system

Abstract

The system according to this embodiment aims to comprehensively monitor a child's health status and suggest appropriate sleep habits and educational play activities. [Solution] The system according to the embodiment comprises a monitoring unit, a sleep habit formation unit, an educational suggestion unit, and an abnormal behavior reporting unit. The monitoring unit monitors the child's health status. The sleep habit formation unit provides advice for forming appropriate sleep habits based on the health data monitored by the monitoring unit. The educational suggestion unit proposes educational play based on the advice provided by the sleep habit formation unit. The abnormal behavior reporting unit immediately reports abnormal behavior based on the results of the play proposed by the educational suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the comprehensive monitoring of a child's health status and the proposal of appropriate sleep habits and educational play have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively monitor a child's health status and propose appropriate sleep habits and educational play.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a sleep habit formation unit, an educational suggestion unit, and an abnormal behavior reporting unit. The monitoring unit monitors the child's health status. The sleep habit formation unit provides advice for forming appropriate sleep habits based on the health data monitored by the monitoring unit. The educational suggestion unit proposes educational play based on the advice provided by the sleep habit formation unit. The abnormal behavior reporting unit immediately reports abnormal behavior based on the results of the play proposed by the educational suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively monitor a child's health status and suggest appropriate sleep habits and educational play activities. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI childcare support agent system according to an embodiment of the present invention is a system that monitors a child's health status, helps establish appropriate sleep habits, suggests educational play, and immediately reports abnormal behavior. This AI childcare support agent system monitors a child's health status in real time, provides advice for establishing appropriate sleep habits, suggests educational play, and immediately reports abnormal behavior. For example, the AI ​​childcare support agent system measures vital signs such as body temperature, heart rate, and respiratory rate using sensors, and the AI ​​analyzes them. As a result, if an abnormality is detected, it is immediately reported, enabling appropriate action. Next, the AI ​​childcare support agent system analyzes the child's sleep patterns and provides advice for establishing appropriate sleep habits. For example, it monitors the child's sleep duration and sleep onset patterns and suggests the optimal bedtime and environment. This supports the child's healthy growth. Furthermore, the AI ​​childcare support agent system suggests educational play appropriate to the child's age and developmental stage. For example, it suggests play using educational toys and learning apps to improve the child's learning effectiveness. In addition, the AI ​​childcare support agent system analyzes the child's behavior patterns and immediately reports if abnormal behavior is detected. For example, if unusual or dangerous behavior is detected, parents will be notified, enabling a quick response. In this way, the AI ​​childcare support agent system monitors the child's health, helps establish appropriate sleep habits, suggests educational play, and provides immediate reporting of abnormal behavior, allowing working parents to focus on their work while supporting the healthy development of their children. The AI ​​childcare support agent system can monitor the child's health in real time, provide advice on establishing appropriate sleep habits, suggest educational play, and immediately report abnormal behavior.

[0029] The AI ​​childcare support agent system according to this embodiment comprises a monitoring unit, a sleep habit formation unit, an educational suggestion unit, and an abnormal behavior reporting unit. The monitoring unit monitors the child's health status. The monitoring unit measures the child's body temperature and heart rate, for example, using a body temperature sensor and a heart rate sensor. The monitoring unit can measure the child's body temperature in real time, for example, using a body temperature sensor. The monitoring unit can measure the child's heart rate in real time, for example, using a heart rate sensor. The monitoring unit can measure the child's respiratory rate in real time, for example, using a respiratory rate sensor. The sleep habit formation unit provides advice for forming appropriate sleep habits based on the health data monitored by the monitoring unit. The sleep habit formation unit can analyze the child's sleep patterns and suggest the optimal bedtime and environment. The sleep habit formation unit can monitor the child's sleep duration and suggest the optimal bedtime. The sleep habit formation unit can monitor the child's sleep onset patterns and suggest the optimal sleep environment. The sleep habit formation unit can monitor the child's sleep quality and suggest the optimal sleep habits. The Educational Proposal Department proposes educational play based on advice provided by the Sleep Habit Formation Department. The Educational Proposal Department proposes, for example, educational toys and learning apps appropriate to the child's age and developmental stage. The Educational Proposal Department can propose, for example, educational toys appropriate to the child's age. The Educational Proposal Department can propose, for example, learning apps appropriate to the child's developmental stage. The Educational Proposal Department can propose, for example, educational play that suits the child's interests and concerns. The Abnormal Behavior Reporting Department immediately reports abnormal behavior based on the results of play proposed by the Educational Proposal Department. The Abnormal Behavior Reporting Department is equipped with, for example, a behavior pattern analysis algorithm for detecting abnormal behavior. The Abnormal Behavior Reporting Department can detect abnormal behavior using, for example, a behavior pattern analysis algorithm. The Abnormal Behavior Reporting Department can immediately notify parents when abnormal behavior is detected. The Abnormal Behavior Reporting Department can propose appropriate responses depending on the type and frequency of the abnormal behavior.As a result, the AI ​​childcare support agent system according to this embodiment can monitor the child's health, provide advice for establishing appropriate sleep habits, suggest educational play activities, and immediately report abnormal behavior.

[0030] The monitoring unit monitors the child's health status. For example, it measures the child's body temperature and heart rate using temperature sensors and heart rate sensors. Specifically, temperature sensors could be types that directly contact the child's skin or non-contact infrared sensors. This allows for real-time measurement of the child's body temperature and early detection of abnormal temperature changes. Heart rate sensors can be attached to the child's chest or wrist, monitoring heart rate fluctuations in real time. This allows for immediate alerts if the child's heart rate is abnormally high or low. Furthermore, respiratory rate sensors detect chest and abdominal movements to measure respiratory rate in real time. This allows for early detection of abnormalities such as shallow or stopped breathing. The monitoring unit centrally manages the data obtained from these sensors and has a function to immediately notify parents or medical institutions if an abnormality is detected. This allows for constant monitoring of the child's health status and rapid response when an abnormality occurs.

[0031] The Sleep Habit Formation Department provides advice on forming appropriate sleep habits based on health data monitored by the Monitoring Department. Specifically, it analyzes a child's sleep patterns and proposes the optimal bedtime and environment. For example, it monitors a child's sleep duration and proposes the optimal bedtime to help them fall asleep at a consistent rhythm. Furthermore, it can analyze a child's sleep onset patterns and propose the optimal sleep environment, including bedroom temperature, humidity, and lighting brightness. To monitor sleep quality, it analyzes the number of times a child turns over in their sleep and the ratio of deep to light sleep, and provides advice to improve sleep quality. For example, it could offer specific suggestions for improving the bedroom environment, or suggest music or scents to promote relaxation. In this way, the Sleep Habit Formation Department can provide specific advice on forming optimal sleep habits while considering the child's health condition, thereby supporting the child's growth and development.

[0032] The Educational Proposal Department proposes educational play activities based on advice provided by the Sleep Habit Formation Department. Specifically, it proposes educational toys and learning apps appropriate to the child's age and developmental stage. For example, for toddlers, it can propose blocks and puzzles to learn colors and shapes, and musical instrument toys to enjoy music. For school-aged children, it can propose educational apps to learn the basics of arithmetic and language, and science experiment kits. Furthermore, in order to propose educational play activities that match the child's interests, it can analyze the child's behavioral data and interest trends to make individually customized suggestions. For example, for a child interested in animals, it can propose a zoo app or an animal encyclopedia, and for a child interested in space, it can propose a space exploration app or a telescope. In this way, the Educational Proposal Department can provide appropriate educational play activities that promote the growth and development of children and stimulate their interests and curiosity.

[0033] The Abnormal Behavior Reporting Department immediately reports abnormal behavior based on the results of play suggested by the Educational Proposal Department. Specifically, it is equipped with a behavioral pattern analysis algorithm for detecting abnormal behavior. For example, it analyzes children's behavioral data to detect unusual behavioral patterns or abnormal behavior. If abnormal behavior is detected, it immediately notifies the parents and suggests appropriate responses. For example, if a child suddenly starts crying or behaves differently than usual, the Abnormal Behavior Reporting Department immediately issues an alert and notifies the parents. It can also suggest appropriate responses depending on the type and frequency of the abnormal behavior. For example, if a child cries frequently, it may suggest consulting a medical institution, and if a specific behavior is repeated, it may suggest seeking advice from a specialist. In this way, the Abnormal Behavior Reporting Department can protect children's health and safety by detecting abnormal behavior early and responding quickly.

[0034] The monitoring unit is equipped with a body temperature sensor and a heart rate sensor. The monitoring unit can, for example, measure a child's body temperature in real time using a body temperature sensor. The monitoring unit can, for example, measure a child's heart rate in real time using a heart rate sensor. The monitoring unit can, for example, measure a child's respiratory rate in real time using a respiratory rate sensor. This allows for detailed monitoring of a child's health status by measuring vital signs such as body temperature and heart rate. Body temperature sensors include, for example, skin temperature sensors and ear temperature sensors. Skin temperature sensors measure body temperature by contacting the child's skin, and ear temperature sensors measure body temperature by inserting them into the child's ear. Heart rate sensors include, for example, photoelectric heart rate sensors and electrode heart rate sensors. Photoelectric heart rate sensors measure heart rate by irradiating light onto the child's skin and detecting changes in reflected light, and electrode heart rate sensors measure heart rate by attaching electrodes to the child's skin. Some or all of the above-described processes in the monitoring unit may be performed using, for example, AI, or without using AI. For example, the monitoring unit inputs data acquired from body temperature sensors and heart rate sensors into a generating AI, which then analyzes the data to monitor the patient's health status.

[0035] The sleep habit formation unit analyzes a child's sleep patterns and proposes the optimal bedtime and environment. For example, the sleep habit formation unit can monitor a child's sleep duration and propose the optimal bedtime. For example, the sleep habit formation unit can monitor a child's sleep onset patterns and propose the optimal sleep environment. For example, the sleep habit formation unit can monitor a child's sleep quality and propose the optimal sleep habits. In this way, by analyzing a child's sleep patterns, it can provide advice for forming healthy sleep habits. Sleep patterns include, for example, sleep depth, sleep cycles, and sleep quality. Sleep depth is evaluated by measuring changes in the child's brain waves during sleep, and sleep cycles indicate the cycles of REM and non-REM sleep during the child's sleep. Sleep quality is evaluated based on the number of awakenings during sleep and the duration of sleep. Optimal bedtimes include, for example, age-specific recommended bedtimes and suggestions based on lifestyle rhythms. Age-specific recommended bedtimes indicate appropriate bedtimes according to the child's age, and suggestions based on lifestyle rhythms propose bedtimes that match the child's daily rhythm. An optimal environment includes, for example, room temperature, lighting, and sound environment. Room temperature indicates an appropriate temperature to support a child's comfortable sleep, and lighting indicates an appropriate brightness to promote a child's sleep. The sound environment indicates a quiet environment that does not disturb a child's sleep. Some or all of the above processing in the sleep habit formation unit may be performed using AI, for example, or without AI. For example, the sleep habit formation unit can input the child's sleep pattern data into a generating AI, which can then analyze the data and suggest the optimal bedtime and environment.

[0036] The Educational Proposal Department proposes educational toys and learning apps that are appropriate for a child's age and developmental stage. For example, the Educational Proposal Department can propose educational toys appropriate for a child's age. For example, the Educational Proposal Department can propose learning apps that are appropriate for a child's developmental stage. For example, the Educational Proposal Department can propose educational play activities that are appropriate for a child's interests and concerns. By proposing educational play activities that are appropriate for a child's age and developmental stage, the learning effect is improved. Educational toys include, for example, age-specific educational toys and toys appropriate for developmental stages. Age-specific educational toys indicate toys that are appropriate for a child's age, and toys appropriate for developmental stages indicate toys that are appropriate for a child's developmental stage. Learning apps include, for example, educational content, target age, and instructions for use. Educational content indicates appropriate content to support a child's learning, and target age indicates an appropriate app that is appropriate for a child's age. Instructions for use indicate how a child can effectively use the app. Some or all of the above processing in the Educational Proposal Department may be performed using, for example, AI, or not using AI. For example, the Educational Proposal Department can input data on a child's age and developmental stage into a generating AI, which then analyzes the data to suggest appropriate educational toys and learning apps.

[0037] The abnormal behavior reporting unit includes a behavior pattern analysis algorithm for detecting abnormal behavior. The abnormal behavior reporting unit can, for example, detect abnormal behavior using the behavior pattern analysis algorithm. The abnormal behavior reporting unit can, for example, immediately notify parents when it detects abnormal behavior. The abnormal behavior reporting unit can, for example, suggest appropriate responses depending on the type and frequency of the abnormal behavior. This allows for the rapid detection and reporting of abnormal behavior by using the behavior pattern analysis algorithm. The behavior pattern analysis algorithm includes, for example, machine learning algorithms and data analysis methods. The machine learning algorithm learns from the child's behavior data and builds a model for detecting abnormal behavior. The data analysis method analyzes the child's behavior data and identifies patterns of abnormal behavior. Some or all of the above processing in the abnormal behavior reporting unit may be performed using, for example, AI, or without AI. For example, the abnormal behavior reporting unit can input the child's behavior data into a generating AI, which can analyze the data and detect abnormal behavior.

[0038] The monitoring unit analyzes the child's past health data and selects a monitoring method that enables early detection of abnormalities. For example, the monitoring unit can detect signs of abnormalities from past health data and issue an early warning. For example, the monitoring unit can perform focused monitoring during specific time periods based on past health data. For example, the monitoring unit can analyze past health data to identify situations where abnormalities are likely to occur and strengthen monitoring. This makes it possible to detect abnormalities early by analyzing past health data. Past health data includes, for example, past body temperature data, heart rate data, and activity level data. Past body temperature data shows changes in the child's body temperature, and heart rate data shows changes in the child's heart rate. Activity level data shows changes in the child's daily activity level. Monitoring methods include, for example, anomaly detection algorithms and data analysis methods. Anomaly detection algorithms build models to detect signs of abnormalities based on past health data. Data analysis methods analyze past health data and identify signs of abnormalities. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without using AI. For example, the monitoring unit can input past health data into a generating AI, which can then analyze the data to detect abnormalities early.

[0039] The monitoring unit filters health data based on the child's daily life circumstances when monitoring it. For example, if the child is exercising, the monitoring unit prioritizes monitoring exercise-related health data. For example, if the child is eating, the monitoring unit can filter and monitor health data related to eating. For example, if the child is sleeping, the monitoring unit can focus on monitoring health data related to sleep. This allows for appropriate monitoring by filtering health data according to the child's daily life circumstances. Daily life circumstances include, for example, activity levels, dietary content, and sleep patterns. Activity levels indicate the child's daily exercise level, and dietary content indicates the type and amount of food the child eats. Sleep patterns indicate the quality and duration of the child's sleep. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's daily life data into a generating AI, which can analyze the data and filter the health data.

[0040] The monitoring unit prioritizes monitoring of highly relevant data when monitoring health data, taking into account the child's geographical location. For example, if the child is at school, the monitoring unit prioritizes monitoring stress levels related to learning. If the child is in a park, the monitoring unit can prioritize monitoring health data related to exercise. If the child is at home, the monitoring unit can prioritize monitoring health data related to daily life. This allows for the priority monitoring of highly relevant health data by considering geographical location information. Geographical location information includes, for example, GPS data and location services. GPS data indicates the child's current location, and location services are services for tracking the child's location in real time. Some or all of the processing described above in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the child's geographical location information into a generating AI, which can analyze the data and prioritize monitoring of highly relevant health data.

[0041] The monitoring unit analyzes a child's social media activity and monitors relevant data when monitoring health data. For example, if a child is experiencing stress from social media, the monitoring unit may prioritize monitoring stress levels. For example, if a child is spending long periods of time on social media, the monitoring unit may prioritize monitoring sleep data. For example, if a child is engaging in positive activities on social media, the monitoring unit may monitor heart rate and respiratory rate. This allows for appropriate monitoring of relevant health data by analyzing social media activity. Social media activity includes, for example, posts, comments, and the number of likes. Posts refer to content posted by the child on social media, comments refer to comments made by the child to other users, and the number of likes refers to the number of likes given by other users to the child's posts. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's social media data into a generating AI, which can then analyze the data and monitor relevant health data.

[0042] The sleep habit formation unit proposes an optimal sleep environment based on the child's health data during sleep habit formation. For example, the sleep habit formation unit can propose an appropriate room temperature based on the child's body temperature data. For example, the sleep habit formation unit can propose relaxing music based on the child's heart rate data. For example, the sleep habit formation unit can propose optimal bedding based on the child's respiratory rate data. In this way, by proposing an optimal sleep environment based on health data, it is possible to support the child's healthy sleep. An optimal sleep environment includes, for example, room temperature, lighting, and sound environment. Room temperature indicates an appropriate temperature to support the child's comfortable sleep, and lighting indicates an appropriate brightness to promote the child's sleep. The sound environment indicates a quiet environment that does not disturb the child's sleep. Some or all of the above processing in the sleep habit formation unit may be performed using, for example, AI, or without AI. For example, the sleep habit formation unit can input the child's health data into a generating AI, and the generating AI can analyze the data and propose an optimal sleep environment.

[0043] The sleep habit formation unit applies different advice algorithms depending on the child's lifestyle rhythm when forming sleep habits. For example, if a child has an early-to-bed, early-to-rise lifestyle rhythm, the sleep habit formation unit can provide advice tailored to that rhythm. For example, if a child has a nocturnal lifestyle rhythm, the sleep habit formation unit can provide advice tailored to that rhythm. For example, if a child has an irregular lifestyle rhythm, the sleep habit formation unit can provide advice to help form a regular rhythm. In this way, by providing advice tailored to the lifestyle rhythm, healthy sleep habits can be formed in children. The advice algorithms include, for example, machine learning algorithms and data analysis methods. The machine learning algorithm learns the child's lifestyle rhythm data and builds a model to provide appropriate advice. The data analysis method shows a method for analyzing the child's lifestyle rhythm data and providing appropriate advice. Some or all of the above processing in the sleep habit formation unit may be performed using, for example, AI, or without AI. For example, the sleep habit formation unit can input the child's lifestyle rhythm data into a generating AI, which can then analyze the data and apply different advice algorithms.

[0044] The sleep habit formation unit prioritizes advice based on the child's sleep history when forming sleep habits. For example, if a child has not had good sleep in the past, the sleep habit formation unit will provide advice to improve it as a top priority. For example, if a child has had good sleep in the past, the sleep habit formation unit can provide advice to maintain that sleep. For example, if a child has had irregular sleep in the past, the sleep habit formation unit can provide advice to establish a regular rhythm. This allows important advice to be provided preferentially by prioritizing advice based on the sleep history. Sleep history includes, for example, past sleep data and changes in sleep patterns. Past sleep data shows the child's past sleep duration and quality, and changes in sleep patterns show changes in the child's sleep quality and duration. Prioritization of advice includes, for example, prioritizing based on sleep history and prioritizing based on health status. Prioritizing based on sleep history determines the priority of advice based on the child's past sleep data, and prioritizing based on health status determines the priority of advice based on the child's health status. Some or all of the above-described processes in the sleep habit formation unit may be performed using AI, for example, or without AI. For example, the sleep habit formation unit can input the child's sleep history data into a generating AI, which can then analyze the data to determine the priority of advice.

[0045] The sleep habit formation unit adjusts the content of advice by referring to the child's relevant data when forming sleep habits. For example, the sleep habit formation unit can suggest an optimal sleep environment by referring to the child's health data. For example, the sleep habit formation unit can suggest an appropriate bedtime by referring to the child's lifestyle rhythm data. For example, the sleep habit formation unit can provide advice for relaxation by referring to the child's emotional data. This allows for the provision of more appropriate advice by referring to relevant data. Relevant data includes, for example, health data, lifestyle rhythm data, and emotional data. Health data shows the child's health status, such as body temperature and heart rate, while lifestyle rhythm data shows the child's daily rhythm. Emotional data shows the child's emotional state. Some or all of the above processing in the sleep habit formation unit may be performed using, for example, AI, or without AI. For example, the sleep habit formation unit can input the child's relevant data into a generating AI, which can analyze the data and adjust the content of the advice.

[0046] The Educational Proposal Department, when making educational proposals, suggests the most suitable educational play activities based on the child's learning history. For example, the Educational Proposal Department may suggest similar educational play activities based on games the child has enjoyed in the past. For example, the Educational Proposal Department may suggest play activities to reinforce areas the child has struggled with in the past. For example, the Educational Proposal Department may suggest play activities to deepen areas the child has shown interest in in the past. In this way, by suggesting the most suitable educational play activities based on the learning history, learning effectiveness is improved. The learning history includes, for example, past learning data and changes in learning outcomes. Past learning data shows the child's past learning content and outcomes, and changes in learning outcomes show changes in the child's learning progress and outcomes. Some or all of the above processing in the Educational Proposal Department may be performed using AI, for example, or without AI. For example, the Educational Proposal Department can input the child's learning history data into a generating AI, and the generating AI can analyze the data and suggest the most suitable educational play activities.

[0047] The Educational Proposal Department applies different proposal algorithms depending on the child's interests when making educational proposals. For example, if a child is interested in science, the Educational Proposal Department might suggest a science experiment kit. If a child is interested in music, the Educational Proposal Department might suggest playing a musical instrument. If a child is interested in art, the Educational Proposal Department might suggest painting or crafts. By applying proposal algorithms tailored to interests, the department can provide educational play that stimulates children's interest. Proposal algorithms include, for example, machine learning algorithms and data analysis methods. Machine learning algorithms learn data on children's interests and build models to make appropriate proposals. Data analysis methods analyze data on children's interests and provide methods for making appropriate proposals. Some or all of the above processes in the Educational Proposal Department may be performed using AI, for example, or without AI. For example, the Educational Proposal Department can input data on children's interests and input into a generating AI, which can then analyze the data and apply different proposal algorithms.

[0048] The Educational Proposal Department prioritizes proposals based on the child's age and developmental stage. For example, it might prioritize suggesting educational toys appropriate for the child's age. It could also prioritize suggesting learning apps appropriate for the child's developmental stage. Furthermore, it could prioritize suggesting picture books and educational materials appropriate for the child's age and developmental stage. By prioritizing proposals based on age and developmental stage, the department can provide children with the most suitable educational play. Proposal priorities include, for example, age-based prioritization and developmental stage-based prioritization. Age-based prioritization indicates the priority for making appropriate proposals according to the child's age, while developmental stage-based prioritization indicates the priority for making appropriate proposals according to the child's developmental stage. Some or all of the above-described processes in the Educational Proposal Department may be performed using AI, or not. For example, the Educational Proposal Department could input child age and developmental stage data into a generating AI, which could then analyze the data to determine the proposal priority.

[0049] The Educational Proposal Department adjusts the content of its proposals by referring to relevant data about the child when making educational proposals. For example, the Educational Proposal Department can refer to the child's learning history to propose the most suitable educational play activities. For example, the Educational Proposal Department can refer to the child's interests and preferences to propose appropriate play activities. For example, the Educational Proposal Department can refer to the child's health data to propose appropriate play activities. This allows for the provision of more appropriate educational play activities by referring to relevant data. Relevant data includes, for example, health data, lifestyle rhythm data, and emotional data. Health data shows the child's health status, such as body temperature and heart rate, while lifestyle rhythm data shows the child's daily rhythm. Emotional data shows the child's emotional state. Some or all of the above processing in the Educational Proposal Department may be performed using, for example, AI, or not using AI. For example, the Educational Proposal Department can input the child's relevant data into a generating AI, which can then analyze the data and adjust the content of the proposals.

[0050] The Abnormal Behavior Reporting Unit detects abnormal behavior early based on the child's past behavioral data when an abnormal behavior report is received. For example, the Abnormal Behavior Reporting Unit can identify patterns of abnormal behavior from past behavioral data and issue warnings early. For example, the Abnormal Behavior Reporting Unit can identify situations in which abnormal behavior is likely to occur at specific times of day based on past behavioral data. For example, the Abnormal Behavior Reporting Unit can analyze past behavioral data and detect signs of abnormal behavior early. This enables a rapid response by detecting abnormal behavior early based on past behavioral data. Past behavioral data includes, for example, past behavioral patterns and changes in behavior. Past behavioral patterns indicate the tendencies of the child's past behavior, and changes in behavior indicate changes in the child's behavior. Some or all of the above processing in the Abnormal Behavior Reporting Unit may be performed using AI, for example, or without AI. For example, the Abnormal Behavior Reporting Unit can input the child's past behavioral data into a generating AI, which can analyze the data to detect abnormal behavior early.

[0051] The abnormal behavior reporting unit applies different reporting algorithms depending on the child's living environment when reporting abnormal behavior. For example, if the child is at school, the abnormal behavior reporting unit can apply a reporting algorithm suitable for the school environment. For example, if the child is at home, the abnormal behavior reporting unit can apply a reporting algorithm suitable for the home environment. For example, if the child is out, the abnormal behavior reporting unit can apply a reporting algorithm suitable for the outing environment. By applying a reporting algorithm appropriate to the living environment, more appropriate reporting of abnormal behavior becomes possible. Reporting algorithms include, for example, machine learning algorithms and data analysis methods. Machine learning algorithms learn the child's living environment data and build models for making appropriate reports. Data analysis methods analyze the child's living environment data and show methods for making appropriate reports. Some or all of the above processing in the abnormal behavior reporting unit may be performed using, for example, AI, or not using AI. For example, the abnormal behavior reporting unit can input the child's living environment data into a generating AI, which can then analyze the data and apply different reporting algorithms.

[0052] The Abnormal Behavior Reporting Unit determines the reporting priority based on the child's behavioral history when an abnormal behavior is reported. For example, if the child has frequently exhibited abnormal behavior in the past, the Abnormal Behavior Reporting Unit will prioritize reporting. For example, if the child has not exhibited abnormal behavior in the past, the Abnormal Behavior Reporting Unit can apply the normal reporting priority. For example, the Abnormal Behavior Reporting Unit can analyze the child's behavioral history and determine the reporting priority according to the frequency of abnormal behavior occurrence. This allows for prioritizing reporting of important abnormal behaviors by determining the reporting priority based on the behavioral history. The behavioral history includes, for example, past behavioral data and changes in behavioral patterns. Past behavioral data shows the content of the child's past behavior, and changes in behavioral patterns show changes in the child's behavior. Reporting priority includes, for example, prioritizing based on behavioral history and prioritizing based on the importance of the abnormal behavior. Prioritizing based on behavioral history determines the reporting priority based on the child's past behavioral data, and prioritizing based on the importance of the abnormal behavior determines the reporting priority based on the importance of the child's abnormal behavior. Some or all of the above processing in the Abnormal Behavior Reporting Unit may be performed using, for example, AI, or not using AI. For example, the abnormal behavior reporting unit can input children's behavioral history data into a generating AI, which then analyzes the data to determine the priority of reports.

[0053] The abnormal behavior reporting unit adjusts the content of the report by referring to relevant data of the child when reporting abnormal behavior. For example, the abnormal behavior reporting unit may refer to the child's health data to make the report of abnormal behavior more detailed. For example, the abnormal behavior reporting unit may refer to the child's daily rhythm data to adjust the content of the report of abnormal behavior. For example, the abnormal behavior reporting unit may refer to the child's emotional data to appropriately adjust the content of the report of abnormal behavior. This makes it possible to report abnormal behavior more appropriately by referring to relevant data. Relevant data includes, for example, health data, daily rhythm data, and emotional data. Health data shows the child's health status, such as body temperature and heart rate, and daily rhythm data shows the child's daily rhythm. Emotional data shows the child's emotional state. Some or all of the above processing in the abnormal behavior reporting unit may be performed using, for example, AI, or not using AI. For example, the abnormal behavior reporting unit may input the child's relevant data into a generating AI, and the generating AI may analyze the data and adjust the content of the report.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The monitoring unit not only monitors a child's health status but also records their daily activities and analyzes their correlation with their health. For example, the monitoring unit can record a child's exercise level and diet, and by comparing this data with their health status, it can identify the causes of changes in their health. The monitoring unit can also predict a child's health status based on their daily activity data. For example, if a child's exercise level decreases, it can predict the possibility of a deterioration in their health and take preventative measures. Furthermore, the monitoring unit can provide advice to improve a child's health based on their daily activity data. For example, if a child is not getting enough exercise, it can suggest appropriate exercises. In this way, the monitoring unit can achieve more effective health management by closely monitoring a child's health status and analyzing its correlation with their daily activities.

[0056] The Sleep Habit Formation Department can not only analyze a child's sleep patterns but also provide more effective sleep advice by considering the child's daily rhythm. For example, it can suggest the optimal bedtime considering the child's school schedule and extracurricular activity time. It can also suggest easily digestible meals considering the child's meal times and content. Furthermore, it can suggest appropriate exercise considering the child's physical activity level. In this way, the Sleep Habit Formation Department can provide more effective sleep advice by considering the child's daily rhythm. Sleep patterns include, for example, sleep depth, sleep cycle, and sleep quality. Sleep depth is evaluated by measuring changes in the child's brain waves during sleep, and the sleep cycle indicates the cycle of REM sleep and non-REM sleep during sleep. Sleep quality is evaluated based on the number of awakenings during sleep and the duration of sleep.

[0057] The Educational Proposal Department can propose educational toys and learning apps that are appropriate for a child's age and developmental stage, as well as suggest more effective educational play activities that take into account the child's interests and concerns. For example, if a child is interested in science, a science experiment kit can be suggested. If a child is interested in music, playing a musical instrument can be suggested. Furthermore, if a child is interested in art, painting and crafts can be suggested. In this way, the Educational Proposal Department can propose more effective educational play activities that take into account the child's interests and concerns. Educational toys include, for example, age-specific educational toys and toys that are appropriate for developmental stages. Age-specific educational toys indicate toys that are appropriate for a child's age, and toys that are appropriate for developmental stages indicate toys that are appropriate for a child's developmental stage. Learning apps include, for example, educational content, target age, and instructions for use. Educational content indicates appropriate content to support a child's learning, and target age indicates an appropriate app for a child's age. Instructions for use indicate how children can effectively use the app.

[0058] The monitoring unit can prioritize monitoring highly relevant data by considering the child's geographical location when monitoring the child's health status. For example, if the child is at school, stress levels related to learning can be prioritized for monitoring. If the child is in a park, health data related to exercise can be prioritized for monitoring. Furthermore, if the child is at home, health data related to daily life can be prioritized for monitoring. In this way, by considering geographical location, highly relevant health data can be prioritized for monitoring. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the child's current location, and location information services are services for tracking the child's location in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the child's geographical location information into a generating AI, and the generating AI can analyze the data and prioritize monitoring highly relevant health data.

[0059] The Abnormal Behavior Reporting Unit can detect abnormal behavior early based on the child's past behavioral data when an abnormal behavior report is received. For example, it can identify patterns of abnormal behavior from past behavioral data and issue warnings early. It can also identify situations in which abnormal behavior is likely to occur at specific times based on past behavioral data. Furthermore, it can analyze past behavioral data to detect signs of abnormal behavior early. This enables a rapid response by detecting abnormal behavior early based on past behavioral data. Past behavioral data includes, for example, past behavioral patterns and changes in behavior. Past behavioral patterns indicate the tendencies of the child's past behavior, and changes in behavior indicate changes in the child's behavior. Some or all of the above processing in the Abnormal Behavior Reporting Unit may be performed using AI, for example, or without AI. For example, the Abnormal Behavior Reporting Unit can input the child's past behavioral data into a generating AI, which can analyze the data to detect abnormal behavior early.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The monitoring unit monitors the child's health status. For example, it can measure the child's body temperature, heart rate, and respiratory rate in real time using a body temperature sensor, heart rate sensor, and respiratory rate sensor. Step 2: The sleep habit formation unit provides advice for forming appropriate sleep habits based on health data monitored by the monitoring unit. For example, it analyzes a child's sleep patterns and suggests the optimal bedtime and environment. Step 3: The Educational Proposal Department proposes educational play activities based on the advice provided by the Sleep Habit Formation Department. For example, they propose educational toys and learning apps appropriate for the child's age and developmental stage. Step 4: The Abnormal Behavior Reporting Department immediately reports abnormal behavior based on the results of play suggested by the Educational Proposal Department. For example, it uses a behavioral pattern analysis algorithm to detect abnormal behavior and immediately notifies the parents.

[0062] (Example of form 2) An AI childcare support agent system according to an embodiment of the present invention is a system that monitors a child's health status, helps establish appropriate sleep habits, suggests educational play, and immediately reports abnormal behavior. This AI childcare support agent system monitors a child's health status in real time, provides advice for establishing appropriate sleep habits, suggests educational play, and immediately reports abnormal behavior. For example, the AI ​​childcare support agent system measures vital signs such as body temperature, heart rate, and respiratory rate using sensors, and the AI ​​analyzes them. As a result, if an abnormality is detected, it is immediately reported, enabling appropriate action. Next, the AI ​​childcare support agent system analyzes the child's sleep patterns and provides advice for establishing appropriate sleep habits. For example, it monitors the child's sleep duration and sleep onset patterns and suggests the optimal bedtime and environment. This supports the child's healthy growth. Furthermore, the AI ​​childcare support agent system suggests educational play appropriate to the child's age and developmental stage. For example, it suggests play using educational toys and learning apps to improve the child's learning effectiveness. In addition, the AI ​​childcare support agent system analyzes the child's behavior patterns and immediately reports if abnormal behavior is detected. For example, if unusual or dangerous behavior is detected, parents will be notified, enabling a quick response. In this way, the AI ​​childcare support agent system monitors the child's health, helps establish appropriate sleep habits, suggests educational play, and provides immediate reporting of abnormal behavior, allowing working parents to focus on their work while supporting the healthy development of their children. The AI ​​childcare support agent system can monitor the child's health in real time, provide advice on establishing appropriate sleep habits, suggest educational play, and immediately report abnormal behavior.

[0063] The AI ​​childcare support agent system according to this embodiment comprises a monitoring unit, a sleep habit formation unit, an educational suggestion unit, and an abnormal behavior reporting unit. The monitoring unit monitors the child's health status. The monitoring unit measures the child's body temperature and heart rate, for example, using a body temperature sensor and a heart rate sensor. The monitoring unit can measure the child's body temperature in real time, for example, using a body temperature sensor. The monitoring unit can measure the child's heart rate in real time, for example, using a heart rate sensor. The monitoring unit can measure the child's respiratory rate in real time, for example, using a respiratory rate sensor. The sleep habit formation unit provides advice for forming appropriate sleep habits based on the health data monitored by the monitoring unit. The sleep habit formation unit can analyze the child's sleep patterns and suggest the optimal bedtime and environment. The sleep habit formation unit can monitor the child's sleep duration and suggest the optimal bedtime. The sleep habit formation unit can monitor the child's sleep onset patterns and suggest the optimal sleep environment. The sleep habit formation unit can monitor the child's sleep quality and suggest the optimal sleep habits. The Educational Proposal Department proposes educational play based on advice provided by the Sleep Habit Formation Department. The Educational Proposal Department proposes, for example, educational toys and learning apps appropriate to the child's age and developmental stage. The Educational Proposal Department can propose, for example, educational toys appropriate to the child's age. The Educational Proposal Department can propose, for example, learning apps appropriate to the child's developmental stage. The Educational Proposal Department can propose, for example, educational play that suits the child's interests and concerns. The Abnormal Behavior Reporting Department immediately reports abnormal behavior based on the results of play proposed by the Educational Proposal Department. The Abnormal Behavior Reporting Department is equipped with, for example, a behavior pattern analysis algorithm for detecting abnormal behavior. The Abnormal Behavior Reporting Department can detect abnormal behavior using, for example, a behavior pattern analysis algorithm. The Abnormal Behavior Reporting Department can immediately notify parents when abnormal behavior is detected. The Abnormal Behavior Reporting Department can propose appropriate responses depending on the type and frequency of the abnormal behavior.As a result, the AI ​​childcare support agent system according to this embodiment can monitor the child's health, provide advice for establishing appropriate sleep habits, suggest educational play activities, and immediately report abnormal behavior.

[0064] The monitoring unit monitors the child's health status. For example, it measures the child's body temperature and heart rate using temperature sensors and heart rate sensors. Specifically, temperature sensors could be types that directly contact the child's skin or non-contact infrared sensors. This allows for real-time measurement of the child's body temperature and early detection of abnormal temperature changes. Heart rate sensors can be attached to the child's chest or wrist, monitoring heart rate fluctuations in real time. This allows for immediate alerts if the child's heart rate is abnormally high or low. Furthermore, respiratory rate sensors detect chest and abdominal movements to measure respiratory rate in real time. This allows for early detection of abnormalities such as shallow or stopped breathing. The monitoring unit centrally manages the data obtained from these sensors and has a function to immediately notify parents or medical institutions if an abnormality is detected. This allows for constant monitoring of the child's health status and rapid response when an abnormality occurs.

[0065] The Sleep Habit Formation Department provides advice on forming appropriate sleep habits based on health data monitored by the Monitoring Department. Specifically, it analyzes a child's sleep patterns and proposes the optimal bedtime and environment. For example, it monitors a child's sleep duration and proposes the optimal bedtime to help them fall asleep at a consistent rhythm. Furthermore, it can analyze a child's sleep onset patterns and propose the optimal sleep environment, including bedroom temperature, humidity, and lighting brightness. To monitor sleep quality, it analyzes the number of times a child turns over in their sleep and the ratio of deep to light sleep, and provides advice to improve sleep quality. For example, it could offer specific suggestions for improving the bedroom environment, or suggest music or scents to promote relaxation. In this way, the Sleep Habit Formation Department can provide specific advice on forming optimal sleep habits while considering the child's health condition, thereby supporting the child's growth and development.

[0066] The Educational Proposal Department proposes educational play activities based on advice provided by the Sleep Habit Formation Department. Specifically, it proposes educational toys and learning apps appropriate to the child's age and developmental stage. For example, for toddlers, it can propose blocks and puzzles to learn colors and shapes, and musical instrument toys to enjoy music. For school-aged children, it can propose educational apps to learn the basics of arithmetic and language, and science experiment kits. Furthermore, in order to propose educational play activities that match the child's interests, it can analyze the child's behavioral data and interest trends to make individually customized suggestions. For example, for a child interested in animals, it can propose a zoo app or an animal encyclopedia, and for a child interested in space, it can propose a space exploration app or a telescope. In this way, the Educational Proposal Department can provide appropriate educational play activities that promote the growth and development of children and stimulate their interests and curiosity.

[0067] The Abnormal Behavior Reporting Department immediately reports abnormal behavior based on the results of play suggested by the Educational Proposal Department. Specifically, it is equipped with a behavioral pattern analysis algorithm for detecting abnormal behavior. For example, it analyzes children's behavioral data to detect unusual behavioral patterns or abnormal behavior. If abnormal behavior is detected, it immediately notifies the parents and suggests appropriate responses. For example, if a child suddenly starts crying or behaves differently than usual, the Abnormal Behavior Reporting Department immediately issues an alert and notifies the parents. It can also suggest appropriate responses depending on the type and frequency of the abnormal behavior. For example, if a child cries frequently, it may suggest consulting a medical institution, and if a specific behavior is repeated, it may suggest seeking advice from a specialist. In this way, the Abnormal Behavior Reporting Department can protect children's health and safety by detecting abnormal behavior early and responding quickly.

[0068] The monitoring unit is equipped with a body temperature sensor and a heart rate sensor. The monitoring unit can, for example, measure a child's body temperature in real time using a body temperature sensor. The monitoring unit can, for example, measure a child's heart rate in real time using a heart rate sensor. The monitoring unit can, for example, measure a child's respiratory rate in real time using a respiratory rate sensor. This allows for detailed monitoring of a child's health status by measuring vital signs such as body temperature and heart rate. Body temperature sensors include, for example, skin temperature sensors and ear temperature sensors. Skin temperature sensors measure body temperature by contacting the child's skin, and ear temperature sensors measure body temperature by inserting them into the child's ear. Heart rate sensors include, for example, photoelectric heart rate sensors and electrode heart rate sensors. Photoelectric heart rate sensors measure heart rate by irradiating light onto the child's skin and detecting changes in reflected light, and electrode heart rate sensors measure heart rate by attaching electrodes to the child's skin. Some or all of the above-described processes in the monitoring unit may be performed using, for example, AI, or without using AI. For example, the monitoring unit inputs data acquired from body temperature sensors and heart rate sensors into a generating AI, which then analyzes the data to monitor the patient's health status.

[0069] The sleep habit formation unit analyzes a child's sleep patterns and proposes the optimal bedtime and environment. For example, the sleep habit formation unit can monitor a child's sleep duration and propose the optimal bedtime. For example, the sleep habit formation unit can monitor a child's sleep onset patterns and propose the optimal sleep environment. For example, the sleep habit formation unit can monitor a child's sleep quality and propose the optimal sleep habits. In this way, by analyzing a child's sleep patterns, it can provide advice for forming healthy sleep habits. Sleep patterns include, for example, sleep depth, sleep cycles, and sleep quality. Sleep depth is evaluated by measuring changes in the child's brain waves during sleep, and sleep cycles indicate the cycles of REM and non-REM sleep during the child's sleep. Sleep quality is evaluated based on the number of awakenings during sleep and the duration of sleep. Optimal bedtimes include, for example, age-specific recommended bedtimes and suggestions based on lifestyle rhythms. Age-specific recommended bedtimes indicate appropriate bedtimes according to the child's age, and suggestions based on lifestyle rhythms propose bedtimes that match the child's daily rhythm. An optimal environment includes, for example, room temperature, lighting, and sound environment. Room temperature indicates an appropriate temperature to support a child's comfortable sleep, and lighting indicates an appropriate brightness to promote a child's sleep. The sound environment indicates a quiet environment that does not disturb a child's sleep. Some or all of the above processing in the sleep habit formation unit may be performed using AI, for example, or without AI. For example, the sleep habit formation unit can input the child's sleep pattern data into a generating AI, which can then analyze the data and suggest the optimal bedtime and environment.

[0070] The Educational Proposal Department proposes educational toys and learning apps that are appropriate for a child's age and developmental stage. For example, the Educational Proposal Department can propose educational toys appropriate for a child's age. For example, the Educational Proposal Department can propose learning apps that are appropriate for a child's developmental stage. For example, the Educational Proposal Department can propose educational play activities that are appropriate for a child's interests and concerns. By proposing educational play activities that are appropriate for a child's age and developmental stage, the learning effect is improved. Educational toys include, for example, age-specific educational toys and toys appropriate for developmental stages. Age-specific educational toys indicate toys that are appropriate for a child's age, and toys appropriate for developmental stages indicate toys that are appropriate for a child's developmental stage. Learning apps include, for example, educational content, target age, and instructions for use. Educational content indicates appropriate content to support a child's learning, and target age indicates an appropriate app that is appropriate for a child's age. Instructions for use indicate how a child can effectively use the app. Some or all of the above processing in the Educational Proposal Department may be performed using, for example, AI, or not using AI. For example, the Educational Proposal Department can input data on a child's age and developmental stage into a generating AI, which then analyzes the data to suggest appropriate educational toys and learning apps.

[0071] The abnormal behavior reporting unit includes a behavior pattern analysis algorithm for detecting abnormal behavior. The abnormal behavior reporting unit can, for example, detect abnormal behavior using the behavior pattern analysis algorithm. The abnormal behavior reporting unit can, for example, immediately notify parents when it detects abnormal behavior. The abnormal behavior reporting unit can, for example, suggest appropriate responses depending on the type and frequency of the abnormal behavior. This allows for the rapid detection and reporting of abnormal behavior by using the behavior pattern analysis algorithm. The behavior pattern analysis algorithm includes, for example, machine learning algorithms and data analysis methods. The machine learning algorithm learns from the child's behavior data and builds a model for detecting abnormal behavior. The data analysis method analyzes the child's behavior data and identifies patterns of abnormal behavior. Some or all of the above processing in the abnormal behavior reporting unit may be performed using, for example, AI, or without AI. For example, the abnormal behavior reporting unit can input the child's behavior data into a generating AI, which can analyze the data and detect abnormal behavior.

[0072] The monitoring unit estimates the child's emotions and adjusts the monitoring frequency of health data based on the estimated emotions. For example, if the child is stressed, the monitoring unit increases the monitoring frequency to closely monitor the child's health. For example, if the child is relaxed, the monitoring unit can reduce the monitoring frequency to lessen the burden. For example, if the child is excited, the monitoring unit can adjust the monitoring frequency appropriately to stabilize the child's health. In this way, the health can be appropriately monitored by adjusting the monitoring frequency according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's emotion data into the generative AI, which can analyze the data to estimate emotions and adjust the monitoring frequency.

[0073] The monitoring unit analyzes the child's past health data and selects a monitoring method that enables early detection of abnormalities. For example, the monitoring unit can detect signs of abnormalities from past health data and issue an early warning. For example, the monitoring unit can perform focused monitoring during specific time periods based on past health data. For example, the monitoring unit can analyze past health data to identify situations where abnormalities are likely to occur and strengthen monitoring. This makes it possible to detect abnormalities early by analyzing past health data. Past health data includes, for example, past body temperature data, heart rate data, and activity level data. Past body temperature data shows changes in the child's body temperature, and heart rate data shows changes in the child's heart rate. Activity level data shows changes in the child's daily activity level. Monitoring methods include, for example, anomaly detection algorithms and data analysis methods. Anomaly detection algorithms build models to detect signs of abnormalities based on past health data. Data analysis methods analyze past health data and identify signs of abnormalities. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without using AI. For example, the monitoring unit can input past health data into a generating AI, which can then analyze the data to detect abnormalities early.

[0074] The monitoring unit filters health data based on the child's daily life circumstances when monitoring it. For example, if the child is exercising, the monitoring unit prioritizes monitoring exercise-related health data. For example, if the child is eating, the monitoring unit can filter and monitor health data related to eating. For example, if the child is sleeping, the monitoring unit can focus on monitoring health data related to sleep. This allows for appropriate monitoring by filtering health data according to the child's daily life circumstances. Daily life circumstances include, for example, activity levels, dietary content, and sleep patterns. Activity levels indicate the child's daily exercise level, and dietary content indicates the type and amount of food the child eats. Sleep patterns indicate the quality and duration of the child's sleep. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's daily life data into a generating AI, which can analyze the data and filter the health data.

[0075] The monitoring unit estimates the child's emotions and determines the priority of health data to monitor based on the estimated emotions. For example, if the child is feeling anxious, the monitoring unit may prioritize monitoring heart rate and respiratory rate. For example, if the child is tired, the monitoring unit may prioritize monitoring body temperature and sleep data. For example, if the child is agitated, the monitoring unit may prioritize monitoring activity level and stress level. This allows for the priority monitoring of important data by prioritizing health data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input the child's emotion data into a generative AI, which can analyze the data to estimate emotions and determine the priority of health data to monitor.

[0076] The monitoring unit prioritizes monitoring of highly relevant data when monitoring health data, taking into account the child's geographical location. For example, if the child is at school, the monitoring unit prioritizes monitoring stress levels related to learning. If the child is in a park, the monitoring unit can prioritize monitoring health data related to exercise. If the child is at home, the monitoring unit can prioritize monitoring health data related to daily life. This allows for the priority monitoring of highly relevant health data by considering geographical location information. Geographical location information includes, for example, GPS data and location services. GPS data indicates the child's current location, and location services are services for tracking the child's location in real time. Some or all of the processing described above in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the child's geographical location information into a generating AI, which can analyze the data and prioritize monitoring of highly relevant health data.

[0077] The monitoring unit analyzes a child's social media activity and monitors relevant data when monitoring health data. For example, if a child is experiencing stress from social media, the monitoring unit may prioritize monitoring stress levels. For example, if a child is spending long periods of time on social media, the monitoring unit may prioritize monitoring sleep data. For example, if a child is engaging in positive activities on social media, the monitoring unit may monitor heart rate and respiratory rate. This allows for appropriate monitoring of relevant health data by analyzing social media activity. Social media activity includes, for example, posts, comments, and the number of likes. Posts refer to content posted by the child on social media, comments refer to comments made by the child to other users, and the number of likes refers to the number of likes given by other users to the child's posts. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's social media data into a generating AI, which can then analyze the data and monitor relevant health data.

[0078] The sleep habit formation unit estimates the child's emotions and adjusts the content of sleep advice based on the estimated emotions. For example, if the child is feeling anxious, the sleep habit formation unit can provide advice on how to relax. For example, if the child is excited, the sleep habit formation unit can provide advice on how to calm down. For example, if the child is tired, the sleep habit formation unit can provide advice on going to bed earlier. By adjusting the content of sleep advice according to the child's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The content of sleep advice includes, for example, suggestions for bedtime and methods for improving the sleep environment. Suggestions for bedtime indicate an appropriate bedtime according to the child's age and lifestyle, and methods for improving the sleep environment indicate ways to improve the environment to support the child's comfortable sleep. Some or all of the above processing in the sleep habit formation unit may be performed using AI, for example, or without using AI. For example, the sleep habit formation unit can input a child's emotional data into a generating AI, which then analyzes the data to estimate the child's emotions and adjust the content of the sleep advice accordingly.

[0079] The sleep habit formation unit proposes an optimal sleep environment based on the child's health data during sleep habit formation. For example, the sleep habit formation unit can propose an appropriate room temperature based on the child's body temperature data. For example, the sleep habit formation unit can propose relaxing music based on the child's heart rate data. For example, the sleep habit formation unit can propose optimal bedding based on the child's respiratory rate data. In this way, by proposing an optimal sleep environment based on health data, it is possible to support the child's healthy sleep. An optimal sleep environment includes, for example, room temperature, lighting, and sound environment. Room temperature indicates an appropriate temperature to support the child's comfortable sleep, and lighting indicates an appropriate brightness to promote the child's sleep. The sound environment indicates a quiet environment that does not disturb the child's sleep. Some or all of the above processing in the sleep habit formation unit may be performed using, for example, AI, or without AI. For example, the sleep habit formation unit can input the child's health data into a generating AI, and the generating AI can analyze the data and propose an optimal sleep environment.

[0080] The sleep habit formation unit applies different advice algorithms depending on the child's lifestyle rhythm when forming sleep habits. For example, if a child has an early-to-bed, early-to-rise lifestyle rhythm, the sleep habit formation unit can provide advice tailored to that rhythm. For example, if a child has a nocturnal lifestyle rhythm, the sleep habit formation unit can provide advice tailored to that rhythm. For example, if a child has an irregular lifestyle rhythm, the sleep habit formation unit can provide advice to help form a regular rhythm. In this way, by providing advice tailored to the lifestyle rhythm, healthy sleep habits can be formed in children. The advice algorithms include, for example, machine learning algorithms and data analysis methods. The machine learning algorithm learns the child's lifestyle rhythm data and builds a model to provide appropriate advice. The data analysis method shows a method for analyzing the child's lifestyle rhythm data and providing appropriate advice. Some or all of the above processing in the sleep habit formation unit may be performed using, for example, AI, or without AI. For example, the sleep habit formation unit can input the child's lifestyle rhythm data into a generating AI, which can then analyze the data and apply different advice algorithms.

[0081] The sleep habit formation unit estimates the child's emotions and adjusts the timing of sleep advice based on the estimated emotions. For example, if the child is feeling anxious, the sleep habit formation unit can provide advice to help the child relax before going to sleep. For example, if the child is excited, the sleep habit formation unit can provide advice to help the child calm down before going to sleep. For example, if the child is tired, the sleep habit formation unit can provide advice to help the child go to bed earlier. By adjusting the timing of sleep advice according to the child's emotions, advice can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Timing of sleep advice includes, for example, pre-sleep timing and post-wake-up timing. Pre-sleep timing indicates an appropriate time for the child to relax before going to sleep, and post-wake-up timing indicates an appropriate time for the child to refresh after waking up. Some or all of the above processing in the sleep habit formation unit may be performed using AI, for example, or without using AI. For example, the sleep habit formation unit can input child emotional data into a generating AI, which analyzes the data to estimate emotions and adjust the timing of sleep advice.

[0082] The sleep habit formation unit prioritizes advice based on the child's sleep history when forming sleep habits. For example, if a child has not had good sleep in the past, the sleep habit formation unit will provide advice to improve it as a top priority. For example, if a child has had good sleep in the past, the sleep habit formation unit can provide advice to maintain that sleep. For example, if a child has had irregular sleep in the past, the sleep habit formation unit can provide advice to establish a regular rhythm. This allows important advice to be provided preferentially by prioritizing advice based on the sleep history. Sleep history includes, for example, past sleep data and changes in sleep patterns. Past sleep data shows the child's past sleep duration and quality, and changes in sleep patterns show changes in the child's sleep quality and duration. Prioritization of advice includes, for example, prioritizing based on sleep history and prioritizing based on health status. Prioritizing based on sleep history determines the priority of advice based on the child's past sleep data, and prioritizing based on health status determines the priority of advice based on the child's health status. Some or all of the above-described processes in the sleep habit formation unit may be performed using AI, for example, or without AI. For example, the sleep habit formation unit can input the child's sleep history data into a generating AI, which can then analyze the data to determine the priority of advice.

[0083] The sleep habit formation unit adjusts the content of advice by referring to the child's relevant data when forming sleep habits. For example, the sleep habit formation unit can suggest an optimal sleep environment by referring to the child's health data. For example, the sleep habit formation unit can suggest an appropriate bedtime by referring to the child's lifestyle rhythm data. For example, the sleep habit formation unit can provide advice for relaxation by referring to the child's emotional data. This allows for the provision of more appropriate advice by referring to relevant data. Relevant data includes, for example, health data, lifestyle rhythm data, and emotional data. Health data shows the child's health status, such as body temperature and heart rate, while lifestyle rhythm data shows the child's daily rhythm. Emotional data shows the child's emotional state. Some or all of the above processing in the sleep habit formation unit may be performed using, for example, AI, or without AI. For example, the sleep habit formation unit can input the child's relevant data into a generating AI, which can analyze the data and adjust the content of the advice.

[0084] The Educational Suggestion Department estimates a child's emotions and adjusts the content of educational play based on the estimated emotions. For example, if a child is excited, the Educational Suggestion Department might suggest puzzles or block play to help them calm down. If a child is feeling anxious, the Educational Suggestion Department might suggest reading a relaxing picture book. If a child is relaxed, the Educational Suggestion Department might suggest an educational toy with high learning effectiveness. By adjusting the content of educational play according to the child's emotions, more appropriate play can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The content of educational play includes, for example, the type of educational toy and the content of learning apps. The type of educational toy indicates an appropriate toy according to the child's age and developmental stage, and the content of learning apps indicates appropriate content to support the child's learning. Some or all of the above processing in the Educational Suggestion Department may be performed using AI, for example, or without AI. For example, the Educational Proposal Department can input children's emotional data into a generating AI, which then analyzes the data to estimate emotions and adjust the content of educational play accordingly.

[0085] The Educational Proposal Department, when making educational proposals, suggests the most suitable educational play activities based on the child's learning history. For example, the Educational Proposal Department may suggest similar educational play activities based on games the child has enjoyed in the past. For example, the Educational Proposal Department may suggest play activities to reinforce areas the child has struggled with in the past. For example, the Educational Proposal Department may suggest play activities to deepen areas the child has shown interest in in the past. In this way, by suggesting the most suitable educational play activities based on the learning history, learning effectiveness is improved. The learning history includes, for example, past learning data and changes in learning outcomes. Past learning data shows the child's past learning content and outcomes, and changes in learning outcomes show changes in the child's learning progress and outcomes. Some or all of the above processing in the Educational Proposal Department may be performed using AI, for example, or without AI. For example, the Educational Proposal Department can input the child's learning history data into a generating AI, and the generating AI can analyze the data and suggest the most suitable educational play activities.

[0086] The Educational Proposal Department applies different proposal algorithms depending on the child's interests when making educational proposals. For example, if a child is interested in science, the Educational Proposal Department might suggest a science experiment kit. If a child is interested in music, the Educational Proposal Department might suggest playing a musical instrument. If a child is interested in art, the Educational Proposal Department might suggest painting or crafts. By applying proposal algorithms tailored to interests, the department can provide educational play that stimulates children's interest. Proposal algorithms include, for example, machine learning algorithms and data analysis methods. Machine learning algorithms learn data on children's interests and build models to make appropriate proposals. Data analysis methods analyze data on children's interests and provide methods for making appropriate proposals. Some or all of the above processes in the Educational Proposal Department may be performed using AI, for example, or without AI. For example, the Educational Proposal Department can input data on children's interests and input into a generating AI, which can then analyze the data and apply different proposal algorithms.

[0087] The Educational Suggestion Department estimates a child's emotions and adjusts the timing of suggested educational play based on the estimated emotions. For example, if a child is excited, the Educational Suggestion Department adjusts the timing of suggesting calming play. For example, if a child is feeling anxious, the Educational Suggestion Department can adjust the timing of suggesting relaxing play. For example, if a child is relaxed, the Educational Suggestion Department can adjust the timing of suggesting highly effective learning play. By adjusting the timing of suggested educational play according to the child's emotions, play can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Suggested timing for educational play includes, for example, post-learning timing and break time timing. Post-learning timing indicates an appropriate time for a child to relax after finishing learning, and break time timing indicates an appropriate time for a child to refresh during a break. Some or all of the above processing in the Educational Suggestion Department may be performed using AI, for example, or without using AI. For example, the Educational Proposal Department can input children's emotional data into a generating AI, which analyzes the data to estimate emotions and adjust the timing of educational play suggestions.

[0088] The Educational Proposal Department prioritizes proposals based on the child's age and developmental stage. For example, it might prioritize suggesting educational toys appropriate for the child's age. It could also prioritize suggesting learning apps appropriate for the child's developmental stage. Furthermore, it could prioritize suggesting picture books and educational materials appropriate for the child's age and developmental stage. By prioritizing proposals based on age and developmental stage, the department can provide children with the most suitable educational play. Proposal priorities include, for example, age-based prioritization and developmental stage-based prioritization. Age-based prioritization indicates the priority for making appropriate proposals according to the child's age, while developmental stage-based prioritization indicates the priority for making appropriate proposals according to the child's developmental stage. Some or all of the above-described processes in the Educational Proposal Department may be performed using AI, or not. For example, the Educational Proposal Department could input child age and developmental stage data into a generating AI, which could then analyze the data to determine the proposal priority.

[0089] The Educational Proposal Department adjusts the content of its proposals by referring to relevant data about the child when making educational proposals. For example, the Educational Proposal Department can refer to the child's learning history to propose the most suitable educational play activities. For example, the Educational Proposal Department can refer to the child's interests and preferences to propose appropriate play activities. For example, the Educational Proposal Department can refer to the child's health data to propose appropriate play activities. This allows for the provision of more appropriate educational play activities by referring to relevant data. Relevant data includes, for example, health data, lifestyle rhythm data, and emotional data. Health data shows the child's health status, such as body temperature and heart rate, while lifestyle rhythm data shows the child's daily rhythm. Emotional data shows the child's emotional state. Some or all of the above processing in the Educational Proposal Department may be performed using, for example, AI, or not using AI. For example, the Educational Proposal Department can input the child's relevant data into a generating AI, which can then analyze the data and adjust the content of the proposals.

[0090] The abnormal behavior reporting unit estimates the child's emotions and adjusts the abnormal behavior report based on the estimated emotions. For example, if the child is feeling anxious, the abnormal behavior reporting unit will provide a detailed report. For example, if the child is excited, the abnormal behavior reporting unit may provide a concise report. For example, if the child is relaxed, the abnormal behavior reporting unit may provide a normal report. This allows for more appropriate reporting by adjusting the abnormal behavior report according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The abnormal behavior report includes, for example, details of the abnormal behavior and the format of the report. Details of the abnormal behavior indicate the specific content of the child's abnormal behavior, and the format of the report indicates an appropriate format for reporting abnormal behavior. Some or all of the above processing in the abnormal behavior reporting unit may be performed using AI, for example, or without AI. For example, the abnormal behavior reporting unit can input child emotional data into a generating AI, which analyzes the data to estimate emotions and adjust the content of the abnormal behavior report.

[0091] The Abnormal Behavior Reporting Unit detects abnormal behavior early based on the child's past behavioral data when an abnormal behavior report is received. For example, the Abnormal Behavior Reporting Unit can identify patterns of abnormal behavior from past behavioral data and issue warnings early. For example, the Abnormal Behavior Reporting Unit can identify situations in which abnormal behavior is likely to occur at specific times of day based on past behavioral data. For example, the Abnormal Behavior Reporting Unit can analyze past behavioral data and detect signs of abnormal behavior early. This enables a rapid response by detecting abnormal behavior early based on past behavioral data. Past behavioral data includes, for example, past behavioral patterns and changes in behavior. Past behavioral patterns indicate the tendencies of the child's past behavior, and changes in behavior indicate changes in the child's behavior. Some or all of the above processing in the Abnormal Behavior Reporting Unit may be performed using AI, for example, or without AI. For example, the Abnormal Behavior Reporting Unit can input the child's past behavioral data into a generating AI, which can analyze the data to detect abnormal behavior early.

[0092] The abnormal behavior reporting unit applies different reporting algorithms depending on the child's living environment when reporting abnormal behavior. For example, if the child is at school, the abnormal behavior reporting unit can apply a reporting algorithm suitable for the school environment. For example, if the child is at home, the abnormal behavior reporting unit can apply a reporting algorithm suitable for the home environment. For example, if the child is out, the abnormal behavior reporting unit can apply a reporting algorithm suitable for the outing environment. By applying a reporting algorithm appropriate to the living environment, more appropriate reporting of abnormal behavior becomes possible. Reporting algorithms include, for example, machine learning algorithms and data analysis methods. Machine learning algorithms learn the child's living environment data and build models for making appropriate reports. Data analysis methods analyze the child's living environment data and show methods for making appropriate reports. Some or all of the above processing in the abnormal behavior reporting unit may be performed using, for example, AI, or not using AI. For example, the abnormal behavior reporting unit can input the child's living environment data into a generating AI, which can then analyze the data and apply different reporting algorithms.

[0093] The abnormal behavior reporting unit estimates the child's emotions and adjusts the timing of abnormal behavior reporting based on the estimated emotions. For example, if the child is feeling anxious, the abnormal behavior reporting unit will report the abnormal behavior immediately. For example, if the child is excited, the abnormal behavior reporting unit can adjust the reporting timing according to the situation. For example, if the child is relaxed, the abnormal behavior reporting unit can report the abnormal behavior at the normal reporting timing. This allows for more appropriate reporting by adjusting the reporting timing according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Timing of abnormal behavior reporting includes, for example, the timing immediately after the abnormal behavior occurs and periodic reporting timing. Timing immediately after the abnormal behavior occurs indicates an appropriate timing for reporting immediately after the child exhibits abnormal behavior, and periodic reporting timing indicates an appropriate timing for periodically reporting the child's abnormal behavior. Some or all of the above-described processes in the abnormal behavior reporting unit may be performed using AI, for example, or without AI. For example, the abnormal behavior reporting unit can input child emotional data into a generating AI, which can analyze the data to estimate emotions and adjust the timing of abnormal behavior reporting.

[0094] The Abnormal Behavior Reporting Unit determines the reporting priority based on the child's behavioral history when an abnormal behavior is reported. For example, if the child has frequently exhibited abnormal behavior in the past, the Abnormal Behavior Reporting Unit will prioritize reporting. For example, if the child has not exhibited abnormal behavior in the past, the Abnormal Behavior Reporting Unit can apply the normal reporting priority. For example, the Abnormal Behavior Reporting Unit can analyze the child's behavioral history and determine the reporting priority according to the frequency of abnormal behavior occurrence. This allows for prioritizing reporting of important abnormal behaviors by determining the reporting priority based on the behavioral history. The behavioral history includes, for example, past behavioral data and changes in behavioral patterns. Past behavioral data shows the content of the child's past behavior, and changes in behavioral patterns show changes in the child's behavior. Reporting priority includes, for example, prioritizing based on behavioral history and prioritizing based on the importance of the abnormal behavior. Prioritizing based on behavioral history determines the reporting priority based on the child's past behavioral data, and prioritizing based on the importance of the abnormal behavior determines the reporting priority based on the importance of the child's abnormal behavior. Some or all of the above processing in the Abnormal Behavior Reporting Unit may be performed using, for example, AI, or not using AI. For example, the abnormal behavior reporting unit can input children's behavioral history data into a generating AI, which then analyzes the data to determine the priority of reports.

[0095] The abnormal behavior reporting unit adjusts the content of the report by referring to relevant data of the child when reporting abnormal behavior. For example, the abnormal behavior reporting unit may refer to the child's health data to make the report of abnormal behavior more detailed. For example, the abnormal behavior reporting unit may refer to the child's daily rhythm data to adjust the content of the report of abnormal behavior. For example, the abnormal behavior reporting unit may refer to the child's emotional data to appropriately adjust the content of the report of abnormal behavior. This makes it possible to report abnormal behavior more appropriately by referring to relevant data. Relevant data includes, for example, health data, daily rhythm data, and emotional data. Health data shows the child's health status, such as body temperature and heart rate, and daily rhythm data shows the child's daily rhythm. Emotional data shows the child's emotional state. Some or all of the above processing in the abnormal behavior reporting unit may be performed using, for example, AI, or not using AI. For example, the abnormal behavior reporting unit may input the child's relevant data into a generating AI, and the generating AI may analyze the data and adjust the content of the report.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The monitoring unit not only monitors a child's health status but also records their daily activities and analyzes their correlation with their health. For example, the monitoring unit can record a child's exercise level and diet, and by comparing this data with their health status, it can identify the causes of changes in their health. The monitoring unit can also predict a child's health status based on their daily activity data. For example, if a child's exercise level decreases, it can predict the possibility of a deterioration in their health and take preventative measures. Furthermore, the monitoring unit can provide advice to improve a child's health based on their daily activity data. For example, if a child is not getting enough exercise, it can suggest appropriate exercises. In this way, the monitoring unit can achieve more effective health management by closely monitoring a child's health status and analyzing its correlation with their daily activities.

[0098] The monitoring unit can estimate a child's emotional state when monitoring the child's health and adjust the monitoring method based on that emotional state. For example, if a child is stressed, the monitoring unit can provide advice to reduce stress. If a child is relaxed, the monitoring unit can provide advice to maintain that relaxed state. Furthermore, if a child is agitated, the monitoring unit can provide advice to calm them down. In this way, the monitoring unit can adjust the monitoring method according to the child's emotional state, enabling more appropriate health management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The Sleep Habit Formation Department can not only analyze a child's sleep patterns but also provide more effective sleep advice by considering the child's daily rhythm. For example, it can suggest the optimal bedtime considering the child's school schedule and extracurricular activity time. It can also suggest easily digestible meals considering the child's meal times and content. Furthermore, it can suggest appropriate exercise considering the child's physical activity level. In this way, the Sleep Habit Formation Department can provide more effective sleep advice by considering the child's daily rhythm. Sleep patterns include, for example, sleep depth, sleep cycle, and sleep quality. Sleep depth is evaluated by measuring changes in the child's brain waves during sleep, and the sleep cycle indicates the cycle of REM sleep and non-REM sleep during sleep. Sleep quality is evaluated based on the number of awakenings during sleep and the duration of sleep.

[0100] The Educational Proposal Department can propose educational toys and learning apps that are appropriate for a child's age and developmental stage, as well as suggest more effective educational play activities that take into account the child's interests and concerns. For example, if a child is interested in science, a science experiment kit can be suggested. If a child is interested in music, playing a musical instrument can be suggested. Furthermore, if a child is interested in art, painting and crafts can be suggested. In this way, the Educational Proposal Department can propose more effective educational play activities that take into account the child's interests and concerns. Educational toys include, for example, age-specific educational toys and toys that are appropriate for developmental stages. Age-specific educational toys indicate toys that are appropriate for a child's age, and toys that are appropriate for developmental stages indicate toys that are appropriate for a child's developmental stage. Learning apps include, for example, educational content, target age, and instructions for use. Educational content indicates appropriate content to support a child's learning, and target age indicates an appropriate app for a child's age. Instructions for use indicate how children can effectively use the app.

[0101] The abnormal behavior reporting unit not only has a behavioral pattern analysis algorithm for detecting abnormal behavior, but can also estimate the child's emotional state and adjust the abnormal behavior report based on that state. For example, if the child is feeling anxious, it can provide a detailed report. If the child is excited, it can provide a concise report. Furthermore, if the child is relaxed, it can provide a normal report. In this way, the abnormal behavior reporting unit can adjust the abnormal behavior report according to the child's emotional state, enabling more appropriate reporting. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The abnormal behavior report includes, for example, details of the abnormal behavior and the format of the report. Details of the abnormal behavior indicate the specific content of the child's abnormal behavior, and the format of the report indicates an appropriate format for reporting abnormal behavior.

[0102] The monitoring unit can prioritize monitoring highly relevant data by considering the child's geographical location when monitoring the child's health status. For example, if the child is at school, stress levels related to learning can be prioritized for monitoring. If the child is in a park, health data related to exercise can be prioritized for monitoring. Furthermore, if the child is at home, health data related to daily life can be prioritized for monitoring. In this way, by considering geographical location, highly relevant health data can be prioritized for monitoring. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the child's current location, and location information services are services for tracking the child's location in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the child's geographical location information into a generating AI, and the generating AI can analyze the data and prioritize monitoring highly relevant health data.

[0103] The monitoring unit can estimate a child's emotions and prioritize the health data to monitor based on the estimated emotions. For example, if a child is feeling anxious, heart rate and respiratory rate can be prioritized for monitoring. If a child is tired, body temperature and sleep data can be prioritized for monitoring. Furthermore, if a child is agitated, activity level and stress level can be prioritized for monitoring. This allows for the prioritization of important data by determining the priority of health data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input child emotion data into a generative AI, which can analyze the data to estimate emotions and determine the priority of health data to monitor.

[0104] The sleep habit formation unit can estimate a child's emotions and adjust the content of sleep advice based on the estimated emotions. For example, if a child is feeling anxious, it can provide advice on how to relax. If a child is excited, it can provide advice on how to calm down. Furthermore, if a child is tired, it can provide advice on going to bed earlier. By adjusting the content of sleep advice according to the child's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The content of sleep advice includes, for example, suggestions for bedtime and methods for improving the sleep environment. Suggestions for bedtime indicate an appropriate bedtime according to the child's age and lifestyle, and methods for improving the sleep environment indicate ways to improve the environment to support the child's comfortable sleep.

[0105] The Educational Play Suggestion Department can estimate a child's emotions and adjust the content of educational play based on those emotions. For example, if a child is excited, it can suggest puzzles or block play to help them calm down. If a child is feeling anxious, it can suggest reading a relaxing picture book. Furthermore, if a child is relaxed, it can suggest educational toys that have a high learning effect. In this way, by adjusting the content of educational play according to the child's emotions, more appropriate play can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The content of educational play includes, for example, the type of educational toy and the content of learning apps. The type of educational toy indicates an appropriate toy according to the child's age and developmental stage, and the content of learning apps indicates appropriate content to support the child's learning.

[0106] The Abnormal Behavior Reporting Unit can detect abnormal behavior early based on the child's past behavioral data when an abnormal behavior report is received. For example, it can identify patterns of abnormal behavior from past behavioral data and issue warnings early. It can also identify situations in which abnormal behavior is likely to occur at specific times based on past behavioral data. Furthermore, it can analyze past behavioral data to detect signs of abnormal behavior early. This enables a rapid response by detecting abnormal behavior early based on past behavioral data. Past behavioral data includes, for example, past behavioral patterns and changes in behavior. Past behavioral patterns indicate the tendencies of the child's past behavior, and changes in behavior indicate changes in the child's behavior. Some or all of the above processing in the Abnormal Behavior Reporting Unit may be performed using AI, for example, or without AI. For example, the Abnormal Behavior Reporting Unit can input the child's past behavioral data into a generating AI, which can analyze the data to detect abnormal behavior early.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The monitoring unit monitors the child's health status. For example, it can measure the child's body temperature, heart rate, and respiratory rate in real time using a body temperature sensor, heart rate sensor, and respiratory rate sensor. Step 2: The sleep habit formation unit provides advice for forming appropriate sleep habits based on health data monitored by the monitoring unit. For example, it analyzes a child's sleep patterns and suggests the optimal bedtime and environment. Step 3: The Educational Proposal Department proposes educational play activities based on the advice provided by the Sleep Habit Formation Department. For example, they propose educational toys and learning apps appropriate for the child's age and developmental stage. Step 4: The Abnormal Behavior Reporting Department immediately reports abnormal behavior based on the results of play suggested by the Educational Proposal Department. For example, it uses a behavioral pattern analysis algorithm to detect abnormal behavior and immediately notifies the parents.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the monitoring unit, sleep habit formation unit, educational suggestion unit, and abnormal behavior reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit measures the child's health status in real time using the body temperature sensor and heart rate sensor of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing unit 12. The sleep habit formation unit can propose optimal sleep habits based on the data obtained from the sensors of the smart device 14, with the specific processing unit 290 of the data processing unit 12 proposing the optimal sleep habits. The educational suggestion unit can propose educational play activities appropriate to the child's age and developmental stage using the control unit 46A of the smart device 14, with the specific processing unit 290 of the data processing unit 12 analyzing the content. The abnormal behavior reporting unit can detect abnormal behavior based on the data obtained from the sensors of the smart device 14, with the specific processing unit 290 of the data processing unit 12 detecting and reporting it immediately. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the monitoring unit, sleep habit formation unit, educational suggestion unit, and abnormal behavior reporting unit, can be implemented, for example, in at least one of the smart glasses 224 and the data processing unit 12. For example, the monitoring unit measures the child's health status in real time using the body temperature sensor and heart rate sensor of the smart glasses 224, and the data is analyzed by the specific processing unit 290 of the data processing unit 12. The sleep habit formation unit can propose optimal sleep habits based on the data obtained from the sensors of the smart glasses 224, with the specific processing unit 290 of the data processing unit 12 proposing an optimal sleep habit. The educational suggestion unit can propose educational play activities appropriate to the child's age and developmental stage using the control unit 46A of the smart glasses 224, and the specific processing unit 290 of the data processing unit 12 can analyze the content. The abnormal behavior reporting unit can detect abnormal behavior based on the data obtained from the sensors of the smart glasses 224, with the specific processing unit 290 of the data processing unit 12 detecting and reporting it immediately. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the monitoring unit, sleep habit formation unit, educational suggestion unit, and abnormal behavior reporting unit, can be implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit measures the child's health status in real time using the body temperature sensor and heart rate sensor of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing unit 12. The sleep habit formation unit can propose optimal sleep habits based on the data obtained from the sensors of the headset terminal 314, with the specific processing unit 290 of the data processing unit 12 proposing an optimal sleep habit. The educational suggestion unit can propose educational play activities appropriate to the child's age and developmental stage using the control unit 46A of the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 can analyze the content. The abnormal behavior reporting unit can detect abnormal behavior based on the data obtained from the sensors of the headset terminal 314, with the specific processing unit 290 of the data processing unit 12 detecting and reporting it immediately. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the monitoring unit, sleep habit formation unit, educational suggestion unit, and abnormal behavior reporting unit, can be implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit measures the child's health status in real time using the robot 414's body temperature sensor and heart rate sensor, and the data processing unit 12 analyzes the data. The sleep habit formation unit can propose optimal sleep habits based on the data obtained from the robot 414's sensors, with the data processing unit 12's specific processing unit 290 capable of doing so. The educational suggestion unit can propose educational play activities appropriate to the child's age and developmental stage using the robot 414's control unit 46A, with the data processing unit 12's specific processing unit 290 capable of analyzing the content. The abnormal behavior reporting unit can detect abnormal behavior based on the data obtained from the robot 414's sensors, with the data processing unit 12's specific processing unit 290 capable of reporting it immediately. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The monitoring department monitors the health status of children, A sleep habit formation unit provides advice for forming appropriate sleep habits based on health data monitored by the aforementioned monitoring unit, Based on the advice provided by the aforementioned Sleep Habit Formation Department, the Educational Proposal Department proposes educational play activities, The system includes an abnormal behavior reporting unit that immediately reports abnormal behavior based on the results of play proposed by the aforementioned educational proposal unit. A system characterized by the following features. (Note 2) The monitoring unit, Equipped with body temperature sensors and heart rate sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sleep habit formation unit is We analyze children's sleep patterns and suggest optimal bedtimes and environments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Educational Proposal Department We propose educational toys and learning apps that are appropriate for children's ages and developmental stages. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned abnormal behavior reporting unit, It includes a behavioral pattern analysis algorithm for detecting abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, The system estimates the child's emotions and adjusts the frequency of monitoring health data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, We will analyze the child's past health data and select a monitoring method that enables early detection of abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, When monitoring health data, filtering is performed based on the child's daily life. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, The system estimates a child's emotions and prioritizes health data to monitor based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, When monitoring health data, prioritize monitoring highly relevant data by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, When monitoring health data, analyze children's social media activity and monitor related data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned sleep habit formation unit is The system estimates the child's emotions and adjusts the sleep advice based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned sleep habit formation unit is We propose the optimal sleep environment based on the child's health data when establishing sleep habits. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned sleep habit formation unit is When establishing sleep habits, different advice algorithms are applied according to the child's daily rhythm. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned sleep habit formation unit is The system estimates the child's emotions and adjusts the timing of sleep advice based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned sleep habit formation unit is Prioritizing advice based on the child's sleep history when establishing sleep habits. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned sleep habit formation unit is When helping children develop sleep habits, we adjust the advice based on relevant data about the child. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Educational Proposal Department The system estimates the child's emotions and adjusts the content of educational play based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Educational Proposal Department When proposing educational activities, we suggest the most suitable educational play based on the child's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Educational Proposal Department When making educational proposals, different proposal algorithms are applied depending on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Educational Proposal Department The system estimates the child's emotions and adjusts the timing of suggested educational play based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Educational Proposal Department When making educational proposals, prioritize the proposals based on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Educational Proposal Department When making educational proposals, we adjust the content of the proposals by referring to relevant data about the children. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned abnormal behavior reporting unit, The system estimates the child's emotions and adjusts the reporting of abnormal behavior based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned abnormal behavior reporting unit, When abnormal behavior is reported, early detection of abnormal behavior is performed based on the child's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned abnormal behavior reporting unit, When reporting abnormal behavior, different reporting algorithms are applied depending on the child's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned abnormal behavior reporting unit, The system estimates the child's emotions and adjusts the timing of reporting abnormal behavior based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned abnormal behavior reporting unit, When reporting abnormal behavior, prioritize reports based on the child's behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned abnormal behavior reporting unit, When reporting abnormal behavior, adjust the report content by referring to relevant data about the child. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors the health status of children, A sleep habit formation unit provides advice for forming appropriate sleep habits based on health data monitored by the aforementioned monitoring unit, Based on the advice provided by the aforementioned Sleep Habit Formation Department, the Educational Proposal Department proposes educational play activities, The system includes an abnormal behavior reporting unit that immediately reports abnormal behavior based on the results of play proposed by the aforementioned educational proposal unit. A system characterized by the following features.

2. The monitoring unit, Equipped with body temperature sensors and heart rate sensors. The system according to feature 1.

3. The aforementioned sleep habit formation unit is We analyze children's sleep patterns and suggest optimal bedtimes and environments. The system according to feature 1.

4. The aforementioned Educational Proposal Department We propose educational toys and learning apps that are appropriate for children's ages and developmental stages. The system according to feature 1.

5. The aforementioned abnormal behavior reporting unit, It includes a behavioral pattern analysis algorithm for detecting abnormal behavior. The system according to feature 1.

6. The monitoring unit, The system estimates the child's emotions and adjusts the frequency of health data monitoring based on the estimated emotions. The system according to feature 1.

7. The monitoring unit, We will analyze the child's past health data and select a monitoring method that enables early detection of abnormalities. The system according to feature 1.

8. The monitoring unit, When monitoring health data, filtering is performed based on the child's daily life. The system according to feature 1.

9. The monitoring unit, The system estimates a child's emotions and prioritizes health data to monitor based on those estimated emotions. The system according to feature 1.

10. The monitoring unit, When monitoring health data, prioritize monitoring highly relevant data by considering the child's geographical location. The system according to feature 1.

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