System

Abstract

An object of the system according to the embodiment is to centrally manage health information of children who visit a nursery school or a Kodomoen and to detect signs of epidemics at an early stage.SOLUTION: A system includes an information aggregation unit, an analysis unit, a notification unit, and a proposal unit. The information aggregation unit aggregates health information input by a parent. The analysis unit analyzes the health information aggregated by the information aggregation unit. The notification unit notifies a sign of the epidemic on the basis of a result analyzed by the analysis unit. The proposal unit proposes a method and an ingredient for immune enhancement based on the signs of the epidemic.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 faced the challenge of centrally managing health information for children attending daycare centers and kindergartens, and making it difficult to detect early signs of an epidemic.

[0005] The system according to the embodiment aims to centrally manage health information of children attending nursery schools and kindergartens, and to detect signs of epidemics early. [Means for solving the problem]

[0006] The system according to the embodiment includes an information aggregation unit, an analysis unit, a notification unit, and a suggestion unit. The information aggregation unit aggregates health information entered by the parent. The analysis unit analyzes the health information aggregated by the information aggregation unit. The notification unit notifies the parent of signs of an epidemic based on the results of the analysis by the analysis unit. The suggestion unit suggests immunity-boosting methods and ingredients based on the signs of an epidemic. [Effects of the Invention]

[0007] The system according to the embodiment centrally manages health information of children attending nursery schools and kindergartens, and can detect signs of epidemics early. [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 health management system according to an embodiment of the present invention is a system that improves the efficiency of health management for children attending nursery schools and kindergartens, and supports the early detection and prevention of epidemics. As a result, the health management system can centrally collect children's health information, detect signs of epidemics early, and provide preventive measures.

[0029] A health management system according to an embodiment includes an information aggregation unit, an analysis unit, a notification unit, and a suggestion unit. The information aggregation unit aggregates health information entered by parents. For example, it centrally aggregates health information entered daily by parents, such as a child's body temperature, appetite, sleep time, and symptoms. The information aggregation unit can also collect health information through a smartphone app or web portal. The analysis unit analyzes the health information aggregated by the information aggregation unit. For example, a generation AI analyzes specific symptoms and patterns based on the aggregated health information to detect signs of an epidemic. The analysis unit can also analyze the health information using natural language processing technology. The notification unit notifies signs of an epidemic based on the results of the analysis by the analysis unit. For example, if there is a sudden increase in fever or cough symptoms in a specific area or nursery, the generation AI notifies parents or nursery schools of the possibility of an epidemic. The notification unit can also send notifications via an app or email. The suggestion unit suggests immune-boosting methods and ingredients based on signs of an epidemic. For example, the generation AI suggests foods rich in vitamin C and lifestyle habits that boost immunity. The suggestion unit can also suggest specific ingredients and methods to parents. This allows the health management system according to the embodiment to improve the efficiency of health management for children attending nursery schools and kindergartens, and to support early detection and prevention of epidemics. For example, early detection of epidemic signs enables a prompt response and prevents the spread of infection. Furthermore, providing information on methods and ingredients that are effective in strengthening the immune system can help maintain children's health and increase their resistance to disease.

[0030] The information aggregation unit can automatically collect the child's activity level and heart rate from the smartwatch in addition to the health information entered by the parent. For example, the information aggregation unit builds a system that automatically collects the child's activity level and heart rate from the smartwatch in addition to the health information entered by the parent. For example, the smartwatch records the child's daily steps and heart rate and sends the data to the generation AI. The information aggregation unit also analyzes the data collected from the smartwatch using the generation AI to comprehensively evaluate the child's health. For example, it issues an alert if the child's activity level is low or their heart rate is abnormally high. The information aggregation unit also integrates the health information entered by the parent with the data from the smartwatch, and the generation AI analyzes health trends. For example, if a decrease in activity level and an increase in body temperature are observed simultaneously, early signs of illness can be detected. This allows for a comprehensive evaluation of the child's health.

[0031] The information aggregation unit can input health information using voice recognition technology. For example, the information aggregation unit could develop a system that allows parents to input their children's health information by voice. For example, using a smartphone microphone, what the parent says is converted into text using voice recognition technology and sent to the generation AI. The information aggregation unit also uses voice recognition technology to automatically classify the health information entered by the parent and store it in a database. For example, it could analyze a voice input such as "Today's temperature is 37.5 degrees" and record it as temperature data. The information aggregation unit also uses voice recognition technology to analyze the health information entered by the parent in real time, and the generation AI provides instant feedback. For example, it could give advice such as "Your temperature is high, so I recommend you consult a doctor." This reduces the burden on parents.

[0032] The information aggregating unit can gamify the input of health information, allowing children to enjoy the process. For example, the information aggregating unit develops an app that gamifies the input of health information, allowing children to enjoy the process. For example, a game format featuring characters is used to input information such as body temperature and appetite. The information aggregating unit also introduces a system in which points can be earned by inputting health information in the game, and the points can be used to purchase in-game items. For example, points can be accumulated each time a body temperature is input, allowing the character's costume to be changed. The information aggregating unit also formats the input of health information in a mini-game format, allowing children to continue without getting bored. For example, an element such as solving a simple puzzle when inputting body temperature can be incorporated. This allows children to enjoy inputting health information.

[0033] When collecting health information, the information aggregation unit can simultaneously collect environmental data within the home and analyze the correlation with health status. For example, the information aggregation unit could build a system that simultaneously collects environmental data such as temperature, humidity, and air quality within the home when health information is entered. For example, it could acquire environmental data from smart home devices. The information aggregation unit then integrates the environmental data with the health information, and the generation AI analyzes the correlation between them. For example, it could identify patterns such as an increase in coughing symptoms on days with high humidity. The information aggregation unit could also identify factors that affect health status based on the environmental data and suggest improvement measures to parents. For example, it could recommend the use of an air purifier if the air quality is poor. This makes it possible to identify factors that affect health status and suggest improvement measures.

[0034] The analysis unit can also collect data from local medical institutions and use the generative AI to perform integrated analysis. For example, the analysis unit could collect data from local medical institutions and build a system in which the generative AI analyzes that data in an integrated manner. For example, it could obtain data on fever and cough cases from local hospitals and clinics. The analysis unit could also integrate the data from medical institutions with health information entered by parents, and the generative AI could analyze the signs of an epidemic. For example, it could issue a warning if there is a sudden increase in fever cases in a particular area. The analysis unit could also work with local medical institutions to develop a data sharing system to detect signs of an epidemic early. For example, medical institutions could provide data in real time, and the generative AI could analyze it immediately. This would allow for integrated analysis of data from local medical institutions, making it possible to more accurately detect signs of an epidemic.

[0035] When the analysis unit detects signs of an epidemic, it can compare the data with past data to identify abnormal values ​​and issue an early warning. For example, when the analysis unit detects signs of an epidemic, it builds a system that compares past data to identify abnormal values. For example, it detects an abnormal increase in cases based on data from the past few years. The analysis unit also develops an algorithm that uses the generative AI to compare past data with current data and identify abnormal values. For example, it issues a warning if there is a sudden increase in a particular symptom. The analysis unit also builds a system that issues an early warning when an abnormal value is identified. For example, it issues an alert to parents or daycare centers to encourage a quick response. This makes it possible to identify abnormal values ​​by comparing the data with past data and issue an early warning, enabling a quick response.

[0036] The notification unit can cooperate with local public health centers and medical institutions to share the results of epidemic detection and promote a rapid response. For example, the notification unit builds a system for sharing the results of epidemic detection in cooperation with local public health centers and medical institutions. For example, it notifies the public health center of the detection results in real time. The notification unit also establishes a data sharing protocol with local public health centers and medical institutions to quickly share the results of epidemic detection. For example, it automatically sends data using an API. The notification unit also develops a system that enables local public health centers and medical institutions to quickly respond based on the results of epidemic detection. For example, it suggests preventive measures based on the detection results. This allows the results of epidemic detection to be shared quickly and promotes a response throughout the region.

[0037] When signs of an epidemic are detected, the notification unit can work with vaccination information and reservation systems to promote vaccinations. For example, the notification unit builds a system that provides vaccination information when signs of an epidemic are detected. For example, it notifies parents of the need for vaccinations against epidemics. The notification unit also works with vaccination reservation systems to promote vaccination reservations when signs of an epidemic are detected. For example, it provides a vaccination reservation link through an app. The notification unit also develops a system that automatically provides vaccination information based on the results of epidemic detection. For example, it notifies vaccination information to areas where signs of an epidemic are seen. This makes it possible to quickly promote vaccinations when signs of an epidemic are detected.

[0038] The suggestion unit can provide information supervised by nutritionists and doctors for the ingredients and methods suggested by the generation AI. For example, the suggestion unit builds a system that provides information supervised by nutritionists and doctors for the ingredients and methods suggested by the generation AI. For example, a nutritionist reviews the ingredient list suggested by the generation AI and adds appropriate advice. The suggestion unit also customizes the ingredients and methods suggested by the generation AI based on information supervised by nutritionists and doctors. For example, when suggesting ingredients that are effective in strengthening immunity against a specific disease, expert opinions are reflected. The suggestion unit also develops a system that provides information supervised by nutritionists and doctors in real time for the ingredients and methods suggested by the generation AI. For example, it displays comments from nutritionists on the ingredients suggested by the generation AI. This enables highly reliable suggestions by providing information supervised by nutritionists and doctors.

[0039] The suggestion unit can automatically generate recipes using the suggested ingredients and provide them to parents. For example, the suggestion unit builds a system that automatically generates recipes using ingredients suggested by the generation AI. For example, it generates recipes for dishes using ingredients rich in vitamin C and provides them to parents. The suggestion unit also automatically generates easy-to-make recipes based on the suggested ingredients and provides them to parents. For example, it suggests recipes for dishes that even busy parents can easily make. The suggestion unit also develops a system that customizes recipes using ingredients suggested by the generation AI to suit parents' preferences. For example, it provides recipes that take into account children's favorite seasonings and ingredients. This reduces the burden on parents by automatically generating recipes using suggested ingredients and providing them to them.

[0040] The proposal department can hold online cooking classes and workshops using the proposed ingredients, allowing parents and children to participate together. For example, the proposal department builds a system to hold online cooking classes and workshops using the proposed ingredients. For example, a cooking class using ingredients rich in vitamin C can be held, allowing parents and children to participate together. The proposal department also uses a generation AI to automatically propose schedules for online cooking classes and workshops and notify parents. For example, it provides information about cooking classes held on weekends. The proposal department also develops a system to collect feedback from participants of online cooking classes and workshops and reflect it in the next event. For example, it improves the content based on participants' impressions and requests. In this way, by holding online cooking classes and workshops that parents and children can participate in together, it is possible to raise health awareness among parents and children.

[0041] The suggestion unit can make the suggested methods and ingredients available for purchase in collaboration with local supermarkets and online shops. For example, the suggestion unit builds a system that enables the suggested methods and ingredients to be purchased in collaboration with local supermarkets and online shops. For example, it provides a link where the ingredients suggested by the generation AI can be purchased at an online shop. The suggestion unit also collaborates with local supermarkets to run a campaign offering the suggested ingredients at a special price. For example, it provides a coupon where the ingredients suggested by the generation AI can be purchased at a discounted price. The suggestion unit also collaborates with online shops to develop a system that enables the suggested ingredients to be easily purchased. For example, it provides a function that allows the ingredients suggested by the generation AI to be purchased with one click. This reduces the burden on parents by making the suggested methods and ingredients easy to purchase.

[0042] The notification unit can customize the notification content according to the parent's level of understanding and provide it in an easy-to-understand format. The notification unit, for example, builds a system that customizes the notification content according to the parent's level of understanding. For example, technical terms are avoided and explanations are given in simple terms. The notification unit also evaluates the parent's level of understanding in advance and adjusts the notification content based on the results. For example, notifications that make extensive use of diagrams and illustrations are provided to parents with low levels of understanding. The notification unit also develops a system that customizes the notification content in real time according to the parent's level of understanding. For example, explanations are given using video or audio to make the notification content easier for parents to understand. In this way, the notification content can be customized according to the parent's level of understanding, thereby increasing the effectiveness of information transmission.

[0043] The notification unit can provide a specific action plan that parents can implement immediately when notified. For example, the notification unit will build a system that provides a specific action plan that parents can implement immediately when notified. For example, it will provide specific instructions on how to wash your hands and gargle if signs of an epidemic are observed. The notification unit will also use a generation AI to automatically generate an action plan that parents should implement based on the content of the notification. For example, it will provide a link to make an appointment for a vaccination. The notification unit will also develop a system that provides an action plan that parents can implement immediately when they receive the notification. For example, it will suggest simple preventive measures that can be taken at home. This will promote a rapid response by providing a specific action plan that parents can implement immediately.

[0044] The notification unit can also display the notification content on bulletin boards and digital signage at the nursery school or kindergarten, allowing all parties involved to share the information. For example, the notification unit will build a system that displays the notification content on bulletin boards and digital signage at the nursery school or kindergarten. For example, if signs of an epidemic are detected, the information will be displayed on the school's bulletin board. The notification unit will also work with the nursery school or kindergarten's digital signage to display the notification content in real time. For example, it will instantly display information about epidemics detected by the generation AI. The notification unit will also develop a system that displays the notification content on bulletin boards and digital signage so that it can be shared by all parties involved. For example, it will allow childcare workers and parents to check the information. This will allow the notification content to be displayed on bulletin boards and digital signage at the nursery school or kindergarten, allowing all parties involved to share the information.

[0045] The notification unit can provide links to related video content or webinars when sending notifications, allowing parents to obtain detailed information. For example, the notification unit builds a system that provides links to related video content or webinars when sending notifications. For example, a video link on preventive measures for epidemics is included in the notification. The notification unit also uses a generation AI to automatically suggest video content or webinars where parents can obtain detailed information, based on the content of the notification. For example, a link to a webinar on strengthening immunity is provided. The notification unit also develops a system that provides links to related video content or webinars when parents receive the content of the notification. For example, a video link explaining preventive measures that can be taken at home is provided. In this way, by providing links to related video content or webinars when sending notifications, parents can obtain detailed information.

[0046] The information aggregating unit can adjust the frequency of data updates to match the parent's lifestyle rhythm, thereby reducing the burden on the parent. For example, the information aggregating unit builds a system that adjusts the frequency of data updates to match the parent's lifestyle rhythm. For example, it encourages the parent to update data while avoiding busy times. The information aggregating unit also analyzes the parent's lifestyle rhythm and adjusts the frequency of data updates based on the results. For example, it sends a notification encouraging the parent to update data during times when the parent has free time. The information aggregating unit also develops a system that adjusts the frequency of data updates in real time to match the parent's lifestyle rhythm. For example, it encourages the parent to update data during times when the parent is relaxed. In this way, the burden on the parent can be reduced by adjusting the frequency of data updates to match the parent's lifestyle rhythm.

[0047] The information aggregating unit can automatically generate an individual health management plan based on the updated data and provide it to parents. The information aggregating unit, for example, builds a system that automatically generates an individual health management plan based on updated data. For example, it proposes a diet and exercise plan according to the child's health condition. The information aggregating unit also has a generation AI that analyzes the updated data and automatically generates an individual health management plan. For example, it provides an appropriate health management plan based on body temperature and activity level. The information aggregating unit also develops a system that automatically generates a health management plan that is easy for parents to follow based on the updated data. For example, it proposes an easy-to-follow health management plan. In this way, the burden on parents can be reduced by automatically generating an individual health management plan based on updated data and providing it to them.

[0048] The information aggregation unit can share the updated data with local medical institutions and health centers, making it useful for health management throughout the region. For example, the information aggregation unit builds a system to share the updated data with local medical institutions and health centers. For example, it sends data analyzed by the generation AI to medical institutions in real time. The information aggregation unit also establishes a data sharing protocol with local medical institutions and health centers to quickly share the updated data. For example, it can automatically send data using an API. The information aggregation unit also develops a system based on the updated data to help with health management throughout the region. For example, it monitors the health status of the region and suggests preventive measures. In this way, by sharing the updated data with local medical institutions and health centers, it can be useful for health management throughout the region.

[0049] The information aggregation unit can visualize the status of data updates, allowing parents to understand their child's health condition at a glance. The information aggregation unit, for example, builds a system that visualizes the status of data updates, allowing parents to understand their child's health condition at a glance. For example, the health condition is displayed using graphs and charts. The information aggregation unit also uses a generation AI to analyze the status of data updates, allowing parents to easily understand the health condition. For example, the health condition trend is visually displayed. The information aggregation unit also develops a system that visualizes the status of data updates in real time, allowing parents to always be aware of their child's health condition. For example, it makes it possible to check the health condition using a smartphone app. In this way, by visualizing the status of data updates, parents can understand their child's health condition at a glance.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The health management system may further include a psychological evaluation unit that monitors the child's psychological state. For example, the psychological evaluation unit may analyze the child's behavior and reactions when entering daily activities and health information to detect signs of psychological stress or anxiety. The psychological evaluation unit may also identify children who need psychological support based on behavioral observation data entered by parents or childcare workers. Furthermore, the psychological evaluation unit may suggest relaxation methods or counseling according to the child's psychological state. This allows for comprehensive management of the child's psychological health state and provides appropriate support early on.

[0052] The health management system can further include a nutritional assessment unit that monitors a child's nutritional status. For example, the nutritional assessment unit analyzes information about meals and ingredients entered by parents and evaluates the child's nutritional balance. The nutritional assessment unit can also suggest specific ingredients and recipes if the child is lacking nutrients necessary for growth. Furthermore, the nutritional assessment unit can take into account the child's eating patterns and preferences and provide a nutritional management plan that is easy for parents to follow. This allows for comprehensive management of a child's nutritional status and supports healthy growth.

[0053] The health management system may further include a sleep evaluation unit that monitors a child's sleep status. For example, the sleep evaluation unit may analyze information about the child's sleep duration and sleep quality entered by the parent and evaluate the child's sleep pattern. The sleep evaluation unit may also detect a child's lack of sleep or irregular sleep patterns and suggest measures to improve them. Furthermore, the sleep evaluation unit may provide advice on optimizing the child's sleep environment. This allows for comprehensive management of a child's sleep status and supports a healthy lifestyle rhythm.

[0054] The health management system may further include an exercise evaluation unit that monitors a child's exercise status. For example, the exercise evaluation unit may analyze information entered by a parent about the amount and type of exercise the child engages in and evaluate the child's exercise pattern. The exercise evaluation unit may also detect a child's lack of exercise or excessive exercise and suggest an appropriate exercise plan. Furthermore, the exercise evaluation unit may provide an exercise program tailored to the child's age and physical strength. This allows for comprehensive management of a child's exercise status and supports healthy physical fitness.

[0055] The health management system may further include an interaction assessment unit that monitors a child's social interaction status. For example, the interaction assessment unit may analyze information about a child's friendships and play situations entered by a parent or childcare worker and evaluate the child's social interaction patterns. The interaction assessment unit may also suggest appropriate support if a child is isolated or has problems with friendships. Furthermore, the interaction assessment unit may provide activities or programs to improve the child's social skills. This allows for comprehensive management of a child's social interaction status and supports the building of healthy human relationships.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information aggregator aggregates health information entered by parents. For example, it centrally aggregates health information entered by parents daily, such as their child's temperature, appetite, sleep duration, and symptoms. The information aggregator can also collect health information via a smartphone app or web portal. Step 2: The analysis unit analyzes the health information aggregated by the information aggregation unit. For example, the generation AI analyzes specific symptoms and patterns based on the aggregated health information to detect signs of an epidemic. The analysis unit can also analyze health information using natural language processing technology. Step 3: The notification unit notifies of signs of an epidemic based on the results of the analysis by the analysis unit. For example, if there is a sudden increase in fever or cough symptoms in a specific area or nursery, the generation AI will notify parents and nurseries of the possibility of an epidemic. The notification unit can also send notifications via app or email. Step 4: The suggestion section suggests immune-boosting methods and foods based on the symptoms of the epidemic. For example, the AI ​​generator can suggest foods rich in vitamin C and lifestyle habits that boost immunity. The suggestion section can also suggest specific foods and methods to parents.

[0058] (Example 2) A health management system according to an embodiment of the present invention is a system that improves the efficiency of health management for children attending nursery schools and kindergartens, and supports the early detection and prevention of epidemics. As a result, the health management system can centrally collect children's health information, detect signs of epidemics early, and provide preventive measures.

[0059] A health management system according to an embodiment includes an information aggregation unit, an analysis unit, a notification unit, and a suggestion unit. The information aggregation unit aggregates health information entered by parents. For example, it centrally aggregates health information entered daily by parents, such as a child's body temperature, appetite, sleep time, and symptoms. The information aggregation unit can also collect health information through a smartphone app or web portal. The analysis unit analyzes the health information aggregated by the information aggregation unit. For example, a generation AI analyzes specific symptoms and patterns based on the aggregated health information to detect signs of an epidemic. The analysis unit can also analyze the health information using natural language processing technology. The notification unit notifies signs of an epidemic based on the results of the analysis by the analysis unit. For example, if there is a sudden increase in fever or cough symptoms in a specific area or nursery, the generation AI notifies parents or nursery schools of the possibility of an epidemic. The notification unit can also send notifications via an app or email. The suggestion unit suggests immune-boosting methods and ingredients based on signs of an epidemic. For example, the generation AI suggests foods rich in vitamin C and lifestyle habits that boost immunity. The suggestion unit can also suggest specific ingredients and methods to parents. This allows the health management system according to the embodiment to improve the efficiency of health management for children attending nursery schools and kindergartens, and to support early detection and prevention of epidemics. For example, early detection of epidemic signs enables a prompt response and prevents the spread of infection. Furthermore, providing information on methods and ingredients that are effective in strengthening the immune system can help maintain children's health and increase their resistance to disease.

[0060] The information aggregation unit can automatically collect the child's activity level and heart rate from the smartwatch in addition to the health information entered by the parent. For example, the information aggregation unit builds a system that automatically collects the child's activity level and heart rate from the smartwatch in addition to the health information entered by the parent. For example, the smartwatch records the child's daily steps and heart rate and sends the data to the generation AI. The information aggregation unit also analyzes the data collected from the smartwatch using the generation AI to comprehensively evaluate the child's health. For example, it issues an alert if the child's activity level is low or their heart rate is abnormally high. The information aggregation unit also integrates the health information entered by the parent with the data from the smartwatch, and the generation AI analyzes health trends. For example, if a decrease in activity level and an increase in body temperature are observed simultaneously, early signs of illness can be detected. This allows for a comprehensive evaluation of the child's health.

[0061] The information aggregation unit can input health information using voice recognition technology. For example, the information aggregation unit could develop a system that allows parents to input their children's health information by voice. For example, using a smartphone microphone, what the parent says is converted into text using voice recognition technology and sent to the generation AI. The information aggregation unit also uses voice recognition technology to automatically classify the health information entered by the parent and store it in a database. For example, it could analyze a voice input such as "Today's temperature is 37.5 degrees" and record it as temperature data. The information aggregation unit also uses voice recognition technology to analyze the health information entered by the parent in real time, and the generation AI provides instant feedback. For example, it could give advice such as "Your temperature is high, so I recommend you consult a doctor." This reduces the burden on parents.

[0062] The information aggregation unit uses the emotion estimation function to analyze the parent's emotions when entering information and can suggest relaxation methods if stress or anxiety is high. For example, the information aggregation unit analyzes the parent's voice and facial expression when entering health information and uses the emotion estimation function to measure the parent's stress or anxiety level. For example, it calculates an emotion score from the tone of the voice and facial expression. The information aggregation unit also uses the emotion estimation function to analyze the parent's emotions in real time when entering information and suggests relaxation methods if stress or anxiety is high. For example, it displays a message such as "Take a deep breath and relax." The information aggregation unit also accumulates emotional data when the parent enters information, and the generation AI uses that data to analyze stress and anxiety trends. For example, if stress tends to increase at a certain time, it can suggest relaxation methods in advance. This helps reduce the parent's stress and anxiety.

[0063] The information aggregating unit can gamify the input of health information, allowing children to enjoy the process. For example, the information aggregating unit develops an app that gamifies the input of health information, allowing children to enjoy the process. For example, a game format featuring characters is used to input information such as body temperature and appetite. The information aggregating unit also introduces a system in which points can be earned by inputting health information in the game, and the points can be used to purchase in-game items. For example, points can be accumulated each time a body temperature is input, allowing the character's costume to be changed. The information aggregating unit also formats the input of health information in a mini-game format, allowing children to continue without getting bored. For example, an element such as solving a simple puzzle when inputting body temperature can be incorporated. This allows children to enjoy inputting health information.

[0064] When collecting health information, the information aggregation unit can simultaneously collect environmental data within the home and analyze the correlation with health status. For example, the information aggregation unit could build a system that simultaneously collects environmental data such as temperature, humidity, and air quality within the home when health information is entered. For example, it could acquire environmental data from smart home devices. The information aggregation unit then integrates the environmental data with the health information, and the generation AI analyzes the correlation between them. For example, it could identify patterns such as an increase in coughing symptoms on days with high humidity. The information aggregation unit could also identify factors that affect health status based on the environmental data and suggest improvement measures to parents. For example, it could recommend the use of an air purifier if the air quality is poor. This makes it possible to identify factors that affect health status and suggest improvement measures.

[0065] The information aggregation unit uses the emotion estimation function to monitor parents' emotions regarding their child's health condition in real time and provide positive feedback. For example, the information aggregation unit will develop a system that monitors emotions regarding health information entered by parents in real time and provides positive feedback. For example, if a parent is feeling anxious, an encouraging message will be displayed. The information aggregation unit also uses the emotion estimation function to analyze the emotions entered by the parent and make suggestions to elicit positive emotions. For example, a message such as "Your child's health management is going well" will be displayed. The information aggregation unit also accumulates parental emotion data, and the generation AI analyzes emotion trends based on that data. For example, if positive emotions tend to increase at certain times of the year, feedback tailored to that time will be provided. This makes it possible to monitor parents' emotions and provide positive feedback.

[0066] The analysis unit can also collect data from local medical institutions and use the generative AI to perform integrated analysis. For example, the analysis unit could collect data from local medical institutions and build a system in which the generative AI analyzes that data in an integrated manner. For example, it could obtain data on fever and cough cases from local hospitals and clinics. The analysis unit could also integrate the data from medical institutions with health information entered by parents, and the generative AI could analyze the signs of an epidemic. For example, it could issue a warning if there is a sudden increase in fever cases in a particular area. The analysis unit could also work with local medical institutions to develop a data sharing system to detect signs of an epidemic early. For example, medical institutions could provide data in real time, and the generative AI could analyze it immediately. This would allow for integrated analysis of data from local medical institutions, making it possible to more accurately detect signs of an epidemic.

[0067] When the analysis unit detects signs of an epidemic, it can compare the data with past data to identify abnormal values ​​and issue an early warning. For example, when the analysis unit detects signs of an epidemic, it builds a system that compares past data to identify abnormal values. For example, it detects an abnormal increase in cases based on data from the past few years. The analysis unit also develops an algorithm that uses the generative AI to compare past data with current data and identify abnormal values. For example, it issues a warning if there is a sudden increase in a particular symptom. The analysis unit also builds a system that issues an early warning when an abnormal value is identified. For example, it issues an alert to parents or daycare centers to encourage a quick response. This makes it possible to identify abnormal values ​​by comparing the data with past data and issue an early warning, enabling a quick response.

[0068] The analysis unit uses the emotion estimation function to analyze the emotions of parents and childcare workers, thereby improving the accuracy of detecting epidemics. The analysis unit, for example, develops a system that analyzes the emotions of parents and childcare workers to improve the accuracy of detecting epidemics. For example, if a parent is feeling anxious, the emotion data is analyzed as a sign of an epidemic. The analysis unit also uses the emotion estimation function to collect emotion data of parents and childcare workers, and the generation AI analyzes signs of an epidemic based on that data. For example, a warning is issued if a childcare worker is feeling anxious about many children. The analysis unit also accumulates emotion data of parents and childcare workers, and the generation AI analyzes emotion trends based on that data. For example, if there is a tendency for anxiety to increase at certain times, it is analyzed as a sign of an epidemic. In this way, by analyzing the emotions of parents and childcare workers, the accuracy of detecting epidemics can be improved.

[0069] The notification unit can cooperate with local public health centers and medical institutions to share the results of epidemic detection and promote a rapid response. For example, the notification unit builds a system for sharing the results of epidemic detection in cooperation with local public health centers and medical institutions. For example, it notifies the public health center of the detection results in real time. The notification unit also establishes a data sharing protocol with local public health centers and medical institutions to quickly share the results of epidemic detection. For example, it automatically sends data using an API. The notification unit also develops a system that enables local public health centers and medical institutions to quickly respond based on the results of epidemic detection. For example, it suggests preventive measures based on the detection results. This allows the results of epidemic detection to be shared quickly and promotes a response throughout the region.

[0070] When signs of an epidemic are detected, the notification unit can work with vaccination information and reservation systems to promote vaccinations. For example, the notification unit builds a system that provides vaccination information when signs of an epidemic are detected. For example, it notifies parents of the need for vaccinations against epidemics. The notification unit also works with vaccination reservation systems to promote vaccination reservations when signs of an epidemic are detected. For example, it provides a vaccination reservation link through an app. The notification unit also develops a system that automatically provides vaccination information based on the results of epidemic detection. For example, it notifies vaccination information to areas where signs of an epidemic are seen. This makes it possible to quickly promote vaccinations when signs of an epidemic are detected.

[0071] The notification unit uses the emotion estimation function to collect parents' emotional reactions to the epidemic detection results and can suggest appropriate countermeasures. For example, the notification unit develops a system that collects parents' emotional reactions to the epidemic detection results and suggests appropriate countermeasures. For example, if a parent is feeling anxious, it suggests relaxation methods. The notification unit also uses the emotion estimation function to collect parents' emotional data in response to the epidemic detection results, and the generation AI suggests countermeasures based on that data. For example, if a parent is feeling anxious, it recommends consulting a doctor. The notification unit also accumulates parents' emotional data, and the generation AI analyzes emotional trends based on that data. For example, if anxiety tends to increase at certain times, it suggests relaxation methods in advance. This makes it possible to collect parents' emotional reactions and suggest appropriate countermeasures.

[0072] The suggestion unit can provide information supervised by nutritionists and doctors for the ingredients and methods suggested by the generation AI. For example, the suggestion unit builds a system that provides information supervised by nutritionists and doctors for the ingredients and methods suggested by the generation AI. For example, a nutritionist reviews the ingredient list suggested by the generation AI and adds appropriate advice. The suggestion unit also customizes the ingredients and methods suggested by the generation AI based on information supervised by nutritionists and doctors. For example, when suggesting ingredients that are effective in strengthening immunity against a specific disease, expert opinions are reflected. The suggestion unit also develops a system that provides information supervised by nutritionists and doctors in real time for the ingredients and methods suggested by the generation AI. For example, it displays comments from nutritionists on the ingredients suggested by the generation AI. This enables highly reliable suggestions by providing information supervised by nutritionists and doctors.

[0073] The suggestion unit can automatically generate recipes using the suggested ingredients and provide them to parents. For example, the suggestion unit builds a system that automatically generates recipes using ingredients suggested by the generation AI. For example, it generates recipes for dishes using ingredients rich in vitamin C and provides them to parents. The suggestion unit also automatically generates easy-to-make recipes based on the suggested ingredients and provides them to parents. For example, it suggests recipes for dishes that even busy parents can easily make. The suggestion unit also develops a system that customizes recipes using ingredients suggested by the generation AI to suit parents' preferences. For example, it provides recipes that take into account children's favorite seasonings and ingredients. This reduces the burden on parents by automatically generating recipes using suggested ingredients and providing them to them.

[0074] The suggestion unit uses the emotion estimation function to analyze the emotions parents have toward the proposed method or ingredients, and can make suggestions that are easy to accept. For example, the suggestion unit uses the emotion estimation function to build a system that analyzes the emotions parents have toward the proposed method or ingredients. For example, it analyzes whether the parents have positive emotions toward the proposed ingredients. The suggestion unit also suggests methods and ingredients that the generation AI will find easy to accept based on the parents' emotion data. For example, it prioritizes suggesting ingredients that the parents have positive emotions about. The suggestion unit also uses the emotion estimation function to analyze the emotions parents have toward the proposed method or ingredients in real time, and the generation AI provides immediate feedback. For example, it suggests alternative suggestions if the parents have negative emotions. This makes it possible to make suggestions that are easy for parents to accept, thereby increasing the effectiveness of the suggestions.

[0075] The proposal department can hold online cooking classes and workshops using the proposed ingredients, allowing parents and children to participate together. For example, the proposal department builds a system to hold online cooking classes and workshops using the proposed ingredients. For example, a cooking class using ingredients rich in vitamin C can be held, allowing parents and children to participate together. The proposal department also uses a generation AI to automatically propose schedules for online cooking classes and workshops and notify parents. For example, it provides information about cooking classes held on weekends. The proposal department also develops a system to collect feedback from participants of online cooking classes and workshops and reflect it in the next event. For example, it improves the content based on participants' impressions and requests. In this way, by holding online cooking classes and workshops that parents and children can participate in together, it is possible to raise health awareness among parents and children.

[0076] The suggestion unit can make the suggested methods and ingredients available for purchase in collaboration with local supermarkets and online shops. For example, the suggestion unit builds a system that enables the suggested methods and ingredients to be purchased in collaboration with local supermarkets and online shops. For example, it provides a link where the ingredients suggested by the generation AI can be purchased at an online shop. The suggestion unit also collaborates with local supermarkets to run a campaign offering the suggested ingredients at a special price. For example, it provides a coupon where the ingredients suggested by the generation AI can be purchased at a discounted price. The suggestion unit also collaborates with online shops to develop a system that enables the suggested ingredients to be easily purchased. For example, it provides a function that allows the ingredients suggested by the generation AI to be purchased with one click. This reduces the burden on parents by making the suggested methods and ingredients easy to purchase.

[0077] The suggestion unit uses the emotion estimation function to collect children's emotional reactions to proposed methods and ingredients, and can make suggestions that will please children. For example, the suggestion unit uses the emotion estimation function to build a system that collects children's emotional reactions to proposed methods and ingredients. For example, it analyzes whether children have positive emotions toward the proposed ingredients. The suggestion unit also uses the child's emotional data to have the generation AI suggest methods and ingredients that will please children. For example, it prioritizes suggesting ingredients that children have positive emotions about. The suggestion unit also uses the emotion estimation function to analyze children's emotional reactions to proposed methods and ingredients in real time, and the generation AI provides immediate feedback. For example, if a child has negative emotions, it suggests an alternative. This makes it possible to make suggestions that will please children, thereby increasing the effectiveness of the suggestions.

[0078] The notification unit can customize the notification content according to the parent's level of understanding and provide it in an easy-to-understand format. The notification unit, for example, builds a system that customizes the notification content according to the parent's level of understanding. For example, technical terms are avoided and explanations are given in simple terms. The notification unit also evaluates the parent's level of understanding in advance and adjusts the notification content based on the results. For example, notifications that make extensive use of diagrams and illustrations are provided to parents with low levels of understanding. The notification unit also develops a system that customizes the notification content in real time according to the parent's level of understanding. For example, explanations are given using video or audio to make the notification content easier for parents to understand. In this way, the notification content can be customized according to the parent's level of understanding, thereby increasing the effectiveness of information transmission.

[0079] The notification unit can provide a specific action plan that parents can implement immediately when notified. For example, the notification unit will build a system that provides a specific action plan that parents can implement immediately when notified. For example, it will provide specific instructions on how to wash your hands and gargle if signs of an epidemic are observed. The notification unit will also use a generation AI to automatically generate an action plan that parents should implement based on the content of the notification. For example, it will provide a link to make an appointment for a vaccination. The notification unit will also develop a system that provides an action plan that parents can implement immediately when they receive the notification. For example, it will suggest simple preventive measures that can be taken at home. This will promote a rapid response by providing a specific action plan that parents can implement immediately.

[0080] The notification unit uses the emotion estimation function to analyze the parent's emotions when receiving a notification and can provide support to reduce stress. The notification unit, for example, builds a system that analyzes the parent's emotions when receiving a notification and provides support to reduce stress. For example, if the parent is feeling anxious, the notification unit suggests relaxation methods. The notification unit also uses the emotion estimation function to collect emotional data of the parent when receiving a notification, and the generation AI suggests stress reduction measures based on that data. For example, if the parent is feeling stressed, the notification unit provides relaxing music. The notification unit also accumulates emotional data of the parent, and the generation AI analyzes emotional trends based on that data. For example, if stress tends to increase at certain times, the notification unit suggests relaxation methods in advance. This reduces the burden on parents by analyzing the parent's emotions when receiving a notification and providing support to reduce stress.

[0081] The notification unit can also display the notification content on bulletin boards and digital signage at the nursery school or kindergarten, allowing all parties involved to share the information. For example, the notification unit will build a system that displays the notification content on bulletin boards and digital signage at the nursery school or kindergarten. For example, if signs of an epidemic are detected, the information will be displayed on the school's bulletin board. The notification unit will also work with the nursery school or kindergarten's digital signage to display the notification content in real time. For example, it will instantly display information about epidemics detected by the generation AI. The notification unit will also develop a system that displays the notification content on bulletin boards and digital signage so that it can be shared by all parties involved. For example, it will allow childcare workers and parents to check the information. This will allow the notification content to be displayed on bulletin boards and digital signage at the nursery school or kindergarten, allowing all parties involved to share the information.

[0082] The notification unit can provide links to related video content or webinars when sending notifications, allowing parents to obtain detailed information. For example, the notification unit builds a system that provides links to related video content or webinars when sending notifications. For example, a video link on preventive measures for epidemics is included in the notification. The notification unit also uses a generation AI to automatically suggest video content or webinars where parents can obtain detailed information, based on the content of the notification. For example, a link to a webinar on strengthening immunity is provided. The notification unit also develops a system that provides links to related video content or webinars when parents receive the content of the notification. For example, a video link explaining preventive measures that can be taken at home is provided. In this way, by providing links to related video content or webinars when sending notifications, parents can obtain detailed information.

[0083] The notification unit can use the emotion estimation function to collect the parent's emotional response to the notification content and improve the content of the next notification. For example, the notification unit can build a system that collects the parent's emotional response to the notification content and improves the content of the next notification. For example, if the parent is feeling anxious, the next notification content can be made easier to understand. The notification unit also uses the emotion estimation function to collect emotional data about the parent regarding the notification content, and the generation AI improves the notification content based on that data. For example, if the parent is feeling stressed, the next notification content can be adjusted. The notification unit also accumulates the parent's emotional data, and the generation AI analyzes emotional trends based on that data. For example, if anxiety tends to increase at certain times, relaxation methods can be suggested in advance. In this way, by collecting the parent's emotional response to the notification content and improving the content of the next notification, the parent's understanding can be improved.

[0084] The information aggregating unit can adjust the frequency of data updates to match the parent's lifestyle rhythm, thereby reducing the burden on the parent. For example, the information aggregating unit builds a system that adjusts the frequency of data updates to match the parent's lifestyle rhythm. For example, it encourages the parent to update data while avoiding busy times. The information aggregating unit also analyzes the parent's lifestyle rhythm and adjusts the frequency of data updates based on the results. For example, it sends a notification encouraging the parent to update data during times when the parent has free time. The information aggregating unit also develops a system that adjusts the frequency of data updates in real time to match the parent's lifestyle rhythm. For example, it encourages the parent to update data during times when the parent is relaxed. In this way, the burden on the parent can be reduced by adjusting the frequency of data updates to match the parent's lifestyle rhythm.

[0085] The information aggregating unit can automatically generate an individual health management plan based on the updated data and provide it to parents. The information aggregating unit, for example, builds a system that automatically generates an individual health management plan based on updated data. For example, it proposes a diet and exercise plan according to the child's health condition. The information aggregating unit also has a generation AI that analyzes the updated data and automatically generates an individual health management plan. For example, it provides an appropriate health management plan based on body temperature and activity level. The information aggregating unit also develops a system that automatically generates a health management plan that is easy for parents to follow based on the updated data. For example, it proposes an easy-to-follow health management plan. In this way, the burden on parents can be reduced by automatically generating an individual health management plan based on updated data and providing it to them.

[0086] The information aggregation unit can use the emotion estimation function to analyze the parent's emotions when entering data and provide support to encourage input. The information aggregation unit, for example, builds a system that analyzes the parent's emotions when entering data and provides support to encourage input. For example, if the parent is feeling stressed, it suggests ways to relax. The information aggregation unit also uses the emotion estimation function to collect emotional data from the parent when entering data, and the generation AI provides support to encourage input based on that data. For example, if the parent is feeling anxious, it displays an encouraging message. The information aggregation unit also accumulates emotional data from the parent, and the generation AI analyzes emotional trends based on that data. For example, if stress tends to increase at certain times, it suggests ways to relax in advance. In this way, the burden on parents can be reduced by analyzing the parent's emotions when entering data and providing support to encourage input.

[0087] The information aggregation unit can share the updated data with local medical institutions and health centers, making it useful for health management throughout the region. For example, the information aggregation unit builds a system to share the updated data with local medical institutions and health centers. For example, it sends data analyzed by the generation AI to medical institutions in real time. The information aggregation unit also establishes a data sharing protocol with local medical institutions and health centers to quickly share the updated data. For example, it can automatically send data using an API. The information aggregation unit also develops a system based on the updated data to help with health management throughout the region. For example, it monitors the health status of the region and suggests preventive measures. In this way, by sharing the updated data with local medical institutions and health centers, it can be useful for health management throughout the region.

[0088] The information aggregation unit can visualize the status of data updates, allowing parents to understand their child's health condition at a glance. The information aggregation unit, for example, builds a system that visualizes the status of data updates, allowing parents to understand their child's health condition at a glance. For example, the health condition is displayed using graphs and charts. The information aggregation unit also uses a generation AI to analyze the status of data updates, allowing parents to easily understand the health condition. For example, the health condition trend is visually displayed. The information aggregation unit also develops a system that visualizes the status of data updates in real time, allowing parents to always be aware of their child's health condition. For example, it makes it possible to check the health condition using a smartphone app. In this way, by visualizing the status of data updates, parents can understand their child's health condition at a glance.

[0089] The information aggregation unit can use the emotion estimation function to collect parents' emotional reactions to data updates and provide feedback to increase their motivation to input. The information aggregation unit, for example, builds a system that collects parents' emotional reactions to data updates and provides feedback to increase their motivation to input. For example, if the parent is feeling positive, a praising message is displayed. The information aggregation unit also uses the emotion estimation function to collect emotional data about parents in response to data updates, and the generation AI provides feedback based on that data. For example, if the parent is feeling stressed, the generation AI suggests ways to relax. The information aggregation unit also accumulates parents' emotional data, and the generation AI analyzes emotional trends based on that data. For example, if there is a tendency for positive emotions to increase at a certain time, feedback tailored to that time is provided. In this way, the burden on parents can be reduced by collecting parents' emotional reactions to data updates and providing feedback to increase their motivation to input.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The health management system may further include a psychological evaluation unit that monitors the child's psychological state. For example, the psychological evaluation unit may analyze the child's behavior and reactions when entering daily activities and health information to detect signs of psychological stress or anxiety. The psychological evaluation unit may also identify children who need psychological support based on behavioral observation data entered by parents or childcare workers. Furthermore, the psychological evaluation unit may suggest relaxation methods or counseling according to the child's psychological state. This allows for comprehensive management of the child's psychological health state and provides appropriate support early on.

[0092] The health management system can further include a nutritional assessment unit that monitors a child's nutritional status. For example, the nutritional assessment unit analyzes information about meals and ingredients entered by parents and evaluates the child's nutritional balance. The nutritional assessment unit can also suggest specific ingredients and recipes if the child is lacking nutrients necessary for growth. Furthermore, the nutritional assessment unit can take into account the child's eating patterns and preferences and provide a nutritional management plan that is easy for parents to follow. This allows for comprehensive management of a child's nutritional status and supports healthy growth.

[0093] The health management system may further include a sleep evaluation unit that monitors a child's sleep status. For example, the sleep evaluation unit may analyze information about the child's sleep duration and sleep quality entered by the parent and evaluate the child's sleep pattern. The sleep evaluation unit may also detect a child's lack of sleep or irregular sleep patterns and suggest measures to improve them. Furthermore, the sleep evaluation unit may provide advice on optimizing the child's sleep environment. This allows for comprehensive management of a child's sleep status and supports a healthy lifestyle rhythm.

[0094] The health management system may further include an exercise evaluation unit that monitors a child's exercise status. For example, the exercise evaluation unit may analyze information entered by a parent about the amount and type of exercise the child engages in and evaluate the child's exercise pattern. The exercise evaluation unit may also detect a child's lack of exercise or excessive exercise and suggest an appropriate exercise plan. Furthermore, the exercise evaluation unit may provide an exercise program tailored to the child's age and physical strength. This allows for comprehensive management of a child's exercise status and supports healthy physical fitness.

[0095] The health management system may further include an interaction assessment unit that monitors a child's social interaction status. For example, the interaction assessment unit may analyze information about a child's friendships and play situations entered by a parent or childcare worker and evaluate the child's social interaction patterns. The interaction assessment unit may also suggest appropriate support if a child is isolated or has problems with friendships. Furthermore, the interaction assessment unit may provide activities or programs to improve the child's social skills. This allows for comprehensive management of a child's social interaction status and supports the building of healthy human relationships.

[0096] The health management system can also analyze parents' emotions and provide support to reduce their stress and anxiety. For example, it can analyze the health information and comments entered by parents and use an emotion estimation function to measure the parent's stress and anxiety levels. If the parent is feeling stressed, it can suggest relaxation methods and activities to reduce stress. Furthermore, by accumulating parental emotional data and using the generation AI to analyze emotional trends based on that data, it can predict when parents' stress and anxiety will increase and provide support in advance. This can reduce parents' stress and anxiety and enable more effective health management for their children.

[0097] The health management system can also analyze children's emotions and support their psychological health. For example, it can analyze the health information and activity data entered by the child and measure the child's emotions using an emotion estimation function. It can also suggest relaxation methods and psychological support if the child is feeling stressed or anxious. Furthermore, by accumulating children's emotional data and using the generation AI to analyze emotional trends based on that data, it can comprehensively evaluate the child's psychological health and provide appropriate support. This can support children's psychological health and promote healthy growth.

[0098] The health management system can also analyze parents' emotions and evaluate how they feel about the proposed health management plan. For example, it can analyze whether parents have positive feelings about the proposed ingredients and methods and make suggestions that are more likely to be accepted. It can also suggest alternatives if parents have negative feelings. Furthermore, it can accumulate parents' emotional data and use the generation AI to analyze emotional trends, making suggestions that are more likely to be accepted by parents. This helps parents to feel positive about the proposed health management plan, increasing the effectiveness of the suggestions.

[0099] The health management system can also analyze children's emotions and evaluate how they feel about the proposed health management plan. For example, it can analyze whether the child has positive feelings about the proposed ingredients and methods and make suggestions that are easy for the child to accept. It can also suggest alternatives if the child has negative feelings. Furthermore, by accumulating children's emotional data and using that data to analyze emotional trends, the generation AI can make suggestions that are easy for the child to accept. This helps the child to feel positive about the proposed health management plan, increasing the effectiveness of the suggestions.

[0100] The health management system can also analyze the parent's emotions and evaluate their emotional reaction when they receive a notification. For example, if the parent feels anxious about the content of the notification, it can suggest relaxation methods and activities to reduce stress. In addition, by accumulating parental emotional data and using that data to analyze emotional trends, the generation AI can support the parent in accepting the notification content more easily. This allows the system to evaluate the parent's emotional reaction when they receive a notification and provide appropriate support to reduce the parent's stress and anxiety.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information aggregator aggregates health information entered by parents. For example, it centrally aggregates health information entered by parents daily, such as their child's temperature, appetite, sleep duration, and symptoms. The information aggregator can also collect health information via a smartphone app or web portal. Step 2: The analysis unit analyzes the health information aggregated by the information aggregation unit. For example, the generation AI analyzes specific symptoms and patterns based on the aggregated health information to detect signs of an epidemic. The analysis unit can also analyze health information using natural language processing technology. Step 3: The notification unit notifies of signs of an epidemic based on the results of the analysis by the analysis unit. For example, if there is a sudden increase in fever or cough symptoms in a specific area or nursery, the generation AI will notify parents and nurseries of the possibility of an epidemic. The notification unit can also send notifications via app or email. Step 4: The suggestion section suggests immune-boosting methods and foods based on the symptoms of the epidemic. For example, the AI ​​generator can suggest foods rich in vitamin C and lifestyle habits that boost immunity. The suggestion section can also suggest specific foods and methods to parents.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. an information aggregation unit that aggregates health information input by a parent; an analysis unit that analyzes the health information collected by the information collection unit; a notification unit that notifies of signs of an epidemic based on the results of the analysis by the analysis unit; A suggestion unit that suggests methods and ingredients for strengthening immunity based on the symptoms of the epidemic. A system characterized by:

2. The information aggregation unit In addition to health information entered by parents, children's activity levels and heart rate are automatically collected from the smartwatch.

2. The system of claim 1.

3. The information aggregation unit Health information is entered using voice recognition technology 2. The system of claim 1.

4. The information aggregation unit Analyzes emotions as parents type and suggests relaxation methods if stress or anxiety levels are high 2. The system of claim 1.

5. The information aggregation unit Turning the process of entering health information into a game so that children can enjoy doing so 2. The system of claim 1.

6. The information aggregation unit When collecting health information, environmental data within the home will also be collected and analyzed to analyze the relationship with health status.

2. The system of claim 1.

Images