System

Abstract

An object of a system according to an embodiment is to quickly and accurately grasp a crying voice of a baby and a change in the physical condition of the baby and take appropriate measures.SOLUTION: A system according to an embodiment includes a wearable terminal, a AI, a smartphone application, and a glasses-type wearable terminal. The wearable device is wound around the ankle of the baby to collect the crying sound, body temperature, and sleep state of the baby. The AI analyzes the date collected by the wearable terminal, identifies the emotion and physical condition of the baby, and proposes a handling method. The smartphone application notifies the protector of the handling method proposed by the AI. The glasses-type wearable terminal displays information on the lens in cooperation with the smartphone application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately detect a baby's crying or changes in its physical condition and take appropriate action.

[0005] The system according to the embodiment aims to quickly and accurately detect a baby's crying or changes in physical condition and take appropriate action. [Means for solving the problem]

[0006] The system according to the embodiment comprises a wearable device, AI, a smartphone app, and a glasses-type wearable device. The wearable device is worn around the baby's ankle and collects the baby's crying, body temperature, and sleep state. The AI ​​analyzes the data collected by the wearable device, identifies the baby's emotions and physical condition, and suggests ways to respond. The smartphone app notifies the parent of the ways to respond suggested by the AI. The glasses-type wearable device works in conjunction with the smartphone app to display information on the lenses. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately detect a baby's crying or changes in physical condition, and can respond appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A childcare support system according to an embodiment of the present invention identifies why a baby is crying and supports parents and caregivers in their childcare. This system uses a wearable device worn around the baby's ankle to monitor the baby's crying, body temperature, sleep status, and other information. AI installed on the device analyzes this data. The AI ​​analyzes the baby's emotions and physical condition and shares recommended responses with a smartphone app. In addition to the app, the system can also be connected to a wearable eyeglasses-type device, which displays information on the lenses, freeing up both hands for busy parenting. Furthermore, to monitor the baby's health, parents are required to record when the baby is sleeping and when the diaper was changed. The information shared with the app is automatically recorded as a history, providing assistance to busy parents. This allows the childcare support system to identify why the baby is crying and provide appropriate responses. For example, even if a baby won't stop crying, the AI ​​can identify the cause and suggest specific responses, allowing parents and caregivers to feel at ease. Furthermore, using the wearable eyeglasses-type device allows parents to check the baby's condition while keeping their hands free, making it ideal for busy parenting. In addition, the automatic recording function allows you to efficiently manage your baby's health.

[0029] A childcare support system according to an embodiment includes a wearable device, AI, a smartphone app, and a glasses-type wearable device. The wearable device is worn around a baby's ankle and collects the baby's crying, body temperature, and sleep state. For example, the wearable device collects the baby's crying using a high-sensitivity microphone and stores the collected data as audio data. The wearable device also measures the baby's body temperature using a temperature sensor and stores the data as body temperature data. The wearable device also monitors the baby's sleep state using a sleep sensor and stores the data as sleep data. The AI ​​analyzes the data collected by the wearable device to identify the baby's emotions and physical condition and proposes appropriate responses. For example, the AI ​​analyzes the crying pattern to determine whether the baby is hungry, has a wet diaper, or is sleepy. The AI ​​also analyzes the body temperature data to determine whether the baby's body temperature is normal. The AI ​​also analyzes the sleep data to evaluate the baby's sleep quality. The smartphone app notifies the parent of the appropriate responses suggested by the AI. For example, a smartphone app might display a notification such as, "Your baby may be hungry. Please feed him / her." The smartphone app also visualizes the baby's health data in graphs and charts, allowing parents to intuitively understand the condition. A wearable eyeglass device works in conjunction with the smartphone app to display information on the lenses. For example, a wearable eyeglass device might display information such as, "Your baby's temperature is rising. Please take measures to lower his / her temperature." This allows the childcare support system to identify why the baby is crying and take appropriate action. For example, even if a baby won't stop crying, the AI ​​can identify the cause and suggest specific measures, allowing parents and caregivers to raise their child with peace of mind. Furthermore, wearable eyeglass devices allow parents to check on their baby's condition with both hands free, making them ideal for busy parents. Furthermore, the automatic recording function allows for efficient management of a baby's health.

[0030] Wearable devices are equipped with high-sensitivity microphones to collect a baby's crying with high accuracy, and when analyzing the audio data, they can record changes in the frequency and volume of the crying in detail, allowing them to identify crying patterns. Wearable devices, for example, are equipped with high-sensitivity microphones to collect a baby's crying with high accuracy. This allows them to filter out ambient noise and clearly capture only the crying. When analyzing the audio data, wearable devices also record changes in the frequency and volume of the crying in detail to identify crying patterns. For example, they can analyze the intensity and rhythm of the crying to determine the baby's condition. Furthermore, wearable devices transmit the crying audio data to AI in real time for instant analysis, allowing them to suggest appropriate responses the moment the baby starts crying. By collecting a baby's crying with high accuracy and analyzing the audio data, a more accurate understanding of the baby's condition can be achieved.

[0031] Wearable devices are equipped with high-precision temperature sensors to accurately measure a baby's body temperature, and can incorporate correction algorithms to minimize the effects of external environments when collecting temperature data. For example, wearable devices are equipped with high-precision temperature sensors to accurately measure a baby's body temperature. This allows them to record even the slightest fluctuations in body temperature in real time. Furthermore, wearable devices incorporate correction algorithms to minimize the effects of external environments when collecting body temperature data. For example, they correct body temperature data taking into account the ambient temperature and humidity. Furthermore, wearable devices transmit body temperature data to AI in real time and analyze the temperature fluctuation patterns. This allows them to instantly detect changes in the baby's health and suggest appropriate responses. Accurately measuring a baby's body temperature and collecting body temperature data allows for a more accurate understanding of the baby's health.

[0032] Wearable devices are equipped with an accelerometer to monitor a baby's sleep state in detail, and can simultaneously monitor the baby's heart rate and breathing patterns when collecting sleep data. Wearable devices, for example, are equipped with an accelerometer to monitor a baby's sleep state in detail. This allows them to record a baby's movements and changes in posture in real time. Furthermore, when collecting sleep data, wearable devices simultaneously monitor the baby's heart rate and breathing patterns. This allows for detailed analysis of the quality and depth of sleep. Furthermore, wearable devices transmit sleep data to AI in real time and analyze sleep patterns. This allows for accurate determination of when a baby falls asleep and how long they sleep for. By closely monitoring a baby's sleep state and collecting sleep data, it is possible to understand the quality of a baby's sleep.

[0033] Wearable devices can be equipped with a skin humidity sensor to measure the baby's skin humidity in real time, and a heart rate sensor to monitor the baby's heart rate in real time. For example, wearable devices can be equipped with a skin humidity sensor to measure the baby's skin humidity in real time. This allows for understanding the baby's sweating level and dryness. Furthermore, wearable devices can be equipped with a heart rate sensor to monitor the baby's heart rate in real time. This allows for analyzing heart rate fluctuations to understand the baby's physical condition and stress level. Furthermore, wearable devices can transmit skin humidity and heart rate data to AI for comprehensive analysis of the physical condition data. This allows for detailed understanding of changes in the baby's physical condition and suggests appropriate responses. By measuring the baby's skin humidity and heart rate, more detailed physical condition data can be collected.

[0034] Wearable devices are equipped with GPS functionality and can collect baby's location information in real time, send the location information to AI, and analyze movement patterns. Wearable devices, for example, are equipped with GPS functionality and collect baby's location information in real time. This allows for accurate tracking of the baby's location. The wearable device also sends location data to AI to analyze the baby's movement patterns. This allows for tracking the distance and speed of movement and analyzing the correlation with the baby's physical condition. The wearable device also comprehensively analyzes movement patterns and physical condition data to predict changes in the baby's physical condition. For example, it can analyze the impact of long-term travel on physical condition and suggest appropriate responses. This allows for detailed tracking of changes in the baby's physical condition by collecting the baby's location information and analyzing the correlation between movement patterns and physical condition.

[0035] AI learns from past data, analyzes individual patterns, and can propose more accurate responses based on those patterns. For example, AI learns from a baby's past data and analyzes individual patterns. For example, it identifies a baby's specific patterns based on past crying, body temperature, and sleep data. AI also develops algorithms that predict changes in a baby's physical condition and emotions based on the learned data. This allows it to propose with high accuracy what response is needed in what situation the baby is in. AI also proposes responses based on past data that meet the baby's individual needs. For example, if a baby is prone to crying at certain times of the day, it suggests special care for those times. In this way, by learning from past data and analyzing individual patterns, it can propose more accurate responses.

[0036] When analyzing health data, AI can take into account external environmental data such as season and weather, and correct the health data based on that data. For example, when analyzing a baby's health data, AI collects external environmental data such as season and weather and incorporates it into the analysis. For example, it corrects the health data by taking into account changes in temperature, humidity, and weather. AI also develops algorithms that predict changes in a baby's health based on external environmental data. This makes it possible to propose response methods that take into account the effects of season and weather. AI also sends seasonal and weather data to the AI ​​in real time, which is integrated with the health data for analysis. This makes it possible to propose appropriate response methods in response to changes in the external environment. This improves the accuracy of health data analysis by taking into account external environmental data such as season and weather.

[0037] AI can predict a baby's health condition based on data and suggest preventive care based on the predicted health condition. For example, AI develops an algorithm to predict a baby's health condition based on analyzed data. For example, it analyzes fluctuations in body temperature and heart rate to predict changes in health condition. AI can also build a system that suggests preventive care based on the predicted health condition. For example, it can suggest cooling measures before the body temperature rises. AI can also monitor a baby's health condition based on the predicted data and respond before an abnormality occurs. This makes it possible to provide preventive care to maintain the baby's health. By predicting a baby's health condition and suggesting preventive care, it is possible to maintain the baby's health.

[0038] AI stores data in the cloud and makes it accessible from multiple devices, allowing the whole family to share its status. For example, AI builds a system that stores analyzed data in the cloud and makes it accessible from multiple devices. This allows the whole family to check the baby's condition in real time. AI also develops an app that uses data stored in the cloud to share the baby's status with the whole family. For example, it provides an app that can be accessed from smartphones and tablets. AI also builds a system that securely manages data stored in the cloud and protects privacy. This allows data to be shared with peace of mind. This allows the whole family to share the baby's status by making data stored in the cloud accessible from multiple devices.

[0039] Smartphone apps have a function to notify parents of changes in their baby's physical condition or emotions in real time, and can automatically issue alerts when changes in their baby's physical condition or emotions are detected. For example, smartphone apps can add a real-time notification function to instantly notify parents of changes in their baby's physical condition or emotions. For example, a notification can be sent if their body temperature rises or the tone of their crying changes. Smartphone apps can also add a function to automatically issue alerts when changes in their baby's physical condition or emotions are detected. For example, an alert can be sent the moment the baby starts crying. Smartphone apps can also use the real-time notification function to allow parents to instantly check changes in their baby's physical condition or emotions. For example, a parent who receives a notification can open the app to check more information. This allows parents to respond immediately by being notified of changes in their baby's physical condition or emotions in real time.

[0040] Smartphone apps have a function for visualizing health data in graphs and charts, and can display health data visually. For example, smartphone apps may add a graph or chart display function to visually display a baby's health data. For example, graphs may be used to show fluctuations in body temperature and sleep patterns. Smartphone apps may also display a baby's health data in chart format to allow parents to intuitively understand the data. For example, charts may be used to show crying patterns and heart rate fluctuations. Smartphone apps may also use graphs and charts to allow parents to grasp a baby's health data at a glance. For example, a comparison may be made with past data to visually show the current condition. In this way, parents may intuitively understand the data by visualizing the baby's health data in graphs and charts.

[0041] The smartphone app has a function to automatically display childcare advice and articles according to the baby's physical condition and emotions, and can provide childcare advice and articles based on physical condition and emotional data. For example, the smartphone app can add a childcare advice display function to automatically display advice according to the baby's physical condition and emotions. For example, if the baby is crying, it can suggest ways to make the baby stop crying. The smartphone app also builds a system that automatically displays childcare advice and related articles based on the baby's physical condition and emotional data. For example, if the baby is sleepy, it can suggest ways to put the baby to sleep. The smartphone app also uses a function to automatically display childcare advice and articles to allow parents to instantly obtain the information they need. For example, it can suggest care methods according to the baby's physical condition. This allows parents to instantly obtain the information they need by automatically displaying childcare advice and articles according to the baby's physical condition and emotions.

[0042] The smartphone app has a function that allows parents to share health data with other parents and exchange information in a community, and can provide an environment where parents can support each other based on the health data. For example, the smartphone app can add a data sharing function to allow parents to share their baby's health data with other parents. For example, parents who use the same app can share data. The smartphone app can also add a community function to create a system where parents can exchange information based on their baby's health data. For example, they can share child-rearing concerns and advice. The smartphone app can also use the data sharing function to share their baby's health data with other parents and provide an environment where parents can support each other in a community. For example, parents who have the same concerns can exchange information. This can provide an environment where parents can support each other by sharing their baby's health data with other parents and exchanging information in a community.

[0043] The glasses-type wearable device has a function to display changes in physical condition and emotions in real time, and can automatically issue an alert when changes in physical condition or emotions are detected. For example, the glasses-type wearable device can add a real-time display function to instantly notify parents of changes in the baby's physical condition or emotions. For example, it can display an alert if the baby's body temperature rises or the tone of the crying changes. The glasses-type wearable device can also add a function to automatically issue an alert when changes in the baby's physical condition or emotions are detected. For example, it can display an alert the moment the baby starts crying. The glasses-type wearable device can also use the real-time display function to allow parents to instantly check changes in the baby's physical condition and emotions. For example, the parents who receive the display can check detailed information. This allows parents to respond immediately by displaying changes in the baby's physical condition and emotions in real time.

[0044] Glasses-type wearable devices have the function of displaying health data in 3D graphs, allowing for visual display of health data. Glasses-type wearable devices, for example, can add a 3D graph display function to visually display a baby's health data. For example, body temperature fluctuations and sleep patterns are shown in 3D graphs. Glasses-type wearable devices also display a baby's health data in 3D graph format, allowing parents to intuitively understand. For example, crying patterns and heart rate fluctuations are shown in 3D graphs. Glasses-type wearable devices also use 3D graphs to allow parents to grasp a baby's health data at a glance. For example, the current condition is visually shown in comparison with past data. In this way, parents can intuitively understand a baby's health data by visualizing it in a 3D graph.

[0045] The glasses-type wearable device has a function for providing voice-based parenting advice according to the baby's physical condition and emotions, and can automatically generate parenting advice based on physical condition and emotional data. For example, the glasses-type wearable device can be equipped with a voice advice function to provide voice-based advice according to the baby's physical condition and emotions. For example, if the baby is crying, it can provide voice-based suggestions on how to make the baby stop crying. Furthermore, the glasses-type wearable device can be configured to create a system in which the glasses-type device automatically generates voice advice based on the baby's physical condition and emotional data. For example, if the baby is sleepy, it can provide voice-based suggestions on how to get the baby to sleep. Furthermore, the glasses-type wearable device can use the voice advice function to enable parents to instantly obtain necessary information. For example, it can provide voice-based suggestions on care methods according to the baby's physical condition. This allows parents to respond immediately by providing voice-based parenting advice according to the baby's physical condition and emotions.

[0046] The glasses-type wearable device has a function to link health data with other devices and share health data with the entire family, allowing health data to be checked in real time. For example, the glasses-type wearable device can be equipped with a data linking function to enable sharing of baby's health data with other devices. For example, data can be shared by linking with a smartphone or tablet. Furthermore, a system can be built to link the glasses-type wearable device with other devices so that all family members can check the baby's health data in real time. For example, all family members can share the same data. Furthermore, the glasses-type wearable device can use the data linking function to share the baby's health data with the entire family, providing an environment that supports childcare. For example, all family members can understand the baby's health and work together to care for the baby. This allows the baby's health data to be linked with other devices and information to be shared with the entire family, providing an environment that supports childcare.

[0047] The automatic recording function can analyze long-term trends in health status based on data and suggest preventive care based on the trends. For example, the automatic recording function develops an algorithm that analyzes long-term trends in a baby's health status based on automatically recorded data. For example, it analyzes fluctuations in body temperature and heart rate over a long period of time to predict changes in health status. The automatic recording function also builds a system that suggests preventive care based on the results of long-term trend analysis. For example, it suggests cooling measures before the body temperature rises. The automatic recording function also monitors the baby's health status based on trend data and responds before abnormalities occur. This makes it possible to provide preventive care to maintain the baby's health. This makes it possible to analyze long-term trends in a baby's health status based on automatically recorded data and suggest preventive care, making it possible to maintain the baby's health.

[0048] The automatic recording function stores data in the cloud and makes the data stored in the cloud accessible from multiple devices, allowing the whole family to share the baby's condition. For example, the automatic recording function builds a system that stores automatically recorded data in the cloud and makes it accessible from multiple devices. This allows the whole family to check the baby's condition in real time. The automatic recording function also develops an app that allows the whole family to share the baby's condition based on the data stored in the cloud. For example, it provides an app that can be accessed from smartphones and tablets. The automatic recording function also builds a system that securely manages data stored in the cloud and protects privacy. This allows data to be shared with peace of mind. This allows the whole family to share the baby's condition by making the data stored in the cloud accessible from multiple devices.

[0049] The automatic recording function can predict a baby's health condition based on the data and suggest preventive care based on the predicted health condition. The automatic recording function, for example, develops an algorithm to predict a baby's health condition based on automatically recorded data. For example, it analyzes fluctuations in body temperature and heart rate to predict changes in health condition. The automatic recording function also builds a system to suggest preventive care based on the predicted health condition. For example, it suggests cooling measures before the body temperature rises. The automatic recording function also monitors a baby's health condition based on the predicted data and responds before an abnormality occurs. This makes it possible to provide preventive care to maintain the baby's health. This makes it possible to maintain the baby's health by predicting a baby's health condition based on automatically recorded data and suggesting preventive care.

[0050] The automatic recording function has the ability to share data with other parents and exchange information in a community, and can provide an environment where parents can support each other based on the data. For example, the automatic recording function adds a function to the app that allows automatically recorded data to be shared with other parents. For example, data can be shared between parents using the same app. The automatic recording function also adds a community function to the app, creating a system where parents can exchange information based on their baby's health data. For example, they can share child-rearing concerns and advice. The automatic recording function also uses the data sharing function to allow parents to share their baby's health data with other parents, providing an environment where parents can support each other in a community. For example, parents with the same concerns can exchange information. This allows parents to share automatically recorded data with other parents and exchange information in a community, providing an environment where parents can support each other.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The childcare support system can also be equipped with a humidity sensor that measures the humidity of the baby's skin. For example, a humidity sensor can measure the humidity of the baby's skin in real time, making it possible to understand the baby's sweating level and dryness. The humidity data can also be sent to AI to analyze the baby's physical condition and stress level. By measuring the humidity of the baby's skin, more detailed physical condition data can be collected and appropriate measures can be proposed.

[0053] The childcare support system can also be equipped with a GPS function that collects the baby's location information. For example, the GPS function can be used to collect the baby's location information in real time, allowing the baby's location to be accurately determined. The location data can also be sent to AI to analyze the baby's movement patterns. This makes it possible to understand the distance and speed of movement and analyze the correlation with the baby's physical condition. By collecting the baby's location information and analyzing the correlation between movement patterns and physical condition, changes in the baby's physical condition can be understood in detail.

[0054] The childcare support system can also be equipped with a heart rate sensor to monitor the baby's heart rate. For example, a heart rate sensor can be used to monitor the baby's heart rate in real time and analyze heart rate fluctuations. The heart rate data can also be sent to AI to understand the baby's physical condition and stress level. By monitoring the baby's heart rate, more detailed physical condition data can be collected and appropriate measures can be proposed.

[0055] The childcare support system can also be equipped with an accelerometer to monitor the baby's sleep state in detail. For example, an accelerometer can be used to record the baby's movements and changes in posture in real time, allowing the quality and depth of sleep to be analyzed. The sleep data can also be sent to AI, allowing for accurate information on when the baby fell asleep and how long they slept. This allows for detailed monitoring of the baby's sleep state and the collection of sleep data to help understand the quality of the baby's sleep.

[0056] The childcare support system can also be equipped with a high-precision temperature sensor to accurately measure the baby's temperature. For example, a temperature sensor can be used to measure the baby's temperature in real time and record even the slightest fluctuations. The temperature data can also be sent to AI to determine whether the baby's temperature is normal. This allows for accurate measurement of the baby's temperature and collection of temperature data, allowing for a more accurate understanding of the baby's health condition.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The wearable device is worn around the baby's ankle and collects the baby's crying, body temperature, and sleep state. For example, the wearable device collects crying sounds using a highly sensitive microphone and stores them as audio data. It also measures body temperature using a temperature sensor and stores it as body temperature data. It also monitors the baby's sleep state using a sleep sensor and stores it as sleep data. Step 2: The AI ​​analyzes the data collected by the wearable device to identify the baby's emotions and physical condition and suggest appropriate responses. For example, it analyzes crying patterns to determine whether the baby is hungry, has a wet diaper, or is sleepy. It also analyzes temperature data to determine whether the baby's temperature is normal. It also analyzes sleep data to evaluate the baby's sleep quality. Step 3: The smartphone app notifies the parent of the AI-suggested course of action. For example, it displays a notification such as, "Your baby may be hungry. Please give him / her milk." It also visualizes the baby's health data in graphs and charts, allowing parents to intuitively understand it. Step 4: The glasses-type wearable device works in conjunction with a smartphone app to display information on the lenses. For example, it displays information such as, "Your baby's temperature is rising. Please take measures to lower the temperature."

[0059] (Example 2) A childcare support system according to an embodiment of the present invention identifies why a baby is crying and supports parents and caregivers in their childcare. This system uses a wearable device worn around the baby's ankle to monitor the baby's crying, body temperature, sleep status, and other information. AI installed on the device analyzes this data. The AI ​​analyzes the baby's emotions and physical condition and shares recommended responses with a smartphone app. In addition to the app, the system can also be connected to a wearable eyeglasses-type device, which displays information on the lenses, freeing up both hands for busy parenting. Furthermore, to monitor the baby's health, parents are required to record when the baby is sleeping and when the diaper was changed. The information shared with the app is automatically recorded as a history, providing assistance to busy parents. This allows the childcare support system to identify why the baby is crying and provide appropriate responses. For example, even if a baby won't stop crying, the AI ​​can identify the cause and suggest specific responses, allowing parents and caregivers to feel at ease. Furthermore, using the wearable eyeglasses-type device allows parents to check the baby's condition while keeping their hands free, making it ideal for busy parenting. In addition, the automatic recording function allows you to efficiently manage your baby's health.

[0060] A childcare support system according to an embodiment includes a wearable device, AI, a smartphone app, and a glasses-type wearable device. The wearable device is worn around a baby's ankle and collects the baby's crying, body temperature, and sleep state. For example, the wearable device collects the baby's crying using a high-sensitivity microphone and stores the collected data as audio data. The wearable device also measures the baby's body temperature using a temperature sensor and stores the data as body temperature data. The wearable device also monitors the baby's sleep state using a sleep sensor and stores the data as sleep data. The AI ​​analyzes the data collected by the wearable device to identify the baby's emotions and physical condition and proposes appropriate responses. For example, the AI ​​analyzes the crying pattern to determine whether the baby is hungry, has a wet diaper, or is sleepy. The AI ​​also analyzes the body temperature data to determine whether the baby's body temperature is normal. The AI ​​also analyzes the sleep data to evaluate the baby's sleep quality. The smartphone app notifies the parent of the appropriate responses suggested by the AI. For example, a smartphone app might display a notification such as, "Your baby may be hungry. Please feed him / her." The smartphone app also visualizes the baby's health data in graphs and charts, allowing parents to intuitively understand the condition. A wearable eyeglass device works in conjunction with the smartphone app to display information on the lenses. For example, a wearable eyeglass device might display information such as, "Your baby's temperature is rising. Please take measures to lower his / her temperature." This allows the childcare support system to identify why the baby is crying and take appropriate action. For example, even if a baby won't stop crying, the AI ​​can identify the cause and suggest specific measures, allowing parents and caregivers to raise their child with peace of mind. Furthermore, wearable eyeglass devices allow parents to check on their baby's condition with both hands free, making them ideal for busy parents. Furthermore, the automatic recording function allows for efficient management of a baby's health.

[0061] Wearable devices are equipped with high-sensitivity microphones to collect a baby's crying with high accuracy, and when analyzing the audio data, they can record changes in the frequency and volume of the crying in detail, allowing them to identify crying patterns. Wearable devices, for example, are equipped with high-sensitivity microphones to collect a baby's crying with high accuracy. This allows them to filter out ambient noise and clearly capture only the crying. When analyzing the audio data, wearable devices also record changes in the frequency and volume of the crying in detail to identify crying patterns. For example, they can analyze the intensity and rhythm of the crying to determine the baby's condition. Furthermore, wearable devices transmit the crying audio data to AI in real time for instant analysis, allowing them to suggest appropriate responses the moment the baby starts crying. By collecting a baby's crying with high accuracy and analyzing the audio data, a more accurate understanding of the baby's condition can be achieved.

[0062] Wearable devices are equipped with high-precision temperature sensors to accurately measure a baby's body temperature, and can incorporate correction algorithms to minimize the effects of external environments when collecting temperature data. For example, wearable devices are equipped with high-precision temperature sensors to accurately measure a baby's body temperature. This allows them to record even the slightest fluctuations in body temperature in real time. Furthermore, wearable devices incorporate correction algorithms to minimize the effects of external environments when collecting body temperature data. For example, they correct body temperature data taking into account the ambient temperature and humidity. Furthermore, wearable devices transmit body temperature data to AI in real time and analyze the temperature fluctuation patterns. This allows them to instantly detect changes in the baby's health and suggest appropriate responses. Accurately measuring a baby's body temperature and collecting body temperature data allows for a more accurate understanding of the baby's health.

[0063] Wearable devices are equipped with an accelerometer to monitor a baby's sleep state in detail, and can simultaneously monitor the baby's heart rate and breathing patterns when collecting sleep data. Wearable devices, for example, are equipped with an accelerometer to monitor a baby's sleep state in detail. This allows them to record a baby's movements and changes in posture in real time. Furthermore, when collecting sleep data, wearable devices simultaneously monitor the baby's heart rate and breathing patterns. This allows for detailed analysis of the quality and depth of sleep. Furthermore, wearable devices transmit sleep data to AI in real time and analyze sleep patterns. This allows for accurate determination of when a baby falls asleep and how long they sleep for. By closely monitoring a baby's sleep state and collecting sleep data, it is possible to understand the quality of a baby's sleep.

[0064] Wearable devices can be equipped with a skin humidity sensor to measure the baby's skin humidity in real time, and a heart rate sensor to monitor the baby's heart rate in real time. For example, wearable devices can be equipped with a skin humidity sensor to measure the baby's skin humidity in real time. This allows for understanding the baby's sweating level and dryness. Furthermore, wearable devices can be equipped with a heart rate sensor to monitor the baby's heart rate in real time. This allows for analyzing heart rate fluctuations to understand the baby's physical condition and stress level. Furthermore, wearable devices can transmit skin humidity and heart rate data to AI for comprehensive analysis of the physical condition data. This allows for detailed understanding of changes in the baby's physical condition and suggests appropriate responses. By measuring the baby's skin humidity and heart rate, more detailed physical condition data can be collected.

[0065] Wearable devices are equipped with GPS functionality and can collect baby's location information in real time, send the location information to AI, and analyze movement patterns. Wearable devices, for example, are equipped with GPS functionality and collect baby's location information in real time. This allows for accurate tracking of the baby's location. The wearable device also sends location data to AI to analyze the baby's movement patterns. This allows for tracking the distance and speed of movement and analyzing the correlation with the baby's physical condition. The wearable device also comprehensively analyzes movement patterns and physical condition data to predict changes in the baby's physical condition. For example, it can analyze the impact of long-term travel on physical condition and suggest appropriate responses. This allows for detailed tracking of changes in the baby's physical condition by collecting the baby's location information and analyzing the correlation between movement patterns and physical condition.

[0066] Wearable devices can use their emotion estimation function to analyze the tone and rhythm of a baby's cry to infer its emotion, send the emotion data to an AI, and analyze the emotion in real time using an emotion estimation algorithm. For example, wearable devices can use their emotion estimation function to analyze the tone and rhythm of a baby's cry to infer its emotion. For example, they can determine whether a baby is angry or sad based on changes in the pitch and rhythm of the cry. The wearable device also sends the crying audio data to an AI and analyzes the emotion in real time using an emotion estimation algorithm. This allows for immediate understanding of changes in a baby's emotions. The wearable device also collects emotion data and analyzes trends in the baby's emotions. This allows for a detailed understanding of the emotions a baby feels in different situations and the appropriate responses to be suggested. By estimating emotions from the tone and rhythm of a baby's cry and collecting emotion data, it is possible to understand changes in a baby's emotions in detail.

[0067] AI learns from past data, analyzes individual patterns, and can propose more accurate responses based on those patterns. For example, AI learns from a baby's past data and analyzes individual patterns. For example, it identifies a baby's specific patterns based on past crying, body temperature, and sleep data. AI also develops algorithms that predict changes in a baby's physical condition and emotions based on the learned data. This allows it to propose with high accuracy what response is needed in what situation the baby is in. AI also proposes responses based on past data that meet the baby's individual needs. For example, if a baby is prone to crying at certain times of the day, it suggests special care for those times. In this way, by learning from past data and analyzing individual patterns, it can propose more accurate responses.

[0068] When analyzing health data, AI can take into account external environmental data such as season and weather, and correct the health data based on that data. For example, when analyzing a baby's health data, AI collects external environmental data such as season and weather and incorporates it into the analysis. For example, it corrects the health data by taking into account changes in temperature, humidity, and weather. AI also develops algorithms that predict changes in a baby's health based on external environmental data. This makes it possible to propose response methods that take into account the effects of season and weather. AI also sends seasonal and weather data to the AI ​​in real time, which is integrated with the health data for analysis. This makes it possible to propose appropriate response methods in response to changes in the external environment. This improves the accuracy of health data analysis by taking into account external environmental data such as season and weather.

[0069] AI can use its emotion estimation function to analyze emotional data in real time and suggest ways to respond according to the emotion based on the emotional data. For example, AI can use its emotion estimation function to analyze changes in a baby's emotions in real time. For example, it can identify a baby's emotions based on changes in the tone and rhythm of its crying. AI can also develop an algorithm that suggests ways to respond according to the baby's emotions based on the emotional data. For example, if a baby is angry, it can suggest ways to calm the baby. AI can also build a system that sends emotional data to AI in real time and instantly suggests ways to respond. This makes it possible to respond quickly to changes in a baby's emotions. This makes it possible to use the emotion estimation function to analyze changes in a baby's emotions in real time and suggest ways to respond according to the emotions.

[0070] AI can predict a baby's health condition based on data and suggest preventive care based on the predicted health condition. For example, AI develops an algorithm to predict a baby's health condition based on analyzed data. For example, it analyzes fluctuations in body temperature and heart rate to predict changes in health condition. AI can also build a system that suggests preventive care based on the predicted health condition. For example, it can suggest cooling measures before the body temperature rises. AI can also monitor a baby's health condition based on the predicted data and respond before an abnormality occurs. This makes it possible to provide preventive care to maintain the baby's health. By predicting a baby's health condition and suggesting preventive care, it is possible to maintain the baby's health.

[0071] AI stores data in the cloud and makes it accessible from multiple devices, allowing the whole family to share its status. For example, AI builds a system that stores analyzed data in the cloud and makes it accessible from multiple devices. This allows the whole family to check the baby's condition in real time. AI also develops an app that uses data stored in the cloud to share the baby's status with the whole family. For example, it provides an app that can be accessed from smartphones and tablets. AI also builds a system that securely manages data stored in the cloud and protects privacy. This allows data to be shared with peace of mind. This allows the whole family to share the baby's status by making data stored in the cloud accessible from multiple devices.

[0072] AI can use its emotion estimation function to automatically play music and videos based on emotional data, learn a baby's preferences based on music and video playback history, and provide optimal content. For example, AI can use its emotion estimation function to build a system that automatically plays music that corresponds to a baby's emotions. For example, if a baby is crying, it can play calming music. AI can also develop a system that automatically plays videos that correspond to a baby's emotions based on emotional data. For example, if a baby is excited, it can play videos that help them relax. AI can also build a system that learns a baby's preferences based on music and video playback history and provides optimal content. This makes it possible to provide an environment that is easy for babies to relax. As a result, the emotion estimation function can be used to automatically play music and videos that correspond to a baby's emotions, helping them relax.

[0073] Smartphone apps have a function to notify parents of changes in their baby's physical condition or emotions in real time, and can automatically issue alerts when changes in their baby's physical condition or emotions are detected. For example, smartphone apps can add a real-time notification function to instantly notify parents of changes in their baby's physical condition or emotions. For example, a notification can be sent if their body temperature rises or the tone of their crying changes. Smartphone apps can also add a function to automatically issue alerts when changes in their baby's physical condition or emotions are detected. For example, an alert can be sent the moment the baby starts crying. Smartphone apps can also use the real-time notification function to allow parents to instantly check changes in their baby's physical condition or emotions. For example, a parent who receives a notification can open the app to check more information. This allows parents to respond immediately by being notified of changes in their baby's physical condition or emotions in real time.

[0074] Smartphone apps have a function for visualizing health data in graphs and charts, and can display health data visually. For example, smartphone apps may add a graph or chart display function to visually display a baby's health data. For example, graphs may be used to show fluctuations in body temperature and sleep patterns. Smartphone apps may also display a baby's health data in chart format to allow parents to intuitively understand the data. For example, charts may be used to show crying patterns and heart rate fluctuations. Smartphone apps may also use graphs and charts to allow parents to grasp a baby's health data at a glance. For example, a comparison may be made with past data to visually show the current condition. In this way, parents may intuitively understand the data by visualizing the baby's health data in graphs and charts.

[0075] The smartphone app uses the emotion estimation function to provide advice based on emotion data, and can generate advice in real time based on emotion data. For example, the smartphone app uses the emotion estimation function to provide advice on the smartphone app according to the baby's emotions. For example, if the baby is angry, it suggests ways to calm the baby. The smartphone app also builds a system that automatically generates advice based on the baby's emotion data. For example, if the baby is sad, it suggests ways to comfort the baby. The smartphone app also uses the emotion estimation function to display specific advice on the app according to the baby's emotions. For example, if the baby is excited, it suggests ways to relax the baby. In this way, the emotion estimation function can be used to provide advice in real time according to the baby's emotions.

[0076] The smartphone app has a function to automatically display childcare advice and articles according to the baby's physical condition and emotions, and can provide childcare advice and articles based on physical condition and emotional data. For example, the smartphone app can add a childcare advice display function to automatically display advice according to the baby's physical condition and emotions. For example, if the baby is crying, it can suggest ways to make the baby stop crying. The smartphone app also builds a system that automatically displays childcare advice and related articles based on the baby's physical condition and emotional data. For example, if the baby is sleepy, it can suggest ways to put the baby to sleep. The smartphone app also uses a function to automatically display childcare advice and articles to allow parents to instantly obtain the information they need. For example, it can suggest care methods according to the baby's physical condition. This allows parents to instantly obtain the information they need by automatically displaying childcare advice and articles according to the baby's physical condition and emotions.

[0077] The smartphone app has a function that allows parents to share health data with other parents and exchange information in a community, and can provide an environment where parents can support each other based on the health data. For example, the smartphone app can add a data sharing function to allow parents to share their baby's health data with other parents. For example, parents who use the same app can share data. The smartphone app can also add a community function to create a system where parents can exchange information based on their baby's health data. For example, they can share child-rearing concerns and advice. The smartphone app can also use the data sharing function to share their baby's health data with other parents and provide an environment where parents can support each other in a community. For example, parents who have the same concerns can exchange information. This can provide an environment where parents can support each other by sharing their baby's health data with other parents and exchanging information in a community.

[0078] The smartphone app can use the emotion estimation function to suggest childcare games and activities based on emotion data, and generate childcare games and activities based on emotion data. For example, the smartphone app uses the emotion estimation function to suggest childcare games and activities on the smartphone app according to the baby's emotions. For example, if the baby is excited, it suggests a game to help the baby relax. The smartphone app also builds a system that automatically generates childcare games and activities based on the baby's emotion data. For example, if the baby is sad, it suggests an activity to comfort the baby. The smartphone app also uses the emotion estimation function to display specific childcare games and activities on the app according to the baby's emotions. For example, if the baby is angry, it suggests an activity to calm the baby. In this way, by using the emotion estimation function to suggest childcare games and activities according to the baby's emotions, an appropriate response can be made according to the baby's emotions.

[0079] The glasses-type wearable device has a function to display changes in physical condition and emotions in real time, and can automatically issue an alert when changes in physical condition or emotions are detected. For example, the glasses-type wearable device can add a real-time display function to instantly notify parents of changes in the baby's physical condition or emotions. For example, it can display an alert if the baby's body temperature rises or the tone of the crying changes. The glasses-type wearable device can also add a function to automatically issue an alert when changes in the baby's physical condition or emotions are detected. For example, it can display an alert the moment the baby starts crying. The glasses-type wearable device can also use the real-time display function to allow parents to instantly check changes in the baby's physical condition and emotions. For example, the parents who receive the display can check detailed information. This allows parents to respond immediately by displaying changes in the baby's physical condition and emotions in real time.

[0080] Glasses-type wearable devices have the function of displaying health data in 3D graphs, allowing for visual display of health data. Glasses-type wearable devices, for example, can add a 3D graph display function to visually display a baby's health data. For example, body temperature fluctuations and sleep patterns are shown in 3D graphs. Glasses-type wearable devices also display a baby's health data in 3D graph format, allowing parents to intuitively understand. For example, crying patterns and heart rate fluctuations are shown in 3D graphs. Glasses-type wearable devices also use 3D graphs to allow parents to grasp a baby's health data at a glance. For example, the current condition is visually shown in comparison with past data. In this way, parents can intuitively understand a baby's health data by visualizing it in a 3D graph.

[0081] The eyeglass-type wearable device can use an emotion estimation function to provide advice based on emotion data and generate advice in real time based on emotion data. The eyeglass-type wearable device, for example, uses the emotion estimation function to display advice on the lenses of the glasses according to the baby's emotion. For example, if the baby is angry, it suggests ways to calm the baby. The eyeglass-type wearable device also builds a system in which the eyeglass-type device automatically generates advice based on the baby's emotion data. For example, if the baby is sad, it suggests ways to comfort the baby. The eyeglass-type wearable device also uses the emotion estimation function to display specific advice on the lenses of the glasses according to the baby's emotion. For example, if the baby is excited, it suggests ways to relax the baby. In this way, the emotion estimation function can be used to provide advice in real time according to the baby's emotion.

[0082] The glasses-type wearable device has a function for providing voice-based parenting advice according to the baby's physical condition and emotions, and can automatically generate parenting advice based on physical condition and emotional data. For example, the glasses-type wearable device can be equipped with a voice advice function to provide voice-based advice according to the baby's physical condition and emotions. For example, if the baby is crying, it can provide voice-based suggestions on how to make the baby stop crying. Furthermore, the glasses-type wearable device can be configured to create a system in which the glasses-type device automatically generates voice advice based on the baby's physical condition and emotional data. For example, if the baby is sleepy, it can provide voice-based suggestions on how to get the baby to sleep. Furthermore, the glasses-type wearable device can use the voice advice function to enable parents to instantly obtain necessary information. For example, it can provide voice-based suggestions on care methods according to the baby's physical condition. This allows parents to respond immediately by providing voice-based parenting advice according to the baby's physical condition and emotions.

[0083] The glasses-type wearable device has a function to link health data with other devices and share health data with the entire family, allowing health data to be checked in real time. For example, the glasses-type wearable device can be equipped with a data linking function to enable sharing of baby's health data with other devices. For example, data can be shared by linking with a smartphone or tablet. Furthermore, a system can be built to link the glasses-type wearable device with other devices so that all family members can check the baby's health data in real time. For example, all family members can share the same data. Furthermore, the glasses-type wearable device can use the data linking function to share the baby's health data with the entire family, providing an environment that supports childcare. For example, all family members can understand the baby's health and work together to care for the baby. This allows the baby's health data to be linked with other devices and information to be shared with the entire family, providing an environment that supports childcare.

[0084] The eyeglass-type wearable device can use an emotion estimation function to automatically generate relaxing music and videos based on emotion data and display the relaxing music and videos on the eyeglass lenses. For example, the eyeglass-type wearable device uses the emotion estimation function to build a system that displays relaxing music on the eyeglass lenses according to a baby's emotion. For example, if a baby is crying, calming music is displayed. Furthermore, a system is developed in which the eyeglass-type wearable device automatically generates relaxing videos based on the baby's emotion data. For example, if a baby is excited, a video for relaxing is displayed. Furthermore, the eyeglass-type wearable device uses the emotion estimation function to display specific relaxing music and videos on the eyeglass lenses according to the baby's emotion. For example, if a baby is angry, a video for calming is displayed. In this way, the emotion estimation function can be used to display relaxing music and videos on the eyeglass lenses according to the baby's emotion, helping the baby relax.

[0085] The automatic recording function can analyze long-term trends in health status based on data and suggest preventive care based on the trends. For example, the automatic recording function develops an algorithm that analyzes long-term trends in a baby's health status based on automatically recorded data. For example, it analyzes fluctuations in body temperature and heart rate over a long period of time to predict changes in health status. The automatic recording function also builds a system that suggests preventive care based on the results of long-term trend analysis. For example, it suggests cooling measures before the body temperature rises. The automatic recording function also monitors the baby's health status based on trend data and responds before abnormalities occur. This makes it possible to provide preventive care to maintain the baby's health. This makes it possible to analyze long-term trends in a baby's health status based on automatically recorded data and suggest preventive care, making it possible to maintain the baby's health.

[0086] The automatic recording function stores data in the cloud and makes the data stored in the cloud accessible from multiple devices, allowing the whole family to share the baby's condition. For example, the automatic recording function builds a system that stores automatically recorded data in the cloud and makes it accessible from multiple devices. This allows the whole family to check the baby's condition in real time. The automatic recording function also develops an app that allows the whole family to share the baby's condition based on the data stored in the cloud. For example, it provides an app that can be accessed from smartphones and tablets. The automatic recording function also builds a system that securely manages data stored in the cloud and protects privacy. This allows data to be shared with peace of mind. This allows the whole family to share the baby's condition by making the data stored in the cloud accessible from multiple devices.

[0087] The automatic recording function uses the emotion estimation function to record emotional data and analyze emotional trends based on the emotional data. The automatic recording function, for example, uses the emotion estimation function to build a system that records a baby's emotional changes in real time. For example, emotions are recorded based on changes in the tone and rhythm of the baby's crying. The automatic recording function also develops an algorithm that analyzes a baby's emotional trends based on the recorded emotional data. This makes it possible to understand in detail what emotions a baby feels in what situations. The automatic recording function also builds a system that predicts a baby's emotional changes based on the emotional trend data and suggests appropriate ways to respond. For example, for a baby who tends to cry at certain times of the day, it suggests special care for those times. In this way, by using the emotion estimation function to record a baby's emotional changes and analyze emotional trends, it is possible to understand a baby's emotional changes in detail.

[0088] The automatic recording function can predict a baby's health condition based on the data and suggest preventive care based on the predicted health condition. The automatic recording function, for example, develops an algorithm to predict a baby's health condition based on automatically recorded data. For example, it analyzes fluctuations in body temperature and heart rate to predict changes in health condition. The automatic recording function also builds a system to suggest preventive care based on the predicted health condition. For example, it suggests cooling measures before the body temperature rises. The automatic recording function also monitors a baby's health condition based on the predicted data and responds before an abnormality occurs. This makes it possible to provide preventive care to maintain the baby's health. This makes it possible to maintain the baby's health by predicting a baby's health condition based on automatically recorded data and suggesting preventive care.

[0089] The automatic recording function has the ability to share data with other parents and exchange information in a community, and can provide an environment where parents can support each other based on the data. For example, the automatic recording function adds a function to the app that allows automatically recorded data to be shared with other parents. For example, data can be shared between parents using the same app. The automatic recording function also adds a community function to the app, creating a system where parents can exchange information based on their baby's health data. For example, they can share child-rearing concerns and advice. The automatic recording function also uses the data sharing function to allow parents to share their baby's health data with other parents, providing an environment where parents can support each other in a community. For example, parents with the same concerns can exchange information. This allows parents to share automatically recorded data with other parents and exchange information in a community, providing an environment where parents can support each other.

[0090] The automatic recording function uses the emotion estimation function to automatically display childcare advice and articles based on emotion data, and can provide childcare advice and articles based on emotion data. For example, the automatic recording function uses the emotion estimation function to automatically display childcare advice and articles on a smartphone app according to the baby's emotions. For example, if the baby is crying, it suggests ways to make the baby stop crying. The automatic recording function also builds a system in which the app automatically displays childcare advice and related articles based on the baby's emotion data. For example, if the baby is sleepy, it suggests ways to put the baby to sleep. The automatic recording function also uses a function to automatically display childcare advice and articles so that parents can instantly obtain the information they need. For example, it suggests care methods according to the baby's physical condition. In this way, the emotion estimation function is used to automatically display childcare advice and articles according to the baby's emotions, allowing parents to instantly obtain the information they need.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The childcare support system can also be equipped with a humidity sensor that measures the humidity of the baby's skin. For example, a humidity sensor can measure the humidity of the baby's skin in real time, making it possible to understand the baby's sweating level and dryness. The humidity data can also be sent to AI to analyze the baby's physical condition and stress level. By measuring the humidity of the baby's skin, more detailed physical condition data can be collected and appropriate measures can be proposed.

[0093] The childcare support system can also be equipped with a GPS function that collects the baby's location information. For example, the GPS function can be used to collect the baby's location information in real time, allowing the baby's location to be accurately determined. The location data can also be sent to AI to analyze the baby's movement patterns. This makes it possible to understand the distance and speed of movement and analyze the correlation with the baby's physical condition. By collecting the baby's location information and analyzing the correlation between movement patterns and physical condition, changes in the baby's physical condition can be understood in detail.

[0094] The childcare support system can also be equipped with a heart rate sensor to monitor the baby's heart rate. For example, a heart rate sensor can be used to monitor the baby's heart rate in real time and analyze heart rate fluctuations. The heart rate data can also be sent to AI to understand the baby's physical condition and stress level. By monitoring the baby's heart rate, more detailed physical condition data can be collected and appropriate measures can be proposed.

[0095] The childcare support system can also be equipped with an accelerometer to monitor the baby's sleep state in detail. For example, an accelerometer can be used to record the baby's movements and changes in posture in real time, allowing the quality and depth of sleep to be analyzed. The sleep data can also be sent to AI, allowing for accurate information on when the baby fell asleep and how long they slept. This allows for detailed monitoring of the baby's sleep state and the collection of sleep data to help understand the quality of the baby's sleep.

[0096] The childcare support system can also be equipped with a high-precision temperature sensor to accurately measure the baby's temperature. For example, a temperature sensor can be used to measure the baby's temperature in real time and record even the slightest fluctuations. The temperature data can also be sent to AI to determine whether the baby's temperature is normal. This allows for accurate measurement of the baby's temperature and collection of temperature data, allowing for a more accurate understanding of the baby's health condition.

[0097] The childcare support system can use its emotion estimation function to analyze the tone and rhythm of a baby's cry to infer its emotions. For example, it can determine whether a baby is angry or sad based on changes in the pitch and rhythm of the cry. It can also send the crying audio data to AI and analyze the emotion in real time using an emotion estimation algorithm. This allows it to instantly grasp changes in a baby's emotions and propose appropriate responses. By estimating emotions from the tone and rhythm of a baby's cry and collecting emotion data, it is possible to grasp changes in a baby's emotions in detail.

[0098] The childcare support system can use the emotion estimation function to suggest ways to respond based on the baby's emotional data. For example, if the baby is angry, it will suggest ways to calm the baby. If the baby is sad, it will suggest ways to comfort the baby. Furthermore, the emotional data can be sent to AI, which can then use the emotion estimation algorithm to analyze the emotions in real time. This allows the system to respond quickly to changes in the baby's emotions. This allows the emotion estimation function to analyze changes in the baby's emotions in real time and suggest ways to respond based on the emotions.

[0099] The childcare support system can use the emotion estimation function to automatically play music and videos based on the baby's emotional data. For example, if the baby is crying, it can play calming music. If the baby is excited, it can play videos to help the baby relax. Furthermore, it can learn the baby's preferences based on the music and video playback history and provide the most suitable content. This makes it possible to provide an environment where the baby can easily relax. The emotion estimation function can then automatically play music and videos that correspond to the baby's emotions to help them relax.

[0100] The childcare support system can use the emotion estimation function to suggest childcare games and activities based on the baby's emotional data. For example, if the baby is excited, it can suggest a game to help the baby relax. If the baby is sad, it can suggest an activity to comfort the baby. Furthermore, a system can be constructed in which the app automatically generates childcare games and activities based on the emotional data. This allows for an appropriate response according to the baby's emotions. By using the emotion estimation function to suggest childcare games and activities according to the baby's emotions, it is possible to respond appropriately according to the baby's emotions.

[0101] The childcare support system can use the emotion estimation function to provide advice based on the baby's emotional data. For example, if the baby is angry, it will suggest ways to calm the baby. Also, if the baby is sad, it will suggest ways to comfort the baby. Furthermore, a system can be built in which the app automatically generates advice based on the emotional data. This allows for quick response to changes in the baby's emotions. This allows the emotion estimation function to provide advice in real time according to the baby's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The wearable device is worn around the baby's ankle and collects the baby's crying, body temperature, and sleep state. For example, the wearable device collects crying sounds using a highly sensitive microphone and stores them as audio data. It also measures body temperature using a temperature sensor and stores it as body temperature data. It also monitors the baby's sleep state using a sleep sensor and stores it as sleep data. Step 2: The AI ​​analyzes the data collected by the wearable device to identify the baby's emotions and physical condition and suggest appropriate responses. For example, it analyzes crying patterns to determine whether the baby is hungry, has a wet diaper, or is sleepy. It also analyzes temperature data to determine whether the baby's temperature is normal. It also analyzes sleep data to evaluate the baby's sleep quality. Step 3: The smartphone app notifies the parent of the AI-suggested course of action. For example, it displays a notification such as, "Your baby may be hungry. Please give him / her milk." It also visualizes the baby's health data in graphs and charts, allowing parents to intuitively understand it. Step 4: The glasses-type wearable device works in conjunction with a smartphone app to display information on the lenses. For example, it displays information such as, "Your baby's temperature is rising. Please take measures to lower the temperature."

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A wearable device wrapped around the baby's ankle, AI installed in the wearable device; A smartphone app that shares the information analyzed by the AI, a glasses-type wearable terminal that cooperates with the smartphone app; The wearable terminal includes: Collects baby's crying, body temperature, and sleep status, The AI ​​is Analyzing the data collected by the wearable device, identifying the baby's emotions and physical condition, and suggesting ways to respond; The smartphone app is Notify the parent of the response suggested by the AI, The glasses-type wearable terminal includes: Linking with the smartphone app, it displays information on the lens. A system characterized by:

2. The wearable terminal includes: Equipped with a highly sensitive microphone to accurately capture the baby's crying voice, When analyzing the audio data, changes in the frequency and volume of the crying are recorded in detail to identify crying patterns.

2. The system of claim 1.

3. The wearable terminal includes: Equipped with a high-precision body temperature sensor to accurately measure the baby's body temperature, Implement a compensation algorithm to minimize the influence of external environments when collecting body temperature data.

2. The system of claim 1.

4. The wearable terminal includes: Equipped with an acceleration sensor to monitor the baby's sleep state in detail, While collecting sleep data, the device also monitors the baby's heart rate and breathing patterns.

2. The system of claim 1.

5. The wearable terminal includes: Add a skin humidity sensor to measure the baby's skin humidity in real time, Equipped with a heart rate sensor to monitor your baby's heart rate in real time 2. The system of claim 1.

6. The wearable terminal includes: Equipped with a GPS function, it collects baby's location information in real time, The location information is sent to AI to analyze movement patterns.

2. The system of claim 1.

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