In this invention, the server includes means for inputting and saving pet information, sensors and monitors for acquiring video data and biometric data, means for transmitting the acquired data to the server, means for analyzing the data on the server and detecting abnormalities, means for sending notifications to the caregiver based on the analysis results, emotion recognition means for optimizing the content of the notifications, and means for generating different training plans and meal plans depending on the situation. This makes it possible to monitor the health condition of the pet in real time and provide optimal notifications and personalized training plans and meal plans that take the user's emotional state into consideration.

[1357] "Means for entering and storing pet information" means an interface that allows a user to enter basic data about a pet (such as name, breed, age, medical history, etc.) and store it in a database or cloud.

[1358] "Sensors and monitors for acquiring video data and biometric data" refers to cameras that capture pet behavior and devices that measure pet biometric information (heart rate, body temperature, activity level, etc.).

[1359] The "means for transmitting acquired data to a server" refers to a function for transmitting data acquired from sensors and monitors to a cloud server using a communication means such as the Internet.

[1360] "Means for analyzing the data on the server and detecting abnormalities" refers to a method in which the cloud server processes the data received using an analytical algorithm and detects abnormalities in the pet's behavior and biological information.

[1361] "Means for sending a notification to the breeder based on the analysis results" refers to a function that notifies the user of the details and how to respond when an abnormality is detected through data analysis.

[1362] "Emotion recognition means for optimizing notification content" refers to an algorithm or system that analyzes the user's emotional state from their voice or text and adjusts the notification content based on the results.

[1363] "A means for generating different training plans and meal plans according to the situation" refers to a system or algorithm that automatically generates customized training methods and meal plans based on the behavioral data and biological information of a pet.

[1364] The system of the present invention not only monitors the health condition of a pet in real time, detects abnormalities and notifies the owner, but also recognizes the user's emotions and optimizes notifications and various plans based on the results. This system includes a means for inputting and saving pet information, sensors and monitors for acquiring video data and biological data, a means for transmitting data to a cloud server, a means for analyzing data and detecting abnormalities on the cloud server, a means for notifying based on the analysis results, and a means for optimizing notifications based on the analysis results, combining an emotion recognition means.

[1365] First, the user uses a dedicated app to input and save information about their pet. For example, they can use the app installed on their smartphone to input and save basic information about their pet, such as its name, breed, age, and medical history. This information is then stored on a cloud server.

[1366] Next, a camera is used to capture pet behavior and a health device is used to acquire biometric information. The pet camera captures video data of the pet in real time. The health device acquires biometric data such as the pet's heart rate, body temperature, and activity level. This data is sent to a cloud server via the Internet or Wi-Fi, and the sent data is encrypted and protected.

[1367] The cloud server analyzes the received video and biometric data to evaluate the pet's behavioral patterns and health status. Machine learning algorithms are used for data analysis. For example, the video data can identify behavioral patterns, such as whether the pet is walking, resting, or eating, and the biometric data can be used to evaluate whether the pet's heart rate is within a normal range.

[1368] If the anomaly detection method detects an abnormality in a pet's behavior or biometric data, it generates a notification containing the details and recommended measures. For example, it may generate a notification that reads, "Your heart rate is outside the normal range. Your body temperature is also elevated. Please refrain from walks and let your pet rest in a cool place. If necessary, consult a veterinarian." The generated notification is immediately sent to the user via a dedicated app or messaging app.

[1369] Furthermore, an emotion recognition system on the server analyzes the user's emotional state from their voice and text data. For example, if the user is feeling stressed, the notification content is optimized to take that emotional state into account. The notification may include customized suggestions such as "If you are concerned, contact your veterinarian immediately."

[1370] The user checks the notification and takes necessary measures to manage the pet's health. For example, if the heart rate is abnormally high, the user is advised to immediately consult a veterinarian. In addition, the cloud server uses a generative AI model to automatically generate individual training methods and diet plans tailored to the pet's characteristics based on the data collected daily. Emotion recognition means adjusts the way these plans are presented based on the user's emotional state. Training methods and diet plans are suggested to the user through a dedicated app, which tracks the implementation of each suggestion and adjusts the plan as needed.

[1371] For example, the following prompt sentence is used:

[1372] User input:

[1373] Pet Name: Coco

[1374] Breed: French Bulldog

[1375] Age: 3 years old

[1376] Medical history: Allergies

[1377] Pet camera and health device setup complete.

[1378] Sending data to cloud server...

[1379] Start the analysis process on the server:

[1380] Identifying behavioral patterns from video data

[1381] Evaluating biological status from health device data

[1382] Anomaly detection:

[1383] If the heart rate is higher than normal, it will generate a notification saying "Pet's heart rate is outside of normal range"

[1384] Analyze user sentiment and optimize notifications

[1385] Final notice details:

[1386] "Your pet's heart rate is outside of the normal range. They also have an elevated body temperature. Please refrain from walks and allow them to rest in a cool place. Consult your veterinarian if necessary. If you are concerned, contact your veterinarian immediately."

[1387] In this way, the entire system works together to manage the pet's health in real time, while providing notifications and optimizing plans that take the user's emotions into account, enabling more effective health management.

[1388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1389] Step 1:

[1390] User: The user installs the dedicated app on their smartphone, creates an account, and logs in. As input, they enter basic information about their pet, such as its name, breed, age, and medical history. This data is sent to the server and stored in the cloud. As output, they receive the saved pet information.

[1391] Step 2:

[1392] User: The user installs the pet camera in the living room and attaches the health device to the pet's collar. The user inputs the camera's installation location and the device's location information into the app. Based on this, the app connects with these devices and confirms the completion of the setup. The connection status between the camera and the device is obtained as an output.

[1393] Step 3:

[1394] Terminal: The pet camera captures the pet's video data in real time, and the health device obtains the pet's biometric data such as heart rate, body temperature, and activity level. The pet's movements and biometric information are captured by the camera and device as input. The video data and biometric data are obtained as output.

[1395] Step 4:

[1396] Terminal: The acquired video data and biometric data are sent to the cloud server via the Internet or Wi-Fi. The captured data is provided as input. The data is encrypted and transmitted. The output is the data stored on the cloud server.

[1397] Step 5:

[1398] Server: The cloud server analyzes the received video data and biometric data. The data stored on the server is fed as input to the analytical algorithm. The algorithm identifies the pet's behavioral patterns and evaluates its health status from the biometric data. The analysis results are provided as output.

[1399] Step 6:

[1400] Server: The anomaly detection means detects abnormalities in the pet's behavior and biometric data based on the analysis results. The data analysis results are provided as input. Based on this, a notification is generated containing the details of the abnormality and recommended measures. The output is an anomaly notification.

[1401] Step 7:

[1402] Server: The generated notification is sent to the user immediately via a dedicated app or messaging app. The abnormality notification is provided as input, allowing the user to instantly check the status of their pet. The notification sent to the user is obtained as output.

[1403] Step 8:

[1404] Server: The emotion recognition means analyzes the user's voice and text data to recognize the emotional state. The user's voice and text data are provided as input. The emotion engine analyzes this and evaluates the emotional state. The analysis result of the emotional state is obtained as output.

[1405] Step 9:

[1406] Server: The notification content is optimized based on the emotional state analysis results. The emotional state analysis results are provided as input. The notification generation algorithm regenerates the notification content according to the emotion. The optimized notification is obtained as output.

[1407] Step 10:

[1408] User: The user checks the notification and takes appropriate action to manage the health of the pet as needed. The input is the notification sent to the user. The user takes appropriate action based on the notification. The output is an improvement in the pet's health.

[1409] Step 11:

[1410] Server: Analyzes the data collected daily and generates training methods and diet plans tailored to the pet's characteristics. The accumulated data is provided as input. The generative AI model analyzes this and generates a customized plan. The output is the training method and diet plan.

[1411] Step 12:

[1412] Server: The generated training method and meal plan are proposed to the user through a dedicated app. The generated plan is provided as input. It is sent to the user, providing a specific action plan. The plan provided to the user is obtained as output.

[1413] Step 13:

[1414] User: The user checks the proposed training methods and diet plans through a dedicated app, selects the ones that apply, and implements them on their pet. The proposed plan is provided as input. The user manages their pet's health based on that plan. The implementation status and its effects are obtained as output.

[1415] In this way, each processing step works together to manage the pet's health condition in real time and generate optimal notifications and plans that take the user's emotions into consideration, thereby achieving more effective health management.

[1416] (Application example 2)

[1417] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] Systems already exist that monitor pet health in real time and detect and notify users of abnormalities. However, these systems lack the ability to optimize notification content based on the user's emotional state. As a result, they have been unable to fully reduce the psychological stress and burden on pet owners involved in managing their pet's health. Furthermore, training and feeding plans based on individual pet characteristics also fail to reflect the user's emotional state. This results in a lack of comprehensive support for both pets and owners.

[1419] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1420] In this invention, the server includes a means for analyzing the user's emotions and optimizing the notification content, an artificial intelligence means for generating new training plans and meal plans based on the pet's health condition, and a means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state, thereby making it possible to provide appropriate notifications and plans while monitoring the pet's health condition in real time and taking the caretaker's emotional state into consideration.

[1421] "Means for inputting and saving pet information" refers to a device or software that allows a user to input information such as the pet's name, breed, age, and health status, and save it in a database.

[1422] A "camera" is a device for capturing a pet's behavior and the surrounding environment as video data.

[1423] A "health device" is a device for collecting biometric data such as a pet's heart rate, body temperature, and activity level.

[1424] The "means for transmitting to a cloud server" refers to a device or software for transmitting acquired data to a remote cloud server via the Internet.

[1425] The "means for analyzing the data on the cloud server and detecting abnormalities" refers to software or algorithms that analyze the acquired data within the cloud server and diagnose whether there are any abnormalities in the pet's health condition.

[1426] The "means for sending a notification to the keeper based on the analysis results" refers to a messaging system or application that notifies the user of any abnormalities that are detected.

[1427] "Means for analyzing user emotions and optimizing notification content" refers to algorithms or software that analyze the user's voice and text data to determine their emotional state and send notifications in the most appropriate format accordingly.

[1428] "Artificial intelligence means for generating new training and diet plans based on the health status of pets" refers to AI models and algorithms that automatically generate optimal training methods and diet plans based on pet health data.

[1429] "Means for generating personalized training methods and meal plans based on the pet's characteristics and the user's emotional state" refers to an AI and analysis system that combines the pet's unique characteristics with the user's emotional state to customize a suitable training and meal plan.

[1430] This invention is a system that monitors the health status of pets in real time, detects abnormalities, and notifies owners. The system also analyzes the user's emotions and optimizes notification content and various plans based on those emotions. This provides comprehensive support for pet health management.

[1431] System configuration and functions

[1432] 1. Users

[1433] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information such as their pet's name, breed, age, and health condition. Users also install the pet camera in an appropriate location in their home or at a physical store (pet shop or pet hotel), and attach the health device to their pet's collar or other device.

[1434] 2. Terminal

[1435] The pet camera captures video data of your pet's behavior and surrounding environment, while the health device captures your pet's real-time biometric data, such as heart rate, body temperature, and activity level, which are then sent to a cloud server via the internet.

[1436] 3. Server

[1437] The server is in a cloud environment and has multiple analysis functions, including:

[1438] Data analysis and anomaly detection methods: Analyze your pet's video data and biometric data to detect anomalies. For this purpose, various algorithms and anomaly detection models are used.

[1439] Notification method: If an abnormality is detected, the user will be notified immediately via a dedicated smartphone app or messaging app.

[1440] Sentiment analysis engine: Analyzes the user's voice and text data to determine their emotional state. Emotion analysis uses emotion recognition algorithms and natural language processing technology.

[1441] Training and Feeding Plan Generation: AI models are available to generate personalized training and feeding plans based on your pet's health and characteristics, and the plan content and suggestions are automatically adjusted based on the user's emotional state.

[1442] Specific examples

[1443] For example, if a German Shepherd named Leo at a pet hotel exhibits an abnormal heart rate:

[1444] 1. The health device detects an abnormal heart rate.

[1445] 2. The data is sent to a cloud server in real time and any abnormalities are checked.

[1446] 3. The customer's (pet owner's) emotional data is sent to the server and analyzed by the emotion analysis engine.

[1447] 4. You will receive a notification saying, "Leo's heart rate is high. We recommend that you consult your veterinarian. You appear to be stressed and we also recommend that you take some time to relax."

[1448] Prompt Sentence Examples

[1449] "If a pet's heart rate is higher than normal, detect the abnormality and notify them. Also, take into account the customer's emotional state and suggest appropriate measures."

[1450] "Please create a program that analyzes pet health data and sends a notification if it detects any abnormalities. The content of the notification should also reflect the customer's emotional state."

[1451] In this way, the pet's health condition is monitored, and notifications and suggestions are given that take the user's emotional state into account, providing more effective and personal support.

[1452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1453] Step 1:

[1454] Users install the dedicated app on their smartphone, create an account, and log in. Next, they enter information about their pet, such as its name, breed, age, and health condition. The entered data is sent from the smartphone app to a cloud server and stored in a database.

[1455] Input: Pet information

[1456] Output: Pet information stored on the cloud server

[1457] Step 2:

[1458] Users install the pet camera in an appropriate location in their home or a physical store (pet shop or pet hotel) and attach the health device to their pet's collar, etc. The camera and health device collect video data and biometric data (heart rate, body temperature, activity level, etc.) of their pet in real time.

[1459] Input: Installed cameras and health devices

[1460] Output: Real-time video and biometric data

[1461] Step 3:

[1462] The terminal transmits the video data and biometric data acquired from the camera and health device to a cloud server via the Internet.

[1463] Input: Video data and biometric data

[1464] Output: Data sent to the cloud server

[1465] Step 4:

[1466] The server, in a cloud environment, analyzes the received data. Machine learning algorithms are used to analyze the data and evaluate the pet's health and detect abnormalities. The algorithms analyze behavioral patterns from video data and identify abnormal patterns from biometric data.

[1467] Input: Data sent to the cloud server

[1468] Output: Health status assessment results and whether there are any abnormalities

[1469] Step 5:

[1470] If the cloud server detects an abnormality, it immediately notifies the user with details, which are sent to the user via a dedicated app or messaging app.

[1471] Input: Health status assessment results and whether there are any abnormalities

[1472] Output: User notification

[1473] Step 6:

[1474] The server receives the user's voice and text data and analyzes it with an emotion analysis engine. An emotion recognition algorithm is used to determine the user's emotional state (e.g., stress, relief, etc.).

[1475] Input: User voice and text data

[1476] Output: Evaluation result of the user's emotional state

[1477] Step 7:

[1478] The server generates new training and feeding plans based on the pet's health and the user's emotional state. The AI ​​model automatically generates individually optimized plans, taking into account the pet's characteristics and the user's emotions.

[1479] Input: Evaluation results of pet health status and evaluation results of user emotional status

[1480] Output: Personalized training and meal plans

[1481] Step 8:

[1482] The generated training and meal plans are sent from the cloud server to a dedicated app, where users can review the proposed plans, select the ones they want, and implement them on their pet.

[1483] Input: Personalized training and meal plans

[1484] Output: User informed of plan and execution

[1485] In this way, a system is realized that monitors the health condition of a pet in real time and provides notifications and suggests plans that take into account the user's emotional state.

[1486] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1487] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1488] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1489] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1490] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1491] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1492] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1493] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1494] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1495] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1496] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1497] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1498] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1499] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1500] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1501] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1502] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1503] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1504] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1505] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1506] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1507] The following is further disclosed regarding the above embodiment.

[1508] (Claim 1)

[1509] a means for entering and storing pet information;

[1510] a camera and a health device for acquiring video data and biometric data;

[1511] means for transmitting the acquired data to a cloud server;

[1512] means for analyzing the data on a cloud server and detecting anomalies;

[1513] The system includes a means for sending a notification to the breeder based on the analysis results.

[1514] (Claim 2)

[1515] 10. The system of claim 1, wherein the cloud server further comprises means for generating a personalized training method and / or diet plan based on the characteristics of the pet.

[1516] (Claim 3)

[1517] 2. The system according to claim 1, wherein the camera and health device are provided with means for acquiring video data and biometric data in real time and transmitting the data to a cloud server.

[1518] "Example 1"

[1519] (Claim 1)

[1520] a means for entering and storing pet information;

[1521] a device for acquiring video data and biometric data;

[1522] means for transmitting the acquired data to a cloud server;

[1523] means for analyzing the data on a cloud server and identifying behavioral patterns;

[1524] A means for assessing health status and detecting abnormalities from biometric data;

[1525] A means for sending notifications of appropriate measures to the breeder based on the analysis results;

[1526] A system including a means for generating training methods and diet plans for pets from the cloud server.

[1527] (Claim 2)

[1528] 2. The system according to claim 1, further comprising means for the cloud server to analyze biometric data and behavioral patterns in real time, and to immediately send a notification including the analysis results if an abnormality is detected.

[1529] (Claim 3)

[1530] 2. The system of claim 1, wherein the device is provided with means for taking photographs and measurements in real time and periodically transmitting the data to a cloud server.

[1531] "Application Example 1"

[1532] (Claim 1)

[1533] a means for entering and storing pet information;

[1534] a camera and a health device for acquiring video data and biometric data;

[1535] means for transmitting the acquired data to a cloud server;

[1536] means for analyzing the data on a cloud server and detecting anomalies;

[1537] a means for sending a notification to the breeder based on the analysis results;

[1538] The system includes a means of monitoring suspicious movements and sounds, detecting abnormalities, and notifying the keeper.

[1539] (Claim 2)

[1540] 10. The system of claim 1, wherein the cloud server further comprises means for generating a personalized training method and / or diet plan based on the characteristics of the pet.

[1541] (Claim 3)

[1542] 2. The system according to claim 1, wherein the camera and health device are provided with means for acquiring video data and biometric data in real time and transmitting the data to a cloud server.

[1543] "Example 2: Combining Emotion Engines"

[1544] (Claim 1)

[1545] a means for entering and storing pet information;

[1546] a sensor and a monitor for acquiring video data and biometric data;

[1547] means for transmitting the acquired data to a server;

[1548] means for analyzing the data on a server and detecting anomalies;

[1549] a means for sending a notification to the breeder based on the analysis results;

[1550] emotion recognition means for optimizing notification content;

[1551] A generating means for generating different training plans and meal plans according to the situation;

[1552] A system including:

[1553] (Claim 2)

[1554] 10. The system of claim 1, wherein the server further comprises means for generating a personalized training regimen and / or meal plan based on the pet's characteristics.

[1555] (Claim 3)

[1556] 2. The system according to claim 1, wherein the sensor and monitor are provided with means for acquiring video data and biometric data in real time and transmitting the data to a server.

[1557] "Application example 2 when combining emotion engines"

[1558] (Claim 1)

[1559] a means for entering and storing pet information;

[1560] a camera and a health device for acquiring video data and biometric data;

[1561] means for transmitting the acquired data to a cloud server;

[1562] means for analyzing the data on a cloud server and detecting anomalies;

[1563] a means for sending a notification to the breeder based on the analysis results;

[1564] A means for analyzing user emotions and optimizing notification content;

[1565] A system including artificial intelligence means for generating new training and feeding plans based on the pet's health status.

[1566] (Claim 2)

[1567] 10. The system of claim 1, wherein the cloud server further comprises means for generating a personalized training regimen and / or meal plan based on the pet's characteristics and the user's emotional state.

[1568] (Claim 3)

[1569] 2. The system according to claim 1, wherein the camera and health device are provided with means for acquiring video data and biometric data in real time and transmitting the data to a cloud server. [Explanation of symbols]

[1570] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for entering and storing pet information; a camera and a health device for acquiring video data and biometric data; means for transmitting the acquired data to a cloud server; means for analyzing the data on a cloud server and detecting anomalies; The system includes a means for sending a notification to the breeder based on the analysis results.

2. The system of claim 1 , wherein the cloud server further comprises means for generating a personalized training regimen and / or meal plan based on the characteristics of the pet.

3. The system according to claim 1 , wherein the camera and health device are provided with means for acquiring video data and biometric data in real time and transmitting the data to a cloud server.

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