system

Abstract

The system according to this embodiment aims to monitor the health status of babies and infants in real time and to immediately detect and respond to any abnormalities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a collaboration unit, and a sharing unit. The collection unit monitors the vital signs of babies and infants. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice to parents and guardians based on the analysis results obtained by the analysis unit. The collaboration unit collaborates with childcare facilities and medical institutions based on the advice provided by the provision unit. The sharing unit shares the data collaborated by the collaboration unit among family members.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, it is difficult to monitor the health status of a baby or an infant in real time, immediately detect an abnormality, and take corresponding measures, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the health status of a baby or an infant in real time, immediately detect an abnormality, and take corresponding measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a collaboration unit, and a sharing unit. The data collection unit monitors the vital signs of babies and infants. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides advice to parents and guardians based on the analysis results obtained by the analysis unit. The collaboration unit collaborates with childcare facilities and medical institutions based on the advice provided by the data provision unit. The sharing unit shares the data collaborated by the collaboration unit among family members. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the health status of babies and infants in real time and immediately detect and respond to any abnormalities. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 reception 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 reception 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) The infant health monitoring system according to an embodiment of the present invention is a system that monitors the vital signs (body temperature, heart rate, respiration) of babies and infants in real time, as well as their facial expressions, body movements, skin color, and behavioral patterns (sleep, activity, eating), and immediately issues a warning when an abnormality is detected. The infant health monitoring system utilizes data analysis by an AI agent to predict changes in health and future risks, and provides specific advice and countermeasures to parents and guardians. This makes it possible to predict poor health before the onset of illness and to take preventative measures. For example, the infant health monitoring system monitors the vital signs of babies and infants using sensor technology. This includes measuring body temperature, heart rate, and respiration. Furthermore, the infant health monitoring system analyzes facial expressions, body movements, skin color, crying, and movement patterns using cameras and microphones. This allows for real-time understanding of the child's needs and condition. Next, the infant health monitoring system analyzes the collected data using an AI agent. The AI ​​agent predicts changes in health and future risks, and provides specific advice and countermeasures to parents and guardians. For example, if signs of illness are detected, the AI ​​agent will advise parents to seek medical attention. It also provides personalized advice based on daily routines and data, enabling child-centered care. Furthermore, the infant health monitoring system can collaborate with childcare facilities and medical institutions. Collected data is shared to quickly receive advice and feedback from experts. This allows for early health management, predicting illness before it manifests and taking preventative measures. The infant health monitoring system supports modern parenting by ensuring that changes in a child's health are not overlooked, providing peace of mind and reducing the burden of childcare. Additionally, based on the collected data, the AI ​​agent can personalize and provide childcare content and advice tailored to each family, improving the quality of childcare. Moreover, by sharing information among family members, a system is in place to check the child's health data and condition in real time, and immediately send notifications to all family members if any abnormalities are detected.This allows infant health monitoring systems to improve the quality of childcare by monitoring the health and safety of babies and toddlers and providing parents and guardians with specific advice and measures.

[0029] The infant health monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a linkage unit, and a sharing unit. The data collection unit monitors the vital signs of babies and infants. The data collection unit measures, for example, body temperature, heart rate, and respiration. The data collection unit can use an ear thermometer or an axillary thermometer to measure body temperature. The data collection unit can use an electrocardiogram or a pulse oximeter to measure heart rate. The data collection unit can measure respiratory rate and analyze respiratory patterns to measure respiration. The data collection unit analyzes facial expressions, body movements, skin color, crying, and movement patterns using a camera and a microphone. The data collection unit can analyze the facial expressions of babies and infants using a camera. The data collection unit can use a camera and sensors to analyze body movements. The data collection unit can use a camera and a light sensor to analyze skin color. The data collection unit can use a microphone to analyze crying. The data collection unit can use a camera and sensors to analyze movement patterns. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit predicts changes in physical condition and future risks. The analysis unit can analyze changes in body temperature and heart rate to detect changes in physical condition. The analysis unit can analyze the risk of developing diseases and developmental delays to predict future risks. The provision unit provides advice to parents and guardians based on the analysis results obtained by the analysis unit. For example, the provision unit will recommend that parents visit a medical institution if signs of illness are observed. The provision unit provides individualized advice as feedback based on daily routines and data. The provision unit can analyze fever and difficulty breathing to detect signs of illness. The provision unit can analyze sleep patterns and meal timing to provide individualized advice. The collaboration unit collaborates with childcare facilities and medical institutions based on the advice provided by the provision unit. The collaboration unit shares the collected data to quickly receive advice and feedback from experts. The collaboration unit can use data sharing methods and communication means to receive advice from experts. The shared section allows family members to share data that has been linked by the collaborative section.The shared unit allows for real-time monitoring of the child's health data and condition, and immediately sends notifications to all family members if any abnormalities are detected. The shared unit can also control the method and scope of data sharing. As a result, the infant health monitoring system according to this embodiment can improve the quality of childcare by monitoring the health and safety of babies and infants and providing specific advice and measures to parents and guardians.

[0030] The data collection unit monitors the vital signs of babies and infants. For example, it measures body temperature, heart rate, and respiration. To measure body temperature, the data collection unit can use ear thermometers or axillary thermometers. Ear thermometers are inserted into the baby's ear to measure body temperature quickly, while axillary thermometers are placed under the baby's armpit to measure body temperature. To measure heart rate, the data collection unit can use electrocardiograms (ECGs) or pulse oximeters. ECGs measure heart rate by attaching electrodes to the baby's chest, while pulse oximeters measure heart rate and blood oxygen saturation by attaching sensors to the baby's fingertips or toes. To measure respiration, the data collection unit can measure respiratory rate and analyze breathing patterns. Respiratory rate is measured by detecting movement in the baby's chest or abdomen with sensors, and breathing pattern analysis analyzes the rhythm and depth of breathing. The data collection unit uses cameras and microphones to analyze facial expressions, body movements, skin color, crying, and movement patterns. The data collection unit can, for example, use a camera to analyze the facial expressions of babies and toddlers. The camera captures the baby's face and analyzes changes in expression, such as smiles and crying. The data collection unit can use cameras and sensors to analyze body movements. The camera captures the baby's whole body movements, and the sensors detect the baby's arm and leg movements and the number of times they roll over. The data collection unit can use cameras and light sensors to analyze skin color. The camera captures changes in the baby's skin color, and the light sensors measure blood flow and oxygen saturation in the skin. The data collection unit can use a microphone to analyze crying. The microphone records the baby's crying and analyzes the intensity and pattern of the crying. The data collection unit can use cameras and sensors to analyze movement patterns. The camera captures the baby's movement patterns, and the sensors detect the frequency and intensity of the baby's movements. As a result, the data collection unit can comprehensively monitor the health of babies and toddlers, helping with early detection of abnormalities and health management.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts changes in physical condition and future risks. To detect changes in physical condition, the analysis unit can analyze increases in body temperature and fluctuations in heart rate. An increase in body temperature may indicate a fever, and fluctuations in heart rate may indicate stress or poor health. To predict future risks, the analysis unit can analyze the risk of disease onset and developmental delays. For example, by analyzing body temperature and heart rate data over a long period and detecting abnormal patterns and trends, it can be used to help in the early detection and prevention of diseases. The analysis unit uses AI to analyze data and predict changes in physical condition and risks with high accuracy. The AI ​​can learn from the collected data and automatically detect abnormal patterns and risk factors. For example, the AI ​​can analyze a baby's body temperature and heart rate data to detect fever and abnormal heart rate at an early stage. The AI ​​can also analyze a baby's facial expressions and crying data to detect signs of stress or discomfort. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past body temperature data, it can predict the risk of fever during specific seasons or time periods and implement preventive measures. This allows the analysis unit to quickly and accurately analyze collected data and understand the health status of babies and young children in real time.

[0032] The service provider provides advice to parents and guardians based on the analysis results obtained by the analysis provider. For example, if signs of illness are observed, the service provider will recommend that parents seek medical attention. The service provider provides individualized advice based on daily routines and data. For example, if a baby's body temperature is high, the service provider will recommend methods to lower the temperature or to seek medical attention. Also, if a baby's heart rate is abnormally high, the service provider will recommend methods to reduce stress or to seek medical attention. The service provider can analyze fever and difficulty breathing to detect signs of illness. For example, if a baby's body temperature rises rapidly, the service provider will inform the parents of the signs of fever and advise them to take appropriate measures. Also, if a baby is having difficulty breathing, the service provider will inform the parents of the signs of difficulty breathing and advise them to seek medical attention. The service provider can analyze sleep patterns and feeding timing to provide individualized advice. For example, if a baby's sleep pattern is disrupted, the service provider will advise the parents to improve the sleep environment or establish a proper sleep rhythm. Furthermore, if a baby's feeding schedule is irregular, the service provider will advise parents on appropriate feeding times and nutritionally balanced meals. This allows the service provider to provide parents and guardians with specific and practical advice, supporting the health management of babies and toddlers.

[0033] The Liaison Department collaborates with childcare facilities and medical institutions based on advice provided by the Service Provider Department. The Liaison Department shares collected data to quickly receive advice and feedback from experts. For example, if an abnormality is observed in a baby's health, the Liaison Department sends the collected data to a medical institution to receive a diagnosis and advice from experts. Childcare facilities can also share data to monitor the baby's health in real time and take appropriate action. The Liaison Department can use data sharing methods and communication means to receive advice from experts. For example, it can use a cloud-based data sharing platform to share data with medical institutions and childcare facilities and transmit information quickly and efficiently. It can also use email and messaging apps to facilitate communication with experts. This allows the Liaison Department to quickly share information on the health status of babies and toddlers and receive appropriate advice from experts, thereby supporting health management.

[0034] The shared section shares data linked by the linked section among family members. The shared section allows real-time monitoring of a child's health data and condition, and immediately sends notifications to all family members if there are any abnormalities. For example, if a baby's body temperature rises rapidly, the shared section notifies all family members of the temperature increase and encourages appropriate action. Also, if a baby's heart rate is abnormally high, the shared section notifies all family members of the abnormal heart rate and recommends seeking medical attention. The shared section can use the method and scope of data sharing. For example, a dedicated application can be used to allow all family members to check the baby's health data in real time. By setting the scope of data sharing, only necessary information can be shared, protecting privacy. This allows the shared section to support health management by enabling all family members to understand the baby's and toddler's health status and take appropriate action. Furthermore, the shared section can facilitate communication among family members and reduce the burden of childcare. For example, by sharing information about the baby's health status, all family members can cooperate in childcare. In addition, the shared section can store health data over a long period of time and use it as a growth record, allowing all family members to monitor the baby's growth and development. This allows the shared area to support health management by enabling all family members to monitor the baby's and toddler's health status in real time and take appropriate action.

[0035] The data collection unit can measure body temperature, heart rate, and respiration. For example, the data collection unit can use an ear thermometer to measure body temperature. The data collection unit can also use an axillary thermometer to measure body temperature. The data collection unit can also use a skin temperature sensor to measure body temperature. The data collection unit can use an electrocardiogram to measure heart rate. The data collection unit can also use a pulse oximeter to measure heart rate. The data collection unit can also use a photoplethysmography (PPE) recorder to measure heart rate. The data collection unit can measure respiratory rate to measure respiration. The data collection unit can also analyze respiratory patterns to measure respiration. The data collection unit can also use a respiratory sound sensor to measure respiration. This allows for accurate monitoring of the vital signs of infants and young children by measuring body temperature, heart rate, and respiration. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs measurement data of body temperature, heart rate, and respiration into a generating AI, which then analyzes the data to monitor vital signs.

[0036] The data collection unit can analyze facial expressions, body movements, skin color, crying, and movement patterns using cameras and microphones. For example, the data collection unit can analyze the facial expressions of babies and toddlers using a camera. As a method of facial expression analysis, the data collection unit can recognize expressions such as smiles and crying faces. The data collection unit can use cameras and sensors to analyze body movements. As a method of analyzing body movements, the data collection unit can analyze the movements of the arms and legs and changes in posture. The data collection unit can use cameras and light sensors to analyze skin color. As a method of analyzing skin color, the data collection unit can analyze changes in complexion and the presence or absence of rashes. The data collection unit can use microphones to analyze crying. As a method of analyzing crying, the data collection unit can analyze the volume of crying and crying patterns. The data collection unit can use cameras and sensors to analyze movement patterns. As a method of analyzing movement patterns, the data collection unit can analyze the frequency of rolling over and the rhythm of arm and leg movements. In this way, by using cameras and microphones, the condition of babies and toddlers can be understood in detail. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by a camera or microphone into a generating AI, which can then analyze the data to understand the condition of the baby or infant.

[0037] The analysis unit can predict changes in physical condition and future risks. For example, to detect changes in physical condition, the analysis unit can analyze increases in body temperature and fluctuations in heart rate. As a method of detecting changes in physical condition, the analysis unit can predict changes in physical condition based on increases in body temperature and fluctuations in heart rate. To predict future risks, the analysis unit can analyze the risk of disease onset and developmental delays. As a method of predicting future risks, the analysis unit can predict future risks based on the risk of disease onset and developmental delays. This allows for early countermeasures to be taken by predicting changes in physical condition and future risks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected by the collection unit into a generating AI, and the generating AI can analyze the data to predict changes in physical condition and future risks.

[0038] The service provider can recommend that parents seek medical attention if signs of illness are observed. For example, the service provider can analyze fever and difficulty breathing to detect signs of illness. The service provider can analyze an increase in body temperature to detect signs of fever. The service provider can analyze an increase in respiratory rate and abnormal respiratory patterns to detect signs of difficulty breathing. If signs of illness are observed, the service provider can send a notification to parents recommending that they seek medical attention. This enables early intervention by promptly recommending medical attention when signs of illness are observed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results obtained by the analysis unit into a generating AI, which can then detect signs of illness and send a notification to the parents.

[0039] The service provider can provide personalized advice based on daily lifestyle rhythms and data. For example, to analyze daily lifestyle rhythms, the service provider can analyze sleep patterns and meal timings. To analyze sleep patterns, the service provider can analyze sleep start and end times and sleep quality. To analyze meal timing, the service provider can analyze meal times and meal content. To provide personalized advice, the service provider can provide suggestions for health management and methods for improving lifestyle habits based on the analyzed data. For example, based on sleep patterns, the service provider can suggest appropriate sleep duration and methods for improving the sleep environment. Based on meal timing, the service provider can suggest balanced meals and adjustments to meal timings. This enables appropriate care by providing personalized advice based on daily lifestyle rhythms and data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results obtained by the analysis unit into a generating AI, which can generate personalized advice and provide feedback to the parent.

[0040] The collaboration unit can share collected data to quickly receive advice and feedback from experts. For example, the collaboration unit can transmit data via the internet as a method of data sharing. The collaboration unit can use cloud storage as a method of data sharing. The collaboration unit can use a dedicated application as a method of data sharing. When sharing data to receive advice from experts, the collaboration unit can use encryption technology to ensure data security. The collaboration unit can share data in real time to quickly receive feedback from experts. This enables appropriate action by quickly receiving advice and feedback from experts. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input data collected by the collection unit into a generation AI, which can then analyze the data and share it with experts.

[0041] The sharing unit can monitor a child's health data and condition in real time, and immediately send notifications to all family members if an abnormality is detected. The sharing unit can, for example, transmit data via the internet as a method of data sharing. The sharing unit can use cloud storage as a method of data sharing. The sharing unit can use a dedicated application as a method of data sharing. The sharing unit can share data with all family members as a scope of data sharing. The sharing unit can share data with specific family members as a scope of data sharing. The sharing unit can monitor data in real time to send notifications if an abnormality is detected. The sharing unit can set the timing of notifications to send notifications if an abnormality is detected. This allows for real-time monitoring of a child's health data and condition, and enables quick response if an abnormality is detected. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input data collected by the collection unit into a generation AI, which can analyze the data to detect abnormalities and send notifications to all family members.

[0042] The data collection unit can analyze past health data of infants and toddlers and select the optimal data collection method. For example, the data collection unit can identify from past health data that a child's physical condition is likely to change during specific time periods and focus data collection during those times. Based on past data, the data collection unit can identify that a child's physical condition is likely to change during specific activities and collect data during those activities. The data collection unit can analyze past data to identify that a child's physical condition is likely to change under specific environmental conditions and collect data under those conditions. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past health data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, which can analyze the data and select the optimal data collection method.

[0043] The data collection unit can filter vital signs based on the baby's or toddler's current activity level and environment. For example, when the baby is sleeping, the data collection unit can reduce the amount of data collected to avoid disturbing sleep. When the baby is playing, the data collection unit can collect data according to the level of activity and detect abnormalities. When the baby is eating, the data collection unit can filter the data taking into account the effects of eating. This allows for accurate data collection by filtering the data based on the current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the baby's or toddler's activity level and environment into a generating AI, which can then filter the data to perform accurate data collection.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the baby or infant when collecting vital signs. For example, if the baby is out, the data collection unit can collect vital signs in response to changes in the environment. If the baby is at home, the data collection unit can collect vital signs in response to the indoor environment. If the baby is at a childcare facility, the data collection unit can collect vital signs in response to the facility environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the baby or infant into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0045] The data collection unit can analyze the social media activity of babies and toddlers and collect relevant data when collecting vital signs. For example, the data collection unit can analyze facial expressions and body movements from photos and videos of babies and collect vital signs. The data collection unit can analyze activity patterns from babies' social media posts and collect vital signs. The data collection unit can estimate emotions from comments and reactions on babies' social media and collect vital signs. This allows for more accurate monitoring by collecting relevant data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data of babies and toddlers into a generating AI, which can analyze the data and collect relevant vital signs.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of vital signs during the analysis. For example, the analysis unit can perform a detailed analysis for important vital signs. For vital signs of low importance, the analysis unit can perform a simplified analysis. The analysis unit can determine the priority of the analysis according to importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of vital signs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0047] The analysis unit can apply different analysis algorithms depending on the category of vital signs during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate. For body temperature, it can apply a body temperature variability analysis algorithm. For respiration, it can apply a respiratory pattern analysis algorithm. By applying different analysis algorithms depending on the category of vital signs, accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0048] The analysis unit can determine the priority of analysis based on the timing of vital sign collection during the analysis. For example, the analysis unit can prioritize the analysis of recently collected vital signs. The analysis unit can also prioritize the analysis of data from important periods by referring to past data. The analysis unit can determine the order of analysis based on the collection timing. This enables efficient analysis by determining the priority of analysis based on the timing of vital sign collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign collection timing data into a generating AI, and the generating AI can analyze the data and determine the priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of vital signs during the analysis. For example, the analysis unit can prioritize the analysis of vital signs with high relevance. The analysis unit can postpone the analysis of vital signs with low relevance. The analysis unit can adjust the order of analysis based on relevance. This allows for accurate analysis by adjusting the order of analysis based on the relevance of vital signs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign relevance data into a generating AI, and the generating AI can analyze the data and adjust the order.

[0050] The service provider can adjust the level of detail in advice based on the importance of vital signs when providing advice. For example, the service provider can provide detailed advice for important vital signs. For less important vital signs, the service provider can provide simplified advice. The service provider can determine the priority of advice according to its importance. This allows for efficient advice by adjusting the level of detail based on the importance of vital signs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign importance data into a generating AI, which can then analyze the data and adjust the level of detail.

[0051] The service provider can apply different advice algorithms depending on the category of vital signs when providing advice. For example, for heart rate, the service provider can provide advice based on heart rate variability. For body temperature, the service provider can provide advice based on body temperature variability. For respiration, the service provider can provide advice based on respiration patterns. This allows for accurate advice by applying different advice algorithms depending on the category of vital signs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0052] The service provider can determine the priority of advice based on the timing of vital sign collection when providing advice. For example, the service provider can provide advice based on recently collected vital signs. The service provider can also provide advice based on data from important periods, referencing past data. The service provider can determine the order of advice based on the collection timing. This enables efficient advice by prioritizing advice based on the timing of vital sign collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign collection timing data into a generating AI, which can then analyze the data and determine the priority.

[0053] The service provider can adjust the order of advice based on the relevance of vital signs when providing advice. For example, the service provider can provide advice based on highly relevant vital signs. The service provider can postpone advice based on less relevant vital signs. The service provider can adjust the order of advice based on relevance. This allows for more accurate advice by adjusting the order of advice based on the relevance of vital signs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign relevance data into a generating AI, which can then analyze the data and adjust the order.

[0054] The collaboration unit can adjust the level of detail in the collaboration based on the importance of vital signs during the collaboration process. For example, the collaboration unit can perform detailed collaboration for important vital signs. For vital signs of lower importance, the collaboration unit can perform simplified collaboration. The collaboration unit can determine the priority of collaboration according to importance. This enables efficient collaboration by adjusting the level of detail in the collaboration based on the importance of vital signs. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input vital sign importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0055] The integration unit can apply different integration algorithms depending on the category of vital signs during integration. For example, for heart rate, the integration unit can apply an integration algorithm based on heart rate variability. For body temperature, the integration unit can apply an integration algorithm based on body temperature variability. For respiration, the integration unit can apply an integration algorithm based on respiration patterns. This enables accurate integration by applying different integration algorithms depending on the category of vital signs. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0056] The integration unit can adjust the order of integration based on the timing of vital sign collection during integration. For example, the integration unit can prioritize the integration of recently collected vital signs. The integration unit can also prioritize the integration of data from important periods by referring to past data. The integration unit can adjust the order of integration based on the collection timing. This enables efficient integration by adjusting the order of integration based on the timing of vital sign collection. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input vital sign collection timing data into a generating AI, which can then analyze the data and adjust the order.

[0057] The integration unit can adjust the integration method based on the relevance of vital signs during integration. For example, the integration unit can prioritize integration of highly relevant vital signs. The integration unit can postpone integration of less relevant vital signs. The integration unit can adjust the integration method based on relevance. This allows for accurate integration by adjusting the integration method based on the relevance of vital signs. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input vital sign relevance data into a generating AI, and the generating AI can analyze the data and adjust the method.

[0058] The sharing unit can adjust the level of detail of the sharing based on the importance of the vital signs. For example, the sharing unit can provide detailed sharing for important vital signs. For less important vital signs, it can provide simplified sharing. The sharing unit can determine the priority of sharing according to importance. This allows for efficient sharing by adjusting the level of detail of sharing based on the importance of the vital signs. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign importance data into a generating AI, which can then analyze the data and adjust the level of detail.

[0059] The sharing unit can apply different sharing algorithms depending on the category of vital signs during sharing. For example, for heart rate, it can apply a sharing algorithm based on heart rate variability. For body temperature, it can apply a sharing algorithm based on body temperature variability. For respiration, it can apply a sharing algorithm based on respiratory patterns. This enables accurate sharing by applying different sharing algorithms depending on the category of vital signs. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0060] The sharing unit can adjust the order of sharing based on the timing of vital sign collection. For example, the sharing unit can prioritize the sharing of recently collected vital signs. The sharing unit can also prioritize the sharing of data from important periods by referring to past data. The sharing unit can adjust the order of sharing based on the collection timing. This allows for efficient sharing by adjusting the order of sharing based on the timing of vital sign collection. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign collection timing data into a generating AI, which can then analyze the data and adjust the order.

[0061] The sharing unit can adjust the sharing method based on the relevance of vital signs during sharing. For example, the sharing unit can prioritize sharing highly relevant vital signs. The sharing unit can postpone sharing less relevant vital signs. The sharing unit can adjust the sharing method based on relevance. This allows for accurate sharing by adjusting the sharing method based on the relevance of vital signs. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign relevance data into a generating AI, which can then analyze the data and adjust the method.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can analyze past health data of infants and toddlers and select the optimal data collection method. For example, it can identify periods in time when a child's physical condition is likely to change based on past health data and focus data collection during those periods. Based on past data, the data collection unit can identify periods during specific activities when a child's physical condition is likely to change and collect data during those activities. The data collection unit can analyze past data to identify periods under specific environmental conditions when a child's physical condition is likely to change and collect data under those conditions. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past health data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past health data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0064] The data collection unit can filter vital signs based on the baby's or toddler's current activity level and environment. For example, when the baby is sleeping, the amount of data collected can be reduced to avoid disturbing sleep. When the baby is playing, data can be collected according to the level of activity to detect abnormalities. When the baby is eating, data can be filtered to take into account the effects of eating. This allows for accurate data collection by filtering data based on the current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the baby's or toddler's activity level and environment into a generating AI, which can then filter the data to perform accurate data collection.

[0065] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the baby or infant when collecting vital signs. For example, if the baby is out, vital signs can be collected in accordance with changes in the environment. If the baby is at home, vital signs can be collected in accordance with the indoor environment. If the baby is in a childcare facility, vital signs can be collected in accordance with the facility's environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of the baby or infant into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0066] The data collection unit can analyze the social media activity of babies and toddlers and collect relevant data when collecting vital signs. For example, it can analyze facial expressions and body movements from photos and videos of babies and collect vital signs. The data collection unit can analyze activity patterns from babies' social media posts and collect vital signs. The data collection unit can estimate emotions from comments and reactions on babies' social media and collect vital signs. This allows for more accurate monitoring by collecting relevant data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data of babies and toddlers into a generating AI, which can analyze the data and collect relevant vital signs.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of vital signs during the analysis. For example, a detailed analysis can be performed for important vital signs, while a simplified analysis can be performed for less important vital signs. The analysis unit can determine the priority of the analysis according to importance. This allows for efficient analysis by adjusting the level of detail based on the importance of vital signs. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input vital sign importance data into a generating AI, which can then analyze the data and adjust the level of detail.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit monitors the vital signs of the baby or infant. The unit measures body temperature, heart rate, and respiration, and uses ear thermometers, axillary thermometers, electrocardiograms, pulse oximeters, cameras, and microphones to analyze facial expressions, body movements, skin color, crying, and movement patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit predicts changes in physical condition and future risks, and analyzes increases in body temperature, fluctuations in heart rate, the risk of disease onset, and developmental delays. Step 3: The service provider provides advice to parents and guardians based on the analysis results obtained by the analysis provider. The service provider recommends visiting a medical institution if signs of illness are observed and provides individualized advice based on daily routines and data. Step 4: The Liaison Department collaborates with childcare facilities and medical institutions based on the advice provided by the Service Provider Department. The Liaison Department shares the collected data to quickly receive advice and feedback from experts. Step 5: The sharing unit shares the data linked by the collaboration unit among family members. The sharing unit can check the child's health data and condition in real time, and if there is an abnormality, a notification is immediately sent to all family members.

[0070] (Example of form 2) The infant health monitoring system according to an embodiment of the present invention is a system that monitors the vital signs (body temperature, heart rate, respiration) of babies and infants in real time, as well as their facial expressions, body movements, skin color, and behavioral patterns (sleep, activity, eating), and immediately issues a warning when an abnormality is detected. The infant health monitoring system utilizes data analysis by an AI agent to predict changes in health and future risks, and provides specific advice and countermeasures to parents and guardians. This makes it possible to predict poor health before the onset of illness and to take preventative measures. For example, the infant health monitoring system monitors the vital signs of babies and infants using sensor technology. This includes measuring body temperature, heart rate, and respiration. Furthermore, the infant health monitoring system analyzes facial expressions, body movements, skin color, crying, and movement patterns using cameras and microphones. This allows for real-time understanding of the child's needs and condition. Next, the infant health monitoring system analyzes the collected data using an AI agent. The AI ​​agent predicts changes in health and future risks, and provides specific advice and countermeasures to parents and guardians. For example, if signs of illness are detected, the AI ​​agent will advise parents to seek medical attention. It also provides personalized advice based on daily routines and data, enabling child-centered care. Furthermore, the infant health monitoring system can collaborate with childcare facilities and medical institutions. Collected data is shared to quickly receive advice and feedback from experts. This allows for early health management, predicting illness before it manifests and taking preventative measures. The infant health monitoring system supports modern parenting by ensuring that changes in a child's health are not overlooked, providing peace of mind and reducing the burden of childcare. Additionally, based on the collected data, the AI ​​agent can personalize and provide childcare content and advice tailored to each family, improving the quality of childcare. Moreover, by sharing information among family members, a system is in place to check the child's health data and condition in real time, and immediately send notifications to all family members if any abnormalities are detected.This allows infant health monitoring systems to improve the quality of childcare by monitoring the health and safety of babies and toddlers and providing parents and guardians with specific advice and measures.

[0071] The infant health monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a linkage unit, and a sharing unit. The data collection unit monitors the vital signs of babies and infants. The data collection unit measures, for example, body temperature, heart rate, and respiration. The data collection unit can use an ear thermometer or an axillary thermometer to measure body temperature. The data collection unit can use an electrocardiogram or a pulse oximeter to measure heart rate. The data collection unit can measure respiratory rate and analyze respiratory patterns to measure respiration. The data collection unit analyzes facial expressions, body movements, skin color, crying, and movement patterns using a camera and a microphone. The data collection unit can analyze the facial expressions of babies and infants using a camera. The data collection unit can use a camera and sensors to analyze body movements. The data collection unit can use a camera and a light sensor to analyze skin color. The data collection unit can use a microphone to analyze crying. The data collection unit can use a camera and sensors to analyze movement patterns. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit predicts changes in physical condition and future risks. The analysis unit can analyze changes in body temperature and heart rate to detect changes in physical condition. The analysis unit can analyze the risk of developing diseases and developmental delays to predict future risks. The provision unit provides advice to parents and guardians based on the analysis results obtained by the analysis unit. For example, the provision unit will recommend that parents visit a medical institution if signs of illness are observed. The provision unit provides individualized advice as feedback based on daily routines and data. The provision unit can analyze fever and difficulty breathing to detect signs of illness. The provision unit can analyze sleep patterns and meal timing to provide individualized advice. The collaboration unit collaborates with childcare facilities and medical institutions based on the advice provided by the provision unit. The collaboration unit shares the collected data to quickly receive advice and feedback from experts. The collaboration unit can use data sharing methods and communication means to receive advice from experts. The shared section allows family members to share data that has been linked by the collaborative section.The shared unit allows for real-time monitoring of the child's health data and condition, and immediately sends notifications to all family members if any abnormalities are detected. The shared unit can also control the method and scope of data sharing. As a result, the infant health monitoring system according to this embodiment can improve the quality of childcare by monitoring the health and safety of babies and infants and providing specific advice and measures to parents and guardians.

[0072] The data collection unit monitors the vital signs of babies and infants. For example, it measures body temperature, heart rate, and respiration. To measure body temperature, the data collection unit can use ear thermometers or axillary thermometers. Ear thermometers are inserted into the baby's ear to measure body temperature quickly, while axillary thermometers are placed under the baby's armpit to measure body temperature. To measure heart rate, the data collection unit can use electrocardiograms (ECGs) or pulse oximeters. ECGs measure heart rate by attaching electrodes to the baby's chest, while pulse oximeters measure heart rate and blood oxygen saturation by attaching sensors to the baby's fingertips or toes. To measure respiration, the data collection unit can measure respiratory rate and analyze breathing patterns. Respiratory rate is measured by detecting movement in the baby's chest or abdomen with sensors, and breathing pattern analysis analyzes the rhythm and depth of breathing. The data collection unit uses cameras and microphones to analyze facial expressions, body movements, skin color, crying, and movement patterns. The data collection unit can, for example, use a camera to analyze the facial expressions of babies and toddlers. The camera captures the baby's face and analyzes changes in expression, such as smiles and crying. The data collection unit can use cameras and sensors to analyze body movements. The camera captures the baby's whole body movements, and the sensors detect the baby's arm and leg movements and the number of times they roll over. The data collection unit can use cameras and light sensors to analyze skin color. The camera captures changes in the baby's skin color, and the light sensors measure blood flow and oxygen saturation in the skin. The data collection unit can use a microphone to analyze crying. The microphone records the baby's crying and analyzes the intensity and pattern of the crying. The data collection unit can use cameras and sensors to analyze movement patterns. The camera captures the baby's movement patterns, and the sensors detect the frequency and intensity of the baby's movements. As a result, the data collection unit can comprehensively monitor the health of babies and toddlers, helping with early detection of abnormalities and health management.

[0073] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts changes in physical condition and future risks. To detect changes in physical condition, the analysis unit can analyze increases in body temperature and fluctuations in heart rate. An increase in body temperature may indicate a fever, and fluctuations in heart rate may indicate stress or poor health. To predict future risks, the analysis unit can analyze the risk of disease onset and developmental delays. For example, by analyzing body temperature and heart rate data over a long period and detecting abnormal patterns and trends, it can be used to help in the early detection and prevention of diseases. The analysis unit uses AI to analyze data and predict changes in physical condition and risks with high accuracy. The AI ​​can learn from the collected data and automatically detect abnormal patterns and risk factors. For example, the AI ​​can analyze a baby's body temperature and heart rate data to detect fever and abnormal heart rate at an early stage. The AI ​​can also analyze a baby's facial expressions and crying data to detect signs of stress or discomfort. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past body temperature data, it can predict the risk of fever during specific seasons or time periods and implement preventive measures. This allows the analysis unit to quickly and accurately analyze collected data and understand the health status of babies and young children in real time.

[0074] The service provider provides advice to parents and guardians based on the analysis results obtained by the analysis provider. For example, if signs of illness are observed, the service provider will recommend that parents seek medical attention. The service provider provides individualized advice based on daily routines and data. For example, if a baby's body temperature is high, the service provider will recommend methods to lower the temperature or to seek medical attention. Also, if a baby's heart rate is abnormally high, the service provider will recommend methods to reduce stress or to seek medical attention. The service provider can analyze fever and difficulty breathing to detect signs of illness. For example, if a baby's body temperature rises rapidly, the service provider will inform the parents of the signs of fever and advise them to take appropriate measures. Also, if a baby is having difficulty breathing, the service provider will inform the parents of the signs of difficulty breathing and advise them to seek medical attention. The service provider can analyze sleep patterns and feeding timing to provide individualized advice. For example, if a baby's sleep pattern is disrupted, the service provider will advise the parents to improve the sleep environment or establish a proper sleep rhythm. Furthermore, if a baby's feeding schedule is irregular, the service provider will advise parents on appropriate feeding times and nutritionally balanced meals. This allows the service provider to provide parents and guardians with specific and practical advice, supporting the health management of babies and toddlers.

[0075] The Liaison Department collaborates with childcare facilities and medical institutions based on advice provided by the Service Provider Department. The Liaison Department shares collected data to quickly receive advice and feedback from experts. For example, if an abnormality is observed in a baby's health, the Liaison Department sends the collected data to a medical institution to receive a diagnosis and advice from experts. Childcare facilities can also share data to monitor the baby's health in real time and take appropriate action. The Liaison Department can use data sharing methods and communication means to receive advice from experts. For example, it can use a cloud-based data sharing platform to share data with medical institutions and childcare facilities and transmit information quickly and efficiently. It can also use email and messaging apps to facilitate communication with experts. This allows the Liaison Department to quickly share information on the health status of babies and toddlers and receive appropriate advice from experts, thereby supporting health management.

[0076] The shared section shares data linked by the linked section among family members. The shared section allows real-time monitoring of a child's health data and condition, and immediately sends notifications to all family members if there are any abnormalities. For example, if a baby's body temperature rises rapidly, the shared section notifies all family members of the temperature increase and encourages appropriate action. Also, if a baby's heart rate is abnormally high, the shared section notifies all family members of the abnormal heart rate and recommends seeking medical attention. The shared section can use the method and scope of data sharing. For example, a dedicated application can be used to allow all family members to check the baby's health data in real time. By setting the scope of data sharing, only necessary information can be shared, protecting privacy. This allows the shared section to support health management by enabling all family members to understand the baby's and toddler's health status and take appropriate action. Furthermore, the shared section can facilitate communication among family members and reduce the burden of childcare. For example, by sharing information about the baby's health status, all family members can cooperate in childcare. In addition, the shared section can store health data over a long period of time and use it as a growth record, allowing all family members to monitor the baby's growth and development. This allows the shared area to support health management by enabling all family members to monitor the baby's and toddler's health status in real time and take appropriate action.

[0077] The data collection unit can measure body temperature, heart rate, and respiration. For example, the data collection unit can use an ear thermometer to measure body temperature. The data collection unit can also use an axillary thermometer to measure body temperature. The data collection unit can also use a skin temperature sensor to measure body temperature. The data collection unit can use an electrocardiogram to measure heart rate. The data collection unit can also use a pulse oximeter to measure heart rate. The data collection unit can also use a photoplethysmography (PPE) recorder to measure heart rate. The data collection unit can measure respiratory rate to measure respiration. The data collection unit can also analyze respiratory patterns to measure respiration. The data collection unit can also use a respiratory sound sensor to measure respiration. This allows for accurate monitoring of the vital signs of infants and young children by measuring body temperature, heart rate, and respiration. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs measurement data of body temperature, heart rate, and respiration into a generating AI, which then analyzes the data to monitor vital signs.

[0078] The data collection unit can analyze facial expressions, body movements, skin color, crying, and movement patterns using cameras and microphones. For example, the data collection unit can analyze the facial expressions of babies and toddlers using a camera. As a method of facial expression analysis, the data collection unit can recognize expressions such as smiles and crying faces. The data collection unit can use cameras and sensors to analyze body movements. As a method of analyzing body movements, the data collection unit can analyze the movements of the arms and legs and changes in posture. The data collection unit can use cameras and light sensors to analyze skin color. As a method of analyzing skin color, the data collection unit can analyze changes in complexion and the presence or absence of rashes. The data collection unit can use microphones to analyze crying. As a method of analyzing crying, the data collection unit can analyze the volume of crying and crying patterns. The data collection unit can use cameras and sensors to analyze movement patterns. As a method of analyzing movement patterns, the data collection unit can analyze the frequency of rolling over and the rhythm of arm and leg movements. In this way, by using cameras and microphones, the condition of babies and toddlers can be understood in detail. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by a camera or microphone into a generating AI, which can then analyze the data to understand the condition of the baby or infant.

[0079] The analysis unit can predict changes in physical condition and future risks. For example, to detect changes in physical condition, the analysis unit can analyze increases in body temperature and fluctuations in heart rate. As a method of detecting changes in physical condition, the analysis unit can predict changes in physical condition based on increases in body temperature and fluctuations in heart rate. To predict future risks, the analysis unit can analyze the risk of disease onset and developmental delays. As a method of predicting future risks, the analysis unit can predict future risks based on the risk of disease onset and developmental delays. This allows for early countermeasures to be taken by predicting changes in physical condition and future risks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected by the collection unit into a generating AI, and the generating AI can analyze the data to predict changes in physical condition and future risks.

[0080] The service provider can recommend that parents seek medical attention if signs of illness are observed. For example, the service provider can analyze fever and difficulty breathing to detect signs of illness. The service provider can analyze an increase in body temperature to detect signs of fever. The service provider can analyze an increase in respiratory rate and abnormal respiratory patterns to detect signs of difficulty breathing. If signs of illness are observed, the service provider can send a notification to parents recommending that they seek medical attention. This enables early intervention by promptly recommending medical attention when signs of illness are observed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results obtained by the analysis unit into a generating AI, which can then detect signs of illness and send a notification to the parents.

[0081] The service provider can provide personalized advice based on daily lifestyle rhythms and data. For example, to analyze daily lifestyle rhythms, the service provider can analyze sleep patterns and meal timings. To analyze sleep patterns, the service provider can analyze sleep start and end times and sleep quality. To analyze meal timing, the service provider can analyze meal times and meal content. To provide personalized advice, the service provider can provide suggestions for health management and methods for improving lifestyle habits based on the analyzed data. For example, based on sleep patterns, the service provider can suggest appropriate sleep duration and methods for improving the sleep environment. Based on meal timing, the service provider can suggest balanced meals and adjustments to meal timings. This enables appropriate care by providing personalized advice based on daily lifestyle rhythms and data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results obtained by the analysis unit into a generating AI, which can generate personalized advice and provide feedback to the parent.

[0082] The collaboration unit can share collected data to quickly receive advice and feedback from experts. For example, the collaboration unit can transmit data via the internet as a method of data sharing. The collaboration unit can use cloud storage as a method of data sharing. The collaboration unit can use a dedicated application as a method of data sharing. When sharing data to receive advice from experts, the collaboration unit can use encryption technology to ensure data security. The collaboration unit can share data in real time to quickly receive feedback from experts. This enables appropriate action by quickly receiving advice and feedback from experts. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input data collected by the collection unit into a generation AI, which can then analyze the data and share it with experts.

[0083] The sharing unit can monitor a child's health data and condition in real time, and immediately send notifications to all family members if an abnormality is detected. The sharing unit can, for example, transmit data via the internet as a method of data sharing. The sharing unit can use cloud storage as a method of data sharing. The sharing unit can use a dedicated application as a method of data sharing. The sharing unit can share data with all family members as a scope of data sharing. The sharing unit can share data with specific family members as a scope of data sharing. The sharing unit can monitor data in real time to send notifications if an abnormality is detected. The sharing unit can set the timing of notifications to send notifications if an abnormality is detected. This allows for real-time monitoring of a child's health data and condition, and enables quick response if an abnormality is detected. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input data collected by the collection unit into a generation AI, which can analyze the data to detect abnormalities and send notifications to all family members.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of vital sign data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to provide a sense of security. If the user is relaxed, the data collection unit can decrease the collection frequency to reduce the burden. If the user is busy, the data collection unit can optimize the collection timing to efficiently collect data. This allows for a sense of security and reduced burden by adjusting the collection timing according to the user'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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0085] The data collection unit can analyze past health data of infants and toddlers and select the optimal data collection method. For example, the data collection unit can identify from past health data that a child's physical condition is likely to change during specific time periods and focus data collection during those times. Based on past data, the data collection unit can identify that a child's physical condition is likely to change during specific activities and collect data during those activities. The data collection unit can analyze past data to identify that a child's physical condition is likely to change under specific environmental conditions and collect data under those conditions. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past health data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, which can analyze the data and select the optimal data collection method.

[0086] The data collection unit can filter vital signs based on the baby's or toddler's current activity level and environment. For example, when the baby is sleeping, the data collection unit can reduce the amount of data collected to avoid disturbing sleep. When the baby is playing, the data collection unit can collect data according to the level of activity and detect abnormalities. When the baby is eating, the data collection unit can filter the data taking into account the effects of eating. This allows for accurate data collection by filtering the data based on the current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the baby's or toddler's activity level and environment into a generating AI, which can then filter the data to perform accurate data collection.

[0087] The data collection unit can estimate the user's emotions and determine the priority of vital signs to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting important vital signs such as heart rate and respiration. If the user is relaxed, the data collection unit can prioritize collecting vital signs such as body temperature and skin color. If the user is busy, the data collection unit can collect only the most important vital signs. This allows for the priority collection of important data by determining the priority of vital signs to collect according to the user'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. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotions and determine the priority of vital signs to collect.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the baby or infant when collecting vital signs. For example, if the baby is out, the data collection unit can collect vital signs in response to changes in the environment. If the baby is at home, the data collection unit can collect vital signs in response to the indoor environment. If the baby is at a childcare facility, the data collection unit can collect vital signs in response to the facility environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the baby or infant into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0089] The data collection unit can analyze the social media activity of babies and toddlers and collect relevant data when collecting vital signs. For example, the data collection unit can analyze facial expressions and body movements from photos and videos of babies and collect vital signs. The data collection unit can analyze activity patterns from babies' social media posts and collect vital signs. The data collection unit can estimate emotions from comments and reactions on babies' social media and collect vital signs. This allows for more accurate monitoring by collecting relevant data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data of babies and toddlers into a generating AI, which can analyze the data and collect relevant vital signs.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, an easy-to-understand analysis result 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of vital signs during the analysis. For example, the analysis unit can perform a detailed analysis for important vital signs. For vital signs of low importance, the analysis unit can perform a simplified analysis. The analysis unit can determine the priority of the analysis according to importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of vital signs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0092] The analysis unit can apply different analysis algorithms depending on the category of vital signs during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate. For body temperature, it can apply a body temperature variability analysis algorithm. For respiration, it can apply a respiratory pattern analysis algorithm. By applying different analysis algorithms depending on the category of vital signs, accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a brief analysis result. By adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the timing of vital sign collection during the analysis. For example, the analysis unit can prioritize the analysis of recently collected vital signs. The analysis unit can also prioritize the analysis of data from important periods by referring to past data. The analysis unit can determine the order of analysis based on the collection timing. This enables efficient analysis by determining the priority of analysis based on the timing of vital sign collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign collection timing data into a generating AI, and the generating AI can analyze the data and determine the priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of vital signs during the analysis. For example, the analysis unit can prioritize the analysis of vital signs with high relevance. The analysis unit can postpone the analysis of vital signs with low relevance. The analysis unit can adjust the order of analysis based on relevance. This allows for accurate analysis by adjusting the order of analysis based on the relevance of vital signs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign relevance data into a generating AI, and the generating AI can analyze the data and adjust the order.

[0096] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide simple and easy-to-understand advice. If the user is relaxed, the service provider can provide detailed advice. If the user is in a hurry, the service provider can provide concise advice. In this way, easy-to-understand advice can be provided by adjusting the way advice is expressed according to the user'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. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the way advice is expressed.

[0097] The service provider can adjust the level of detail in advice based on the importance of vital signs when providing advice. For example, the service provider can provide detailed advice for important vital signs. For less important vital signs, the service provider can provide simplified advice. The service provider can determine the priority of advice according to its importance. This allows for efficient advice by adjusting the level of detail based on the importance of vital signs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign importance data into a generating AI, which can then analyze the data and adjust the level of detail.

[0098] The service provider can apply different advice algorithms depending on the category of vital signs when providing advice. For example, for heart rate, the service provider can provide advice based on heart rate variability. For body temperature, the service provider can provide advice based on body temperature variability. For respiration, the service provider can provide advice based on respiration patterns. This allows for accurate advice by applying different advice algorithms depending on the category of vital signs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0099] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide short, concise advice. If the user is relaxed, the service provider can provide detailed advice. If the user is in a hurry, the service provider can provide brief advice. This allows the service provider to provide appropriate advice by adjusting the length of the advice according to the user'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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the advice.

[0100] The service provider can determine the priority of advice based on the timing of vital sign collection when providing advice. For example, the service provider can provide advice based on recently collected vital signs. The service provider can also provide advice based on data from important periods, referencing past data. The service provider can determine the order of advice based on the collection timing. This enables efficient advice by prioritizing advice based on the timing of vital sign collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign collection timing data into a generating AI, which can then analyze the data and determine the priority.

[0101] The service provider can adjust the order of advice based on the relevance of vital signs when providing advice. For example, the service provider can provide advice based on highly relevant vital signs. The service provider can postpone advice based on less relevant vital signs. The service provider can adjust the order of advice based on relevance. This allows for more accurate advice by adjusting the order of advice based on the relevance of vital signs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input vital sign relevance data into a generating AI, which can then analyze the data and adjust the order.

[0102] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is feeling anxious, the interaction unit can provide a simple and easy-to-understand interaction method. If the user is relaxed, the interaction unit can provide a detailed interaction method. If the user is in a hurry, the interaction unit can provide a quick interaction method. This allows for easy-to-understand interaction by adjusting the interaction method according to the user'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 interaction unit may be performed using AI or not using AI. For example, the interaction unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the interaction method.

[0103] The collaboration unit can adjust the level of detail in the collaboration based on the importance of vital signs during the collaboration process. For example, the collaboration unit can perform detailed collaboration for important vital signs. For vital signs of lower importance, the collaboration unit can perform simplified collaboration. The collaboration unit can determine the priority of collaboration according to importance. This enables efficient collaboration by adjusting the level of detail in the collaboration based on the importance of vital signs. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input vital sign importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0104] The integration unit can apply different integration algorithms depending on the category of vital signs during integration. For example, for heart rate, the integration unit can apply an integration algorithm based on heart rate variability. For body temperature, the integration unit can apply an integration algorithm based on body temperature variability. For respiration, the integration unit can apply an integration algorithm based on respiration patterns. This enables accurate integration by applying different integration algorithms depending on the category of vital signs. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0105] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the user is feeling anxious, the collaboration unit can prioritize important collaborations. If the user is relaxed, the collaboration unit can perform detailed collaborations. If the user is in a hurry, the collaboration unit can perform rapid collaborations. This allows for prioritizing important collaborations by determining the priority of collaborations according to the user'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. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of collaborations.

[0106] The integration unit can adjust the order of integration based on the timing of vital sign collection during integration. For example, the integration unit can prioritize the integration of recently collected vital signs. The integration unit can also prioritize the integration of data from important periods by referring to past data. The integration unit can adjust the order of integration based on the collection timing. This enables efficient integration by adjusting the order of integration based on the timing of vital sign collection. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input vital sign collection timing data into a generating AI, which can then analyze the data and adjust the order.

[0107] The integration unit can adjust the integration method based on the relevance of vital signs during integration. For example, the integration unit can prioritize integration of highly relevant vital signs. The integration unit can postpone integration of less relevant vital signs. The integration unit can adjust the integration method based on relevance. This allows for accurate integration by adjusting the integration method based on the relevance of vital signs. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input vital sign relevance data into a generating AI, and the generating AI can analyze the data and adjust the method.

[0108] The sharing unit can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is feeling anxious, the sharing unit can provide a simple and easy-to-understand sharing method. If the user is relaxed, the sharing unit can provide a detailed sharing method. If the user is in a hurry, the sharing unit can provide a quick sharing method. This allows for easy-to-understand sharing by adjusting the sharing method according to the user'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 sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the sharing method.

[0109] The sharing unit can adjust the level of detail of the sharing based on the importance of the vital signs. For example, the sharing unit can provide detailed sharing for important vital signs. For less important vital signs, it can provide simplified sharing. The sharing unit can determine the priority of sharing according to importance. This allows for efficient sharing by adjusting the level of detail of sharing based on the importance of the vital signs. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign importance data into a generating AI, which can then analyze the data and adjust the level of detail.

[0110] The sharing unit can apply different sharing algorithms depending on the category of vital signs during sharing. For example, for heart rate, it can apply a sharing algorithm based on heart rate variability. For body temperature, it can apply a sharing algorithm based on body temperature variability. For respiration, it can apply a sharing algorithm based on respiratory patterns. This enables accurate sharing by applying different sharing algorithms depending on the category of vital signs. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0111] The sharing unit can estimate the user's emotions and determine the priority of sharing based on the estimated emotions. For example, if the user is feeling anxious, the sharing unit can prioritize important sharing. If the user is relaxed, the sharing unit can provide detailed sharing. If the user is in a hurry, the sharing unit can provide quick sharing. This allows for prioritizing important sharing by determining the priority of sharing according to the user'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. Some or all of the above processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of sharing.

[0112] The sharing unit can adjust the order of sharing based on the timing of vital sign collection. For example, the sharing unit can prioritize the sharing of recently collected vital signs. The sharing unit can also prioritize the sharing of data from important periods by referring to past data. The sharing unit can adjust the order of sharing based on the collection timing. This allows for efficient sharing by adjusting the order of sharing based on the timing of vital sign collection. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign collection timing data into a generating AI, which can then analyze the data and adjust the order.

[0113] The sharing unit can adjust the sharing method based on the relevance of vital signs during sharing. For example, the sharing unit can prioritize sharing highly relevant vital signs. The sharing unit can postpone sharing less relevant vital signs. The sharing unit can adjust the sharing method based on relevance. This allows for accurate sharing by adjusting the sharing method based on the relevance of vital signs. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input vital sign relevance data into a generating AI, which can then analyze the data and adjust the method.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis of important vital signs can be prioritized. If the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a concise analysis can be performed. This allows for the provision of appropriate analysis results by prioritizing the analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of the analysis.

[0116] The service provider can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling anxious, it can provide reassuring advice. If the user is relaxed, it can provide detailed advice. If the user is in a hurry, it can provide concise advice. In this way, appropriate advice can be provided by adjusting the content of the advice according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the content of the advice.

[0117] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the estimated emotions. For example, if the user is feeling anxious, it can provide a simple and easy-to-understand collaboration method. If the user is relaxed, it can provide a detailed collaboration method. If the user is in a hurry, it can provide a quick collaboration method. This allows for easy-to-understand collaboration by adjusting the collaboration method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collaboration method.

[0118] The sharing section can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is feeling anxious, a simple and easy-to-understand sharing method can be provided. If the user is relaxed, a detailed sharing method can be provided. If the user is in a hurry, a quick sharing method can be provided. This allows for easy-to-understand sharing by adjusting the sharing method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section can input user emotion data into a generative AI, which can estimate the emotions and adjust the sharing method.

[0119] The data collection unit can estimate the user's emotions and adjust the timing of vital sign data collection based on the estimated emotions. For example, if the user is stressed, the data collection timing can be increased to provide a sense of security. If the user is relaxed, the data collection timing can be reduced to lessen the burden. If the user is busy, the data collection timing can be optimized to efficiently collect data. In this way, by adjusting the data collection timing according to the user's emotions, a sense of security can be provided and the burden reduced. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the data collection timing.

[0120] The data collection unit can analyze past health data of infants and toddlers and select the optimal data collection method. For example, it can identify periods in time when a child's physical condition is likely to change based on past health data and focus data collection during those periods. Based on past data, the data collection unit can identify periods during specific activities when a child's physical condition is likely to change and collect data during those activities. The data collection unit can analyze past data to identify periods under specific environmental conditions when a child's physical condition is likely to change and collect data under those conditions. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past health data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past health data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0121] The data collection unit can filter vital signs based on the baby's or toddler's current activity level and environment. For example, when the baby is sleeping, the amount of data collected can be reduced to avoid disturbing sleep. When the baby is playing, data can be collected according to the level of activity to detect abnormalities. When the baby is eating, data can be filtered to take into account the effects of eating. This allows for accurate data collection by filtering data based on the current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the baby's or toddler's activity level and environment into a generating AI, which can then filter the data to perform accurate data collection.

[0122] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the baby or infant when collecting vital signs. For example, if the baby is out, vital signs can be collected in accordance with changes in the environment. If the baby is at home, vital signs can be collected in accordance with the indoor environment. If the baby is in a childcare facility, vital signs can be collected in accordance with the facility's environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of the baby or infant into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0123] The data collection unit can analyze the social media activity of babies and toddlers and collect relevant data when collecting vital signs. For example, it can analyze facial expressions and body movements from photos and videos of babies and collect vital signs. The data collection unit can analyze activity patterns from babies' social media posts and collect vital signs. The data collection unit can estimate emotions from comments and reactions on babies' social media and collect vital signs. This allows for more accurate monitoring by collecting relevant data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data of babies and toddlers into a generating AI, which can analyze the data and collect relevant vital signs.

[0124] The analysis unit can adjust the level of detail of the analysis based on the importance of vital signs during the analysis. For example, a detailed analysis can be performed for important vital signs, while a simplified analysis can be performed for less important vital signs. The analysis unit can determine the priority of the analysis according to importance. This allows for efficient analysis by adjusting the level of detail based on the importance of vital signs. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input vital sign importance data into a generating AI, which can then analyze the data and adjust the level of detail.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit monitors the vital signs of the baby or infant. The unit measures body temperature, heart rate, and respiration, and uses ear thermometers, axillary thermometers, electrocardiograms, pulse oximeters, cameras, and microphones to analyze facial expressions, body movements, skin color, crying, and movement patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit predicts changes in physical condition and future risks, and analyzes increases in body temperature, fluctuations in heart rate, the risk of disease onset, and developmental delays. Step 3: The service provider provides advice to parents and guardians based on the analysis results obtained by the analysis provider. The service provider recommends visiting a medical institution if signs of illness are observed and provides individualized advice based on daily routines and data. Step 4: The Liaison Department collaborates with childcare facilities and medical institutions based on the advice provided by the Service Provider Department. The Liaison Department shares the collected data to quickly receive advice and feedback from experts. Step 5: The sharing unit shares the data linked by the collaboration unit among family members. The sharing unit can check the child's health data and condition in real time, and if there is an abnormality, a notification is immediately sent to all family members.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, collaboration unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to analyze the vital signs, facial expressions, body movements, skin color, crying, and movement patterns of babies and infants. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to predict changes in health and future risks. The provision unit provides advice to parents and guardians based on the analysis results using the specific processing unit 290 of the data processing unit 12. The collaboration unit collaborates with childcare facilities and medical institutions using the specific processing unit 290 of the data processing unit 12 to share the collected data. The sharing unit shares data among family members using the control unit 46A of the smart device 14 and immediately sends notifications to all family members if there is an abnormality. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, collaboration unit, and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to analyze the vital signs, facial expressions, body movements, skin color, crying, and movement patterns of babies and infants. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to predict changes in health and future risks. The provision unit provides advice to parents and guardians based on the analysis results using the identification processing unit 290 of the data processing unit 12. The collaboration unit collaborates with childcare facilities and medical institutions using the identification processing unit 290 of the data processing unit 12 to share the collected data. The sharing unit shares data among family members using the control unit 46A of the smart glasses 214 and immediately sends notifications to all family members if there is an abnormality. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, collaboration unit, and sharing unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to analyze the vital signs, facial expressions, body movements, skin color, crying, and movement patterns of babies and infants. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12, to predict changes in physical condition and future risks. The provision unit provides advice to parents and guardians based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The collaboration unit collaborates with childcare facilities and medical institutions, for example, by the specific processing unit 290 of the data processing unit 12, and shares the collected data. The sharing unit shares data among family members, for example, by the control unit 46A of the headset terminal 314, and immediately sends a notification to all family members if there is an abnormality. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, collaboration unit, and sharing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to analyze the vital signs, facial expressions, body movements, skin color, crying, and movement patterns of babies and infants. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12, to predict changes in physical condition and future risks. The provision unit provides advice to parents and guardians based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The collaboration unit collaborates with childcare facilities and medical institutions, for example, by the specific processing unit 290 of the data processing unit 12, to share the collected data. The sharing unit shares data among family members, for example, by the control unit 46A of the robot 414, and immediately sends a notification to all family members if there is an abnormality. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A collection unit that monitors the vital signs of babies and infants, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides advice to parents and guardians based on the analysis results obtained by the aforementioned analysis unit, Based on the advice provided by the aforementioned provision department, the liaison department collaborates with childcare facilities and medical institutions. The system includes a sharing unit that shares the data linked by the aforementioned linking unit among family members. A system characterized by the following features. (Note 2) The aforementioned collection unit is Measure body temperature, heart rate, and respiration. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Cameras and microphones are used to analyze facial expressions, body movements, skin color, crying sounds, and movement patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Predicting changes in physical condition and future risks The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, If signs of illness are observed, parents should be advised to seek medical attention. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on your daily routine and data, we provide personalized advice and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned linkage unit is, The collected data will be shared to quickly receive advice and feedback from experts. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned shared portion is, You can check your child's health data and condition in real time, and if there is an abnormality, a notification will be sent immediately to the whole family. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of vital sign collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past health data of babies and toddlers to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting vital signs, filter them based on the baby's or toddler's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of vital signs to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting vital signs, prioritize the collection of highly relevant data, taking into account the geographical location of the infant or toddler. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting vital signs, analyze the social media activity of babies and toddlers and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when vital signs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing advice, prioritize the advice based on when vital signs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, During collaboration, the level of detail in the collaboration is adjusted based on the importance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, During integration, different integration algorithms are applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, During the collaboration process, the order of collaboration will be adjusted based on the timing of vital sign collection. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, During collaboration, the method of collaboration will be adjusted based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, It estimates the user's emotions and adjusts the sharing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shared portion is, When sharing, adjust the level of detail based on the importance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shared portion is, When sharing, different sharing algorithms are applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned shared portion is, When sharing, adjust the sharing order based on when vital signs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned shared portion is, When sharing, adjust the sharing method based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 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. A collection unit that monitors the vital signs of babies and infants, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides advice to parents and guardians based on the analysis results obtained by the aforementioned analysis unit, Based on the advice provided by the aforementioned provision department, the liaison department collaborates with childcare facilities and medical institutions. The system includes a sharing unit that shares the data linked by the aforementioned linking unit among family members. A system characterized by the following features.

2. The aforementioned collection unit is Measure body temperature, heart rate, and respiration. The system according to feature 1.

3. The aforementioned collection unit is Cameras and microphones are used to analyze facial expressions, body movements, skin color, crying sounds, and movement patterns. The system according to feature 1.

4. The aforementioned analysis unit, Predicting changes in physical condition and future risks The system according to feature 1.

5. The aforementioned supply unit is, If signs of illness are observed, parents should be advised to seek medical attention. The system according to feature 1.

6. The aforementioned supply unit is, Based on your daily routine and data, we provide personalized advice and feedback. The system according to feature 1.

7. The aforementioned linkage unit is, The collected data will be shared to quickly receive advice and feedback from experts. The system according to feature 1.

8. The aforementioned shared portion is, You can check your child's health data and condition in real time, and if there is an abnormality, a notification will be sent immediately to the whole family. The system according to feature 1.

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