System

Abstract

A system is provided.SOLUTION: A system for monitoring the health condition of a pet, comprising: means for collecting behavior data of the pet; means for receiving health information provided by a veterinarian; means for recording the health condition of the pet input by a user; means for integrating and analyzing these data and learning specific health patterns; means for notifying the user in real time when an abnormality is detected; means for supporting communication with the veterinarian when the user confirms the abnormality; and means for providing a community for the users to share information and exchange knowledge and experience related to the health of the pet.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] There is a problem with keeping track of pet health in real time. It is particularly difficult to detect signs of health problems with pets like cats, and sudden changes often occur. Regular veterinary checkups are also a significant burden for owners, making early detection of illness difficult. Furthermore, there is a lack of opportunities for pet owners to share information and exchange health knowledge and experiences, which can lead to isolation. To solve these issues, an integrated system is needed that thoroughly cares for pet health and alleviates owner anxiety. [Means for solving the problem]

[0005] The present invention is a system for monitoring the health condition of a pet in real time, detecting abnormalities early and notifying the pet owner. Specifically, the system includes the following means.

[0006] Means of collecting pet behavior data: Collect data from IoT devices attached to pets to monitor their behavior.

[0007] A means of receiving health information provided by your veterinarian: Obtaining routine diagnostic results and health records.

[0008] User-entered means of recording pet health status: Records health information entered by owners through the app.

[0009] A means of integrating and analyzing this data to learn specific health patterns: Using AI technology, we learn your pet's normal behavioral patterns and detect abnormalities.

[0010] A means of notifying users in real time when an abnormality is detected: As soon as an abnormality is detected, a notification is sent to the user's device in real time.

[0011] A means to assist users in communicating with a veterinarian when they notice an abnormality: Provide a function that allows users to easily contact a veterinarian when they notice an abnormality.

[0012] A means of providing a community where users can share information and exchange knowledge and experiences about pet health: Providing a platform where pet owners can interact with each other, with AI suggesting appropriate information and experiences.

[0013] This allows for detailed and real-time information on pet health, reducing owner anxiety and improving pet health care.

[0014] "Pet behavior data" refers to data collected from IoT devices attached to pets, and includes behavioral information such as the amount of exercise, sleep time, and eating patterns of pets.

[0015] "Veterinarian-provided health information" is information provided by a veterinarian as a result of routine examinations and health checks, and includes data such as weight, blood test results, and vaccination history.

[0016] "Pet health status entered by the user" refers to health information entered by the owner through the application, including data such as observed abnormal behavior, changes in appetite, and excretion status.

[0017] "Means for integrating and analyzing" refers to the function of centralizing pet behavior data, health information, and user input data and analyzing them using AI technology.

[0018] "Specific health patterns" refer to the normal behavior and health patterns of individual pets, which the AI ​​has learned and modeled from past data.

[0019] "Means for detecting anomalies" refers to features that use AI algorithms to compare normal health patterns with real-time data and detect anomalies.

[0020] "Means of notifying users in real time" refers to a function that immediately sends a push notification to a user's device when an abnormality is detected.

[0021] "Means to support communication with veterinarians" refers to functions that make it easier for users to contact veterinarians when they notice an abnormality, and includes creating and sending reports.

[0022] The "community for users to share information" is a platform that provides a place for pet owners to share their health knowledge and experiences and exchange opinions. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] Basic system configuration

[0045] The pet health monitoring system of the present invention includes the following major components:

[0046] 1. Pet-worn IoT devices

[0047] 2. Server

[0048] 3. User Device

[0049] 4. User Community Platform

[0050] Detailed program description

[0051] The core of this system is a program that collects data on pet behavior and detects abnormalities. The specific processing content of this program is explained in natural language below.

[0052] Data collection

[0053] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[0054] Data storage and integration

[0055] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[0056] AI-powered data analysis and health pattern learning

[0057] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[0058] Anomaly detection and notification

[0059] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0060] Support for communication with veterinarians

[0061] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0062] User Community Platform

[0063] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[0064] Specific examples

[0065] Example 1: Detecting abnormal pet behavior

[0066] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[0067] 2. The IoT device sends the abnormal data to the server.

[0068] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[0069] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0070] Example 2: Sharing information in the community

[0071] 1. A user posts a question to the community saying, "My pet is eating less."

[0072] 2. The server searches its historical database and suggests similar questions and their answers.

[0073] 3. Other users also respond to this question with their own experiences and advice.

[0074] In this way, this system comprehensively monitors the health of pets, alleviating owners' anxiety and making a significant contribution to maintaining the health of pets.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The IoT device worn by pets uses sensors to collect real-time data on pet behavior, such as the amount of exercise, sleep duration, and eating patterns.

[0078] Step 2:

[0079] IoT devices periodically send collected data to a server via Bluetooth or Wi-Fi.

[0080] Step 3:

[0081] The server stores the received pet behavior data in a database, including past data.

[0082] Step 4:

[0083] The user enters health information provided by the veterinarian through the application, as well as daily health conditions and abnormal symptoms, and this information is sent from the device to the server.

[0084] Step 5:

[0085] The server receives the veterinarian's health information and user-entered data, consolidates it into a database, and stores it.

[0086] Step 6:

[0087] The server analyzes past behavioral data and health information, and uses AI algorithms to learn your pet's normal behavioral patterns, during which specific health patterns are modeled.

[0088] Step 7:

[0089] The server periodically analyzes newly collected data in real time and compares it with learned health patterns.

[0090] Step 8:

[0091] If the server detects any behavior or health condition that differs from normal patterns, it determines that it is an abnormality.

[0092] Step 9:

[0093] If the server detects an abnormality, it immediately sends a push notification to the user's device, which includes specific details about the abnormality and a warning.

[0094] Step 10:

[0095] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[0096] Step 11:

[0097] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[0098] Step 12:

[0099] The server also provides a community platform where users can share information with each other. When a user posts a question to the community, the server searches a database of past questions and suggests similar questions and answers.

[0100] This series of steps allows for efficient and comprehensive monitoring and management of pet health, reducing owner anxiety.

[0101] Example 1

[0102] 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."

[0103] There is a demand for systems that can continuously and precisely monitor pet health, detect abnormalities early, and enable users to respond quickly and appropriately. However, existing systems lack the functionality to collect and analyze a pet's overall behavioral data in real time and immediately notify users of abnormalities. Furthermore, they lack a smooth way to share information with veterinarians or a community platform where users can exchange experiences and knowledge with each other. This makes it difficult to adequately manage pet health.

[0104] 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.

[0105] In this invention, the server includes means for collecting pet behavioral data, means for transmitting the data to the server, means for receiving health information provided by a veterinarian, means for recording the pet's health status input by the user, means for integrating and storing this data in a database, means for training an AI model using the stored data to learn the specific health patterns of each pet, means for analyzing new behavioral data in real time and sending a push notification to the user's device if an abnormality is detected, means for generating a report and sending it to the veterinarian when the user confirms the abnormality notification, and means for providing a community for users to share information and exchange knowledge and experiences regarding pet health. This enables comprehensive monitoring of pet health status, immediate notification if an abnormality is detected, and efficient collaboration with veterinarians, thereby enabling effective pet health management.

[0106] "Pet behavior data" refers to information about your pet's lifestyle habits, such as the amount of exercise, sleep time, and eating patterns.

[0107] "Server" refers to the central computer system that receives, stores, and analyzes pet behavioral and health data.

[0108] A "pet-worn IoT device" refers to hardware that is worn by a pet and uses built-in sensors to collect data on the pet's behavior.

[0109] "User device" refers to a device, such as a smartphone or tablet, that the user uses to receive pet behavior data and abnormality notifications.

[0110] "Database" refers to an information system for integrated management of pet behavior data, health information, and data entered by users, stored on a server.

[0111] "AI model" refers to the artificial intelligence algorithm used to analyze pet behavior data and learn specific health patterns.

[0112] "Push notification" refers to an instant notification message sent from the server to the user's device when an abnormality is detected.

[0113] "Abnormal data" refers to data that indicates behavior that differs from your pet's normal health pattern.

[0114] A "veterinarian" is a professional who provides specialized diagnosis and treatment for pet health problems.

[0115] A "user community platform" refers to an online community where users can share knowledge and experiences about pet health and exchange information.

[0116] MODE FOR CARRYING OUT THE INVENTION

[0117] Basic system configuration

[0118] The pet health monitoring system of the present invention includes the following main components: an IoT device worn by a pet, a server, a user terminal, and a user community platform.

[0119] Detailed program description

[0120] This system operates based on a program that acquires pet behavior data and detects abnormalities. Each process is performed using specific hardware and software.

[0121] Data collection

[0122] First, the IoT device worn by the pet collects the pet's behavior data in real time. This device has built-in sensors such as a pedometer, vibration sensor, accelerometer, camera, and microphone. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0123] Data storage and integration

[0124] The server stores the received pet behavior data in a database. It also integrates and stores health information provided by veterinarians and pet health status entered by users through the application. This integrated data is used to centrally manage all information necessary for pet health management.

[0125] AI-powered data analysis and health pattern learning

[0126] The server uses the stored data to train an AI algorithm, specifically a generative AI model that learns each pet's specific health patterns. This process allows the server to model a pet's normal behavior and health status and detect abnormalities.

[0127] Anomaly detection and notification

[0128] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0129] Support for communication with veterinarians

[0130] When the user checks the abnormality notification, they can contact a veterinarian through the app based on the information. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0131] User Community Platform

[0132] There are also community platforms where users can share information and exchange knowledge and experiences about pet health. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[0133] Specific examples

[0134] Example 1: Detecting abnormal pet behavior

[0135] 1. An IoT device worn by a pet collects data on the pet's activity.

[0136] 2. The device detects that you are sleeping for longer than usual.

[0137] 3. The device sends the abnormal data to the server.

[0138] 4. The server compares this data with normal health patterns and determines any abnormalities.

[0139] 5. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0140] Example 2: Sharing information in the community

[0141] 1. A user uses the application to post a question to the community: "My pet is eating less."

[0142] 2. The server searches its historical database and suggests similar questions and their answers.

[0143] 3. As a result, other users will also contribute their own experiences and advice to this question.

[0144] Prompt Sentence Examples

[0145] "Please explain each processing step of the system that collects real-time behavioral data of pets and detects abnormalities. Also, please explain in detail the process by which users send abnormality notifications to their veterinarians."

[0146] This invention enables effective health management of pets by comprehensively monitoring their health status, immediately notifying them if an abnormality is detected, and enabling efficient collaboration with veterinarians.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1:

[0149] Collecting pet behavior data

[0150] The IoT device worn by pets uses a built-in pedometer, vibration sensor, accelerometer, camera, and microphone to collect real-time behavioral data such as exercise volume, sleep duration, and eating patterns.

[0151] Input: Pet behavior (exercise, sleep, eating, etc.)

[0152] Data processing: collection of raw data obtained from sensors

[0153] Output: Generate behavioral data (amount of exercise, sleep time, dietary patterns, etc.)

[0154] Step 2:

[0155] Sending data to the server

[0156] IoT devices send the collected data to a server via Bluetooth or Wi-Fi.

[0157] Input: Behavioral data collected by IoT devices

[0158] Data processing: Packetizing data and converting it to transmission protocol

[0159] Output: Send data to the server

[0160] Step 3:

[0161] Data storage and integration

[0162] The server centrally manages the received data and stores it in a database, and also integrates and stores health information provided by veterinarians and pet health conditions entered by users through the app.

[0163] Input: Submitted behavioral data, health information, and user-entered data

[0164] Data processing: Data integration and centralization

[0165] Output: Consolidated database updates

[0166] Step 4:

[0167] AI-based data analysis and learning

[0168] The server uses the stored data to train AI algorithms to learn each pet's specific health patterns, using generative AI models to learn normal and abnormal behavior patterns.

[0169] Input: Data from the integrated database

[0170] Data processing: Data analysis and learning using AI algorithms

[0171] Output: A model of health patterns

[0172] Step 5:

[0173] Anomaly detection and notification

[0174] The server analyzes new behavioral data in real time, compares it with learned health patterns, and immediately sends a push notification to the user's device if an abnormality is detected.

[0175] Input: New behavioral data and AI model comparison results

[0176] Data processing: Running anomaly detection algorithms

[0177] Output: Generate and send a push notification

[0178] Step 6:

[0179] Support for communication with veterinarians

[0180] When the user confirms the abnormality notification, they can use the app to contact a veterinarian. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0181] Input: Anomaly detection notification

[0182] Data processing: Reporting abnormal data

[0183] Output: Generate a report and send it to your veterinarian

[0184] Step 7:

[0185] Information sharing on user community platforms

[0186] Users use the application to post questions to the community and get answers, and the server searches a database of past questions to suggest similar questions and answers.

[0187] Input: User question

[0188] Data processing: database search and suggestion of similar queries

[0189] Output: Displaying recommendations

[0190] These processing steps enable monitoring of the pet's health condition and early detection and management of abnormalities.

[0191] (Application example 1)

[0192] 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."

[0193] Conventional pet health monitoring systems primarily focus on detecting abnormalities based on the pet's own behavioral data. However, the health of the pet owner often has a significant impact on the pet's health. Furthermore, there is a lack of support for taking appropriate actions based on the user's health status. Therefore, there is a need for a system that takes the user's health status into consideration comprehensively. This will enable a system that can optimize the health of not only the user but also the pet.

[0194] 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.

[0195] In this invention, the server includes means for collecting pet behavior data, means for receiving health information provided by a veterinarian, means for recording the pet's health status entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication between the user and the veterinarian when the user confirms an abnormality, means for providing a community for users to share information and exchange knowledge and experiences regarding pet health, a smart device for collecting the user's health status, and means for notifying the user of meal provision contents based on the user's health data. This enables comprehensive monitoring and support for maintaining the health of the pet while taking the user's health status into consideration.

[0196] The "means for collecting pet behavioral data" refers to a device that measures and records a pet's daily behavior and activities in real time through an IoT device attached to the pet.

[0197] The "means for receiving health information provided by veterinarians" is an interface for aggregating and incorporating into the system the health status and medical records provided by veterinarians regarding diagnosis and treatment.

[0198] "User-entered means for recording pet health conditions" refers to a function that allows pet owners to use an application to input, save, and record their pet's health conditions and symptoms.

[0199] "Means for learning specific health patterns" refers to the process of analyzing collected data with AI algorithms to learn and detect your pet's normal health patterns.

[0200] "Means of notifying users in real time when abnormalities are detected" refers to a function that immediately notifies users when abnormalities in their health status are detected based on the analysis of collected data.

[0201] The "means for supporting communication with a veterinarian" is an interface that helps the user easily contact a veterinarian when an abnormality is detected.

[0202] "A means of providing a community for users to share information and exchange knowledge and experiences related to pet health" is an online platform where pet owners can share health information and care methods and help each other.

[0203] A "smart device that collects user health status" is a wearable device used to collect user health data (e.g., heart rate, blood sugar level, body temperature, etc.) in real time.

[0204] "Means for notifying the user of meal contents based on the user's health data" refers to a function that analyzes the user's collected health data and notifies the user of appropriate meal contents and suggestions based on the results.

[0205] The system of this invention comprehensively monitors the health status of both pets and their owners and supports appropriate actions, which requires an IoT device worn by the pet, a smart device, a server, and a user terminal.

[0206] Hardware Configuration

[0207] 1. Pet-worn IoT devices

[0208] A device that collects pet behavioral data (such as exercise, sleep, and eating patterns).

[0209] 2. User's smart device

[0210] A wearable device for collecting user health data (heart rate, blood sugar level, body temperature, etc.) in real time.

[0211] 3. Server

[0212] It centralizes data on both pets and users, and is used for data analysis and pattern recognition, using programming languages ​​such as Python and frameworks such as Django and Flask.

[0213] 4. User Device

[0214] A device such as a smartphone that allows you to receive real-time updates on your pet's health and any abnormalities.

[0215] Software Configuration

[0216] 1. Data Collection

[0217] The server receives data from the pet's IoT device and the user's smart device via Bluetooth and Wi-Fi, and the data is stored in a database on the server.

[0218] 2. Data integration and analysis

[0219] The server combines the collected pet and user data and analyzes it using AI algorithms, including deep learning and machine learning models, to detect abnormal patterns.

[0220] 3. Abnormality notification

[0221] If an anomaly is detected, the server will send a real-time push notification to the user's device, which will include details of the anomaly and recommended actions.

[0222] 4. Communication support

[0223] When users receive an abnormality notification, they are provided with a function that allows them to easily contact a veterinarian via the server, and they can also send a report containing health data to the veterinarian.

[0224] 5. User Community Platform

[0225] The server hosts a community platform where users can post questions about pet health and receive answers from other users. The platform is built using Django.

[0226] Specific examples

[0227] Example 1: Pet and user anomaly detection

[0228] The IoT device worn by the pet detects when the pet is sleeping for longer than usual.

[0229] A smart wearable device detects when a user's blood sugar level is high.

[0230] The server analyzes both sets of data and determines that there is an anomaly.

[0231] The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required. Also, your blood sugar level is high. We recommend that you feed it appropriately."

[0232] Prompt Sentence Examples

[0233] "Please suggest a special menu for those with high blood sugar levels and dietary restrictions."

[0234] "If my blood sugar level is over 150, please suggest a meal plan that is suitable for high blood sugar."

[0235] The system monitors the health of both pets and owners in real time, immediately recommends necessary actions, and facilitates collaboration with veterinarians and communication between users.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] Data collection

[0239] Subject: Server

[0240] How it works: The server receives data from the pet's IoT device and the user's smart device.

[0241] Input: Pet behavior data (amount of exercise, sleep time, eating patterns) and user health data (heart rate, blood sugar level, body temperature).

[0242] Data processing: The received data is stored in the server database. This data is recorded with a timestamp.

[0243] Output: Real-time pet and user health data stored in a database on the server.

[0244] Step 2:

[0245] Data Integration and Preprocessing

[0246] Subject: Server

[0247] How it works: The server consolidates collected pet and user health data.

[0248] Input: Pet and user health data stored in a database.

[0249] Data processing: Filling in missing data, filtering outliers, and standardizing data.

[0250] Output: Preprocessed consolidated data in a parseable format.

[0251] Step 3:

[0252] AI-based data analysis and anomaly detection

[0253] Subject: Server

[0254] How it works: The server feeds pre-processed data into an AI algorithm to detect anomalies.

[0255] Input: Preprocessed integrated data, existing health pattern models.

[0256] Data Computing: Deep learning and machine learning models (e.g., using TensorFlow or PyTorch) to analyze health patterns and detect anomalies.

[0257] Output: Anomaly detection results (type and details of anomaly).

[0258] Step 4:

[0259] Abnormal notification

[0260] Subject: Server

[0261] Operation: The server pushes a notification to the user's device about any detected anomalies.

[0262] Input: Anomaly detection results.

[0263] Data Transformation: Generate notification messages with details of the anomaly and recommended actions.

[0264] Output: Push notification to user device.

[0265] Step 5:

[0266] User support

[0267] Subject: Server

[0268] Behavior: The server supports the user in taking action after receiving an error notification.

[0269] Input: A response selection from the user (e.g., contacting a veterinarian, entering additional data).

[0270] Data Processing: Collects the necessary data and generates reports based on user selections.

[0271] Output: User guide and veterinarian report.

[0272] Step 6:

[0273] Managing the Community Platform

[0274] Subject: Server

[0275] How it works: The server allows users to post questions about their pet's health to the community.

[0276] Input: Questions submitted by users.

[0277] Data processing: Compare with past posted data and suggest similar questions and their answers.

[0278] Output: Answers and advice within the user community.

[0279] Step 7:

[0280] Proposing actions based on health data

[0281] Subject: Server

[0282] How it works: The server generates and notifies the user of appropriate dietary and behavioral suggestions based on their health data.

[0283] Input: User health data, existing medical and nutritional data.

[0284] Data calculation: Analyzes user data and generates dietary and behavioral recommendations based on health status.

[0285] Output: A notification of suggested actions sent to the user's device. A specific example would be something like, "Because your blood sugar level is high, we suggest a low-carb menu for your next meal."

[0286] 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.

[0287] Basic system configuration

[0288] The pet health monitoring system of the present invention includes the following major components:

[0289] 1. Pet-worn IoT devices

[0290] 2. Server

[0291] 3. User Device

[0292] 4. User Community Platform

[0293] 5. Emotion Engine

[0294] Detailed program description

[0295] The core of this system is a program that collects data on pet behavior and detects abnormalities. In addition, by combining it with an emotion engine that recognizes and analyzes the user's emotions, more advanced health management and user support are realized. The specific processing contents of the program are explained in natural language below.

[0296] Data collection

[0297] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[0298] Data storage and integration

[0299] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[0300] AI-powered data analysis and health pattern learning

[0301] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[0302] Anomaly detection and notification

[0303] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0304] Emotional analysis of users using an emotion engine

[0305] As users interact with the application, the emotion engine recognizes emotions by analyzing their input and interaction history, for example, by inferring their emotions from the tone of their text input and the choice of words.

[0306] Providing advice based on user emotions

[0307] The server provides appropriate advice and measures based on the user's emotions recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and comforting materials.

[0308] Support for communication with veterinarians

[0309] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0310] User Community Platform

[0311] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[0312] Specific examples

[0313] Example 1: Detecting abnormal pet behavior

[0314] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[0315] 2. The IoT device sends the abnormal data to the server.

[0316] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[0317] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0318] Example 2: Sharing information in the community

[0319] 1. A user posts a question to the community saying, "My pet is eating less."

[0320] 2. The server searches its historical database and suggests similar questions and their answers.

[0321] 3. Other users also respond to this question with their own experiences and advice.

[0322] Example 3: Providing advice using an emotion engine

[0323] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[0324] 2. The server provides relaxation techniques to ease anxiety and reassurance for similar cases.

[0325] In this way, the system efficiently and comprehensively monitors and manages pet health, and also provides emotional support to users, allowing for more peace of mind in health management.

[0326] The processing flow will be explained below.

[0327] Step 1:

[0328] The IoT device worn by pets uses sensors to collect real-time behavioral data (such as the amount of exercise, sleep time, and eating patterns), which is then sent to a server via Bluetooth or Wi-Fi.

[0329] Step 2:

[0330] The server stores the received pet behavior data in a database, as well as health information provided by veterinarians and health condition data entered by users through the app.

[0331] Step 3:

[0332] Users enter any abnormal symptoms or daily health conditions through the application and send the data to the server.

[0333] Step 4:

[0334] The server consolidates all stored data and analyzes it using AI algorithms, learning your pet's normal behavior patterns and modeling specific health patterns.

[0335] Step 5:

[0336] The server analyzes newly collected behavioral data in real time and compares it with learned health patterns. If abnormal behavior or health conditions are detected, they are deemed abnormal.

[0337] Step 6:

[0338] If an anomaly is detected, the server will send a real-time push notification to the user's device, detailing the specific anomaly and any points requiring attention.

[0339] Step 7:

[0340] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[0341] Step 8:

[0342] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[0343] Step 9:

[0344] The emotion engine analyzes text input and dialogue history when a user uses the app to recognize the user's emotions.

[0345] Step 10:

[0346] The server then proposes appropriate advice and measures based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling anxious, it will provide relaxation techniques and comforting materials.

[0347] Step 11:

[0348] Users receive advice and take appropriate action, and if necessary, can use the community to seek advice from other users.

[0349] Step 12:

[0350] The server manages the user community platform, and when a user posts a question, it searches the database to suggest similar questions and answers, and immediately notifies the user when a new answer is posted by another user.

[0351] This series of steps allows you to comprehensively monitor and manage your pet's health and reduces your anxiety.

[0352] Example 2

[0353] 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."

[0354] In pet health management, conventional systems did not adequately collect pet behavior data or detect abnormalities. Furthermore, communication with veterinarians and knowledge sharing between users were not smooth, making it difficult to efficiently monitor and manage pet health. Furthermore, support tailored to the user's emotions was not provided, making it difficult to alleviate user anxiety.

[0355] 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.

[0356] In this invention, the server includes: means for collecting pet behavioral data and transferring it to the server in real time; means for integrating and storing the behavioral data and health information in a database; means for providing artificial intelligence that uses the stored data to learn specific health patterns; means for detecting abnormalities and notifying the user's device in real time; means for providing a natural language processing engine that analyzes emotions from the user's text input and dialogue history; means for providing advice to the user based on the analyzed emotions; means for transmitting data to support communication with veterinarians when an abnormality is detected; and means for providing a community platform for users to share information and exchange health-related knowledge and experiences. This allows for real-time monitoring and management of pet health conditions and prompt notification to the user when an abnormality is detected. Furthermore, by providing advice based on the user's emotions, facilitating smooth communication with veterinarians, and allowing users to share information, the system provides comprehensive support for pet health management.

[0357] "Pet behavior data" refers to data related to the behavior of pets, such as the amount of exercise, sleeping hours, and eating patterns.

[0358] A "server" is a computer system that collects, stores, and analyzes pet behavior data and provides various notifications and communication support.

[0359] "Real time" means being able to process phenomena occurring at that time immediately without delay.

[0360] A "database" is an information storage system for effectively storing and managing pet behavioral data and health information.

[0361] "Artificial intelligence" refers to machine learning algorithms that learn your pet's specific health patterns and detect abnormalities.

[0362] A "natural language processing engine" refers to technology for analyzing emotions from user text input and dialogue history.

[0363] "Push notification" is a communication method that sends information from a server to a user's device in real time.

[0364] A "veterinarian" is a professional who diagnoses the health of animals and provides appropriate treatment.

[0365] "Emotion" refers to the psychological state inferred from the content of a user's text or dialogue.

[0366] A "community platform" is an online service that provides a place for users to share information and exchange knowledge and experiences regarding pet health.

[0367] The present invention is a system for efficiently monitoring and managing the health condition of a pet and providing support according to the user's emotions. Specific embodiments of the system are described below.

[0368] Basic system configuration

[0369] The system of the present invention includes the following major components:

[0370] 1. Pet-worn IoT devices

[0371] 2. Server

[0372] 3. User Device

[0373] 4. User Community Platform

[0374] 5. Emotion Engine

[0375] Data collection

[0376] First, the IoT device worn by the pet uses an accelerometer and temperature sensor to collect real-time behavioral data about the pet, including the pet's activity level, sleep duration, and eating patterns, and the collected data is transmitted to a server via Bluetooth or Wi-Fi.

[0377] Data storage and integration

[0378] The server stores the received pet behavior data in a MySQL database. Health information provided by veterinarians and pet health conditions entered by users through the application are also stored in the same database. This allows all data to be managed centrally.

[0379] AI-powered data analysis and health pattern learning

[0380] The server uses the stored data to train machine learning algorithms, specifically building health pattern learning models using Python and TensorFlow, which then identify and analyze normal and abnormal behavior patterns in pets.

[0381] Anomaly detection and notification

[0382] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device. This notification contains information such as, "Your pet is sleeping longer than usual. Attention is required."

[0383] Emotional analysis of users using an emotion engine

[0384] As users interact with the application, the emotion engine analyzes their text input and dialogue history to identify their emotions, using natural language processing (NLP) techniques to infer emotions such as "anxiety" or "relief" from the tone of the text and word choice.

[0385] Providing advice based on user emotions

[0386] The server provides appropriate advice and countermeasures based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and examples of success in similar cases.

[0387] Support for communication with veterinarians

[0388] When a user confirms an abnormality, the app provides a function that allows them to easily contact a veterinarian. The user's device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0389] User Community Platform

[0390] The server manages questions and answers posted by users and searches related past data to suggest appropriate information, allowing users to share their knowledge and experiences regarding health management.

[0391] Specific examples

[0392] Below is a concrete example of how this system works.

[0393] Example 1: Detecting abnormal pet behavior

[0394] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[0395] 2. The IoT device sends the abnormal data to the server.

[0396] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[0397] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0398] Example 2: Sharing information in the community

[0399] 1. A user posts a question to the community saying, "My pet is eating less."

[0400] 2. The server searches its historical database and suggests similar questions and their answers.

[0401] 3. Other users also respond to this question with their own experiences and advice.

[0402] Example 3: Providing advice using an emotion engine

[0403] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[0404] 2. The server will provide relaxation techniques and reassurance from similar cases to ease anxiety.

[0405] Prompt Sentence Examples

[0406] "Please give us a concrete example of a pet health monitoring system. Please explain in detail how it detects abnormal pet behavior, shares information with the community, and provides advice using an emotion engine."

[0407] As described above, the system of the present invention is capable of comprehensively monitoring and managing the health condition of pets and efficiently providing the necessary support to users.

[0408] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0409] Step 1:

[0410] The IoT device worn by pets uses accelerometers and temperature sensors to collect pet behavior data in real time. The sensors detect the pet's physical movements as input and convert the data into digital form. As output, the collected data is sent to a server via Bluetooth or Wi-Fi. Specific actions such as running, eating, and sleeping are captured once per second.

[0411] Step 2:

[0412] The server stores the received pet behavior data in a database. As input, it receives behavior data sent in real time from IoT devices. As output, it stores the data in the "Behavior Data" table in a MySQL database. Specific operations include adding new data entries and integrating them with existing data.

[0413] Step 3:

[0414] The user enters the pet's health status through the application. As input, the user fills in the application's form with the pet's recent health status. As output, the information is sent to the server and stored in a database. Specifically, the user selects a health checklist within the app and enters conditions such as "no appetite" or "lack of energy."

[0415] Step 4:

[0416] The server uses artificial intelligence to integrate pet behavioral and health data and learn specific health patterns. It uses behavioral and health data stored in a database as input. It generates a trained health pattern model as output. Specifically, it uses Python and TensorFlow to train a machine learning algorithm and model the health patterns.

[0417] Step 5:

[0418] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. It uses real-time behavioral data as input. As output, it generates abnormality warning data if an abnormality is detected. Specifically, the analysis algorithm compares the data with a normal behavior model and executes the process of detecting an abnormality.

[0419] Step 6:

[0420] If the server detects an abnormality, it immediately sends a push notification to the user's device. The input is abnormality warning data. The output is a notification message sent to the user's device. Specifically, the server sends a message via the push notification service, such as "Your pet is sleeping longer than usual. Attention is required."

[0421] Step 7:

[0422] When a user uses an application, the emotion engine analyzes the user's input and interaction history to recognize emotions. It uses text data from the user as input and generates data on the user's emotional state as output. Specifically, it uses natural language processing technology to perform sentiment analysis on the user's input text and infers emotions such as anxiety or relief.

[0423] Step 8:

[0424] The server provides appropriate advice and countermeasures to the user based on the emotional information recognized by the emotion engine. The server receives the user's emotional state data as input. The server generates an advice message as output and sends it to the user. Specific actions include providing users who are feeling anxious with "relaxation methods" and "examples of previous successes."

[0425] Step 9:

[0426] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian. The input is detailed information including the abnormality data. The output is a report that is sent to the veterinarian. Specifically, when the user presses the "Contact" button, a detailed report is generated and sent to the veterinarian via the server.

[0427] Step 10:

[0428] The server manages questions and answers posted by users and shares information through a community platform. It uses question data from users as input. It searches related past data and suggests appropriate information as output. Specifically, a database search algorithm searches past questions and answers and suggests similar questions and answers.

[0429] (Application example 2)

[0430] 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."

[0431] To effectively monitor the health of pets, conventional technologies lack the ability to detect and notify abnormalities in real time, and no method has been established to provide appropriate support based on the user's emotions.In order to effectively monitor the health of elderly people and detect abnormalities early, a system is needed that applies conventional pet monitoring technology, has the ability to detect abnormalities in real time, and provides support based on the user's emotions.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral data, means for receiving health information provided by medical professionals, means for recording health conditions entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication with medical professionals when the user confirms an abnormality, means for providing a community where users can share information and exchange health-related knowledge and experiences, and means for analyzing the user's emotions and providing corresponding advice. This enables efficient and comprehensive monitoring and management of the health of elderly people and rapid response when an abnormality occurs. Furthermore, providing support tailored to the user's emotions can provide a sense of security and contribute to improving their health.

[0433] Key Word Definitions

[0434] "Behavioral data" refers to information about the behavior of subjects such as pets and elderly people in their daily lives, and specifically refers to data such as heart rate, distance traveled, sleeping patterns, and eating patterns.

[0435] "Medical personnel" refers to people who have specialized knowledge about the health of subjects such as pets and elderly people and who diagnose and treat them, and generally refers to veterinarians and doctors.

[0436] "Health patterns" refer to the normal health status and behavioral tendencies of subjects, such as pets and elderly people, formed based on daily behavioral data.

[0437] "Real-time" refers to a state in which data can be processed and analyzed immediately at the moment it is generated or collected, and results can be obtained.

[0438] A "community" is a place or platform where users interested in the health of pets, the elderly, etc. gather, and where information is shared and mutual support is provided.

[0439] "Sentiment analysis" refers to the process of inferring and analyzing a user's emotions from the text they enter and their behavior.

[0440] "Anomaly detection" refers to the process of identifying unusual behaviors or conditions that deviate from the norm by comparing them with previously learned health patterns.

[0441] "Notification" refers to the act of providing information and sending alert messages in real time through a user device or application.

[0442] "Server" refers to a central processing unit or system that receives, stores, analyzes behavioral data of pets, elderly people, etc., and provides the results.

[0443] "Advice" refers to specific advice or countermeasures provided based on the user's situation and emotions.

[0444] MODE FOR CARRYING OUT THE INVENTION

[0445] Basic system configuration

[0446] The present invention is a system for monitoring the health status of elderly people, which includes the following main components:

[0447] 1. Wearable devices: worn by seniors, they collect real-time behavioral data such as heart rate, distance traveled, sleep patterns, and eating patterns.

[0448] 2. Server: Receives data sent from the wearable device, stores it in a database, and analyzes it using AI algorithms.

[0449] 3. User's device: A device such as a smartphone or tablet that receives notifications from the server.

[0450] 4. User community platform: An online platform for users to share information and exchange knowledge and experiences related to elderly health.

[0451] 5. Sentiment Analysis Engine: Analyzes the text entered by the user to recognize emotions and provide advice accordingly.

[0452] Specific processing details of the system

[0453] 1. Data Collection

[0454] The wearable device uses built-in sensors to collect real-time behavioral data of the elderly, including heart rate, distance traveled, sleep duration, and dietary patterns, and transmits this data to a server via Bluetooth or Wi-Fi.

[0455] 2. Data storage and integration

[0456] The server centrally manages the behavioral data received from the wearable devices and stores it in a database, as well as health information provided by medical professionals and health condition information entered by users through their devices.

[0457] 3. AI-powered data analysis and health pattern learning

[0458] The server uses the stored data to train a generative AI model to learn the specific health patterns of each elderly person, in the process modeling their normal behavior and health status so that anomalies can be detected.

[0459] 4. Anomaly detection and notification

[0460] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0461] 5. Supporting users through sentiment analysis

[0462] When a user checks an abnormality notification, the emotion analysis engine analyzes the user's input and dialogue history to recognize their emotions. For example, it infers the user's emotions from the tone of their text input and their choice of words. Based on the results of the emotion analysis, the server provides the user with appropriate advice and countermeasures. For example, if the user is feeling anxious, it provides relaxation techniques and reassurance materials.

[0463] Specific use cases

[0464] Example 1: Detecting abnormal behavior in elderly people

[0465] 1. A wearable device detects when an elderly person's heart rate is higher than normal.

[0466] 2. The wearable device sends the abnormality data to the server.

[0467] 3. The server compares this data with normal health patterns and determines abnormalities.

[0468] 4. The server immediately sends a notification to the user's device saying, "Your heart rate is higher than normal. Attention is required."

[0469] Example 2: Providing advice based on sentiment analysis

[0470] 1. When a user receives a notification of an abnormality and checks the details in the app, the sentiment analysis engine detects "anxiety" from the user's text input.

[0471] 2. The server provides relaxation techniques to ease anxiety and ways to deal with similar cases.

[0472] Prompt Sentence Examples

[0473] Explain how to apply this technology to a pet health monitoring system. This system uses IoT devices to collect pet behavior data and uses AI analytics to detect abnormalities. Consider a scenario in which this technology is used to develop a health monitoring and security support app for the elderly, and explain the specific application content and processing steps.

[0474] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0475] Explanation of the program's processing steps

[0476] Processing flow and explanation of each step

[0477] Step 1:

[0478] Data collection

[0479] Subject: Wearable devices

[0480] Input: Elderly person's heart rate, distance traveled, sleep duration, and dietary patterns

[0481] How it works: The wearable device uses built-in sensors to measure an older adult's heart rate, distance traveled, sleep duration, and eating patterns in real time.

[0482] Output: Collected behavioral data

[0483] Specific operation: Measurement data is transferred to the server via Bluetooth or Wi-Fi.

[0484] Step 2:

[0485] Data storage and integration

[0486] Subject: Server

[0487] Input: Behavioral data sent from wearable devices, health information provided by healthcare professionals, and user-entered health conditions

[0488] How it works: The server stores the received data in a centralized database.

[0489] Output: Integrated database

[0490] Specific operation: Data is formatted and converted to save it in the database.

[0491] Step 3:

[0492] AI-powered data analysis and health pattern learning

[0493] Subject: Server

[0494] Input: Integrated Database

[0495] How it works: The server uses a generative AI model to analyze the stored data and learn the specific health patterns of each elderly person.

[0496] Output: Learned health patterns

[0497] Specific operation: Data is input into the AI ​​model, and health patterns are generated and saved as analysis results.

[0498] Step 4:

[0499] Anomaly detection and notification

[0500] Subject: Server

[0501] Input: New behavioral data and learned health patterns

[0502] How it works: The server analyzes new behavioral data in real time and compares it with learned health patterns to detect anomalies.

[0503] Output: Anomaly detection notification

[0504] Specific behavior: If an anomaly is detected, a push notification is sent to the user's device.

[0505] Step 5:

[0506] Supporting users with sentiment analysis

[0507] Subject: Sentiment analysis engine

[0508] Input: User-entered text and interaction history

[0509] How it works: The sentiment analysis engine analyzes the user's sentiment from the input text and generates advice based on that sentiment.

[0510] Output: Sentiment analysis results and advice

[0511] Specific operation: A text analysis algorithm is used to determine emotions, and appropriate relaxation methods and advice are sent to the user's device via a server.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] [Second embodiment]

[0516] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0517] 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.

[0518] 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).

[0519] 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.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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.

[0526] 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.

[0527] 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."

[0528] Basic system configuration

[0529] The pet health monitoring system of the present invention includes the following major components:

[0530] 1. Pet-worn IoT devices

[0531] 2. Server

[0532] 3. User Device

[0533] 4. User Community Platform

[0534] Detailed program description

[0535] The core of this system is a program that collects data on pet behavior and detects abnormalities. The specific processing content of this program is explained in natural language below.

[0536] Data collection

[0537] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[0538] Data storage and integration

[0539] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[0540] AI-powered data analysis and health pattern learning

[0541] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[0542] Anomaly detection and notification

[0543] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0544] Support for communication with veterinarians

[0545] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0546] User Community Platform

[0547] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[0548] Specific examples

[0549] Example 1: Detecting abnormal pet behavior

[0550] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[0551] 2. The IoT device sends the abnormal data to the server.

[0552] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[0553] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0554] Example 2: Sharing information in the community

[0555] 1. A user posts a question to the community saying, "My pet is eating less."

[0556] 2. The server searches its historical database and suggests similar questions and their answers.

[0557] 3. Other users also respond to this question with their own experiences and advice.

[0558] In this way, this system comprehensively monitors the health of pets, alleviating owners' anxiety and making a significant contribution to maintaining the health of pets.

[0559] The processing flow will be explained below.

[0560] Step 1:

[0561] The IoT device worn by pets uses sensors to collect real-time data on pet behavior, such as the amount of exercise, sleep duration, and eating patterns.

[0562] Step 2:

[0563] IoT devices periodically send collected data to a server via Bluetooth or Wi-Fi.

[0564] Step 3:

[0565] The server stores the received pet behavior data in a database, including past data.

[0566] Step 4:

[0567] The user enters health information provided by the veterinarian through the application, as well as daily health conditions and abnormal symptoms, and this information is sent from the device to the server.

[0568] Step 5:

[0569] The server receives the veterinarian's health information and user-entered data, consolidates it into a database, and stores it.

[0570] Step 6:

[0571] The server analyzes past behavioral data and health information, and uses AI algorithms to learn your pet's normal behavioral patterns, during which specific health patterns are modeled.

[0572] Step 7:

[0573] The server periodically analyzes newly collected data in real time and compares it with learned health patterns.

[0574] Step 8:

[0575] If the server detects any behavior or health condition that differs from normal patterns, it determines that it is an abnormality.

[0576] Step 9:

[0577] If the server detects an abnormality, it immediately sends a push notification to the user's device, which includes specific details about the abnormality and a warning.

[0578] Step 10:

[0579] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[0580] Step 11:

[0581] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[0582] Step 12:

[0583] The server also provides a community platform where users can share information with each other. When a user posts a question to the community, the server searches a database of past questions and suggests similar questions and answers.

[0584] This series of steps allows for efficient and comprehensive monitoring and management of pet health, reducing owner anxiety.

[0585] Example 1

[0586] 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."

[0587] There is a demand for systems that can continuously and precisely monitor pet health, detect abnormalities early, and enable users to respond quickly and appropriately. However, existing systems lack the functionality to collect and analyze a pet's overall behavioral data in real time and immediately notify users of abnormalities. Furthermore, they lack a smooth way to share information with veterinarians or a community platform where users can exchange experiences and knowledge with each other. This makes it difficult to adequately manage pet health.

[0588] 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.

[0589] In this invention, the server includes means for collecting pet behavioral data, means for transmitting the data to the server, means for receiving health information provided by a veterinarian, means for recording the pet's health status input by the user, means for integrating and storing this data in a database, means for training an AI model using the stored data to learn the specific health patterns of each pet, means for analyzing new behavioral data in real time and sending a push notification to the user's device if an abnormality is detected, means for generating a report and sending it to the veterinarian when the user confirms the abnormality notification, and means for providing a community for users to share information and exchange knowledge and experiences regarding pet health. This enables comprehensive monitoring of pet health status, immediate notification if an abnormality is detected, and efficient collaboration with veterinarians, thereby enabling effective pet health management.

[0590] "Pet behavior data" refers to information about your pet's lifestyle habits, such as the amount of exercise, sleep time, and eating patterns.

[0591] "Server" refers to the central computer system that receives, stores, and analyzes pet behavioral and health data.

[0592] A "pet-worn IoT device" refers to hardware that is worn by a pet and uses built-in sensors to collect data on the pet's behavior.

[0593] "User device" refers to a device, such as a smartphone or tablet, that the user uses to receive pet behavior data and abnormality notifications.

[0594] "Database" refers to an information system for integrated management of pet behavior data, health information, and data entered by users, stored on a server.

[0595] "AI model" refers to the artificial intelligence algorithm used to analyze pet behavior data and learn specific health patterns.

[0596] "Push notification" refers to an instant notification message sent from the server to the user's device when an abnormality is detected.

[0597] "Abnormal data" refers to data that indicates behavior that differs from your pet's normal health pattern.

[0598] A "veterinarian" is a professional who provides specialized diagnosis and treatment for pet health problems.

[0599] A "user community platform" refers to an online community where users can share knowledge and experiences about pet health and exchange information.

[0600] MODE FOR CARRYING OUT THE INVENTION

[0601] Basic system configuration

[0602] The pet health monitoring system of the present invention includes the following main components: an IoT device worn by a pet, a server, a user terminal, and a user community platform.

[0603] Detailed program description

[0604] This system operates based on a program that acquires pet behavior data and detects abnormalities. Each process is performed using specific hardware and software.

[0605] Data collection

[0606] First, the IoT device worn by the pet collects the pet's behavior data in real time. This device has built-in sensors such as a pedometer, vibration sensor, accelerometer, camera, and microphone. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0607] Data storage and integration

[0608] The server stores the received pet behavior data in a database. It also integrates and stores health information provided by veterinarians and pet health status entered by users through the application. This integrated data is used to centrally manage all information necessary for pet health management.

[0609] AI-powered data analysis and health pattern learning

[0610] The server uses the stored data to train an AI algorithm, specifically a generative AI model that learns each pet's specific health patterns. This process allows the server to model a pet's normal behavior and health status and detect abnormalities.

[0611] Anomaly detection and notification

[0612] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0613] Support for communication with veterinarians

[0614] When the user checks the abnormality notification, they can contact a veterinarian through the app based on the information. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0615] User Community Platform

[0616] There are also community platforms where users can share information and exchange knowledge and experiences about pet health. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[0617] Specific examples

[0618] Example 1: Detecting abnormal pet behavior

[0619] 1. An IoT device worn by a pet collects data on the pet's activity.

[0620] 2. The device detects that you are sleeping for longer than usual.

[0621] 3. The device sends the abnormal data to the server.

[0622] 4. The server compares this data with normal health patterns and determines any abnormalities.

[0623] 5. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0624] Example 2: Sharing information in the community

[0625] 1. A user uses the application to post a question to the community: "My pet is eating less."

[0626] 2. The server searches its historical database and suggests similar questions and their answers.

[0627] 3. As a result, other users will also contribute their own experiences and advice to this question.

[0628] Prompt Sentence Examples

[0629] "Please explain each processing step of the system that collects real-time behavioral data of pets and detects abnormalities. Also, please explain in detail the process by which users send abnormality notifications to their veterinarians."

[0630] This invention enables effective health management of pets by comprehensively monitoring their health status, immediately notifying them if an abnormality is detected, and enabling efficient collaboration with veterinarians.

[0631] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0632] Step 1:

[0633] Collecting pet behavior data

[0634] The IoT device worn by pets uses a built-in pedometer, vibration sensor, accelerometer, camera, and microphone to collect real-time behavioral data such as exercise volume, sleep duration, and eating patterns.

[0635] Input: Pet behavior (exercise, sleep, eating, etc.)

[0636] Data processing: collection of raw data obtained from sensors

[0637] Output: Generate behavioral data (amount of exercise, sleep time, dietary patterns, etc.)

[0638] Step 2:

[0639] Sending data to the server

[0640] IoT devices send the collected data to a server via Bluetooth or Wi-Fi.

[0641] Input: Behavioral data collected by IoT devices

[0642] Data processing: Packetizing data and converting it to transmission protocol

[0643] Output: Send data to the server

[0644] Step 3:

[0645] Data storage and integration

[0646] The server centrally manages the received data and stores it in a database, and also integrates and stores health information provided by veterinarians and pet health conditions entered by users through the app.

[0647] Input: Submitted behavioral data, health information, and user-entered data

[0648] Data processing: Data integration and centralization

[0649] Output: Consolidated database updates

[0650] Step 4:

[0651] AI-based data analysis and learning

[0652] The server uses the stored data to train AI algorithms to learn each pet's specific health patterns, using generative AI models to learn normal and abnormal behavior patterns.

[0653] Input: Data from the integrated database

[0654] Data processing: Data analysis and learning using AI algorithms

[0655] Output: A model of health patterns

[0656] Step 5:

[0657] Anomaly detection and notification

[0658] The server analyzes new behavioral data in real time, compares it with learned health patterns, and immediately sends a push notification to the user's device if an abnormality is detected.

[0659] Input: New behavioral data and AI model comparison results

[0660] Data processing: Running anomaly detection algorithms

[0661] Output: Generate and send a push notification

[0662] Step 6:

[0663] Support for communication with veterinarians

[0664] When the user confirms the abnormality notification, they can use the app to contact a veterinarian. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0665] Input: Anomaly detection notification

[0666] Data processing: Reporting abnormal data

[0667] Output: Generate a report and send it to your veterinarian

[0668] Step 7:

[0669] Information sharing on user community platforms

[0670] Users use the application to post questions to the community and get answers, and the server searches a database of past questions to suggest similar questions and answers.

[0671] Input: User question

[0672] Data processing: database search and suggestion of similar queries

[0673] Output: Displaying recommendations

[0674] These processing steps enable monitoring of the pet's health condition and early detection and management of abnormalities.

[0675] (Application example 1)

[0676] 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."

[0677] Conventional pet health monitoring systems primarily focus on detecting abnormalities based on the pet's own behavioral data. However, the health of the pet owner often has a significant impact on the pet's health. Furthermore, there is a lack of support for taking appropriate actions based on the user's health status. Therefore, there is a need for a system that takes the user's health status into consideration comprehensively. This will enable a system that can optimize the health of not only the user but also the pet.

[0678] 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.

[0679] In this invention, the server includes means for collecting pet behavior data, means for receiving health information provided by a veterinarian, means for recording the pet's health status entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication between the user and the veterinarian when the user confirms an abnormality, means for providing a community for users to share information and exchange knowledge and experiences regarding pet health, a smart device for collecting the user's health status, and means for notifying the user of meal provision contents based on the user's health data. This enables comprehensive monitoring and support for maintaining the health of the pet while taking the user's health status into consideration.

[0680] The "means for collecting pet behavioral data" refers to a device that measures and records a pet's daily behavior and activities in real time through an IoT device attached to the pet.

[0681] The "means for receiving health information provided by veterinarians" is an interface for aggregating and incorporating into the system the health status and medical records provided by veterinarians regarding diagnosis and treatment.

[0682] "User-entered means for recording pet health conditions" refers to a function that allows pet owners to use an application to input, save, and record their pet's health conditions and symptoms.

[0683] "Means for learning specific health patterns" refers to the process of analyzing collected data with AI algorithms to learn and detect your pet's normal health patterns.

[0684] "Means of notifying users in real time when abnormalities are detected" refers to a function that immediately notifies users when abnormalities in their health status are detected based on the analysis of collected data.

[0685] The "means for supporting communication with a veterinarian" is an interface that helps the user easily contact a veterinarian when an abnormality is detected.

[0686] "A means of providing a community for users to share information and exchange knowledge and experiences related to pet health" is an online platform where pet owners can share health information and care methods and help each other.

[0687] A "smart device that collects user health status" is a wearable device used to collect user health data (e.g., heart rate, blood sugar level, body temperature, etc.) in real time.

[0688] "Means for notifying the user of meal contents based on the user's health data" refers to a function that analyzes the user's collected health data and notifies the user of appropriate meal contents and suggestions based on the results.

[0689] The system of this invention comprehensively monitors the health status of both pets and their owners and supports appropriate actions, which requires an IoT device worn by the pet, a smart device, a server, and a user terminal.

[0690] Hardware Configuration

[0691] 1. Pet-worn IoT devices

[0692] A device that collects pet behavioral data (such as exercise, sleep, and eating patterns).

[0693] 2. User's smart device

[0694] A wearable device for collecting user health data (heart rate, blood sugar level, body temperature, etc.) in real time.

[0695] 3. Server

[0696] It centralizes data on both pets and users, and is used for data analysis and pattern recognition, using programming languages ​​such as Python and frameworks such as Django and Flask.

[0697] 4. User Device

[0698] A device such as a smartphone that allows you to receive real-time updates on your pet's health and any abnormalities.

[0699] Software Configuration

[0700] 1. Data Collection

[0701] The server receives data from the pet's IoT device and the user's smart device via Bluetooth and Wi-Fi, and the data is stored in a database on the server.

[0702] 2. Data integration and analysis

[0703] The server combines the collected pet and user data and analyzes it using AI algorithms, including deep learning and machine learning models, to detect abnormal patterns.

[0704] 3. Abnormality notification

[0705] If an anomaly is detected, the server will send a real-time push notification to the user's device, which will include details of the anomaly and recommended actions.

[0706] 4. Communication support

[0707] When users receive an abnormality notification, they are provided with a function that allows them to easily contact a veterinarian via the server, and they can also send a report containing health data to the veterinarian.

[0708] 5. User Community Platform

[0709] The server hosts a community platform where users can post questions about pet health and receive answers from other users. The platform is built using Django.

[0710] Specific examples

[0711] Example 1: Pet and user anomaly detection

[0712] The IoT device worn by the pet detects when the pet is sleeping for longer than usual.

[0713] A smart wearable device detects when a user's blood sugar level is high.

[0714] The server analyzes both sets of data and determines that there is an anomaly.

[0715] The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required. Also, your blood sugar level is high. We recommend that you feed it appropriately."

[0716] Prompt Sentence Examples

[0717] "Please suggest a special menu for those with high blood sugar levels and dietary restrictions."

[0718] "If my blood sugar level is over 150, please suggest a meal plan that is suitable for high blood sugar."

[0719] The system monitors the health of both pets and owners in real time, immediately recommends necessary actions, and facilitates collaboration with veterinarians and communication between users.

[0720] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0721] Step 1:

[0722] Data collection

[0723] Subject: Server

[0724] How it works: The server receives data from the pet's IoT device and the user's smart device.

[0725] Input: Pet behavior data (amount of exercise, sleep time, eating patterns) and user health data (heart rate, blood sugar level, body temperature).

[0726] Data processing: The received data is stored in the server database. This data is recorded with a timestamp.

[0727] Output: Real-time pet and user health data stored in a database on the server.

[0728] Step 2:

[0729] Data Integration and Preprocessing

[0730] Subject: Server

[0731] How it works: The server consolidates collected pet and user health data.

[0732] Input: Pet and user health data stored in a database.

[0733] Data processing: Filling in missing data, filtering outliers, and standardizing data.

[0734] Output: Preprocessed consolidated data in a parseable format.

[0735] Step 3:

[0736] AI-based data analysis and anomaly detection

[0737] Subject: Server

[0738] How it works: The server feeds pre-processed data into an AI algorithm to detect anomalies.

[0739] Input: Preprocessed integrated data, existing health pattern models.

[0740] Data Computing: Deep learning and machine learning models (e.g., using TensorFlow or PyTorch) to analyze health patterns and detect anomalies.

[0741] Output: Anomaly detection results (type and details of anomaly).

[0742] Step 4:

[0743] Abnormal notification

[0744] Subject: Server

[0745] Operation: The server pushes a notification to the user's device about any detected anomalies.

[0746] Input: Anomaly detection results.

[0747] Data Transformation: Generate notification messages with details of the anomaly and recommended actions.

[0748] Output: Push notification to user device.

[0749] Step 5:

[0750] User support

[0751] Subject: Server

[0752] Behavior: The server supports the user in taking action after receiving an error notification.

[0753] Input: A response selection from the user (e.g., contacting a veterinarian, entering additional data).

[0754] Data Processing: Collects the necessary data and generates reports based on user selections.

[0755] Output: User guide and veterinarian report.

[0756] Step 6:

[0757] Managing the Community Platform

[0758] Subject: Server

[0759] How it works: The server allows users to post questions about their pet's health to the community.

[0760] Input: Questions submitted by users.

[0761] Data processing: Compare with past posted data and suggest similar questions and their answers.

[0762] Output: Answers and advice within the user community.

[0763] Step 7:

[0764] Proposing actions based on health data

[0765] Subject: Server

[0766] How it works: The server generates and notifies the user of appropriate dietary and behavioral suggestions based on their health data.

[0767] Input: User health data, existing medical and nutritional data.

[0768] Data calculation: Analyzes user data and generates dietary and behavioral recommendations based on health status.

[0769] Output: A notification of suggested actions sent to the user's device. A specific example would be something like, "Because your blood sugar level is high, we suggest a low-carb menu for your next meal."

[0770] 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.

[0771] Basic system configuration

[0772] The pet health monitoring system of the present invention includes the following major components:

[0773] 1. Pet-worn IoT devices

[0774] 2. Server

[0775] 3. User Device

[0776] 4. User Community Platform

[0777] 5. Emotion Engine

[0778] Detailed program description

[0779] The core of this system is a program that collects data on pet behavior and detects abnormalities. In addition, by combining it with an emotion engine that recognizes and analyzes the user's emotions, more advanced health management and user support are realized. The specific processing contents of the program are explained in natural language below.

[0780] Data collection

[0781] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[0782] Data storage and integration

[0783] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[0784] AI-powered data analysis and health pattern learning

[0785] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[0786] Anomaly detection and notification

[0787] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0788] Emotional analysis of users using an emotion engine

[0789] As users interact with the application, the emotion engine recognizes emotions by analyzing their input and interaction history, for example, by inferring their emotions from the tone of their text input and the choice of words.

[0790] Providing advice based on user emotions

[0791] The server provides appropriate advice and measures based on the user's emotions recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and comforting materials.

[0792] Support for communication with veterinarians

[0793] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0794] User Community Platform

[0795] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[0796] Specific examples

[0797] Example 1: Detecting abnormal pet behavior

[0798] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[0799] 2. The IoT device sends the abnormal data to the server.

[0800] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[0801] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0802] Example 2: Sharing information in the community

[0803] 1. A user posts a question to the community saying, "My pet is eating less."

[0804] 2. The server searches its historical database and suggests similar questions and their answers.

[0805] 3. Other users also respond to this question with their own experiences and advice.

[0806] Example 3: Providing advice using an emotion engine

[0807] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[0808] 2. The server provides relaxation techniques to ease anxiety and reassurance for similar cases.

[0809] In this way, the system efficiently and comprehensively monitors and manages pet health, and also provides emotional support to users, allowing for more peace of mind in health management.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] The IoT device worn by pets uses sensors to collect real-time behavioral data (such as the amount of exercise, sleep time, and eating patterns), which is then sent to a server via Bluetooth or Wi-Fi.

[0813] Step 2:

[0814] The server stores the received pet behavior data in a database, as well as health information provided by veterinarians and health condition data entered by users through the app.

[0815] Step 3:

[0816] Users enter any abnormal symptoms or daily health conditions through the application and send the data to the server.

[0817] Step 4:

[0818] The server consolidates all stored data and analyzes it using AI algorithms, learning your pet's normal behavior patterns and modeling specific health patterns.

[0819] Step 5:

[0820] The server analyzes newly collected behavioral data in real time and compares it with learned health patterns. If abnormal behavior or health conditions are detected, they are deemed abnormal.

[0821] Step 6:

[0822] If an anomaly is detected, the server will send a real-time push notification to the user's device, detailing the specific anomaly and any points requiring attention.

[0823] Step 7:

[0824] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[0825] Step 8:

[0826] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[0827] Step 9:

[0828] The emotion engine analyzes text input and dialogue history when a user uses the app to recognize the user's emotions.

[0829] Step 10:

[0830] The server then proposes appropriate advice and measures based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling anxious, it will provide relaxation techniques and comforting materials.

[0831] Step 11:

[0832] Users receive advice and take appropriate action, and if necessary, can use the community to seek advice from other users.

[0833] Step 12:

[0834] The server manages the user community platform, and when a user posts a question, it searches the database to suggest similar questions and answers, and immediately notifies the user when a new answer is posted by another user.

[0835] This series of steps allows you to comprehensively monitor and manage your pet's health and reduces your anxiety.

[0836] Example 2

[0837] 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."

[0838] In pet health management, conventional systems did not adequately collect pet behavior data or detect abnormalities. Furthermore, communication with veterinarians and knowledge sharing between users were not smooth, making it difficult to efficiently monitor and manage pet health. Furthermore, support tailored to the user's emotions was not provided, making it difficult to alleviate user anxiety.

[0839] 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.

[0840] In this invention, the server includes: means for collecting pet behavioral data and transferring it to the server in real time; means for integrating and storing the behavioral data and health information in a database; means for providing artificial intelligence that uses the stored data to learn specific health patterns; means for detecting abnormalities and notifying the user's device in real time; means for providing a natural language processing engine that analyzes emotions from the user's text input and dialogue history; means for providing advice to the user based on the analyzed emotions; means for transmitting data to support communication with veterinarians when an abnormality is detected; and means for providing a community platform for users to share information and exchange health-related knowledge and experiences. This allows for real-time monitoring and management of pet health conditions and prompt notification to the user when an abnormality is detected. Furthermore, by providing advice based on the user's emotions, facilitating smooth communication with veterinarians, and allowing users to share information, the system provides comprehensive support for pet health management.

[0841] "Pet behavior data" refers to data related to the behavior of pets, such as the amount of exercise, sleeping hours, and eating patterns.

[0842] A "server" is a computer system that collects, stores, and analyzes pet behavior data and provides various notifications and communication support.

[0843] "Real time" means being able to process phenomena occurring at that time immediately without delay.

[0844] A "database" is an information storage system for effectively storing and managing pet behavioral data and health information.

[0845] "Artificial intelligence" refers to machine learning algorithms that learn your pet's specific health patterns and detect abnormalities.

[0846] A "natural language processing engine" refers to technology for analyzing emotions from user text input and dialogue history.

[0847] "Push notification" is a communication method that sends information from a server to a user's device in real time.

[0848] A "veterinarian" is a professional who diagnoses the health of animals and provides appropriate treatment.

[0849] "Emotion" refers to the psychological state inferred from the content of a user's text or dialogue.

[0850] A "community platform" is an online service that provides a place for users to share information and exchange knowledge and experiences regarding pet health.

[0851] The present invention is a system for efficiently monitoring and managing the health condition of a pet and providing support according to the user's emotions. Specific embodiments of the system are described below.

[0852] Basic system configuration

[0853] The system of the present invention includes the following major components:

[0854] 1. Pet-worn IoT devices

[0855] 2. Server

[0856] 3. User Device

[0857] 4. User Community Platform

[0858] 5. Emotion Engine

[0859] Data collection

[0860] First, the IoT device worn by the pet uses an accelerometer and temperature sensor to collect real-time behavioral data about the pet, including the pet's activity level, sleep duration, and eating patterns, and the collected data is transmitted to a server via Bluetooth or Wi-Fi.

[0861] Data storage and integration

[0862] The server stores the received pet behavior data in a MySQL database. Health information provided by veterinarians and pet health conditions entered by users through the application are also stored in the same database. This allows all data to be managed centrally.

[0863] AI-powered data analysis and health pattern learning

[0864] The server uses the stored data to train machine learning algorithms, specifically building health pattern learning models using Python and TensorFlow, which then identify and analyze normal and abnormal behavior patterns in pets.

[0865] Anomaly detection and notification

[0866] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device. This notification contains information such as, "Your pet is sleeping longer than usual. Attention is required."

[0867] Emotional analysis of users using an emotion engine

[0868] As users interact with the application, the emotion engine analyzes their text input and dialogue history to identify their emotions, using natural language processing (NLP) techniques to infer emotions such as "anxiety" or "relief" from the tone of the text and word choice.

[0869] Providing advice based on user emotions

[0870] The server provides appropriate advice and countermeasures based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and examples of success in similar cases.

[0871] Support for communication with veterinarians

[0872] When a user confirms an abnormality, the app provides a function that allows them to easily contact a veterinarian. The user's device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[0873] User Community Platform

[0874] The server manages questions and answers posted by users and searches related past data to suggest appropriate information, allowing users to share their knowledge and experiences regarding health management.

[0875] Specific examples

[0876] Below is a concrete example of how this system works.

[0877] Example 1: Detecting abnormal pet behavior

[0878] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[0879] 2. The IoT device sends the abnormal data to the server.

[0880] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[0881] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[0882] Example 2: Sharing information in the community

[0883] 1. A user posts a question to the community saying, "My pet is eating less."

[0884] 2. The server searches its historical database and suggests similar questions and their answers.

[0885] 3. Other users also respond to this question with their own experiences and advice.

[0886] Example 3: Providing advice using an emotion engine

[0887] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[0888] 2. The server will provide relaxation techniques and reassurance from similar cases to ease anxiety.

[0889] Prompt Sentence Examples

[0890] "Please give us a concrete example of a pet health monitoring system. Please explain in detail how it detects abnormal pet behavior, shares information with the community, and provides advice using an emotion engine."

[0891] As described above, the system of the present invention is capable of comprehensively monitoring and managing the health condition of pets and efficiently providing the necessary support to users.

[0892] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0893] Step 1:

[0894] The IoT device worn by pets uses accelerometers and temperature sensors to collect pet behavior data in real time. The sensors detect the pet's physical movements as input and convert the data into digital form. As output, the collected data is sent to a server via Bluetooth or Wi-Fi. Specific actions such as running, eating, and sleeping are captured once per second.

[0895] Step 2:

[0896] The server stores the received pet behavior data in a database. As input, it receives behavior data sent in real time from IoT devices. As output, it stores the data in the "Behavior Data" table in a MySQL database. Specific operations include adding new data entries and integrating them with existing data.

[0897] Step 3:

[0898] The user enters the pet's health status through the application. As input, the user fills in the application's form with the pet's recent health status. As output, the information is sent to the server and stored in a database. Specifically, the user selects a health checklist within the app and enters conditions such as "no appetite" or "lack of energy."

[0899] Step 4:

[0900] The server uses artificial intelligence to integrate pet behavioral and health data and learn specific health patterns. It uses behavioral and health data stored in a database as input. It generates a trained health pattern model as output. Specifically, it uses Python and TensorFlow to train a machine learning algorithm and model the health patterns.

[0901] Step 5:

[0902] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. It uses real-time behavioral data as input. As output, it generates abnormality warning data if an abnormality is detected. Specifically, the analysis algorithm compares the data with a normal behavior model and executes the process of detecting an abnormality.

[0903] Step 6:

[0904] If the server detects an abnormality, it immediately sends a push notification to the user's device. The input is abnormality warning data. The output is a notification message sent to the user's device. Specifically, the server sends a message via the push notification service, such as "Your pet is sleeping longer than usual. Attention is required."

[0905] Step 7:

[0906] When a user uses an application, the emotion engine analyzes the user's input and interaction history to recognize emotions. It uses text data from the user as input and generates data on the user's emotional state as output. Specifically, it uses natural language processing technology to perform sentiment analysis on the user's input text and infers emotions such as anxiety or relief.

[0907] Step 8:

[0908] The server provides appropriate advice and countermeasures to the user based on the emotional information recognized by the emotion engine. The server receives the user's emotional state data as input. The server generates an advice message as output and sends it to the user. Specific actions include providing users who are feeling anxious with "relaxation methods" and "examples of previous successes."

[0909] Step 9:

[0910] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian. The input is detailed information including the abnormality data. The output is a report that is sent to the veterinarian. Specifically, when the user presses the "Contact" button, a detailed report is generated and sent to the veterinarian via the server.

[0911] Step 10:

[0912] The server manages questions and answers posted by users and shares information through a community platform. It uses question data from users as input. It searches related past data and suggests appropriate information as output. Specifically, a database search algorithm searches past questions and answers and suggests similar questions and answers.

[0913] (Application example 2)

[0914] 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."

[0915] To effectively monitor the health of pets, conventional technologies lack the ability to detect and notify abnormalities in real time, and no method has been established to provide appropriate support based on the user's emotions.In order to effectively monitor the health of elderly people and detect abnormalities early, a system is needed that applies conventional pet monitoring technology, has the ability to detect abnormalities in real time, and provides support based on the user's emotions.

[0916] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral data, means for receiving health information provided by medical professionals, means for recording health conditions entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication with medical professionals when the user confirms an abnormality, means for providing a community where users can share information and exchange health-related knowledge and experiences, and means for analyzing the user's emotions and providing corresponding advice. This enables efficient and comprehensive monitoring and management of the health of elderly people and rapid response when an abnormality occurs. Furthermore, providing support tailored to the user's emotions can provide a sense of security and contribute to improving their health.

[0917] Key Word Definitions

[0918] "Behavioral data" refers to information about the behavior of subjects such as pets and elderly people in their daily lives, and specifically refers to data such as heart rate, distance traveled, sleeping patterns, and eating patterns.

[0919] "Medical personnel" refers to people who have specialized knowledge about the health of subjects such as pets and elderly people and who diagnose and treat them, and generally refers to veterinarians and doctors.

[0920] "Health patterns" refer to the normal health status and behavioral tendencies of subjects, such as pets and elderly people, formed based on daily behavioral data.

[0921] "Real-time" refers to a state in which data can be processed and analyzed immediately at the moment it is generated or collected, and results can be obtained.

[0922] A "community" is a place or platform where users interested in the health of pets, the elderly, etc. gather, and where information is shared and mutual support is provided.

[0923] "Sentiment analysis" refers to the process of inferring and analyzing a user's emotions from the text they enter and their behavior.

[0924] "Anomaly detection" refers to the process of identifying unusual behaviors or conditions that deviate from the norm by comparing them with previously learned health patterns.

[0925] "Notification" refers to the act of providing information and sending alert messages in real time through a user device or application.

[0926] "Server" refers to a central processing unit or system that receives, stores, analyzes behavioral data of pets, elderly people, etc., and provides the results.

[0927] "Advice" refers to specific advice or countermeasures provided based on the user's situation and emotions.

[0928] MODE FOR CARRYING OUT THE INVENTION

[0929] Basic system configuration

[0930] The present invention is a system for monitoring the health status of elderly people, which includes the following main components:

[0931] 1. Wearable devices: worn by seniors, they collect real-time behavioral data such as heart rate, distance traveled, sleep patterns, and eating patterns.

[0932] 2. Server: Receives data sent from the wearable device, stores it in a database, and analyzes it using AI algorithms.

[0933] 3. User's device: A device such as a smartphone or tablet that receives notifications from the server.

[0934] 4. User community platform: An online platform for users to share information and exchange knowledge and experiences related to elderly health.

[0935] 5. Sentiment Analysis Engine: Analyzes the text entered by the user to recognize emotions and provide advice accordingly.

[0936] Specific processing details of the system

[0937] 1. Data Collection

[0938] The wearable device uses built-in sensors to collect real-time behavioral data of the elderly, including heart rate, distance traveled, sleep duration, and dietary patterns, and transmits this data to a server via Bluetooth or Wi-Fi.

[0939] 2. Data storage and integration

[0940] The server centrally manages the behavioral data received from the wearable devices and stores it in a database, as well as health information provided by medical professionals and health condition information entered by users through their devices.

[0941] 3. AI-powered data analysis and health pattern learning

[0942] The server uses the stored data to train a generative AI model to learn the specific health patterns of each elderly person, in the process modeling their normal behavior and health status so that anomalies can be detected.

[0943] 4. Anomaly detection and notification

[0944] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[0945] 5. Supporting users through sentiment analysis

[0946] When a user checks an abnormality notification, the emotion analysis engine analyzes the user's input and dialogue history to recognize their emotions. For example, it infers the user's emotions from the tone of their text input and their choice of words. Based on the results of the emotion analysis, the server provides the user with appropriate advice and countermeasures. For example, if the user is feeling anxious, it provides relaxation techniques and reassurance materials.

[0947] Specific use cases

[0948] Example 1: Detecting abnormal behavior in elderly people

[0949] 1. A wearable device detects when an elderly person's heart rate is higher than normal.

[0950] 2. The wearable device sends the abnormality data to the server.

[0951] 3. The server compares this data with normal health patterns and determines abnormalities.

[0952] 4. The server immediately sends a notification to the user's device saying, "Your heart rate is higher than normal. Attention is required."

[0953] Example 2: Providing advice based on sentiment analysis

[0954] 1. When a user receives a notification of an abnormality and checks the details in the app, the sentiment analysis engine detects "anxiety" from the user's text input.

[0955] 2. The server provides relaxation techniques to ease anxiety and ways to deal with similar cases.

[0956] Prompt Sentence Examples

[0957] Explain how to apply this technology to a pet health monitoring system. This system uses IoT devices to collect pet behavior data and uses AI analytics to detect abnormalities. Consider a scenario in which this technology is used to develop a health monitoring and security support app for the elderly, and explain the specific application content and processing steps.

[0958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0959] Explanation of the program's processing steps

[0960] Processing flow and explanation of each step

[0961] Step 1:

[0962] Data collection

[0963] Subject: Wearable devices

[0964] Input: Elderly person's heart rate, distance traveled, sleep duration, and dietary patterns

[0965] How it works: The wearable device uses built-in sensors to measure an older adult's heart rate, distance traveled, sleep duration, and eating patterns in real time.

[0966] Output: Collected behavioral data

[0967] Specific operation: Measurement data is transferred to the server via Bluetooth or Wi-Fi.

[0968] Step 2:

[0969] Data storage and integration

[0970] Subject: Server

[0971] Input: Behavioral data sent from wearable devices, health information provided by healthcare professionals, and user-entered health conditions

[0972] How it works: The server stores the received data in a centralized database.

[0973] Output: Integrated database

[0974] Specific operation: Data is formatted and converted to save it in the database.

[0975] Step 3:

[0976] AI-powered data analysis and health pattern learning

[0977] Subject: Server

[0978] Input: Integrated Database

[0979] How it works: The server uses a generative AI model to analyze the stored data and learn the specific health patterns of each elderly person.

[0980] Output: Learned health patterns

[0981] Specific operation: Data is input into the AI ​​model, and health patterns are generated and saved as analysis results.

[0982] Step 4:

[0983] Anomaly detection and notification

[0984] Subject: Server

[0985] Input: New behavioral data and learned health patterns

[0986] How it works: The server analyzes new behavioral data in real time and compares it with learned health patterns to detect anomalies.

[0987] Output: Anomaly detection notification

[0988] Specific behavior: If an anomaly is detected, a push notification is sent to the user's device.

[0989] Step 5:

[0990] Supporting users with sentiment analysis

[0991] Subject: Sentiment analysis engine

[0992] Input: User-entered text and interaction history

[0993] How it works: The sentiment analysis engine analyzes the user's sentiment from the input text and generates advice based on that sentiment.

[0994] Output: Sentiment analysis results and advice

[0995] Specific operation: A text analysis algorithm is used to determine emotions, and appropriate relaxation methods and advice are sent to the user's device via a server.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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."

[1012] Basic system configuration

[1013] The pet health monitoring system of the present invention includes the following major components:

[1014] 1. Pet-worn IoT devices

[1015] 2. Server

[1016] 3. User Device

[1017] 4. User Community Platform

[1018] Detailed program description

[1019] The core of this system is a program that collects data on pet behavior and detects abnormalities. The specific processing content of this program is explained in natural language below.

[1020] Data collection

[1021] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[1022] Data storage and integration

[1023] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[1024] AI-powered data analysis and health pattern learning

[1025] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[1026] Anomaly detection and notification

[1027] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1028] Support for communication with veterinarians

[1029] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1030] User Community Platform

[1031] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[1032] Specific examples

[1033] Example 1: Detecting abnormal pet behavior

[1034] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[1035] 2. The IoT device sends the abnormal data to the server.

[1036] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[1037] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1038] Example 2: Sharing information in the community

[1039] 1. A user posts a question to the community saying, "My pet is eating less."

[1040] 2. The server searches its historical database and suggests similar questions and their answers.

[1041] 3. Other users also respond to this question with their own experiences and advice.

[1042] In this way, this system comprehensively monitors the health of pets, alleviating owners' anxiety and making a significant contribution to maintaining the health of pets.

[1043] The processing flow will be explained below.

[1044] Step 1:

[1045] The IoT device worn by pets uses sensors to collect real-time data on pet behavior, such as the amount of exercise, sleep duration, and eating patterns.

[1046] Step 2:

[1047] IoT devices periodically send collected data to a server via Bluetooth or Wi-Fi.

[1048] Step 3:

[1049] The server stores the received pet behavior data in a database, including past data.

[1050] Step 4:

[1051] The user enters health information provided by the veterinarian through the application, as well as daily health conditions and abnormal symptoms, and this information is sent from the device to the server.

[1052] Step 5:

[1053] The server receives the veterinarian's health information and user-entered data, consolidates it into a database, and stores it.

[1054] Step 6:

[1055] The server analyzes past behavioral data and health information, and uses AI algorithms to learn your pet's normal behavioral patterns, during which specific health patterns are modeled.

[1056] Step 7:

[1057] The server periodically analyzes newly collected data in real time and compares it with learned health patterns.

[1058] Step 8:

[1059] If the server detects any behavior or health condition that differs from normal patterns, it determines that it is an abnormality.

[1060] Step 9:

[1061] If the server detects an abnormality, it immediately sends a push notification to the user's device, which includes specific details about the abnormality and a warning.

[1062] Step 10:

[1063] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[1064] Step 11:

[1065] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[1066] Step 12:

[1067] The server also provides a community platform where users can share information with each other. When a user posts a question to the community, the server searches a database of past questions and suggests similar questions and answers.

[1068] This series of steps allows for efficient and comprehensive monitoring and management of pet health, reducing owner anxiety.

[1069] Example 1

[1070] 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."

[1071] There is a demand for systems that can continuously and precisely monitor pet health, detect abnormalities early, and enable users to respond quickly and appropriately. However, existing systems lack the functionality to collect and analyze a pet's overall behavioral data in real time and immediately notify users of abnormalities. Furthermore, they lack a smooth way to share information with veterinarians or a community platform where users can exchange experiences and knowledge with each other. This makes it difficult to adequately manage pet health.

[1072] 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.

[1073] In this invention, the server includes means for collecting pet behavioral data, means for transmitting the data to the server, means for receiving health information provided by a veterinarian, means for recording the pet's health status input by the user, means for integrating and storing this data in a database, means for training an AI model using the stored data to learn the specific health patterns of each pet, means for analyzing new behavioral data in real time and sending a push notification to the user's device if an abnormality is detected, means for generating a report and sending it to the veterinarian when the user confirms the abnormality notification, and means for providing a community for users to share information and exchange knowledge and experiences regarding pet health. This enables comprehensive monitoring of pet health status, immediate notification if an abnormality is detected, and efficient collaboration with veterinarians, thereby enabling effective pet health management.

[1074] "Pet behavior data" refers to information about your pet's lifestyle habits, such as the amount of exercise, sleep time, and eating patterns.

[1075] "Server" refers to the central computer system that receives, stores, and analyzes pet behavioral and health data.

[1076] A "pet-worn IoT device" refers to hardware that is worn by a pet and uses built-in sensors to collect data on the pet's behavior.

[1077] "User device" refers to a device, such as a smartphone or tablet, that the user uses to receive pet behavior data and abnormality notifications.

[1078] "Database" refers to an information system for integrated management of pet behavior data, health information, and data entered by users, stored on a server.

[1079] "AI model" refers to the artificial intelligence algorithm used to analyze pet behavior data and learn specific health patterns.

[1080] "Push notification" refers to an instant notification message sent from the server to the user's device when an abnormality is detected.

[1081] "Abnormal data" refers to data that indicates behavior that differs from your pet's normal health pattern.

[1082] A "veterinarian" is a professional who provides specialized diagnosis and treatment for pet health problems.

[1083] A "user community platform" refers to an online community where users can share knowledge and experiences about pet health and exchange information.

[1084] MODE FOR CARRYING OUT THE INVENTION

[1085] Basic system configuration

[1086] The pet health monitoring system of the present invention includes the following main components: an IoT device worn by a pet, a server, a user terminal, and a user community platform.

[1087] Detailed program description

[1088] This system operates based on a program that acquires pet behavior data and detects abnormalities. Each process is performed using specific hardware and software.

[1089] Data collection

[1090] First, the IoT device worn by the pet collects the pet's behavior data in real time. This device has built-in sensors such as a pedometer, vibration sensor, accelerometer, camera, and microphone. This data is transmitted to a server via Bluetooth or Wi-Fi.

[1091] Data storage and integration

[1092] The server stores the received pet behavior data in a database. It also integrates and stores health information provided by veterinarians and pet health status entered by users through the application. This integrated data is used to centrally manage all information necessary for pet health management.

[1093] AI-powered data analysis and health pattern learning

[1094] The server uses the stored data to train an AI algorithm, specifically a generative AI model that learns each pet's specific health patterns. This process allows the server to model a pet's normal behavior and health status and detect abnormalities.

[1095] Anomaly detection and notification

[1096] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1097] Support for communication with veterinarians

[1098] When the user checks the abnormality notification, they can contact a veterinarian through the app based on the information. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1099] User Community Platform

[1100] There are also community platforms where users can share information and exchange knowledge and experiences about pet health. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[1101] Specific examples

[1102] Example 1: Detecting abnormal pet behavior

[1103] 1. An IoT device worn by a pet collects data on the pet's activity.

[1104] 2. The device detects that you are sleeping for longer than usual.

[1105] 3. The device sends the abnormal data to the server.

[1106] 4. The server compares this data with normal health patterns and determines any abnormalities.

[1107] 5. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1108] Example 2: Sharing information in the community

[1109] 1. A user uses the application to post a question to the community: "My pet is eating less."

[1110] 2. The server searches its historical database and suggests similar questions and their answers.

[1111] 3. As a result, other users will also contribute their own experiences and advice to this question.

[1112] Prompt Sentence Examples

[1113] "Please explain each processing step of the system that collects real-time behavioral data of pets and detects abnormalities. Also, please explain in detail the process by which users send abnormality notifications to their veterinarians."

[1114] This invention enables effective health management of pets by comprehensively monitoring their health status, immediately notifying them if an abnormality is detected, and enabling efficient collaboration with veterinarians.

[1115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1116] Step 1:

[1117] Collecting pet behavior data

[1118] The IoT device worn by pets uses a built-in pedometer, vibration sensor, accelerometer, camera, and microphone to collect real-time behavioral data such as exercise volume, sleep duration, and eating patterns.

[1119] Input: Pet behavior (exercise, sleep, eating, etc.)

[1120] Data processing: collection of raw data obtained from sensors

[1121] Output: Generate behavioral data (amount of exercise, sleep time, dietary patterns, etc.)

[1122] Step 2:

[1123] Sending data to the server

[1124] IoT devices send the collected data to a server via Bluetooth or Wi-Fi.

[1125] Input: Behavioral data collected by IoT devices

[1126] Data processing: Packetizing data and converting it to transmission protocol

[1127] Output: Send data to the server

[1128] Step 3:

[1129] Data storage and integration

[1130] The server centrally manages the received data and stores it in a database, and also integrates and stores health information provided by veterinarians and pet health conditions entered by users through the app.

[1131] Input: Submitted behavioral data, health information, and user-entered data

[1132] Data processing: Data integration and centralization

[1133] Output: Consolidated database updates

[1134] Step 4:

[1135] AI-based data analysis and learning

[1136] The server uses the stored data to train AI algorithms to learn each pet's specific health patterns, using generative AI models to learn normal and abnormal behavior patterns.

[1137] Input: Data from the integrated database

[1138] Data processing: Data analysis and learning using AI algorithms

[1139] Output: A model of health patterns

[1140] Step 5:

[1141] Anomaly detection and notification

[1142] The server analyzes new behavioral data in real time, compares it with learned health patterns, and immediately sends a push notification to the user's device if an abnormality is detected.

[1143] Input: New behavioral data and AI model comparison results

[1144] Data processing: Running anomaly detection algorithms

[1145] Output: Generate and send a push notification

[1146] Step 6:

[1147] Support for communication with veterinarians

[1148] When the user confirms the abnormality notification, they can use the app to contact a veterinarian. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1149] Input: Anomaly detection notification

[1150] Data processing: Reporting abnormal data

[1151] Output: Generate a report and send it to your veterinarian

[1152] Step 7:

[1153] Information sharing on user community platforms

[1154] Users use the application to post questions to the community and get answers, and the server searches a database of past questions to suggest similar questions and answers.

[1155] Input: User question

[1156] Data processing: database search and suggestion of similar queries

[1157] Output: Displaying recommendations

[1158] These processing steps enable monitoring of the pet's health condition and early detection and management of abnormalities.

[1159] (Application example 1)

[1160] 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."

[1161] Conventional pet health monitoring systems primarily focus on detecting abnormalities based on the pet's own behavioral data. However, the health of the pet owner often has a significant impact on the pet's health. Furthermore, there is a lack of support for taking appropriate actions based on the user's health status. Therefore, there is a need for a system that takes the user's health status into consideration comprehensively. This will enable a system that can optimize the health of not only the user but also the pet.

[1162] 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.

[1163] In this invention, the server includes means for collecting pet behavior data, means for receiving health information provided by a veterinarian, means for recording the pet's health status entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication between the user and the veterinarian when the user confirms an abnormality, means for providing a community for users to share information and exchange knowledge and experiences regarding pet health, a smart device for collecting the user's health status, and means for notifying the user of meal provision contents based on the user's health data. This enables comprehensive monitoring and support for maintaining the health of the pet while taking the user's health status into consideration.

[1164] The "means for collecting pet behavioral data" refers to a device that measures and records a pet's daily behavior and activities in real time through an IoT device attached to the pet.

[1165] The "means for receiving health information provided by veterinarians" is an interface for aggregating and incorporating into the system the health status and medical records provided by veterinarians regarding diagnosis and treatment.

[1166] "User-entered means for recording pet health conditions" refers to a function that allows pet owners to use an application to input, save, and record their pet's health conditions and symptoms.

[1167] "Means for learning specific health patterns" refers to the process of analyzing collected data with AI algorithms to learn and detect your pet's normal health patterns.

[1168] "Means of notifying users in real time when abnormalities are detected" refers to a function that immediately notifies users when abnormalities in their health status are detected based on the analysis of collected data.

[1169] The "means for supporting communication with a veterinarian" is an interface that helps the user easily contact a veterinarian when an abnormality is detected.

[1170] "A means of providing a community for users to share information and exchange knowledge and experiences related to pet health" is an online platform where pet owners can share health information and care methods and help each other.

[1171] A "smart device that collects user health status" is a wearable device used to collect user health data (e.g., heart rate, blood sugar level, body temperature, etc.) in real time.

[1172] "Means for notifying the user of meal contents based on the user's health data" refers to a function that analyzes the user's collected health data and notifies the user of appropriate meal contents and suggestions based on the results.

[1173] The system of this invention comprehensively monitors the health status of both pets and their owners and supports appropriate actions, which requires an IoT device worn by the pet, a smart device, a server, and a user terminal.

[1174] Hardware Configuration

[1175] 1. Pet-worn IoT devices

[1176] A device that collects pet behavioral data (such as exercise, sleep, and eating patterns).

[1177] 2. User's smart device

[1178] A wearable device for collecting user health data (heart rate, blood sugar level, body temperature, etc.) in real time.

[1179] 3. Server

[1180] It centralizes data on both pets and users, and is used for data analysis and pattern recognition, using programming languages ​​such as Python and frameworks such as Django and Flask.

[1181] 4. User Device

[1182] A device such as a smartphone that allows you to receive real-time updates on your pet's health and any abnormalities.

[1183] Software Configuration

[1184] 1. Data Collection

[1185] The server receives data from the pet's IoT device and the user's smart device via Bluetooth and Wi-Fi, and the data is stored in a database on the server.

[1186] 2. Data integration and analysis

[1187] The server combines the collected pet and user data and analyzes it using AI algorithms, including deep learning and machine learning models, to detect abnormal patterns.

[1188] 3. Abnormality notification

[1189] If an anomaly is detected, the server will send a real-time push notification to the user's device, which will include details of the anomaly and recommended actions.

[1190] 4. Communication support

[1191] When users receive an abnormality notification, they are provided with a function that allows them to easily contact a veterinarian via the server, and they can also send a report containing health data to the veterinarian.

[1192] 5. User Community Platform

[1193] The server hosts a community platform where users can post questions about pet health and receive answers from other users. The platform is built using Django.

[1194] Specific examples

[1195] Example 1: Pet and user anomaly detection

[1196] The IoT device worn by the pet detects when the pet is sleeping for longer than usual.

[1197] A smart wearable device detects when a user's blood sugar level is high.

[1198] The server analyzes both sets of data and determines that there is an anomaly.

[1199] The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required. Also, your blood sugar level is high. We recommend that you feed it appropriately."

[1200] Prompt Sentence Examples

[1201] "Please suggest a special menu for those with high blood sugar levels and dietary restrictions."

[1202] "If my blood sugar level is over 150, please suggest a meal plan that is suitable for high blood sugar."

[1203] The system monitors the health of both pets and owners in real time, immediately recommends necessary actions, and facilitates collaboration with veterinarians and communication between users.

[1204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1205] Step 1:

[1206] Data collection

[1207] Subject: Server

[1208] How it works: The server receives data from the pet's IoT device and the user's smart device.

[1209] Input: Pet behavior data (amount of exercise, sleep time, eating patterns) and user health data (heart rate, blood sugar level, body temperature).

[1210] Data processing: The received data is stored in the server database. This data is recorded with a timestamp.

[1211] Output: Real-time pet and user health data stored in a database on the server.

[1212] Step 2:

[1213] Data Integration and Preprocessing

[1214] Subject: Server

[1215] How it works: The server consolidates collected pet and user health data.

[1216] Input: Pet and user health data stored in a database.

[1217] Data processing: Filling in missing data, filtering outliers, and standardizing data.

[1218] Output: Preprocessed consolidated data in a parseable format.

[1219] Step 3:

[1220] AI-based data analysis and anomaly detection

[1221] Subject: Server

[1222] How it works: The server feeds pre-processed data into an AI algorithm to detect anomalies.

[1223] Input: Preprocessed integrated data, existing health pattern models.

[1224] Data Computing: Deep learning and machine learning models (e.g., using TensorFlow or PyTorch) to analyze health patterns and detect anomalies.

[1225] Output: Anomaly detection results (type and details of anomaly).

[1226] Step 4:

[1227] Abnormal notification

[1228] Subject: Server

[1229] Operation: The server pushes a notification to the user's device about any detected anomalies.

[1230] Input: Anomaly detection results.

[1231] Data Transformation: Generate notification messages with details of the anomaly and recommended actions.

[1232] Output: Push notification to user device.

[1233] Step 5:

[1234] User support

[1235] Subject: Server

[1236] Behavior: The server supports the user in taking action after receiving an error notification.

[1237] Input: A response selection from the user (e.g., contacting a veterinarian, entering additional data).

[1238] Data Processing: Collects the necessary data and generates reports based on user selections.

[1239] Output: User guide and veterinarian report.

[1240] Step 6:

[1241] Managing the Community Platform

[1242] Subject: Server

[1243] How it works: The server allows users to post questions about their pet's health to the community.

[1244] Input: Questions submitted by users.

[1245] Data processing: Compare with past posted data and suggest similar questions and their answers.

[1246] Output: Answers and advice within the user community.

[1247] Step 7:

[1248] Proposing actions based on health data

[1249] Subject: Server

[1250] How it works: The server generates and notifies the user of appropriate dietary and behavioral suggestions based on their health data.

[1251] Input: User health data, existing medical and nutritional data.

[1252] Data calculation: Analyzes user data and generates dietary and behavioral recommendations based on health status.

[1253] Output: A notification of suggested actions sent to the user's device. A specific example would be something like, "Because your blood sugar level is high, we suggest a low-carb menu for your next meal."

[1254] 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.

[1255] Basic system configuration

[1256] The pet health monitoring system of the present invention includes the following major components:

[1257] 1. Pet-worn IoT devices

[1258] 2. Server

[1259] 3. User Device

[1260] 4. User Community Platform

[1261] 5. Emotion Engine

[1262] Detailed program description

[1263] The core of this system is a program that collects data on pet behavior and detects abnormalities. In addition, by combining it with an emotion engine that recognizes and analyzes the user's emotions, more advanced health management and user support are realized. The specific processing contents of the program are explained in natural language below.

[1264] Data collection

[1265] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[1266] Data storage and integration

[1267] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[1268] AI-powered data analysis and health pattern learning

[1269] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[1270] Anomaly detection and notification

[1271] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1272] Emotional analysis of users using an emotion engine

[1273] As users interact with the application, the emotion engine recognizes emotions by analyzing their input and interaction history, for example, by inferring their emotions from the tone of their text input and the choice of words.

[1274] Providing advice based on user emotions

[1275] The server provides appropriate advice and measures based on the user's emotions recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and comforting materials.

[1276] Support for communication with veterinarians

[1277] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1278] User Community Platform

[1279] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[1280] Specific examples

[1281] Example 1: Detecting abnormal pet behavior

[1282] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[1283] 2. The IoT device sends the abnormal data to the server.

[1284] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[1285] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1286] Example 2: Sharing information in the community

[1287] 1. A user posts a question to the community saying, "My pet is eating less."

[1288] 2. The server searches its historical database and suggests similar questions and their answers.

[1289] 3. Other users also respond to this question with their own experiences and advice.

[1290] Example 3: Providing advice using an emotion engine

[1291] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[1292] 2. The server provides relaxation techniques to ease anxiety and reassurance for similar cases.

[1293] In this way, the system efficiently and comprehensively monitors and manages pet health, and also provides emotional support to users, allowing for more peace of mind in health management.

[1294] The processing flow will be explained below.

[1295] Step 1:

[1296] The IoT device worn by pets uses sensors to collect real-time behavioral data (such as the amount of exercise, sleep time, and eating patterns), which is then sent to a server via Bluetooth or Wi-Fi.

[1297] Step 2:

[1298] The server stores the received pet behavior data in a database, as well as health information provided by veterinarians and health condition data entered by users through the app.

[1299] Step 3:

[1300] Users enter any abnormal symptoms or daily health conditions through the application and send the data to the server.

[1301] Step 4:

[1302] The server consolidates all stored data and analyzes it using AI algorithms, learning your pet's normal behavior patterns and modeling specific health patterns.

[1303] Step 5:

[1304] The server analyzes newly collected behavioral data in real time and compares it with learned health patterns. If abnormal behavior or health conditions are detected, they are deemed abnormal.

[1305] Step 6:

[1306] If an anomaly is detected, the server will send a real-time push notification to the user's device, detailing the specific anomaly and any points requiring attention.

[1307] Step 7:

[1308] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[1309] Step 8:

[1310] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[1311] Step 9:

[1312] The emotion engine analyzes text input and dialogue history when a user uses the app to recognize the user's emotions.

[1313] Step 10:

[1314] The server then proposes appropriate advice and measures based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling anxious, it will provide relaxation techniques and comforting materials.

[1315] Step 11:

[1316] Users receive advice and take appropriate action, and if necessary, can use the community to seek advice from other users.

[1317] Step 12:

[1318] The server manages the user community platform, and when a user posts a question, it searches the database to suggest similar questions and answers, and immediately notifies the user when a new answer is posted by another user.

[1319] This series of steps allows you to comprehensively monitor and manage your pet's health and reduces your anxiety.

[1320] Example 2

[1321] 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."

[1322] In pet health management, conventional systems did not adequately collect pet behavior data or detect abnormalities. Furthermore, communication with veterinarians and knowledge sharing between users were not smooth, making it difficult to efficiently monitor and manage pet health. Furthermore, support tailored to the user's emotions was not provided, making it difficult to alleviate user anxiety.

[1323] 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.

[1324] In this invention, the server includes: means for collecting pet behavioral data and transferring it to the server in real time; means for integrating and storing the behavioral data and health information in a database; means for providing artificial intelligence that uses the stored data to learn specific health patterns; means for detecting abnormalities and notifying the user's device in real time; means for providing a natural language processing engine that analyzes emotions from the user's text input and dialogue history; means for providing advice to the user based on the analyzed emotions; means for transmitting data to support communication with veterinarians when an abnormality is detected; and means for providing a community platform for users to share information and exchange health-related knowledge and experiences. This allows for real-time monitoring and management of pet health conditions and prompt notification to the user when an abnormality is detected. Furthermore, by providing advice based on the user's emotions, facilitating smooth communication with veterinarians, and allowing users to share information, the system provides comprehensive support for pet health management.

[1325] "Pet behavior data" refers to data related to the behavior of pets, such as the amount of exercise, sleeping hours, and eating patterns.

[1326] A "server" is a computer system that collects, stores, and analyzes pet behavior data and provides various notifications and communication support.

[1327] "Real time" means being able to process phenomena occurring at that time immediately without delay.

[1328] A "database" is an information storage system for effectively storing and managing pet behavioral data and health information.

[1329] "Artificial intelligence" refers to machine learning algorithms that learn your pet's specific health patterns and detect abnormalities.

[1330] A "natural language processing engine" refers to technology for analyzing emotions from user text input and dialogue history.

[1331] "Push notification" is a communication method that sends information from a server to a user's device in real time.

[1332] A "veterinarian" is a professional who diagnoses the health of animals and provides appropriate treatment.

[1333] "Emotion" refers to the psychological state inferred from the content of a user's text or dialogue.

[1334] A "community platform" is an online service that provides a place for users to share information and exchange knowledge and experiences regarding pet health.

[1335] The present invention is a system for efficiently monitoring and managing the health condition of a pet and providing support according to the user's emotions. Specific embodiments of the system are described below.

[1336] Basic system configuration

[1337] The system of the present invention includes the following major components:

[1338] 1. Pet-worn IoT devices

[1339] 2. Server

[1340] 3. User Device

[1341] 4. User Community Platform

[1342] 5. Emotion Engine

[1343] Data collection

[1344] First, the IoT device worn by the pet uses an accelerometer and temperature sensor to collect real-time behavioral data about the pet, including the pet's activity level, sleep duration, and eating patterns, and the collected data is transmitted to a server via Bluetooth or Wi-Fi.

[1345] Data storage and integration

[1346] The server stores the received pet behavior data in a MySQL database. Health information provided by veterinarians and pet health conditions entered by users through the application are also stored in the same database. This allows all data to be managed centrally.

[1347] AI-powered data analysis and health pattern learning

[1348] The server uses the stored data to train machine learning algorithms, specifically building health pattern learning models using Python and TensorFlow, which then identify and analyze normal and abnormal behavior patterns in pets.

[1349] Anomaly detection and notification

[1350] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device. This notification contains information such as, "Your pet is sleeping longer than usual. Attention is required."

[1351] Emotional analysis of users using an emotion engine

[1352] As users interact with the application, the emotion engine analyzes their text input and dialogue history to identify their emotions, using natural language processing (NLP) techniques to infer emotions such as "anxiety" or "relief" from the tone of the text and word choice.

[1353] Providing advice based on user emotions

[1354] The server provides appropriate advice and countermeasures based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and examples of success in similar cases.

[1355] Support for communication with veterinarians

[1356] When a user confirms an abnormality, the app provides a function that allows them to easily contact a veterinarian. The user's device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1357] User Community Platform

[1358] The server manages questions and answers posted by users and searches related past data to suggest appropriate information, allowing users to share their knowledge and experiences regarding health management.

[1359] Specific examples

[1360] Below is a concrete example of how this system works.

[1361] Example 1: Detecting abnormal pet behavior

[1362] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[1363] 2. The IoT device sends the abnormal data to the server.

[1364] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[1365] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1366] Example 2: Sharing information in the community

[1367] 1. A user posts a question to the community saying, "My pet is eating less."

[1368] 2. The server searches its historical database and suggests similar questions and their answers.

[1369] 3. Other users also respond to this question with their own experiences and advice.

[1370] Example 3: Providing advice using an emotion engine

[1371] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[1372] 2. The server will provide relaxation techniques and reassurance from similar cases to ease anxiety.

[1373] Prompt Sentence Examples

[1374] "Please give us a concrete example of a pet health monitoring system. Please explain in detail how it detects abnormal pet behavior, shares information with the community, and provides advice using an emotion engine."

[1375] As described above, the system of the present invention is capable of comprehensively monitoring and managing the health condition of pets and efficiently providing the necessary support to users.

[1376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1377] Step 1:

[1378] The IoT device worn by pets uses accelerometers and temperature sensors to collect pet behavior data in real time. The sensors detect the pet's physical movements as input and convert the data into digital form. As output, the collected data is sent to a server via Bluetooth or Wi-Fi. Specific actions such as running, eating, and sleeping are captured once per second.

[1379] Step 2:

[1380] The server stores the received pet behavior data in a database. As input, it receives behavior data sent in real time from IoT devices. As output, it stores the data in the "Behavior Data" table in a MySQL database. Specific operations include adding new data entries and integrating them with existing data.

[1381] Step 3:

[1382] The user enters the pet's health status through the application. As input, the user fills in the application's form with the pet's recent health status. As output, the information is sent to the server and stored in a database. Specifically, the user selects a health checklist within the app and enters conditions such as "no appetite" or "lack of energy."

[1383] Step 4:

[1384] The server uses artificial intelligence to integrate pet behavioral and health data and learn specific health patterns. It uses behavioral and health data stored in a database as input. It generates a trained health pattern model as output. Specifically, it uses Python and TensorFlow to train a machine learning algorithm and model the health patterns.

[1385] Step 5:

[1386] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. It uses real-time behavioral data as input. As output, it generates abnormality warning data if an abnormality is detected. Specifically, the analysis algorithm compares the data with a normal behavior model and executes the process of detecting an abnormality.

[1387] Step 6:

[1388] If the server detects an abnormality, it immediately sends a push notification to the user's device. The input is abnormality warning data. The output is a notification message sent to the user's device. Specifically, the server sends a message via the push notification service, such as "Your pet is sleeping longer than usual. Attention is required."

[1389] Step 7:

[1390] When a user uses an application, the emotion engine analyzes the user's input and interaction history to recognize emotions. It uses text data from the user as input and generates data on the user's emotional state as output. Specifically, it uses natural language processing technology to perform sentiment analysis on the user's input text and infers emotions such as anxiety or relief.

[1391] Step 8:

[1392] The server provides appropriate advice and countermeasures to the user based on the emotional information recognized by the emotion engine. The server receives the user's emotional state data as input. The server generates an advice message as output and sends it to the user. Specific actions include providing users who are feeling anxious with "relaxation methods" and "examples of previous successes."

[1393] Step 9:

[1394] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian. The input is detailed information including the abnormality data. The output is a report that is sent to the veterinarian. Specifically, when the user presses the "Contact" button, a detailed report is generated and sent to the veterinarian via the server.

[1395] Step 10:

[1396] The server manages questions and answers posted by users and shares information through a community platform. It uses question data from users as input. It searches related past data and suggests appropriate information as output. Specifically, a database search algorithm searches past questions and answers and suggests similar questions and answers.

[1397] (Application example 2)

[1398] 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."

[1399] To effectively monitor the health of pets, conventional technologies lack the ability to detect and notify abnormalities in real time, and no method has been established to provide appropriate support based on the user's emotions.In order to effectively monitor the health of elderly people and detect abnormalities early, a system is needed that applies conventional pet monitoring technology, has the ability to detect abnormalities in real time, and provides support based on the user's emotions.

[1400] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral data, means for receiving health information provided by medical professionals, means for recording health conditions entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication with medical professionals when the user confirms an abnormality, means for providing a community where users can share information and exchange health-related knowledge and experiences, and means for analyzing the user's emotions and providing corresponding advice. This enables efficient and comprehensive monitoring and management of the health of elderly people and rapid response when an abnormality occurs. Furthermore, providing support tailored to the user's emotions can provide a sense of security and contribute to improving their health.

[1401] Key Word Definitions

[1402] "Behavioral data" refers to information about the behavior of subjects such as pets and elderly people in their daily lives, and specifically refers to data such as heart rate, distance traveled, sleeping patterns, and eating patterns.

[1403] "Medical personnel" refers to people who have specialized knowledge about the health of subjects such as pets and elderly people and who diagnose and treat them, and generally refers to veterinarians and doctors.

[1404] "Health patterns" refer to the normal health status and behavioral tendencies of subjects, such as pets and elderly people, formed based on daily behavioral data.

[1405] "Real-time" refers to a state in which data can be processed and analyzed immediately at the moment it is generated or collected, and results can be obtained.

[1406] A "community" is a place or platform where users interested in the health of pets, the elderly, etc. gather, and where information is shared and mutual support is provided.

[1407] "Sentiment analysis" refers to the process of inferring and analyzing a user's emotions from the text they enter and their behavior.

[1408] "Anomaly detection" refers to the process of identifying unusual behaviors or conditions that deviate from the norm by comparing them with previously learned health patterns.

[1409] "Notification" refers to the act of providing information and sending alert messages in real time through a user device or application.

[1410] "Server" refers to a central processing unit or system that receives, stores, analyzes behavioral data of pets, elderly people, etc., and provides the results.

[1411] "Advice" refers to specific advice or countermeasures provided based on the user's situation and emotions.

[1412] MODE FOR CARRYING OUT THE INVENTION

[1413] Basic system configuration

[1414] The present invention is a system for monitoring the health status of elderly people, which includes the following main components:

[1415] 1. Wearable devices: worn by seniors, they collect real-time behavioral data such as heart rate, distance traveled, sleep patterns, and eating patterns.

[1416] 2. Server: Receives data sent from the wearable device, stores it in a database, and analyzes it using AI algorithms.

[1417] 3. User's device: A device such as a smartphone or tablet that receives notifications from the server.

[1418] 4. User community platform: An online platform for users to share information and exchange knowledge and experiences related to elderly health.

[1419] 5. Sentiment Analysis Engine: Analyzes the text entered by the user to recognize emotions and provide advice accordingly.

[1420] Specific processing details of the system

[1421] 1. Data Collection

[1422] The wearable device uses built-in sensors to collect real-time behavioral data of the elderly, including heart rate, distance traveled, sleep duration, and dietary patterns, and transmits this data to a server via Bluetooth or Wi-Fi.

[1423] 2. Data storage and integration

[1424] The server centrally manages the behavioral data received from the wearable devices and stores it in a database, as well as health information provided by medical professionals and health condition information entered by users through their devices.

[1425] 3. AI-powered data analysis and health pattern learning

[1426] The server uses the stored data to train a generative AI model to learn the specific health patterns of each elderly person, in the process modeling their normal behavior and health status so that anomalies can be detected.

[1427] 4. Anomaly detection and notification

[1428] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1429] 5. Supporting users through sentiment analysis

[1430] When a user checks an abnormality notification, the emotion analysis engine analyzes the user's input and dialogue history to recognize their emotions. For example, it infers the user's emotions from the tone of their text input and their choice of words. Based on the results of the emotion analysis, the server provides the user with appropriate advice and countermeasures. For example, if the user is feeling anxious, it provides relaxation techniques and reassurance materials.

[1431] Specific use cases

[1432] Example 1: Detecting abnormal behavior in elderly people

[1433] 1. A wearable device detects when an elderly person's heart rate is higher than normal.

[1434] 2. The wearable device sends the abnormality data to the server.

[1435] 3. The server compares this data with normal health patterns and determines abnormalities.

[1436] 4. The server immediately sends a notification to the user's device saying, "Your heart rate is higher than normal. Attention is required."

[1437] Example 2: Providing advice based on sentiment analysis

[1438] 1. When a user receives a notification of an abnormality and checks the details in the app, the sentiment analysis engine detects "anxiety" from the user's text input.

[1439] 2. The server provides relaxation techniques to ease anxiety and ways to deal with similar cases.

[1440] Prompt Sentence Examples

[1441] Explain how to apply this technology to a pet health monitoring system. This system uses IoT devices to collect pet behavior data and uses AI analytics to detect abnormalities. Consider a scenario in which this technology is used to develop a health monitoring and security support app for the elderly, and explain the specific application content and processing steps.

[1442] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1443] Explanation of the program's processing steps

[1444] Processing flow and explanation of each step

[1445] Step 1:

[1446] Data collection

[1447] Subject: Wearable devices

[1448] Input: Elderly person's heart rate, distance traveled, sleep duration, and dietary patterns

[1449] How it works: The wearable device uses built-in sensors to measure an older adult's heart rate, distance traveled, sleep duration, and eating patterns in real time.

[1450] Output: Collected behavioral data

[1451] Specific operation: Measurement data is transferred to the server via Bluetooth or Wi-Fi.

[1452] Step 2:

[1453] Data storage and integration

[1454] Subject: Server

[1455] Input: Behavioral data sent from wearable devices, health information provided by healthcare professionals, and user-entered health conditions

[1456] How it works: The server stores the received data in a centralized database.

[1457] Output: Integrated database

[1458] Specific operation: Data is formatted and converted to save it in the database.

[1459] Step 3:

[1460] AI-powered data analysis and health pattern learning

[1461] Subject: Server

[1462] Input: Integrated Database

[1463] How it works: The server uses a generative AI model to analyze the stored data and learn the specific health patterns of each elderly person.

[1464] Output: Learned health patterns

[1465] Specific operation: Data is input into the AI ​​model, and health patterns are generated and saved as analysis results.

[1466] Step 4:

[1467] Anomaly detection and notification

[1468] Subject: Server

[1469] Input: New behavioral data and learned health patterns

[1470] How it works: The server analyzes new behavioral data in real time and compares it with learned health patterns to detect anomalies.

[1471] Output: Anomaly detection notification

[1472] Specific behavior: If an anomaly is detected, a push notification is sent to the user's device.

[1473] Step 5:

[1474] Supporting users with sentiment analysis

[1475] Subject: Sentiment analysis engine

[1476] Input: User-entered text and interaction history

[1477] How it works: The sentiment analysis engine analyzes the user's sentiment from the input text and generates advice based on that sentiment.

[1478] Output: Sentiment analysis results and advice

[1479] Specific operation: A text analysis algorithm is used to determine emotions, and appropriate relaxation methods and advice are sent to the user's device via a server.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] [Fourth embodiment]

[1484] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1485] 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.

[1486] 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).

[1487] 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.

[1488] 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.

[1489] 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).

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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."

[1497] Basic system configuration

[1498] The pet health monitoring system of the present invention includes the following major components:

[1499] 1. Pet-worn IoT devices

[1500] 2. Server

[1501] 3. User Device

[1502] 4. User Community Platform

[1503] Detailed program description

[1504] The core of this system is a program that collects data on pet behavior and detects abnormalities. The specific processing content of this program is explained in natural language below.

[1505] Data collection

[1506] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[1507] Data storage and integration

[1508] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[1509] AI-powered data analysis and health pattern learning

[1510] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[1511] Anomaly detection and notification

[1512] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1513] Support for communication with veterinarians

[1514] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1515] User Community Platform

[1516] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[1517] Specific examples

[1518] Example 1: Detecting abnormal pet behavior

[1519] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[1520] 2. The IoT device sends the abnormal data to the server.

[1521] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[1522] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1523] Example 2: Sharing information in the community

[1524] 1. A user posts a question to the community saying, "My pet is eating less."

[1525] 2. The server searches its historical database and suggests similar questions and their answers.

[1526] 3. Other users also respond to this question with their own experiences and advice.

[1527] In this way, this system comprehensively monitors the health of pets, alleviating owners' anxiety and making a significant contribution to maintaining the health of pets.

[1528] The processing flow will be explained below.

[1529] Step 1:

[1530] The IoT device worn by pets uses sensors to collect real-time data on pet behavior, such as the amount of exercise, sleep duration, and eating patterns.

[1531] Step 2:

[1532] IoT devices periodically send collected data to a server via Bluetooth or Wi-Fi.

[1533] Step 3:

[1534] The server stores the received pet behavior data in a database, including past data.

[1535] Step 4:

[1536] The user enters health information provided by the veterinarian through the application, as well as daily health conditions and abnormal symptoms, and this information is sent from the device to the server.

[1537] Step 5:

[1538] The server receives the veterinarian's health information and user-entered data, consolidates it into a database, and stores it.

[1539] Step 6:

[1540] The server analyzes past behavioral data and health information, and uses AI algorithms to learn your pet's normal behavioral patterns, during which specific health patterns are modeled.

[1541] Step 7:

[1542] The server periodically analyzes newly collected data in real time and compares it with learned health patterns.

[1543] Step 8:

[1544] If the server detects any behavior or health condition that differs from normal patterns, it determines that it is an abnormality.

[1545] Step 9:

[1546] If the server detects an abnormality, it immediately sends a push notification to the user's device, which includes specific details about the abnormality and a warning.

[1547] Step 10:

[1548] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[1549] Step 11:

[1550] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[1551] Step 12:

[1552] The server also provides a community platform where users can share information with each other. When a user posts a question to the community, the server searches a database of past questions and suggests similar questions and answers.

[1553] This series of steps allows for efficient and comprehensive monitoring and management of pet health, reducing owner anxiety.

[1554] Example 1

[1555] 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."

[1556] There is a demand for systems that can continuously and precisely monitor pet health, detect abnormalities early, and enable users to respond quickly and appropriately. However, existing systems lack the functionality to collect and analyze a pet's overall behavioral data in real time and immediately notify users of abnormalities. Furthermore, they lack a smooth way to share information with veterinarians or a community platform where users can exchange experiences and knowledge with each other. This makes it difficult to adequately manage pet health.

[1557] 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.

[1558] In this invention, the server includes means for collecting pet behavioral data, means for transmitting the data to the server, means for receiving health information provided by a veterinarian, means for recording the pet's health status input by the user, means for integrating and storing this data in a database, means for training an AI model using the stored data to learn the specific health patterns of each pet, means for analyzing new behavioral data in real time and sending a push notification to the user's device if an abnormality is detected, means for generating a report and sending it to the veterinarian when the user confirms the abnormality notification, and means for providing a community for users to share information and exchange knowledge and experiences regarding pet health. This enables comprehensive monitoring of pet health status, immediate notification if an abnormality is detected, and efficient collaboration with veterinarians, thereby enabling effective pet health management.

[1559] "Pet behavior data" refers to information about your pet's lifestyle habits, such as the amount of exercise, sleep time, and eating patterns.

[1560] "Server" refers to the central computer system that receives, stores, and analyzes pet behavioral and health data.

[1561] A "pet-worn IoT device" refers to hardware that is worn by a pet and uses built-in sensors to collect data on the pet's behavior.

[1562] "User device" refers to a device, such as a smartphone or tablet, that the user uses to receive pet behavior data and abnormality notifications.

[1563] "Database" refers to an information system for integrated management of pet behavior data, health information, and data entered by users, stored on a server.

[1564] "AI model" refers to the artificial intelligence algorithm used to analyze pet behavior data and learn specific health patterns.

[1565] "Push notification" refers to an instant notification message sent from the server to the user's device when an abnormality is detected.

[1566] "Abnormal data" refers to data that indicates behavior that differs from your pet's normal health pattern.

[1567] A "veterinarian" is a professional who provides specialized diagnosis and treatment for pet health problems.

[1568] A "user community platform" refers to an online community where users can share knowledge and experiences about pet health and exchange information.

[1569] MODE FOR CARRYING OUT THE INVENTION

[1570] Basic system configuration

[1571] The pet health monitoring system of the present invention includes the following main components: an IoT device worn by a pet, a server, a user terminal, and a user community platform.

[1572] Detailed program description

[1573] This system operates based on a program that acquires pet behavior data and detects abnormalities. Each process is performed using specific hardware and software.

[1574] Data collection

[1575] First, the IoT device worn by the pet collects the pet's behavior data in real time. This device has built-in sensors such as a pedometer, vibration sensor, accelerometer, camera, and microphone. This data is transmitted to a server via Bluetooth or Wi-Fi.

[1576] Data storage and integration

[1577] The server stores the received pet behavior data in a database. It also integrates and stores health information provided by veterinarians and pet health status entered by users through the application. This integrated data is used to centrally manage all information necessary for pet health management.

[1578] AI-powered data analysis and health pattern learning

[1579] The server uses the stored data to train an AI algorithm, specifically a generative AI model that learns each pet's specific health patterns. This process allows the server to model a pet's normal behavior and health status and detect abnormalities.

[1580] Anomaly detection and notification

[1581] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1582] Support for communication with veterinarians

[1583] When the user checks the abnormality notification, they can contact a veterinarian through the app based on the information. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1584] User Community Platform

[1585] There are also community platforms where users can share information and exchange knowledge and experiences about pet health. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[1586] Specific examples

[1587] Example 1: Detecting abnormal pet behavior

[1588] 1. An IoT device worn by a pet collects data on the pet's activity.

[1589] 2. The device detects that you are sleeping for longer than usual.

[1590] 3. The device sends the abnormal data to the server.

[1591] 4. The server compares this data with normal health patterns and determines any abnormalities.

[1592] 5. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1593] Example 2: Sharing information in the community

[1594] 1. A user uses the application to post a question to the community: "My pet is eating less."

[1595] 2. The server searches its historical database and suggests similar questions and their answers.

[1596] 3. As a result, other users will also contribute their own experiences and advice to this question.

[1597] Prompt Sentence Examples

[1598] "Please explain each processing step of the system that collects real-time behavioral data of pets and detects abnormalities. Also, please explain in detail the process by which users send abnormality notifications to their veterinarians."

[1599] This invention enables effective health management of pets by comprehensively monitoring their health status, immediately notifying them if an abnormality is detected, and enabling efficient collaboration with veterinarians.

[1600] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1601] Step 1:

[1602] Collecting pet behavior data

[1603] The IoT device worn by pets uses a built-in pedometer, vibration sensor, accelerometer, camera, and microphone to collect real-time behavioral data such as exercise volume, sleep duration, and eating patterns.

[1604] Input: Pet behavior (exercise, sleep, eating, etc.)

[1605] Data processing: collection of raw data obtained from sensors

[1606] Output: Generate behavioral data (amount of exercise, sleep time, dietary patterns, etc.)

[1607] Step 2:

[1608] Sending data to the server

[1609] IoT devices send the collected data to a server via Bluetooth or Wi-Fi.

[1610] Input: Behavioral data collected by IoT devices

[1611] Data processing: Packetizing data and converting it to transmission protocol

[1612] Output: Send data to the server

[1613] Step 3:

[1614] Data storage and integration

[1615] The server centrally manages the received data and stores it in a database, and also integrates and stores health information provided by veterinarians and pet health conditions entered by users through the app.

[1616] Input: Submitted behavioral data, health information, and user-entered data

[1617] Data processing: Data integration and centralization

[1618] Output: Consolidated database updates

[1619] Step 4:

[1620] AI-based data analysis and learning

[1621] The server uses the stored data to train AI algorithms to learn each pet's specific health patterns, using generative AI models to learn normal and abnormal behavior patterns.

[1622] Input: Data from the integrated database

[1623] Data processing: Data analysis and learning using AI algorithms

[1624] Output: A model of health patterns

[1625] Step 5:

[1626] Anomaly detection and notification

[1627] The server analyzes new behavioral data in real time, compares it with learned health patterns, and immediately sends a push notification to the user's device if an abnormality is detected.

[1628] Input: New behavioral data and AI model comparison results

[1629] Data processing: Running anomaly detection algorithms

[1630] Output: Generate and send a push notification

[1631] Step 6:

[1632] Support for communication with veterinarians

[1633] When the user confirms the abnormality notification, they can use the app to contact a veterinarian. The device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1634] Input: Anomaly detection notification

[1635] Data processing: Reporting abnormal data

[1636] Output: Generate a report and send it to your veterinarian

[1637] Step 7:

[1638] Information sharing on user community platforms

[1639] Users use the application to post questions to the community and get answers, and the server searches a database of past questions to suggest similar questions and answers.

[1640] Input: User question

[1641] Data processing: database search and suggestion of similar queries

[1642] Output: Displaying recommendations

[1643] These processing steps enable monitoring of the pet's health condition and early detection and management of abnormalities.

[1644] (Application example 1)

[1645] 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."

[1646] Conventional pet health monitoring systems primarily focus on detecting abnormalities based on the pet's own behavioral data. However, the health of the pet owner often has a significant impact on the pet's health. Furthermore, there is a lack of support for taking appropriate actions based on the user's health status. Therefore, there is a need for a system that takes the user's health status into consideration comprehensively. This will enable a system that can optimize the health of not only the user but also the pet.

[1647] 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.

[1648] In this invention, the server includes means for collecting pet behavior data, means for receiving health information provided by a veterinarian, means for recording the pet's health status entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication between the user and the veterinarian when the user confirms an abnormality, means for providing a community for users to share information and exchange knowledge and experiences regarding pet health, a smart device for collecting the user's health status, and means for notifying the user of meal provision contents based on the user's health data. This enables comprehensive monitoring and support for maintaining the health of the pet while taking the user's health status into consideration.

[1649] The "means for collecting pet behavioral data" refers to a device that measures and records a pet's daily behavior and activities in real time through an IoT device attached to the pet.

[1650] The "means for receiving health information provided by veterinarians" is an interface for aggregating and incorporating into the system the health status and medical records provided by veterinarians regarding diagnosis and treatment.

[1651] "User-entered means for recording pet health conditions" refers to a function that allows pet owners to use an application to input, save, and record their pet's health conditions and symptoms.

[1652] "Means for learning specific health patterns" refers to the process of analyzing collected data with AI algorithms to learn and detect your pet's normal health patterns.

[1653] "Means of notifying users in real time when abnormalities are detected" refers to a function that immediately notifies users when abnormalities in their health status are detected based on the analysis of collected data.

[1654] The "means for supporting communication with a veterinarian" is an interface that helps the user easily contact a veterinarian when an abnormality is detected.

[1655] "A means of providing a community for users to share information and exchange knowledge and experiences related to pet health" is an online platform where pet owners can share health information and care methods and help each other.

[1656] A "smart device that collects user health status" is a wearable device used to collect user health data (e.g., heart rate, blood sugar level, body temperature, etc.) in real time.

[1657] "Means for notifying the user of meal contents based on the user's health data" refers to a function that analyzes the user's collected health data and notifies the user of appropriate meal contents and suggestions based on the results.

[1658] The system of this invention comprehensively monitors the health status of both pets and their owners and supports appropriate actions, which requires an IoT device worn by the pet, a smart device, a server, and a user terminal.

[1659] Hardware Configuration

[1660] 1. Pet-worn IoT devices

[1661] A device that collects pet behavioral data (such as exercise, sleep, and eating patterns).

[1662] 2. User's smart device

[1663] A wearable device for collecting user health data (heart rate, blood sugar level, body temperature, etc.) in real time.

[1664] 3. Server

[1665] It centralizes data on both pets and users, and is used for data analysis and pattern recognition, using programming languages ​​such as Python and frameworks such as Django and Flask.

[1666] 4. User Device

[1667] A device such as a smartphone that allows you to receive real-time updates on your pet's health and any abnormalities.

[1668] Software Configuration

[1669] 1. Data Collection

[1670] The server receives data from the pet's IoT device and the user's smart device via Bluetooth and Wi-Fi, and the data is stored in a database on the server.

[1671] 2. Data integration and analysis

[1672] The server combines the collected pet and user data and analyzes it using AI algorithms, including deep learning and machine learning models, to detect abnormal patterns.

[1673] 3. Abnormality notification

[1674] If an anomaly is detected, the server will send a real-time push notification to the user's device, which will include details of the anomaly and recommended actions.

[1675] 4. Communication support

[1676] When users receive an abnormality notification, they are provided with a function that allows them to easily contact a veterinarian via the server, and they can also send a report containing health data to the veterinarian.

[1677] 5. User Community Platform

[1678] The server hosts a community platform where users can post questions about pet health and receive answers from other users. The platform is built using Django.

[1679] Specific examples

[1680] Example 1: Pet and user anomaly detection

[1681] The IoT device worn by the pet detects when the pet is sleeping for longer than usual.

[1682] A smart wearable device detects when a user's blood sugar level is high.

[1683] The server analyzes both sets of data and determines that there is an anomaly.

[1684] The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required. Also, your blood sugar level is high. We recommend that you feed it appropriately."

[1685] Prompt Sentence Examples

[1686] "Please suggest a special menu for those with high blood sugar levels and dietary restrictions."

[1687] "If my blood sugar level is over 150, please suggest a meal plan that is suitable for high blood sugar."

[1688] The system monitors the health of both pets and owners in real time, immediately recommends necessary actions, and facilitates collaboration with veterinarians and communication between users.

[1689] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1690] Step 1:

[1691] Data collection

[1692] Subject: Server

[1693] How it works: The server receives data from the pet's IoT device and the user's smart device.

[1694] Input: Pet behavior data (amount of exercise, sleep time, eating patterns) and user health data (heart rate, blood sugar level, body temperature).

[1695] Data processing: The received data is stored in the server database. This data is recorded with a timestamp.

[1696] Output: Real-time pet and user health data stored in a database on the server.

[1697] Step 2:

[1698] Data Integration and Preprocessing

[1699] Subject: Server

[1700] How it works: The server consolidates collected pet and user health data.

[1701] Input: Pet and user health data stored in a database.

[1702] Data processing: Filling in missing data, filtering outliers, and standardizing data.

[1703] Output: Preprocessed consolidated data in a parseable format.

[1704] Step 3:

[1705] AI-based data analysis and anomaly detection

[1706] Subject: Server

[1707] How it works: The server feeds pre-processed data into an AI algorithm to detect anomalies.

[1708] Input: Preprocessed integrated data, existing health pattern models.

[1709] Data Computing: Deep learning and machine learning models (e.g., using TensorFlow or PyTorch) to analyze health patterns and detect anomalies.

[1710] Output: Anomaly detection results (type and details of anomaly).

[1711] Step 4:

[1712] Abnormal notification

[1713] Subject: Server

[1714] Operation: The server pushes a notification to the user's device about any detected anomalies.

[1715] Input: Anomaly detection results.

[1716] Data Transformation: Generate notification messages with details of the anomaly and recommended actions.

[1717] Output: Push notification to user device.

[1718] Step 5:

[1719] User support

[1720] Subject: Server

[1721] Behavior: The server supports the user in taking action after receiving an error notification.

[1722] Input: A response selection from the user (e.g., contacting a veterinarian, entering additional data).

[1723] Data Processing: Collects the necessary data and generates reports based on user selections.

[1724] Output: User guide and veterinarian report.

[1725] Step 6:

[1726] Managing the Community Platform

[1727] Subject: Server

[1728] How it works: The server allows users to post questions about their pet's health to the community.

[1729] Input: Questions submitted by users.

[1730] Data processing: Compare with past posted data and suggest similar questions and their answers.

[1731] Output: Answers and advice within the user community.

[1732] Step 7:

[1733] Proposing actions based on health data

[1734] Subject: Server

[1735] How it works: The server generates and notifies the user of appropriate dietary and behavioral suggestions based on their health data.

[1736] Input: User health data, existing medical and nutritional data.

[1737] Data calculation: Analyzes user data and generates dietary and behavioral recommendations based on health status.

[1738] Output: A notification of suggested actions sent to the user's device. A specific example would be something like, "Because your blood sugar level is high, we suggest a low-carb menu for your next meal."

[1739] 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.

[1740] Basic system configuration

[1741] The pet health monitoring system of the present invention includes the following major components:

[1742] 1. Pet-worn IoT devices

[1743] 2. Server

[1744] 3. User Device

[1745] 4. User Community Platform

[1746] 5. Emotion Engine

[1747] Detailed program description

[1748] The core of this system is a program that collects data on pet behavior and detects abnormalities. In addition, by combining it with an emotion engine that recognizes and analyzes the user's emotions, more advanced health management and user support are realized. The specific processing contents of the program are explained in natural language below.

[1749] Data collection

[1750] First, the IoT device worn by the pet uses sensors to collect real-time behavioral data about the pet, such as the amount of exercise, sleep time, and eating patterns, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[1751] Data storage and integration

[1752] The server centralizes and stores pet behavior data, health information provided by veterinarians, and pet health status entered by users through the application in a database.

[1753] AI-powered data analysis and health pattern learning

[1754] The server uses the stored data to train AI algorithms that learn each pet's specific health patterns, modeling their normal behavior and health status so they can detect any abnormalities.

[1755] Anomaly detection and notification

[1756] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1757] Emotional analysis of users using an emotion engine

[1758] As users interact with the application, the emotion engine recognizes emotions by analyzing their input and interaction history, for example, by inferring their emotions from the tone of their text input and the choice of words.

[1759] Providing advice based on user emotions

[1760] The server provides appropriate advice and measures based on the user's emotions recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and comforting materials.

[1761] Support for communication with veterinarians

[1762] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian, and the device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1763] User Community Platform

[1764] The system includes a community platform for pet owners to share information and exchange health-related knowledge and experiences. The server manages questions and answers posted by users and searches related past data to suggest appropriate information.

[1765] Specific examples

[1766] Example 1: Detecting abnormal pet behavior

[1767] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[1768] 2. The IoT device sends the abnormal data to the server.

[1769] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[1770] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1771] Example 2: Sharing information in the community

[1772] 1. A user posts a question to the community saying, "My pet is eating less."

[1773] 2. The server searches its historical database and suggests similar questions and their answers.

[1774] 3. Other users also respond to this question with their own experiences and advice.

[1775] Example 3: Providing advice using an emotion engine

[1776] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[1777] 2. The server provides relaxation techniques to ease anxiety and reassurance for similar cases.

[1778] In this way, the system efficiently and comprehensively monitors and manages pet health, and also provides emotional support to users, allowing for more peace of mind in health management.

[1779] The processing flow will be explained below.

[1780] Step 1:

[1781] The IoT device worn by pets uses sensors to collect real-time behavioral data (such as the amount of exercise, sleep time, and eating patterns), which is then sent to a server via Bluetooth or Wi-Fi.

[1782] Step 2:

[1783] The server stores the received pet behavior data in a database, as well as health information provided by veterinarians and health condition data entered by users through the app.

[1784] Step 3:

[1785] Users enter any abnormal symptoms or daily health conditions through the application and send the data to the server.

[1786] Step 4:

[1787] The server consolidates all stored data and analyzes it using AI algorithms, learning your pet's normal behavior patterns and modeling specific health patterns.

[1788] Step 5:

[1789] The server analyzes newly collected behavioral data in real time and compares it with learned health patterns. If abnormal behavior or health conditions are detected, they are deemed abnormal.

[1790] Step 6:

[1791] If an anomaly is detected, the server will send a real-time push notification to the user's device, detailing the specific anomaly and any points requiring attention.

[1792] Step 7:

[1793] Users receive a notification, open the app to see details of the problem, and, if necessary, contact a veterinarian directly from the app.

[1794] Step 8:

[1795] The terminal generates a report containing the abnormality data entered by the user and sends it to the veterinarian via the server.

[1796] Step 9:

[1797] The emotion engine analyzes text input and dialogue history when a user uses the app to recognize the user's emotions.

[1798] Step 10:

[1799] The server then proposes appropriate advice and measures based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling anxious, it will provide relaxation techniques and comforting materials.

[1800] Step 11:

[1801] Users receive advice and take appropriate action, and if necessary, can use the community to seek advice from other users.

[1802] Step 12:

[1803] The server manages the user community platform, and when a user posts a question, it searches the database to suggest similar questions and answers, and immediately notifies the user when a new answer is posted by another user.

[1804] This series of steps allows you to comprehensively monitor and manage your pet's health and reduces your anxiety.

[1805] Example 2

[1806] 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."

[1807] In pet health management, conventional systems did not adequately collect pet behavior data or detect abnormalities. Furthermore, communication with veterinarians and knowledge sharing between users were not smooth, making it difficult to efficiently monitor and manage pet health. Furthermore, support tailored to the user's emotions was not provided, making it difficult to alleviate user anxiety.

[1808] 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.

[1809] In this invention, the server includes: means for collecting pet behavioral data and transferring it to the server in real time; means for integrating and storing the behavioral data and health information in a database; means for providing artificial intelligence that uses the stored data to learn specific health patterns; means for detecting abnormalities and notifying the user's device in real time; means for providing a natural language processing engine that analyzes emotions from the user's text input and dialogue history; means for providing advice to the user based on the analyzed emotions; means for transmitting data to support communication with veterinarians when an abnormality is detected; and means for providing a community platform for users to share information and exchange health-related knowledge and experiences. This allows for real-time monitoring and management of pet health conditions and prompt notification to the user when an abnormality is detected. Furthermore, by providing advice based on the user's emotions, facilitating smooth communication with veterinarians, and allowing users to share information, the system provides comprehensive support for pet health management.

[1810] "Pet behavior data" refers to data related to the behavior of pets, such as the amount of exercise, sleeping hours, and eating patterns.

[1811] A "server" is a computer system that collects, stores, and analyzes pet behavior data and provides various notifications and communication support.

[1812] "Real time" means being able to process phenomena occurring at that time immediately without delay.

[1813] A "database" is an information storage system for effectively storing and managing pet behavioral data and health information.

[1814] "Artificial intelligence" refers to machine learning algorithms that learn your pet's specific health patterns and detect abnormalities.

[1815] A "natural language processing engine" refers to technology for analyzing emotions from user text input and dialogue history.

[1816] "Push notification" is a communication method that sends information from a server to a user's device in real time.

[1817] A "veterinarian" is a professional who diagnoses the health of animals and provides appropriate treatment.

[1818] "Emotion" refers to the psychological state inferred from the content of a user's text or dialogue.

[1819] A "community platform" is an online service that provides a place for users to share information and exchange knowledge and experiences regarding pet health.

[1820] The present invention is a system for efficiently monitoring and managing the health condition of a pet and providing support according to the user's emotions. Specific embodiments of the system are described below.

[1821] Basic system configuration

[1822] The system of the present invention includes the following major components:

[1823] 1. Pet-worn IoT devices

[1824] 2. Server

[1825] 3. User Device

[1826] 4. User Community Platform

[1827] 5. Emotion Engine

[1828] Data collection

[1829] First, the IoT device worn by the pet uses an accelerometer and temperature sensor to collect real-time behavioral data about the pet, including the pet's activity level, sleep duration, and eating patterns, and the collected data is transmitted to a server via Bluetooth or Wi-Fi.

[1830] Data storage and integration

[1831] The server stores the received pet behavior data in a MySQL database. Health information provided by veterinarians and pet health conditions entered by users through the application are also stored in the same database. This allows all data to be managed centrally.

[1832] AI-powered data analysis and health pattern learning

[1833] The server uses the stored data to train machine learning algorithms, specifically building health pattern learning models using Python and TensorFlow, which then identify and analyze normal and abnormal behavior patterns in pets.

[1834] Anomaly detection and notification

[1835] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device. This notification contains information such as, "Your pet is sleeping longer than usual. Attention is required."

[1836] Emotional analysis of users using an emotion engine

[1837] As users interact with the application, the emotion engine analyzes their text input and dialogue history to identify their emotions, using natural language processing (NLP) techniques to infer emotions such as "anxiety" or "relief" from the tone of the text and word choice.

[1838] Providing advice based on user emotions

[1839] The server provides appropriate advice and countermeasures based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it provides relaxation techniques and examples of success in similar cases.

[1840] Support for communication with veterinarians

[1841] When a user confirms an abnormality, the app provides a function that allows them to easily contact a veterinarian. The user's device generates a report containing the abnormality data and sends it to the veterinarian via the server.

[1842] User Community Platform

[1843] The server manages questions and answers posted by users and searches related past data to suggest appropriate information, allowing users to share their knowledge and experiences regarding health management.

[1844] Specific examples

[1845] Below is a concrete example of how this system works.

[1846] Example 1: Detecting abnormal pet behavior

[1847] 1. An IoT device worn by a pet detects that the pet is sleeping for longer than usual.

[1848] 2. The IoT device sends the abnormal data to the server.

[1849] 3. The server compares this data with normal health patterns and determines whether there are any abnormalities.

[1850] 4. The server immediately sends a notification to the user's device saying, "Your pet is sleeping longer than usual. Attention is required."

[1851] Example 2: Sharing information in the community

[1852] 1. A user posts a question to the community saying, "My pet is eating less."

[1853] 2. The server searches its historical database and suggests similar questions and their answers.

[1854] 3. Other users also respond to this question with their own experiences and advice.

[1855] Example 3: Providing advice using an emotion engine

[1856] 1. When a user receives a notification of an abnormality and checks the details in the app, the emotion engine detects "anxiety" from the user's post.

[1857] 2. The server will provide relaxation techniques and reassurance from similar cases to ease anxiety.

[1858] Prompt Sentence Examples

[1859] "Please give us a concrete example of a pet health monitoring system. Please explain in detail how it detects abnormal pet behavior, shares information with the community, and provides advice using an emotion engine."

[1860] As described above, the system of the present invention is capable of comprehensively monitoring and managing the health condition of pets and efficiently providing the necessary support to users.

[1861] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1862] Step 1:

[1863] The IoT device worn by pets uses accelerometers and temperature sensors to collect pet behavior data in real time. The sensors detect the pet's physical movements as input and convert the data into digital form. As output, the collected data is sent to a server via Bluetooth or Wi-Fi. Specific actions such as running, eating, and sleeping are captured once per second.

[1864] Step 2:

[1865] The server stores the received pet behavior data in a database. As input, it receives behavior data sent in real time from IoT devices. As output, it stores the data in the "Behavior Data" table in a MySQL database. Specific operations include adding new data entries and integrating them with existing data.

[1866] Step 3:

[1867] The user enters the pet's health status through the application. As input, the user fills in the application's form with the pet's recent health status. As output, the information is sent to the server and stored in a database. Specifically, the user selects a health checklist within the app and enters conditions such as "no appetite" or "lack of energy."

[1868] Step 4:

[1869] The server uses artificial intelligence to integrate pet behavioral and health data and learn specific health patterns. It uses behavioral and health data stored in a database as input. It generates a trained health pattern model as output. Specifically, it uses Python and TensorFlow to train a machine learning algorithm and model the health patterns.

[1870] Step 5:

[1871] The server analyzes newly acquired behavioral data in real time and compares it with the learned health patterns. It uses real-time behavioral data as input. As output, it generates abnormality warning data if an abnormality is detected. Specifically, the analysis algorithm compares the data with a normal behavior model and executes the process of detecting an abnormality.

[1872] Step 6:

[1873] If the server detects an abnormality, it immediately sends a push notification to the user's device. The input is abnormality warning data. The output is a notification message sent to the user's device. Specifically, the server sends a message via the push notification service, such as "Your pet is sleeping longer than usual. Attention is required."

[1874] Step 7:

[1875] When a user uses an application, the emotion engine analyzes the user's input and interaction history to recognize emotions. It uses text data from the user as input and generates data on the user's emotional state as output. Specifically, it uses natural language processing technology to perform sentiment analysis on the user's input text and infers emotions such as anxiety or relief.

[1876] Step 8:

[1877] The server provides appropriate advice and countermeasures to the user based on the emotional information recognized by the emotion engine. The server receives the user's emotional state data as input. The server generates an advice message as output and sends it to the user. Specific actions include providing users who are feeling anxious with "relaxation methods" and "examples of previous successes."

[1878] Step 9:

[1879] When the user confirms the abnormality notification, the app provides a function to easily contact a veterinarian. The input is detailed information including the abnormality data. The output is a report that is sent to the veterinarian. Specifically, when the user presses the "Contact" button, a detailed report is generated and sent to the veterinarian via the server.

[1880] Step 10:

[1881] The server manages questions and answers posted by users and shares information through a community platform. It uses question data from users as input. It searches related past data and suggests appropriate information as output. Specifically, a database search algorithm searches past questions and answers and suggests similar questions and answers.

[1882] (Application example 2)

[1883] 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."

[1884] To effectively monitor the health of pets, conventional technologies lack the ability to detect and notify abnormalities in real time, and no method has been established to provide appropriate support based on the user's emotions.In order to effectively monitor the health of elderly people and detect abnormalities early, a system is needed that applies conventional pet monitoring technology, has the ability to detect abnormalities in real time, and provides support based on the user's emotions.

[1885] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral data, means for receiving health information provided by medical professionals, means for recording health conditions entered by the user, means for integrating and analyzing this data to learn specific health patterns, means for notifying the user in real time when an abnormality is detected, means for supporting communication with medical professionals when the user confirms an abnormality, means for providing a community where users can share information and exchange health-related knowledge and experiences, and means for analyzing the user's emotions and providing corresponding advice. This enables efficient and comprehensive monitoring and management of the health of elderly people and rapid response when an abnormality occurs. Furthermore, providing support tailored to the user's emotions can provide a sense of security and contribute to improving their health.

[1886] Key Word Definitions

[1887] "Behavioral data" refers to information about the behavior of subjects such as pets and elderly people in their daily lives, and specifically refers to data such as heart rate, distance traveled, sleeping patterns, and eating patterns.

[1888] "Medical personnel" refers to people who have specialized knowledge about the health of subjects such as pets and elderly people and who diagnose and treat them, and generally refers to veterinarians and doctors.

[1889] "Health patterns" refer to the normal health status and behavioral tendencies of subjects, such as pets and elderly people, formed based on daily behavioral data.

[1890] "Real-time" refers to a state in which data can be processed and analyzed immediately at the moment it is generated or collected, and results can be obtained.

[1891] A "community" is a place or platform where users interested in the health of pets, the elderly, etc. gather, and where information is shared and mutual support is provided.

[1892] "Sentiment analysis" refers to the process of inferring and analyzing a user's emotions from the text they enter and their behavior.

[1893] "Anomaly detection" refers to the process of identifying unusual behaviors or conditions that deviate from the norm by comparing them with previously learned health patterns.

[1894] "Notification" refers to the act of providing information and sending alert messages in real time through a user device or application.

[1895] "Server" refers to a central processing unit or system that receives, stores, analyzes behavioral data of pets, elderly people, etc., and provides the results.

[1896] "Advice" refers to specific advice or countermeasures provided based on the user's situation and emotions.

[1897] MODE FOR CARRYING OUT THE INVENTION

[1898] Basic system configuration

[1899] The present invention is a system for monitoring the health status of elderly people, which includes the following main components:

[1900] 1. Wearable devices: worn by seniors, they collect real-time behavioral data such as heart rate, distance traveled, sleep patterns, and eating patterns.

[1901] 2. Server: Receives data sent from the wearable device, stores it in a database, and analyzes it using AI algorithms.

[1902] 3. User's device: A device such as a smartphone or tablet that receives notifications from the server.

[1903] 4. User community platform: An online platform for users to share information and exchange knowledge and experiences related to elderly health.

[1904] 5. Sentiment Analysis Engine: Analyzes the text entered by the user to recognize emotions and provide advice accordingly.

[1905] Specific processing details of the system

[1906] 1. Data Collection

[1907] The wearable device uses built-in sensors to collect real-time behavioral data of the elderly, including heart rate, distance traveled, sleep duration, and dietary patterns, and transmits this data to a server via Bluetooth or Wi-Fi.

[1908] 2. Data storage and integration

[1909] The server centrally manages the behavioral data received from the wearable devices and stores it in a database, as well as health information provided by medical professionals and health condition information entered by users through their devices.

[1910] 3. AI-powered data analysis and health pattern learning

[1911] The server uses the stored data to train a generative AI model to learn the specific health patterns of each elderly person, in the process modeling their normal behavior and health status so that anomalies can be detected.

[1912] 4. Anomaly detection and notification

[1913] The server analyzes new behavioral data in real time and compares it with learned health patterns. If an abnormality is detected, the server immediately sends a push notification to the user's device, detailing the abnormality and any points requiring attention.

[1914] 5. Supporting users through sentiment analysis

[1915] When a user checks an abnormality notification, the emotion analysis engine analyzes the user's input and dialogue history to recognize their emotions. For example, it infers the user's emotions from the tone of their text input and their choice of words. Based on the results of the emotion analysis, the server provides the user with appropriate advice and countermeasures. For example, if the user is feeling anxious, it provides relaxation techniques and reassurance materials.

[1916] Specific use cases

[1917] Example 1: Detecting abnormal behavior in elderly people

[1918] 1. A wearable device detects when an elderly person's heart rate is higher than normal.

[1919] 2. The wearable device sends the abnormality data to the server.

[1920] 3. The server compares this data with normal health patterns and determines abnormalities.

[1921] 4. The server immediately sends a notification to the user's device saying, "Your heart rate is higher than normal. Attention is required."

[1922] Example 2: Providing advice based on sentiment analysis

[1923] 1. When a user receives a notification of an abnormality and checks the details in the app, the sentiment analysis engine detects "anxiety" from the user's text input.

[1924] 2. The server provides relaxation techniques to ease anxiety and ways to deal with similar cases.

[1925] Prompt Sentence Examples

[1926] Explain how to apply this technology to a pet health monitoring system. This system uses IoT devices to collect pet behavior data and uses AI analytics to detect abnormalities. Consider a scenario in which this technology is used to develop a health monitoring and security support app for the elderly, and explain the specific application content and processing steps.

[1927] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1928] Explanation of the program's processing steps

[1929] Processing flow and explanation of each step

[1930] Step 1:

[1931] Data collection

[1932] Subject: Wearable devices

[1933] Input: Elderly person's heart rate, distance traveled, sleep duration, and dietary patterns

[1934] How it works: The wearable device uses built-in sensors to measure an older adult's heart rate, distance traveled, sleep duration, and eating patterns in real time.

[1935] Output: Collected behavioral data

[1936] Specific operation: Measurement data is transferred to the server via Bluetooth or Wi-Fi.

[1937] Step 2:

[1938] Data storage and integration

[1939] Subject: Server

[1940] Input: Behavioral data sent from wearable devices, health information provided by healthcare professionals, and user-entered health conditions

[1941] How it works: The server stores the received data in a centralized database.

[1942] Output: Integrated database

[1943] Specific operation: Data is formatted and converted to save it in the database.

[1944] Step 3:

[1945] AI-powered data analysis and health pattern learning

[1946] Subject: Server

[1947] Input: Integrated Database

[1948] How it works: The server uses a generative AI model to analyze the stored data and learn the specific health patterns of each elderly person.

[1949] Output: Learned health patterns

[1950] Specific operation: Data is input into the AI ​​model, and health patterns are generated and saved as analysis results.

[1951] Step 4:

[1952] Anomaly detection and notification

[1953] Subject: Server

[1954] Input: New behavioral data and learned health patterns

[1955] How it works: The server analyzes new behavioral data in real time and compares it with learned health patterns to detect anomalies.

[1956] Output: Anomaly detection notification

[1957] Specific behavior: If an anomaly is detected, a push notification is sent to the user's device.

[1958] Step 5:

[1959] Supporting users with sentiment analysis

[1960] Subject: Sentiment analysis engine

[1961] Input: User-entered text and interaction history

[1962] How it works: The sentiment analysis engine analyzes the user's sentiment from the input text and generates advice based on that sentiment.

[1963] Output: Sentiment analysis results and advice

[1964] Specific operation: A text analysis algorithm is used to determine emotions, and appropriate relaxation methods and advice are sent to the user's device via a server.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] FIG. 9 illustrates 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 behaviors 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.

[1970] 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.

[1971] 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).

[1972] 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, and motorcycles, 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.

[1973] 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."

[1974] 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.

[1975] 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).

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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.

[1986] The following is further disclosed regarding the above embodiment.

[1987] (Claim 1)

[1988] A system for monitoring the health of a pet, comprising:

[1989] A means for collecting pet behavior data;

[1990] a means of receiving health information provided by a veterinarian;

[1991] a means for recording the pet's health status entered by the user;

[1992] A means to integrate and analyze this data to learn specific health patterns;

[1993] A means to notify users in real time when an abnormality is detected, and

[1994] A means to assist users in communicating with veterinarians when they notice any abnormalities,

[1995] It provides a community for users to share information and exchange knowledge and experiences about pet health, and

[1996] A system including:

[1997] (Claim 2)

[1998] 10. The system of claim 1, further comprising means for users to post questions to the community, search a database of past posts, and suggest similar questions and answers.

[1999] (Claim 3)

[2000] 10. The system of claim 1, further comprising means for sending a push notification to a user's device in real time when an anomaly is detected.

[2001] "Example 1"

[2002] (Claim 1)

[2003] A system for monitoring the health of a pet, comprising:

[2004] A means for collecting pet behavior data;

[2005] means for transmitting the data to a server;

[2006] a means of receiving health information provided by a veterinarian;

[2007] a means for recording the pet's health status entered by the user;

[2008] A means of integrating and storing this data in a database;

[2009] A means to use the stored data to train an AI model to learn the specific health patterns of each pet; and

[2010] A means to analyze new behavioral data in real time and send push notifications to users' devices if an abnormality is detected.

[2011] When the user sees an abnormality notification, a means to generate a report and send it to a veterinarian.

[2012] Provide a community for users to share information and exchange knowledge and experiences related to pet health;

[2013] A system including:

[2014] (Claim 2)

[2015] 10. The system of claim 1, further comprising means for users to post questions to the community, search a historical database, and suggest similar questions and answers.

[2016] (Claim 3)

[2017] 10. The system of claim 1, further comprising means for immediately sending a push notification to a user's device when an anomaly is detected.

[2018] "Application Example 1"

[2019] (Claim 1)

[2020] A system for monitoring the health of a pet, comprising:

[2021] A means for collecting pet behavior data;

[2022] a means of receiving health information provided by a veterinarian;

[2023] a means for recording the pet's health status entered by the user;

[2024] A means to integrate and analyze this data to learn specific health patterns;

[2025] A means to notify users in real time when an abnormality is detected, and

[2026] A means to assist users in communicating with veterinarians when they notice any abnormalities,

[2027] It provides a community for users to share information and exchange knowledge and experiences about pet health, and

[2028] A smart device that collects the user's health status and

[2029] a means of informing users of meal offers based on their health data;

[2030] A system including:

[2031] (Claim 2)

[2032] 10. The system of claim 1, further comprising means for users to post questions to the community, search a database of past posts, and suggest similar questions and answers.

[2033] (Claim 3)

[2034] 10. The system of claim 1, further comprising means for sending a push notification to a user's device in real time when an anomaly is detected.

[2035] "Example 2: Combining Emotion Engines"

[2036] (Claim 1)

[2037] A system for monitoring the health of a pet, comprising:

[2038] A means for collecting pet behavior data and transmitting it to a server in real time;

[2039] A means of integrating and storing behavioral data and health information in a database;

[2040] means for providing artificial intelligence that uses the stored data to learn specific health patterns;

[2041] A means of detecting abnormalities and notifying users' devices in real time,

[2042] A means for providing a natural language processing engine that analyzes emotions from user text input and dialogue history;

[2043] a means for providing advice to the user based on the analyzed sentiment;

[2044] A data transmission method to assist in communication with a veterinarian when an abnormality is detected;

[2045] A means to provide a community platform for users to share information and exchange health knowledge and experiences;

[2046] A system including:

[2047] (Claim 2)

[2048] 10. The system of claim 1, further comprising means for users to post questions to the community, search a database of past posts, and suggest similar questions and answers.

[2049] (Claim 3)

[2050] 10. The system of claim 1, further comprising means for sending a push notification to a user's device in real time when an anomaly is detected.

[2051] "Application example 2 when combining emotion engines"

[2052] (Claim 1)

[2053] A system for monitoring the health of a pet, comprising:

[2054] A means for collecting pet behavior data;

[2055] a means of receiving health information provided by your healthcare provider;

[2056] a means for recording the pet's health status entered by the user;

[2057] A means to integrate and analyze this data to learn specific health patterns;

[2058] A means to notify users in real time when an abnormality is detected, and

[2059] A means to assist users in communicating with medical personnel when they detect an abnormality, and

[2060] It provides a community for users to share information and exchange knowledge and experiences about pet health, and

[2061] A means of analyzing user sentiment and providing advice accordingly;

[2062] A system including:

[2063] (Claim 2)

[2064] 10. The system of claim 1, further comprising means for users to post questions to the community, search a database of past posts, and suggest similar questions and answers.

[2065] (Claim 3)

[2066] 10. The system of claim 1, further comprising means for sending a push notification to a user's device in real time when an anomaly is detected. [Explanation of symbols]

[2067] 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 system for monitoring the health of a pet, comprising: A means for collecting pet behavior data; a means of receiving health information provided by a veterinarian; a means for recording the pet's health status entered by the user; A means to integrate and analyze this data to learn specific health patterns; A means to notify users in real time when an abnormality is detected, and A means to assist users in communicating with veterinarians when they notice any abnormalities, It provides a community for users to share information and exchange knowledge and experiences about pet health, and A system including:

2. The system of claim 1 further comprising means for users to post questions to the community, search a database of past posts, and suggest similar questions and answers.

3. The system of claim 1 , further comprising means for sending a push notification to a user's terminal in real time when an anomaly is detected.

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