System

Abstract

A system is provided.SOLUTION: A system including means for measuring a heart rate, a body temperature, and an activity level from a biological information terminal attached to a pet, means for transmitting the measured data to a server, means for analyzing the received data and evaluating a health condition and an action of the pet by the server, means for generating and transmitting a command for controlling an IoT device as necessary on the basis of an evaluation result, and means for transmitting an alert to an owner in a case where an abnormality is detected.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] In recent years, there has been an increasing demand for pet health management and monitoring while away from home. However, conventional systems have difficulty accurately monitoring pet health and behavior in real time, and have been unable to respond quickly when abnormalities occur. Furthermore, because pets have diverse behavioral patterns, there is a need for a system that automatically responds appropriately to these patterns. The purpose of this invention is to solve these problems and provide a system that monitors pet health in real time and automatically takes necessary measures. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a biometric information terminal attached to a pet measures the heart rate, body temperature, and activity level. Next, the measured data is sent to a server, which analyzes the received data and evaluates the pet's health condition and behavior. Based on the evaluation results, commands to control IoT devices are generated and sent as needed. Furthermore, if an abnormality is detected, an alert is sent to the owner to prompt a prompt response. Specifically, the system includes means for capturing video data of the pet in real time and sending it to the server, and means for the server to train a machine learning model using continuously collected data to improve the accuracy of analysis and abnormality detection. This makes it possible to reliably monitor the pet's health condition and take prompt action in the event of an abnormality.

[0006] A "biometric information terminal" is a device that is attached to a pet and measures biometric data such as heart rate, body temperature, and activity level, and transmits the data to a server.

[0007] The "server" is a computer system that receives, stores, and analyzes measured biometric data and video data to evaluate the health and behavior of pets.

[0008] An "IoT device" is a device that can be controlled remotely via the Internet, and includes, for example, air conditioners, feeders, and lighting.

[0009] An "alert" is a warning message sent to pet owners when an abnormality is detected, and is sent via a smartphone app or other means.

[0010] "Analysis" is the process of evaluating the pet's health and behavior based on the received biometric and video data, and detecting any abnormalities.

[0011] A "command" is an instruction generated by a server and sent to an IoT device, and includes specific operational instructions for controlling the device.

[0012] A "machine learning model" is a mathematical model that is trained based on continuously collected data to predict pet behavior patterns and health conditions and improve the accuracy of anomaly detection. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention relates to a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. The system mainly consists of the following components:

[0035] 1. Biometric Information Terminal

[0036] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[0037] 2. Surveillance Camera

[0038] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[0039] 3. Server

[0040] The server receives, stores, and analyzes the data sent from the device and camera. Specifically, it performs the following operations:

[0041] The received biometric data is analyzed to assess the pet's health condition.

[0042] The pet's behavior patterns are analyzed based on the received video data.

[0043] Compare with past data to detect anomalies.

[0044] If an abnormality is detected, an alert will be sent to the owner.

[0045] 4. IoT devices

[0046] This is a device that receives commands generated by the server and performs the specified control action (for example, adjusting the temperature of the air conditioner, activating the feeder, etc.).

[0047] 5. Smartphone App

[0048] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[0049] System operation example

[0050] 1. Health monitoring

[0051] Device:

[0052] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[0053] camera:

[0054] Capture footage of your pet relaxing in its dedicated resting area.

[0055] server:

[0056] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[0057] User:

[0058] All you need to do is check the app to see how relaxed your pet is.

[0059] 2. Anomaly detection and response

[0060] Device:

[0061] Measure your pet's abnormally high heart rate and activity level.

[0062] camera:

[0063] Capture footage of your pet moving erratically around the house.

[0064] server:

[0065] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[0066] IoT devices:

[0067] The air conditioner turns on and adjusts to the desired temperature.

[0068] User:

[0069] The app will alert you to any abnormalities and provide detailed information about your pet's condition, allowing you to take action if necessary, such as returning home immediately.

[0070] 3. Continuous learning

[0071] server:

[0072] Continuously collected data is used to train machine learning models to improve analysis and anomaly detection, allowing for better responses in the future.

[0073] This system uses advanced technology to ensure the health and safety of pets, allowing owners to monitor their pet's condition in real time and automatically take necessary measures, allowing them to leave their pets with peace of mind even when they are not at home.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[0077] Step 2:

[0078] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[0079] Step 3:

[0080] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[0081] Step 4:

[0082] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[0083] Step 5:

[0084] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[0085] Step 6:

[0086] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[0087] Step 7:

[0088] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[0089] Step 8:

[0090] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[0091] Step 9:

[0092] The server generates an alert and sends it to the owner's smartphone app to notify them.

[0093] Step 10:

[0094] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[0095] Step 11:

[0096] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[0097] Step 12:

[0098] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[0099] Step 13:

[0100] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[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] In recent years, the importance of pet health management has increased, creating a growing need for real-time monitoring of pet health and behavior and early detection of abnormalities. However, existing technologies offer few systems that comprehensively monitor pets' biometric information and behavior and automatically implement countermeasures, which poses the problem of being unable to quickly respond to abnormalities in pets when their owners are away. Furthermore, there is a lack of technology to improve the accuracy of analyzing pet behavior patterns and detecting abnormalities. Therefore, there is a need for the development of more effective monitoring systems to ensure the health and safety of pets.

[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 analyzing received data and evaluating the health and behavior of the animals, means for generating and transmitting commands to operate the control device as necessary based on the evaluation results, means for sending a notification to an administrator if an abnormality is detected, means for detecting an abnormality by comparing with past data, means for storing multiple pieces of data transmitted from the device in a database, and means for continuously collecting data and training a learning model. This makes it possible to comprehensively monitor the health and behavior of animals in real time, detect abnormalities early, and automatically take appropriate measures.

[0106] "Device" refers to a biometric terminal attached to an animal, which is hardware used to measure heart rate, body temperature, and activity level.

[0107] "Processing Unit" refers to the server used to analyze the received data and assess the health and behavior of the animals.

[0108] "Control device" refers to an IoT device that operates based on commands generated by a server, and includes devices such as air conditioners and feeders.

[0109] A "command" is a command generated and transmitted by the server, and includes specific instructions for operating the control device.

[0110] "Administrator" refers to the user of the system, typically the pet owner.

[0111] "Database" refers to a storage device for storing received biometric data and video data.

[0112] "Learning model" refers to a machine learning algorithm that is trained on continuously collected data to improve the accuracy of analysis and anomaly detection.

[0113] "Abnormality" refers to a condition detected when abnormalities are found in health conditions or behavioral patterns compared with past data.

[0114] The present invention is a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. This system mainly consists of the following components.

[0115] 1. Biometric Information Terminal

[0116] Terminal: The biometric information terminal is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is stored in temporary memory and sent to a server at regular intervals (for example, every minute). The terminal uses various sensor devices to accurately collect this information.

[0117] Example: For example, if your pet's heart rate is 80 beats per minute, its body temperature is 37 degrees, and its activity level is 5 (out of 10), these data will be measured and sent to the server.

[0118] 2. Surveillance Camera

[0119] Surveillance camera: A surveillance camera is a device that captures video data of your pet in real time and sends it to a server as streaming data. This camera can rotate 360 ​​degrees and automatically track your pet's position.

[0120] Example: A pet taking a nap in the living room is captured on camera and transmitted to a server in real time.

[0121] 3. Server

[0122] Server: The server is used to receive, store, and analyze data sent from the devices and cameras. Software used includes database management systems and machine learning algorithms.

[0123] Data analysis: Analyze the received vital data and assess the pet's health status.

[0124] Behavioral pattern analysis: Analyze your pet's behavioral patterns based on video data.

[0125] Anomaly detection: Detect anomalies by comparing with past data.

[0126] Command generation: If an abnormality is detected, an appropriate control command is generated and sent to the IoT device.

[0127] Data storage: The received data is stored in a database with a timestamp.

[0128] Machine learning: Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[0129] Example: If data and video data are received showing a heart rate of 80, body temperature of 37 degrees, and activity level of 5, and based on this the pet's health condition is assessed as normal, the data will be stored in the database and no special action will be required.

[0130] 4. IoT devices

[0131] IoT device: An IoT device is a device that receives commands generated by a server and executes the specified control action (e.g., adjusting the temperature of an air conditioner, activating a feeder, etc.).

[0132] Example: For example, if a pet's heart rate is abnormally high and its activity level is also abnormally high, a command to turn on the air conditioner and lower the room temperature will be generated, and the air conditioner will turn on and adjust to the set temperature.

[0133] 5. Smartphone App

[0134] Users can receive alerts from the server via a smartphone app and monitor their pet's condition in real time. They can also use the app to remotely control IoT devices.

[0135] Example: The owner receives an alert and can view footage of their pet or manually change the air conditioning settings through the app.

[0136] Prompt Sentence Examples

[0137] "Please explain what alerts will be generated if an abnormality is detected based on the latest data collection results from this pet health monitoring system."

[0138] By inputting such prompt statements into the generative AI model, it is possible to provide detailed information such as what specific alerts will be sent when an abnormality is detected and what countermeasures will be taken.

[0139] The above is an embodiment of the present invention.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] Device: Measures heart rate, body temperature, and activity levels. The biometric device attached to your pet uses various built-in sensors to measure these health indicators.

[0143] Input: Your pet's current vitals.

[0144] Output: Measured heart rate, body temperature, and activity level data.

[0145] Specific actions: For example, your pet's heart rate is measured at 80 beats per minute, body temperature is measured at 37 degrees, and activity level is measured at 5 (out of 10).

[0146] Step 2:

[0147] Terminal: Sends measurement data to the server. The acquired data is encrypted and then sent to the server via Wi-Fi.

[0148] Input: Measured heart rate, temperature, and activity level data.

[0149] Output: Encrypted biometric data sent to the server.

[0150] Specific operation: Data on the pet's heart rate (80), body temperature (37 degrees), and activity level (5) are encrypted and sent to the server.

[0151] Step 3:

[0152] Device: The monitoring camera captures video of your pet in real time. The camera rotates 360 degrees and automatically tracks your pet's position.

[0153] Input: Pet movements captured on surveillance camera.

[0154] Output: Real-time captured video data.

[0155] Specific operation: The camera captures a pet taking a nap in the living room, and the image is then used as video data.

[0156] Step 4:

[0157] Terminal: Captured video data is sent to the server in streaming format. Compressed video data is sent.

[0158] Input: Video data captured in real time.

[0159] Output: Compressed video data sent to the server.

[0160] Specific operation: The captured video (of a pet taking a nap in the living room) is compressed in MPEG format and sent to a server via Wi-Fi.

[0161] Step 5:

[0162] Server: Receives biometric and video data and stores them in a database. Received data is recorded with a timestamp.

[0163] Input: Transmitted biometric and video data.

[0164] Output: Biometric and video data stored in a database.

[0165] Specific operation: Data such as a heart rate of 80, body temperature of 37 degrees, and activity level of 5, as well as footage of a napping pet, are stored in a database.

[0166] Step 6:

[0167] Server: Analyzes the received vital data and evaluates the pet's health condition. It compares it with the normal range and checks for any abnormalities.

[0168] Input: Stored biometric data.

[0169] Output: Evaluation result (normal or abnormal).

[0170] Specific operation: Confirm that the heart rate of 80 is within the normal range (60-100) and the body temperature of 37 degrees is within the normal range (36-39 degrees), and evaluate the health condition as normal.

[0171] Step 7:

[0172] Server: Analyzes the video data and evaluates the pet's behavioral patterns. It analyzes behavior based on the frequency of movement and changes in position.

[0173] Input: Stored video data.

[0174] Output: Behavioral pattern evaluation results.

[0175] Specific behavior: Confirms that a napping pet has been in the same place for more than an hour and determines that it is in a relaxed state.

[0176] Step 8:

[0177] Server: Compares biometric data and behavioral patterns with past data to detect anomalies. If anomalies are found, it determines the appropriate course of action.

[0178] Input: Assessment results and historical data.

[0179] Output: Anomaly detection results and countermeasures.

[0180] Specific operation: If the relative activity level is 10 (the previous maximum activity level is 8) and the heart rate is 120 (the previous maximum heart rate is 100), it is determined to be abnormal.

[0181] Step 9:

[0182] Server: When an abnormality is detected, it generates and sends control commands to the appropriate IoT devices.

[0183] Input: Anomaly detection results.

[0184] Output: Control command.

[0185] Specific operation: Because the room temperature is high, a command to operate the air conditioner is generated and sent to the IoT device.

[0186] Step 10:

[0187] IoT device: Receives commands from the server and performs the specified control action.

[0188] Input: Control command.

[0189] Output: The control action that was performed.

[0190] Specific action: The air conditioner is turned on and the room temperature is set to 24 degrees.

[0191] Step 11:

[0192] User: Monitors pet status in real time using a smartphone app and receives alerts sent from the server.

[0193] Input: Alert from the server.

[0194] Output: Alert and pet information displayed in the app.

[0195] Specific behavior: The app will receive a push notification indicating an abnormality has occurred and will display your pet's detailed information (heart rate 120, activity level 10).

[0196] Step 12:

[0197] User: Use the app to remotely control IoT devices as needed.

[0198] Input: User instructions.

[0199] Output: The control action that was performed.

[0200] Specific actions: For example, manually change the air conditioner temperature setting from 24 degrees to 22 degrees.

[0201] Step 13:

[0202] Server: Continuously collects data and trains machine learning models to improve analysis and anomaly detection.

[0203] Input: Continuously collected biometric and behavioral data.

[0204] Output: An improved machine learning model.

[0205] What it does: The server periodically learns from the data and understands new abnormal patterns.

[0206] (Application example 1)

[0207] 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."

[0208] Conventional pet monitoring systems can monitor pets' health and behavior in real time, but their application is limited to the home. Systems are needed to ensure that pets can live in a safe and comfortable environment even when out and about or on the move. In particular, when using autonomous vehicles, controlling the in-car environment can have a significant impact on pet health. However, currently, there are no systems that manage pet conditions in conjunction with the autonomous vehicle's environmental control. Therefore, there is a need for a system that can monitor pet health in real time while traveling and take appropriate measures if an abnormality is detected.

[0209] 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.

[0210] In this invention, the server includes means for measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet, means for transmitting the measured data to the server, means for the server to analyze the received data and evaluate the pet's health condition and behavior, means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results, means for sending an alert to the owner if an abnormality is detected, and means for enabling cooperation with the control device of the autonomous vehicle and controlling the environment inside the vehicle. This makes it possible to monitor the pet's health condition and behavior in real time and to appropriately control the environment inside the autonomous vehicle if an abnormality occurs.

[0211] A "biometric information terminal" is a device that is attached to a pet and measures biometric data such as heart rate, body temperature, and activity level.

[0212] The "means for transmitting data" is a device or function that transmits measured data from the biometric information terminal to the server.

[0213] The "server" is a central data processing unit that receives and analyzes pet biometric and video data to assess the pet's health and behavior.

[0214] An "IoT device" is a device that has the ability to communicate with other devices via the Internet and perform control operations based on instructions.

[0215] "Means for sending an alert" refers to a device or function that sends a warning notice to the owner when an abnormality is detected.

[0216] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without driver operation.

[0217] "Means for environmental control" refers to a device or function for adjusting the internal environment of an autonomous vehicle (e.g., adjusting the temperature of the air conditioner).

[0218] "Means for capturing video data" refers to a device or function that uses a camera to capture video of the pet in real time and transmits the data to a server.

[0219] "Continuously collected data" is a general term for biometric information and behavioral data acquired continuously over a certain period of time.

[0220] A "machine learning model" refers to an algorithm or method for analyzing and learning about a pet's behavioral patterns and health condition.

[0221] An "autonomous vehicle control device" is a central control device for managing the driving and environmental settings of an autonomous vehicle.

[0222] The present invention relates to a system that monitors the health and behavior of pets in real time in cooperation with an autonomous vehicle and automatically takes necessary measures. This system mainly consists of the following components.

[0223] System configuration

[0224] 1. Biometric Information Terminal

[0225] The biometric information terminal is attached to the pet and measures vital data such as heart rate, body temperature, and activity level, and the measured data is sent to a server at regular intervals.

[0226] 2. Surveillance Camera

[0227] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[0228] 3. Server

[0229] The server receives, stores, and analyzes the data sent from the biometric terminal and the surveillance camera. Specifically, it performs the following operations:

[0230] Analyze biological data to assess your pet's health.

[0231] Analyze your pet's behavior patterns based on video data.

[0232] Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[0233] If an abnormality is detected, an alert will be sent to the owner.

[0234] It works in conjunction with the control device of the autonomous vehicle to control the in-car environment (such as adjusting the air conditioning temperature).

[0235] 4. IoT devices

[0236] It receives commands generated from the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner).

[0237] 5. Smartphone App

[0238] Using this app, owners can monitor their pets' status in real time, receive alerts when something abnormal occurs, and remotely control IoT devices as needed.

[0239] Example of a system

[0240] First, let's say the vital signs terminal measures a pet's heart rate at 120 bpm, body temperature at 38.5 °C, and activity level at 8. This data is sent to a server, which analyzes it in real time. The server determines from the data that the pet is stressed. The server then sends a command to activate the air conditioning in the autonomous vehicle and appropriately lower the temperature inside the vehicle. An alert is then sent to the owner via a smartphone app to notify them of the situation.

[0241] Prompt Sentence Examples

[0242] Build a system that monitors your pet's health and adjusts the car's air conditioning if an abnormal condition is detected. Specifically, data including the following parameters will be sent to a server: heart rate (bpm), body temperature (°C), and activity level. Based on the results of data analysis, appropriate action will be taken, such as adjusting the air conditioning temperature or stopping the car.

[0243] Thus, the present invention is a system for ensuring the health and safety of pets, and also provides an environment in which owners can travel with their pets with peace of mind.

[0244] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0245] Step 1:

[0246] Measurement and transmission of vital signs:

[0247] The terminal measures the heart rate, body temperature, and activity level every minute from the biometric information terminal attached to the pet. These measurement data are sent to the server as biometric data. It receives biometric information (heart rate, body temperature, activity level) as input and performs measurements. It generates the measured data as output and sends it to the server.

[0248] Step 2:

[0249] Video data capture and transmission:

[0250] The surveillance camera captures video of your pet in real time and sends the video data to the server in streaming format. The input is continuous video capture, and the output is captured video data that is sent to the server.

[0251] Step 3:

[0252] Data reception and storage:

[0253] The server receives the biometric data sent from the device and the video data sent from the surveillance camera and stores the data in a temporary database. The server receives the data sent as input and stores it in the database. The server uses the stored data as output in subsequent analysis steps.

[0254] Step 4:

[0255] Data analysis:

[0256] The server analyzes the received biometric data and video data to evaluate the pet's health and behavior. A pre-trained machine learning model is used for the analysis. The stored data is read as input and the machine learning model is applied. The server generates an evaluation result as output and determines whether or not there are any abnormalities.

[0257] Step 5:

[0258] Anomaly detection and response command generation:

[0259] If the server detects an abnormality based on the analysis results, it generates a response command. If a specific abnormality (e.g., stress due to high temperature) is detected, it generates a command to adjust the air conditioner temperature. It uses the evaluation results as input to determine whether an abnormality exists. It generates a response command as output and saves the response command.

[0260] Step 6:

[0261] Integration with IoT devices:

[0262] The server sends the generated corresponding command to the IoT device to execute a remote control action (e.g., adjusting the temperature of an air conditioner). The server uses the generated corresponding command as input and sends it to the IoT device. The IoT device executes the action as output.

[0263] Step 7:

[0264] Sending alerts:

[0265] When an abnormality is detected and a response is taken, the server sends the details as an alert to the owner's smartphone app. The server receives the execution result of the response command as input and generates an alert. The server sends a notification to the owner as output.

[0266] Step 8:

[0267] Continuous learning:

[0268] The server continuously trains the machine learning model using continuously collected biometric and video data. It receives the accumulated data as input and trains the model. As output, it generates a machine learning model with improved accuracy.

[0269] By linking these steps together, a series of systems are created that monitors the health of pets in real time and appropriately controls the environment of the self-driving vehicle.

[0270] 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.

[0271] This invention combines a system that monitors the health and behavior of pets in real time and automatically takes necessary measures with an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[0272] 1. Biometric Information Terminal

[0273] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[0274] 2. Surveillance Camera

[0275] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[0276] 3. Server

[0277] The server receives, stores, and analyzes data sent from the device and camera. It uses the analysis to evaluate the pet's health and behavior, and sends an alert to the owner if an abnormality is detected. It also generates and sends commands to control IoT devices. Furthermore, it uses an emotion engine to analyze the user's emotional state and adjust the content and method of the alert.

[0278] 4. Emotion Engine

[0279] The emotion engine recognizes the user's emotions and evaluates the user's stress level in response to abnormal notifications or pet conditions. The emotion engine can also learn from the user's past emotional data and suggest more appropriate responses.

[0280] 5. IoT devices

[0281] This is a device that receives commands generated by the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner, activating a feeder, etc.).

[0282] 6. Smartphone App

[0283] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[0284] System operation example

[0285] Example 1: Health monitoring

[0286] Device:

[0287] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[0288] camera:

[0289] Capture footage of your pet relaxing in its dedicated resting area.

[0290] server:

[0291] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[0292] Emotion Engine:

[0293] Determine that the user's stress level is low and decide that there is no need to send an alert.

[0294] User:

[0295] Use the app to check how relaxed your pet is and feel reassured.

[0296] Example 2: Anomaly detection and response

[0297] Device:

[0298] Measure your pet's abnormally high heart rate and activity level.

[0299] camera:

[0300] Capture footage of your pet moving erratically around the house.

[0301] server:

[0302] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[0303] Emotion Engine:

[0304] If the system analyzes the user's emotional state and determines that the user is in a state of high stress, it generates an emotionally sensitive alert notification, such as sending an alert urging the user to return home quickly.

[0305] IoT devices:

[0306] The air conditioner turns on and adjusts to the desired temperature.

[0307] User:

[0308] Receive alerts in the app when something unusual happens, get detailed information about your pet's condition, and take appropriate action based on the response suggested by the emotion engine.

[0309] Example 3: Continuous learning

[0310] server:

[0311] Train machine learning models on continuously collected data to improve the accuracy of analysis and anomaly detection.

[0312] Emotion Engine:

[0313] The system learns the user's past emotional data and can respond more appropriately when the user's emotional pattern differs from the normal pattern.

[0314] User:

[0315] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[0316] This system uses advanced technology to ensure the health and safety of pets, allowing it to monitor their condition in real time and automatically take necessary measures. Furthermore, by taking the user's emotions into consideration, it can respond more appropriately and effectively, giving owners a greater sense of security.

[0317] The processing flow will be explained below.

[0318] Step 1:

[0319] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[0320] Step 2:

[0321] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[0322] Step 3:

[0323] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[0324] Step 4:

[0325] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[0326] Step 5:

[0327] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[0328] Step 6:

[0329] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[0330] Step 7:

[0331] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[0332] Step 8:

[0333] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[0334] Step 9:

[0335] The server generates an alert and sends it to the owner's smartphone app to notify them.

[0336] Step 10:

[0337] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[0338] Step 11:

[0339] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[0340] Step 12:

[0341] The server uses an emotion engine to analyze the user's emotion data, which evaluates whether the user is currently relaxed or stressed.

[0342] Step 13:

[0343] The server adjusts the content and format of the alert notification based on the analysis results of the emotion engine. For example, if the user is under stress, it uses gentle and reassuring language.

[0344] Step 14:

[0345] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[0346] Step 15:

[0347] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[0348] Example 2

[0349] 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."

[0350] Systems that monitor pet health and behavior in real time and automatically take necessary measures are important. However, conventional systems often fail to consider the owner's emotions or stress level when detecting an abnormality, making owners anxious. Furthermore, data analysis accuracy is insufficient, potentially delaying appropriate responses. Furthermore, limited remote control options mean owners are sometimes unable to respond quickly. There is a need to resolve these issues and provide a more reliable and effective pet monitoring system.

[0351] 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.

[0352] In this invention, the server includes: means for measuring the heart rate, body temperature, and activity level from a biometric information terminal worn by the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means including an emotion engine for evaluating the user's emotional data and adjusting the alert and notification method; means for the server to continuously analyze the data based on a machine learning model to improve accuracy; and means for the owner to receive alerts via a smartphone app and remotely check and control the pet's condition. This allows for a response that takes the user's emotions into consideration when an abnormality is detected, allowing the owner to respond quickly and with peace of mind. Furthermore, the improved accuracy of data analysis allows for more accurate monitoring of the pet's health condition and behavior. Furthermore, remote control using a smartphone app allows owners to monitor their pet's condition from anywhere and take appropriate action in emergencies.

[0353] A "biometric information terminal" is a device that is attached to a pet to measure biometric data such as heart rate, body temperature, and activity level.

[0354] The "measuring means" is a means for measuring heart rate, body temperature, and activity level using a biometric information terminal.

[0355] The "transmission means" is a means for transmitting the measured data to the server.

[0356] The "server" is a central processing unit that receives, stores, and analyzes data sent from the terminals and cameras, and generates and sends necessary commands based on the evaluation results.

[0357] "Analysis means" refers to means for analyzing the received data and assessing the health and behavior of the pet.

[0358] The "assessment means" is a means for assessing the health and behavior of a pet based on the analyzed data.

[0359] The "control means" is a means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results.

[0360] The "alert sending means" is a means for sending an alert to the owner when an abnormality is detected.

[0361] The "emotion engine" is an engine that evaluates the user's emotional data and adjusts alerts and notification methods.

[0362] A "machine learning model" is a model that the server trains using continuously collected data to improve the accuracy of analysis and anomaly detection.

[0363] The "smartphone app" is an application that allows owners to receive alerts and remotely check and control their pet's condition.

[0364] This invention is a system that monitors the health condition and behavior of pets in real time and automatically takes necessary measures. This system includes a biometric information terminal that acquires the pet's biometric information, a monitoring camera that captures the pet's video, a server that receives and analyzes the data, an emotion engine that analyzes the user's emotions, IoT devices, and a smartphone app used by pet owners.

[0365] Biometric information terminal

[0366] The device is attached to the pet and has built-in heart rate, body temperature, and activity level sensors, which measure the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute).

[0367] Surveillance camera

[0368] The camera captures images of your pet in real time and sends them to a server as streaming data, allowing you to constantly monitor your pet's behavior and environment.

[0369] server

[0370] The server receives data sent from the device and camera and stores it in a database. The received data is analyzed using machine learning models and statistical methods. The server detects abnormalities when a pet's heart rate or body temperature is outside of normal ranges or when its activity level is abnormally high. When an abnormality is detected, the server determines the necessary countermeasures based on the evaluation results and generates commands to control the IoT device.

[0371] Emotion Engine

[0372] The emotion engine has the ability to evaluate the user's emotional data. It learns from past emotional data and adjusts the alert content and notification method. For example, if the user is in a stressful state, it generates an alert that prompts a prompt response.

[0373] IoT equipment

[0374] The IoT device receives commands sent from the server and executes the specified control action. For example, if an abnormality is detected, the device can activate the air conditioner to lower the room temperature.

[0375] Smartphone app

[0376] Users can receive alerts sent from the server via a smartphone app and check the status of their pets. If necessary, they can also remotely control IoT devices through the app.

[0377] Specific examples

[0378] Example 1: Health monitoring

[0379] Device: Measure your pet's heart rate to 80 bpm, body temperature to 38°C, and activity level to ensure it is calm.

[0380] Camera: Capture footage of your pet relaxing in its dedicated resting area.

[0381] Server: Analyzes the received data and determines that the pet is healthy and relaxed. If no action is required, saves the data in a database.

[0382] Emotion engine: Determines that the user's stress level is low and no alert needs to be sent.

[0383] Users: Use the app to see their pets relaxing and feel reassured.

[0384] Prompt for generative AI model: "Describe the results of monitoring your pet's heart rate and body temperature when they are within normal ranges and relaxed."

[0385] Example 2: Anomaly detection and response

[0386] Device: Pet's heart rate is measured at 150 bpm, body temperature at 39.5°C, and activity level is abnormally high.

[0387] Camera: Capture footage of your pet moving erratically around the house.

[0388] Server: Determines through analysis that the pet is stressed or affected by high temperatures. Generates and sends a command to turn on the air conditioner and lower the room temperature.

[0389] Emotion engine: Analyzes the user's emotional state and generates an alert urging them to return home quickly if it determines that they are in a state of high stress.

[0390] IoT device: The air conditioner turns on and adjusts to the set temperature.

[0391] User: Receives an alert in the app about an abnormality, checks video footage and detailed information about the pet, and takes appropriate action based on the response suggested by the emotion engine.

[0392] Prompt for generative AI model: "Describe the process for detecting abnormal behavior and health conditions in pets and taking necessary action."

[0393] Example 3: Continuous learning

[0394] Server: Trains machine learning models based on continuously collected data to improve analysis accuracy and anomaly detection.

[0395] Emotion engine: Learns from the user's past emotional data and responds appropriately when their emotional patterns deviate from normal ones.

[0396] Users can check their pet's current condition and past activity records using a smartphone app. If an abnormality is detected, they can take prompt action based on the information provided by the app.

[0397] Prompt for generative AI model: "Describe the process by which the system continuously learns from pet health data and user emotion data to improve its accuracy."

[0398] In this way, the present invention uses advanced technology to ensure the health and safety of pets, allowing for real-time monitoring of pet conditions and automatic implementation of necessary measures. Furthermore, by taking into account the user's emotions, more appropriate and reassuring responses are possible.

[0399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] The device measures the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute) using the heart rate sensor, body temperature sensor, and activity level sensor attached to the pet. The measurement data is assumed to be a stable heart rate of 80 bpm, body temperature of 38°C, and activity level. The measurement data is saved in a buffer along with the pet ID and a timestamp.

[0402] Input: Biometric data from sensors

[0403] Output: Measurement data (heart rate, body temperature, activity level)

[0404] Step 2:

[0405] The device transmits the measured data, including the pet's ID, timestamp, heart rate, body temperature, and activity level, to the server at regular intervals. This transmitted data reaches the server via the network.

[0406] Input: Measurement data (heart rate, body temperature, activity level)

[0407] Output: Data sent to the server

[0408] Step 3:

[0409] The camera captures video data of your pet in real time—for example, a video of your pet relaxing in a designated resting area—and sends the captured video data to a server in streaming format.

[0410] Input: Real-time video data

[0411] Output: Video data sent to the server

[0412] Step 4:

[0413] The server receives the biometric data sent from the device and the video data from the camera and stores them in a database. The received data is associated with the pet ID and a timestamp.

[0414] Input: Biometric data and video data from the device

[0415] Output: Data stored in the database

[0416] Step 5:

[0417] The server analyzes the stored data using statistical methods and machine learning models. For example, it evaluates whether the pet's heart rate and body temperature are within healthy ranges and detects whether its activity level is unusual. Based on the results of this analysis, it determines whether the pet's condition is normal or abnormal.

[0418] Input: Stored biometric and video data

[0419] Output: Analysis and evaluation results (health status, behavioral status)

[0420] Step 6:

[0421] The server generates commands to control IoT devices as needed based on the analysis results. For example, if a pet's heart rate is high and the room temperature is high, it generates a command to turn on the air conditioner. This command is sent to the IoT device.

[0422] Input: Analysis results

[0423] Output: IoT device control command

[0424] Step 7:

[0425] If an abnormality is detected, the server sends an alert to the owner. At this time, the emotion engine adjusts the alert content and notification method based on the user's past emotional data. For example, if the user is stressed, it generates an alert that emphasizes urgency.

[0426] Input: Anomaly detection results and user emotion data

[0427] Output: User alert

[0428] Step 8:

[0429] The IoT device executes the commands received from the server, for example, turning on the air conditioner and adjusting the room temperature to the set value, thereby maintaining a comfortable environment for the pet.

[0430] Input: Control command from the server

[0431] Output: IoT device operation (e.g., air conditioner operation)

[0432] Step 9:

[0433] Users can receive alerts and check their pet's condition through a smartphone app, and if necessary, can remotely control IoT devices through the app to quickly adjust the temperature or take other actions.

[0434] Input: User alert from server

[0435] Output: User action (e.g. remote control, confirmation)

[0436] In this way, through the specific actions performed at each step and the chain of inputs and outputs, the system can monitor the pet's health in real time and automatically take necessary measures.

[0437] (Application example 2)

[0438] 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."

[0439] There is a need to monitor pet health and behavior in real time and take prompt and appropriate measures when abnormalities are detected. It is also important to reduce stress and increase peace of mind for pet owners by providing notification methods that take into consideration the owner's feelings regarding their pet's condition. Current systems do not comprehensively cover these elements, so a system that solves these issues is needed.

[0440] The identification process by the identification 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 measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means for analyzing the user's emotional state and adjusting the content and method of the notification; and means for automatically controlling environmental control devices such as air conditioners and feeders when an abnormality is detected in the pet's health condition or behavior. This makes it possible to monitor the pet's health condition and behavior in real time and take prompt and appropriate measures when an abnormality occurs, reducing the owner's stress and providing a sense of security through a notification method that takes the user's emotions into consideration.

[0441] A "biometric terminal" is a device worn by a pet to measure its heart rate, body temperature, and activity level.

[0442] A "server" is a device that receives and analyzes data sent from biometric information terminals and surveillance cameras, and controls IoT devices as necessary.

[0443] "Analysis" is the process of evaluating the pet's health and behavior based on the data received by the server.

[0444] An "IoT device" is a device that receives commands from a server and performs control operations, and includes air conditioners and bird feeders.

[0445] The "emotion engine" is a system that analyzes the user's emotional state and adjusts the content and method of notifications.

[0446] An "air conditioner" is an environmental control device for adjusting the temperature inside a room.

[0447] A "feeder" is an automatic device for providing food to pets.

[0448] "Notifications" are alerts or messages sent to owners when an abnormality is detected.

[0449] "Real-time" refers to data acquisition and analysis occurring immediately, without delay.

[0450] A "machine learning model" is an algorithm that uses continuously collected data to improve the accuracy of analysis and anomaly detection.

[0451] "User" refers to the owner who uses the system to monitor the health and behavior of their pet.

[0452] System Configuration

[0453] The invention requires the following components:

[0454] Bio-information terminal: Attached to your pet, it measures heart rate, body temperature, and activity level.

[0455] Surveillance camera: Captures video data of your pet in real time and sends it to a server.

[0456] Server: Analyzes the received data, evaluates the pet's health and behavior, and generates and sends the necessary commands.

[0457] Emotion engine: Analyzes the user's emotional state and adjusts the content and method of notifications.

[0458] IoT devices: Control air conditioners, feeders, etc. according to instructions from the server.

[0459] Smartphone app: A way for owners to monitor their pet's condition and receive alerts if anything unusual happens.

[0460] Process Overview

[0461] The server receives data sent from the vital signs terminal and monitoring camera, analyzes it, and evaluates the pet's health and behavior. If an abnormality is detected, the server generates and sends commands to control the IoT device and sends appropriate alerts to the owner.

[0462] The emotion engine analyzes the owner's emotional state and adjusts the content and method of notifications. For example, if the owner is stressed, the system will adjust to send faster and clearer alerts.

[0463] Hardware and Software

[0464] The hardware and software used to implement this system includes:

[0465] Hardware:

[0466] Biometric information terminals (e.g. smart bands for pets)

[0467] Surveillance cameras (e.g. network cameras)

[0468] Air conditioner (IoT-compatible smart air conditioner)

[0469] Feeder (IoT-enabled automatic feeder)

[0470] software:

[0471] Servers for data analysis (e.g., database servers and analysis servers)

[0472] Sentiment engines for sentiment analysis (e.g., sentiment analysis software using machine learning models)

[0473] Smartphone app (monitoring and alarm app for owners)

[0474] Specific examples of processing

[0475] For example, if your pet is exhibiting abnormal behavior in the living room (heart rate: 130, body temperature: 40, activity level: 85) and you are feeling stressed, the system will act as follows:

[0476] 1. The vital signs terminal measures the pet's abnormal heart rate, body temperature, and activity level and transmits the data to a server.

[0477] 2. The monitoring camera captures your pet's erratic movements in real time and sends streaming data to the server.

[0478] 3. The server analyzes this data and determines that the pet's condition is abnormal.

[0479] 4. The server sends a command to the IoT device (air conditioner) to set it to "cooling" and performs automatic control.

[0480] 5. The emotion engine analyzes the owner's emotional state and generates a strong alert message.

[0481] 6. Send a strong alert to owners via a smartphone app to return home quickly.

[0482] Prompt Sentence Examples

[0483] The system's behavior can be triggered by inputting a prompt like the following into the generative AI model:

[0484] Data required for a pet health monitoring program:

[0485] Health data (heart rate, body temperature, activity level)

[0486] User emotional state

[0487] Pet Health Data:

[0488] Heart rate: 130

[0489] Body temperature: 40 degrees

[0490] Activity level: 85

[0491] User emotional state:

[0492] stress

[0493] Expected system behavior:

[0494] 1. Analyze health data and detect abnormalities.

[0495] 2. Set the air conditioner to "cooling" and turn it on.

[0496] 3. Send a strong alert to the user to get home quickly.

[0497] In this way, it is possible to monitor the health and behavior of pets in real time, and to take prompt and appropriate action when an abnormality is detected.

[0498] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0499] Step 1:

[0500] The terminal measures the pet's vital signs (heart rate, body temperature, activity level). This data is collected in real time and sent to a server at regular intervals. The input is sensor data from the vital signs terminal, and the output is measurement data sent to the server. Specifically, the terminal's sensors collect various vital signs and send this data to the server via wireless communication.

[0501] Step 2:

[0502] The surveillance camera captures video of your pet in real time and sends it to a server as streaming data. The input is the video data captured by the surveillance camera, and the output is the data converted to digital format and sent to the server. The camera captures the video, digitizes the video signal, and sends it to the server over the network.

[0503] Step 3:

[0504] The server receives data sent from the biometric information terminal and the surveillance camera. The input is biometric information and video data, which are then stored in a database. Specifically, the server captures this data in real time using the data receiving module and stores it in the storage system.

[0505] Step 4:

[0506] The server analyzes the received data and evaluates the pet's health and behavior. The input is the stored biometric information and video data, and the output is the analysis results (health and behavior assessment). The data analysis module evaluates the data using machine learning algorithms and detects abnormalities.

[0507] Step 5:

[0508] Based on the analysis results, the server generates and sends commands to control IoT devices as needed. The input is the analysis results, and the output is the generated control commands. Specifically, it monitors the results of the analysis module, and if an abnormality is detected, it generates and sends appropriate commands to the air conditioner or feeder.

[0509] Step 6:

[0510] If an abnormality is detected, the server sends an alert to the owner. The input is the analysis result and the output of the emotion engine, and the output is an alert message. The notification module takes into account the analysis result of the emotion engine, generates an alert message to be sent to the owner, and notifies the smartphone app.

[0511] Step 7:

[0512] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications. The input is the user's emotional data, and the output is the intensity and format of the notification message. Specifically, the emotion engine uses machine learning to learn past emotional data, analyzes the user's current emotional state, and determines the optimal notification method.

[0513] Step 8:

[0514] IoT devices (air conditioners, feeders, etc.) receive commands from the server and execute the specified control actions. The input is the control command from the server, and the output is the executed control action. For example, the air conditioner adjusts the temperature, and the feeder dispenses food.

[0515] 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.

[0516] 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.

[0517] 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.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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."

[0531] The present invention relates to a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. The system mainly consists of the following components:

[0532] 1. Biometric Information Terminal

[0533] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[0534] 2. Surveillance Camera

[0535] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[0536] 3. Server

[0537] The server receives, stores, and analyzes the data sent from the device and camera. Specifically, it performs the following operations:

[0538] The received biometric data is analyzed to assess the pet's health condition.

[0539] The pet's behavior patterns are analyzed based on the received video data.

[0540] Compare with past data to detect anomalies.

[0541] If an abnormality is detected, an alert will be sent to the owner.

[0542] 4. IoT devices

[0543] This is a device that receives commands generated by the server and performs the specified control action (for example, adjusting the temperature of the air conditioner, activating the feeder, etc.).

[0544] 5. Smartphone App

[0545] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[0546] System operation example

[0547] 1. Health monitoring

[0548] Device:

[0549] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[0550] camera:

[0551] Capture footage of your pet relaxing in its dedicated resting area.

[0552] server:

[0553] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[0554] User:

[0555] All you need to do is check the app to see how relaxed your pet is.

[0556] 2. Anomaly detection and response

[0557] Device:

[0558] Measure your pet's abnormally high heart rate and activity level.

[0559] camera:

[0560] Capture footage of your pet moving erratically around the house.

[0561] server:

[0562] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[0563] IoT devices:

[0564] The air conditioner turns on and adjusts to the desired temperature.

[0565] User:

[0566] The app will alert you to any abnormalities and provide detailed information about your pet's condition, allowing you to take action if necessary, such as returning home immediately.

[0567] 3. Continuous learning

[0568] server:

[0569] Continuously collected data is used to train machine learning models to improve analysis and anomaly detection, allowing for better responses in the future.

[0570] This system uses advanced technology to ensure the health and safety of pets, allowing owners to monitor their pet's condition in real time and automatically take necessary measures, allowing them to leave their pets with peace of mind even when they are not at home.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[0574] Step 2:

[0575] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[0576] Step 3:

[0577] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[0578] Step 4:

[0579] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[0580] Step 5:

[0581] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[0582] Step 6:

[0583] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[0584] Step 7:

[0585] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[0586] Step 8:

[0587] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[0588] Step 9:

[0589] The server generates an alert and sends it to the owner's smartphone app to notify them.

[0590] Step 10:

[0591] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[0592] Step 11:

[0593] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[0594] Step 12:

[0595] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[0596] Step 13:

[0597] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[0598] Example 1

[0599] 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."

[0600] In recent years, the importance of pet health management has increased, creating a growing need for real-time monitoring of pet health and behavior and early detection of abnormalities. However, existing technologies offer few systems that comprehensively monitor pets' biometric information and behavior and automatically implement countermeasures, which poses the problem of being unable to quickly respond to abnormalities in pets when their owners are away. Furthermore, there is a lack of technology to improve the accuracy of analyzing pet behavior patterns and detecting abnormalities. Therefore, there is a need for the development of more effective monitoring systems to ensure the health and safety of pets.

[0601] 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.

[0602] In this invention, the server includes means for analyzing received data and evaluating the health and behavior of the animals, means for generating and transmitting commands to operate the control device as necessary based on the evaluation results, means for sending a notification to an administrator if an abnormality is detected, means for detecting an abnormality by comparing with past data, means for storing multiple pieces of data transmitted from the device in a database, and means for continuously collecting data and training a learning model. This makes it possible to comprehensively monitor the health and behavior of animals in real time, detect abnormalities early, and automatically take appropriate measures.

[0603] "Device" refers to a biometric terminal attached to an animal, which is hardware used to measure heart rate, body temperature, and activity level.

[0604] "Processing Unit" refers to the server used to analyze the received data and assess the health and behavior of the animals.

[0605] "Control device" refers to an IoT device that operates based on commands generated by a server, and includes devices such as air conditioners and feeders.

[0606] A "command" is a command generated and transmitted by the server, and includes specific instructions for operating the control device.

[0607] "Administrator" refers to the user of the system, typically the pet owner.

[0608] "Database" refers to a storage device for storing received biometric data and video data.

[0609] "Learning model" refers to a machine learning algorithm that is trained on continuously collected data to improve the accuracy of analysis and anomaly detection.

[0610] "Abnormality" refers to a condition detected when abnormalities are found in health conditions or behavioral patterns compared with past data.

[0611] The present invention is a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. This system mainly consists of the following components.

[0612] 1. Biometric Information Terminal

[0613] Terminal: The biometric information terminal is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is stored in temporary memory and sent to a server at regular intervals (for example, every minute). The terminal uses various sensor devices to accurately collect this information.

[0614] Example: For example, if your pet's heart rate is 80 beats per minute, its body temperature is 37 degrees, and its activity level is 5 (out of 10), these data will be measured and sent to the server.

[0615] 2. Surveillance Camera

[0616] Surveillance camera: A surveillance camera is a device that captures video data of your pet in real time and sends it to a server as streaming data. This camera can rotate 360 ​​degrees and automatically track your pet's position.

[0617] Example: A pet taking a nap in the living room is captured on camera and transmitted to a server in real time.

[0618] 3. Server

[0619] Server: The server is used to receive, store, and analyze data sent from the devices and cameras. Software used includes database management systems and machine learning algorithms.

[0620] Data analysis: Analyze the received vital data and assess the pet's health status.

[0621] Behavioral pattern analysis: Analyze your pet's behavioral patterns based on video data.

[0622] Anomaly detection: Detect anomalies by comparing with past data.

[0623] Command generation: If an abnormality is detected, an appropriate control command is generated and sent to the IoT device.

[0624] Data storage: The received data is stored in a database with a timestamp.

[0625] Machine learning: Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[0626] Example: If data and video data are received showing a heart rate of 80, body temperature of 37 degrees, and activity level of 5, and based on this the pet's health condition is assessed as normal, the data will be stored in the database and no special action will be required.

[0627] 4. IoT devices

[0628] IoT device: An IoT device is a device that receives commands generated by a server and executes the specified control action (e.g., adjusting the temperature of an air conditioner, activating a feeder, etc.).

[0629] Example: For example, if a pet's heart rate is abnormally high and its activity level is also abnormally high, a command to turn on the air conditioner and lower the room temperature will be generated, and the air conditioner will turn on and adjust to the set temperature.

[0630] 5. Smartphone App

[0631] Users can receive alerts from the server via a smartphone app and monitor their pet's condition in real time. They can also use the app to remotely control IoT devices.

[0632] Example: The owner receives an alert and can view footage of their pet or manually change the air conditioning settings through the app.

[0633] Prompt Sentence Examples

[0634] "Please explain what alerts will be generated if an abnormality is detected based on the latest data collection results from this pet health monitoring system."

[0635] By inputting such prompt statements into the generative AI model, it is possible to provide detailed information such as what specific alerts will be sent when an abnormality is detected and what countermeasures will be taken.

[0636] The above is an embodiment of the present invention.

[0637] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0638] Step 1:

[0639] Device: Measures heart rate, body temperature, and activity levels. The biometric device attached to your pet uses various built-in sensors to measure these health indicators.

[0640] Input: Your pet's current vitals.

[0641] Output: Measured heart rate, body temperature, and activity level data.

[0642] Specific actions: For example, your pet's heart rate is measured at 80 beats per minute, body temperature is measured at 37 degrees, and activity level is measured at 5 (out of 10).

[0643] Step 2:

[0644] Terminal: Sends measurement data to the server. The acquired data is encrypted and then sent to the server via Wi-Fi.

[0645] Input: Measured heart rate, temperature, and activity level data.

[0646] Output: Encrypted biometric data sent to the server.

[0647] Specific operation: Data on the pet's heart rate (80), body temperature (37 degrees), and activity level (5) are encrypted and sent to the server.

[0648] Step 3:

[0649] Device: The monitoring camera captures video of your pet in real time. The camera rotates 360 degrees and automatically tracks your pet's position.

[0650] Input: Pet movements captured on surveillance camera.

[0651] Output: Real-time captured video data.

[0652] Specific operation: The camera captures a pet taking a nap in the living room, and the image is then used as video data.

[0653] Step 4:

[0654] Terminal: Captured video data is sent to the server in streaming format. Compressed video data is sent.

[0655] Input: Video data captured in real time.

[0656] Output: Compressed video data sent to the server.

[0657] Specific operation: The captured video (of a pet taking a nap in the living room) is compressed in MPEG format and sent to a server via Wi-Fi.

[0658] Step 5:

[0659] Server: Receives biometric and video data and stores them in a database. Received data is recorded with a timestamp.

[0660] Input: Transmitted biometric and video data.

[0661] Output: Biometric and video data stored in a database.

[0662] Specific operation: Data such as a heart rate of 80, body temperature of 37 degrees, and activity level of 5, as well as footage of a napping pet, are stored in a database.

[0663] Step 6:

[0664] Server: Analyzes the received vital data and evaluates the pet's health condition. It compares it with the normal range and checks for any abnormalities.

[0665] Input: Stored biometric data.

[0666] Output: Evaluation result (normal or abnormal).

[0667] Specific operation: Confirm that the heart rate of 80 is within the normal range (60-100) and the body temperature of 37 degrees is within the normal range (36-39 degrees), and evaluate the health condition as normal.

[0668] Step 7:

[0669] Server: Analyzes the video data and evaluates the pet's behavioral patterns. It analyzes behavior based on the frequency of movement and changes in position.

[0670] Input: Stored video data.

[0671] Output: Behavioral pattern evaluation results.

[0672] Specific behavior: Confirms that a napping pet has been in the same place for more than an hour and determines that it is in a relaxed state.

[0673] Step 8:

[0674] Server: Compares biometric data and behavioral patterns with past data to detect anomalies. If anomalies are found, it determines the appropriate course of action.

[0675] Input: Assessment results and historical data.

[0676] Output: Anomaly detection results and countermeasures.

[0677] Specific operation: If the relative activity level is 10 (the previous maximum activity level is 8) and the heart rate is 120 (the previous maximum heart rate is 100), it is determined to be abnormal.

[0678] Step 9:

[0679] Server: When an abnormality is detected, it generates and sends control commands to the appropriate IoT devices.

[0680] Input: Anomaly detection results.

[0681] Output: Control command.

[0682] Specific operation: Because the room temperature is high, a command to operate the air conditioner is generated and sent to the IoT device.

[0683] Step 10:

[0684] IoT device: Receives commands from the server and performs the specified control action.

[0685] Input: Control command.

[0686] Output: The control action that was performed.

[0687] Specific action: The air conditioner is turned on and the room temperature is set to 24 degrees.

[0688] Step 11:

[0689] User: Monitors pet status in real time using a smartphone app and receives alerts sent from the server.

[0690] Input: Alert from the server.

[0691] Output: Alert and pet information displayed in the app.

[0692] Specific behavior: The app will receive a push notification indicating an abnormality has occurred and will display your pet's detailed information (heart rate 120, activity level 10).

[0693] Step 12:

[0694] User: Use the app to remotely control IoT devices as needed.

[0695] Input: User instructions.

[0696] Output: The control action that was performed.

[0697] Specific actions: For example, manually change the air conditioner temperature setting from 24 degrees to 22 degrees.

[0698] Step 13:

[0699] Server: Continuously collects data and trains machine learning models to improve analysis and anomaly detection.

[0700] Input: Continuously collected biometric and behavioral data.

[0701] Output: An improved machine learning model.

[0702] What it does: The server periodically learns from the data and understands new abnormal patterns.

[0703] (Application example 1)

[0704] 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."

[0705] Conventional pet monitoring systems can monitor pets' health and behavior in real time, but their application is limited to the home. Systems are needed to ensure that pets can live in a safe and comfortable environment even when out and about or on the move. In particular, when using autonomous vehicles, controlling the in-car environment can have a significant impact on pet health. However, currently, there are no systems that manage pet conditions in conjunction with the autonomous vehicle's environmental control. Therefore, there is a need for a system that can monitor pet health in real time while traveling and take appropriate measures if an abnormality is detected.

[0706] 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.

[0707] In this invention, the server includes means for measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet, means for transmitting the measured data to the server, means for the server to analyze the received data and evaluate the pet's health condition and behavior, means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results, means for sending an alert to the owner if an abnormality is detected, and means for enabling cooperation with the control device of the autonomous vehicle and controlling the environment inside the vehicle. This makes it possible to monitor the pet's health condition and behavior in real time and to appropriately control the environment inside the autonomous vehicle if an abnormality occurs.

[0708] A "biometric information terminal" is a device that is attached to a pet and measures biometric data such as heart rate, body temperature, and activity level.

[0709] The "means for transmitting data" is a device or function that transmits measured data from the biometric information terminal to the server.

[0710] The "server" is a central data processing unit that receives and analyzes pet biometric and video data to assess the pet's health and behavior.

[0711] An "IoT device" is a device that has the ability to communicate with other devices via the Internet and perform control operations based on instructions.

[0712] "Means for sending an alert" refers to a device or function that sends a warning notice to the owner when an abnormality is detected.

[0713] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without driver operation.

[0714] "Means for environmental control" refers to a device or function for adjusting the internal environment of an autonomous vehicle (e.g., adjusting the temperature of the air conditioner).

[0715] "Means for capturing video data" refers to a device or function that uses a camera to capture video of the pet in real time and transmits the data to a server.

[0716] "Continuously collected data" is a general term for biometric information and behavioral data acquired continuously over a certain period of time.

[0717] A "machine learning model" refers to an algorithm or method for analyzing and learning about a pet's behavioral patterns and health condition.

[0718] An "autonomous vehicle control device" is a central control device for managing the driving and environmental settings of an autonomous vehicle.

[0719] The present invention relates to a system that monitors the health and behavior of pets in real time in cooperation with an autonomous vehicle and automatically takes necessary measures. This system mainly consists of the following components.

[0720] System configuration

[0721] 1. Biometric Information Terminal

[0722] The biometric information terminal is attached to the pet and measures vital data such as heart rate, body temperature, and activity level, and the measured data is sent to a server at regular intervals.

[0723] 2. Surveillance Camera

[0724] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[0725] 3. Server

[0726] The server receives, stores, and analyzes the data sent from the biometric terminal and the surveillance camera. Specifically, it performs the following operations:

[0727] Analyze biological data to assess your pet's health.

[0728] Analyze your pet's behavior patterns based on video data.

[0729] Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[0730] If an abnormality is detected, an alert will be sent to the owner.

[0731] It works in conjunction with the control device of the autonomous vehicle to control the in-car environment (such as adjusting the air conditioning temperature).

[0732] 4. IoT devices

[0733] It receives commands generated from the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner).

[0734] 5. Smartphone App

[0735] Using this app, owners can monitor their pets' status in real time, receive alerts when something abnormal occurs, and remotely control IoT devices as needed.

[0736] Example of a system

[0737] First, let's say the vital signs terminal measures a pet's heart rate at 120 bpm, body temperature at 38.5 °C, and activity level at 8. This data is sent to a server, which analyzes it in real time. The server determines from the data that the pet is stressed. The server then sends a command to activate the air conditioning in the autonomous vehicle and appropriately lower the temperature inside the vehicle. An alert is then sent to the owner via a smartphone app to notify them of the situation.

[0738] Prompt Sentence Examples

[0739] Build a system that monitors your pet's health and adjusts the car's air conditioning if an abnormal condition is detected. Specifically, data including the following parameters will be sent to a server: heart rate (bpm), body temperature (°C), and activity level. Based on the results of data analysis, appropriate action will be taken, such as adjusting the air conditioning temperature or stopping the car.

[0740] Thus, the present invention is a system for ensuring the health and safety of pets, and also provides an environment in which owners can travel with their pets with peace of mind.

[0741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0742] Step 1:

[0743] Measurement and transmission of vital signs:

[0744] The terminal measures the heart rate, body temperature, and activity level every minute from the biometric information terminal attached to the pet. These measurement data are sent to the server as biometric data. It receives biometric information (heart rate, body temperature, activity level) as input and performs measurements. It generates the measured data as output and sends it to the server.

[0745] Step 2:

[0746] Video data capture and transmission:

[0747] The surveillance camera captures video of your pet in real time and sends the video data to the server in streaming format. The input is continuous video capture, and the output is captured video data that is sent to the server.

[0748] Step 3:

[0749] Data reception and storage:

[0750] The server receives the biometric data sent from the device and the video data sent from the surveillance camera and stores the data in a temporary database. The server receives the data sent as input and stores it in the database. The server uses the stored data as output in subsequent analysis steps.

[0751] Step 4:

[0752] Data analysis:

[0753] The server analyzes the received biometric data and video data to evaluate the pet's health and behavior. A pre-trained machine learning model is used for the analysis. The stored data is read as input and the machine learning model is applied. The server generates an evaluation result as output and determines whether or not there are any abnormalities.

[0754] Step 5:

[0755] Anomaly detection and response command generation:

[0756] If the server detects an abnormality based on the analysis results, it generates a response command. If a specific abnormality (e.g., stress due to high temperature) is detected, it generates a command to adjust the air conditioner temperature. It uses the evaluation results as input to determine whether an abnormality exists. It generates a response command as output and saves the response command.

[0757] Step 6:

[0758] Integration with IoT devices:

[0759] The server sends the generated corresponding command to the IoT device to execute a remote control action (e.g., adjusting the temperature of an air conditioner). The server uses the generated corresponding command as input and sends it to the IoT device. The IoT device executes the action as output.

[0760] Step 7:

[0761] Sending alerts:

[0762] When an abnormality is detected and a response is taken, the server sends the details as an alert to the owner's smartphone app. The server receives the execution result of the response command as input and generates an alert. The server sends a notification to the owner as output.

[0763] Step 8:

[0764] Continuous learning:

[0765] The server continuously trains the machine learning model using continuously collected biometric and video data. It receives the accumulated data as input and trains the model. As output, it generates a machine learning model with improved accuracy.

[0766] By linking these steps together, a series of systems are created that monitors the health of pets in real time and appropriately controls the environment of the self-driving vehicle.

[0767] 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.

[0768] This invention combines a system that monitors the health and behavior of pets in real time and automatically takes necessary measures with an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[0769] 1. Biometric Information Terminal

[0770] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[0771] 2. Surveillance Camera

[0772] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[0773] 3. Server

[0774] The server receives, stores, and analyzes data sent from the device and camera. It uses the analysis to evaluate the pet's health and behavior, and sends an alert to the owner if an abnormality is detected. It also generates and sends commands to control IoT devices. Furthermore, it uses an emotion engine to analyze the user's emotional state and adjust the content and method of the alert.

[0775] 4. Emotion Engine

[0776] The emotion engine recognizes the user's emotions and evaluates the user's stress level in response to abnormal notifications or pet conditions. The emotion engine can also learn from the user's past emotional data and suggest more appropriate responses.

[0777] 5. IoT devices

[0778] This is a device that receives commands generated by the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner, activating a feeder, etc.).

[0779] 6. Smartphone App

[0780] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[0781] System operation example

[0782] Example 1: Health monitoring

[0783] Device:

[0784] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[0785] camera:

[0786] Capture footage of your pet relaxing in its dedicated resting area.

[0787] server:

[0788] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[0789] Emotion Engine:

[0790] Determine that the user's stress level is low and decide that there is no need to send an alert.

[0791] User:

[0792] Use the app to check how relaxed your pet is and feel reassured.

[0793] Example 2: Anomaly detection and response

[0794] Device:

[0795] Measure your pet's abnormally high heart rate and activity level.

[0796] camera:

[0797] Capture footage of your pet moving erratically around the house.

[0798] server:

[0799] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[0800] Emotion Engine:

[0801] If the system analyzes the user's emotional state and determines that the user is in a state of high stress, it generates an emotionally sensitive alert notification, such as sending an alert urging the user to return home quickly.

[0802] IoT devices:

[0803] The air conditioner turns on and adjusts to the desired temperature.

[0804] User:

[0805] Receive alerts in the app when something unusual happens, get detailed information about your pet's condition, and take appropriate action based on the response suggested by the emotion engine.

[0806] Example 3: Continuous learning

[0807] server:

[0808] Train machine learning models on continuously collected data to improve the accuracy of analysis and anomaly detection.

[0809] Emotion Engine:

[0810] The system learns the user's past emotional data and can respond more appropriately when the user's emotional pattern differs from the normal pattern.

[0811] User:

[0812] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[0813] This system uses advanced technology to ensure the health and safety of pets, allowing it to monitor their condition in real time and automatically take necessary measures. Furthermore, by taking the user's emotions into consideration, it can respond more appropriately and effectively, giving owners a greater sense of security.

[0814] The processing flow will be explained below.

[0815] Step 1:

[0816] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[0817] Step 2:

[0818] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[0819] Step 3:

[0820] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[0821] Step 4:

[0822] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[0823] Step 5:

[0824] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[0825] Step 6:

[0826] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[0827] Step 7:

[0828] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[0829] Step 8:

[0830] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[0831] Step 9:

[0832] The server generates an alert and sends it to the owner's smartphone app to notify them.

[0833] Step 10:

[0834] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[0835] Step 11:

[0836] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[0837] Step 12:

[0838] The server uses an emotion engine to analyze the user's emotion data, which evaluates whether the user is currently relaxed or stressed.

[0839] Step 13:

[0840] The server adjusts the content and format of the alert notification based on the analysis results of the emotion engine. For example, if the user is under stress, it uses gentle and reassuring language.

[0841] Step 14:

[0842] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[0843] Step 15:

[0844] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[0845] Example 2

[0846] 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."

[0847] Systems that monitor pet health and behavior in real time and automatically take necessary measures are important. However, conventional systems often fail to consider the owner's emotions or stress level when detecting an abnormality, making owners anxious. Furthermore, data analysis accuracy is insufficient, potentially delaying appropriate responses. Furthermore, limited remote control options mean owners are sometimes unable to respond quickly. There is a need to resolve these issues and provide a more reliable and effective pet monitoring system.

[0848] 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.

[0849] In this invention, the server includes: means for measuring the heart rate, body temperature, and activity level from a biometric information terminal worn by the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means including an emotion engine for evaluating the user's emotional data and adjusting the alert and notification method; means for the server to continuously analyze the data based on a machine learning model to improve accuracy; and means for the owner to receive alerts via a smartphone app and remotely check and control the pet's condition. This allows for a response that takes the user's emotions into consideration when an abnormality is detected, allowing the owner to respond quickly and with peace of mind. Furthermore, the improved accuracy of data analysis allows for more accurate monitoring of the pet's health condition and behavior. Furthermore, remote control using a smartphone app allows owners to monitor their pet's condition from anywhere and take appropriate action in emergencies.

[0850] A "biometric information terminal" is a device that is attached to a pet to measure biometric data such as heart rate, body temperature, and activity level.

[0851] The "measuring means" is a means for measuring heart rate, body temperature, and activity level using a biometric information terminal.

[0852] The "transmission means" is a means for transmitting the measured data to the server.

[0853] The "server" is a central processing unit that receives, stores, and analyzes data sent from the terminals and cameras, and generates and sends necessary commands based on the evaluation results.

[0854] "Analysis means" refers to means for analyzing the received data and assessing the health and behavior of the pet.

[0855] The "assessment means" is a means for assessing the health and behavior of a pet based on the analyzed data.

[0856] The "control means" is a means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results.

[0857] The "alert sending means" is a means for sending an alert to the owner when an abnormality is detected.

[0858] The "emotion engine" is an engine that evaluates the user's emotional data and adjusts alerts and notification methods.

[0859] A "machine learning model" is a model that the server trains using continuously collected data to improve the accuracy of analysis and anomaly detection.

[0860] The "smartphone app" is an application that allows owners to receive alerts and remotely check and control their pet's condition.

[0861] This invention is a system that monitors the health condition and behavior of pets in real time and automatically takes necessary measures. This system includes a biometric information terminal that acquires the pet's biometric information, a monitoring camera that captures the pet's video, a server that receives and analyzes the data, an emotion engine that analyzes the user's emotions, IoT devices, and a smartphone app used by pet owners.

[0862] Biometric information terminal

[0863] The device is attached to the pet and has built-in heart rate, body temperature, and activity level sensors, which measure the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute).

[0864] Surveillance camera

[0865] The camera captures images of your pet in real time and sends them to a server as streaming data, allowing you to constantly monitor your pet's behavior and environment.

[0866] server

[0867] The server receives data sent from the device and camera and stores it in a database. The received data is analyzed using machine learning models and statistical methods. The server detects abnormalities when a pet's heart rate or body temperature is outside of normal ranges or when its activity level is abnormally high. When an abnormality is detected, the server determines the necessary countermeasures based on the evaluation results and generates commands to control the IoT device.

[0868] Emotion Engine

[0869] The emotion engine has the ability to evaluate the user's emotional data. It learns from past emotional data and adjusts the alert content and notification method. For example, if the user is in a stressful state, it generates an alert that prompts a prompt response.

[0870] IoT equipment

[0871] The IoT device receives commands sent from the server and executes the specified control action. For example, if an abnormality is detected, the device can activate the air conditioner to lower the room temperature.

[0872] Smartphone app

[0873] Users can receive alerts sent from the server via a smartphone app and check the status of their pets. If necessary, they can also remotely control IoT devices through the app.

[0874] Specific examples

[0875] Example 1: Health monitoring

[0876] Device: Measure your pet's heart rate to 80 bpm, body temperature to 38°C, and activity level to ensure it is calm.

[0877] Camera: Capture footage of your pet relaxing in its dedicated resting area.

[0878] Server: Analyzes the received data and determines that the pet is healthy and relaxed. If no action is required, saves the data in a database.

[0879] Emotion engine: Determines that the user's stress level is low and no alert needs to be sent.

[0880] Users: Use the app to see their pets relaxing and feel reassured.

[0881] Prompt for generative AI model: "Describe the results of monitoring your pet's heart rate and body temperature when they are within normal ranges and relaxed."

[0882] Example 2: Anomaly detection and response

[0883] Device: Pet's heart rate is measured at 150 bpm, body temperature at 39.5°C, and activity level is abnormally high.

[0884] Camera: Capture footage of your pet moving erratically around the house.

[0885] Server: Determines through analysis that the pet is stressed or affected by high temperatures. Generates and sends a command to turn on the air conditioner and lower the room temperature.

[0886] Emotion engine: Analyzes the user's emotional state and generates an alert urging them to return home quickly if it determines that they are in a state of high stress.

[0887] IoT device: The air conditioner turns on and adjusts to the set temperature.

[0888] User: Receives an alert in the app about an abnormality, checks video footage and detailed information about the pet, and takes appropriate action based on the response suggested by the emotion engine.

[0889] Prompt for generative AI model: "Describe the process for detecting abnormal behavior and health conditions in pets and taking necessary action."

[0890] Example 3: Continuous learning

[0891] Server: Trains machine learning models based on continuously collected data to improve analysis accuracy and anomaly detection.

[0892] Emotion engine: Learns from the user's past emotional data and responds appropriately when their emotional patterns deviate from normal ones.

[0893] Users can check their pet's current condition and past activity records using a smartphone app. If an abnormality is detected, they can take prompt action based on the information provided by the app.

[0894] Prompt for generative AI model: "Describe the process by which the system continuously learns from pet health data and user emotion data to improve its accuracy."

[0895] In this way, the present invention uses advanced technology to ensure the health and safety of pets, allowing for real-time monitoring of pet conditions and automatic implementation of necessary measures. Furthermore, by taking into account the user's emotions, more appropriate and reassuring responses are possible.

[0896] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0897] Step 1:

[0898] The device measures the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute) using the heart rate sensor, body temperature sensor, and activity level sensor attached to the pet. The measurement data is assumed to be a stable heart rate of 80 bpm, body temperature of 38°C, and activity level. The measurement data is saved in a buffer along with the pet ID and a timestamp.

[0899] Input: Biometric data from sensors

[0900] Output: Measurement data (heart rate, body temperature, activity level)

[0901] Step 2:

[0902] The device transmits the measured data, including the pet's ID, timestamp, heart rate, body temperature, and activity level, to the server at regular intervals. This transmitted data reaches the server via the network.

[0903] Input: Measurement data (heart rate, body temperature, activity level)

[0904] Output: Data sent to the server

[0905] Step 3:

[0906] The camera captures video data of your pet in real time—for example, a video of your pet relaxing in a designated resting area—and sends the captured video data to a server in streaming format.

[0907] Input: Real-time video data

[0908] Output: Video data sent to the server

[0909] Step 4:

[0910] The server receives the biometric data sent from the device and the video data from the camera and stores them in a database. The received data is associated with the pet ID and a timestamp.

[0911] Input: Biometric data and video data from the device

[0912] Output: Data stored in the database

[0913] Step 5:

[0914] The server analyzes the stored data using statistical methods and machine learning models. For example, it evaluates whether the pet's heart rate and body temperature are within healthy ranges and detects whether its activity level is unusual. Based on the results of this analysis, it determines whether the pet's condition is normal or abnormal.

[0915] Input: Stored biometric and video data

[0916] Output: Analysis and evaluation results (health status, behavioral status)

[0917] Step 6:

[0918] The server generates commands to control IoT devices as needed based on the analysis results. For example, if a pet's heart rate is high and the room temperature is high, it generates a command to turn on the air conditioner. This command is sent to the IoT device.

[0919] Input: Analysis results

[0920] Output: IoT device control command

[0921] Step 7:

[0922] If an abnormality is detected, the server sends an alert to the owner. At this time, the emotion engine adjusts the alert content and notification method based on the user's past emotional data. For example, if the user is stressed, it generates an alert that emphasizes urgency.

[0923] Input: Anomaly detection results and user emotion data

[0924] Output: User alert

[0925] Step 8:

[0926] The IoT device executes the commands received from the server, for example, turning on the air conditioner and adjusting the room temperature to the set value, thereby maintaining a comfortable environment for the pet.

[0927] Input: Control command from the server

[0928] Output: IoT device operation (e.g., air conditioner operation)

[0929] Step 9:

[0930] Users can receive alerts and check their pet's condition through a smartphone app, and if necessary, can remotely control IoT devices through the app to quickly adjust the temperature or take other actions.

[0931] Input: User alert from server

[0932] Output: User action (e.g. remote control, confirmation)

[0933] In this way, through the specific actions performed at each step and the chain of inputs and outputs, the system can monitor the pet's health in real time and automatically take necessary measures.

[0934] (Application example 2)

[0935] 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."

[0936] There is a need to monitor pet health and behavior in real time and take prompt and appropriate measures when abnormalities are detected. It is also important to reduce stress and increase peace of mind for pet owners by providing notification methods that take into consideration the owner's feelings regarding their pet's condition. Current systems do not comprehensively cover these elements, so a system that solves these issues is needed.

[0937] The identification process by the identification 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 measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means for analyzing the user's emotional state and adjusting the content and method of the notification; and means for automatically controlling environmental control devices such as air conditioners and feeders when an abnormality is detected in the pet's health condition or behavior. This makes it possible to monitor the pet's health condition and behavior in real time and take prompt and appropriate measures when an abnormality occurs, reducing the owner's stress and providing a sense of security through a notification method that takes the user's emotions into consideration.

[0938] A "biometric terminal" is a device worn by a pet to measure its heart rate, body temperature, and activity level.

[0939] A "server" is a device that receives and analyzes data sent from biometric information terminals and surveillance cameras, and controls IoT devices as necessary.

[0940] "Analysis" is the process of evaluating the pet's health and behavior based on the data received by the server.

[0941] An "IoT device" is a device that receives commands from a server and performs control operations, and includes air conditioners and bird feeders.

[0942] The "emotion engine" is a system that analyzes the user's emotional state and adjusts the content and method of notifications.

[0943] An "air conditioner" is an environmental control device for adjusting the temperature inside a room.

[0944] A "feeder" is an automatic device for providing food to pets.

[0945] "Notifications" are alerts or messages sent to owners when an abnormality is detected.

[0946] "Real-time" refers to data acquisition and analysis occurring immediately, without delay.

[0947] A "machine learning model" is an algorithm that uses continuously collected data to improve the accuracy of analysis and anomaly detection.

[0948] "User" refers to the owner who uses the system to monitor the health and behavior of their pet.

[0949] System Configuration

[0950] The invention requires the following components:

[0951] Bio-information terminal: Attached to your pet, it measures heart rate, body temperature, and activity level.

[0952] Surveillance camera: Captures video data of your pet in real time and sends it to a server.

[0953] Server: Analyzes the received data, evaluates the pet's health and behavior, and generates and sends the necessary commands.

[0954] Emotion engine: Analyzes the user's emotional state and adjusts the content and method of notifications.

[0955] IoT devices: Control air conditioners, feeders, etc. according to instructions from the server.

[0956] Smartphone app: A way for owners to monitor their pet's condition and receive alerts if anything unusual happens.

[0957] Process Overview

[0958] The server receives data sent from the vital signs terminal and monitoring camera, analyzes it, and evaluates the pet's health and behavior. If an abnormality is detected, the server generates and sends commands to control the IoT device and sends appropriate alerts to the owner.

[0959] The emotion engine analyzes the owner's emotional state and adjusts the content and method of notifications. For example, if the owner is stressed, the system will adjust to send faster and clearer alerts.

[0960] Hardware and Software

[0961] The hardware and software used to implement this system includes:

[0962] Hardware:

[0963] Biometric information terminals (e.g. smart bands for pets)

[0964] Surveillance cameras (e.g. network cameras)

[0965] Air conditioner (IoT-compatible smart air conditioner)

[0966] Feeder (IoT-enabled automatic feeder)

[0967] software:

[0968] Servers for data analysis (e.g., database servers and analysis servers)

[0969] Sentiment engines for sentiment analysis (e.g., sentiment analysis software using machine learning models)

[0970] Smartphone app (monitoring and alarm app for owners)

[0971] Specific examples of processing

[0972] For example, if your pet is exhibiting abnormal behavior in the living room (heart rate: 130, body temperature: 40, activity level: 85) and you are feeling stressed, the system will act as follows:

[0973] 1. The vital signs terminal measures the pet's abnormal heart rate, body temperature, and activity level and transmits the data to a server.

[0974] 2. The monitoring camera captures your pet's erratic movements in real time and sends streaming data to the server.

[0975] 3. The server analyzes this data and determines that the pet's condition is abnormal.

[0976] 4. The server sends a command to the IoT device (air conditioner) to set it to "cooling" and performs automatic control.

[0977] 5. The emotion engine analyzes the owner's emotional state and generates a strong alert message.

[0978] 6. Send a strong alert to owners via a smartphone app to return home quickly.

[0979] Prompt Sentence Examples

[0980] The system's behavior can be triggered by inputting a prompt like the following into the generative AI model:

[0981] Data required for a pet health monitoring program:

[0982] Health data (heart rate, body temperature, activity level)

[0983] User emotional state

[0984] Pet Health Data:

[0985] Heart rate: 130

[0986] Body temperature: 40 degrees

[0987] Activity level: 85

[0988] User emotional state:

[0989] stress

[0990] Expected system behavior:

[0991] 1. Analyze health data and detect abnormalities.

[0992] 2. Set the air conditioner to "cooling" and turn it on.

[0993] 3. Send a strong alert to the user to get home quickly.

[0994] In this way, it is possible to monitor the health and behavior of pets in real time, and to take prompt and appropriate action when an abnormality is detected.

[0995] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0996] Step 1:

[0997] The terminal measures the pet's vital signs (heart rate, body temperature, activity level). This data is collected in real time and sent to a server at regular intervals. The input is sensor data from the vital signs terminal, and the output is measurement data sent to the server. Specifically, the terminal's sensors collect various vital signs and send this data to the server via wireless communication.

[0998] Step 2:

[0999] The surveillance camera captures video of your pet in real time and sends it to a server as streaming data. The input is the video data captured by the surveillance camera, and the output is the data converted to digital format and sent to the server. The camera captures the video, digitizes the video signal, and sends it to the server over the network.

[1000] Step 3:

[1001] The server receives data sent from the biometric information terminal and the surveillance camera. The input is biometric information and video data, which are then stored in a database. Specifically, the server captures this data in real time using the data receiving module and stores it in the storage system.

[1002] Step 4:

[1003] The server analyzes the received data and evaluates the pet's health and behavior. The input is the stored biometric information and video data, and the output is the analysis results (health and behavior assessment). The data analysis module evaluates the data using machine learning algorithms and detects abnormalities.

[1004] Step 5:

[1005] Based on the analysis results, the server generates and sends commands to control IoT devices as needed. The input is the analysis results, and the output is the generated control commands. Specifically, it monitors the results of the analysis module, and if an abnormality is detected, it generates and sends appropriate commands to the air conditioner or feeder.

[1006] Step 6:

[1007] If an abnormality is detected, the server sends an alert to the owner. The input is the analysis result and the output of the emotion engine, and the output is an alert message. The notification module takes into account the analysis result of the emotion engine, generates an alert message to be sent to the owner, and notifies the smartphone app.

[1008] Step 7:

[1009] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications. The input is the user's emotional data, and the output is the intensity and format of the notification message. Specifically, the emotion engine uses machine learning to learn past emotional data, analyzes the user's current emotional state, and determines the optimal notification method.

[1010] Step 8:

[1011] IoT devices (air conditioners, feeders, etc.) receive commands from the server and execute the specified control actions. The input is the control command from the server, and the output is the executed control action. For example, the air conditioner adjusts the temperature, and the feeder dispenses food.

[1012] 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.

[1013] 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.

[1014] 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.

[1015] [Third embodiment]

[1016] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1017] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1018] 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).

[1019] 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.

[1020] 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.

[1021] 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).

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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."

[1028] The present invention relates to a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. The system mainly consists of the following components:

[1029] 1. Biometric Information Terminal

[1030] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[1031] 2. Surveillance Camera

[1032] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[1033] 3. Server

[1034] The server receives, stores, and analyzes the data sent from the device and camera. Specifically, it performs the following operations:

[1035] The received biometric data is analyzed to assess the pet's health condition.

[1036] The pet's behavior patterns are analyzed based on the received video data.

[1037] Compare with past data to detect anomalies.

[1038] If an abnormality is detected, an alert will be sent to the owner.

[1039] 4. IoT devices

[1040] This is a device that receives commands generated by the server and performs the specified control action (for example, adjusting the temperature of the air conditioner, activating the feeder, etc.).

[1041] 5. Smartphone App

[1042] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[1043] System operation example

[1044] 1. Health monitoring

[1045] Device:

[1046] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[1047] camera:

[1048] Capture footage of your pet relaxing in its dedicated resting area.

[1049] server:

[1050] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[1051] User:

[1052] All you need to do is check the app to see how relaxed your pet is.

[1053] 2. Anomaly detection and response

[1054] Device:

[1055] Measure your pet's abnormally high heart rate and activity level.

[1056] camera:

[1057] Capture footage of your pet moving erratically around the house.

[1058] server:

[1059] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[1060] IoT devices:

[1061] The air conditioner turns on and adjusts to the desired temperature.

[1062] User:

[1063] The app will alert you to any abnormalities and provide detailed information about your pet's condition, allowing you to take action if necessary, such as returning home immediately.

[1064] 3. Continuous learning

[1065] server:

[1066] Continuously collected data is used to train machine learning models to improve analysis and anomaly detection, allowing for better responses in the future.

[1067] This system uses advanced technology to ensure the health and safety of pets, allowing owners to monitor their pet's condition in real time and automatically take necessary measures, allowing them to leave their pets with peace of mind even when they are not at home.

[1068] The processing flow will be explained below.

[1069] Step 1:

[1070] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[1071] Step 2:

[1072] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[1073] Step 3:

[1074] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[1075] Step 4:

[1076] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[1077] Step 5:

[1078] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[1079] Step 6:

[1080] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[1081] Step 7:

[1082] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[1083] Step 8:

[1084] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[1085] Step 9:

[1086] The server generates an alert and sends it to the owner's smartphone app to notify them.

[1087] Step 10:

[1088] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[1089] Step 11:

[1090] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[1091] Step 12:

[1092] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[1093] Step 13:

[1094] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[1095] Example 1

[1096] 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."

[1097] In recent years, the importance of pet health management has increased, creating a growing need for real-time monitoring of pet health and behavior and early detection of abnormalities. However, existing technologies offer few systems that comprehensively monitor pets' biometric information and behavior and automatically implement countermeasures, which poses the problem of being unable to quickly respond to abnormalities in pets when their owners are away. Furthermore, there is a lack of technology to improve the accuracy of analyzing pet behavior patterns and detecting abnormalities. Therefore, there is a need for the development of more effective monitoring systems to ensure the health and safety of pets.

[1098] 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.

[1099] In this invention, the server includes means for analyzing received data and evaluating the health and behavior of the animals, means for generating and transmitting commands to operate the control device as necessary based on the evaluation results, means for sending a notification to an administrator if an abnormality is detected, means for detecting an abnormality by comparing with past data, means for storing multiple pieces of data transmitted from the device in a database, and means for continuously collecting data and training a learning model. This makes it possible to comprehensively monitor the health and behavior of animals in real time, detect abnormalities early, and automatically take appropriate measures.

[1100] "Device" refers to a biometric terminal attached to an animal, which is hardware used to measure heart rate, body temperature, and activity level.

[1101] "Processing Unit" refers to the server used to analyze the received data and assess the health and behavior of the animals.

[1102] "Control device" refers to an IoT device that operates based on commands generated by a server, and includes devices such as air conditioners and feeders.

[1103] A "command" is a command generated and transmitted by the server, and includes specific instructions for operating the control device.

[1104] "Administrator" refers to the user of the system, typically the pet owner.

[1105] "Database" refers to a storage device for storing received biometric data and video data.

[1106] "Learning model" refers to a machine learning algorithm that is trained on continuously collected data to improve the accuracy of analysis and anomaly detection.

[1107] "Abnormality" refers to a condition detected when abnormalities are found in health conditions or behavioral patterns compared with past data.

[1108] The present invention is a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. This system mainly consists of the following components.

[1109] 1. Biometric Information Terminal

[1110] Terminal: The biometric information terminal is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is stored in temporary memory and sent to a server at regular intervals (for example, every minute). The terminal uses various sensor devices to accurately collect this information.

[1111] Example: For example, if your pet's heart rate is 80 beats per minute, its body temperature is 37 degrees, and its activity level is 5 (out of 10), these data will be measured and sent to the server.

[1112] 2. Surveillance Camera

[1113] Surveillance camera: A surveillance camera is a device that captures video data of your pet in real time and sends it to a server as streaming data. This camera can rotate 360 ​​degrees and automatically track your pet's position.

[1114] Example: A pet taking a nap in the living room is captured on camera and transmitted to a server in real time.

[1115] 3. Server

[1116] Server: The server is used to receive, store, and analyze data sent from the devices and cameras. Software used includes database management systems and machine learning algorithms.

[1117] Data analysis: Analyze the received vital data and assess the pet's health status.

[1118] Behavioral pattern analysis: Analyze your pet's behavioral patterns based on video data.

[1119] Anomaly detection: Detect anomalies by comparing with past data.

[1120] Command generation: If an abnormality is detected, an appropriate control command is generated and sent to the IoT device.

[1121] Data storage: The received data is stored in a database with a timestamp.

[1122] Machine learning: Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[1123] Example: If data and video data are received showing a heart rate of 80, body temperature of 37 degrees, and activity level of 5, and based on this the pet's health condition is assessed as normal, the data will be stored in the database and no special action will be required.

[1124] 4. IoT devices

[1125] IoT device: An IoT device is a device that receives commands generated by a server and executes the specified control action (e.g., adjusting the temperature of an air conditioner, activating a feeder, etc.).

[1126] Example: For example, if a pet's heart rate is abnormally high and its activity level is also abnormally high, a command to turn on the air conditioner and lower the room temperature will be generated, and the air conditioner will turn on and adjust to the set temperature.

[1127] 5. Smartphone App

[1128] Users can receive alerts from the server via a smartphone app and monitor their pet's condition in real time. They can also use the app to remotely control IoT devices.

[1129] Example: The owner receives an alert and can view footage of their pet or manually change the air conditioning settings through the app.

[1130] Prompt Sentence Examples

[1131] "Please explain what alerts will be generated if an abnormality is detected based on the latest data collection results from this pet health monitoring system."

[1132] By inputting such prompt statements into the generative AI model, it is possible to provide detailed information such as what specific alerts will be sent when an abnormality is detected and what countermeasures will be taken.

[1133] The above is an embodiment of the present invention.

[1134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1135] Step 1:

[1136] Device: Measures heart rate, body temperature, and activity levels. The biometric device attached to your pet uses various built-in sensors to measure these health indicators.

[1137] Input: Your pet's current vitals.

[1138] Output: Measured heart rate, body temperature, and activity level data.

[1139] Specific actions: For example, your pet's heart rate is measured at 80 beats per minute, body temperature is measured at 37 degrees, and activity level is measured at 5 (out of 10).

[1140] Step 2:

[1141] Terminal: Sends measurement data to the server. The acquired data is encrypted and then sent to the server via Wi-Fi.

[1142] Input: Measured heart rate, temperature, and activity level data.

[1143] Output: Encrypted biometric data sent to the server.

[1144] Specific operation: Data on the pet's heart rate (80), body temperature (37 degrees), and activity level (5) are encrypted and sent to the server.

[1145] Step 3:

[1146] Device: The monitoring camera captures video of your pet in real time. The camera rotates 360 degrees and automatically tracks your pet's position.

[1147] Input: Pet movements captured on surveillance camera.

[1148] Output: Real-time captured video data.

[1149] Specific operation: The camera captures a pet taking a nap in the living room, and the image is then used as video data.

[1150] Step 4:

[1151] Terminal: Captured video data is sent to the server in streaming format. Compressed video data is sent.

[1152] Input: Video data captured in real time.

[1153] Output: Compressed video data sent to the server.

[1154] Specific operation: The captured video (of a pet taking a nap in the living room) is compressed in MPEG format and sent to a server via Wi-Fi.

[1155] Step 5:

[1156] Server: Receives biometric and video data and stores them in a database. Received data is recorded with a timestamp.

[1157] Input: Transmitted biometric and video data.

[1158] Output: Biometric and video data stored in a database.

[1159] Specific operation: Data such as a heart rate of 80, body temperature of 37 degrees, and activity level of 5, as well as footage of a napping pet, are stored in a database.

[1160] Step 6:

[1161] Server: Analyzes the received vital data and evaluates the pet's health condition. It compares it with the normal range and checks for any abnormalities.

[1162] Input: Stored biometric data.

[1163] Output: Evaluation result (normal or abnormal).

[1164] Specific operation: Confirm that the heart rate of 80 is within the normal range (60-100) and the body temperature of 37 degrees is within the normal range (36-39 degrees), and evaluate the health condition as normal.

[1165] Step 7:

[1166] Server: Analyzes the video data and evaluates the pet's behavioral patterns. It analyzes behavior based on the frequency of movement and changes in position.

[1167] Input: Stored video data.

[1168] Output: Behavioral pattern evaluation results.

[1169] Specific behavior: Confirms that a napping pet has been in the same place for more than an hour and determines that it is in a relaxed state.

[1170] Step 8:

[1171] Server: Compares biometric data and behavioral patterns with past data to detect anomalies. If anomalies are found, it determines the appropriate course of action.

[1172] Input: Assessment results and historical data.

[1173] Output: Anomaly detection results and countermeasures.

[1174] Specific operation: If the relative activity level is 10 (the previous maximum activity level is 8) and the heart rate is 120 (the previous maximum heart rate is 100), it is determined to be abnormal.

[1175] Step 9:

[1176] Server: When an abnormality is detected, it generates and sends control commands to the appropriate IoT devices.

[1177] Input: Anomaly detection results.

[1178] Output: Control command.

[1179] Specific operation: Because the room temperature is high, a command to operate the air conditioner is generated and sent to the IoT device.

[1180] Step 10:

[1181] IoT device: Receives commands from the server and performs the specified control action.

[1182] Input: Control command.

[1183] Output: The control action that was performed.

[1184] Specific action: The air conditioner is turned on and the room temperature is set to 24 degrees.

[1185] Step 11:

[1186] User: Monitors pet status in real time using a smartphone app and receives alerts sent from the server.

[1187] Input: Alert from the server.

[1188] Output: Alert and pet information displayed in the app.

[1189] Specific behavior: The app will receive a push notification indicating an abnormality has occurred and will display your pet's detailed information (heart rate 120, activity level 10).

[1190] Step 12:

[1191] User: Use the app to remotely control IoT devices as needed.

[1192] Input: User instructions.

[1193] Output: The control action that was performed.

[1194] Specific actions: For example, manually change the air conditioner temperature setting from 24 degrees to 22 degrees.

[1195] Step 13:

[1196] Server: Continuously collects data and trains machine learning models to improve analysis and anomaly detection.

[1197] Input: Continuously collected biometric and behavioral data.

[1198] Output: An improved machine learning model.

[1199] What it does: The server periodically learns from the data and understands new abnormal patterns.

[1200] (Application example 1)

[1201] 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."

[1202] Conventional pet monitoring systems can monitor pets' health and behavior in real time, but their application is limited to the home. Systems are needed to ensure that pets can live in a safe and comfortable environment even when out and about or on the move. In particular, when using autonomous vehicles, controlling the in-car environment can have a significant impact on pet health. However, currently, there are no systems that manage pet conditions in conjunction with the autonomous vehicle's environmental control. Therefore, there is a need for a system that can monitor pet health in real time while traveling and take appropriate measures if an abnormality is detected.

[1203] 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.

[1204] In this invention, the server includes means for measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet, means for transmitting the measured data to the server, means for the server to analyze the received data and evaluate the pet's health condition and behavior, means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results, means for sending an alert to the owner if an abnormality is detected, and means for enabling cooperation with the control device of the autonomous vehicle and controlling the environment inside the vehicle. This makes it possible to monitor the pet's health condition and behavior in real time and to appropriately control the environment inside the autonomous vehicle if an abnormality occurs.

[1205] A "biometric information terminal" is a device that is attached to a pet and measures biometric data such as heart rate, body temperature, and activity level.

[1206] The "means for transmitting data" is a device or function that transmits measured data from the biometric information terminal to the server.

[1207] The "server" is a central data processing unit that receives and analyzes pet biometric and video data to assess the pet's health and behavior.

[1208] An "IoT device" is a device that has the ability to communicate with other devices via the Internet and perform control operations based on instructions.

[1209] "Means for sending an alert" refers to a device or function that sends a warning notice to the owner when an abnormality is detected.

[1210] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without driver operation.

[1211] "Means for environmental control" refers to a device or function for adjusting the internal environment of an autonomous vehicle (e.g., adjusting the temperature of the air conditioner).

[1212] "Means for capturing video data" refers to a device or function that uses a camera to capture video of the pet in real time and transmits the data to a server.

[1213] "Continuously collected data" is a general term for biometric information and behavioral data acquired continuously over a certain period of time.

[1214] A "machine learning model" refers to an algorithm or method for analyzing and learning about a pet's behavioral patterns and health condition.

[1215] An "autonomous vehicle control device" is a central control device for managing the driving and environmental settings of an autonomous vehicle.

[1216] The present invention relates to a system that monitors the health and behavior of pets in real time in cooperation with an autonomous vehicle and automatically takes necessary measures. This system mainly consists of the following components.

[1217] System configuration

[1218] 1. Biometric Information Terminal

[1219] The biometric information terminal is attached to the pet and measures vital data such as heart rate, body temperature, and activity level, and the measured data is sent to a server at regular intervals.

[1220] 2. Surveillance Camera

[1221] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[1222] 3. Server

[1223] The server receives, stores, and analyzes the data sent from the biometric terminal and the surveillance camera. Specifically, it performs the following operations:

[1224] Analyze biological data to assess your pet's health.

[1225] Analyze your pet's behavior patterns based on video data.

[1226] Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[1227] If an abnormality is detected, an alert will be sent to the owner.

[1228] It works in conjunction with the control device of the autonomous vehicle to control the in-car environment (such as adjusting the air conditioning temperature).

[1229] 4. IoT devices

[1230] It receives commands generated from the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner).

[1231] 5. Smartphone App

[1232] Using this app, owners can monitor their pets' status in real time, receive alerts when something abnormal occurs, and remotely control IoT devices as needed.

[1233] Example of a system

[1234] First, let's say the vital signs terminal measures a pet's heart rate at 120 bpm, body temperature at 38.5 °C, and activity level at 8. This data is sent to a server, which analyzes it in real time. The server determines from the data that the pet is stressed. The server then sends a command to activate the air conditioning in the autonomous vehicle and appropriately lower the temperature inside the vehicle. An alert is then sent to the owner via a smartphone app to notify them of the situation.

[1235] Prompt Sentence Examples

[1236] Build a system that monitors your pet's health and adjusts the car's air conditioning if an abnormal condition is detected. Specifically, data including the following parameters will be sent to a server: heart rate (bpm), body temperature (°C), and activity level. Based on the results of data analysis, appropriate action will be taken, such as adjusting the air conditioning temperature or stopping the car.

[1237] Thus, the present invention is a system for ensuring the health and safety of pets, and also provides an environment in which owners can travel with their pets with peace of mind.

[1238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1239] Step 1:

[1240] Measurement and transmission of vital signs:

[1241] The terminal measures the heart rate, body temperature, and activity level every minute from the biometric information terminal attached to the pet. These measurement data are sent to the server as biometric data. It receives biometric information (heart rate, body temperature, activity level) as input and performs measurements. It generates the measured data as output and sends it to the server.

[1242] Step 2:

[1243] Video data capture and transmission:

[1244] The surveillance camera captures video of your pet in real time and sends the video data to the server in streaming format. The input is continuous video capture, and the output is captured video data that is sent to the server.

[1245] Step 3:

[1246] Data reception and storage:

[1247] The server receives the biometric data sent from the device and the video data sent from the surveillance camera and stores the data in a temporary database. The server receives the data sent as input and stores it in the database. The server uses the stored data as output in subsequent analysis steps.

[1248] Step 4:

[1249] Data analysis:

[1250] The server analyzes the received biometric data and video data to evaluate the pet's health and behavior. A pre-trained machine learning model is used for the analysis. The stored data is read as input and the machine learning model is applied. The server generates an evaluation result as output and determines whether or not there are any abnormalities.

[1251] Step 5:

[1252] Anomaly detection and response command generation:

[1253] If the server detects an abnormality based on the analysis results, it generates a response command. If a specific abnormality (e.g., stress due to high temperature) is detected, it generates a command to adjust the air conditioner temperature. It uses the evaluation results as input to determine whether an abnormality exists. It generates a response command as output and saves the response command.

[1254] Step 6:

[1255] Integration with IoT devices:

[1256] The server sends the generated corresponding command to the IoT device to execute a remote control action (e.g., adjusting the temperature of an air conditioner). The server uses the generated corresponding command as input and sends it to the IoT device. The IoT device executes the action as output.

[1257] Step 7:

[1258] Sending alerts:

[1259] When an abnormality is detected and a response is taken, the server sends the details as an alert to the owner's smartphone app. The server receives the execution result of the response command as input and generates an alert. The server sends a notification to the owner as output.

[1260] Step 8:

[1261] Continuous learning:

[1262] The server continuously trains the machine learning model using continuously collected biometric and video data. It receives the accumulated data as input and trains the model. As output, it generates a machine learning model with improved accuracy.

[1263] By linking these steps together, a series of systems are created that monitors the health of pets in real time and appropriately controls the environment of the self-driving vehicle.

[1264] 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.

[1265] This invention combines a system that monitors the health and behavior of pets in real time and automatically takes necessary measures with an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[1266] 1. Biometric Information Terminal

[1267] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[1268] 2. Surveillance Camera

[1269] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[1270] 3. Server

[1271] The server receives, stores, and analyzes data sent from the device and camera. It uses the analysis to evaluate the pet's health and behavior, and sends an alert to the owner if an abnormality is detected. It also generates and sends commands to control IoT devices. Furthermore, it uses an emotion engine to analyze the user's emotional state and adjust the content and method of the alert.

[1272] 4. Emotion Engine

[1273] The emotion engine recognizes the user's emotions and evaluates the user's stress level in response to abnormal notifications or pet conditions. The emotion engine can also learn from the user's past emotional data and suggest more appropriate responses.

[1274] 5. IoT devices

[1275] This is a device that receives commands generated by the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner, activating a feeder, etc.).

[1276] 6. Smartphone App

[1277] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[1278] System operation example

[1279] Example 1: Health monitoring

[1280] Device:

[1281] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[1282] camera:

[1283] Capture footage of your pet relaxing in its dedicated resting area.

[1284] server:

[1285] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[1286] Emotion Engine:

[1287] Determine that the user's stress level is low and decide that there is no need to send an alert.

[1288] User:

[1289] Use the app to check how relaxed your pet is and feel reassured.

[1290] Example 2: Anomaly detection and response

[1291] Device:

[1292] Measure your pet's abnormally high heart rate and activity level.

[1293] camera:

[1294] Capture footage of your pet moving erratically around the house.

[1295] server:

[1296] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[1297] Emotion Engine:

[1298] If the system analyzes the user's emotional state and determines that the user is in a state of high stress, it generates an emotionally sensitive alert notification, such as sending an alert urging the user to return home quickly.

[1299] IoT devices:

[1300] The air conditioner turns on and adjusts to the desired temperature.

[1301] User:

[1302] Receive alerts in the app when something unusual happens, get detailed information about your pet's condition, and take appropriate action based on the response suggested by the emotion engine.

[1303] Example 3: Continuous learning

[1304] server:

[1305] Train machine learning models on continuously collected data to improve the accuracy of analysis and anomaly detection.

[1306] Emotion Engine:

[1307] The system learns the user's past emotional data and can respond more appropriately when the user's emotional pattern differs from the normal pattern.

[1308] User:

[1309] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[1310] This system uses advanced technology to ensure the health and safety of pets, allowing it to monitor their condition in real time and automatically take necessary measures. Furthermore, by taking the user's emotions into consideration, it can respond more appropriately and effectively, giving owners a greater sense of security.

[1311] The processing flow will be explained below.

[1312] Step 1:

[1313] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[1314] Step 2:

[1315] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[1316] Step 3:

[1317] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[1318] Step 4:

[1319] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[1320] Step 5:

[1321] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[1322] Step 6:

[1323] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[1324] Step 7:

[1325] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[1326] Step 8:

[1327] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[1328] Step 9:

[1329] The server generates an alert and sends it to the owner's smartphone app to notify them.

[1330] Step 10:

[1331] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[1332] Step 11:

[1333] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[1334] Step 12:

[1335] The server uses an emotion engine to analyze the user's emotion data, which evaluates whether the user is currently relaxed or stressed.

[1336] Step 13:

[1337] The server adjusts the content and format of the alert notification based on the analysis results of the emotion engine. For example, if the user is under stress, it uses gentle and reassuring language.

[1338] Step 14:

[1339] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[1340] Step 15:

[1341] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[1342] Example 2

[1343] 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."

[1344] Systems that monitor pet health and behavior in real time and automatically take necessary measures are important. However, conventional systems often fail to consider the owner's emotions or stress level when detecting an abnormality, making owners anxious. Furthermore, data analysis accuracy is insufficient, potentially delaying appropriate responses. Furthermore, limited remote control options mean owners are sometimes unable to respond quickly. There is a need to resolve these issues and provide a more reliable and effective pet monitoring system.

[1345] 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.

[1346] In this invention, the server includes: means for measuring the heart rate, body temperature, and activity level from a biometric information terminal worn by the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means including an emotion engine for evaluating the user's emotional data and adjusting the alert and notification method; means for the server to continuously analyze the data based on a machine learning model to improve accuracy; and means for the owner to receive alerts via a smartphone app and remotely check and control the pet's condition. This allows for a response that takes the user's emotions into consideration when an abnormality is detected, allowing the owner to respond quickly and with peace of mind. Furthermore, the improved accuracy of data analysis allows for more accurate monitoring of the pet's health condition and behavior. Furthermore, remote control using a smartphone app allows owners to monitor their pet's condition from anywhere and take appropriate action in emergencies.

[1347] A "biometric information terminal" is a device that is attached to a pet to measure biometric data such as heart rate, body temperature, and activity level.

[1348] The "measuring means" is a means for measuring heart rate, body temperature, and activity level using a biometric information terminal.

[1349] The "transmission means" is a means for transmitting the measured data to the server.

[1350] The "server" is a central processing unit that receives, stores, and analyzes data sent from the terminals and cameras, and generates and sends necessary commands based on the evaluation results.

[1351] "Analysis means" refers to means for analyzing the received data and assessing the health and behavior of the pet.

[1352] The "assessment means" is a means for assessing the health and behavior of a pet based on the analyzed data.

[1353] The "control means" is a means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results.

[1354] The "alert sending means" is a means for sending an alert to the owner when an abnormality is detected.

[1355] The "emotion engine" is an engine that evaluates the user's emotional data and adjusts alerts and notification methods.

[1356] A "machine learning model" is a model that the server trains using continuously collected data to improve the accuracy of analysis and anomaly detection.

[1357] The "smartphone app" is an application that allows owners to receive alerts and remotely check and control their pet's condition.

[1358] This invention is a system that monitors the health condition and behavior of pets in real time and automatically takes necessary measures. This system includes a biometric information terminal that acquires the pet's biometric information, a monitoring camera that captures the pet's video, a server that receives and analyzes the data, an emotion engine that analyzes the user's emotions, IoT devices, and a smartphone app used by pet owners.

[1359] Biometric information terminal

[1360] The device is attached to the pet and has built-in heart rate, body temperature, and activity level sensors, which measure the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute).

[1361] Surveillance camera

[1362] The camera captures images of your pet in real time and sends them to a server as streaming data, allowing you to constantly monitor your pet's behavior and environment.

[1363] server

[1364] The server receives data sent from the device and camera and stores it in a database. The received data is analyzed using machine learning models and statistical methods. The server detects abnormalities when a pet's heart rate or body temperature is outside of normal ranges or when its activity level is abnormally high. When an abnormality is detected, the server determines the necessary countermeasures based on the evaluation results and generates commands to control the IoT device.

[1365] Emotion Engine

[1366] The emotion engine has the ability to evaluate the user's emotional data. It learns from past emotional data and adjusts the alert content and notification method. For example, if the user is in a stressful state, it generates an alert that prompts a prompt response.

[1367] IoT equipment

[1368] The IoT device receives commands sent from the server and executes the specified control action. For example, if an abnormality is detected, the device can activate the air conditioner to lower the room temperature.

[1369] Smartphone app

[1370] Users can receive alerts sent from the server via a smartphone app and check the status of their pets. If necessary, they can also remotely control IoT devices through the app.

[1371] Specific examples

[1372] Example 1: Health monitoring

[1373] Device: Measure your pet's heart rate to 80 bpm, body temperature to 38°C, and activity level to ensure it is calm.

[1374] Camera: Capture footage of your pet relaxing in its dedicated resting area.

[1375] Server: Analyzes the received data and determines that the pet is healthy and relaxed. If no action is required, saves the data in a database.

[1376] Emotion engine: Determines that the user's stress level is low and no alert needs to be sent.

[1377] Users: Use the app to see their pets relaxing and feel reassured.

[1378] Prompt for generative AI model: "Describe the results of monitoring your pet's heart rate and body temperature when they are within normal ranges and relaxed."

[1379] Example 2: Anomaly detection and response

[1380] Device: Pet's heart rate is measured at 150 bpm, body temperature at 39.5°C, and activity level is abnormally high.

[1381] Camera: Capture footage of your pet moving erratically around the house.

[1382] Server: Determines through analysis that the pet is stressed or affected by high temperatures. Generates and sends a command to turn on the air conditioner and lower the room temperature.

[1383] Emotion engine: Analyzes the user's emotional state and generates an alert urging them to return home quickly if it determines that they are in a state of high stress.

[1384] IoT device: The air conditioner turns on and adjusts to the set temperature.

[1385] User: Receives an alert in the app about an abnormality, checks video footage and detailed information about the pet, and takes appropriate action based on the response suggested by the emotion engine.

[1386] Prompt for generative AI model: "Describe the process for detecting abnormal behavior and health conditions in pets and taking necessary action."

[1387] Example 3: Continuous learning

[1388] Server: Trains machine learning models based on continuously collected data to improve analysis accuracy and anomaly detection.

[1389] Emotion engine: Learns from the user's past emotional data and responds appropriately when their emotional patterns deviate from normal ones.

[1390] Users can check their pet's current condition and past activity records using a smartphone app. If an abnormality is detected, they can take prompt action based on the information provided by the app.

[1391] Prompt for generative AI model: "Describe the process by which the system continuously learns from pet health data and user emotion data to improve its accuracy."

[1392] In this way, the present invention uses advanced technology to ensure the health and safety of pets, allowing for real-time monitoring of pet conditions and automatic implementation of necessary measures. Furthermore, by taking into account the user's emotions, more appropriate and reassuring responses are possible.

[1393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1394] Step 1:

[1395] The device measures the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute) using the heart rate sensor, body temperature sensor, and activity level sensor attached to the pet. The measurement data is assumed to be a stable heart rate of 80 bpm, body temperature of 38°C, and activity level. The measurement data is saved in a buffer along with the pet ID and a timestamp.

[1396] Input: Biometric data from sensors

[1397] Output: Measurement data (heart rate, body temperature, activity level)

[1398] Step 2:

[1399] The device transmits the measured data, including the pet's ID, timestamp, heart rate, body temperature, and activity level, to the server at regular intervals. This transmitted data reaches the server via the network.

[1400] Input: Measurement data (heart rate, body temperature, activity level)

[1401] Output: Data sent to the server

[1402] Step 3:

[1403] The camera captures video data of your pet in real time—for example, a video of your pet relaxing in a designated resting area—and sends the captured video data to a server in streaming format.

[1404] Input: Real-time video data

[1405] Output: Video data sent to the server

[1406] Step 4:

[1407] The server receives the biometric data sent from the device and the video data from the camera and stores them in a database. The received data is associated with the pet ID and a timestamp.

[1408] Input: Biometric data and video data from the device

[1409] Output: Data stored in the database

[1410] Step 5:

[1411] The server analyzes the stored data using statistical methods and machine learning models. For example, it evaluates whether the pet's heart rate and body temperature are within healthy ranges and detects whether its activity level is unusual. Based on the results of this analysis, it determines whether the pet's condition is normal or abnormal.

[1412] Input: Stored biometric and video data

[1413] Output: Analysis and evaluation results (health status, behavioral status)

[1414] Step 6:

[1415] The server generates commands to control IoT devices as needed based on the analysis results. For example, if a pet's heart rate is high and the room temperature is high, it generates a command to turn on the air conditioner. This command is sent to the IoT device.

[1416] Input: Analysis results

[1417] Output: IoT device control command

[1418] Step 7:

[1419] If an abnormality is detected, the server sends an alert to the owner. At this time, the emotion engine adjusts the alert content and notification method based on the user's past emotional data. For example, if the user is stressed, it generates an alert that emphasizes urgency.

[1420] Input: Anomaly detection results and user emotion data

[1421] Output: User alert

[1422] Step 8:

[1423] The IoT device executes the commands received from the server, for example, turning on the air conditioner and adjusting the room temperature to the set value, thereby maintaining a comfortable environment for the pet.

[1424] Input: Control command from the server

[1425] Output: IoT device operation (e.g., air conditioner operation)

[1426] Step 9:

[1427] Users can receive alerts and check their pet's condition through a smartphone app, and if necessary, can remotely control IoT devices through the app to quickly adjust the temperature or take other actions.

[1428] Input: User alert from server

[1429] Output: User action (e.g. remote control, confirmation)

[1430] In this way, through the specific actions performed at each step and the chain of inputs and outputs, the system can monitor the pet's health in real time and automatically take necessary measures.

[1431] (Application example 2)

[1432] 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."

[1433] There is a need to monitor pet health and behavior in real time and take prompt and appropriate measures when abnormalities are detected. It is also important to reduce stress and increase peace of mind for pet owners by providing notification methods that take into consideration the owner's feelings regarding their pet's condition. Current systems do not comprehensively cover these elements, so a system that solves these issues is needed.

[1434] The identification process by the identification 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 measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means for analyzing the user's emotional state and adjusting the content and method of the notification; and means for automatically controlling environmental control devices such as air conditioners and feeders when an abnormality is detected in the pet's health condition or behavior. This makes it possible to monitor the pet's health condition and behavior in real time and take prompt and appropriate measures when an abnormality occurs, reducing the owner's stress and providing a sense of security through a notification method that takes the user's emotions into consideration.

[1435] A "biometric terminal" is a device worn by a pet to measure its heart rate, body temperature, and activity level.

[1436] A "server" is a device that receives and analyzes data sent from biometric information terminals and surveillance cameras, and controls IoT devices as necessary.

[1437] "Analysis" is the process of evaluating the pet's health and behavior based on the data received by the server.

[1438] An "IoT device" is a device that receives commands from a server and performs control operations, and includes air conditioners and bird feeders.

[1439] The "emotion engine" is a system that analyzes the user's emotional state and adjusts the content and method of notifications.

[1440] An "air conditioner" is an environmental control device for adjusting the temperature inside a room.

[1441] A "feeder" is an automatic device for providing food to pets.

[1442] "Notifications" are alerts or messages sent to owners when an abnormality is detected.

[1443] "Real-time" refers to data acquisition and analysis occurring immediately, without delay.

[1444] A "machine learning model" is an algorithm that uses continuously collected data to improve the accuracy of analysis and anomaly detection.

[1445] "User" refers to the owner who uses the system to monitor the health and behavior of their pet.

[1446] System Configuration

[1447] The invention requires the following components:

[1448] Bio-information terminal: Attached to your pet, it measures heart rate, body temperature, and activity level.

[1449] Surveillance camera: Captures video data of your pet in real time and sends it to a server.

[1450] Server: Analyzes the received data, evaluates the pet's health and behavior, and generates and sends the necessary commands.

[1451] Emotion engine: Analyzes the user's emotional state and adjusts the content and method of notifications.

[1452] IoT devices: Control air conditioners, feeders, etc. according to instructions from the server.

[1453] Smartphone app: A way for owners to monitor their pet's condition and receive alerts if anything unusual happens.

[1454] Process Overview

[1455] The server receives data sent from the vital signs terminal and monitoring camera, analyzes it, and evaluates the pet's health and behavior. If an abnormality is detected, the server generates and sends commands to control the IoT device and sends appropriate alerts to the owner.

[1456] The emotion engine analyzes the owner's emotional state and adjusts the content and method of notifications. For example, if the owner is stressed, the system will adjust to send faster and clearer alerts.

[1457] Hardware and Software

[1458] The hardware and software used to implement this system includes:

[1459] Hardware:

[1460] Biometric information terminals (e.g. smart bands for pets)

[1461] Surveillance cameras (e.g. network cameras)

[1462] Air conditioner (IoT-compatible smart air conditioner)

[1463] Feeder (IoT-enabled automatic feeder)

[1464] software:

[1465] Servers for data analysis (e.g., database servers and analysis servers)

[1466] Sentiment engines for sentiment analysis (e.g., sentiment analysis software using machine learning models)

[1467] Smartphone app (monitoring and alarm app for owners)

[1468] Specific examples of processing

[1469] For example, if your pet is exhibiting abnormal behavior in the living room (heart rate: 130, body temperature: 40, activity level: 85) and you are feeling stressed, the system will act as follows:

[1470] 1. The vital signs terminal measures the pet's abnormal heart rate, body temperature, and activity level and transmits the data to a server.

[1471] 2. The monitoring camera captures your pet's erratic movements in real time and sends streaming data to the server.

[1472] 3. The server analyzes this data and determines that the pet's condition is abnormal.

[1473] 4. The server sends a command to the IoT device (air conditioner) to set it to "cooling" and performs automatic control.

[1474] 5. The emotion engine analyzes the owner's emotional state and generates a strong alert message.

[1475] 6. Send a strong alert to owners via a smartphone app to return home quickly.

[1476] Prompt Sentence Examples

[1477] The system's behavior can be triggered by inputting a prompt like the following into the generative AI model:

[1478] Data required for a pet health monitoring program:

[1479] Health data (heart rate, body temperature, activity level)

[1480] User emotional state

[1481] Pet Health Data:

[1482] Heart rate: 130

[1483] Body temperature: 40 degrees

[1484] Activity level: 85

[1485] User emotional state:

[1486] stress

[1487] Expected system behavior:

[1488] 1. Analyze health data and detect abnormalities.

[1489] 2. Set the air conditioner to "cooling" and turn it on.

[1490] 3. Send a strong alert to the user to get home quickly.

[1491] In this way, it is possible to monitor the health and behavior of pets in real time, and to take prompt and appropriate action when an abnormality is detected.

[1492] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1493] Step 1:

[1494] The terminal measures the pet's vital signs (heart rate, body temperature, activity level). This data is collected in real time and sent to a server at regular intervals. The input is sensor data from the vital signs terminal, and the output is measurement data sent to the server. Specifically, the terminal's sensors collect various vital signs and send this data to the server via wireless communication.

[1495] Step 2:

[1496] The surveillance camera captures video of your pet in real time and sends it to a server as streaming data. The input is the video data captured by the surveillance camera, and the output is the data converted to digital format and sent to the server. The camera captures the video, digitizes the video signal, and sends it to the server over the network.

[1497] Step 3:

[1498] The server receives data sent from the biometric information terminal and the surveillance camera. The input is biometric information and video data, which are then stored in a database. Specifically, the server captures this data in real time using the data receiving module and stores it in the storage system.

[1499] Step 4:

[1500] The server analyzes the received data and evaluates the pet's health and behavior. The input is the stored biometric information and video data, and the output is the analysis results (health and behavior assessment). The data analysis module evaluates the data using machine learning algorithms and detects abnormalities.

[1501] Step 5:

[1502] Based on the analysis results, the server generates and sends commands to control IoT devices as needed. The input is the analysis results, and the output is the generated control commands. Specifically, it monitors the results of the analysis module, and if an abnormality is detected, it generates and sends appropriate commands to the air conditioner or feeder.

[1503] Step 6:

[1504] If an abnormality is detected, the server sends an alert to the owner. The input is the analysis result and the output of the emotion engine, and the output is an alert message. The notification module takes into account the analysis result of the emotion engine, generates an alert message to be sent to the owner, and notifies the smartphone app.

[1505] Step 7:

[1506] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications. The input is the user's emotional data, and the output is the intensity and format of the notification message. Specifically, the emotion engine uses machine learning to learn past emotional data, analyzes the user's current emotional state, and determines the optimal notification method.

[1507] Step 8:

[1508] IoT devices (air conditioners, feeders, etc.) receive commands from the server and execute the specified control actions. The input is the control command from the server, and the output is the executed control action. For example, the air conditioner adjusts the temperature, and the feeder dispenses food.

[1509] 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.

[1510] 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.

[1511] 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.

[1512] [Fourth embodiment]

[1513] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1514] 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.

[1515] 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).

[1516] 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.

[1517] 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.

[1518] 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).

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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."

[1526] The present invention relates to a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. The system mainly consists of the following components:

[1527] 1. Biometric Information Terminal

[1528] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[1529] 2. Surveillance Camera

[1530] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[1531] 3. Server

[1532] The server receives, stores, and analyzes the data sent from the device and camera. Specifically, it performs the following operations:

[1533] The received biometric data is analyzed to assess the pet's health condition.

[1534] The pet's behavior patterns are analyzed based on the received video data.

[1535] Compare with past data to detect anomalies.

[1536] If an abnormality is detected, an alert will be sent to the owner.

[1537] 4. IoT devices

[1538] This is a device that receives commands generated by the server and performs the specified control action (for example, adjusting the temperature of the air conditioner, activating the feeder, etc.).

[1539] 5. Smartphone App

[1540] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[1541] System operation example

[1542] 1. Health monitoring

[1543] Device:

[1544] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[1545] camera:

[1546] Capture footage of your pet relaxing in its dedicated resting area.

[1547] server:

[1548] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[1549] User:

[1550] All you need to do is check the app to see how relaxed your pet is.

[1551] 2. Anomaly detection and response

[1552] Device:

[1553] Measure your pet's abnormally high heart rate and activity level.

[1554] camera:

[1555] Capture footage of your pet moving erratically around the house.

[1556] server:

[1557] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[1558] IoT devices:

[1559] The air conditioner turns on and adjusts to the desired temperature.

[1560] User:

[1561] The app will alert you to any abnormalities and provide detailed information about your pet's condition, allowing you to take action if necessary, such as returning home immediately.

[1562] 3. Continuous learning

[1563] server:

[1564] Continuously collected data is used to train machine learning models to improve analysis and anomaly detection, allowing for better responses in the future.

[1565] This system uses advanced technology to ensure the health and safety of pets, allowing owners to monitor their pet's condition in real time and automatically take necessary measures, allowing them to leave their pets with peace of mind even when they are not at home.

[1566] The processing flow will be explained below.

[1567] Step 1:

[1568] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[1569] Step 2:

[1570] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[1571] Step 3:

[1572] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[1573] Step 4:

[1574] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[1575] Step 5:

[1576] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[1577] Step 6:

[1578] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[1579] Step 7:

[1580] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[1581] Step 8:

[1582] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[1583] Step 9:

[1584] The server generates an alert and sends it to the owner's smartphone app to notify them.

[1585] Step 10:

[1586] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[1587] Step 11:

[1588] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[1589] Step 12:

[1590] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[1591] Step 13:

[1592] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[1593] Example 1

[1594] 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."

[1595] In recent years, the importance of pet health management has increased, creating a growing need for real-time monitoring of pet health and behavior and early detection of abnormalities. However, existing technologies offer few systems that comprehensively monitor pets' biometric information and behavior and automatically implement countermeasures, which poses the problem of being unable to quickly respond to abnormalities in pets when their owners are away. Furthermore, there is a lack of technology to improve the accuracy of analyzing pet behavior patterns and detecting abnormalities. Therefore, there is a need for the development of more effective monitoring systems to ensure the health and safety of pets.

[1596] 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.

[1597] In this invention, the server includes means for analyzing received data and evaluating the health and behavior of the animals, means for generating and transmitting commands to operate the control device as necessary based on the evaluation results, means for sending a notification to an administrator if an abnormality is detected, means for detecting an abnormality by comparing with past data, means for storing multiple pieces of data transmitted from the device in a database, and means for continuously collecting data and training a learning model. This makes it possible to comprehensively monitor the health and behavior of animals in real time, detect abnormalities early, and automatically take appropriate measures.

[1598] "Device" refers to a biometric terminal attached to an animal, which is hardware used to measure heart rate, body temperature, and activity level.

[1599] "Processing Unit" refers to the server used to analyze the received data and assess the health and behavior of the animals.

[1600] "Control device" refers to an IoT device that operates based on commands generated by a server, and includes devices such as air conditioners and feeders.

[1601] A "command" is a command generated and transmitted by the server, and includes specific instructions for operating the control device.

[1602] "Administrator" refers to the user of the system, typically the pet owner.

[1603] "Database" refers to a storage device for storing received biometric data and video data.

[1604] "Learning model" refers to a machine learning algorithm that is trained on continuously collected data to improve the accuracy of analysis and anomaly detection.

[1605] "Abnormality" refers to a condition detected when abnormalities are found in health conditions or behavioral patterns compared with past data.

[1606] The present invention is a system that monitors the health and behavior of pets in real time and automatically takes necessary measures. This system mainly consists of the following components.

[1607] 1. Biometric Information Terminal

[1608] Terminal: The biometric information terminal is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is stored in temporary memory and sent to a server at regular intervals (for example, every minute). The terminal uses various sensor devices to accurately collect this information.

[1609] Example: For example, if your pet's heart rate is 80 beats per minute, its body temperature is 37 degrees, and its activity level is 5 (out of 10), these data will be measured and sent to the server.

[1610] 2. Surveillance Camera

[1611] Surveillance camera: A surveillance camera is a device that captures video data of your pet in real time and sends it to a server as streaming data. This camera can rotate 360 ​​degrees and automatically track your pet's position.

[1612] Example: A pet taking a nap in the living room is captured on camera and transmitted to a server in real time.

[1613] 3. Server

[1614] Server: The server is used to receive, store, and analyze data sent from the devices and cameras. Software used includes database management systems and machine learning algorithms.

[1615] Data analysis: Analyze the received vital data and assess the pet's health status.

[1616] Behavioral pattern analysis: Analyze your pet's behavioral patterns based on video data.

[1617] Anomaly detection: Detect anomalies by comparing with past data.

[1618] Command generation: If an abnormality is detected, an appropriate control command is generated and sent to the IoT device.

[1619] Data storage: The received data is stored in a database with a timestamp.

[1620] Machine learning: Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[1621] Example: If data and video data are received showing a heart rate of 80, body temperature of 37 degrees, and activity level of 5, and based on this the pet's health condition is assessed as normal, the data will be stored in the database and no special action will be required.

[1622] 4. IoT devices

[1623] IoT device: An IoT device is a device that receives commands generated by a server and executes the specified control action (e.g., adjusting the temperature of an air conditioner, activating a feeder, etc.).

[1624] Example: For example, if a pet's heart rate is abnormally high and its activity level is also abnormally high, a command to turn on the air conditioner and lower the room temperature will be generated, and the air conditioner will turn on and adjust to the set temperature.

[1625] 5. Smartphone App

[1626] Users can receive alerts from the server via a smartphone app and monitor their pet's condition in real time. They can also use the app to remotely control IoT devices.

[1627] Example: The owner receives an alert and can view footage of their pet or manually change the air conditioning settings through the app.

[1628] Prompt Sentence Examples

[1629] "Please explain what alerts will be generated if an abnormality is detected based on the latest data collection results from this pet health monitoring system."

[1630] By inputting such prompt statements into the generative AI model, it is possible to provide detailed information such as what specific alerts will be sent when an abnormality is detected and what countermeasures will be taken.

[1631] The above is an embodiment of the present invention.

[1632] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1633] Step 1:

[1634] Device: Measures heart rate, body temperature, and activity levels. The biometric device attached to your pet uses various built-in sensors to measure these health indicators.

[1635] Input: Your pet's current vitals.

[1636] Output: Measured heart rate, body temperature, and activity level data.

[1637] Specific actions: For example, your pet's heart rate is measured at 80 beats per minute, body temperature is measured at 37 degrees, and activity level is measured at 5 (out of 10).

[1638] Step 2:

[1639] Terminal: Sends measurement data to the server. The acquired data is encrypted and then sent to the server via Wi-Fi.

[1640] Input: Measured heart rate, temperature, and activity level data.

[1641] Output: Encrypted biometric data sent to the server.

[1642] Specific operation: Data on the pet's heart rate (80), body temperature (37 degrees), and activity level (5) are encrypted and sent to the server.

[1643] Step 3:

[1644] Device: The monitoring camera captures video of your pet in real time. The camera rotates 360 degrees and automatically tracks your pet's position.

[1645] Input: Pet movements captured on surveillance camera.

[1646] Output: Real-time captured video data.

[1647] Specific operation: The camera captures a pet taking a nap in the living room, and the image is then used as video data.

[1648] Step 4:

[1649] Terminal: Captured video data is sent to the server in streaming format. Compressed video data is sent.

[1650] Input: Video data captured in real time.

[1651] Output: Compressed video data sent to the server.

[1652] Specific operation: The captured video (of a pet taking a nap in the living room) is compressed in MPEG format and sent to a server via Wi-Fi.

[1653] Step 5:

[1654] Server: Receives biometric and video data and stores them in a database. Received data is recorded with a timestamp.

[1655] Input: Transmitted biometric and video data.

[1656] Output: Biometric and video data stored in a database.

[1657] Specific operation: Data such as a heart rate of 80, body temperature of 37 degrees, and activity level of 5, as well as footage of a napping pet, are stored in a database.

[1658] Step 6:

[1659] Server: Analyzes the received vital data and evaluates the pet's health condition. It compares it with the normal range and checks for any abnormalities.

[1660] Input: Stored biometric data.

[1661] Output: Evaluation result (normal or abnormal).

[1662] Specific operation: Confirm that the heart rate of 80 is within the normal range (60-100) and the body temperature of 37 degrees is within the normal range (36-39 degrees), and evaluate the health condition as normal.

[1663] Step 7:

[1664] Server: Analyzes the video data and evaluates the pet's behavioral patterns. It analyzes behavior based on the frequency of movement and changes in position.

[1665] Input: Stored video data.

[1666] Output: Behavioral pattern evaluation results.

[1667] Specific behavior: Confirms that a napping pet has been in the same place for more than an hour and determines that it is in a relaxed state.

[1668] Step 8:

[1669] Server: Compares biometric data and behavioral patterns with past data to detect anomalies. If anomalies are found, it determines the appropriate course of action.

[1670] Input: Assessment results and historical data.

[1671] Output: Anomaly detection results and countermeasures.

[1672] Specific operation: If the relative activity level is 10 (the previous maximum activity level is 8) and the heart rate is 120 (the previous maximum heart rate is 100), it is determined to be abnormal.

[1673] Step 9:

[1674] Server: When an abnormality is detected, it generates and sends control commands to the appropriate IoT devices.

[1675] Input: Anomaly detection results.

[1676] Output: Control command.

[1677] Specific operation: Because the room temperature is high, a command to operate the air conditioner is generated and sent to the IoT device.

[1678] Step 10:

[1679] IoT device: Receives commands from the server and performs the specified control action.

[1680] Input: Control command.

[1681] Output: The control action that was performed.

[1682] Specific action: The air conditioner is turned on and the room temperature is set to 24 degrees.

[1683] Step 11:

[1684] User: Monitors pet status in real time using a smartphone app and receives alerts sent from the server.

[1685] Input: Alert from the server.

[1686] Output: Alert and pet information displayed in the app.

[1687] Specific behavior: The app will receive a push notification indicating an abnormality has occurred and will display your pet's detailed information (heart rate 120, activity level 10).

[1688] Step 12:

[1689] User: Use the app to remotely control IoT devices as needed.

[1690] Input: User instructions.

[1691] Output: The control action that was performed.

[1692] Specific actions: For example, manually change the air conditioner temperature setting from 24 degrees to 22 degrees.

[1693] Step 13:

[1694] Server: Continuously collects data and trains machine learning models to improve analysis and anomaly detection.

[1695] Input: Continuously collected biometric and behavioral data.

[1696] Output: An improved machine learning model.

[1697] What it does: The server periodically learns from the data and understands new abnormal patterns.

[1698] (Application example 1)

[1699] 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."

[1700] Conventional pet monitoring systems can monitor pets' health and behavior in real time, but their application is limited to the home. Systems are needed to ensure that pets can live in a safe and comfortable environment even when out and about or on the move. In particular, when using autonomous vehicles, controlling the in-car environment can have a significant impact on pet health. However, currently, there are no systems that manage pet conditions in conjunction with the autonomous vehicle's environmental control. Therefore, there is a need for a system that can monitor pet health in real time while traveling and take appropriate measures if an abnormality is detected.

[1701] 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.

[1702] In this invention, the server includes means for measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet, means for transmitting the measured data to the server, means for the server to analyze the received data and evaluate the pet's health condition and behavior, means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results, means for sending an alert to the owner if an abnormality is detected, and means for enabling cooperation with the control device of the autonomous vehicle and controlling the environment inside the vehicle. This makes it possible to monitor the pet's health condition and behavior in real time and to appropriately control the environment inside the autonomous vehicle if an abnormality occurs.

[1703] A "biometric information terminal" is a device that is attached to a pet and measures biometric data such as heart rate, body temperature, and activity level.

[1704] The "means for transmitting data" is a device or function that transmits measured data from the biometric information terminal to the server.

[1705] The "server" is a central data processing unit that receives and analyzes pet biometric and video data to assess the pet's health and behavior.

[1706] An "IoT device" is a device that has the ability to communicate with other devices via the Internet and perform control operations based on instructions.

[1707] "Means for sending an alert" refers to a device or function that sends a warning notice to the owner when an abnormality is detected.

[1708] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without driver operation.

[1709] "Means for environmental control" refers to a device or function for adjusting the internal environment of an autonomous vehicle (e.g., adjusting the temperature of the air conditioner).

[1710] "Means for capturing video data" refers to a device or function that uses a camera to capture video of the pet in real time and transmits the data to a server.

[1711] "Continuously collected data" is a general term for biometric information and behavioral data acquired continuously over a certain period of time.

[1712] A "machine learning model" refers to an algorithm or method for analyzing and learning about a pet's behavioral patterns and health condition.

[1713] An "autonomous vehicle control device" is a central control device for managing the driving and environmental settings of an autonomous vehicle.

[1714] The present invention relates to a system that monitors the health and behavior of pets in real time in cooperation with an autonomous vehicle and automatically takes necessary measures. This system mainly consists of the following components.

[1715] System configuration

[1716] 1. Biometric Information Terminal

[1717] The biometric information terminal is attached to the pet and measures vital data such as heart rate, body temperature, and activity level, and the measured data is sent to a server at regular intervals.

[1718] 2. Surveillance Camera

[1719] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[1720] 3. Server

[1721] The server receives, stores, and analyzes the data sent from the biometric terminal and the surveillance camera. Specifically, it performs the following operations:

[1722] Analyze biological data to assess your pet's health.

[1723] Analyze your pet's behavior patterns based on video data.

[1724] Continuously collected data is used to train machine learning models to improve the accuracy of analysis and anomaly detection.

[1725] If an abnormality is detected, an alert will be sent to the owner.

[1726] It works in conjunction with the control device of the autonomous vehicle to control the in-car environment (such as adjusting the air conditioning temperature).

[1727] 4. IoT devices

[1728] It receives commands generated from the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner).

[1729] 5. Smartphone App

[1730] Using this app, owners can monitor their pets' status in real time, receive alerts when something abnormal occurs, and remotely control IoT devices as needed.

[1731] Example of a system

[1732] First, let's say the vital signs terminal measures a pet's heart rate at 120 bpm, body temperature at 38.5 °C, and activity level at 8. This data is sent to a server, which analyzes it in real time. The server determines from the data that the pet is stressed. The server then sends a command to activate the air conditioning in the autonomous vehicle and appropriately lower the temperature inside the vehicle. An alert is then sent to the owner via a smartphone app to notify them of the situation.

[1733] Prompt Sentence Examples

[1734] Build a system that monitors your pet's health and adjusts the car's air conditioning if an abnormal condition is detected. Specifically, data including the following parameters will be sent to a server: heart rate (bpm), body temperature (°C), and activity level. Based on the results of data analysis, appropriate action will be taken, such as adjusting the air conditioning temperature or stopping the car.

[1735] Thus, the present invention is a system for ensuring the health and safety of pets, and also provides an environment in which owners can travel with their pets with peace of mind.

[1736] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1737] Step 1:

[1738] Measurement and transmission of vital signs:

[1739] The terminal measures the heart rate, body temperature, and activity level every minute from the biometric information terminal attached to the pet. These measurement data are sent to the server as biometric data. It receives biometric information (heart rate, body temperature, activity level) as input and performs measurements. It generates the measured data as output and sends it to the server.

[1740] Step 2:

[1741] Video data capture and transmission:

[1742] The surveillance camera captures video of your pet in real time and sends the video data to the server in streaming format. The input is continuous video capture, and the output is captured video data that is sent to the server.

[1743] Step 3:

[1744] Data reception and storage:

[1745] The server receives the biometric data sent from the device and the video data sent from the surveillance camera and stores the data in a temporary database. The server receives the data sent as input and stores it in the database. The server uses the stored data as output in subsequent analysis steps.

[1746] Step 4:

[1747] Data analysis:

[1748] The server analyzes the received biometric data and video data to evaluate the pet's health and behavior. A pre-trained machine learning model is used for the analysis. The stored data is read as input and the machine learning model is applied. The server generates an evaluation result as output and determines whether or not there are any abnormalities.

[1749] Step 5:

[1750] Anomaly detection and response command generation:

[1751] If the server detects an abnormality based on the analysis results, it generates a response command. If a specific abnormality (e.g., stress due to high temperature) is detected, it generates a command to adjust the air conditioner temperature. It uses the evaluation results as input to determine whether an abnormality exists. It generates a response command as output and saves the response command.

[1752] Step 6:

[1753] Integration with IoT devices:

[1754] The server sends the generated corresponding command to the IoT device to execute a remote control action (e.g., adjusting the temperature of an air conditioner). The server uses the generated corresponding command as input and sends it to the IoT device. The IoT device executes the action as output.

[1755] Step 7:

[1756] Sending alerts:

[1757] When an abnormality is detected and a response is taken, the server sends the details as an alert to the owner's smartphone app. The server receives the execution result of the response command as input and generates an alert. The server sends a notification to the owner as output.

[1758] Step 8:

[1759] Continuous learning:

[1760] The server continuously trains the machine learning model using continuously collected biometric and video data. It receives the accumulated data as input and trains the model. As output, it generates a machine learning model with improved accuracy.

[1761] By linking these steps together, a series of systems are created that monitors the health of pets in real time and appropriately controls the environment of the self-driving vehicle.

[1762] 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.

[1763] This invention combines a system that monitors the health and behavior of pets in real time and automatically takes necessary measures with an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[1764] 1. Biometric Information Terminal

[1765] The device is attached to the pet and measures biometric data such as heart rate, body temperature, and activity level. The measured data is designed to be sent to a server at regular intervals (for example, every minute).

[1766] 2. Surveillance Camera

[1767] A surveillance camera is a device that captures video data of pets in real time and transmits it to a server as streaming data.

[1768] 3. Server

[1769] The server receives, stores, and analyzes data sent from the device and camera. It uses the analysis to evaluate the pet's health and behavior, and sends an alert to the owner if an abnormality is detected. It also generates and sends commands to control IoT devices. Furthermore, it uses an emotion engine to analyze the user's emotional state and adjust the content and method of the alert.

[1770] 4. Emotion Engine

[1771] The emotion engine recognizes the user's emotions and evaluates the user's stress level in response to abnormal notifications or pet conditions. The emotion engine can also learn from the user's past emotional data and suggest more appropriate responses.

[1772] 5. IoT devices

[1773] This is a device that receives commands generated by the server and performs the specified control action (e.g., adjusting the temperature of the air conditioner, activating a feeder, etc.).

[1774] 6. Smartphone App

[1775] Through this app, owners can receive alerts sent from the server and monitor their pet's condition, and can also remotely control IoT devices if necessary.

[1776] System operation example

[1777] Example 1: Health monitoring

[1778] Device:

[1779] Measure your pet's heart rate and body temperature to make sure they are within normal ranges and that their activity level is calm.

[1780] camera:

[1781] Capture footage of your pet relaxing in its dedicated resting area.

[1782] server:

[1783] The data is analyzed and determined to be healthy and relaxed. If no action is required, the data is simply stored.

[1784] Emotion Engine:

[1785] Determine that the user's stress level is low and decide that there is no need to send an alert.

[1786] User:

[1787] Use the app to check how relaxed your pet is and feel reassured.

[1788] Example 2: Anomaly detection and response

[1789] Device:

[1790] Measure your pet's abnormally high heart rate and activity level.

[1791] camera:

[1792] Capture footage of your pet moving erratically around the house.

[1793] server:

[1794] Analysis determines that the pet is experiencing stress or is affected by high temperatures. When an abnormality is detected, a command is generated and sent to turn on the air conditioner and lower the room temperature.

[1795] Emotion Engine:

[1796] If the system analyzes the user's emotional state and determines that the user is in a state of high stress, it generates an emotionally sensitive alert notification, such as sending an alert urging the user to return home quickly.

[1797] IoT devices:

[1798] The air conditioner turns on and adjusts to the desired temperature.

[1799] User:

[1800] Receive alerts in the app when something unusual happens, get detailed information about your pet's condition, and take appropriate action based on the response suggested by the emotion engine.

[1801] Example 3: Continuous learning

[1802] server:

[1803] Train machine learning models on continuously collected data to improve the accuracy of analysis and anomaly detection.

[1804] Emotion Engine:

[1805] The system learns the user's past emotional data and can respond more appropriately when the user's emotional pattern differs from the normal pattern.

[1806] User:

[1807] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[1808] This system uses advanced technology to ensure the health and safety of pets, allowing it to monitor their condition in real time and automatically take necessary measures. Furthermore, by taking the user's emotions into consideration, it can respond more appropriately and effectively, giving owners a greater sense of security.

[1809] The processing flow will be explained below.

[1810] Step 1:

[1811] The device measures your pet's vital signs (heart rate, body temperature, activity level) at regular intervals (e.g., every minute), and the measured data is temporarily stored in the device's internal memory.

[1812] Step 2:

[1813] The camera captures video data of the pet in real time and transmits it as streaming data to a server via a network.

[1814] Step 3:

[1815] The device sends the measured biometric information to the server periodically (e.g., every minute) using Wi-Fi or Bluetooth.

[1816] Step 4:

[1817] The server receives the biometric data sent from the device and stores it in a database. Similarly, it receives and stores video data from the camera.

[1818] Step 5:

[1819] The server analyzes the received biometric data and runs algorithms to assess whether the values ​​for heart rate, body temperature, and activity level are within normal ranges.

[1820] Step 6:

[1821] The server analyzes the received video data and runs an image analysis algorithm to evaluate the pet's behavioral patterns, providing behavioral data such as whether the pet is resting or running around.

[1822] Step 7:

[1823] The server combines the analysis results of the biometric data and video data to evaluate the pet's health and behavior, and compares it with past data to determine whether any abnormalities have been detected.

[1824] Step 8:

[1825] If an anomaly is detected, the server generates an alert, which includes details such as the type of anomaly, the time it occurred, and recommended actions to take.

[1826] Step 9:

[1827] The server generates an alert and sends it to the owner's smartphone app to notify them.

[1828] Step 10:

[1829] Based on the analysis results, the server generates commands to control IoT devices as needed. For example, if a pet's activity level and heart rate are high, it generates a command to turn on the air conditioner to lower the room temperature.

[1830] Step 11:

[1831] The IoT device receives commands from the server and performs the specified action (e.g., turning on the air conditioner).

[1832] Step 12:

[1833] The server uses an emotion engine to analyze the user's emotion data, which evaluates whether the user is currently relaxed or stressed.

[1834] Step 13:

[1835] The server adjusts the content and format of the alert notification based on the analysis results of the emotion engine. For example, if the user is under stress, it uses gentle and reassuring language.

[1836] Step 14:

[1837] The server continuously trains machine learning models based on collected data to improve the accuracy of analysis and anomaly detection, enabling more sophisticated anomaly detection and appropriate response in the future.

[1838] Step 15:

[1839] Users can check their pet's current condition and past activity records through a smartphone app. If an abnormality is detected, users can take prompt action based on the information provided by the app.

[1840] Example 2

[1841] 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."

[1842] Systems that monitor pet health and behavior in real time and automatically take necessary measures are important. However, conventional systems often fail to consider the owner's emotions or stress level when detecting an abnormality, making owners anxious. Furthermore, data analysis accuracy is insufficient, potentially delaying appropriate responses. Furthermore, limited remote control options mean owners are sometimes unable to respond quickly. There is a need to resolve these issues and provide a more reliable and effective pet monitoring system.

[1843] 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.

[1844] In this invention, the server includes: means for measuring the heart rate, body temperature, and activity level from a biometric information terminal worn by the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means including an emotion engine for evaluating the user's emotional data and adjusting the alert and notification method; means for the server to continuously analyze the data based on a machine learning model to improve accuracy; and means for the owner to receive alerts via a smartphone app and remotely check and control the pet's condition. This allows for a response that takes the user's emotions into consideration when an abnormality is detected, allowing the owner to respond quickly and with peace of mind. Furthermore, the improved accuracy of data analysis allows for more accurate monitoring of the pet's health condition and behavior. Furthermore, remote control using a smartphone app allows owners to monitor their pet's condition from anywhere and take appropriate action in emergencies.

[1845] A "biometric information terminal" is a device that is attached to a pet to measure biometric data such as heart rate, body temperature, and activity level.

[1846] The "measuring means" is a means for measuring heart rate, body temperature, and activity level using a biometric information terminal.

[1847] The "transmission means" is a means for transmitting the measured data to the server.

[1848] The "server" is a central processing unit that receives, stores, and analyzes data sent from the terminals and cameras, and generates and sends necessary commands based on the evaluation results.

[1849] "Analysis means" refers to means for analyzing the received data and assessing the health and behavior of the pet.

[1850] The "assessment means" is a means for assessing the health and behavior of a pet based on the analyzed data.

[1851] The "control means" is a means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results.

[1852] The "alert sending means" is a means for sending an alert to the owner when an abnormality is detected.

[1853] The "emotion engine" is an engine that evaluates the user's emotional data and adjusts alerts and notification methods.

[1854] A "machine learning model" is a model that the server trains using continuously collected data to improve the accuracy of analysis and anomaly detection.

[1855] The "smartphone app" is an application that allows owners to receive alerts and remotely check and control their pet's condition.

[1856] This invention is a system that monitors the health condition and behavior of pets in real time and automatically takes necessary measures. This system includes a biometric information terminal that acquires the pet's biometric information, a monitoring camera that captures the pet's video, a server that receives and analyzes the data, an emotion engine that analyzes the user's emotions, IoT devices, and a smartphone app used by pet owners.

[1857] Biometric information terminal

[1858] The device is attached to the pet and has built-in heart rate, body temperature, and activity level sensors, which measure the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute).

[1859] Surveillance camera

[1860] The camera captures images of your pet in real time and sends them to a server as streaming data, allowing you to constantly monitor your pet's behavior and environment.

[1861] server

[1862] The server receives data sent from the device and camera and stores it in a database. The received data is analyzed using machine learning models and statistical methods. The server detects abnormalities when a pet's heart rate or body temperature is outside of normal ranges or when its activity level is abnormally high. When an abnormality is detected, the server determines the necessary countermeasures based on the evaluation results and generates commands to control the IoT device.

[1863] Emotion Engine

[1864] The emotion engine has the ability to evaluate the user's emotional data. It learns from past emotional data and adjusts the alert content and notification method. For example, if the user is in a stressful state, it generates an alert that prompts a prompt response.

[1865] IoT equipment

[1866] The IoT device receives commands sent from the server and executes the specified control action. For example, if an abnormality is detected, the device can activate the air conditioner to lower the room temperature.

[1867] Smartphone app

[1868] Users can receive alerts sent from the server via a smartphone app and check the status of their pets. If necessary, they can also remotely control IoT devices through the app.

[1869] Specific examples

[1870] Example 1: Health monitoring

[1871] Device: Measure your pet's heart rate to 80 bpm, body temperature to 38°C, and activity level to ensure it is calm.

[1872] Camera: Capture footage of your pet relaxing in its dedicated resting area.

[1873] Server: Analyzes the received data and determines that the pet is healthy and relaxed. If no action is required, saves the data in a database.

[1874] Emotion engine: Determines that the user's stress level is low and no alert needs to be sent.

[1875] Users: Use the app to see their pets relaxing and feel reassured.

[1876] Prompt for generative AI model: "Describe the results of monitoring your pet's heart rate and body temperature when they are within normal ranges and relaxed."

[1877] Example 2: Anomaly detection and response

[1878] Device: Pet's heart rate is measured at 150 bpm, body temperature at 39.5°C, and activity level is abnormally high.

[1879] Camera: Capture footage of your pet moving erratically around the house.

[1880] Server: Determines through analysis that the pet is stressed or affected by high temperatures. Generates and sends a command to turn on the air conditioner and lower the room temperature.

[1881] Emotion engine: Analyzes the user's emotional state and generates an alert urging them to return home quickly if it determines that they are in a state of high stress.

[1882] IoT device: The air conditioner turns on and adjusts to the set temperature.

[1883] User: Receives an alert in the app about an abnormality, checks video footage and detailed information about the pet, and takes appropriate action based on the response suggested by the emotion engine.

[1884] Prompt for generative AI model: "Describe the process for detecting abnormal behavior and health conditions in pets and taking necessary action."

[1885] Example 3: Continuous learning

[1886] Server: Trains machine learning models based on continuously collected data to improve analysis accuracy and anomaly detection.

[1887] Emotion engine: Learns from the user's past emotional data and responds appropriately when their emotional patterns deviate from normal ones.

[1888] Users can check their pet's current condition and past activity records using a smartphone app. If an abnormality is detected, they can take prompt action based on the information provided by the app.

[1889] Prompt for generative AI model: "Describe the process by which the system continuously learns from pet health data and user emotion data to improve its accuracy."

[1890] In this way, the present invention uses advanced technology to ensure the health and safety of pets, allowing for real-time monitoring of pet conditions and automatic implementation of necessary measures. Furthermore, by taking into account the user's emotions, more appropriate and reassuring responses are possible.

[1891] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1892] Step 1:

[1893] The device measures the pet's heart rate, body temperature, and activity level at regular intervals (for example, every minute) using the heart rate sensor, body temperature sensor, and activity level sensor attached to the pet. The measurement data is assumed to be a stable heart rate of 80 bpm, body temperature of 38°C, and activity level. The measurement data is saved in a buffer along with the pet ID and a timestamp.

[1894] Input: Biometric data from sensors

[1895] Output: Measurement data (heart rate, body temperature, activity level)

[1896] Step 2:

[1897] The device transmits the measured data, including the pet's ID, timestamp, heart rate, body temperature, and activity level, to the server at regular intervals. This transmitted data reaches the server via the network.

[1898] Input: Measurement data (heart rate, body temperature, activity level)

[1899] Output: Data sent to the server

[1900] Step 3:

[1901] The camera captures video data of your pet in real time—for example, a video of your pet relaxing in a designated resting area—and sends the captured video data to a server in streaming format.

[1902] Input: Real-time video data

[1903] Output: Video data sent to the server

[1904] Step 4:

[1905] The server receives the biometric data sent from the device and the video data from the camera and stores them in a database. The received data is associated with the pet ID and a timestamp.

[1906] Input: Biometric data and video data from the device

[1907] Output: Data stored in the database

[1908] Step 5:

[1909] The server analyzes the stored data using statistical methods and machine learning models. For example, it evaluates whether the pet's heart rate and body temperature are within healthy ranges and detects whether its activity level is unusual. Based on the results of this analysis, it determines whether the pet's condition is normal or abnormal.

[1910] Input: Stored biometric and video data

[1911] Output: Analysis and evaluation results (health status, behavioral status)

[1912] Step 6:

[1913] The server generates commands to control IoT devices as needed based on the analysis results. For example, if a pet's heart rate is high and the room temperature is high, it generates a command to turn on the air conditioner. This command is sent to the IoT device.

[1914] Input: Analysis results

[1915] Output: IoT device control command

[1916] Step 7:

[1917] If an abnormality is detected, the server sends an alert to the owner. At this time, the emotion engine adjusts the alert content and notification method based on the user's past emotional data. For example, if the user is stressed, it generates an alert that emphasizes urgency.

[1918] Input: Anomaly detection results and user emotion data

[1919] Output: User alert

[1920] Step 8:

[1921] The IoT device executes the commands received from the server, for example, turning on the air conditioner and adjusting the room temperature to the set value, thereby maintaining a comfortable environment for the pet.

[1922] Input: Control command from the server

[1923] Output: IoT device operation (e.g., air conditioner operation)

[1924] Step 9:

[1925] Users can receive alerts and check their pet's condition through a smartphone app, and if necessary, can remotely control IoT devices through the app to quickly adjust the temperature or take other actions.

[1926] Input: User alert from server

[1927] Output: User action (e.g. remote control, confirmation)

[1928] In this way, through the specific actions performed at each step and the chain of inputs and outputs, the system can monitor the pet's health in real time and automatically take necessary measures.

[1929] (Application example 2)

[1930] 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."

[1931] There is a need to monitor pet health and behavior in real time and take prompt and appropriate measures when abnormalities are detected. It is also important to reduce stress and increase peace of mind for pet owners by providing notification methods that take into consideration the owner's feelings regarding their pet's condition. Current systems do not comprehensively cover these elements, so a system that solves these issues is needed.

[1932] The identification process by the identification 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 measuring the heart rate, body temperature, and activity level from a biometric information terminal attached to the pet; means for transmitting the measured data to the server; means for analyzing the received data and evaluating the pet's health condition and behavior by the server; means for generating and transmitting commands to control IoT devices as necessary based on the evaluation results; means for sending an alert to the owner if an abnormality is detected; means for analyzing the user's emotional state and adjusting the content and method of the notification; and means for automatically controlling environmental control devices such as air conditioners and feeders when an abnormality is detected in the pet's health condition or behavior. This makes it possible to monitor the pet's health condition and behavior in real time and take prompt and appropriate measures when an abnormality occurs, reducing the owner's stress and providing a sense of security through a notification method that takes the user's emotions into consideration.

[1933] A "biometric terminal" is a device worn by a pet to measure its heart rate, body temperature, and activity level.

[1934] A "server" is a device that receives and analyzes data sent from biometric information terminals and surveillance cameras, and controls IoT devices as necessary.

[1935] "Analysis" is the process of evaluating the pet's health and behavior based on the data received by the server.

[1936] An "IoT device" is a device that receives commands from a server and performs control operations, and includes air conditioners and bird feeders.

[1937] The "emotion engine" is a system that analyzes the user's emotional state and adjusts the content and method of notifications.

[1938] An "air conditioner" is an environmental control device for adjusting the temperature inside a room.

[1939] A "feeder" is an automatic device for providing food to pets.

[1940] "Notifications" are alerts or messages sent to owners when an abnormality is detected.

[1941] "Real-time" refers to data acquisition and analysis occurring immediately, without delay.

[1942] A "machine learning model" is an algorithm that uses continuously collected data to improve the accuracy of analysis and anomaly detection.

[1943] "User" refers to the owner who uses the system to monitor the health and behavior of their pet.

[1944] System Configuration

[1945] The invention requires the following components:

[1946] Bio-information terminal: Attached to your pet, it measures heart rate, body temperature, and activity level.

[1947] Surveillance camera: Captures video data of your pet in real time and sends it to a server.

[1948] Server: Analyzes the received data, evaluates the pet's health and behavior, and generates and sends the necessary commands.

[1949] Emotion engine: Analyzes the user's emotional state and adjusts the content and method of notifications.

[1950] IoT devices: Control air conditioners, feeders, etc. according to instructions from the server.

[1951] Smartphone app: A way for owners to monitor their pet's condition and receive alerts if anything unusual happens.

[1952] Process Overview

[1953] The server receives data sent from the vital signs terminal and monitoring camera, analyzes it, and evaluates the pet's health and behavior. If an abnormality is detected, the server generates and sends commands to control the IoT device and sends appropriate alerts to the owner.

[1954] The emotion engine analyzes the owner's emotional state and adjusts the content and method of notifications. For example, if the owner is stressed, the system will adjust to send faster and clearer alerts.

[1955] Hardware and Software

[1956] The hardware and software used to implement this system includes:

[1957] Hardware:

[1958] Biometric information terminals (e.g. smart bands for pets)

[1959] Surveillance cameras (e.g. network cameras)

[1960] Air conditioner (IoT-compatible smart air conditioner)

[1961] Feeder (IoT-enabled automatic feeder)

[1962] software:

[1963] Servers for data analysis (e.g., database servers and analysis servers)

[1964] Sentiment engines for sentiment analysis (e.g., sentiment analysis software using machine learning models)

[1965] Smartphone app (monitoring and alarm app for owners)

[1966] Specific examples of processing

[1967] For example, if your pet is exhibiting abnormal behavior in the living room (heart rate: 130, body temperature: 40, activity level: 85) and you are feeling stressed, the system will act as follows:

[1968] 1. The vital signs terminal measures the pet's abnormal heart rate, body temperature, and activity level and transmits the data to a server.

[1969] 2. The monitoring camera captures your pet's erratic movements in real time and sends streaming data to the server.

[1970] 3. The server analyzes this data and determines that the pet's condition is abnormal.

[1971] 4. The server sends a command to the IoT device (air conditioner) to set it to "cooling" and performs automatic control.

[1972] 5. The emotion engine analyzes the owner's emotional state and generates a strong alert message.

[1973] 6. Send a strong alert to owners via a smartphone app to return home quickly.

[1974] Prompt Sentence Examples

[1975] The system's behavior can be triggered by inputting a prompt like the following into the generative AI model:

[1976] Data required for a pet health monitoring program:

[1977] Health data (heart rate, body temperature, activity level)

[1978] User emotional state

[1979] Pet Health Data:

[1980] Heart rate: 130

[1981] Body temperature: 40 degrees

[1982] Activity level: 85

[1983] User emotional state:

[1984] stress

[1985] Expected system behavior:

[1986] 1. Analyze health data and detect abnormalities.

[1987] 2. Set the air conditioner to "cooling" and turn it on.

[1988] 3. Send a strong alert to the user to get home quickly.

[1989] In this way, it is possible to monitor the health and behavior of pets in real time, and to take prompt and appropriate action when an abnormality is detected.

[1990] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1991] Step 1:

[1992] The terminal measures the pet's vital signs (heart rate, body temperature, activity level). This data is collected in real time and sent to a server at regular intervals. The input is sensor data from the vital signs terminal, and the output is measurement data sent to the server. Specifically, the terminal's sensors collect various vital signs and send this data to the server via wireless communication.

[1993] Step 2:

[1994] The surveillance camera captures video of your pet in real time and sends it to a server as streaming data. The input is the video data captured by the surveillance camera, and the output is the data converted to digital format and sent to the server. The camera captures the video, digitizes the video signal, and sends it to the server over the network.

[1995] Step 3:

[1996] The server receives data sent from the biometric information terminal and the surveillance camera. The input is biometric information and video data, which are then stored in a database. Specifically, the server captures this data in real time using the data receiving module and stores it in the storage system.

[1997] Step 4:

[1998] The server analyzes the received data and evaluates the pet's health and behavior. The input is the stored biometric information and video data, and the output is the analysis results (health and behavior assessment). The data analysis module evaluates the data using machine learning algorithms and detects abnormalities.

[1999] Step 5:

[2000] Based on the analysis results, the server generates and sends commands to control IoT devices as needed. The input is the analysis results, and the output is the generated control commands. Specifically, it monitors the results of the analysis module, and if an abnormality is detected, it generates and sends appropriate commands to the air conditioner or feeder.

[2001] Step 6:

[2002] If an abnormality is detected, the server sends an alert to the owner. The input is the analysis result and the output of the emotion engine, and the output is an alert message. The notification module takes into account the analysis result of the emotion engine, generates an alert message to be sent to the owner, and notifies the smartphone app.

[2003] Step 7:

[2004] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications. The input is the user's emotional data, and the output is the intensity and format of the notification message. Specifically, the emotion engine uses machine learning to learn past emotional data, analyzes the user's current emotional state, and determines the optimal notification method.

[2005] Step 8:

[2006] IoT devices (air conditioners, feeders, etc.) receive commands from the server and execute the specified control actions. The input is the control command from the server, and the output is the executed control action. For example, the air conditioner adjusts the temperature, and the feeder dispenses food.

[2007] 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.

[2008] 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.

[2009] 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.

[2010] 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.

[2011] 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.

[2012] 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.

[2013] 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).

[2014] 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.

[2015] 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."

[2016] 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.

[2017] 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).

[2018] 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.

[2019] 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.

[2020] 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.

[2021] 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.

[2022] 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.

[2023] 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.

[2024] 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.

[2025] 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.

[2026] 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.

[2027] 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.

[2028] The following is further disclosed regarding the above embodiment.

[2029] (Claim 1)

[2030] A means for measuring heart rate, body temperature, and activity level from a vital sign terminal attached to the pet;

[2031] means for transmitting the measured data to a server;

[2032] a server for analyzing the received data and assessing the health and behavior of the pet;

[2033] A means for generating and transmitting commands to control the IoT device as needed based on the evaluation results;

[2034] The system includes a means for sending an alert to the owner if an abnormality is detected.

[2035] (Claim 2)

[2036] 10. The system of claim 1, further comprising: means for capturing video data of the pet in real time and transmitting the video data to the server.

[2037] (Claim 3)

[2038] 10. The system of claim 1, wherein the server comprises means for training a machine learning model with continuously collected data to improve accuracy of analysis and anomaly detection.

[2039] "Example 1"

[2040] (Claim 1)

[2041] means for measuring heart rate, body temperature, and activity level from the device;

[2042] means for transmitting the measured data to a processing device;

[2043] means for analyzing the received data and assessing the animal's health and behavior by a processing device;

[2044] means for generating and transmitting commands to operate the control device as necessary based on the evaluation results;

[2045] a means for sending a notification to an administrator if an anomaly is detected;

[2046] A means of comparing with past data to detect anomalies;

[2047] a means for storing the plurality of data transmitted from the device in a database;

[2048] A means to continuously collect data and train learning models;

[2049] A system including:

[2050] (Claim 2)

[2051] further, means for capturing video data of the animal in real time and transmitting the video data to a processing device;

[2052] means for analyzing the received video data and assessing animal behavior patterns;

[2053] 10. The system of claim 1.

[2054] (Claim 3)

[2055] the processing unit includes means for using the continuously collected data to improve the accuracy of the analysis and anomaly detection;

[2056] 10. The system of claim 1.

[2057] "Application Example 1"

[2058] (Claim 1)

[2059] A means for measuring heart rate, body temperature, and activity level from a vital sign terminal attached to the pet;

[2060] means for transmitting the measured data to a server;

[2061] a server for analyzing the received data and assessing the health and behavior of the pet;

[2062] A means for generating and transmitting commands to control the IoT device as needed based on the evaluation results;

[2063] A means of sending an alert to the owner if an abnormality is detected;

[2064] A means for enabling cooperation with a control device of an autonomous vehicle and controlling the environment inside the vehicle;

[2065] A system including:

[2066] (Claim 2)

[2067] A means for capturing video data of the pet in real time and transmitting it to a server;

[2068] 10. The system of claim 1, further comprising means for in-vehicle video monitoring and analysis.

[2069] (Claim 3)

[2070] means for the server to use the continuously collected data to train a machine learning model to improve the accuracy of the analysis and anomaly detection;

[2071] The system according to claim 1, which cooperates with the system control of the self-driving vehicle and takes appropriate action depending on the condition of the pet.

[2072] "Example 2: Combining Emotion Engines"

[2073] (Claim 1)

[2074] A means for measuring heart rate, body temperature, and activity level from a vital sign terminal attached to the pet;

[2075] means for transmitting the measured data to a server;

[2076] a server for analyzing the received data and assessing the health and behavior of the pet;

[2077] A means for generating and transmitting commands to control the IoT device as needed based on the evaluation results;

[2078] A means of sending an alert to the owner if an abnormality is detected;

[2079] means including an emotion engine for evaluating the user's emotion data and adjusting alerts and notification methods;

[2080] A means for continuously analyzing the data based on machine learning models by a server to improve accuracy;

[2081] A way for owners to receive alerts via a smartphone app and remotely check and control their pet's condition,

[2082] A system including:

[2083] (Claim 2)

[2084] 10. The system of claim 1, further comprising means for capturing and transmitting video data of the pet in real time to a server.

[2085] (Claim 3)

[2086] 10. The system of claim 1, wherein the server comprises means for training a machine learning model with continuously collected data to improve accuracy of analysis and anomaly detection.

[2087] "Application example 2 when combining emotion engines"

[2088] (Claim 1)

[2089] A means for measuring heart rate, body temperature, and activity level from a vital sign terminal attached to the pet;

[2090] means for transmitting the measured data to a server;

[2091] a server for analyzing the received data and assessing the health and behavior of the pet;

[2092] A means for generating and transmitting commands to control the IoT device as needed based on the evaluation results;

[2093] A means of sending an alert to the owner if an abnormality is detected;

[2094] A means for analyzing the user's emotional state and adjusting the content and method of notifications;

[2095] A means to automatically control environmental control devices such as air conditioners and feeders when abnormalities are detected in the pet's health or behavior,

[2096] A system including:

[2097] (Claim 2)

[2098] 10. The system of claim 1, further comprising means for capturing and transmitting video data of the pet in real time to a server.

[2099] (Claim 3)

[2100] 10. The system of claim 1, wherein the server comprises means for training a machine learning model with continuously collected data to improve accuracy of analysis and anomaly detection. [Explanation of symbols]

[2101] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for measuring heart rate, body temperature, and activity level from a vital sign terminal attached to the pet; means for transmitting the measured data to a server; a server for analyzing the received data and assessing the health and behavior of the pet; A means for generating and transmitting commands to control the IoT device as needed based on the evaluation results; The system includes a means for sending an alert to the owner if an abnormality is detected.

2. 10. The system of claim 1, further comprising means for capturing video data of the pet in real time and transmitting the video data to the server.

3. 10. The system of claim 1, wherein the server includes means for training a machine learning model using continuously collected data to improve accuracy of analysis and anomaly detection.

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