System

Abstract

A system is provided.SOLUTION: The system includes a means for obtaining basic information of the child, a means for monitoring the child's health condition, a means for analyzing the monitored data and detecting abnormalities, a means for notifying abnormalities in real time, a means for recording the child's sleep pattern and generating an appropriate sleep schedule, a means for guiding the child's sleep based on the sleep schedule, a means for suggesting educational play based on the child's age and interests, and a means for detecting and reporting abnormal behavior in real time.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 modern society, the number of dual-income households is increasing, placing a heavy burden on childcare. Balancing childcare with work and ensuring children's health and safety is a major challenge, especially for working parents and single-person households. While fostering proper sleep habits and providing educational activities are also important, doing so effectively can be difficult. To address these challenges, the present invention provides a childcare support system that monitors children's health, fosters sleep habits, suggests educational activities, and immediately reports abnormal behavior, thereby reducing the burden of childcare and supporting the healthy development of children. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] To provide a childcare support system including a means for acquiring basic information about a child, a means for monitoring the child's health condition, a means for analyzing the monitored data and detecting abnormalities, a means for immediately notifying the abnormalities, a means for recording the child's sleep pattern and generating an appropriate sleep schedule, a means for inducing sleep for the child based on the sleep schedule, a means for suggesting educational games based on the child's age and interests, and a means for detecting abnormal behavior and immediately reporting it.

[0007] Specifically, the user enters basic information about their child into the device, which then sends it to the server. The server uses this information to formulate monitoring standards and educational plans. The device monitors the child's health in real time and sends the data to the server. The server analyzes the data and immediately notifies the user if any abnormalities are detected. The device also records the child's sleep patterns and sends the data to the server. The server analyzes the data and suggests an appropriate sleep schedule to the user. The user uses the device to induce sleep in their child, and the recorded data is analyzed by the server. The server also suggests educational activities based on the child's age and interests. The device then notifies the user of the suggestions based on this list, and the user then plays the suggested activities. The recorded data is analyzed by the server and reflected in future suggestions. The device also analyzes the child's behavior and immediately reports any abnormal behavior detected, ensuring the child's safety.

[0008] This will reduce the burden of childcare and support the healthy development of children.

[0009] "Basic information about the child" refers to basic information such as the child's name, age, gender, allergy information, and health condition.

[0010] "Monitoring" refers to the act of observing a child's health and behavior in real time and collecting data.

[0011] "Health status" refers to a child's vital signs, such as heart rate, temperature, and respiratory rate, as well as their overall physical condition.

[0012] "Analysis" refers to the act of analyzing collected data using AI or algorithms to detect anomalies and find trends.

[0013] "Abnormal" refers to a state that deviates from pre-established standards or a significant deviation from normal behavior or health.

[0014] "Notification" refers to the act of sending a warning or information to the user when an abnormality is detected.

[0015] "Sleep patterns" refers to data such as the time of day a child sleeps, the duration of their sleep, and the number of times they wake up.

[0016] A "sleep schedule" refers to the recommended times and rhythms for children to get adequate sleep.

[0017] "Guidance" refers to the act of encouraging children to adopt healthy and appropriate behaviors and lifestyles.

[0018] "Educational play" refers to activities that aim to develop children's knowledge and skills.

[0019] "Abnormal behavior" refers to unnatural or dangerous behavior that deviates from normal patterns of behavior.

[0020] "Immediate reporting" refers to the act of immediately sending information to users and related parties when an abnormality is detected. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[0043] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0044] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0045] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0046] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0047] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[0048] For example, if a child's heart rate suddenly rises, the wearable device will send the data to the terminal, which will then forward it to the server. The server will then use AI to analyze the data and notify the user if an abnormality is detected. The user will then receive the notification and be able to take the necessary action promptly.

[0049] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The user can then use the device's music playback and light adjustment functions to improve their child's sleep.

[0050] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and allows users to take prompt action. It also supports healthy development of children by suggesting sleep habits and educational activities.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[0054] Step 2:

[0055] Device: Sends the child's basic information to the server.

[0056] Step 3:

[0057] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[0058] Step 4:

[0059] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, collecting data such as their heart rate, body temperature, and movement.

[0060] Step 5:

[0061] Terminal: Sends collected health data to the server in real time.

[0062] Step 6:

[0063] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[0064] Step 7:

[0065] Server: Sends alert information to the user's terminal.

[0066] Step 8:

[0067] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[0068] Step 9:

[0069] User: Receives notification and takes necessary action. Takes specific action, such as contacting a doctor.

[0070] Step 10:

[0071] Device: Continuous monitoring is performed to record the child's sleep patterns. Data is collected on the device, including movements during sleep and environmental sounds.

[0072] Step 11:

[0073] Device: Periodically sends recorded sleep data to the server.

[0074] Step 12:

[0075] Server: AI analyzes the received sleep data and generates an appropriate sleep schedule.

[0076] Step 13:

[0077] Server: Sends the generated sleep schedule to the user's device.

[0078] Step 14:

[0079] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[0080] Step 15:

[0081] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[0082] Step 16:

[0083] Server: Sends the generated educational pretend play list to the user's terminal.

[0084] Step 17:

[0085] Terminal: Notify the user of the suggested role play list.

[0086] Step 18:

[0087] User: Carry out the proposed play and record the results and reactions on the device.

[0088] Step 19:

[0089] Terminal: Sends recorded data to the server.

[0090] Step 20:

[0091] Server: Analyzes the received data and reflects it in suggestions for future educational pretend play.

[0092] Step 21:

[0093] Device: Monitors children's behavior in real time and analyzes abnormal behavior.

[0094] Step 22:

[0095] Terminal: If abnormal behavior (long periods of no response, abnormal movements, etc.) is detected, the information is sent to the server.

[0096] Step 23:

[0097] Server: Receives abnormal behavior data and evaluates the urgency.

[0098] Step 24:

[0099] Server: If the emergency is high, a mass notification is sent to the user and other emergency contacts.

[0100] Step 25:

[0101] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take necessary action.

[0102] These are the specific processing steps of a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior.

[0103] Example 1

[0104] 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."

[0105] In recent years, the trend toward nuclear families and dual-income households has increased the burden of childcare. Furthermore, the difficulty of understanding children's health conditions and behavior in real time and responding appropriately presents challenges in managing children's health and ensuring their safety. Furthermore, there is a need for methods to effectively support children's development of sleep habits and educational activities. However, no system currently exists that comprehensively solves these challenges. The present invention aims to provide a comprehensive system that solves these challenges and supports children's health management, ensuring their safety, and healthy upbringing.

[0106] 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.

[0107] In this invention, the server includes means for acquiring basic information about the child, means for monitoring the child's health condition, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the child of abnormalities, means for recording the child's sleep patterns and generating an appropriate sleep schedule, means for guiding the child to sleep based on the sleep schedule, means for suggesting educational play based on the child's age and interests, means for detecting and immediately reporting abnormal behavior, means for notifying the child of abnormalities based on the analysis results, and means for analyzing behavioral data using a generative AI model and detecting abnormalities. This enables real-time monitoring of the child's health condition, immediate response to abnormalities, formation of appropriate sleep habits, and suggestion of educational activities, thereby realizing comprehensive child-rearing support.

[0108] "Basic information" refers to basic information about an individual, such as the child's name, age, gender, and allergy information.

[0109] "Health status" refers to a child's physical condition and vital signs, such as heart rate, temperature, and movement.

[0110] "Monitoring" is the act of observing a child's health and behavior in real time and collecting the data.

[0111] "Data analysis" is the analytical process of using monitored data to detect anomalies.

[0112] "Abnormal" refers to any deviation from normal health or behavior, including, for example, a sudden increase in heart rate or abnormal behavior.

[0113] "Notification" is a means of immediately conveying information to the user when an abnormality is detected.

[0114] "Sleep patterns" refer to a child's set of sleep-related behaviors and habits, such as bedtimes and wake-up times.

[0115] A "sleep schedule" is a plan that determines bedtimes and wake-up times that are appropriate for a child's health.

[0116] "Educational play" refers to activities and games that are designed to educate children and are chosen according to their age and interests.

[0117] "Abnormal behavior" refers to irregular behavior or unresponsiveness that differs from normal behavior.

[0118] A "generative AI model" is an artificial intelligence model used for data analysis and anomaly detection, for example, by learning behavioral patterns to detect anomalies.

[0119] "Real-time" refers to processing and responses that are nearly simultaneous, and refers to a state in which information is collected and notified without delay.

[0120] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[0121] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0122] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0123] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0124] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0125] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[0126] The analysis uses AI models, such as frameworks like TensorFlow and PyTorch, and Pandas and NumPy are used for data analysis. Generative AI models like OpenAI's GPT-3 are also used for anomaly detection and schedule generation.

[0127] As a concrete example, the following scenario can be considered.

[0128] Example 1: A child's heart rate is abnormally high

[0129] The wearable device monitors your heart rate and detects any readings outside the normal range.

[0130] The device sends this data to the terminal.

[0131] The terminal transfers the data to the server.

[0132] The server performs AI analysis and detects abnormalities.

[0133] The server will immediately notify the user.

[0134] The user receives a notification and opens the application to take immediate action.

[0135] Example prompt:

[0136] "A 3-year-old child's heart rate has suddenly increased. AI analysis has detected an abnormality. Please take immediate action."

[0137] Example 2: Your child's sleep schedule is irregular

[0138] The device records your sleep patterns (e.g., your daily bedtime and wake-up times).

[0139] The terminal transmits the recorded data to the server.

[0140] The server analyzes the data and generates an appropriate sleep schedule.

[0141] The server notifies the user of the schedule.

[0142] Users receive notifications and can set their smartphone's light adjustment and music playback according to a schedule.

[0143] Example prompt:

[0144] "Your child's sleep patterns are irregular. Based on the AI ​​analysis results, we have suggested an appropriate sleep schedule. Please set it according to the schedule."

[0145] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and supports healthy development of children by forming good sleep habits and suggesting educational activities.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1: Enter and submit basic information

[0148] The user opens the smartphone application and enters basic information about the child (such as name, age, gender, and allergy information).

[0149] The entered information is saved on the terminal and sent to the server.

[0150] Input: Child's basic information

[0151] Output: Basic information data sent to the server

[0152] The server stores the received basic information in a database.

[0153] Step 2: Real-time health monitoring

[0154] Monitoring devices (wearable devices and cameras) collect data such as a child's heart rate, body temperature, and movement.

[0155] The collected data is sent to the terminal in real time.

[0156] Input: Child health data

[0157] Output: Health data sent to the device

[0158] The terminal transfers the received data to the server.

[0159] Input: Health data received by the device

[0160] Output: Health data transmitted to the server

[0161] Step 3: Anomaly detection and notification

[0162] The server analyzes the received health data using AI models such as TensorFlow and PyTorch.

[0163] Input: Health data received by the server

[0164] Output: Health data analysis results

[0165] The server detects abnormalities based on the analysis results, such as when the heart rate rises above a certain level.

[0166] Input: Health data analysis results

[0167] Output: Anomaly detection results

[0168] If an abnormality is detected, the server immediately sends a notification to the user's terminal.

[0169] Input: Anomaly detection results

[0170] Output: Notification sent to the user's device

[0171] Step 4: Record your sleep patterns and create a schedule

[0172] The device records the child's sleep patterns (e.g., bedtime, wake-up time, etc.).

[0173] Input: Child's sleep data

[0174] Output: Recorded sleep data

[0175] The device sends the recorded sleep data to a server.

[0176] Input: Sleep data recorded on the device

[0177] Output: Sleep data sent to the server

[0178] The server analyzes the received data and generates an appropriate sleep schedule using R or Python data analysis libraries (such as Pandas and NumPy).

[0179] Input: Sleep data received by the server

[0180] Output: Generated sleep schedule

[0181] The schedule is sent to the user's terminal and notified to the user.

[0182] Input: Generated sleep schedule

[0183] Output: Schedule notification sent to the user's device

[0184] Based on the suggested schedule, users can use the music and light adjustment functions provided by the device to help their child sleep.

[0185] Input: Schedule Notification

[0186] Output: Playing music and adjusting the light

[0187] Step 5: Propose educational activities

[0188] The server collects and analyzes data such as the child's age, past play history, and interests.

[0189] Input: Child's personal data and play history

[0190] Output: Analysis results

[0191] The server generates a list of educational activities based on the analysis results, using an AI model such as OpenAI's GPT-3.

[0192] Input: Analysis results

[0193] Output: Generated activity list

[0194] The activity list is sent to the user's terminal and notified to the user.

[0195] Input: Generated activity list

[0196] Output: Activity notification sent to the user's device

[0197] The user performs the suggested activities and records their results and reactions on the device.

[0198] Input: Activity execution result

[0199] Output: Performance data recorded on the device

[0200] The recorded data is sent to the server and reflected in future proposals.

[0201] Input: Performance data recorded on the device

[0202] Output: Results data sent to the server

[0203] Step 6: Detect and immediately report abnormal behavior

[0204] The monitoring device collects data on children's behavior.

[0205] Input: Child behavior data

[0206] Output: Collected behavioral data

[0207] The terminal transmits the received behavioral data to the server.

[0208] Input: Behavioral data received by the device

[0209] Output: Behavioral data sent to the server

[0210] The server analyzes the received behavioral data and detects abnormal behavior using anomaly detection algorithms and AI models.

[0211] Input: Behavioral data received by the server

[0212] Output: Analysis results of behavioral data

[0213] If any abnormal activity is detected, the server will notify the user and, if necessary, other emergency contacts.

[0214] Input: Anomalous behavior analysis results

[0215] Output: Notifications sent to the user and emergency contacts

[0216] In this way, the system comprehensively monitors, analyzes, and notifies children's health status, sleep habits, educational activities, and abnormal behaviors, helping users effectively manage their children's health and safety and supporting their healthy development.

[0217] (Application example 1)

[0218] 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."

[0219] Currently, there are limited systems in factories that can monitor the operating status and abnormalities of robots in real time and respond quickly. Furthermore, there are insufficient means to efficiently propose regular maintenance for robots and improve the working environment. This can lead to a decline in the operating efficiency of robots in factories, potentially resulting in problems with productivity and safety. Furthermore, integrating these systems with existing childcare support systems is expected to increase convenience and centralize widespread monitoring.

[0220] 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.

[0221] In this invention, the server includes: means for acquiring basic information about the child; means for monitoring the child's health; means for analyzing the monitored data and detecting abnormalities; means for immediately notifying the child of abnormalities; means for recording the child's sleep patterns and generating an appropriate sleep schedule; means for inducing the child to sleep based on the sleep schedule; means for suggesting educational activities based on the child's age and interests; means for detecting abnormal behavior and immediately reporting the abnormal behavior; means for monitoring the robot's operating status; means for analyzing the monitored robot's operating data and detecting abnormalities; means for immediately notifying the child of robot abnormalities; and means for suggesting robot maintenance. This enables real-time monitoring and abnormality detection of the operating status of robots in factories, enabling rapid response and appropriate maintenance suggestions. Furthermore, integration with a childcare support system enables centralized monitoring and efficient information management.

[0222] "Means for obtaining basic information about children" refers to devices or systems for collecting basic information such as a child's name, age, gender, and allergy information.

[0223] "Child health monitoring means" refers to devices or systems for real-time monitoring of a child's health, including heart rate, body temperature, and movement.

[0224] "Means for analyzing monitored data and detecting abnormalities" refers to devices or systems that analyze collected data and detect abnormalities.

[0225] The "means for immediately notifying an abnormality" refers to a device or system that immediately sends a notification to the user when an abnormality is detected.

[0226] "Means for recording a child's sleep patterns and generating an appropriate sleep schedule" refers to a device or system for recording a child's sleep data and creating an optimal sleep schedule based on that data.

[0227] "Means for inducing sleep in children based on a sleep schedule" refers to a device or system that adjusts music and lighting based on a created sleep schedule to induce sleep in children.

[0228] "Means for suggesting educational play based on a child's age and interests" refers to a device or system for suggesting educational activities based on data on a child's age, past play history, and interests.

[0229] "Means for detecting abnormal behavior and reporting it immediately" refers to a device or system that analyzes children's behavioral data, detects abnormal behavior, and immediately reports it to the user.

[0230] "Means for monitoring the operating status of robots" refers to devices and systems for monitoring the operating status of robots in factories in real time.

[0231] "Means for analyzing the monitored robot operation data and detecting abnormalities" refers to a device or system for analyzing collected robot operation data and detecting any abnormalities.

[0232] "Means for immediately notifying robot abnormalities" refers to devices or systems that immediately send a notification to a manager when an abnormality is detected within the factory.

[0233] "Means for proposing robot maintenance" refers to a device or system for proposing appropriate maintenance based on the robot's operational data.

[0234] This is a system that monitors the health of children and the operating status of robots, provides immediate notification of abnormal behavior, and provides maintenance support. Users operate the system using a smartphone or tablet, and the system functions by combining a server and various monitoring devices (wearable devices, sensor devices, cameras).

[0235] The server collects basic information about the child and the robot and sets various monitoring standards based on that information. The child's health status is monitored using a wearable device, including heart rate, body temperature, and movement, and the collected data is analyzed in real time. The robot's operating status is monitored using sensor devices and cameras, including its speed, temperature, vibration, and current, and the data is similarly collected and analyzed.

[0236] If an abnormality is detected, the server immediately sends a notification to the user's smartphone or tablet. In the case of a child, if an abnormal heart rate or rise in body temperature is detected, a notification is sent to the user, allowing for a prompt response. In the case of a robot, if an abnormal vibration or rise in temperature is detected, a notification is sent to the administrator, allowing for immediate action to be taken.

[0237] The server also records the child's sleep patterns and generates an appropriate sleep schedule, which is then sent to the user's device, where it uses the device's music playback and light adjustment functions to guide the child to sleep.

[0238] The server then suggests educational play based on the child's age and interests, including a list of educational activities generated based on data such as the child's age, past play history, and interests. The user can then try out the suggested play and record their results and reactions, which will be reflected in future suggestions.

[0239] The server also has the function of proposing regular maintenance based on the robot's operational data, which allows for effective maintenance of the robot and prevents a decline in operational efficiency.

[0240] Specific hardware used includes temperature sensors, vibration sensors, current sensors, humidity sensors, communication modules (Wi-Fi, LTE), wearable devices, and cameras. For software, a program is run using Python to collect and send data, and an API server (e.g., Flask or Django) analyzes the received data, detects anomalies, and sends notifications. A database (e.g., MySQL, PostgreSQL) stores and analyzes the collected data.

[0241] For example, if the temperature of a robot in a factory rises to a dangerous level, the system will detect the abnormality in real time and send a notification to the factory manager's smartphone, allowing the manager to immediately inspect the cooling system and take appropriate action. Similarly, if a child deviates from their normal sleep schedule, the system will detect this and adjust the music and lighting based on the suggested sleep schedule to encourage healthy sleep.

[0242] Examples of input prompts for a generative AI model include:

[0243] Create a Python program that monitors the temperature, vibration, current, and humidity of a robot in a factory in real time. The data will be sent to a server every 5 seconds. The server will notify the administrator if it detects an abnormality. The temperature range is 20.0 to 70.0°C, vibration range is 0.0 to 10.0Hz, current range is 0.0 to 5.0A, and humidity range is 0.0 to 100.0%.

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

[0245] Step 1:

[0246] The user uses the terminal to input basic information about the child and the robot's identification information.

[0247] Input: Child's name, age, gender, allergy information and robot identification information.

[0248] Data processing / calculation: This information is sent to the server and stored in a database.

[0249] Output: Monitoring criteria are set based on the stored baseline data.

[0250] Step 2:

[0251] Monitoring devices (wearable devices, sensor devices, cameras) collect data.

[0252] Input: Heart rate, body temperature, movement, robot speed, temperature, vibration, current, and other data.

[0253] Data processing / calculation: The device collects this data in real time and sends it to the server via the terminal.

[0254] Output: Real-time monitoring data.

[0255] Step 3:

[0256] The server analyzes the data it receives and detects any abnormalities.

[0257] Input: Real-time monitoring data.

[0258] Data processing / calculation: An AI model on the server analyzes the data and detects anomalies that fall outside the normal range.

[0259] Output: Anomaly detection results.

[0260] Step 4:

[0261] The server will immediately notify you of any abnormalities.

[0262] Input: Anomaly detection results.

[0263] Data processing / calculation: If an abnormality is detected, the server immediately sends a notification to the user's device.

[0264] Output: Abnormality notification.

[0265] Step 5:

[0266] The server records the child's sleep patterns and generates an appropriate sleep schedule.

[0267] Input: Child sleep data.

[0268] Data processing / calculation: The server analyzes the sleep data and generates an optimal sleep schedule based on statistics and past data.

[0269] Output: A suggested sleep schedule.

[0270] Step 6:

[0271] The user uses the device's functions to guide the child to sleep based on the suggested sleep schedule.

[0272] Input: A suggested sleep schedule.

[0273] Data processing / calculation: The device uses music playback and light adjustment functions to guide the child to sleep according to a schedule.

[0274] Output: Improved sleep patterns in children.

[0275] Step 7:

[0276] The server suggests educational activities based on the child's age and interests.

[0277] Input: Child's age, past play history, and interest data.

[0278] Data processing / calculation: Based on this data, the server generates a list of optimal educational activities.

[0279] Output: A list of suggested educational activities.

[0280] Step 8:

[0281] The user performs the suggested play and records the results.

[0282] Input: Proposed play, outcomes and responses to the play.

[0283] Data processing / calculation: The user records their results and reactions on the device and sends them to the server.

[0284] Output: The recorded data will be reflected in future proposals.

[0285] Step 9:

[0286] The server suggests robot maintenance.

[0287] Input: Robot operation data.

[0288] Data processing / calculation: The server analyzes operational data and evaluates the need for maintenance based on the operational status.

[0289] Output: Proposed maintenance schedule.

[0290] Step 10:

[0291] The user performs the proposed maintenance.

[0292] Input: Proposed maintenance schedule.

[0293] Data processing / calculation: The user performs maintenance on the robot according to the proposed schedule.

[0294] Output: Efficient operation and improved performance of the robot.

[0295] 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.

[0296] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[0297] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0298] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0299] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0300] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0301] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational games and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice via the camera and microphone to determine emotions.

[0302] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts will also be notified.

[0303] (Example)

[0304] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[0305] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[0306] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[0307] The processing flow will be explained below.

[0308] Step 1:

[0309] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[0310] Step 2:

[0311] Device: Sends the child's basic information to the server.

[0312] Step 3:

[0313] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[0314] Step 4:

[0315] Devices: Receive established monitoring standards and educational plans and use wearable devices and cameras to monitor children's health, continuously collecting data such as their heart rate, temperature, and movement.

[0316] Step 5:

[0317] Terminal: Sends collected health data to the server in real time.

[0318] Step 6:

[0319] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[0320] Step 7:

[0321] Server: Based on the generated alert, the emotion engine analyzes camera images and audio data to recognize the user's emotions. It determines the user's current emotions.

[0322] Step 8:

[0323] Server: Adjust the urgency and content of the emergency notification based on the user's emotions. For example, if the user is feeling very stressed, the content of the emergency notification will be limited and concise, and other emergency contacts will also be notified.

[0324] Step 9:

[0325] Server: Sends the adjusted abnormality notification to the user's device.

[0326] Step 10:

[0327] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[0328] Step 11:

[0329] User: Receives notification and takes necessary action, for example, taking specific action such as contacting a doctor.

[0330] Step 12:

[0331] Device: Continuously monitors your child's sleep patterns, collecting and recording data on movements and environmental sounds during sleep.

[0332] Step 13:

[0333] Device: Periodically sends recorded sleep data to the server.

[0334] Step 14:

[0335] Server: Analyzes the received sleep data using AI and generates an appropriate sleep schedule.

[0336] Step 15:

[0337] Server: The emotion engine recognizes the user's emotions and adjusts the sleep schedule accordingly. For example, if the user is relaxed, it will suggest a standard schedule, but if the user is stressed, it will suggest a schedule that includes relaxing music and environmental adjustments.

[0338] Step 16:

[0339] Server: Sends the adjusted sleep schedule to the user's device.

[0340] Step 17:

[0341] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[0342] Step 18:

[0343] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[0344] Step 19:

[0345] Server: Sends the generated educational pretend play list to the user's terminal.

[0346] Step 20:

[0347] Terminal: Notify the user of the suggested role play list.

[0348] Step 21:

[0349] User: Carry out the proposed play and record the results and reactions on the device.

[0350] Step 22:

[0351] Terminal: Sends recorded data to the server.

[0352] Step 23:

[0353] Server: Analyzes the received data and reflects it in future educational pretend play suggestions.

[0354] Step 24:

[0355] Device: Monitors children's behavior in real time and analyzes abnormal behavior, such as prolonged periods of unresponsiveness or abnormal movements.

[0356] Step 25:

[0357] Terminal: If abnormal behavior is detected, it sends the information to the server.

[0358] Step 26:

[0359] Server: Analyzes the received abnormal behavior data, and the emotion engine recognizes the user's emotions. If the user is feeling particularly anxious, it generates a notification to encourage more proactive action.

[0360] Step 27:

[0361] Server: Sends coordinated anomaly notifications to users and other emergency contacts.

[0362] Step 28:

[0363] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take the necessary action.

[0364] These are the specific processing steps of the invention that combines the emotion engine. By taking the user's emotions into consideration and adjusting the system's operation, it is possible to provide more effective and personalized childcare support.

[0365] Example 2

[0366] 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."

[0367] In modern childcare, managing a child's health, sleep habits, and educational activities is extremely important, but it is difficult for parents to consistently monitor these and respond appropriately. Parents are also required to care for their children while taking into account their own emotional state, but there is no effective system for doing so. Therefore, there is a need for a system that monitors a child's health in real time, promptly notifies parents when abnormalities are detected, and supports appropriate responses while taking into account the parent's emotional state.

[0368] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a device for acquiring basic information about the child, a device for monitoring the child's health condition, a device for analyzing the monitored data and detecting abnormalities, a device for immediately notifying the abnormality, a device for recording the child's sleep pattern and generating an appropriate sleep schedule, a device for guiding the child's sleep based on the sleep schedule, a device for suggesting educational activities based on the child's age and interests, a device for detecting abnormal behavior and immediately reporting it, an emotion engine for analyzing the user's emotions, and a device for adjusting the system operation based on the user's emotions. This makes it possible to monitor the child's health condition in real time and take appropriate measures, as well as provide appropriate support taking into account the parent's emotional state.

[0369] "Basic information" refers to basic data about the child, such as the child's name, age, gender, and allergies.

[0370] "Health status" refers to information about a child's physical and physiological state, such as heart rate, temperature, and movement.

[0371] "Monitoring" refers to the act of continuously acquiring data and observing its status.

[0372] "Analysis" refers to the process of analyzing acquired data and making it meaningful.

[0373] "Abnormal" refers to data or conditions that fall outside a predefined normal range.

[0374] "Notification" refers to a message that notifies the user of an abnormality or important information.

[0375] "Sleep patterns" refers to information such as a child's sleep duration, depth, and cycle.

[0376] A "sleep schedule" refers to a plan or timetable for ensuring a child gets adequate sleep.

[0377] "Educational activities" refer to play and experiences that promote children's learning and development.

[0378] "Abnormal behavior" refers to behavior that significantly deviates from normal behavior patterns.

[0379] "Reporting" refers to the act of communicating anomalies or important data to users or other systems.

[0380] An "emotion engine" refers to software or hardware for analyzing a user's emotions.

[0381] "Device for adjusting operation" refers to a control device for optimizing the operation of the system based on the analysis results and the user's emotions.

[0382] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[0383] First, the user installs a dedicated application on their smartphone and enters basic information about their child (such as name, age, gender, and allergies). This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child. Specific hardware used at this stage includes a smartphone and a database on the server.

[0384] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data using an analysis engine and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. Specific wearable devices used include a heart rate monitor and a thermometer.

[0385] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to a server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user guides the child to sleep using the music and light adjustment functions provided by the device based on the proposed schedule. Specific hardware examples include smartphones, music playback devices, and lighting control devices.

[0386] Furthermore, the system suggests educational games based on the child's age and interests. The server generates a list of educational pretend games based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions. Specific hardware examples include a server and a smartphone.

[0387] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational activities and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine emotions. Specific software examples include facial recognition algorithms and voice analysis algorithms.

[0388] The system also detects abnormal child behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts are also notified. Specific hardware examples include a camera, microphone, and smartphone.

[0389] (Example)

[0390] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[0391] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[0392] Prompt Sentence Examples

[0393] "What should I do if my child's heart rate spikes? Include emergency procedures if the user is stressed."

[0394] "Please give me some suggestions to improve my child's irregular sleep pattern. Please also tell me how to make suggestions based on the user's emotions."

[0395] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[0396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0397] Step 1:

[0398] The user enters basic information about their child. Specifically, the user launches a dedicated application on their smartphone and enters the child's name, age, gender, and allergy information. This basic information is sent from the device to the server. Input data: name, age, gender, and allergy information. Output data: child's basic information saved on the server. Specifically, the user taps the "Send" button, which sends the information to the server.

[0399] Step 2:

[0400] The server formulates monitoring standards and educational plans. Based on the basic information received, the server sets monitoring standards based on the child's age and allergy information. An initial educational plan is also automatically generated. Input data: Basic information about the child stored on the server. Output data: Monitoring standards and educational plans. Specifically, the server's algorithm analyzes the data and sets standards and plans.

[0401] Step 3:

[0402] The system monitors a child's health. The user has the child wear a wearable device (e.g., a heart rate monitor or thermometer). This device collects real-time health data such as heart rate, body temperature, and movement and sends it to a terminal. The terminal then transfers this data to a server. Input data: heart rate, body temperature, movement. Output data: health data stored on the server. Specifically, the wearable device periodically collects data and sends it to the terminal.

[0403] Step 4:

[0404] The server analyzes the health data and detects abnormalities. The server analyzes the received health data in real time using an analysis engine. If an abnormality is detected as a result of the analysis, the type of abnormality and its urgency are identified. Input data: Health data sent to the server. Output data: Analysis results and abnormality detection results. Specifically, the server uses a data analysis algorithm to detect abnormalities.

[0405] Step 5:

[0406] The server will send notifications to the user as needed. If an anomaly is detected, the server will immediately send a push notification to the user's device. The notification will include the type of anomaly, its urgency, and recommended countermeasures. Input data: Anomaly detection results. Output data: Notification sent to the user's device. Specifically, the server will generate notification content according to the urgency and send it to the device.

[0407] Step 6:

[0408] The device records the child's sleep patterns. The device periodically collects the child's sleep data from the wearable device and sends it to the server. Input data: Sleep data from the wearable device. Output data: Sleep data stored on the server. Specifically, the device periodically collects data and automatically sends it to the server.

[0409] Step 7:

[0410] The server analyzes the sleep data and creates an appropriate schedule. The server analyzes the received sleep data and generates a sleep schedule suitable for the child. This schedule is sent to the user's device. Input data: Sleep data sent to the server. Output data: Generated sleep schedule. Specifically, the server runs an analysis algorithm and generates an optimal schedule.

[0411] Step 8:

[0412] The server suggests educational activities. The server creates a list of suitable educational activities based on the child's age, interests, and past play history. The list is sent to the user's device. Input data: child's age, interests, play history. Output data: list of suggested educational activities. Specifically, the server references the database and generates a list of suggestions.

[0413] Step 9:

[0414] The emotion engine analyzes the user's emotions. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time to determine their emotions. Input data: camera footage, audio data. Output data: user emotion analysis results. Specifically, the emotion engine runs a facial expression recognition algorithm and a voice analysis algorithm.

[0415] Step 10:

[0416] The server adjusts the content of the abnormality notification and the content of the suggestions. The server adjusts the content of the abnormality notification and the content of the educational activity suggestions based on the emotional data sent from the emotion engine. Input data: Emotion analysis results. Output data: Adjusted notifications and suggestions. Specifically, the server analyzes the emotional data and dynamically changes the content of the notifications and suggestions.

[0417] (Application example 2)

[0418] 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."

[0419] Current self-driving vehicles have difficulty providing a comfortable riding environment that responds promptly to passengers' health and psychological state. Furthermore, mechanisms for quickly responding to sudden changes in a passenger's health or increased psychological stress are not adequately developed. Therefore, there is a need for a system that allows passengers to use self-driving vehicles safely and comfortably.

[0420] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0421] In this invention, the server includes means for acquiring basic passenger information, means for monitoring the passenger's health status, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the passenger of the abnormality, means for analyzing the passenger's emotional state and automatically adjusting the in-vehicle environment, means for adjusting the urgency and content of the abnormality notification based on the passenger's emotional state, means for automatically adjusting in-vehicle comfort functions based on the passenger's health status and emotional state, and means for stopping the vehicle and taking emergency action when an abnormality occurs. This makes it possible to flexibly respond to the passenger's health status and psychological state and provide a safe and comfortable riding environment.

[0422] "Passenger basic information" is basic data about the passenger, such as the passenger's name, age, and health status.

[0423] "Health status" refers to a passenger's physical condition, such as heart rate, body temperature, or blood pressure, as measured by wearable devices.

[0424] "Monitoring" is the process of observing and recording passenger health and behavior in real time.

[0425] "Analyzing data" means analyzing the monitored information and detecting anomalies and characteristics.

[0426] "Detecting anomalies" means finding abnormal data that deviates from a passenger's normal state.

[0427] "Immediate notification" refers to the process of issuing an alert immediately when an abnormality is detected and prompting the necessary response.

[0428] "Emotional state" refers to the psychological state of a passenger analyzed based on facial expressions, tone of voice, etc.

[0429] "Automatically adjusting the in-car environment" means changing settings such as lighting, music, and temperature in the car to match the passenger's emotional state.

[0430] "Comfort features" refer to the in-car environmental settings and facilities that make passengers comfortable.

[0431] "Emergency response" means taking necessary measures in response to a sudden change in a person's health or emotional state, such as stopping the vehicle or contacting a medical institution.

[0432] The present invention provides a system for monitoring the health and emotional state of passengers in real time and for responding quickly when an abnormality occurs. This system is implemented by combining a server, an in-vehicle terminal, a wearable device, a camera, a microphone, and an emotion engine. A specific embodiment of this system will be described below.

[0433] System Configuration

[0434] 1. Obtain basic passenger information

[0435] When passengers get into the vehicle, the server collects basic information (such as name, age, and health condition) from their smartphones or the touch panel of the in-vehicle terminal. This information is stored on the server and used as basic data for analysis.

[0436] 2. Health monitoring

[0437] The in-car terminal will link with a wearable device (capable of measuring heart rate, body temperature, etc.) to monitor the passenger's health. The wearable device will measure data in real time and send it to the in-car terminal via Bluetooth or other means.

[0438] 3. Emotional state analysis

[0439] Cameras and microphones installed in the in-car terminal analyze passengers' facial expressions and tone of voice, allowing the passenger's emotional state to be grasped in real time, and the analysis results are output by the emotion engine.

[0440] 4. Anomaly detection and notification

[0441] The server receives monitoring data and emotion analysis results sent from the in-vehicle device and analyzes this data using AI algorithms. If an abnormality is detected, the server immediately sends a notification to the in-vehicle device, and if necessary, notifies passengers' smartphones and emergency contacts.

[0442] 5. Automatic adjustment of the in-car environment

[0443] The server sends instructions to the in-car device to automatically adjust the in-car environment (lighting, music, air conditioning settings, etc.) based on the passenger's emotional state, providing a relaxing environment for passengers who are feeling stressed.

[0444] 6. Emergency Response

[0445] If a serious abnormality in the passenger's health condition is detected, the on-board device will automatically stop the vehicle and contact the nearest medical facility, based on pre-registered emergency contact information.

[0446] Hardware and Software Used

[0447] Wearable devices: heart rate monitors, thermometers, etc.

[0448] In-car terminal: Android display, touch panel

[0449] Camera and microphone: High-resolution camera and high-sensitivity microphone for analyzing passengers' facial expressions and voices

[0450] Emotion Engine: Dlib library and custom AI models

[0451] Communication protocols: Bluetooth, Wi-Fi, 4G / 5G networks

[0452] For example, a prompt to instruct a generative AI model might look like this:

[0453] "Generate a Python program that uses the in-car camera and microphone to analyze the passenger's facial expressions and voice, and heart rate data from a smartphone-connected wearable device to detect abnormalities in real time and automatically adjust the in-car environment. Use the presence or absence of a smile as a simple way to determine emotions."

[0454] As a result, the present invention is a system that improves passenger safety and comfort and enables rapid response in the event of an emergency.

[0455] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0456] Step 1:

[0457] Enter and save passenger basic information

[0458] Input: Basic passenger information (such as name, age, and health status) entered via smartphone or in-car device.

[0459] Operation: The user enters basic passenger information into the touch panel of a smartphone or in-car terminal. The entered information is sent to the server.

[0460] Output: Passenger basic information data stored on the server.

[0461] Step 2:

[0462] Health monitoring

[0463] Input: Health data sent from wearable devices (heart rate, temperature, etc.).

[0464] How it works: The wearable device measures heart rate and body temperature and transmits the data via Bluetooth to the in-car terminal, which receives the data and sends it to a server.

[0465] Output: Passenger health status data stored on the server.

[0466] Step 3:

[0467] Emotional state analysis

[0468] Input: Video data from the in-car camera and audio data from the microphone.

[0469] How it works: The onboard camera captures the passenger's facial expressions, and the microphone records the passenger's voice. This data is sent to the onboard device and analyzed by the emotion engine. The analysis results are then sent to the server.

[0470] Output: Passenger emotional state data stored on the server.

[0471] Step 4:

[0472] Anomaly detection

[0473] Input: Health and emotional state data stored on the server.

[0474] How it works: The server analyzes the health and emotional state data using AI algorithms to check for abnormalities. If an abnormality is detected, an immediate response is decided.

[0475] Output: Alert notification data when an anomaly is detected.

[0476] Step 5:

[0477] Abnormality notification

[0478] Input: Alert notification data when an abnormality is detected.

[0479] How it works: The server sends an alert to the in-car device. If the abnormality is serious, a notification is also sent to the passenger's smartphone and emergency contacts.

[0480] Output: An abnormality notification displayed on the in-car device and an alert message sent to a smartphone.

[0481] Step 6:

[0482] Automatic adjustment of the in-car environment

[0483] Input: Parsed emotional state data.

[0484] Operation: Based on the emotion analysis results, the server sends instructions to the in-car device to change the in-car environment, such as lighting, music, and air conditioning settings.

[0485] Output: In-car environment adjustment instruction data and actual changed in-car settings.

[0486] Step 7:

[0487] Emergency response

[0488] Input: Anomaly detection data and emergency response instruction data.

[0489] Operation: The server sends a command to the in-vehicle terminal to stop the vehicle and contacts the nearest medical institution. This contact is made based on the emergency contact information registered in advance.

[0490] Output: Stop the vehicle, notify emergency contacts, and contact the nearest medical facility.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] [Second embodiment]

[0495] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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).

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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."

[0507] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[0508] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0509] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0510] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0511] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0512] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[0513] For example, if a child's heart rate suddenly rises, the wearable device will send the data to the terminal, which will then forward it to the server. The server will then use AI to analyze the data and notify the user if an abnormality is detected. The user will then receive the notification and be able to take the necessary action promptly.

[0514] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The user can then use the device's music playback and light adjustment functions to improve their child's sleep.

[0515] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and allows users to take prompt action. It also supports healthy development of children by suggesting sleep habits and educational activities.

[0516] The processing flow will be explained below.

[0517] Step 1:

[0518] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[0519] Step 2:

[0520] Device: Sends the child's basic information to the server.

[0521] Step 3:

[0522] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[0523] Step 4:

[0524] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, collecting data such as their heart rate, body temperature, and movement.

[0525] Step 5:

[0526] Terminal: Sends collected health data to the server in real time.

[0527] Step 6:

[0528] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[0529] Step 7:

[0530] Server: Sends alert information to the user's terminal.

[0531] Step 8:

[0532] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[0533] Step 9:

[0534] User: Receives notification and takes necessary action. Takes specific action, such as contacting a doctor.

[0535] Step 10:

[0536] Device: Continuous monitoring is performed to record the child's sleep patterns. Data is collected on the device, including movements during sleep and environmental sounds.

[0537] Step 11:

[0538] Device: Periodically sends recorded sleep data to the server.

[0539] Step 12:

[0540] Server: AI analyzes the received sleep data and generates an appropriate sleep schedule.

[0541] Step 13:

[0542] Server: Sends the generated sleep schedule to the user's device.

[0543] Step 14:

[0544] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[0545] Step 15:

[0546] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[0547] Step 16:

[0548] Server: Sends the generated educational pretend play list to the user's terminal.

[0549] Step 17:

[0550] Terminal: Notify the user of the suggested role play list.

[0551] Step 18:

[0552] User: Carry out the proposed play and record the results and reactions on the device.

[0553] Step 19:

[0554] Terminal: Sends recorded data to the server.

[0555] Step 20:

[0556] Server: Analyzes the received data and reflects it in suggestions for future educational pretend play.

[0557] Step 21:

[0558] Device: Monitors children's behavior in real time and analyzes abnormal behavior.

[0559] Step 22:

[0560] Terminal: If abnormal behavior (long periods of no response, abnormal movements, etc.) is detected, the information is sent to the server.

[0561] Step 23:

[0562] Server: Receives abnormal behavior data and evaluates the urgency.

[0563] Step 24:

[0564] Server: If the emergency is high, a mass notification is sent to the user and other emergency contacts.

[0565] Step 25:

[0566] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take necessary action.

[0567] These are the specific processing steps of a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior.

[0568] Example 1

[0569] 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."

[0570] In recent years, the trend toward nuclear families and dual-income households has increased the burden of childcare. Furthermore, the difficulty of understanding children's health conditions and behavior in real time and responding appropriately presents challenges in managing children's health and ensuring their safety. Furthermore, there is a need for methods to effectively support children's development of sleep habits and educational activities. However, no system currently exists that comprehensively solves these challenges. The present invention aims to provide a comprehensive system that solves these challenges and supports children's health management, ensuring their safety, and healthy upbringing.

[0571] 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.

[0572] In this invention, the server includes means for acquiring basic information about the child, means for monitoring the child's health condition, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the child of abnormalities, means for recording the child's sleep patterns and generating an appropriate sleep schedule, means for guiding the child to sleep based on the sleep schedule, means for suggesting educational play based on the child's age and interests, means for detecting and immediately reporting abnormal behavior, means for notifying the child of abnormalities based on the analysis results, and means for analyzing behavioral data using a generative AI model and detecting abnormalities. This enables real-time monitoring of the child's health condition, immediate response to abnormalities, formation of appropriate sleep habits, and suggestion of educational activities, thereby realizing comprehensive child-rearing support.

[0573] "Basic information" refers to basic information about an individual, such as the child's name, age, gender, and allergy information.

[0574] "Health status" refers to a child's physical condition and vital signs, such as heart rate, temperature, and movement.

[0575] "Monitoring" is the act of observing a child's health and behavior in real time and collecting the data.

[0576] "Data analysis" is the analytical process of using monitored data to detect anomalies.

[0577] "Abnormal" refers to any deviation from normal health or behavior, including, for example, a sudden increase in heart rate or abnormal behavior.

[0578] "Notification" is a means of immediately conveying information to the user when an abnormality is detected.

[0579] "Sleep patterns" refer to a child's set of sleep-related behaviors and habits, such as bedtimes and wake-up times.

[0580] A "sleep schedule" is a plan that determines bedtimes and wake-up times that are appropriate for a child's health.

[0581] "Educational play" refers to activities and games that are designed to educate children and are chosen according to their age and interests.

[0582] "Abnormal behavior" refers to irregular behavior or unresponsiveness that differs from normal behavior.

[0583] A "generative AI model" is an artificial intelligence model used for data analysis and anomaly detection, for example, by learning behavioral patterns to detect anomalies.

[0584] "Real-time" refers to processing and responses that are nearly simultaneous, and refers to a state in which information is collected and notified without delay.

[0585] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[0586] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0587] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0588] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0589] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0590] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[0591] The analysis uses AI models, such as frameworks like TensorFlow and PyTorch, and Pandas and NumPy are used for data analysis. Generative AI models like OpenAI's GPT-3 are also used for anomaly detection and schedule generation.

[0592] As a concrete example, the following scenario can be considered.

[0593] Example 1: A child's heart rate is abnormally high

[0594] The wearable device monitors your heart rate and detects any readings outside the normal range.

[0595] The device sends this data to the terminal.

[0596] The terminal transfers the data to the server.

[0597] The server performs AI analysis and detects abnormalities.

[0598] The server will immediately notify the user.

[0599] The user receives a notification and opens the application to take immediate action.

[0600] Example prompt:

[0601] "A 3-year-old child's heart rate has suddenly increased. AI analysis has detected an abnormality. Please take immediate action."

[0602] Example 2: Your child's sleep schedule is irregular

[0603] The device records your sleep patterns (e.g., your daily bedtime and wake-up times).

[0604] The terminal transmits the recorded data to the server.

[0605] The server analyzes the data and generates an appropriate sleep schedule.

[0606] The server notifies the user of the schedule.

[0607] Users receive notifications and can set their smartphone's light adjustment and music playback according to a schedule.

[0608] Example prompt:

[0609] "Your child's sleep patterns are irregular. Based on the AI ​​analysis results, we have suggested an appropriate sleep schedule. Please set it according to the schedule."

[0610] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and supports healthy development of children by forming good sleep habits and suggesting educational activities.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1: Enter and submit basic information

[0613] The user opens the smartphone application and enters basic information about the child (such as name, age, gender, and allergy information).

[0614] The entered information is saved on the terminal and sent to the server.

[0615] Input: Child's basic information

[0616] Output: Basic information data sent to the server

[0617] The server stores the received basic information in a database.

[0618] Step 2: Real-time health monitoring

[0619] Monitoring devices (wearable devices and cameras) collect data such as a child's heart rate, body temperature, and movement.

[0620] The collected data is sent to the terminal in real time.

[0621] Input: Child health data

[0622] Output: Health data sent to the device

[0623] The terminal transfers the received data to the server.

[0624] Input: Health data received by the device

[0625] Output: Health data transmitted to the server

[0626] Step 3: Anomaly detection and notification

[0627] The server analyzes the received health data using AI models such as TensorFlow and PyTorch.

[0628] Input: Health data received by the server

[0629] Output: Health data analysis results

[0630] The server detects abnormalities based on the analysis results, such as when the heart rate rises above a certain level.

[0631] Input: Health data analysis results

[0632] Output: Anomaly detection results

[0633] If an abnormality is detected, the server immediately sends a notification to the user's terminal.

[0634] Input: Anomaly detection results

[0635] Output: Notification sent to the user's device

[0636] Step 4: Record your sleep patterns and create a schedule

[0637] The device records the child's sleep patterns (e.g., bedtime, wake-up time, etc.).

[0638] Input: Child's sleep data

[0639] Output: Recorded sleep data

[0640] The device sends the recorded sleep data to a server.

[0641] Input: Sleep data recorded on the device

[0642] Output: Sleep data sent to the server

[0643] The server analyzes the received data and generates an appropriate sleep schedule using R or Python data analysis libraries (such as Pandas and NumPy).

[0644] Input: Sleep data received by the server

[0645] Output: Generated sleep schedule

[0646] The schedule is sent to the user's terminal and notified to the user.

[0647] Input: Generated sleep schedule

[0648] Output: Schedule notification sent to the user's device

[0649] Based on the suggested schedule, users can use the music and light adjustment functions provided by the device to help their child sleep.

[0650] Input: Schedule Notification

[0651] Output: Playing music and adjusting the light

[0652] Step 5: Propose educational activities

[0653] The server collects and analyzes data such as the child's age, past play history, and interests.

[0654] Input: Child's personal data and play history

[0655] Output: Analysis results

[0656] The server generates a list of educational activities based on the analysis results, using an AI model such as OpenAI's GPT-3.

[0657] Input: Analysis results

[0658] Output: Generated activity list

[0659] The activity list is sent to the user's terminal and notified to the user.

[0660] Input: Generated activity list

[0661] Output: Activity notification sent to the user's device

[0662] The user performs the suggested activities and records their results and reactions on the device.

[0663] Input: Activity execution result

[0664] Output: Performance data recorded on the device

[0665] The recorded data is sent to the server and reflected in future proposals.

[0666] Input: Performance data recorded on the device

[0667] Output: Results data sent to the server

[0668] Step 6: Detect and immediately report abnormal behavior

[0669] The monitoring device collects data on children's behavior.

[0670] Input: Child behavior data

[0671] Output: Collected behavioral data

[0672] The terminal transmits the received behavioral data to the server.

[0673] Input: Behavioral data received by the device

[0674] Output: Behavioral data sent to the server

[0675] The server analyzes the received behavioral data and detects abnormal behavior using anomaly detection algorithms and AI models.

[0676] Input: Behavioral data received by the server

[0677] Output: Analysis results of behavioral data

[0678] If any abnormal activity is detected, the server will notify the user and, if necessary, other emergency contacts.

[0679] Input: Anomalous behavior analysis results

[0680] Output: Notifications sent to the user and emergency contacts

[0681] In this way, the system comprehensively monitors, analyzes, and notifies children's health status, sleep habits, educational activities, and abnormal behaviors, helping users effectively manage their children's health and safety and supporting their healthy development.

[0682] (Application example 1)

[0683] 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."

[0684] Currently, there are limited systems in factories that can monitor the operating status and abnormalities of robots in real time and respond quickly. Furthermore, there are insufficient means to efficiently propose regular maintenance for robots and improve the working environment. This can lead to a decline in the operating efficiency of robots in factories, potentially resulting in problems with productivity and safety. Furthermore, integrating these systems with existing childcare support systems is expected to increase convenience and centralize widespread monitoring.

[0685] 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.

[0686] In this invention, the server includes: means for acquiring basic information about the child; means for monitoring the child's health; means for analyzing the monitored data and detecting abnormalities; means for immediately notifying the child of abnormalities; means for recording the child's sleep patterns and generating an appropriate sleep schedule; means for inducing the child to sleep based on the sleep schedule; means for suggesting educational activities based on the child's age and interests; means for detecting abnormal behavior and immediately reporting the abnormal behavior; means for monitoring the robot's operating status; means for analyzing the monitored robot's operating data and detecting abnormalities; means for immediately notifying the child of robot abnormalities; and means for suggesting robot maintenance. This enables real-time monitoring and abnormality detection of the operating status of robots in factories, enabling rapid response and appropriate maintenance suggestions. Furthermore, integration with a childcare support system enables centralized monitoring and efficient information management.

[0687] "Means for obtaining basic information about children" refers to devices or systems for collecting basic information such as a child's name, age, gender, and allergy information.

[0688] "Child health monitoring means" refers to devices or systems for real-time monitoring of a child's health, including heart rate, body temperature, and movement.

[0689] "Means for analyzing monitored data and detecting abnormalities" refers to devices or systems that analyze collected data and detect abnormalities.

[0690] The "means for immediately notifying an abnormality" refers to a device or system that immediately sends a notification to the user when an abnormality is detected.

[0691] "Means for recording a child's sleep patterns and generating an appropriate sleep schedule" refers to a device or system for recording a child's sleep data and creating an optimal sleep schedule based on that data.

[0692] "Means for inducing sleep in children based on a sleep schedule" refers to a device or system that adjusts music and lighting based on a created sleep schedule to induce sleep in children.

[0693] "Means for suggesting educational play based on a child's age and interests" refers to a device or system for suggesting educational activities based on data on a child's age, past play history, and interests.

[0694] "Means for detecting abnormal behavior and reporting it immediately" refers to a device or system that analyzes children's behavioral data, detects abnormal behavior, and immediately reports it to the user.

[0695] "Means for monitoring the operating status of robots" refers to devices and systems for monitoring the operating status of robots in factories in real time.

[0696] "Means for analyzing the monitored robot operation data and detecting abnormalities" refers to a device or system for analyzing collected robot operation data and detecting any abnormalities.

[0697] "Means for immediately notifying robot abnormalities" refers to devices or systems that immediately send a notification to a manager when an abnormality is detected within the factory.

[0698] "Means for proposing robot maintenance" refers to a device or system for proposing appropriate maintenance based on the robot's operational data.

[0699] This is a system that monitors the health of children and the operating status of robots, provides immediate notification of abnormal behavior, and provides maintenance support. Users operate the system using a smartphone or tablet, and the system functions by combining a server and various monitoring devices (wearable devices, sensor devices, cameras).

[0700] The server collects basic information about the child and the robot and sets various monitoring standards based on that information. The child's health status is monitored using a wearable device, including heart rate, body temperature, and movement, and the collected data is analyzed in real time. The robot's operating status is monitored using sensor devices and cameras, including its speed, temperature, vibration, and current, and the data is similarly collected and analyzed.

[0701] If an abnormality is detected, the server immediately sends a notification to the user's smartphone or tablet. In the case of a child, if an abnormal heart rate or rise in body temperature is detected, a notification is sent to the user, allowing for a prompt response. In the case of a robot, if an abnormal vibration or rise in temperature is detected, a notification is sent to the administrator, allowing for immediate action to be taken.

[0702] The server also records the child's sleep patterns and generates an appropriate sleep schedule, which is then sent to the user's device, where it uses the device's music playback and light adjustment functions to guide the child to sleep.

[0703] The server then suggests educational play based on the child's age and interests, including a list of educational activities generated based on data such as the child's age, past play history, and interests. The user can then try out the suggested play and record their results and reactions, which will be reflected in future suggestions.

[0704] The server also has the function of proposing regular maintenance based on the robot's operational data, which allows for effective maintenance of the robot and prevents a decline in operational efficiency.

[0705] Specific hardware used includes temperature sensors, vibration sensors, current sensors, humidity sensors, communication modules (Wi-Fi, LTE), wearable devices, and cameras. For software, a program is run using Python to collect and send data, and an API server (e.g., Flask or Django) analyzes the received data, detects anomalies, and sends notifications. A database (e.g., MySQL, PostgreSQL) stores and analyzes the collected data.

[0706] For example, if the temperature of a robot in a factory rises to a dangerous level, the system will detect the abnormality in real time and send a notification to the factory manager's smartphone, allowing the manager to immediately inspect the cooling system and take appropriate action. Similarly, if a child deviates from their normal sleep schedule, the system will detect this and adjust the music and lighting based on the suggested sleep schedule to encourage healthy sleep.

[0707] Examples of input prompts for a generative AI model include:

[0708] Create a Python program that monitors the temperature, vibration, current, and humidity of a robot in a factory in real time. The data will be sent to a server every 5 seconds. The server will notify the administrator if it detects an abnormality. The temperature range is 20.0 to 70.0°C, vibration range is 0.0 to 10.0Hz, current range is 0.0 to 5.0A, and humidity range is 0.0 to 100.0%.

[0709] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0710] Step 1:

[0711] The user uses the terminal to input basic information about the child and the robot's identification information.

[0712] Input: Child's name, age, gender, allergy information and robot identification information.

[0713] Data processing / calculation: This information is sent to the server and stored in a database.

[0714] Output: Monitoring criteria are set based on the stored baseline data.

[0715] Step 2:

[0716] Monitoring devices (wearable devices, sensor devices, cameras) collect data.

[0717] Input: Heart rate, body temperature, movement, robot speed, temperature, vibration, current, and other data.

[0718] Data processing / calculation: The device collects this data in real time and sends it to the server via the terminal.

[0719] Output: Real-time monitoring data.

[0720] Step 3:

[0721] The server analyzes the data it receives and detects any abnormalities.

[0722] Input: Real-time monitoring data.

[0723] Data processing / calculation: An AI model on the server analyzes the data and detects anomalies that fall outside the normal range.

[0724] Output: Anomaly detection results.

[0725] Step 4:

[0726] The server will immediately notify you of any abnormalities.

[0727] Input: Anomaly detection results.

[0728] Data processing / calculation: If an abnormality is detected, the server immediately sends a notification to the user's device.

[0729] Output: Abnormality notification.

[0730] Step 5:

[0731] The server records the child's sleep patterns and generates an appropriate sleep schedule.

[0732] Input: Child sleep data.

[0733] Data processing / calculation: The server analyzes the sleep data and generates an optimal sleep schedule based on statistics and past data.

[0734] Output: A suggested sleep schedule.

[0735] Step 6:

[0736] The user uses the device's functions to guide the child to sleep based on the suggested sleep schedule.

[0737] Input: A suggested sleep schedule.

[0738] Data processing / calculation: The device uses music playback and light adjustment functions to guide the child to sleep according to a schedule.

[0739] Output: Improved sleep patterns in children.

[0740] Step 7:

[0741] The server suggests educational activities based on the child's age and interests.

[0742] Input: Child's age, past play history, and interest data.

[0743] Data processing / calculation: The server generates a list of optimal educational activities based on this data.

[0744] Output: A list of suggested educational activities.

[0745] Step 8:

[0746] The user performs the suggested play and records the results.

[0747] Input: Proposed play, outcomes and responses to the play.

[0748] Data processing / calculation: The user records their results and reactions on the device and sends them to the server.

[0749] Output: The recorded data will be reflected in future proposals.

[0750] Step 9:

[0751] The server suggests robot maintenance.

[0752] Input: Robot operation data.

[0753] Data processing / calculation: The server analyzes operational data and evaluates the need for maintenance based on the operational status.

[0754] Output: Proposed maintenance schedule.

[0755] Step 10:

[0756] The user performs the proposed maintenance.

[0757] Input: Proposed maintenance schedule.

[0758] Data processing / calculation: The user performs maintenance on the robot according to the proposed schedule.

[0759] Output: Efficient operation and improved performance of the robot.

[0760] 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.

[0761] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[0762] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0763] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0764] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0765] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0766] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational games and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice via the camera and microphone to determine emotions.

[0767] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts will also be notified.

[0768] (Example)

[0769] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[0770] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[0771] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[0772] The processing flow will be explained below.

[0773] Step 1:

[0774] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[0775] Step 2:

[0776] Device: Sends the child's basic information to the server.

[0777] Step 3:

[0778] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[0779] Step 4:

[0780] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, continuously collecting data such as their heart rate, temperature, and movement.

[0781] Step 5:

[0782] Terminal: Sends collected health data to the server in real time.

[0783] Step 6:

[0784] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[0785] Step 7:

[0786] Server: Based on the generated alert, the emotion engine analyzes camera images and audio data to recognize the user's emotions. It determines the user's current emotions.

[0787] Step 8:

[0788] Server: Adjust the urgency and content of the emergency notification based on the user's emotions. For example, if the user is feeling very stressed, the content of the emergency notification will be limited and concise, and other emergency contacts will also be notified.

[0789] Step 9:

[0790] Server: Sends the adjusted abnormality notification to the user's device.

[0791] Step 10:

[0792] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[0793] Step 11:

[0794] User: Receives notification and takes necessary action, for example, taking specific action such as contacting a doctor.

[0795] Step 12:

[0796] Device: Continuously monitors your child's sleep patterns, collecting and recording data on movements and environmental sounds during sleep.

[0797] Step 13:

[0798] Device: Periodically sends recorded sleep data to the server.

[0799] Step 14:

[0800] Server: Analyzes the received sleep data using AI and generates an appropriate sleep schedule.

[0801] Step 15:

[0802] Server: The emotion engine recognizes the user's emotions and adjusts the sleep schedule accordingly. For example, if the user is relaxed, it will suggest a standard schedule, but if the user is stressed, it will suggest a schedule that includes relaxing music and environmental adjustments.

[0803] Step 16:

[0804] Server: Sends the adjusted sleep schedule to the user's device.

[0805] Step 17:

[0806] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[0807] Step 18:

[0808] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[0809] Step 19:

[0810] Server: Sends the generated educational pretend play list to the user's terminal.

[0811] Step 20:

[0812] Terminal: Notify the user of the suggested role play list.

[0813] Step 21:

[0814] User: Carry out the proposed play and record the results and reactions on the device.

[0815] Step 22:

[0816] Terminal: Sends recorded data to the server.

[0817] Step 23:

[0818] Server: Analyzes the received data and reflects it in future educational pretend play suggestions.

[0819] Step 24:

[0820] Device: Monitors children's behavior in real time and analyzes abnormal behavior, such as prolonged periods of unresponsiveness or abnormal movements.

[0821] Step 25:

[0822] Terminal: If abnormal behavior is detected, it sends the information to the server.

[0823] Step 26:

[0824] Server: Analyzes the received abnormal behavior data, and the emotion engine recognizes the user's emotions. If the user is feeling particularly anxious, it generates a notification to encourage more proactive action.

[0825] Step 27:

[0826] Server: Sends coordinated anomaly notifications to users and other emergency contacts.

[0827] Step 28:

[0828] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take the necessary action.

[0829] These are the specific processing steps of the invention that combines the emotion engine. By taking the user's emotions into consideration and adjusting the system's operation, it is possible to provide more effective and personalized childcare support.

[0830] Example 2

[0831] 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."

[0832] In modern childcare, managing a child's health, sleep habits, and educational activities is extremely important, but it is difficult for parents to consistently monitor these and respond appropriately. Parents are also required to care for their children while taking into account their own emotional state, but there is no effective system for doing so. Therefore, there is a need for a system that monitors a child's health in real time, promptly notifies parents when abnormalities are detected, and supports appropriate responses while taking into account the parent's emotional state.

[0833] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a device for acquiring basic information about the child, a device for monitoring the child's health condition, a device for analyzing the monitored data and detecting abnormalities, a device for immediately notifying the abnormality, a device for recording the child's sleep pattern and generating an appropriate sleep schedule, a device for guiding the child's sleep based on the sleep schedule, a device for suggesting educational activities based on the child's age and interests, a device for detecting abnormal behavior and immediately reporting it, an emotion engine for analyzing the user's emotions, and a device for adjusting the system operation based on the user's emotions. This makes it possible to monitor the child's health condition in real time and take appropriate measures, as well as provide appropriate support taking into account the parent's emotional state.

[0834] "Basic information" refers to basic data about the child, such as the child's name, age, gender, and allergies.

[0835] "Health status" refers to information about a child's physical and physiological state, such as heart rate, temperature, and movement.

[0836] "Monitoring" refers to the act of continuously acquiring data and observing its status.

[0837] "Analysis" refers to the process of analyzing acquired data and making it meaningful.

[0838] "Abnormal" refers to data or conditions that fall outside a predefined normal range.

[0839] "Notification" refers to a message that notifies the user of an abnormality or important information.

[0840] "Sleep patterns" refers to information such as a child's sleep duration, depth, and cycle.

[0841] A "sleep schedule" refers to a plan or timetable for ensuring a child gets adequate sleep.

[0842] "Educational activities" refer to play and experiences that promote children's learning and development.

[0843] "Abnormal behavior" refers to behavior that significantly deviates from normal behavior patterns.

[0844] "Reporting" refers to the act of communicating anomalies or important data to users or other systems.

[0845] An "emotion engine" refers to software or hardware for analyzing a user's emotions.

[0846] "Device for adjusting operation" refers to a control device for optimizing the operation of the system based on the analysis results and the user's emotions.

[0847] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[0848] First, the user installs a dedicated application on their smartphone and enters basic information about their child (such as name, age, gender, and allergies). This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child. Specific hardware used at this stage includes a smartphone and a database on the server.

[0849] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data using an analysis engine and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. Specific wearable devices used include a heart rate monitor and a thermometer.

[0850] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to a server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user guides the child to sleep using the music and light adjustment functions provided by the device based on the proposed schedule. Specific hardware examples include smartphones, music playback devices, and lighting control devices.

[0851] Furthermore, the system suggests educational games based on the child's age and interests. The server generates a list of educational pretend games based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions. Specific hardware examples include a server and a smartphone.

[0852] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational activities and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine emotions. Specific software examples include facial recognition algorithms and voice analysis algorithms.

[0853] The system also detects abnormal child behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts are also notified. Specific hardware examples include a camera, microphone, and smartphone.

[0854] (Example)

[0855] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[0856] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[0857] Prompt Sentence Examples

[0858] "What should I do if my child's heart rate spikes? Include emergency procedures if the user is stressed."

[0859] "Please give me some suggestions to improve my child's irregular sleep pattern. Please also tell me how to make suggestions based on the user's emotions."

[0860] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[0861] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0862] Step 1:

[0863] The user enters basic information about their child. Specifically, the user launches a dedicated application on their smartphone and enters the child's name, age, gender, and allergy information. This basic information is sent from the device to the server. Input data: name, age, gender, and allergy information. Output data: child's basic information saved on the server. Specifically, the user taps the "Send" button, which sends the information to the server.

[0864] Step 2:

[0865] The server formulates monitoring standards and educational plans. Based on the basic information received, the server sets monitoring standards based on the child's age and allergy information. An initial educational plan is also automatically generated. Input data: Basic information about the child stored on the server. Output data: Monitoring standards and educational plans. Specifically, the server's algorithm analyzes the data and sets standards and plans.

[0866] Step 3:

[0867] The system monitors a child's health. The user has the child wear a wearable device (e.g., a heart rate monitor or thermometer). This device collects real-time health data such as heart rate, body temperature, and movement and sends it to a terminal. The terminal then transfers this data to a server. Input data: heart rate, body temperature, movement. Output data: health data stored on the server. Specifically, the wearable device periodically collects data and sends it to the terminal.

[0868] Step 4:

[0869] The server analyzes the health data and detects abnormalities. The server analyzes the received health data in real time using an analysis engine. If an abnormality is detected as a result of the analysis, the type of abnormality and its urgency are identified. Input data: Health data sent to the server. Output data: Analysis results and abnormality detection results. Specifically, the server uses a data analysis algorithm to detect abnormalities.

[0870] Step 5:

[0871] The server will send notifications to the user as needed. If an anomaly is detected, the server will immediately send a push notification to the user's device. The notification will include the type of anomaly, its urgency, and recommended countermeasures. Input data: Anomaly detection results. Output data: Notification sent to the user's device. Specifically, the server will generate notification content according to the urgency and send it to the device.

[0872] Step 6:

[0873] The device records the child's sleep patterns. The device periodically collects the child's sleep data from the wearable device and sends it to the server. Input data: Sleep data from the wearable device. Output data: Sleep data stored on the server. Specifically, the device periodically collects data and automatically sends it to the server.

[0874] Step 7:

[0875] The server analyzes the sleep data and creates an appropriate schedule. The server analyzes the received sleep data and generates a sleep schedule suitable for the child. This schedule is sent to the user's device. Input data: Sleep data sent to the server. Output data: Generated sleep schedule. Specifically, the server runs an analysis algorithm and generates an optimal schedule.

[0876] Step 8:

[0877] The server suggests educational activities. The server creates a list of suitable educational activities based on the child's age, interests, and past play history. The list is sent to the user's device. Input data: child's age, interests, play history. Output data: list of suggested educational activities. Specifically, the server references the database and generates a list of suggestions.

[0878] Step 9:

[0879] The emotion engine analyzes the user's emotions. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time to determine their emotions. Input data: camera footage, audio data. Output data: user emotion analysis results. Specifically, the emotion engine runs a facial expression recognition algorithm and a voice analysis algorithm.

[0880] Step 10:

[0881] The server adjusts the content of the abnormality notification and the content of the suggestions. The server adjusts the content of the abnormality notification and the content of the educational activity suggestions based on the emotional data sent from the emotion engine. Input data: Emotion analysis results. Output data: Adjusted notifications and suggestions. Specifically, the server analyzes the emotional data and dynamically changes the content of the notifications and suggestions.

[0882] (Application example 2)

[0883] 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."

[0884] Current self-driving vehicles have difficulty providing a comfortable riding environment that responds promptly to passengers' health and psychological state. Furthermore, mechanisms for quickly responding to sudden changes in a passenger's health or increased psychological stress are not adequately developed. Therefore, there is a need for a system that allows passengers to use self-driving vehicles safely and comfortably.

[0885] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0886] In this invention, the server includes means for acquiring basic passenger information, means for monitoring the passenger's health status, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the passenger of the abnormality, means for analyzing the passenger's emotional state and automatically adjusting the in-vehicle environment, means for adjusting the urgency and content of the abnormality notification based on the passenger's emotional state, means for automatically adjusting in-vehicle comfort functions based on the passenger's health status and emotional state, and means for stopping the vehicle and taking emergency action when an abnormality occurs. This makes it possible to flexibly respond to the passenger's health status and psychological state and provide a safe and comfortable riding environment.

[0887] "Passenger basic information" is basic data about the passenger, such as the passenger's name, age, and health status.

[0888] "Health status" refers to a passenger's physical condition, such as heart rate, body temperature, or blood pressure, as measured by wearable devices.

[0889] "Monitoring" is the process of observing and recording passenger health and behavior in real time.

[0890] "Analyzing data" means analyzing the monitored information and detecting anomalies and characteristics.

[0891] "Detecting anomalies" means finding abnormal data that deviates from a passenger's normal state.

[0892] "Immediate notification" refers to the process of issuing an alert immediately when an abnormality is detected and prompting the necessary response.

[0893] "Emotional state" refers to the psychological state of a passenger analyzed based on facial expressions, tone of voice, etc.

[0894] "Automatically adjusting the in-car environment" means changing settings such as lighting, music, and temperature in the car to match the passenger's emotional state.

[0895] "Comfort features" refer to the in-car environmental settings and facilities that make passengers comfortable.

[0896] "Emergency response" means taking necessary measures in response to a sudden change in a person's health or emotional state, such as stopping the vehicle or contacting a medical institution.

[0897] The present invention provides a system for monitoring the health and emotional state of passengers in real time and for responding quickly when an abnormality occurs. This system is implemented by combining a server, an in-vehicle terminal, a wearable device, a camera, a microphone, and an emotion engine. A specific embodiment of this system will be described below.

[0898] System Configuration

[0899] 1. Obtain basic passenger information

[0900] When passengers get into the vehicle, the server collects basic information (such as name, age, and health condition) from their smartphones or the touch panel of the in-vehicle terminal. This information is stored on the server and used as basic data for analysis.

[0901] 2. Health monitoring

[0902] The in-car terminal will link with a wearable device (capable of measuring heart rate, body temperature, etc.) to monitor the passenger's health. The wearable device will measure data in real time and send it to the in-car terminal via Bluetooth or other means.

[0903] 3. Emotional state analysis

[0904] Cameras and microphones installed in the in-car terminal analyze passengers' facial expressions and tone of voice, allowing the passenger's emotional state to be grasped in real time, and the analysis results are output by the emotion engine.

[0905] 4. Anomaly detection and notification

[0906] The server receives monitoring data and emotion analysis results sent from the in-vehicle device and analyzes this data using AI algorithms. If an abnormality is detected, the server immediately sends a notification to the in-vehicle device, and if necessary, notifies passengers' smartphones and emergency contacts.

[0907] 5. Automatic adjustment of the in-car environment

[0908] The server sends instructions to the in-car device to automatically adjust the in-car environment (lighting, music, air conditioning settings, etc.) based on the passenger's emotional state, providing a relaxing environment for passengers who are feeling stressed.

[0909] 6. Emergency Response

[0910] If a serious abnormality in the passenger's health condition is detected, the on-board device will automatically stop the vehicle and contact the nearest medical facility, based on pre-registered emergency contact information.

[0911] Hardware and Software Used

[0912] Wearable devices: heart rate monitors, thermometers, etc.

[0913] In-car terminal: Android display, touch panel

[0914] Camera and microphone: High-resolution camera and high-sensitivity microphone for analyzing passengers' facial expressions and voices

[0915] Emotion Engine: Dlib library and custom AI models

[0916] Communication protocols: Bluetooth, Wi-Fi, 4G / 5G networks

[0917] For example, a prompt to instruct a generative AI model might look like this:

[0918] "Generate a Python program that uses the in-car camera and microphone to analyze the passenger's facial expressions and voice, and heart rate data from a smartphone-connected wearable device to detect abnormalities in real time and automatically adjust the in-car environment. Use the presence or absence of a smile as a simple way to determine emotions."

[0919] As a result, the present invention is a system that improves passenger safety and comfort and enables rapid response in the event of an emergency.

[0920] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0921] Step 1:

[0922] Enter and save passenger basic information

[0923] Input: Basic passenger information (such as name, age, and health status) entered via smartphone or in-car device.

[0924] Operation: The user enters basic passenger information into the touch panel of a smartphone or in-car terminal. The entered information is sent to the server.

[0925] Output: Passenger basic information data stored on the server.

[0926] Step 2:

[0927] Health monitoring

[0928] Input: Health data sent from wearable devices (heart rate, temperature, etc.).

[0929] How it works: The wearable device measures heart rate and body temperature and transmits the data via Bluetooth to the in-car terminal, which receives the data and sends it to a server.

[0930] Output: Passenger health status data stored on the server.

[0931] Step 3:

[0932] Emotional state analysis

[0933] Input: Video data from the in-car camera and audio data from the microphone.

[0934] How it works: The onboard camera captures the passenger's facial expressions, and the microphone records the passenger's voice. This data is sent to the onboard device and analyzed by the emotion engine. The analysis results are then sent to the server.

[0935] Output: Passenger emotional state data stored on the server.

[0936] Step 4:

[0937] Anomaly detection

[0938] Input: Health and emotional state data stored on the server.

[0939] How it works: The server analyzes the health and emotional state data using AI algorithms to check for abnormalities. If an abnormality is detected, an immediate response is decided.

[0940] Output: Alert notification data when an anomaly is detected.

[0941] Step 5:

[0942] Abnormality notification

[0943] Input: Alert notification data when an abnormality is detected.

[0944] How it works: The server sends an alert to the in-car device. If the abnormality is serious, a notification is also sent to the passenger's smartphone and emergency contacts.

[0945] Output: An abnormality notification displayed on the in-car device and an alert message sent to a smartphone.

[0946] Step 6:

[0947] Automatic adjustment of the in-car environment

[0948] Input: Parsed emotional state data.

[0949] Operation: Based on the emotion analysis results, the server sends instructions to the in-car device to change the in-car environment, such as lighting, music, and air conditioning settings.

[0950] Output: In-car environment adjustment instruction data and actual changed in-car settings.

[0951] Step 7:

[0952] Emergency response

[0953] Input: Anomaly detection data and emergency response instruction data.

[0954] Operation: The server sends a command to the in-vehicle terminal to stop the vehicle and contacts the nearest medical institution. This contact is made based on the emergency contact information registered in advance.

[0955] Output: Stop the vehicle, notify emergency contacts, and contact the nearest medical facility.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] [Third embodiment]

[0960] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0961] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0962] 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).

[0963] 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.

[0964] 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.

[0965] 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).

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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."

[0972] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[0973] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[0974] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[0975] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[0976] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[0977] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[0978] For example, if a child's heart rate suddenly rises, the wearable device will send the data to the terminal, which will then forward it to the server. The server will then use AI to analyze the data and notify the user if an abnormality is detected. The user will then receive the notification and be able to take the necessary action promptly.

[0979] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The user can then use the device's music playback and light adjustment functions to improve their child's sleep.

[0980] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and allows users to take prompt action. It also supports healthy development of children by suggesting sleep habits and educational activities.

[0981] The processing flow will be explained below.

[0982] Step 1:

[0983] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[0984] Step 2:

[0985] Device: Sends the child's basic information to the server.

[0986] Step 3:

[0987] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[0988] Step 4:

[0989] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, collecting data such as their heart rate, body temperature, and movement.

[0990] Step 5:

[0991] Terminal: Sends collected health data to the server in real time.

[0992] Step 6:

[0993] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[0994] Step 7:

[0995] Server: Sends alert information to the user's terminal.

[0996] Step 8:

[0997] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[0998] Step 9:

[0999] User: Receives notification and takes necessary action. Takes specific action, such as contacting a doctor.

[1000] Step 10:

[1001] Device: Continuous monitoring is performed to record the child's sleep patterns. Data is collected on the device, including movements during sleep and environmental sounds.

[1002] Step 11:

[1003] Device: Periodically sends recorded sleep data to the server.

[1004] Step 12:

[1005] Server: AI analyzes the received sleep data and generates an appropriate sleep schedule.

[1006] Step 13:

[1007] Server: Sends the generated sleep schedule to the user's device.

[1008] Step 14:

[1009] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[1010] Step 15:

[1011] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[1012] Step 16:

[1013] Server: Sends the generated educational pretend play list to the user's terminal.

[1014] Step 17:

[1015] Terminal: Notify the user of the suggested role play list.

[1016] Step 18:

[1017] User: Carry out the proposed play and record the results and reactions on the device.

[1018] Step 19:

[1019] Terminal: Sends recorded data to the server.

[1020] Step 20:

[1021] Server: Analyzes the received data and reflects it in suggestions for future educational pretend play.

[1022] Step 21:

[1023] Device: Monitors children's behavior in real time and analyzes abnormal behavior.

[1024] Step 22:

[1025] Terminal: If abnormal behavior (long periods of no response, abnormal movements, etc.) is detected, the information is sent to the server.

[1026] Step 23:

[1027] Server: Receives abnormal behavior data and evaluates the urgency.

[1028] Step 24:

[1029] Server: If the emergency is high, a mass notification is sent to the user and other emergency contacts.

[1030] Step 25:

[1031] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take necessary action.

[1032] These are the specific processing steps of a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior.

[1033] Example 1

[1034] 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."

[1035] In recent years, the trend toward nuclear families and dual-income households has increased the burden of childcare. Furthermore, the difficulty of understanding children's health conditions and behavior in real time and responding appropriately presents challenges in managing children's health and ensuring their safety. Furthermore, there is a need for methods to effectively support children's development of sleep habits and educational activities. However, no system currently exists that comprehensively solves these challenges. The present invention aims to provide a comprehensive system that solves these challenges and supports children's health management, ensuring their safety, and healthy upbringing.

[1036] 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.

[1037] In this invention, the server includes means for acquiring basic information about the child, means for monitoring the child's health condition, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the child of abnormalities, means for recording the child's sleep patterns and generating an appropriate sleep schedule, means for guiding the child to sleep based on the sleep schedule, means for suggesting educational play based on the child's age and interests, means for detecting and immediately reporting abnormal behavior, means for notifying the child of abnormalities based on the analysis results, and means for analyzing behavioral data using a generative AI model and detecting abnormalities. This enables real-time monitoring of the child's health condition, immediate response to abnormalities, formation of appropriate sleep habits, and suggestion of educational activities, thereby realizing comprehensive child-rearing support.

[1038] "Basic information" refers to basic information about an individual, such as the child's name, age, gender, and allergy information.

[1039] "Health status" refers to a child's physical condition and vital signs, such as heart rate, temperature, and movement.

[1040] "Monitoring" is the act of observing a child's health and behavior in real time and collecting the data.

[1041] "Data analysis" is the analytical process of using monitored data to detect anomalies.

[1042] "Abnormal" refers to any deviation from normal health or behavior, including, for example, a sudden increase in heart rate or abnormal behavior.

[1043] "Notification" is a means of immediately conveying information to the user when an abnormality is detected.

[1044] "Sleep patterns" refer to a child's set of sleep-related behaviors and habits, such as bedtimes and wake-up times.

[1045] A "sleep schedule" is a plan that determines bedtimes and wake-up times that are appropriate for a child's health.

[1046] "Educational play" refers to activities and games that are designed to educate children and are chosen according to their age and interests.

[1047] "Abnormal behavior" refers to irregular behavior or unresponsiveness that differs from normal behavior.

[1048] A "generative AI model" is an artificial intelligence model used for data analysis and anomaly detection, for example, by learning behavioral patterns to detect anomalies.

[1049] "Real-time" refers to processing and responses that are nearly simultaneous, and refers to a state in which information is collected and notified without delay.

[1050] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[1051] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[1052] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[1053] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[1054] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[1055] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[1056] The analysis uses AI models, such as frameworks like TensorFlow and PyTorch, and Pandas and NumPy are used for data analysis. Generative AI models like OpenAI's GPT-3 are also used for anomaly detection and schedule generation.

[1057] As a concrete example, the following scenario can be considered.

[1058] Example 1: A child's heart rate is abnormally high

[1059] The wearable device monitors your heart rate and detects any readings outside the normal range.

[1060] The device sends this data to the terminal.

[1061] The terminal transfers the data to the server.

[1062] The server performs AI analysis and detects abnormalities.

[1063] The server will immediately notify the user.

[1064] The user receives a notification and opens the application to take immediate action.

[1065] Example prompt:

[1066] "A 3-year-old child's heart rate has suddenly increased. AI analysis has detected an abnormality. Please take immediate action."

[1067] Example 2: Your child's sleep schedule is irregular

[1068] The device records your sleep patterns (e.g., your daily bedtime and wake-up times).

[1069] The terminal transmits the recorded data to the server.

[1070] The server analyzes the data and generates an appropriate sleep schedule.

[1071] The server notifies the user of the schedule.

[1072] Users receive notifications and can set their smartphone's light adjustment and music playback according to a schedule.

[1073] Example prompt:

[1074] "Your child's sleep patterns are irregular. Based on the AI ​​analysis results, we have suggested an appropriate sleep schedule. Please set it according to the schedule."

[1075] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and supports healthy development of children by forming good sleep habits and suggesting educational activities.

[1076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1077] Step 1: Enter and submit basic information

[1078] The user opens the smartphone application and enters basic information about the child (such as name, age, gender, and allergy information).

[1079] The entered information is saved on the terminal and sent to the server.

[1080] Input: Child's basic information

[1081] Output: Basic information data sent to the server

[1082] The server stores the received basic information in a database.

[1083] Step 2: Real-time health monitoring

[1084] Monitoring devices (wearable devices and cameras) collect data such as a child's heart rate, body temperature, and movement.

[1085] The collected data is sent to the terminal in real time.

[1086] Input: Child health data

[1087] Output: Health data sent to the device

[1088] The terminal transfers the received data to the server.

[1089] Input: Health data received by the device

[1090] Output: Health data transmitted to the server

[1091] Step 3: Anomaly detection and notification

[1092] The server analyzes the received health data using AI models such as TensorFlow and PyTorch.

[1093] Input: Health data received by the server

[1094] Output: Health data analysis results

[1095] The server detects abnormalities based on the analysis results, such as when the heart rate rises above a certain level.

[1096] Input: Health data analysis results

[1097] Output: Anomaly detection results

[1098] If an abnormality is detected, the server immediately sends a notification to the user's terminal.

[1099] Input: Anomaly detection results

[1100] Output: Notification sent to the user's device

[1101] Step 4: Record your sleep patterns and create a schedule

[1102] The device records the child's sleep patterns (e.g., bedtime, wake-up time, etc.).

[1103] Input: Child's sleep data

[1104] Output: Recorded sleep data

[1105] The device sends the recorded sleep data to a server.

[1106] Input: Sleep data recorded on the device

[1107] Output: Sleep data sent to the server

[1108] The server analyzes the received data and generates an appropriate sleep schedule using R or Python data analysis libraries (such as Pandas and NumPy).

[1109] Input: Sleep data received by the server

[1110] Output: Generated sleep schedule

[1111] The schedule is sent to the user's terminal and notified to the user.

[1112] Input: Generated sleep schedule

[1113] Output: Schedule notification sent to the user's device

[1114] Based on the suggested schedule, users can use the music and light adjustment functions provided by the device to help their child sleep.

[1115] Input: Schedule Notification

[1116] Output: Playing music and adjusting the light

[1117] Step 5: Propose educational activities

[1118] The server collects and analyzes data such as the child's age, past play history, and interests.

[1119] Input: Child's personal data and play history

[1120] Output: Analysis results

[1121] The server generates a list of educational activities based on the analysis results, using an AI model such as OpenAI's GPT-3.

[1122] Input: Analysis results

[1123] Output: Generated activity list

[1124] The activity list is sent to the user's terminal and notified to the user.

[1125] Input: Generated activity list

[1126] Output: Activity notification sent to the user's device

[1127] The user performs the suggested activities and records their results and reactions on the device.

[1128] Input: Activity execution result

[1129] Output: Performance data recorded on the device

[1130] The recorded data is sent to the server and reflected in future proposals.

[1131] Input: Performance data recorded on the device

[1132] Output: Results data sent to the server

[1133] Step 6: Detect and immediately report abnormal behavior

[1134] The monitoring device collects data on children's behavior.

[1135] Input: Child behavior data

[1136] Output: Collected behavioral data

[1137] The terminal transmits the received behavioral data to the server.

[1138] Input: Behavioral data received by the device

[1139] Output: Behavioral data sent to the server

[1140] The server analyzes the received behavioral data and detects abnormal behavior using anomaly detection algorithms and AI models.

[1141] Input: Behavioral data received by the server

[1142] Output: Analysis results of behavioral data

[1143] If any abnormal activity is detected, the server will notify the user and, if necessary, other emergency contacts.

[1144] Input: Anomalous behavior analysis results

[1145] Output: Notifications sent to the user and emergency contacts

[1146] In this way, the system comprehensively monitors, analyzes, and notifies children's health status, sleep habits, educational activities, and abnormal behaviors, helping users effectively manage their children's health and safety and supporting their healthy development.

[1147] (Application example 1)

[1148] 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."

[1149] Currently, there are limited systems in factories that can monitor the operating status and abnormalities of robots in real time and respond quickly. Furthermore, there are insufficient means to efficiently propose regular maintenance for robots and improve the working environment. This can lead to a decline in the operating efficiency of robots in factories, potentially resulting in problems with productivity and safety. Furthermore, integrating these systems with existing childcare support systems is expected to increase convenience and centralize widespread monitoring.

[1150] 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.

[1151] In this invention, the server includes: means for acquiring basic information about the child; means for monitoring the child's health; means for analyzing the monitored data and detecting abnormalities; means for immediately notifying the child of abnormalities; means for recording the child's sleep patterns and generating an appropriate sleep schedule; means for inducing the child to sleep based on the sleep schedule; means for suggesting educational activities based on the child's age and interests; means for detecting abnormal behavior and immediately reporting the abnormal behavior; means for monitoring the robot's operating status; means for analyzing the monitored robot's operating data and detecting abnormalities; means for immediately notifying the child of robot abnormalities; and means for suggesting robot maintenance. This enables real-time monitoring and abnormality detection of the operating status of robots in factories, enabling rapid response and appropriate maintenance suggestions. Furthermore, integration with a childcare support system enables centralized monitoring and efficient information management.

[1152] "Means for obtaining basic information about children" refers to devices or systems for collecting basic information such as a child's name, age, gender, and allergy information.

[1153] "Child health monitoring means" refers to devices or systems for real-time monitoring of a child's health, including heart rate, body temperature, and movement.

[1154] "Means for analyzing monitored data and detecting abnormalities" refers to devices or systems that analyze collected data and detect abnormalities.

[1155] The "means for immediately notifying an abnormality" refers to a device or system that immediately sends a notification to the user when an abnormality is detected.

[1156] "Means for recording a child's sleep patterns and generating an appropriate sleep schedule" refers to a device or system for recording a child's sleep data and creating an optimal sleep schedule based on that data.

[1157] "Means for inducing sleep in children based on a sleep schedule" refers to a device or system that adjusts music and lighting based on a created sleep schedule to induce sleep in children.

[1158] "Means for suggesting educational play based on a child's age and interests" refers to a device or system for suggesting educational activities based on data on a child's age, past play history, and interests.

[1159] "Means for detecting abnormal behavior and reporting it immediately" refers to a device or system that analyzes children's behavioral data, detects abnormal behavior, and immediately reports it to the user.

[1160] "Means for monitoring the operating status of robots" refers to devices and systems for monitoring the operating status of robots in factories in real time.

[1161] "Means for analyzing the monitored robot operation data and detecting abnormalities" refers to a device or system for analyzing collected robot operation data and detecting any abnormalities.

[1162] "Means for immediately notifying robot abnormalities" refers to devices or systems that immediately send a notification to a manager when an abnormality is detected within the factory.

[1163] "Means for proposing robot maintenance" refers to a device or system for proposing appropriate maintenance based on the robot's operational data.

[1164] This is a system that monitors the health of children and the operating status of robots, provides immediate notification of abnormal behavior, and provides maintenance support. Users operate the system using a smartphone or tablet, and the system functions by combining a server and various monitoring devices (wearable devices, sensor devices, cameras).

[1165] The server collects basic information about the child and the robot and sets various monitoring standards based on that information. The child's health status is monitored using a wearable device, including heart rate, body temperature, and movement, and the collected data is analyzed in real time. The robot's operating status is monitored using sensor devices and cameras, including its speed, temperature, vibration, and current, and the data is similarly collected and analyzed.

[1166] If an abnormality is detected, the server immediately sends a notification to the user's smartphone or tablet. In the case of a child, if an abnormal heart rate or rise in body temperature is detected, a notification is sent to the user, allowing for a prompt response. In the case of a robot, if an abnormal vibration or rise in temperature is detected, a notification is sent to the administrator, allowing for immediate action to be taken.

[1167] The server also records the child's sleep patterns and generates an appropriate sleep schedule, which is then sent to the user's device, where it uses the device's music playback and light adjustment functions to guide the child to sleep.

[1168] The server then suggests educational play based on the child's age and interests, including a list of educational activities generated based on data such as the child's age, past play history, and interests. The user can then try out the suggested play and record their results and reactions, which will be reflected in future suggestions.

[1169] The server also has the function of proposing regular maintenance based on the robot's operational data, which allows for effective maintenance of the robot and prevents a decline in operational efficiency.

[1170] Specific hardware used includes temperature sensors, vibration sensors, current sensors, humidity sensors, communication modules (Wi-Fi, LTE), wearable devices, and cameras. For software, a program is run using Python to collect and send data, and an API server (e.g., Flask or Django) analyzes the received data, detects anomalies, and sends notifications. A database (e.g., MySQL, PostgreSQL) stores and analyzes the collected data.

[1171] For example, if the temperature of a robot in a factory rises to a dangerous level, the system will detect the abnormality in real time and send a notification to the factory manager's smartphone, allowing the manager to immediately inspect the cooling system and take appropriate action. Similarly, if a child deviates from their normal sleep schedule, the system will detect this and adjust the music and lighting based on the suggested sleep schedule to encourage healthy sleep.

[1172] Examples of input prompts for a generative AI model include:

[1173] Create a Python program that monitors the temperature, vibration, current, and humidity of a robot in a factory in real time. The data will be sent to a server every 5 seconds. The server will notify the administrator if it detects an abnormality. The temperature range is 20.0 to 70.0°C, vibration range is 0.0 to 10.0Hz, current range is 0.0 to 5.0A, and humidity range is 0.0 to 100.0%.

[1174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1175] Step 1:

[1176] The user uses the terminal to input basic information about the child and the robot's identification information.

[1177] Input: Child's name, age, gender, allergy information and robot identification information.

[1178] Data processing / calculation: This information is sent to the server and stored in a database.

[1179] Output: Monitoring criteria are set based on the stored baseline data.

[1180] Step 2:

[1181] Monitoring devices (wearable devices, sensor devices, cameras) collect data.

[1182] Input: Heart rate, body temperature, movement, robot speed, temperature, vibration, current, and other data.

[1183] Data processing / calculation: The device collects this data in real time and sends it to the server via the terminal.

[1184] Output: Real-time monitoring data.

[1185] Step 3:

[1186] The server analyzes the data it receives and detects any abnormalities.

[1187] Input: Real-time monitoring data.

[1188] Data processing / calculation: An AI model on the server analyzes the data and detects anomalies that fall outside the normal range.

[1189] Output: Anomaly detection results.

[1190] Step 4:

[1191] The server will immediately notify you of any abnormalities.

[1192] Input: Anomaly detection results.

[1193] Data processing / calculation: If an abnormality is detected, the server immediately sends a notification to the user's device.

[1194] Output: Abnormality notification.

[1195] Step 5:

[1196] The server records the child's sleep patterns and generates an appropriate sleep schedule.

[1197] Input: Child sleep data.

[1198] Data processing / calculation: The server analyzes the sleep data and generates an optimal sleep schedule based on statistics and past data.

[1199] Output: A suggested sleep schedule.

[1200] Step 6:

[1201] The user uses the device's functions to guide the child to sleep based on the suggested sleep schedule.

[1202] Input: A suggested sleep schedule.

[1203] Data processing / calculation: The device uses music playback and light adjustment functions to guide the child to sleep according to a schedule.

[1204] Output: Improved sleep patterns in children.

[1205] Step 7:

[1206] The server suggests educational activities based on the child's age and interests.

[1207] Input: Child's age, past play history, and interest data.

[1208] Data processing / calculation: The server generates a list of optimal educational activities based on this data.

[1209] Output: A list of suggested educational activities.

[1210] Step 8:

[1211] The user performs the suggested play and records the results.

[1212] Input: Proposed play, outcomes and responses to the play.

[1213] Data processing / calculation: The user records their results and reactions on the device and sends them to the server.

[1214] Output: The recorded data will be reflected in future proposals.

[1215] Step 9:

[1216] The server suggests robot maintenance.

[1217] Input: Robot operation data.

[1218] Data processing / calculation: The server analyzes operational data and evaluates the need for maintenance based on the operational status.

[1219] Output: Proposed maintenance schedule.

[1220] Step 10:

[1221] The user performs the proposed maintenance.

[1222] Input: Proposed maintenance schedule.

[1223] Data processing / calculation: The user performs maintenance on the robot according to the proposed schedule.

[1224] Output: Efficient operation and improved performance of the robot.

[1225] 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.

[1226] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[1227] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[1228] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[1229] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[1230] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[1231] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational games and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice via the camera and microphone to determine emotions.

[1232] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts will also be notified.

[1233] (Example)

[1234] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[1235] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[1236] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[1237] The processing flow will be explained below.

[1238] Step 1:

[1239] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[1240] Step 2:

[1241] Device: Sends the child's basic information to the server.

[1242] Step 3:

[1243] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[1244] Step 4:

[1245] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, continuously collecting data such as their heart rate, temperature, and movement.

[1246] Step 5:

[1247] Terminal: Sends collected health data to the server in real time.

[1248] Step 6:

[1249] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[1250] Step 7:

[1251] Server: Based on the generated alert, the emotion engine analyzes camera images and audio data to recognize the user's emotions. It determines the user's current emotions.

[1252] Step 8:

[1253] Server: Adjust the urgency and content of the emergency notification based on the user's emotions. For example, if the user is feeling very stressed, the content of the emergency notification will be limited and concise, and other emergency contacts will also be notified.

[1254] Step 9:

[1255] Server: Sends the adjusted abnormality notification to the user's device.

[1256] Step 10:

[1257] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[1258] Step 11:

[1259] User: Receives notification and takes necessary action, for example, taking specific action such as contacting a doctor.

[1260] Step 12:

[1261] Device: Continuously monitors your child's sleep patterns, collecting and recording data on movements and environmental sounds during sleep.

[1262] Step 13:

[1263] Device: Periodically sends recorded sleep data to the server.

[1264] Step 14:

[1265] Server: Analyzes the received sleep data using AI and generates an appropriate sleep schedule.

[1266] Step 15:

[1267] Server: The emotion engine recognizes the user's emotions and adjusts the sleep schedule accordingly. For example, if the user is relaxed, it will suggest a standard schedule, but if the user is stressed, it will suggest a schedule that includes relaxing music and environmental adjustments.

[1268] Step 16:

[1269] Server: Sends the adjusted sleep schedule to the user's device.

[1270] Step 17:

[1271] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[1272] Step 18:

[1273] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[1274] Step 19:

[1275] Server: Sends the generated educational pretend play list to the user's terminal.

[1276] Step 20:

[1277] Terminal: Notify the user of the suggested role play list.

[1278] Step 21:

[1279] User: Carry out the proposed play and record the results and reactions on the device.

[1280] Step 22:

[1281] Terminal: Sends recorded data to the server.

[1282] Step 23:

[1283] Server: Analyzes the received data and reflects it in future educational pretend play suggestions.

[1284] Step 24:

[1285] Device: Monitors children's behavior in real time and analyzes abnormal behavior, such as prolonged periods of unresponsiveness or abnormal movements.

[1286] Step 25:

[1287] Terminal: If abnormal behavior is detected, it sends the information to the server.

[1288] Step 26:

[1289] Server: Analyzes the received abnormal behavior data, and the emotion engine recognizes the user's emotions. If the user is feeling particularly anxious, it generates a notification to encourage more proactive action.

[1290] Step 27:

[1291] Server: Sends coordinated anomaly notifications to users and other emergency contacts.

[1292] Step 28:

[1293] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take the necessary action.

[1294] These are the specific processing steps of the invention that combines the emotion engine. By taking the user's emotions into consideration and adjusting the system's operation, it is possible to provide more effective and personalized childcare support.

[1295] Example 2

[1296] 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."

[1297] In modern childcare, managing a child's health, sleep habits, and educational activities is extremely important, but it is difficult for parents to consistently monitor these and respond appropriately. Parents are also required to care for their children while taking into account their own emotional state, but there is no effective system for doing so. Therefore, there is a need for a system that monitors a child's health in real time, promptly notifies parents when abnormalities are detected, and supports appropriate responses while taking into account the parent's emotional state.

[1298] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a device for acquiring basic information about the child, a device for monitoring the child's health condition, a device for analyzing the monitored data and detecting abnormalities, a device for immediately notifying the abnormality, a device for recording the child's sleep pattern and generating an appropriate sleep schedule, a device for guiding the child's sleep based on the sleep schedule, a device for suggesting educational activities based on the child's age and interests, a device for detecting abnormal behavior and immediately reporting it, an emotion engine for analyzing the user's emotions, and a device for adjusting the system operation based on the user's emotions. This makes it possible to monitor the child's health condition in real time and take appropriate measures, as well as provide appropriate support taking into account the parent's emotional state.

[1299] "Basic information" refers to basic data about the child, such as the child's name, age, gender, and allergies.

[1300] "Health status" refers to information about a child's physical and physiological state, such as heart rate, temperature, and movement.

[1301] "Monitoring" refers to the act of continuously acquiring data and observing its status.

[1302] "Analysis" refers to the process of analyzing acquired data and making it meaningful.

[1303] "Abnormal" refers to data or conditions that fall outside a predefined normal range.

[1304] "Notification" refers to a message that notifies the user of an abnormality or important information.

[1305] "Sleep patterns" refers to information such as a child's sleep duration, depth, and cycle.

[1306] A "sleep schedule" refers to a plan or timetable for ensuring a child gets adequate sleep.

[1307] "Educational activities" refer to play and experiences that promote children's learning and development.

[1308] "Abnormal behavior" refers to behavior that significantly deviates from normal behavior patterns.

[1309] "Reporting" refers to the act of communicating anomalies or important data to users or other systems.

[1310] An "emotion engine" refers to software or hardware for analyzing a user's emotions.

[1311] "Device for adjusting operation" refers to a control device for optimizing the operation of the system based on the analysis results and the user's emotions.

[1312] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[1313] First, the user installs a dedicated application on their smartphone and enters basic information about their child (such as name, age, gender, and allergies). This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child. Specific hardware used at this stage includes a smartphone and a database on the server.

[1314] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data using an analysis engine and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. Specific wearable devices used include a heart rate monitor and a thermometer.

[1315] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to a server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user guides the child to sleep using the music and light adjustment functions provided by the device based on the proposed schedule. Specific hardware examples include smartphones, music playback devices, and lighting control devices.

[1316] Furthermore, the system suggests educational games based on the child's age and interests. The server generates a list of educational pretend games based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions. Specific hardware examples include a server and a smartphone.

[1317] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational activities and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine emotions. Specific software examples include facial recognition algorithms and voice analysis algorithms.

[1318] The system also detects abnormal child behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts are also notified. Specific hardware examples include a camera, microphone, and smartphone.

[1319] (Example)

[1320] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[1321] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[1322] Prompt Sentence Examples

[1323] "What should I do if my child's heart rate spikes? Include emergency procedures if the user is stressed."

[1324] "Please give me some suggestions to improve my child's irregular sleep pattern. Please also tell me how to make suggestions based on the user's emotions."

[1325] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[1326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1327] Step 1:

[1328] The user enters basic information about their child. Specifically, the user launches a dedicated application on their smartphone and enters the child's name, age, gender, and allergy information. This basic information is sent from the device to the server. Input data: name, age, gender, and allergy information. Output data: child's basic information saved on the server. Specifically, the user taps the "Send" button, which sends the information to the server.

[1329] Step 2:

[1330] The server formulates monitoring standards and educational plans. Based on the basic information received, the server sets monitoring standards based on the child's age and allergy information. An initial educational plan is also automatically generated. Input data: Basic information about the child stored on the server. Output data: Monitoring standards and educational plans. Specifically, the server's algorithm analyzes the data and sets standards and plans.

[1331] Step 3:

[1332] The system monitors a child's health. The user has the child wear a wearable device (e.g., a heart rate monitor or thermometer). This device collects real-time health data such as heart rate, body temperature, and movement and sends it to a terminal. The terminal then transfers this data to a server. Input data: heart rate, body temperature, movement. Output data: health data stored on the server. Specifically, the wearable device periodically collects data and sends it to the terminal.

[1333] Step 4:

[1334] The server analyzes the health data and detects abnormalities. The server analyzes the received health data in real time using an analysis engine. If an abnormality is detected as a result of the analysis, the type of abnormality and its urgency are identified. Input data: Health data sent to the server. Output data: Analysis results and abnormality detection results. Specifically, the server uses a data analysis algorithm to detect abnormalities.

[1335] Step 5:

[1336] The server will send notifications to the user as needed. If an anomaly is detected, the server will immediately send a push notification to the user's device. The notification will include the type of anomaly, its urgency, and recommended countermeasures. Input data: Anomaly detection results. Output data: Notification sent to the user's device. Specifically, the server will generate notification content according to the urgency and send it to the device.

[1337] Step 6:

[1338] The device records the child's sleep patterns. The device periodically collects the child's sleep data from the wearable device and sends it to the server. Input data: Sleep data from the wearable device. Output data: Sleep data stored on the server. Specifically, the device periodically collects data and automatically sends it to the server.

[1339] Step 7:

[1340] The server analyzes the sleep data and creates an appropriate schedule. The server analyzes the received sleep data and generates a sleep schedule suitable for the child. This schedule is sent to the user's device. Input data: Sleep data sent to the server. Output data: Generated sleep schedule. Specifically, the server runs an analysis algorithm and generates an optimal schedule.

[1341] Step 8:

[1342] The server suggests educational activities. The server creates a list of suitable educational activities based on the child's age, interests, and past play history. The list is sent to the user's device. Input data: child's age, interests, play history. Output data: list of suggested educational activities. Specifically, the server references the database and generates a list of suggestions.

[1343] Step 9:

[1344] The emotion engine analyzes the user's emotions. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time to determine their emotions. Input data: camera footage, audio data. Output data: user emotion analysis results. Specifically, the emotion engine runs a facial expression recognition algorithm and a voice analysis algorithm.

[1345] Step 10:

[1346] The server adjusts the content of the abnormality notification and the content of the suggestions. The server adjusts the content of the abnormality notification and the content of the educational activity suggestions based on the emotional data sent from the emotion engine. Input data: Emotion analysis results. Output data: Adjusted notifications and suggestions. Specifically, the server analyzes the emotional data and dynamically changes the content of the notifications and suggestions.

[1347] (Application example 2)

[1348] 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."

[1349] Current self-driving vehicles have difficulty providing a comfortable riding environment that responds promptly to passengers' health and psychological state. Furthermore, mechanisms for quickly responding to sudden changes in a passenger's health or increased psychological stress are not adequately developed. Therefore, there is a need for a system that allows passengers to use self-driving vehicles safely and comfortably.

[1350] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1351] In this invention, the server includes means for acquiring basic passenger information, means for monitoring the passenger's health status, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the passenger of the abnormality, means for analyzing the passenger's emotional state and automatically adjusting the in-vehicle environment, means for adjusting the urgency and content of the abnormality notification based on the passenger's emotional state, means for automatically adjusting in-vehicle comfort functions based on the passenger's health status and emotional state, and means for stopping the vehicle and taking emergency action when an abnormality occurs. This makes it possible to flexibly respond to the passenger's health status and psychological state and provide a safe and comfortable riding environment.

[1352] "Passenger basic information" is basic data about the passenger, such as the passenger's name, age, and health status.

[1353] "Health status" refers to a passenger's physical condition, such as heart rate, body temperature, or blood pressure, as measured by wearable devices.

[1354] "Monitoring" is the process of observing and recording passenger health and behavior in real time.

[1355] "Analyzing data" means analyzing the monitored information and detecting anomalies and characteristics.

[1356] "Detecting anomalies" means finding abnormal data that deviates from a passenger's normal state.

[1357] "Immediate notification" refers to the process of issuing an alert immediately when an abnormality is detected and prompting the necessary response.

[1358] "Emotional state" refers to the psychological state of a passenger analyzed based on facial expressions, tone of voice, etc.

[1359] "Automatically adjusting the in-car environment" means changing settings such as lighting, music, and temperature in the car to match the passenger's emotional state.

[1360] "Comfort features" refer to the in-car environmental settings and facilities that make passengers comfortable.

[1361] "Emergency response" means taking necessary measures in response to a sudden change in a person's health or emotional state, such as stopping the vehicle or contacting a medical institution.

[1362] The present invention provides a system for monitoring the health and emotional state of passengers in real time and for responding quickly when an abnormality occurs. This system is implemented by combining a server, an in-vehicle terminal, a wearable device, a camera, a microphone, and an emotion engine. A specific embodiment of this system will be described below.

[1363] System Configuration

[1364] 1. Obtain basic passenger information

[1365] When passengers get into the vehicle, the server collects basic information (such as name, age, and health condition) from their smartphones or the touch panel of the in-vehicle terminal. This information is stored on the server and used as basic data for analysis.

[1366] 2. Health monitoring

[1367] The in-car terminal will link with a wearable device (capable of measuring heart rate, body temperature, etc.) to monitor the passenger's health. The wearable device will measure data in real time and send it to the in-car terminal via Bluetooth or other means.

[1368] 3. Emotional state analysis

[1369] Cameras and microphones installed in the in-car terminal analyze passengers' facial expressions and tone of voice, allowing the passenger's emotional state to be grasped in real time, and the analysis results are output by the emotion engine.

[1370] 4. Anomaly detection and notification

[1371] The server receives monitoring data and emotion analysis results sent from the in-vehicle device and analyzes this data using AI algorithms. If an abnormality is detected, the server immediately sends a notification to the in-vehicle device, and if necessary, notifies passengers' smartphones and emergency contacts.

[1372] 5. Automatic adjustment of the in-car environment

[1373] The server sends instructions to the in-car device to automatically adjust the in-car environment (lighting, music, air conditioning settings, etc.) based on the passenger's emotional state, providing a relaxing environment for passengers who are feeling stressed.

[1374] 6. Emergency Response

[1375] If a serious abnormality in the passenger's health condition is detected, the on-board device will automatically stop the vehicle and contact the nearest medical facility, based on pre-registered emergency contact information.

[1376] Hardware and Software Used

[1377] Wearable devices: heart rate monitors, thermometers, etc.

[1378] In-car terminal: Android display, touch panel

[1379] Camera and microphone: High-resolution camera and high-sensitivity microphone for analyzing passengers' facial expressions and voices

[1380] Emotion Engine: Dlib library and custom AI models

[1381] Communication protocols: Bluetooth, Wi-Fi, 4G / 5G networks

[1382] For example, a prompt to instruct a generative AI model might look like this:

[1383] "Generate a Python program that uses the in-car camera and microphone to analyze the passenger's facial expressions and voice, and heart rate data from a smartphone-connected wearable device to detect abnormalities in real time and automatically adjust the in-car environment. Use the presence or absence of a smile as a simple way to determine emotions."

[1384] As a result, the present invention is a system that improves passenger safety and comfort and enables rapid response in the event of an emergency.

[1385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1386] Step 1:

[1387] Enter and save passenger basic information

[1388] Input: Basic passenger information (such as name, age, and health status) entered via smartphone or in-car device.

[1389] Operation: The user enters basic passenger information into the touch panel of a smartphone or in-car terminal. The entered information is sent to the server.

[1390] Output: Passenger basic information data stored on the server.

[1391] Step 2:

[1392] Health monitoring

[1393] Input: Health data sent from wearable devices (heart rate, temperature, etc.).

[1394] How it works: The wearable device measures heart rate and body temperature and transmits the data via Bluetooth to the in-car terminal, which receives the data and sends it to a server.

[1395] Output: Passenger health status data stored on the server.

[1396] Step 3:

[1397] Emotional state analysis

[1398] Input: Video data from the in-car camera and audio data from the microphone.

[1399] How it works: The onboard camera captures the passenger's facial expressions, and the microphone records the passenger's voice. This data is sent to the onboard device and analyzed by the emotion engine. The analysis results are then sent to the server.

[1400] Output: Passenger emotional state data stored on the server.

[1401] Step 4:

[1402] Anomaly detection

[1403] Input: Health and emotional state data stored on the server.

[1404] How it works: The server analyzes the health and emotional state data using AI algorithms to check for abnormalities. If an abnormality is detected, an immediate response is decided.

[1405] Output: Alert notification data when an anomaly is detected.

[1406] Step 5:

[1407] Abnormality notification

[1408] Input: Alert notification data when an abnormality is detected.

[1409] How it works: The server sends an alert to the in-car device. If the abnormality is serious, a notification is also sent to the passenger's smartphone and emergency contacts.

[1410] Output: An abnormality notification displayed on the in-car device and an alert message sent to a smartphone.

[1411] Step 6:

[1412] Automatic adjustment of the in-car environment

[1413] Input: Parsed emotional state data.

[1414] Operation: Based on the emotion analysis results, the server sends instructions to the in-car device to change the in-car environment, such as lighting, music, and air conditioning settings.

[1415] Output: In-car environment adjustment instruction data and actual changed in-car settings.

[1416] Step 7:

[1417] Emergency response

[1418] Input: Anomaly detection data and emergency response instruction data.

[1419] Operation: The server sends a command to the in-vehicle terminal to stop the vehicle and contacts the nearest medical institution. This contact is made based on the emergency contact information registered in advance.

[1420] Output: Stop the vehicle, notify emergency contacts, and contact the nearest medical facility.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] [Fourth embodiment]

[1425] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1426] 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.

[1427] 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).

[1428] 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.

[1429] 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.

[1430] 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).

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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."

[1438] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[1439] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[1440] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[1441] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[1442] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[1443] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[1444] For example, if a child's heart rate suddenly rises, the wearable device will send the data to the terminal, which will then forward it to the server. The server will then use AI to analyze the data and notify the user if an abnormality is detected. The user will then receive the notification and be able to take the necessary action promptly.

[1445] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The user can then use the device's music playback and light adjustment functions to improve their child's sleep.

[1446] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and allows users to take prompt action. It also supports healthy development of children by suggesting sleep habits and educational activities.

[1447] The processing flow will be explained below.

[1448] Step 1:

[1449] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[1450] Step 2:

[1451] Device: Sends the child's basic information to the server.

[1452] Step 3:

[1453] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[1454] Step 4:

[1455] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, collecting data such as their heart rate, body temperature, and movement.

[1456] Step 5:

[1457] Terminal: Sends collected health data to the server in real time.

[1458] Step 6:

[1459] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[1460] Step 7:

[1461] Server: Sends alert information to the user's terminal.

[1462] Step 8:

[1463] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[1464] Step 9:

[1465] User: Receives notification and takes necessary action. Takes specific action, such as contacting a doctor.

[1466] Step 10:

[1467] Device: Continuous monitoring is performed to record the child's sleep patterns. Data is collected on the device, including movements during sleep and environmental sounds.

[1468] Step 11:

[1469] Device: Periodically sends recorded sleep data to the server.

[1470] Step 12:

[1471] Server: AI analyzes the received sleep data and generates an appropriate sleep schedule.

[1472] Step 13:

[1473] Server: Sends the generated sleep schedule to the user's device.

[1474] Step 14:

[1475] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[1476] Step 15:

[1477] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[1478] Step 16:

[1479] Server: Sends the generated educational pretend play list to the user's terminal.

[1480] Step 17:

[1481] Terminal: Notify the user of the suggested role play list.

[1482] Step 18:

[1483] User: Carry out the proposed play and record the results and reactions on the device.

[1484] Step 19:

[1485] Terminal: Sends recorded data to the server.

[1486] Step 20:

[1487] Server: Analyzes the received data and reflects it in suggestions for future educational pretend play.

[1488] Step 21:

[1489] Device: Monitors children's behavior in real time and analyzes abnormal behavior.

[1490] Step 22:

[1491] Terminal: If abnormal behavior (long periods of no response, abnormal movements, etc.) is detected, the information is sent to the server.

[1492] Step 23:

[1493] Server: Receives abnormal behavior data and evaluates the urgency.

[1494] Step 24:

[1495] Server: If the emergency is high, a mass notification is sent to the user and other emergency contacts.

[1496] Step 25:

[1497] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take necessary action.

[1498] These are the specific processing steps of a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior.

[1499] Example 1

[1500] 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."

[1501] In recent years, the trend toward nuclear families and dual-income households has increased the burden of childcare. Furthermore, the difficulty of understanding children's health conditions and behavior in real time and responding appropriately presents challenges in managing children's health and ensuring their safety. Furthermore, there is a need for methods to effectively support children's development of sleep habits and educational activities. However, no system currently exists that comprehensively solves these challenges. The present invention aims to provide a comprehensive system that solves these challenges and supports children's health management, ensuring their safety, and healthy upbringing.

[1502] 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.

[1503] In this invention, the server includes means for acquiring basic information about the child, means for monitoring the child's health condition, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the child of abnormalities, means for recording the child's sleep patterns and generating an appropriate sleep schedule, means for guiding the child to sleep based on the sleep schedule, means for suggesting educational play based on the child's age and interests, means for detecting and immediately reporting abnormal behavior, means for notifying the child of abnormalities based on the analysis results, and means for analyzing behavioral data using a generative AI model and detecting abnormalities. This enables real-time monitoring of the child's health condition, immediate response to abnormalities, formation of appropriate sleep habits, and suggestion of educational activities, thereby realizing comprehensive child-rearing support.

[1504] "Basic information" refers to basic information about an individual, such as the child's name, age, gender, and allergy information.

[1505] "Health status" refers to a child's physical condition and vital signs, such as heart rate, temperature, and movement.

[1506] "Monitoring" is the act of observing a child's health and behavior in real time and collecting the data.

[1507] "Data analysis" is the analytical process of using monitored data to detect anomalies.

[1508] "Abnormal" refers to any deviation from normal health or behavior, including, for example, a sudden increase in heart rate or abnormal behavior.

[1509] "Notification" is a means of immediately conveying information to the user when an abnormality is detected.

[1510] "Sleep patterns" refer to a child's set of sleep-related behaviors and habits, such as bedtimes and wake-up times.

[1511] A "sleep schedule" is a plan that determines bedtimes and wake-up times that are appropriate for a child's health.

[1512] "Educational play" refers to activities and games that are designed to educate children and are chosen according to their age and interests.

[1513] "Abnormal behavior" refers to irregular behavior or unresponsiveness that differs from normal behavior.

[1514] A "generative AI model" is an artificial intelligence model used for data analysis and anomaly detection, for example, by learning behavioral patterns to detect anomalies.

[1515] "Real-time" refers to processing and responses that are nearly simultaneous, and refers to a state in which information is collected and notified without delay.

[1516] This is a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior. This system is implemented by combining a user's terminal, a server, and monitoring devices (wearable devices and cameras).

[1517] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[1518] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[1519] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[1520] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[1521] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data, evaluates the urgency, and notifies the user. If necessary, reports are also made to other emergency contacts.

[1522] The analysis uses AI models, such as frameworks like TensorFlow and PyTorch, and Pandas and NumPy are used for data analysis. Generative AI models like OpenAI's GPT-3 are also used for anomaly detection and schedule generation.

[1523] As a concrete example, the following scenario can be considered.

[1524] Example 1: A child's heart rate is abnormally high

[1525] The wearable device monitors your heart rate and detects any readings outside the normal range.

[1526] The device sends this data to the terminal.

[1527] The terminal transfers the data to the server.

[1528] The server performs AI analysis and detects abnormalities.

[1529] The server will immediately notify the user.

[1530] The user receives a notification and opens the application to take immediate action.

[1531] Example prompt:

[1532] "A 3-year-old child's heart rate has suddenly increased. AI analysis has detected an abnormality. Please take immediate action."

[1533] Example 2: Your child's sleep schedule is irregular

[1534] The device records your sleep patterns (e.g., your daily bedtime and wake-up times).

[1535] The terminal transmits the recorded data to the server.

[1536] The server analyzes the data and generates an appropriate sleep schedule.

[1537] The server notifies the user of the schedule.

[1538] Users receive notifications and can set their smartphone's light adjustment and music playback according to a schedule.

[1539] Example prompt:

[1540] "Your child's sleep patterns are irregular. Based on the AI ​​analysis results, we have suggested an appropriate sleep schedule. Please set it according to the schedule."

[1541] As described above, this system monitors children's health and safety in real time, notifies users immediately if an abnormality occurs, and supports healthy development of children by forming good sleep habits and suggesting educational activities.

[1542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1543] Step 1: Enter and submit basic information

[1544] The user opens the smartphone application and enters basic information about the child (such as name, age, gender, and allergy information).

[1545] The entered information is saved on the terminal and sent to the server.

[1546] Input: Child's basic information

[1547] Output: Basic information data sent to the server

[1548] The server stores the received basic information in a database.

[1549] Step 2: Real-time health monitoring

[1550] Monitoring devices (wearable devices and cameras) collect data such as a child's heart rate, body temperature, and movement.

[1551] The collected data is sent to the terminal in real time.

[1552] Input: Child health data

[1553] Output: Health data sent to the device

[1554] The terminal transfers the received data to the server.

[1555] Input: Health data received by the device

[1556] Output: Health data transmitted to the server

[1557] Step 3: Anomaly detection and notification

[1558] The server analyzes the received health data using AI models such as TensorFlow and PyTorch.

[1559] Input: Health data received by the server

[1560] Output: Health data analysis results

[1561] The server detects abnormalities based on the analysis results, such as when the heart rate rises above a certain level.

[1562] Input: Health data analysis results

[1563] Output: Anomaly detection results

[1564] If an abnormality is detected, the server immediately sends a notification to the user's terminal.

[1565] Input: Anomaly detection results

[1566] Output: Notification sent to the user's device

[1567] Step 4: Record your sleep patterns and create a schedule

[1568] The device records the child's sleep patterns (e.g., bedtime, wake-up time, etc.).

[1569] Input: Child's sleep data

[1570] Output: Recorded sleep data

[1571] The device sends the recorded sleep data to a server.

[1572] Input: Sleep data recorded on the device

[1573] Output: Sleep data sent to the server

[1574] The server analyzes the received data and generates an appropriate sleep schedule using R or Python data analysis libraries (such as Pandas and NumPy).

[1575] Input: Sleep data received by the server

[1576] Output: Generated sleep schedule

[1577] The schedule is sent to the user's terminal and notified to the user.

[1578] Input: Generated sleep schedule

[1579] Output: Schedule notification sent to the user's device

[1580] Based on the suggested schedule, users can use the music and light adjustment functions provided by the device to help their child sleep.

[1581] Input: Schedule Notification

[1582] Output: Playing music and adjusting the light

[1583] Step 5: Propose educational activities

[1584] The server collects and analyzes data such as the child's age, past play history, and interests.

[1585] Input: Child's personal data and play history

[1586] Output: Analysis results

[1587] The server generates a list of educational activities based on the analysis results, using an AI model such as OpenAI's GPT-3.

[1588] Input: Analysis results

[1589] Output: Generated activity list

[1590] The activity list is sent to the user's terminal and notified to the user.

[1591] Input: Generated activity list

[1592] Output: Activity notification sent to the user's device

[1593] The user performs the suggested activities and records their results and reactions on the device.

[1594] Input: Activity execution result

[1595] Output: Performance data recorded on the device

[1596] The recorded data is sent to the server and reflected in future proposals.

[1597] Input: Performance data recorded on the device

[1598] Output: Results data sent to the server

[1599] Step 6: Detect and immediately report abnormal behavior

[1600] The monitoring device collects data on children's behavior.

[1601] Input: Child behavior data

[1602] Output: Collected behavioral data

[1603] The terminal transmits the received behavioral data to the server.

[1604] Input: Behavioral data received by the device

[1605] Output: Behavioral data sent to the server

[1606] The server analyzes the received behavioral data and detects abnormal behavior using anomaly detection algorithms and AI models.

[1607] Input: Behavioral data received by the server

[1608] Output: Analysis results of behavioral data

[1609] If any abnormal activity is detected, the server will notify the user and, if necessary, other emergency contacts.

[1610] Input: Anomalous behavior analysis results

[1611] Output: Notifications sent to the user and emergency contacts

[1612] In this way, the system comprehensively monitors, analyzes, and notifies children's health status, sleep habits, educational activities, and abnormal behaviors, helping users effectively manage their children's health and safety and supporting their healthy development.

[1613] (Application example 1)

[1614] 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."

[1615] Currently, there are limited systems in factories that can monitor the operating status and abnormalities of robots in real time and respond quickly. Furthermore, there are insufficient means to efficiently propose regular maintenance for robots and improve the working environment. This can lead to a decline in the operating efficiency of robots in factories, potentially resulting in problems with productivity and safety. Furthermore, integrating these systems with existing childcare support systems is expected to increase convenience and centralize widespread monitoring.

[1616] 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.

[1617] In this invention, the server includes: means for acquiring basic information about the child; means for monitoring the child's health; means for analyzing the monitored data and detecting abnormalities; means for immediately notifying the child of abnormalities; means for recording the child's sleep patterns and generating an appropriate sleep schedule; means for inducing the child to sleep based on the sleep schedule; means for suggesting educational activities based on the child's age and interests; means for detecting abnormal behavior and immediately reporting the abnormal behavior; means for monitoring the robot's operating status; means for analyzing the monitored robot's operating data and detecting abnormalities; means for immediately notifying the child of robot abnormalities; and means for suggesting robot maintenance. This enables real-time monitoring and abnormality detection of the operating status of robots in factories, enabling rapid response and appropriate maintenance suggestions. Furthermore, integration with a childcare support system enables centralized monitoring and efficient information management.

[1618] "Means for obtaining basic information about children" refers to devices or systems for collecting basic information such as a child's name, age, gender, and allergy information.

[1619] "Child health monitoring means" refers to devices or systems for real-time monitoring of a child's health, including heart rate, body temperature, and movement.

[1620] "Means for analyzing monitored data and detecting abnormalities" refers to devices or systems that analyze collected data and detect abnormalities.

[1621] The "means for immediately notifying an abnormality" refers to a device or system that immediately sends a notification to the user when an abnormality is detected.

[1622] "Means for recording a child's sleep patterns and generating an appropriate sleep schedule" refers to a device or system for recording a child's sleep data and creating an optimal sleep schedule based on that data.

[1623] "Means for inducing sleep in children based on a sleep schedule" refers to a device or system that adjusts music and lighting based on a created sleep schedule to induce sleep in children.

[1624] "Means for suggesting educational play based on a child's age and interests" refers to a device or system for suggesting educational activities based on data on a child's age, past play history, and interests.

[1625] "Means for detecting abnormal behavior and reporting it immediately" refers to a device or system that analyzes children's behavioral data, detects abnormal behavior, and immediately reports it to the user.

[1626] "Means for monitoring the operating status of robots" refers to devices and systems for monitoring the operating status of robots in factories in real time.

[1627] "Means for analyzing the monitored robot operation data and detecting abnormalities" refers to a device or system for analyzing collected robot operation data and detecting any abnormalities.

[1628] "Means for immediately notifying robot abnormalities" refers to devices or systems that immediately send a notification to a manager when an abnormality is detected within the factory.

[1629] "Means for proposing robot maintenance" refers to a device or system for proposing appropriate maintenance based on the robot's operational data.

[1630] This is a system that monitors the health of children and the operating status of robots, provides immediate notification of abnormal behavior, and provides maintenance support. Users operate the system using a smartphone or tablet, and the system functions by combining a server and various monitoring devices (wearable devices, sensor devices, cameras).

[1631] The server collects basic information about the child and the robot and sets various monitoring standards based on that information. The child's health status is monitored using a wearable device, including heart rate, body temperature, and movement, and the collected data is analyzed in real time. The robot's operating status is monitored using sensor devices and cameras, including its speed, temperature, vibration, and current, and the data is similarly collected and analyzed.

[1632] If an abnormality is detected, the server immediately sends a notification to the user's smartphone or tablet. In the case of a child, if an abnormal heart rate or rise in body temperature is detected, a notification is sent to the user, allowing for a prompt response. In the case of a robot, if an abnormal vibration or rise in temperature is detected, a notification is sent to the administrator, allowing for immediate action to be taken.

[1633] The server also records the child's sleep patterns and generates an appropriate sleep schedule, which is then sent to the user's device, where it uses the device's music playback and light adjustment functions to guide the child to sleep.

[1634] The server then suggests educational play based on the child's age and interests, including a list of educational activities generated based on data such as the child's age, past play history, and interests. The user can then try out the suggested play and record their results and reactions, which will be reflected in future suggestions.

[1635] The server also has the function of proposing regular maintenance based on the robot's operational data, which allows for effective maintenance of the robot and prevents a decline in operational efficiency.

[1636] Specific hardware used includes temperature sensors, vibration sensors, current sensors, humidity sensors, communication modules (Wi-Fi, LTE), wearable devices, and cameras. For software, a program is run using Python to collect and send data, and an API server (e.g., Flask or Django) analyzes the received data, detects anomalies, and sends notifications. A database (e.g., MySQL, PostgreSQL) stores and analyzes the collected data.

[1637] For example, if the temperature of a robot in a factory rises to a dangerous level, the system will detect the abnormality in real time and send a notification to the factory manager's smartphone, allowing the manager to immediately inspect the cooling system and take appropriate action. Similarly, if a child deviates from their normal sleep schedule, the system will detect this and adjust the music and lighting based on the suggested sleep schedule to encourage healthy sleep.

[1638] Examples of input prompts for a generative AI model include:

[1639] Create a Python program that monitors the temperature, vibration, current, and humidity of a robot in a factory in real time. The data will be sent to a server every 5 seconds. The server will notify the administrator if it detects an abnormality. The temperature range is 20.0 to 70.0°C, vibration range is 0.0 to 10.0Hz, current range is 0.0 to 5.0A, and humidity range is 0.0 to 100.0%.

[1640] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1641] Step 1:

[1642] The user uses the terminal to input basic information about the child and the robot's identification information.

[1643] Input: Child's name, age, gender, allergy information and robot identification information.

[1644] Data processing / calculation: This information is sent to the server and stored in a database.

[1645] Output: Monitoring criteria are set based on the stored baseline data.

[1646] Step 2:

[1647] Monitoring devices (wearable devices, sensor devices, cameras) collect data.

[1648] Input: Heart rate, body temperature, movement, robot speed, temperature, vibration, current, and other data.

[1649] Data processing / calculation: The device collects this data in real time and sends it to the server via the terminal.

[1650] Output: Real-time monitoring data.

[1651] Step 3:

[1652] The server analyzes the data it receives and detects any abnormalities.

[1653] Input: Real-time monitoring data.

[1654] Data processing / calculation: An AI model on the server analyzes the data and detects anomalies that fall outside the normal range.

[1655] Output: Anomaly detection results.

[1656] Step 4:

[1657] The server will immediately notify you of any abnormalities.

[1658] Input: Anomaly detection results.

[1659] Data processing / calculation: If an abnormality is detected, the server immediately sends a notification to the user's device.

[1660] Output: Abnormality notification.

[1661] Step 5:

[1662] The server records the child's sleep patterns and generates an appropriate sleep schedule.

[1663] Input: Child sleep data.

[1664] Data processing / calculation: The server analyzes the sleep data and generates an optimal sleep schedule based on statistics and past data.

[1665] Output: A suggested sleep schedule.

[1666] Step 6:

[1667] The user uses the device's functions to guide the child to sleep based on the suggested sleep schedule.

[1668] Input: A suggested sleep schedule.

[1669] Data processing / calculation: The device uses music playback and light adjustment functions to guide the child to sleep according to a schedule.

[1670] Output: Improved sleep patterns in children.

[1671] Step 7:

[1672] The server suggests educational activities based on the child's age and interests.

[1673] Input: Child's age, past play history, and interest data.

[1674] Data processing / calculation: The server generates a list of optimal educational activities based on this data.

[1675] Output: A list of suggested educational activities.

[1676] Step 8:

[1677] The user performs the suggested play and records the results.

[1678] Input: Proposed play, outcomes and responses to the play.

[1679] Data processing / calculation: The user records their results and reactions on the device and sends them to the server.

[1680] Output: The recorded data will be reflected in future proposals.

[1681] Step 9:

[1682] The server suggests robot maintenance.

[1683] Input: Robot operation data.

[1684] Data processing / calculation: The server analyzes operational data and evaluates the need for maintenance based on the operational status.

[1685] Output: Proposed maintenance schedule.

[1686] Step 10:

[1687] The user performs the proposed maintenance.

[1688] Input: Proposed maintenance schedule.

[1689] Data processing / calculation: The user performs maintenance on the robot according to the proposed schedule.

[1690] Output: Efficient operation and improved performance of the robot.

[1691] 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.

[1692] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[1693] First, the user installs the application on their smartphone and enters basic information about their child (name, age, gender, allergies, etc.) This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child.

[1694] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. The user receives the notification and can take the necessary action.

[1695] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to the server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user can use the device's music and light control functions to guide the child to sleep based on the proposed schedule.

[1696] The system also suggests educational games based on the child's age and interests. The server generates a list of educational pretend play based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which then notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions.

[1697] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational games and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice via the camera and microphone to determine emotions.

[1698] The system also detects abnormal behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts will also be notified.

[1699] (Example)

[1700] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[1701] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[1702] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[1703] The processing flow will be explained below.

[1704] Step 1:

[1705] User: Installs the application on a smartphone, opens the initial setup screen, enters the child's basic information (name, age, gender, allergy information, etc.), and submits it.

[1706] Step 2:

[1707] Device: Sends the child's basic information to the server.

[1708] Step 3:

[1709] Server: Based on the received basic information, the server formulates appropriate monitoring standards and educational plans for the child. The formulated standards and plans are then sent to the device.

[1710] Step 4:

[1711] Devices: Receive established monitoring standards and educational plans, and use wearable devices and cameras to monitor children's health, continuously collecting data such as their heart rate, temperature, and movement.

[1712] Step 5:

[1713] Terminal: Sends collected health data to the server in real time.

[1714] Step 6:

[1715] Server: Analyzes the received health data using AI and compares it with standard values. If an abnormality is detected, an alert is generated.

[1716] Step 7:

[1717] Server: Based on the generated alert, the emotion engine analyzes camera images and audio data to recognize the user's emotions. It determines the user's current emotions.

[1718] Step 8:

[1719] Server: Adjust the urgency and content of the emergency notification based on the user's emotions. For example, if the user is feeling very stressed, the content of the emergency notification will be limited and concise, and other emergency contacts will also be notified.

[1720] Step 9:

[1721] Server: Sends the adjusted abnormality notification to the user's device.

[1722] Step 10:

[1723] Device: An abnormality notification is displayed on the user's smartphone, and an alert is given via sound and vibration.

[1724] Step 11:

[1725] User: Receives notification and takes necessary action, for example, taking specific action such as contacting a doctor.

[1726] Step 12:

[1727] Device: Continuously monitors your child's sleep patterns, collecting and recording data on movements and environmental sounds during sleep.

[1728] Step 13:

[1729] Device: Periodically sends recorded sleep data to the server.

[1730] Step 14:

[1731] Server: Analyzes the received sleep data using AI and generates an appropriate sleep schedule.

[1732] Step 15:

[1733] Server: The emotion engine recognizes the user's emotions and adjusts the sleep schedule accordingly. For example, if the user is relaxed, it will suggest a standard schedule, but if the user is stressed, it will suggest a schedule that includes relaxing music and environmental adjustments.

[1734] Step 16:

[1735] Server: Sends the adjusted sleep schedule to the user's device.

[1736] Step 17:

[1737] User: Review the suggested sleep schedule and use the device's music and light controls to help guide their child to sleep.

[1738] Step 18:

[1739] Server: Analyzes data to suggest educational play based on the child's age and interests. Generates a list of pretend play activities based on past play history and interest data.

[1740] Step 19:

[1741] Server: Sends the generated educational pretend play list to the user's terminal.

[1742] Step 20:

[1743] Terminal: Notify the user of the suggested role play list.

[1744] Step 21:

[1745] User: Carry out the proposed play and record the results and reactions on the device.

[1746] Step 22:

[1747] Terminal: Sends recorded data to the server.

[1748] Step 23:

[1749] Server: Analyzes the received data and reflects it in future educational pretend play suggestions.

[1750] Step 24:

[1751] Device: Monitors children's behavior in real time and analyzes abnormal behavior, such as prolonged periods of unresponsiveness or abnormal movements.

[1752] Step 25:

[1753] Terminal: If abnormal behavior is detected, it sends the information to the server.

[1754] Step 26:

[1755] Server: Analyzes the received abnormal behavior data, and the emotion engine recognizes the user's emotions. If the user is feeling particularly anxious, it generates a notification to encourage more proactive action.

[1756] Step 27:

[1757] Server: Sends coordinated anomaly notifications to users and other emergency contacts.

[1758] Step 28:

[1759] Device: Displays an alert of abnormal behavior on the user's smartphone or notification device, prompting them to take the necessary action.

[1760] These are the specific processing steps of the invention that combines the emotion engine. By taking the user's emotions into consideration and adjusting the system's operation, it is possible to provide more effective and personalized childcare support.

[1761] Example 2

[1762] 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."

[1763] In modern childcare, managing a child's health, sleep habits, and educational activities is extremely important, but it is difficult for parents to consistently monitor these and respond appropriately. Parents are also required to care for their children while taking into account their own emotional state, but there is no effective system for doing so. Therefore, there is a need for a system that monitors a child's health in real time, promptly notifies parents when abnormalities are detected, and supports appropriate responses while taking into account the parent's emotional state.

[1764] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a device for acquiring basic information about the child, a device for monitoring the child's health condition, a device for analyzing the monitored data and detecting abnormalities, a device for immediately notifying the abnormality, a device for recording the child's sleep pattern and generating an appropriate sleep schedule, a device for guiding the child's sleep based on the sleep schedule, a device for suggesting educational activities based on the child's age and interests, a device for detecting abnormal behavior and immediately reporting it, an emotion engine for analyzing the user's emotions, and a device for adjusting the system operation based on the user's emotions. This makes it possible to monitor the child's health condition in real time and take appropriate measures, as well as provide appropriate support taking into account the parent's emotional state.

[1765] "Basic information" refers to basic data about the child, such as the child's name, age, gender, and allergies.

[1766] "Health status" refers to information about a child's physical and physiological state, such as heart rate, temperature, and movement.

[1767] "Monitoring" refers to the act of continuously acquiring data and observing its status.

[1768] "Analysis" refers to the process of analyzing acquired data and making it meaningful.

[1769] "Abnormal" refers to data or conditions that fall outside a predefined normal range.

[1770] "Notification" refers to a message that notifies the user of an abnormality or important information.

[1771] "Sleep patterns" refers to information such as a child's sleep duration, depth, and cycle.

[1772] A "sleep schedule" refers to a plan or timetable for ensuring a child gets adequate sleep.

[1773] "Educational activities" refer to play and experiences that promote children's learning and development.

[1774] "Abnormal behavior" refers to behavior that significantly deviates from normal behavior patterns.

[1775] "Reporting" refers to the act of communicating anomalies or important data to users or other systems.

[1776] An "emotion engine" refers to software or hardware for analyzing a user's emotions.

[1777] "Device for adjusting operation" refers to a control device for optimizing the operation of the system based on the analysis results and the user's emotions.

[1778] This invention combines a childcare support system that monitors children's health, develops sleep habits, suggests educational activities, and immediately reports abnormal behavior with an emotion engine that recognizes the user's emotions. This system is implemented by combining a user's terminal, a server, monitoring devices (wearable devices and cameras), and the emotion engine.

[1779] First, the user installs a dedicated application on their smartphone and enters basic information about their child (such as name, age, gender, and allergies). This information is sent from the device to a server, which then creates appropriate monitoring standards and educational plans for the child. Specific hardware used at this stage includes a smartphone and a database on the server.

[1780] The system uses wearable devices and cameras to monitor children's health in real time. The monitored data (heart rate, body temperature, movement, etc.) is sent from the device to a server in real time. The server analyzes the received data using an analysis engine and detects abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user's device. Specific wearable devices used include a heart rate monitor and a thermometer.

[1781] The system then helps children develop good sleep habits. The device records the child's sleep patterns and sends them to a server. The server analyzes the received sleep data and generates an appropriate sleep schedule. This schedule is then sent to the user's device, and the user guides the child to sleep using the music and light adjustment functions provided by the device based on the proposed schedule. Specific hardware examples include smartphones, music playback devices, and lighting control devices.

[1782] Furthermore, the system suggests educational games based on the child's age and interests. The server generates a list of educational pretend games based on data such as the child's age, past play history, and interests. This list is sent to the user's device, which notifies the user of the suggested games. The user then plays the suggested games and records their results and reactions on the device. This data is sent to the server, and the analysis results are reflected in future suggestions. Specific hardware examples include a server and a smartphone.

[1783] The emotion engine recognizes the user's emotions and adjusts the system's behavior accordingly. For example, if the user is feeling stressed or anxious, the emotion engine can recognize that emotion and adjust the educational activities and abnormality notifications suggested by the server. The emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine emotions. Specific software examples include facial recognition algorithms and voice analysis algorithms.

[1784] The system also detects abnormal child behavior in real time and reports it immediately. The device analyzes the child's behavior, and if abnormal behavior (such as unresponsiveness or abnormal movements) is detected, it sends the information to the server. The server receives the abnormal behavior data and adjusts the urgency and content of the abnormality notification, taking into account the user's emotions recognized by the emotion engine. If the urgency is high, the user and other emergency contacts are also notified. Specific hardware examples include a camera, microphone, and smartphone.

[1785] (Example)

[1786] For example, if a child's heart rate suddenly rises, the wearable device sends the data to the terminal, which then forwards it to the server. The server analyzes the data using AI, and if an abnormality is detected, the emotion engine analyzes the user's emotions. If the user is already stressed, the server will narrow the content of the abnormality notification to emergency response measures and send a notification to other emergency contacts.

[1787] If a child's sleep is irregular, the device records their sleep patterns and sends them to the server. The server then uses the analysis results to suggest an appropriate sleep schedule to the user. The emotion engine analyzes the user's emotions and, if it determines that the user is relaxed, suggests a normal sleep schedule. If the user is feeling stressed, it suggests music or environmental adjustments that will have a more relaxing effect.

[1788] Prompt Sentence Examples

[1789] "What should I do if my child's heart rate spikes? Include emergency procedures if the user is stressed."

[1790] "Please give me some suggestions to improve my child's irregular sleep pattern. Please also tell me how to make suggestions based on the user's emotions."

[1791] As described above, this system monitors children's health and safety in real time, and if an abnormality occurs, it uses an emotion engine to take the user's emotions into consideration and respond quickly and appropriately. It also suggests educational activities and supports the formation of sleeping habits, ensuring the healthy development of children.

[1792] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1793] Step 1:

[1794] The user enters basic information about their child. Specifically, the user launches a dedicated application on their smartphone and enters the child's name, age, gender, and allergy information. This basic information is sent from the device to the server. Input data: name, age, gender, and allergy information. Output data: child's basic information saved on the server. Specifically, the user taps the "Send" button, which sends the information to the server.

[1795] Step 2:

[1796] The server formulates monitoring standards and educational plans. Based on the basic information received, the server sets monitoring standards based on the child's age and allergy information. An initial educational plan is also automatically generated. Input data: Basic information about the child stored on the server. Output data: Monitoring standards and educational plans. Specifically, the server's algorithm analyzes the data and sets standards and plans.

[1797] Step 3:

[1798] The system monitors a child's health. The user has the child wear a wearable device (e.g., a heart rate monitor or thermometer). This device collects real-time health data such as heart rate, body temperature, and movement and sends it to a terminal. The terminal then transfers this data to a server. Input data: heart rate, body temperature, movement. Output data: health data stored on the server. Specifically, the wearable device periodically collects data and sends it to the terminal.

[1799] Step 4:

[1800] The server analyzes the health data and detects abnormalities. The server analyzes the received health data in real time using an analysis engine. If an abnormality is detected as a result of the analysis, the type of abnormality and its urgency are identified. Input data: Health data sent to the server. Output data: Analysis results and abnormality detection results. Specifically, the server uses a data analysis algorithm to detect abnormalities.

[1801] Step 5:

[1802] The server will send notifications to the user as needed. If an anomaly is detected, the server will immediately send a push notification to the user's device. The notification will include the type of anomaly, its urgency, and recommended countermeasures. Input data: Anomaly detection results. Output data: Notification sent to the user's device. Specifically, the server will generate notification content according to the urgency and send it to the device.

[1803] Step 6:

[1804] The device records the child's sleep patterns. The device periodically collects the child's sleep data from the wearable device and sends it to the server. Input data: Sleep data from the wearable device. Output data: Sleep data stored on the server. Specifically, the device periodically collects data and automatically sends it to the server.

[1805] Step 7:

[1806] The server analyzes the sleep data and creates an appropriate schedule. The server analyzes the received sleep data and generates a sleep schedule suitable for the child. This schedule is sent to the user's device. Input data: Sleep data sent to the server. Output data: Generated sleep schedule. Specifically, the server runs an analysis algorithm and generates an optimal schedule.

[1807] Step 8:

[1808] The server suggests educational activities. The server creates a list of suitable educational activities based on the child's age, interests, and past play history. The list is sent to the user's device. Input data: child's age, interests, play history. Output data: list of suggested educational activities. Specifically, the server references the database and generates a list of suggestions.

[1809] Step 9:

[1810] The emotion engine analyzes the user's emotions. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time to determine their emotions. Input data: camera footage, audio data. Output data: user emotion analysis results. Specifically, the emotion engine runs a facial expression recognition algorithm and a voice analysis algorithm.

[1811] Step 10:

[1812] The server adjusts the content of the abnormality notification and the content of the suggestions. The server adjusts the content of the abnormality notification and the content of the educational activity suggestions based on the emotional data sent from the emotion engine. Input data: Emotion analysis results. Output data: Adjusted notifications and suggestions. Specifically, the server analyzes the emotional data and dynamically changes the content of the notifications and suggestions.

[1813] (Application example 2)

[1814] 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."

[1815] Current self-driving vehicles have difficulty providing a comfortable riding environment that responds promptly to passengers' health and psychological state. Furthermore, mechanisms for quickly responding to sudden changes in a passenger's health or increased psychological stress are not adequately developed. Therefore, there is a need for a system that allows passengers to use self-driving vehicles safely and comfortably.

[1816] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1817] In this invention, the server includes means for acquiring basic passenger information, means for monitoring the passenger's health status, means for analyzing the monitored data and detecting abnormalities, means for immediately notifying the passenger of the abnormality, means for analyzing the passenger's emotional state and automatically adjusting the in-vehicle environment, means for adjusting the urgency and content of the abnormality notification based on the passenger's emotional state, means for automatically adjusting in-vehicle comfort functions based on the passenger's health status and emotional state, and means for stopping the vehicle and taking emergency action when an abnormality occurs. This makes it possible to flexibly respond to the passenger's health status and psychological state and provide a safe and comfortable riding environment.

[1818] "Passenger basic information" is basic data about the passenger, such as the passenger's name, age, and health status.

[1819] "Health status" refers to a passenger's physical condition, such as heart rate, body temperature, or blood pressure, as measured by wearable devices.

[1820] "Monitoring" is the process of observing and recording passenger health and behavior in real time.

[1821] "Analyzing data" means analyzing the monitored information and detecting anomalies and characteristics.

[1822] "Detecting anomalies" means finding abnormal data that deviates from a passenger's normal state.

[1823] "Immediate notification" refers to the process of issuing an alert immediately when an abnormality is detected and prompting the necessary response.

[1824] "Emotional state" refers to the psychological state of a passenger analyzed based on facial expressions, tone of voice, etc.

[1825] "Automatically adjusting the in-car environment" means changing settings such as lighting, music, and temperature in the car to match the passenger's emotional state.

[1826] "Comfort features" refer to the in-car environmental settings and facilities that make passengers comfortable.

[1827] "Emergency response" means taking necessary measures in response to a sudden change in a person's health or emotional state, such as stopping the vehicle or contacting a medical institution.

[1828] The present invention provides a system for monitoring the health and emotional state of passengers in real time and for responding quickly when an abnormality occurs. This system is implemented by combining a server, an in-vehicle terminal, a wearable device, a camera, a microphone, and an emotion engine. A specific embodiment of this system will be described below.

[1829] System Configuration

[1830] 1. Obtain basic passenger information

[1831] When passengers get into the vehicle, the server collects basic information (such as name, age, and health condition) from their smartphones or the touch panel of the in-vehicle terminal. This information is stored on the server and used as basic data for analysis.

[1832] 2. Health monitoring

[1833] The in-car terminal will link with a wearable device (capable of measuring heart rate, body temperature, etc.) to monitor the passenger's health. The wearable device will measure data in real time and send it to the in-car terminal via Bluetooth or other means.

[1834] 3. Emotional state analysis

[1835] Cameras and microphones installed in the in-car terminal analyze passengers' facial expressions and tone of voice, allowing the passenger's emotional state to be grasped in real time, and the analysis results are output by the emotion engine.

[1836] 4. Anomaly detection and notification

[1837] The server receives monitoring data and emotion analysis results sent from the in-vehicle device and analyzes this data using AI algorithms. If an abnormality is detected, the server immediately sends a notification to the in-vehicle device, and if necessary, notifies passengers' smartphones and emergency contacts.

[1838] 5. Automatic adjustment of the in-car environment

[1839] The server sends instructions to the in-car device to automatically adjust the in-car environment (lighting, music, air conditioning settings, etc.) based on the passenger's emotional state, providing a relaxing environment for passengers who are feeling stressed.

[1840] 6. Emergency Response

[1841] If a serious abnormality in the passenger's health condition is detected, the on-board device will automatically stop the vehicle and contact the nearest medical facility, based on pre-registered emergency contact information.

[1842] Hardware and Software Used

[1843] Wearable devices: heart rate monitors, thermometers, etc.

[1844] In-car terminal: Android display, touch panel

[1845] Camera and microphone: High-resolution camera and high-sensitivity microphone for analyzing passengers' facial expressions and voices

[1846] Emotion Engine: Dlib library and custom AI models

[1847] Communication protocols: Bluetooth, Wi-Fi, 4G / 5G networks

[1848] For example, a prompt to instruct a generative AI model might look like this:

[1849] "Generate a Python program that uses the in-car camera and microphone to analyze the passenger's facial expressions and voice, and heart rate data from a smartphone-connected wearable device to detect abnormalities in real time and automatically adjust the in-car environment. Use the presence or absence of a smile as a simple way to determine emotions."

[1850] As a result, the present invention is a system that improves passenger safety and comfort and enables rapid response in the event of an emergency.

[1851] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1852] Step 1:

[1853] Enter and save passenger basic information

[1854] Input: Basic passenger information (such as name, age, and health status) entered via smartphone or in-car device.

[1855] Operation: The user enters basic passenger information into the touch panel of a smartphone or in-car terminal. The entered information is sent to the server.

[1856] Output: Passenger basic information data stored on the server.

[1857] Step 2:

[1858] Health monitoring

[1859] Input: Health data sent from wearable devices (heart rate, temperature, etc.).

[1860] How it works: The wearable device measures heart rate and body temperature and transmits the data via Bluetooth to the in-car terminal, which receives the data and sends it to a server.

[1861] Output: Passenger health status data stored on the server.

[1862] Step 3:

[1863] Emotional state analysis

[1864] Input: Video data from the in-car camera and audio data from the microphone.

[1865] How it works: The onboard camera captures the passenger's facial expressions, and the microphone records the passenger's voice. This data is sent to the onboard device and analyzed by the emotion engine. The analysis results are then sent to the server.

[1866] Output: Passenger emotional state data stored on the server.

[1867] Step 4:

[1868] Anomaly detection

[1869] Input: Health and emotional state data stored on the server.

[1870] How it works: The server analyzes the health and emotional state data using AI algorithms to check for abnormalities. If an abnormality is detected, an immediate response is decided.

[1871] Output: Alert notification data when an anomaly is detected.

[1872] Step 5:

[1873] Abnormality notification

[1874] Input: Alert notification data when an abnormality is detected.

[1875] How it works: The server sends an alert to the in-car device. If the abnormality is serious, a notification is also sent to the passenger's smartphone and emergency contacts.

[1876] Output: An abnormality notification displayed on the in-car device and an alert message sent to a smartphone.

[1877] Step 6:

[1878] Automatic adjustment of the in-car environment

[1879] Input: Parsed emotional state data.

[1880] Operation: Based on the emotion analysis results, the server sends instructions to the in-car device to change the in-car environment, such as lighting, music, and air conditioning settings.

[1881] Output: In-car environment adjustment instruction data and actual changed in-car settings.

[1882] Step 7:

[1883] Emergency response

[1884] Input: Anomaly detection data and emergency response instruction data.

[1885] Operation: The server sends a command to the in-vehicle terminal to stop the vehicle and contacts the nearest medical institution. This contact is made based on the emergency contact information registered in advance.

[1886] Output: Stop the vehicle, notify emergency contacts, and contact the nearest medical facility.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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.

[1891] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are ar...

Claims

1. A means of obtaining basic information about the child; means of monitoring children's health; A means for analyzing the monitored data and detecting abnormalities; A means for immediately notifying abnormalities; a means for recording the child's sleep patterns and generating an appropriate sleep schedule; A method for guiding children to sleep based on a sleep schedule; A means of suggesting educational play based on the child's age and interests, A means to detect and immediately report abnormal behavior A system including:

2. The system of claim 1 , comprising a wearable device for monitoring a child's health.

3. 10. The system of claim 1, further comprising a camera for analyzing the child's behavior and detecting abnormal behavior.

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