System

Abstract

An object of a system according to an embodiment is to appropriately manage information related to child care and provide support.SOLUTION: A system includes a management part, a notification part, a reception part, an answer part, a community formation part, an analysis part, an alert part, and a recording part. The management unit manages vaccination information or a schedule. The notification unit notifies a next injection schedule or a change based on the information managed by the management unit. The receiving unit receives a question related to pregnancy, childbirth, and child care. The answer unit provides an expert answer based on the question received by the reception unit. The community forming unit connects users who gave birth at the same time. The analysis unit analyzes the health information of the child. The alert unit detects an abnormal value or a preventive measure based on the information analyzed by the analysis unit, and transmits an alert to the parent. The recording unit records the growth of the child.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 technologies do not adequately manage and support information related to childcare, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately manage information related to childcare and provide support. [Means for solving the problem]

[0006] The system according to the embodiment includes a management unit, a notification unit, a reception unit, a response unit, a community creation unit, an analysis unit, an alert unit, and a recording unit. The management unit manages vaccination information or schedules. The notification unit notifies users of upcoming or changed vaccination dates based on the information managed by the management unit. The reception unit accepts questions about pregnancy, childbirth, and childcare. The response unit provides expert answers based on the questions accepted by the reception unit. The community creation unit connects users who gave birth around the same time. The analysis unit analyzes children's health information. The alert unit detects abnormal values ​​or preventive measures based on the information analyzed by the analysis unit and sends an alert to parents. The recording unit records the child's growth. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately manage information related to childcare and provide support. [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 child-rearing support system according to an embodiment of the present invention utilizes generative AI to manage vaccination information and schedules, accept and answer questions about pregnancy, childbirth, and childcare, build communities, analyze children's health information, send alerts, and record children's growth. For example, the child-rearing support system manages vaccination information and schedules, notifies users of upcoming vaccination dates and alternative dates if changes occur, accepts questions about pregnancy, childbirth, and childcare, and provides expert answers. Furthermore, it connects users who gave birth around the same time to form a community for information exchange and communication. It analyzes children's health information, detects abnormalities and preventive measures, sends alerts to parents, and suggests emergency response agencies. Finally, it tracks and records children's growth as a digital footprint, recording weight, height, newly acquired skills, and other data, and saving photos and videos. This enables the child-rearing support system to provide a wide range of child-rearing support, including childcare management, childcare support, community building, early detection and prevention, and child growth recording. For example, by utilizing generative AI, vaccination information management and notifications can be automated, allowing users to easily keep track of upcoming vaccination dates and changes. It also alleviates users' anxieties and doubts by providing fast, expert answers to questions about pregnancy, childbirth, and childcare. Furthermore, by connecting users who gave birth around the same time, they can exchange information and support each other, promoting the formation of a community. It analyzes children's health information in real time, detects abnormalities and preventative measures, and sends alerts, enabling prompt action. Finally, it records children's growth as a digital footprint, allowing parents to easily track and monitor their children's development.

[0029] A child-rearing support system according to an embodiment includes a management unit, a notification unit, a reception unit, a response unit, a community building unit, an analysis unit, an alert unit, and a recording unit. The management unit manages vaccination information or schedules. For example, the management unit manages information such as vaccination dates, vaccination sites, and the types of vaccines used. The management unit can also manage the next vaccination schedule and alternative dates if changes occur. The notification unit notifies the user of the next vaccination schedule or changes based on the information managed by the management unit. For example, the notification unit can send reminders via a smartphone app. The reception unit accepts questions about pregnancy, childbirth, and child-rearing. For example, the reception unit can accept questions in text, audio, image, or other formats. The response unit provides expert answers based on the questions accepted by the reception unit. For example, the response unit can provide expert answers using a collaboration system with experts. The community building unit connects users who gave birth around the same time. For example, the community building unit can suggest appropriate communities based on the user's birth period and local information. The analysis unit analyzes the child's health information. For example, the analysis unit can monitor health information such as body temperature, weight, height, and medical history in real time. The alert unit detects abnormal values ​​or preventive measures based on the information analyzed by the analysis unit and sends an alert to the parent. For example, the alert unit can send an alert to the parent when an abnormal value is detected and suggest an emergency response agency. The recording unit records the child's growth. For example, the recording unit can record weight, height, newly acquired skills, etc., and save photos and videos. As a result, the child-rearing support system according to the embodiment can manage vaccination information, send notifications, receive and answer questions, form communities, analyze health information, send alerts, and record growth.

[0030] The management unit automatically acquires data from medical institutions. Examples of data from medical institutions include, but are not limited to, electronic medical records, medical records, and test results. The management unit can, for example, acquire data from medical institutions in real time and manage the latest information. The management unit can also compare data from medical institutions with past data and detect abnormal values. Furthermore, the management unit can cross-check data from medical institutions from multiple sources and evaluate reliability. This allows the latest information to be managed by automatically acquiring data from medical institutions. Some or all of the above-mentioned processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input data acquired from medical institutions into a generation AI and have the generation AI analyze the data and detect abnormal values.

[0031] The notification unit can send a reminder through a smartphone app. Reminders include, but are not limited to, notification timing and notification methods (push notification, email, etc.). The notification unit can, for example, select an optimal notification time based on the user's schedule. The notification unit can also select an optimal notification time taking into account the user's schedule and past notification history. Furthermore, the notification unit can analyze the user's schedule and select a notification time that causes the least stress. By sending a reminder through the smartphone app, the user can be notified at an appropriate time. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the notification unit can input the user's schedule data into the generation AI and have the generation AI select the optimal notification time.

[0032] The answering unit can provide a professional answer using a system for collaboration with an expert. Examples of the system for collaboration with an expert include, but are not limited to, an online consultation system and an expert database. For example, the answering unit can provide an answer that reflects expert advice based on the user's past question history. The answering unit can also analyze the user's past question history and provide an optimal answer based on the expert advice. Furthermore, the answering unit can improve the answer that reflects the expert advice by referring to the user's past question history. In this way, by using the system for collaboration with an expert, more accurate and professional answers can be provided. Some or all of the above-described processing in the answering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answering unit can input the user's question data into the generation AI and cause the generation AI to generate an answer based on the expert advice.

[0033] The community formation unit can propose an appropriate community based on information about the user's birth date and region. Examples of birth date include, but are not limited to, the expected birth date and the actual birth date. Examples of region information include, but are not limited to, the city or ward where the user lives and information about local medical institutions. The community formation unit can propose an optimal community based on, for example, the user's birth date and region information. The community formation unit can also propose an optimal community based on past community formation data, taking into account the user's birth date and region information. Furthermore, the community formation unit can analyze information about the user's birth date and region and propose an optimal community. This allows users to exchange information and support each other by proposing an appropriate community based on information about the user's birth date and region. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input information about the user's birth date and region into the generation AI and have the generation AI propose an optimal community.

[0034] The analysis unit can monitor the child's health information in real time. Examples of real-time monitoring include, but are not limited to, the frequency of data updates and the type of sensor used. For example, the analysis unit can monitor the child's health information in real time and immediately detect abnormal values. The analysis unit can also compare the child's health information with past data to detect abnormal values. Furthermore, the analysis unit can cross-check the child's health information from multiple sources to improve the accuracy of abnormal value detection. Thus, by monitoring the child's health information in real time, abnormal values ​​can be immediately detected. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the child's health information into the generation AI and have the generation AI detect abnormal values.

[0035] The alert unit can send an alert to the parent when an abnormal value is detected and suggest available emergency response agencies. Examples of available emergency response agencies include, but are not limited to, the contact information for the nearest hospital or an ambulance. For example, the alert unit can send an alert to the parent when an abnormal value is detected and suggest available emergency response agencies. The alert unit can also refer to the user's past health data to improve the accuracy of abnormal value detection. Furthermore, the alert unit can customize the alert based on the user's current situation and areas of interest. This allows for a prompt response by sending an alert to the parent when an abnormal value is detected and suggesting available emergency response agencies. Some or all of the above-described processing in the alert unit can be performed, for example, using a generation AI or without a generation AI. For example, the alert unit can input abnormal value data into the generation AI and have the generation AI generate and send an alert.

[0036] The recording unit can record weight, height, newly acquired skills, etc., and save photos and videos. Examples of newly acquired skills include, but are not limited to, walking, speech development, and manual dexterity. The recording unit can record weight, height, newly acquired skills, etc., and save photos and videos. The recording unit can also improve the accuracy of the recording by referring to the user's past recording data. Furthermore, the recording unit can customize growth records based on the user's current situation and areas of interest. This allows digital management of a child's growth by recording weight, height, newly acquired skills, etc., and saving photos and videos. Some or all of the above-described processing in the recording unit can be performed using, or without, a generation AI. For example, the recording unit can input growth record data into the generation AI and have the generation AI generate and save records.

[0037] The management unit can automatically acquire data from medical institutions and evaluate the reliability of the data. Criteria for evaluating the reliability of the data include, but are not limited to, the origin of the data and the consistency of the data. For example, the management unit can acquire data from medical institutions in real time and display only reliable data. The management unit can also compare data from medical institutions with past data to detect outliers. Furthermore, the management unit can cross-check data from medical institutions from multiple sources and evaluate its reliability. This allows accurate information to be provided by automatically acquiring data from medical institutions and evaluating its reliability. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input data acquired from medical institutions into a generation AI and have the generation AI evaluate the reliability of the data.

[0038] When acquiring vaccination information, the management unit can propose an optimal schedule taking into account the user's past vaccination history. To propose an optimal schedule, criteria such as, but not limited to, past vaccination history and recommended vaccination intervals are used. For example, the management unit can automatically propose the next vaccination schedule based on the user's past vaccination history. The management unit can also propose an optimal vaccination schedule taking into account the user's past vaccination history and current health status. Furthermore, the management unit can analyze the user's past vaccination history and optimize the vaccination interval. This enables efficient vaccination by proposing an optimal schedule taking into account the user's past vaccination history. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's vaccination history data into the generation AI and have the generation AI propose an optimal schedule.

[0039] When acquiring vaccination information, the management unit can suggest the optimal vaccination site based on information about medical institutions in the user's area. Criteria used to suggest the optimal vaccination site include, but are not limited to, distance, facility facilities, and medical staff qualifications. The management unit can also suggest the optimal vaccination site based on information about medical institutions in the user's area. The management unit can also suggest the optimal vaccination site taking into account the congestion status of medical institutions in the user's area. Furthermore, the management unit can suggest a reliable vaccination site based on the evaluations of medical institutions in the user's area. By suggesting the optimal vaccination site based on information about medical institutions in the user's area, a convenient vaccination site can be provided to the user. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input information about local medical institutions into the generation AI and have the generation AI suggest the optimal vaccination site.

[0040] When acquiring vaccination information, the management unit can integrate all members' vaccination schedules taking into account the user's family composition. Family composition includes, but is not limited to, the number of family members, their ages, and their health status. The management unit can integrate all members' vaccination schedules based on the user's family composition, for example. The management unit can also propose an optimal schedule taking into account the user's family composition and each member's vaccination history. Furthermore, the management unit can analyze the user's family composition and propose a schedule that allows all members to be vaccinated at the same time. This allows for efficient management of vaccinations for all family members by integrating all members' schedules taking into account the user's family composition. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input family composition data into the generation AI and have the generation AI integrate the schedules.

[0041] When acquiring vaccination information, the management unit can analyze the user's lifestyle rhythm and suggest an optimal vaccination time. Lifestyle rhythms include, but are not limited to, wake-up time, bedtime, and meal times. The management unit can, for example, suggest an optimal vaccination time based on the user's lifestyle rhythm. The management unit can also suggest an optimal vaccination time taking into account the user's lifestyle rhythm and past vaccination history. Furthermore, the management unit can analyze the user's lifestyle rhythm and suggest a less stressful vaccination time. By analyzing the user's lifestyle rhythm and suggesting an optimal vaccination time, a less stressful vaccination can be achieved. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input lifestyle rhythm data into the generation AI and have the generation AI suggest an optimal vaccination time.

[0042] When acquiring vaccination information, the management unit can customize the acquisition method by reflecting the user's past feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. The management unit can customize the acquisition method for vaccination information, for example, based on the user's past feedback. The management unit can also analyze the user's past feedback and suggest the optimal acquisition method. Furthermore, the management unit can improve the acquisition method by reflecting the user's past feedback. By customizing the acquisition method by reflecting the user's past feedback, optimal information acquisition for the user is possible. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input feedback data into the generation AI and have the generation AI customize the acquisition method.

[0043] When sending a reminder through a smartphone app, the notification unit can select an optimal notification time taking into account the user's schedule. The optimal notification time includes, but is not limited to, the user's schedule and past notification history. For example, the notification unit can select the optimal notification time based on the user's schedule. The notification unit can also select the optimal notification time taking into account the user's schedule and past notification history. Furthermore, the notification unit can analyze the user's schedule and select a notification time that causes the least stress. By selecting the optimal notification time taking into account the user's schedule, notifications can be sent at the optimal time for the user. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's schedule data into the generation AI and have the generation AI select the optimal notification time.

[0044] The notification unit can customize the notification content based on the user's past behavioral history. The behavioral history includes, but is not limited to, past reactions to notifications and app usage history. The notification unit can customize the notification content based on, for example, the user's past behavioral history. The notification unit can also analyze the user's past behavioral history and suggest optimal notification content. Furthermore, the notification unit can improve the notification content by reflecting the user's past behavioral history. By customizing the notification content based on the user's past behavioral history, it is possible to provide information that is highly relevant to the user. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can input the user's behavioral history data into the generation AI and have the generation AI customize the notification content.

[0045] The notification unit can make the notification content multilingual according to the user's language setting. Multilingual support includes, but is not limited to, the types of supported languages ​​and the accuracy of translation. The notification unit can, for example, automatically translate the notification content based on the user's language setting. The notification unit can also provide a language switching function when the user uses multiple languages. Furthermore, the notification unit can send notifications in the optimal language based on the user's language setting. This makes it possible to provide information that is easy for the user to understand by making the notification content multilingual according to the user's language setting. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI or without using a generation AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI perform translation and language switching.

[0046] When sending a notification, the notification unit can select the optimal notification method by taking into account the user's device information. Device information includes, but is not limited to, a smartphone, tablet, or PC. For example, if the user is using a smartphone, the notification unit can send a push notification. Furthermore, if the user is using a tablet, the notification unit can also send an email notification. Furthermore, if the user is using a smartwatch, the notification unit can also send a vibration notification. This allows the optimal notification method to be selected by taking into account the user's device information, thereby providing notifications in the optimal manner for the user. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input device information into the generation AI and have the generation AI select the optimal notification method.

[0047] When sending a notification, the notification unit can provide highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the notification unit can provide information about nearby medical institutions based on the user's geographical location information. The notification unit can also suggest optimal vaccination locations by taking into account the user's geographical location information. Furthermore, the notification unit can analyze the user's geographical location information and provide highly relevant information. By providing highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the notification unit can input geographical location information into the generation AI and cause the generation AI to provide highly relevant information.

[0048] When sending a notification, the notification unit can customize the notification method by reflecting the user's past feedback. Examples of notification methods include, but are not limited to, push notification, email, and SMS. The notification unit can customize the notification method based on, for example, the user's past feedback. The notification unit can also analyze the user's past feedback and propose an optimal notification method. The notification unit can also improve the notification method by reflecting the user's past feedback. By customizing the notification method by reflecting the user's past feedback, the optimal notification method for the user can be provided. Some or all of the above-described processing in the notification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the notification unit can input feedback data into the generation AI and have the generation AI customize the notification method.

[0049] When accepting a question, the reception unit can select the optimal reception method by taking into account the user's past question history. The question history includes, for example, past question content, a history of answers, and the like, but is not limited to such examples. The reception unit can select the optimal reception method based on, for example, the user's past question history. The reception unit can also analyze the user's past question history and suggest the optimal reception method. Furthermore, the reception unit can improve the reception method by reflecting the user's past question history. Thus, by selecting the optimal reception method by taking into account the user's past question history, it is possible to accept questions in the optimal method for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input question history data into the generation AI and have the generation AI select the optimal reception method.

[0050] When accepting questions, the reception unit may perform filtering based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The reception unit may, for example, preferentially accept questions that are highly relevant based on the user's current situation. The reception unit may also accept optimal questions taking into account the user's areas of interest. Furthermore, the reception unit may analyze the user's current situation and areas of interest and suggest optimal questions. In this way, filtering based on the user's current situation and areas of interest allows highly relevant questions to be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's situation data into the generation AI and cause the generation AI to perform filtering.

[0051] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, text input, voice input, and image input. For example, when a user inputs a question by voice, the acceptance unit can accept the question using voice recognition technology. Furthermore, when a user inputs a question by text, the acceptance unit can accept the question using text analysis technology. Furthermore, when a user inputs a question by image, the acceptance unit can accept the question using image recognition technology. By selecting the optimal acceptance means depending on the user's input method, the question can be accepted in the optimal way for the user. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input input data to a generation AI and have the generation AI select the optimal acceptance means.

[0052] When accepting questions, the reception unit can prioritize highly relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the reception unit can prioritize highly relevant questions based on the user's geographical location information. The reception unit can also accept optimal questions by taking into account the user's geographical location information. Furthermore, the reception unit can analyze the user's geographical location information and suggest highly relevant questions. Thus, by taking into account the user's geographical location information and preferentially accepting highly relevant questions, it is possible to provide useful information to the user. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input geographical location information to the generation AI and cause the generation AI to accept highly relevant questions.

[0053] When accepting a question, the reception unit can analyze the user's social media activity and accept related questions. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can prioritize accepting highly relevant questions based on the user's social media activity. The reception unit can also accept optimal questions taking the user's social media activity into consideration. Furthermore, the reception unit can analyze the user's social media activity and suggest highly relevant questions. In this way, by analyzing the user's social media activity and accepting related questions, it is possible to provide information that is highly relevant to the user. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the reception unit can input social media data into the generation AI and cause the generation AI to accept related questions.

[0054] When accepting a question, the reception unit can customize the reception method by reflecting the user's past feedback. Feedback includes, but is not limited to, the user's ratings, comments, and usage history. The reception unit can customize the question reception method based on, for example, the user's past feedback. The reception unit can also analyze the user's past feedback and suggest an optimal reception method. The reception unit can also improve the reception method by reflecting the user's past feedback. By customizing the reception method by reflecting the user's past feedback, questions can be accepted in a method optimal for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input feedback data into the generation AI and have the generation AI customize the reception method.

[0055] The answering unit can improve the accuracy of the answer by referring to the user's past question history using a system for collaborating with experts. The question history includes, for example, past question content and answer history, but is not limited to such examples. The answering unit can provide, for example, an answer that reflects expert advice based on the user's past question history. The answering unit can also analyze the user's past question history and provide an optimal answer based on the expert advice. Furthermore, the answering unit can improve the answer that reflects the expert advice by referring to the user's past question history. In this way, the answer that is optimal for the user can be provided by improving the accuracy of the answer by referring to the user's past question history. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the answering unit can input question history data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0056] When providing an answer, the answering unit can customize the answer based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The answering unit can provide a highly relevant answer based on the user's current situation. The answering unit can also provide an optimal answer taking into account the user's areas of interest. Furthermore, the answering unit can analyze the user's current situation and areas of interest and suggest an optimal answer. This allows for providing a highly relevant answer to the user by customizing the answer based on the user's current situation and areas of interest. Some or all of the above-described processing in the answering unit can be performed using, or without, a generation AI. For example, the answering unit can input the user's situation data into the generation AI and have the generation AI customize the answer.

[0057] The answering unit can provide answers in multiple languages ​​according to the user's language settings when providing answers. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. For example, the answering unit can automatically translate answers based on the user's language settings. The answering unit can also provide a language switching function when the user uses multiple languages. Furthermore, the answering unit can provide answers in the most appropriate language based on the user's language settings. This allows for multilingual support according to the user's language settings, making it possible to provide answers that are easy for the user to understand. Some or all of the above-described processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the content of the answer into the generation AI and have the generation AI perform translation and language switching.

[0058] When providing an answer, the answering unit can adjust the use of technical terminology according to the user's level of expertise. Technical terminology includes, but is not limited to, medical terminology, technical terminology, etc. For example, if the user has technical expertise, the answering unit can provide an answer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the answering unit can provide an answer in easy-to-understand language. Furthermore, the answering unit can analyze the user's level of expertise and suggest the optimal use of technical terminology. This allows the use of technical terminology to be adjusted according to the user's level of expertise, thereby providing an answer that is easy for the user to understand. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the user's technical expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0059] When providing an answer, the answering unit can provide highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the answering unit can provide highly relevant information based on the user's geographical location information. The answering unit can also provide an optimal answer by taking into account the user's geographical location information. Furthermore, the answering unit can analyze the user's geographical location information and suggest highly relevant information. By providing highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the answering unit can be performed, for example, using a generation AI or without using a generation AI. For example, the answering unit can input geographical location information to the generation AI and cause the generation AI to provide highly relevant information.

[0060] When providing an answer, the answering unit can customize the answering method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, the user's ratings, comments, and usage history. The answering unit can customize the answering method based on, for example, the user's past feedback. The answering unit can also analyze the user's past feedback and suggest an optimal answering method. Furthermore, the answering unit can improve the answering method by reflecting the user's past feedback. By customizing the answering method by reflecting the user's past feedback, an answer can be provided in a way that is optimal for the user. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input feedback data into the generation AI and have the generation AI customize the answering method.

[0061] The community formation unit can propose an optimal community based on information about the user's birth period and region, and by referring to past community formation data. Community formation data includes, for example, the number of past participants and activity details, but is not limited to these examples. The community formation unit can propose an optimal community based on, for example, information about the user's birth period and region. The community formation unit can also propose an optimal community based on information about the user's birth period and region, and by referring to past community formation data. The community formation unit can also analyze information about the user's birth period and region and propose an optimal community. This allows the optimal community to be proposed based on information about the user's birth period and region, and by referring to past community formation data, thereby providing a community that is highly relevant to the user. Some or all of the above-described processing in the community formation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the community formation unit can input information about the birth period and region into the generation AI and have the generation AI propose an optimal community.

[0062] When proposing a community, the community formation unit can customize it based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The community formation unit can, for example, propose a highly relevant community based on the user's current situation. The community formation unit can also propose an optimal community taking into account the user's areas of interest. Furthermore, the community formation unit can analyze the user's current situation and areas of interest and propose an optimal community. This allows for customization based on the user's current situation and areas of interest, thereby providing a highly relevant community to the user. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input the user's situation data into the generation AI and have the generation AI customize the community.

[0063] When proposing a community, the community formation unit can support multiple languages ​​according to the user's language settings. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. For example, the community formation unit can automatically translate the community proposal based on the user's language settings. The community formation unit can also provide a language switching function when the user speaks multiple languages. Furthermore, the community formation unit can also propose a community in the most appropriate language based on the user's language settings. This allows for multilingual support according to the user's language settings, thereby providing a community that is easy for the user to understand. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input the community proposal into the generation AI and have the generation AI perform translation and language switching.

[0064] When proposing a community, the community formation unit can prioritize proposing highly relevant communities by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the community formation unit can prioritize proposing highly relevant communities based on the user's geographical location information. The community formation unit can also propose optimal communities by taking into account the user's geographical location information. Furthermore, the community formation unit can analyze the user's geographical location information and propose highly relevant communities. This allows for providing a community that is beneficial to the user by preferentially proposing highly relevant communities by taking into account the user's geographical location information. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input geographical location information to the generation AI and cause the generation AI to propose highly relevant communities.

[0065] When suggesting a community, the community formation unit can analyze the user's social media activity and suggest relevant communities. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the community formation unit can prioritize suggesting highly relevant communities based on the user's social media activity. The community formation unit can also suggest optimal communities taking the user's social media activity into consideration. Furthermore, the community formation unit can analyze the user's social media activity and suggest highly relevant communities. In this way, by analyzing the user's social media activity and suggesting related communities, it is possible to provide highly relevant communities to the user. Some or all of the above-described processing in the community formation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the community formation unit can input social media data into the generation AI and cause the generation AI to suggest related communities.

[0066] When proposing a community, the community formation unit can customize the proposal method by reflecting the user's past feedback. Examples of the feedback include, but are not limited to, the user's ratings, comments, and usage history. The community formation unit can customize the community proposal method based on, for example, the user's past feedback. The community formation unit can also analyze the user's past feedback and propose an optimal community proposal method. Furthermore, the community formation unit can improve the community proposal method by reflecting the user's past feedback. By customizing the proposal method by reflecting the user's past feedback, a community can be proposed in a way that is optimal for the user. Some or all of the above-described processing in the community formation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the community formation unit can input feedback data into the generation AI and cause the generation AI to customize the proposal method.

[0067] The analysis unit can monitor the child's health information in real time and improve the accuracy of detecting abnormal values. Examples of the accuracy of detecting abnormal values ​​include, but are not limited to, the accuracy of the detection algorithm and the false positive rate. For example, the analysis unit can monitor the child's health information in real time and immediately detect abnormal values. The analysis unit can also compare the child's health information with past data to detect abnormal values. Furthermore, the analysis unit can cross-check the child's health information from multiple sources to improve the accuracy of detecting abnormal values. This enables real-time monitoring of the child's health information and improved accuracy in detecting abnormal values, enabling prompt responses. Some or all of the above-described processing in the analysis unit can be performed, for example, using or without the generation AI. For example, the analysis unit can input health information data into the generation AI and have the generation AI perform abnormal value detection.

[0068] When analyzing health information, the analysis unit can improve the accuracy of the analysis by referring to the user's past health data. Past health data includes, but is not limited to, past medical records and test results. The analysis unit can improve the accuracy of the analysis, for example, based on the user's past health data. The analysis unit can also analyze the user's past health data and propose an optimal analysis method. Furthermore, the analysis unit can improve the analysis method by referring to the user's past health data. This improves the accuracy of the analysis by referring to the user's past health data, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input past health data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0069] When analyzing the health information, the analysis unit can propose optimal preventive measures based on information about medical institutions in the user's area. Information about local medical institutions includes, but is not limited to, the location, medical specialty, and opening hours of the medical institution. The analysis unit can propose optimal preventive measures based on, for example, information about medical institutions in the user's area. The analysis unit can also propose optimal preventive measures taking into account the evaluation of medical institutions in the user's area. Furthermore, the analysis unit can analyze information about medical institutions in the user's area and propose optimal preventive measures. By proposing optimal preventive measures based on information about medical institutions in the user's area, it is possible to provide beneficial preventive measures to the user. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information about local medical institutions into the generation AI and have the generation AI propose optimal preventive measures.

[0070] When analyzing health information, the analysis unit can prioritize analysis of highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The analysis unit can, for example, prioritize analysis of highly relevant health information based on the user's geographical location information. The analysis unit can also analyze optimal health information taking into account the user's geographical location information. Furthermore, the analysis unit can analyze the user's geographical location information and prioritize analysis of highly relevant health information. This allows for providing useful information to the user by prioritizing analysis of highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input geographical location information to the generation AI and cause the generation AI to analyze highly relevant information.

[0071] When analyzing health information, the analysis unit may analyze the user's social media activity and analyze related information. Social media activity includes, but is not limited to, post content, number of likes, and number of followers. For example, the analysis unit may prioritize analysis of highly relevant health information based on the user's social media activity. The analysis unit may also analyze optimal health information taking the user's social media activity into consideration. Furthermore, the analysis unit may analyze the user's social media activity and prioritize analysis of highly relevant health information. In this way, by analyzing the user's social media activity and analyzing related information, highly relevant information can be provided to the user. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI or without a generation AI. For example, the analysis unit may input social media data into a generation AI and have the generation AI analyze the related information.

[0072] When analyzing health information, the analysis unit can customize the analysis method by reflecting the user's past feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. The analysis unit can customize the analysis method for health information based on, for example, the user's past feedback. The analysis unit can also analyze the user's past feedback and propose an optimal analysis method. Furthermore, the analysis unit can improve the analysis method by reflecting the user's past feedback. By customizing the analysis method by reflecting the user's past feedback, information can be analyzed in a way that is optimal for the user. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input feedback data into the generation AI and have the generation AI customize the analysis method.

[0073] When an abnormal value is detected, the alert unit can improve the accuracy of the alert by referring to the user's past health data. Past health data includes, but is not limited to, past medical records and test results. For example, the alert unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. The alert unit can also analyze the user's past health data to improve the accuracy of detecting abnormal values. Furthermore, the alert unit can also improve the accuracy of detecting abnormal values ​​by referring to the user's past health data. This allows for more accurate alerts to be provided by improving the accuracy of alerts by referring to the user's past health data. Some or all of the above-described processing in the alert unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the alert unit can input past health data into the generation AI and cause the generation AI to improve the accuracy of detecting abnormal values.

[0074] When sending an alert, the alert unit can customize the alert based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The alert unit can, for example, send a highly relevant alert based on the user's current situation. The alert unit can also send an optimal alert taking into account the user's areas of interest. Furthermore, the alert unit can analyze the user's current situation and areas of interest and suggest an optimal alert. This allows for customization based on the user's current situation and areas of interest, thereby providing a highly relevant alert to the user. Some or all of the above-described processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input user situation data into the generation AI and have the generation AI customize the alert.

[0075] The alert unit can support multiple languages ​​when sending an alert according to the user's language setting. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. The alert unit can automatically translate the alert based on the user's language setting, for example. The alert unit can also provide a language switching function when the user uses multiple languages. Furthermore, the alert unit can send the alert in the optimal language based on the user's language setting. This allows for multilingual support according to the user's language setting, making it possible to provide an alert that is easy for the user to understand. Some or all of the above-described processing in the alert unit can be performed using, or without, a generation AI. For example, the alert unit can input the alert content to the generation AI and have the generation AI perform translation and language switching.

[0076] When sending an alert, the alert unit can provide highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The alert unit can provide highly relevant information based on, for example, the user's geographical location information. The alert unit can also send an optimal alert by taking into account the user's geographical location information. Furthermore, the alert unit can analyze the user's geographical location information and provide highly relevant information. As a result, by providing highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the alert unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the alert unit can input geographical location information to the generation AI and cause the generation AI to provide highly relevant information.

[0077] When sending an alert, the alert unit can analyze the user's social media activity and provide relevant information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The alert unit can provide relevant information based on the user's social media activity. The alert unit can also send an optimal alert taking the user's social media activity into consideration. Furthermore, the alert unit can analyze the user's social media activity and provide relevant information. In this way, by analyzing the user's social media activity and providing relevant information, it is possible to provide information that is relevant to the user. Some or all of the above-described processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input social media data into the generation AI and cause the generation AI to provide relevant information.

[0078] When sending an alert, the alert unit can customize the alert method by reflecting the user's past feedback. Examples of alert methods include, but are not limited to, pop-up notification, audio notification, and email notification. The alert unit can customize the alert method based on, for example, the user's past feedback. The alert unit can also analyze the user's past feedback and suggest an optimal alert method. Furthermore, the alert unit can also improve the alert method by reflecting the user's past feedback. By customizing the alert method by reflecting the user's past feedback, an alert can be provided in an optimal manner for the user. Some or all of the above-described processing in the alert unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the alert unit can input feedback data into the generation AI and have the generation AI customize the alert method.

[0079] The recording unit can improve the accuracy of recording weight, height, newly acquired skills, etc. by referring to the user's past record data. Past record data includes, but is not limited to, past weight, height, developmental milestones, etc. For example, the recording unit can improve the accuracy of recording weight and height based on the user's past record data. The recording unit can also analyze the user's past record data and improve the accuracy of recording newly acquired skills. Furthermore, the recording unit can improve the recording method by referring to the user's past record data. By doing so, by improving the accuracy of recording by referring to the user's past record data, a more accurate growth record can be provided. Some or all of the above-described processing in the recording unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the recording unit can input past record data into the generation AI and cause the generation AI to improve the accuracy of the recording.

[0080] When digitally managing the growth record, the recording unit can customize it based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The recording unit can provide a highly relevant growth record based on the user's current situation. The recording unit can also provide an optimal growth record taking into account the user's areas of interest. Furthermore, the recording unit can analyze the user's current situation and areas of interest and suggest an optimal growth record. This allows for customization based on the user's current situation and areas of interest to provide a highly relevant growth record for the user. Some or all of the above-described processing in the recording unit can be performed using, or without, a generation AI. For example, the recording unit can input the user's situation data into the generation AI and have the generation AI customize the growth record.

[0081] When digitally managing the growth record, the recording unit can support multiple languages ​​according to the user's language setting. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. For example, the recording unit can automatically translate the growth record based on the user's language setting. The recording unit can also provide a language switching function when the user speaks multiple languages. Furthermore, the recording unit can provide the growth record in the optimal language based on the user's language setting. This allows for multilingual support according to the user's language setting, making it possible to provide a growth record that is easy for the user to understand. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input the growth record into the generation AI and have the generation AI perform translation and language switching.

[0082] When digitally managing the growth record, the recording unit can prioritize recording highly relevant information taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the recording unit can prioritize recording highly relevant growth records based on the user's geographical location information. The recording unit can also record optimal growth records taking into account the user's geographical location information. Furthermore, the recording unit can analyze the user's geographical location information and prioritize recording highly relevant growth records. This allows for providing a growth record that is beneficial to the user by prioritizing recording highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input geographical location information to the generation AI and cause the generation AI to record highly relevant information.

[0083] When digitally managing the growth record, the recording unit can analyze the user's social media activity and record related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the recording unit can prioritize recording relevant growth records based on the user's social media activity. The recording unit can also record optimal growth records taking the user's social media activity into consideration. Furthermore, the recording unit can analyze the user's social media activity and prioritize recording relevant growth records. In this way, by analyzing the user's social media activity and recording relevant information, a growth record that is relevant to the user can be provided. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input social media data into the generation AI and cause the generation AI to record relevant information.

[0084] When digitally managing the growth record, the recording unit can customize the recording method by reflecting the user's past feedback. Feedback includes, but is not limited to, the user's ratings, comments, and usage history. The recording unit can customize the growth record method based on, for example, the user's past feedback. The recording unit can also analyze the user's past feedback and suggest an optimal growth record method. Furthermore, the recording unit can also improve the growth record method by reflecting the user's past feedback. By customizing the recording method by reflecting the user's past feedback, the growth record can be provided in an optimal manner for the user. Some or all of the above-described processing in the recording unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the recording unit can input feedback data into the generation AI and have the generation AI customize the recording method.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The management unit can analyze the user's lifestyle and optimize the timing of vaccination information notifications. For example, it can select the time when notifications are most effectively received, taking into account the user's wake-up time, bedtime, meal times, etc. It can also identify the time periods when notifications are most likely to be received based on the user's past notification history and adjust the notification timing. Furthermore, if the user's lifestyle changes, it can perform analysis in real time and dynamically change the notification timing. This allows for optimal notifications that match the user's lifestyle.

[0087] The reception unit can analyze the user's past question history and suggest the optimal question reception method. For example, for a user who has previously asked many questions in text format, text input can be suggested as a priority. Also, for a user who prefers voice input, the voice input option can be emphasized. Furthermore, for a user who asks many questions in image format, the image upload function can be highlighted. In this way, the optimal question reception method can be provided based on the user's past question history.

[0088] The community creation unit can analyze the user's social media activity and suggest related communities. For example, if the user frequently participates in a specific childcare group, it can suggest communities related to that group. Also, if the user shows interest in a specific childcare topic, it can suggest communities related to that topic. Furthermore, it can preferentially suggest active communities based on the frequency of the user's social media activity. This makes it possible to provide the most suitable community based on the user's social media activity.

[0089] The alert unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. For example, it can optimize the abnormal value detection algorithm by referring to past medical records and test results. It can also improve the accuracy of detecting abnormal values ​​by analyzing past health data and finding specific patterns. It can also adjust the parameters required for detecting abnormal values ​​based on past health data. This makes it possible to more accurately detect abnormal values ​​by utilizing the user's past health data.

[0090] The recording unit can customize the growth record based on the user's geographic location information. For example, if the user lives in a specific area, childcare information and event information for that area can be added to the growth record. Information about local medical institutions can also be reflected in the growth record based on the user's geographic location information. Furthermore, the user's geographic location information can be analyzed to include region-specific childcare topics in the growth record. This makes it possible to provide an optimal growth record based on the user's geographic location information.

[0091] The processing flow of the first embodiment will be briefly explained below.

[0092] Step 1: The administration department manages vaccination information or schedules. For example, it manages information such as vaccination dates, vaccination sites, and the type of vaccine used, as well as the next vaccination date and alternative dates in case of changes. Step 2: The notification department notifies the next vaccination date or change based on the information managed by the management department, for example, by sending a reminder via a smartphone app. Step 3: The reception unit accepts questions about pregnancy, childbirth, and childcare. For example, questions can be accepted in text, audio, or image format. Step 4: The answering unit provides a professional answer based on the question received by the receiving unit. For example, the professional answer can be provided using a system for collaborating with experts. Step 5: The community building unit connects users who gave birth around the same time. For example, it can suggest appropriate communities based on the user's birth time and region. Step 6: The analysis unit analyzes the child's health information, such as temperature, weight, height, and medical history, in real time. Step 7: The alert unit detects abnormal values ​​or preventive measures based on the information analyzed by the analysis unit and sends an alert to the parent. For example, if an abnormal value is detected, an alert can be sent to the parent and an emergency response agency can be suggested. Step 8: The recorder will record your child's growth, such as weight, height, newly acquired skills, etc., and can save photos and videos.

[0093] (Example 2) A child-rearing support system according to an embodiment of the present invention utilizes generative AI to manage vaccination information and schedules, accept and answer questions about pregnancy, childbirth, and childcare, build communities, analyze children's health information, send alerts, and record children's growth. For example, the child-rearing support system manages vaccination information and schedules, notifies users of upcoming vaccination dates and alternative dates if changes occur, accepts questions about pregnancy, childbirth, and childcare, and provides expert answers. Furthermore, it connects users who gave birth around the same time to form a community for information exchange and communication. It analyzes children's health information, detects abnormalities and preventive measures, sends alerts to parents, and suggests emergency response agencies. Finally, it tracks and records children's growth as a digital footprint, recording weight, height, newly acquired skills, and other data, and saving photos and videos. This enables the child-rearing support system to provide a wide range of child-rearing support, including childcare management, childcare support, community building, early detection and prevention, and child growth recording. For example, by utilizing generative AI, vaccination information management and notifications can be automated, allowing users to easily keep track of upcoming vaccination dates and changes. It also alleviates users' anxieties and doubts by providing fast, expert answers to questions about pregnancy, childbirth, and childcare. Furthermore, by connecting users who gave birth around the same time, they can exchange information and support each other, promoting the formation of a community. It analyzes children's health information in real time, detects abnormalities and preventative measures, and sends alerts, enabling prompt action. Finally, it records children's growth as a digital footprint, allowing parents to easily track and monitor their children's development.

[0094] A child-rearing support system according to an embodiment includes a management unit, a notification unit, a reception unit, a response unit, a community building unit, an analysis unit, an alert unit, and a recording unit. The management unit manages vaccination information or schedules. For example, the management unit manages information such as vaccination dates, vaccination sites, and the types of vaccines used. The management unit can also manage the next vaccination schedule and alternative dates if changes occur. The notification unit notifies the user of the next vaccination schedule or changes based on the information managed by the management unit. For example, the notification unit can send reminders via a smartphone app. The reception unit accepts questions about pregnancy, childbirth, and child-rearing. For example, the reception unit can accept questions in text, audio, image, or other formats. The response unit provides expert answers based on the questions accepted by the reception unit. For example, the response unit can provide expert answers using a collaboration system with experts. The community building unit connects users who gave birth around the same time. For example, the community building unit can suggest appropriate communities based on the user's birth period and local information. The analysis unit analyzes the child's health information. For example, the analysis unit can monitor health information such as body temperature, weight, height, and medical history in real time. The alert unit detects abnormal values ​​or preventive measures based on the information analyzed by the analysis unit and sends an alert to the parent. For example, the alert unit can send an alert to the parent when an abnormal value is detected and suggest an emergency response agency. The recording unit records the child's growth. For example, the recording unit can record weight, height, newly acquired skills, etc., and save photos and videos. As a result, the child-rearing support system according to the embodiment can manage vaccination information, send notifications, receive and answer questions, form communities, analyze health information, send alerts, and record growth.

[0095] The management unit automatically acquires data from medical institutions. Examples of data from medical institutions include, but are not limited to, electronic medical records, medical records, and test results. The management unit can, for example, acquire data from medical institutions in real time and manage the latest information. The management unit can also compare data from medical institutions with past data and detect abnormal values. Furthermore, the management unit can cross-check data from medical institutions from multiple sources and evaluate reliability. This allows the latest information to be managed by automatically acquiring data from medical institutions. Some or all of the above-mentioned processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input data acquired from medical institutions into a generation AI and have the generation AI analyze the data and detect abnormal values.

[0096] The notification unit can send a reminder through a smartphone app. Reminders include, but are not limited to, notification timing and notification methods (push notification, email, etc.). The notification unit can, for example, select an optimal notification time based on the user's schedule. The notification unit can also select an optimal notification time taking into account the user's schedule and past notification history. Furthermore, the notification unit can analyze the user's schedule and select a notification time that causes the least stress. By sending a reminder through the smartphone app, the user can be notified at an appropriate time. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the notification unit can input the user's schedule data into the generation AI and have the generation AI select the optimal notification time.

[0097] The answering unit can provide a professional answer using a system for collaboration with an expert. Examples of the system for collaboration with an expert include, but are not limited to, an online consultation system and an expert database. For example, the answering unit can provide an answer that reflects expert advice based on the user's past question history. The answering unit can also analyze the user's past question history and provide an optimal answer based on the expert advice. Furthermore, the answering unit can improve the answer that reflects the expert advice by referring to the user's past question history. In this way, by using the system for collaboration with an expert, more accurate and professional answers can be provided. Some or all of the above-described processing in the answering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answering unit can input the user's question data into the generation AI and cause the generation AI to generate an answer based on the expert advice.

[0098] The community formation unit can propose an appropriate community based on information about the user's birth date and region. Examples of birth date include, but are not limited to, the expected birth date and the actual birth date. Examples of region information include, but are not limited to, the city or ward where the user lives and information about local medical institutions. The community formation unit can propose an optimal community based on, for example, the user's birth date and region information. The community formation unit can also propose an optimal community based on past community formation data, taking into account the user's birth date and region information. Furthermore, the community formation unit can analyze information about the user's birth date and region and propose an optimal community. This allows users to exchange information and support each other by proposing an appropriate community based on information about the user's birth date and region. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input information about the user's birth date and region into the generation AI and have the generation AI propose an optimal community.

[0099] The analysis unit can monitor the child's health information in real time. Examples of real-time monitoring include, but are not limited to, the frequency of data updates and the type of sensor used. For example, the analysis unit can monitor the child's health information in real time and immediately detect abnormal values. The analysis unit can also compare the child's health information with past data to detect abnormal values. Furthermore, the analysis unit can cross-check the child's health information from multiple sources to improve the accuracy of abnormal value detection. Thus, by monitoring the child's health information in real time, abnormal values ​​can be immediately detected. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the child's health information into the generation AI and have the generation AI detect abnormal values.

[0100] The alert unit can send an alert to the parent when an abnormal value is detected and suggest available emergency response agencies. Examples of available emergency response agencies include, but are not limited to, the contact information for the nearest hospital or an ambulance. For example, the alert unit can send an alert to the parent when an abnormal value is detected and suggest available emergency response agencies. The alert unit can also refer to the user's past health data to improve the accuracy of abnormal value detection. Furthermore, the alert unit can customize the alert based on the user's current situation and areas of interest. This allows for a prompt response by sending an alert to the parent when an abnormal value is detected and suggesting available emergency response agencies. Some or all of the above-described processing in the alert unit can be performed, for example, using a generation AI or without a generation AI. For example, the alert unit can input abnormal value data into the generation AI and have the generation AI generate and send an alert.

[0101] The recording unit can record weight, height, newly acquired skills, etc., and save photos and videos. Examples of newly acquired skills include, but are not limited to, walking, speech development, and manual dexterity. The recording unit can record weight, height, newly acquired skills, etc., and save photos and videos. The recording unit can also improve the accuracy of the recording by referring to the user's past recording data. Furthermore, the recording unit can customize growth records based on the user's current situation and areas of interest. This allows digital management of a child's growth by recording weight, height, newly acquired skills, etc., and saving photos and videos. Some or all of the above-described processing in the recording unit can be performed using, or without, a generation AI. For example, the recording unit can input growth record data into the generation AI and have the generation AI generate and save records.

[0102] The management unit can estimate the user's emotions and adjust the display method of vaccination information based on the estimated user emotions. Technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis can be used to estimate emotions. For example, if the user is feeling stressed, the management unit can provide a simple, visually easy-to-understand display method. Furthermore, if the user is relaxed, the management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a concise display method that focuses on the main points. This allows the display method of vaccination information to be adjusted according to the user's emotions, thereby enabling optimal information display for the user. Some or all of the above-described processing in the management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0103] The management unit can automatically acquire data from medical institutions and evaluate the reliability of the data. Criteria for evaluating the reliability of the data include, but are not limited to, the origin of the data and the consistency of the data. For example, the management unit can acquire data from medical institutions in real time and display only reliable data. The management unit can also compare data from medical institutions with past data to detect outliers. Furthermore, the management unit can cross-check data from medical institutions from multiple sources and evaluate its reliability. This allows accurate information to be provided by automatically acquiring data from medical institutions and evaluating its reliability. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input data acquired from medical institutions into a generation AI and have the generation AI evaluate the reliability of the data.

[0104] When acquiring vaccination information, the management unit can propose an optimal schedule taking into account the user's past vaccination history. To propose an optimal schedule, criteria such as, but not limited to, past vaccination history and recommended vaccination intervals are used. For example, the management unit can automatically propose the next vaccination schedule based on the user's past vaccination history. The management unit can also propose an optimal vaccination schedule taking into account the user's past vaccination history and current health status. Furthermore, the management unit can analyze the user's past vaccination history and optimize the vaccination interval. This enables efficient vaccination by proposing an optimal schedule taking into account the user's past vaccination history. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's vaccination history data into the generation AI and have the generation AI propose an optimal schedule.

[0105] When acquiring vaccination information, the management unit can suggest the optimal vaccination site based on information about medical institutions in the user's area. Criteria used to suggest the optimal vaccination site include, but are not limited to, distance, facility facilities, and medical staff qualifications. The management unit can also suggest the optimal vaccination site based on information about medical institutions in the user's area. The management unit can also suggest the optimal vaccination site taking into account the congestion status of medical institutions in the user's area. Furthermore, the management unit can suggest a reliable vaccination site based on the evaluations of medical institutions in the user's area. By suggesting the optimal vaccination site based on information about medical institutions in the user's area, a convenient vaccination site can be provided to the user. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input information about local medical institutions into the generation AI and have the generation AI suggest the optimal vaccination site.

[0106] The management unit can estimate the user's emotions and prioritize vaccination information based on the estimated user emotions. Criteria for determining the priorities include, but are not limited to, importance, urgency, and user interest. For example, when the user is stressed, the management unit can prioritize displaying important information. Furthermore, when the user is relaxed, the management unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the management unit can prioritize displaying information that focuses on the main points. Thus, by prioritizing vaccination information according to the user's emotions, important information can be prioritized. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0107] When acquiring vaccination information, the management unit can integrate all members' vaccination schedules taking into account the user's family composition. Family composition includes, but is not limited to, the number of family members, their ages, and their health status. The management unit can integrate all members' vaccination schedules based on the user's family composition, for example. The management unit can also propose an optimal schedule taking into account the user's family composition and each member's vaccination history. Furthermore, the management unit can analyze the user's family composition and propose a schedule that allows all members to be vaccinated at the same time. This allows for efficient management of vaccinations for all family members by integrating all members' schedules taking into account the user's family composition. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input family composition data into the generation AI and have the generation AI integrate the schedules.

[0108] When acquiring vaccination information, the management unit can analyze the user's lifestyle rhythm and suggest an optimal vaccination time. Lifestyle rhythms include, but are not limited to, wake-up time, bedtime, and meal times. The management unit can, for example, suggest an optimal vaccination time based on the user's lifestyle rhythm. The management unit can also suggest an optimal vaccination time taking into account the user's lifestyle rhythm and past vaccination history. Furthermore, the management unit can analyze the user's lifestyle rhythm and suggest a less stressful vaccination time. By analyzing the user's lifestyle rhythm and suggesting an optimal vaccination time, a less stressful vaccination can be achieved. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input lifestyle rhythm data into the generation AI and have the generation AI suggest an optimal vaccination time.

[0109] When acquiring vaccination information, the management unit can customize the acquisition method by reflecting the user's past feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. The management unit can customize the acquisition method for vaccination information, for example, based on the user's past feedback. The management unit can also analyze the user's past feedback and suggest the optimal acquisition method. Furthermore, the management unit can improve the acquisition method by reflecting the user's past feedback. By customizing the acquisition method by reflecting the user's past feedback, optimal information acquisition for the user is possible. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input feedback data into the generation AI and have the generation AI customize the acquisition method.

[0110] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. Examples of notification timing include, but are not limited to, the user's active time, before and after an important event, and the like. For example, the notification unit can delay the timing of the notification when the user is stressed. The notification unit can also advance the timing of the notification when the user is relaxed. Furthermore, the notification unit can send a notification immediately when the user is in a hurry. This allows the notification to be sent at an appropriate time by adjusting the timing of the notification according to the user's emotions. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of the notification.

[0111] When sending a reminder through a smartphone app, the notification unit can select an optimal notification time taking into account the user's schedule. The optimal notification time includes, but is not limited to, the user's schedule and past notification history. For example, the notification unit can select the optimal notification time based on the user's schedule. The notification unit can also select the optimal notification time taking into account the user's schedule and past notification history. Furthermore, the notification unit can analyze the user's schedule and select a notification time that causes the least stress. By selecting the optimal notification time taking into account the user's schedule, notifications can be sent at the optimal time for the user. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's schedule data into the generation AI and have the generation AI select the optimal notification time.

[0112] The notification unit can customize the notification content based on the user's past behavioral history. The behavioral history includes, but is not limited to, past reactions to notifications and app usage history. The notification unit can customize the notification content based on, for example, the user's past behavioral history. The notification unit can also analyze the user's past behavioral history and suggest optimal notification content. Furthermore, the notification unit can improve the notification content by reflecting the user's past behavioral history. By customizing the notification content based on the user's past behavioral history, it is possible to provide information that is highly relevant to the user. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can input the user's behavioral history data into the generation AI and have the generation AI customize the notification content.

[0113] The notification unit can make the notification content multilingual according to the user's language setting. Multilingual support includes, but is not limited to, the types of supported languages ​​and the accuracy of translation. The notification unit can, for example, automatically translate the notification content based on the user's language setting. The notification unit can also provide a language switching function when the user uses multiple languages. Furthermore, the notification unit can send notifications in the optimal language based on the user's language setting. This makes it possible to provide information that is easy for the user to understand by making the notification content multilingual according to the user's language setting. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI or without using a generation AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI perform translation and language switching.

[0114] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. Notification priorities include, but are not limited to, importance, urgency, and user interest. For example, if the user is feeling stressed, the notification unit can prioritize sending important notifications. Furthermore, if the user is relaxed, the notification unit can prioritize sending detailed notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize sending notifications that focus on the main points. Thus, by determining the priority of notifications according to the user's emotions, important information can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of notifications.

[0115] When sending a notification, the notification unit can select the optimal notification method by taking into account the user's device information. Device information includes, but is not limited to, a smartphone, tablet, or PC. For example, if the user is using a smartphone, the notification unit can send a push notification. Furthermore, if the user is using a tablet, the notification unit can also send an email notification. Furthermore, if the user is using a smartwatch, the notification unit can also send a vibration notification. This allows the optimal notification method to be selected by taking into account the user's device information, thereby providing notifications in the optimal manner for the user. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input device information into the generation AI and have the generation AI select the optimal notification method.

[0116] When sending a notification, the notification unit can provide highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the notification unit can provide information about nearby medical institutions based on the user's geographical location information. The notification unit can also suggest optimal vaccination locations by taking into account the user's geographical location information. Furthermore, the notification unit can analyze the user's geographical location information and provide highly relevant information. By providing highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the notification unit can input geographical location information into the generation AI and cause the generation AI to provide highly relevant information.

[0117] When sending a notification, the notification unit can customize the notification method by reflecting the user's past feedback. Examples of notification methods include, but are not limited to, push notification, email, and SMS. The notification unit can customize the notification method based on, for example, the user's past feedback. The notification unit can also analyze the user's past feedback and propose an optimal notification method. The notification unit can also improve the notification method by reflecting the user's past feedback. By customizing the notification method by reflecting the user's past feedback, the optimal notification method for the user can be provided. Some or all of the above-described processing in the notification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the notification unit can input feedback data into the generation AI and have the generation AI customize the notification method.

[0118] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user emotions. Examples of methods for receiving questions include, but are not limited to, text input, voice input, and image input. For example, when the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly receive questions. This allows questions to be received in a manner optimal for the user by adjusting the method of receiving questions according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input user emotion data into the generation AI and have the generation AI adjust the method of receiving questions.

[0119] When accepting a question, the reception unit can select the optimal reception method by taking into account the user's past question history. The question history includes, for example, past question content, a history of answers, and the like, but is not limited to such examples. The reception unit can select the optimal reception method based on, for example, the user's past question history. The reception unit can also analyze the user's past question history and suggest the optimal reception method. Furthermore, the reception unit can improve the reception method by reflecting the user's past question history. Thus, by selecting the optimal reception method by taking into account the user's past question history, it is possible to accept questions in the optimal method for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input question history data into the generation AI and have the generation AI select the optimal reception method.

[0120] When accepting questions, the reception unit may perform filtering based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The reception unit may, for example, preferentially accept questions that are highly relevant based on the user's current situation. The reception unit may also accept optimal questions taking into account the user's areas of interest. Furthermore, the reception unit may analyze the user's current situation and areas of interest and suggest optimal questions. In this way, filtering based on the user's current situation and areas of interest allows highly relevant questions to be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's situation data into the generation AI and cause the generation AI to perform filtering.

[0121] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, text input, voice input, and image input. For example, when a user inputs a question by voice, the acceptance unit can accept the question using voice recognition technology. Furthermore, when a user inputs a question by text, the acceptance unit can accept the question using text analysis technology. Furthermore, when a user inputs a question by image, the acceptance unit can accept the question using image recognition technology. By selecting the optimal acceptance means depending on the user's input method, the question can be accepted in the optimal way for the user. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input input data to a generation AI and have the generation AI select the optimal acceptance means.

[0122] The reception unit can estimate the user's emotions and determine the priority of questions based on the estimated user's emotions. The priority of questions can include, but is not limited to, importance, urgency, and the user's level of interest. For example, when the user is stressed, the reception unit can prioritize important questions. Furthermore, when the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize questions that focus on the main points. Thus, by determining the priority of questions according to the user's emotions, important questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0123] When accepting questions, the reception unit can prioritize highly relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the reception unit can prioritize highly relevant questions based on the user's geographical location information. The reception unit can also accept optimal questions by taking into account the user's geographical location information. Furthermore, the reception unit can analyze the user's geographical location information and suggest highly relevant questions. Thus, by taking into account the user's geographical location information and preferentially accepting highly relevant questions, it is possible to provide useful information to the user. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input geographical location information to the generation AI and cause the generation AI to accept highly relevant questions.

[0124] When accepting a question, the reception unit can analyze the user's social media activity and accept related questions. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can prioritize accepting highly relevant questions based on the user's social media activity. The reception unit can also accept optimal questions taking the user's social media activity into consideration. Furthermore, the reception unit can analyze the user's social media activity and suggest highly relevant questions. In this way, by analyzing the user's social media activity and accepting related questions, it is possible to provide information that is highly relevant to the user. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the reception unit can input social media data into the generation AI and cause the generation AI to accept related questions.

[0125] When accepting a question, the reception unit can customize the reception method by reflecting the user's past feedback. Feedback includes, but is not limited to, the user's ratings, comments, and usage history. The reception unit can customize the question reception method based on, for example, the user's past feedback. The reception unit can also analyze the user's past feedback and suggest an optimal reception method. The reception unit can also improve the reception method by reflecting the user's past feedback. By customizing the reception method by reflecting the user's past feedback, questions can be accepted in a method optimal for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input feedback data into the generation AI and have the generation AI customize the reception method.

[0126] The answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. Answer expression methods include, but are not limited to, text, audio, and image formats. For example, if the user is stressed, the answering unit can provide a simple and easy-to-understand expression. Furthermore, if the user is relaxed, the answering unit can provide a detailed expression. Furthermore, if the user is in a hurry, the answering unit can provide a concise expression that focuses on the main points. By adjusting the way the answer is expressed based on the user's emotions, an answer that is easy for the user to understand can be provided. Some or all of the above-described processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0127] The answering unit can improve the accuracy of the answer by referring to the user's past question history using a system for collaborating with experts. The question history includes, for example, past question content and answer history, but is not limited to such examples. The answering unit can provide, for example, an answer that reflects expert advice based on the user's past question history. The answering unit can also analyze the user's past question history and provide an optimal answer based on the expert advice. Furthermore, the answering unit can improve the answer that reflects the expert advice by referring to the user's past question history. In this way, the answer that is optimal for the user can be provided by improving the accuracy of the answer by referring to the user's past question history. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the answering unit can input question history data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0128] When providing an answer, the answering unit can customize the answer based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The answering unit can provide a highly relevant answer based on the user's current situation. The answering unit can also provide an optimal answer taking into account the user's areas of interest. Furthermore, the answering unit can analyze the user's current situation and areas of interest and suggest an optimal answer. This allows for providing a highly relevant answer to the user by customizing the answer based on the user's current situation and areas of interest. Some or all of the above-described processing in the answering unit can be performed using, or without, a generation AI. For example, the answering unit can input the user's situation data into the generation AI and have the generation AI customize the answer.

[0129] The answering unit can provide answers in multiple languages ​​according to the user's language settings when providing answers. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. For example, the answering unit can automatically translate answers based on the user's language settings. The answering unit can also provide a language switching function when the user uses multiple languages. Furthermore, the answering unit can provide answers in the most appropriate language based on the user's language settings. This allows for multilingual support according to the user's language settings, making it possible to provide answers that are easy for the user to understand. Some or all of the above-described processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the content of the answer into the generation AI and have the generation AI perform translation and language switching.

[0130] The answering unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. Examples of answer length include, but are not limited to, the number of characters and the level of detail. For example, if the user is stressed, the answering unit can provide a short, to-the-point answer. Furthermore, if the user is relaxed, the answering unit can provide a longer answer with detailed explanations. Furthermore, if the user is in a hurry, the answering unit can provide a concise, quick answer. By adjusting the length of the answer according to the user's emotions, an answer of optimal length for the user can be provided. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0131] When providing an answer, the answering unit can adjust the use of technical terminology according to the user's level of expertise. Technical terminology includes, but is not limited to, medical terminology, technical terminology, etc. For example, if the user has technical expertise, the answering unit can provide an answer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the answering unit can provide an answer in easy-to-understand language. Furthermore, the answering unit can analyze the user's level of expertise and suggest the optimal use of technical terminology. This allows the use of technical terminology to be adjusted according to the user's level of expertise, thereby providing an answer that is easy for the user to understand. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the user's technical expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0132] When providing an answer, the answering unit can provide highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the answering unit can provide highly relevant information based on the user's geographical location information. The answering unit can also provide an optimal answer by taking into account the user's geographical location information. Furthermore, the answering unit can analyze the user's geographical location information and suggest highly relevant information. By providing highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the answering unit can be performed, for example, using a generation AI or without using a generation AI. For example, the answering unit can input geographical location information to the generation AI and cause the generation AI to provide highly relevant information.

[0133] When providing an answer, the answering unit can customize the answering method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, the user's ratings, comments, and usage history. The answering unit can customize the answering method based on, for example, the user's past feedback. The answering unit can also analyze the user's past feedback and suggest an optimal answering method. Furthermore, the answering unit can improve the answering method by reflecting the user's past feedback. By customizing the answering method by reflecting the user's past feedback, an answer can be provided in a way that is optimal for the user. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input feedback data into the generation AI and have the generation AI customize the answering method.

[0134] The community formation unit can estimate a user's emotions and adjust the community proposal method based on the estimated user's emotions. Examples of community proposal methods include, but are not limited to, online forums and chat groups. For example, when a user is stressed, the community formation unit can propose a simple and easy-to-understand community. Furthermore, when a user is relaxed, the community formation unit can propose a community that includes detailed information. Furthermore, when a user is in a hurry, the community formation unit can propose a concise community that focuses on the main points. By adjusting the community proposal method according to the user's emotions, it is possible to propose an optimal community for the user. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input user emotion data into the generation AI and have the generation AI adjust the community proposal method.

[0135] The community formation unit can propose an optimal community based on information about the user's birth period and region, and by referring to past community formation data. Community formation data includes, for example, the number of past participants and activity details, but is not limited to these examples. The community formation unit can propose an optimal community based on, for example, information about the user's birth period and region. The community formation unit can also propose an optimal community based on information about the user's birth period and region, and by referring to past community formation data. The community formation unit can also analyze information about the user's birth period and region and propose an optimal community. This allows the optimal community to be proposed based on information about the user's birth period and region, and by referring to past community formation data, thereby providing a community that is highly relevant to the user. Some or all of the above-described processing in the community formation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the community formation unit can input information about the birth period and region into the generation AI and have the generation AI propose an optimal community.

[0136] When proposing a community, the community formation unit can customize it based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The community formation unit can, for example, propose a highly relevant community based on the user's current situation. The community formation unit can also propose an optimal community taking into account the user's areas of interest. Furthermore, the community formation unit can analyze the user's current situation and areas of interest and propose an optimal community. This allows for customization based on the user's current situation and areas of interest, thereby providing a highly relevant community to the user. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input the user's situation data into the generation AI and have the generation AI customize the community.

[0137] When proposing a community, the community formation unit can support multiple languages ​​according to the user's language settings. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. For example, the community formation unit can automatically translate the community proposal based on the user's language settings. The community formation unit can also provide a language switching function when the user speaks multiple languages. Furthermore, the community formation unit can also propose a community in the most appropriate language based on the user's language settings. This allows for multilingual support according to the user's language settings, thereby providing a community that is easy for the user to understand. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input the community proposal into the generation AI and have the generation AI perform translation and language switching.

[0138] The community formation unit can estimate the user's emotions and determine the priority of communities based on the estimated user emotions. Community priorities include, but are not limited to, importance, urgency, and user interest. For example, when the user is stressed, the community formation unit can prioritize suggesting important communities. Furthermore, when the user is relaxed, the community formation unit can prioritize suggesting detailed communities. Furthermore, when the user is in a hurry, the community formation unit can prioritize suggesting communities that focus on the main points. Thus, by determining the priority of communities according to the user's emotions, important communities can be prioritized. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input user emotion data into the generation AI and have the generation AI determine the priority of communities.

[0139] When proposing a community, the community formation unit can prioritize proposing highly relevant communities by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the community formation unit can prioritize proposing highly relevant communities based on the user's geographical location information. The community formation unit can also propose optimal communities by taking into account the user's geographical location information. Furthermore, the community formation unit can analyze the user's geographical location information and propose highly relevant communities. This allows for providing a community that is beneficial to the user by preferentially proposing highly relevant communities by taking into account the user's geographical location information. Some or all of the above-described processing in the community formation unit may be performed using, or without, a generation AI. For example, the community formation unit can input geographical location information to the generation AI and cause the generation AI to propose highly relevant communities.

[0140] When suggesting a community, the community formation unit can analyze the user's social media activity and suggest relevant communities. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the community formation unit can prioritize suggesting highly relevant communities based on the user's social media activity. The community formation unit can also suggest optimal communities taking the user's social media activity into consideration. Furthermore, the community formation unit can analyze the user's social media activity and suggest highly relevant communities. In this way, by analyzing the user's social media activity and suggesting related communities, it is possible to provide highly relevant communities to the user. Some or all of the above-described processing in the community formation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the community formation unit can input social media data into the generation AI and cause the generation AI to suggest related communities.

[0141] When proposing a community, the community formation unit can customize the proposal method by reflecting the user's past feedback. Examples of the feedback include, but are not limited to, the user's ratings, comments, and usage history. The community formation unit can customize the community proposal method based on, for example, the user's past feedback. The community formation unit can also analyze the user's past feedback and propose an optimal community proposal method. Furthermore, the community formation unit can improve the community proposal method by reflecting the user's past feedback. By customizing the proposal method by reflecting the user's past feedback, a community can be proposed in a way that is optimal for the user. Some or all of the above-described processing in the community formation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the community formation unit can input feedback data into the generation AI and cause the generation AI to customize the proposal method.

[0142] The analysis unit can estimate the user's emotions and adjust the health information analysis method based on the estimated user emotions. Examples of health information analysis methods include, but are not limited to, data analysis techniques and algorithms. For example, the analysis unit can provide simple and easy-to-understand analysis results when the user is stressed. Furthermore, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. By adjusting the health information analysis method according to the user's emotions, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0143] The analysis unit can monitor the child's health information in real time and improve the accuracy of detecting abnormal values. Examples of the accuracy of detecting abnormal values ​​include, but are not limited to, the accuracy of the detection algorithm and the false positive rate. For example, the analysis unit can monitor the child's health information in real time and immediately detect abnormal values. The analysis unit can also compare the child's health information with past data to detect abnormal values. Furthermore, the analysis unit can cross-check the child's health information from multiple sources to improve the accuracy of detecting abnormal values. This enables real-time monitoring of the child's health information and improved accuracy in detecting abnormal values, enabling prompt responses. Some or all of the above-described processing in the analysis unit can be performed, for example, using or without the generation AI. For example, the analysis unit can input health information data into the generation AI and have the generation AI perform abnormal value detection.

[0144] When analyzing health information, the analysis unit can improve the accuracy of the analysis by referring to the user's past health data. Past health data includes, but is not limited to, past medical records and test results. The analysis unit can improve the accuracy of the analysis, for example, based on the user's past health data. The analysis unit can also analyze the user's past health data and propose an optimal analysis method. Furthermore, the analysis unit can improve the analysis method by referring to the user's past health data. This improves the accuracy of the analysis by referring to the user's past health data, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input past health data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0145] When analyzing the health information, the analysis unit can propose optimal preventive measures based on information about medical institutions in the user's area. Information about local medical institutions includes, but is not limited to, the location, medical specialty, and opening hours of the medical institution. The analysis unit can propose optimal preventive measures based on, for example, information about medical institutions in the user's area. The analysis unit can also propose optimal preventive measures taking into account the evaluation of medical institutions in the user's area. Furthermore, the analysis unit can analyze information about medical institutions in the user's area and propose optimal preventive measures. By proposing optimal preventive measures based on information about medical institutions in the user's area, it is possible to provide beneficial preventive measures to the user. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information about local medical institutions into the generation AI and have the generation AI propose optimal preventive measures.

[0146] The analysis unit can estimate the user's emotions and prioritize health information based on the estimated user's emotions. Examples of health information prioritization include, but are not limited to, importance, urgency, and user interest. For example, the analysis unit can prioritize analysis of important health information when the user is stressed. Furthermore, the analysis unit can prioritize analysis of detailed health information when the user is relaxed. Furthermore, the analysis unit can prioritize analysis of key health information when the user is in a hurry. Thus, by prioritizing health information according to the user's emotions, important information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of health information.

[0147] When analyzing health information, the analysis unit can prioritize analysis of highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The analysis unit can, for example, prioritize analysis of highly relevant health information based on the user's geographical location information. The analysis unit can also analyze optimal health information taking into account the user's geographical location information. Furthermore, the analysis unit can analyze the user's geographical location information and prioritize analysis of highly relevant health information. This allows for providing useful information to the user by prioritizing analysis of highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input geographical location information to the generation AI and cause the generation AI to analyze highly relevant information.

[0148] When analyzing health information, the analysis unit may analyze the user's social media activity and analyze related information. Social media activity includes, but is not limited to, post content, number of likes, and number of followers. For example, the analysis unit may prioritize analysis of highly relevant health information based on the user's social media activity. The analysis unit may also analyze optimal health information taking the user's social media activity into consideration. Furthermore, the analysis unit may analyze the user's social media activity and prioritize analysis of highly relevant health information. In this way, by analyzing the user's social media activity and analyzing related information, highly relevant information can be provided to the user. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI or without a generation AI. For example, the analysis unit may input social media data into a generation AI and have the generation AI analyze the related information.

[0149] When analyzing health information, the analysis unit can customize the analysis method by reflecting the user's past feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. The analysis unit can customize the analysis method for health information based on, for example, the user's past feedback. The analysis unit can also analyze the user's past feedback and propose an optimal analysis method. Furthermore, the analysis unit can improve the analysis method by reflecting the user's past feedback. By customizing the analysis method by reflecting the user's past feedback, information can be analyzed in a way that is optimal for the user. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input feedback data into the generation AI and have the generation AI customize the analysis method.

[0150] The alert unit can estimate the user's emotions and adjust the alert display method based on the estimated user emotions. Examples of alert display methods include, but are not limited to, pop-up notifications, audio notifications, and email notifications. For example, the alert unit can display a simple and easy-to-understand alert when the user is stressed. Furthermore, the alert unit can display an alert that includes detailed information when the user is relaxed. Furthermore, the alert unit can display a concise alert that focuses on the main points when the user is in a hurry. By adjusting the alert display method according to the user's emotions, it is possible to provide an alert that is easy for the user to understand. Some or all of the above-described processing in the alert unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the alert unit can input user emotion data into the generation AI and have the generation AI adjust the alert display method.

[0151] When an abnormal value is detected, the alert unit can improve the accuracy of the alert by referring to the user's past health data. Past health data includes, but is not limited to, past medical records and test results. For example, the alert unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. The alert unit can also analyze the user's past health data to improve the accuracy of detecting abnormal values. Furthermore, the alert unit can also improve the accuracy of detecting abnormal values ​​by referring to the user's past health data. This allows for more accurate alerts to be provided by improving the accuracy of alerts by referring to the user's past health data. Some or all of the above-described processing in the alert unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the alert unit can input past health data into the generation AI and cause the generation AI to improve the accuracy of detecting abnormal values.

[0152] When sending an alert, the alert unit can customize the alert based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The alert unit can, for example, send a highly relevant alert based on the user's current situation. The alert unit can also send an optimal alert taking into account the user's areas of interest. Furthermore, the alert unit can analyze the user's current situation and areas of interest and suggest an optimal alert. This allows for customization based on the user's current situation and areas of interest, thereby providing a highly relevant alert to the user. Some or all of the above-described processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input user situation data into the generation AI and have the generation AI customize the alert.

[0153] The alert unit can support multiple languages ​​when sending an alert according to the user's language setting. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. The alert unit can automatically translate the alert based on the user's language setting, for example. The alert unit can also provide a language switching function when the user uses multiple languages. Furthermore, the alert unit can send the alert in the optimal language based on the user's language setting. This allows for multilingual support according to the user's language setting, making it possible to provide an alert that is easy for the user to understand. Some or all of the above-described processing in the alert unit can be performed using, or without, a generation AI. For example, the alert unit can input the alert content to the generation AI and have the generation AI perform translation and language switching.

[0154] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated user emotions. Examples of alert priorities include, but are not limited to, importance, urgency, and user interest. For example, the alert unit can prioritize sending important alerts when the user is stressed. Furthermore, the alert unit can prioritize sending detailed alerts when the user is relaxed. Furthermore, the alert unit can prioritize sending alerts that focus on the main points when the user is in a hurry. This allows important alerts to be prioritized by determining the priority of alerts according to the user's emotions. Some or all of the above-described processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input user emotion data into the generation AI and have the generation AI determine the priority of alerts.

[0155] When sending an alert, the alert unit can provide highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The alert unit can provide highly relevant information based on, for example, the user's geographical location information. The alert unit can also send an optimal alert by taking into account the user's geographical location information. Furthermore, the alert unit can analyze the user's geographical location information and provide highly relevant information. As a result, by providing highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing in the alert unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the alert unit can input geographical location information to the generation AI and cause the generation AI to provide highly relevant information.

[0156] When sending an alert, the alert unit can analyze the user's social media activity and provide relevant information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The alert unit can provide relevant information based on the user's social media activity. The alert unit can also send an optimal alert taking the user's social media activity into consideration. Furthermore, the alert unit can analyze the user's social media activity and provide relevant information. In this way, by analyzing the user's social media activity and providing relevant information, it is possible to provide information that is relevant to the user. Some or all of the above-described processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input social media data into the generation AI and cause the generation AI to provide relevant information.

[0157] When sending an alert, the alert unit can customize the alert method by reflecting the user's past feedback. Examples of alert methods include, but are not limited to, pop-up notification, audio notification, and email notification. The alert unit can customize the alert method based on, for example, the user's past feedback. The alert unit can also analyze the user's past feedback and suggest an optimal alert method. Furthermore, the alert unit can also improve the alert method by reflecting the user's past feedback. By customizing the alert method by reflecting the user's past feedback, an alert can be provided in an optimal manner for the user. Some or all of the above-described processing in the alert unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the alert unit can input feedback data into the generation AI and have the generation AI customize the alert method.

[0158] The recording unit can estimate the user's emotions and adjust the display method of the growth record based on the estimated user emotions. Examples of display methods for the growth record include, but are not limited to, graph display, list display, and timeline display. For example, when the user is stressed, the recording unit can provide a simple and easy-to-understand display method. Furthermore, when the user is relaxed, the recording unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the recording unit can provide a concise display method that focuses on the main points. By adjusting the display method of the growth record according to the user's emotions, a display that is easy for the user to understand can be provided. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the growth record.

[0159] The recording unit can improve the accuracy of recording weight, height, newly acquired skills, etc. by referring to the user's past record data. Past record data includes, but is not limited to, past weight, height, developmental milestones, etc. For example, the recording unit can improve the accuracy of recording weight and height based on the user's past record data. The recording unit can also analyze the user's past record data and improve the accuracy of recording newly acquired skills. Furthermore, the recording unit can improve the recording method by referring to the user's past record data. By doing so, by improving the accuracy of recording by referring to the user's past record data, a more accurate growth record can be provided. Some or all of the above-described processing in the recording unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the recording unit can input past record data into the generation AI and cause the generation AI to improve the accuracy of the recording.

[0160] When digitally managing the growth record, the recording unit can customize it based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, health status, living environment, and areas of interest. The recording unit can provide a highly relevant growth record based on the user's current situation. The recording unit can also provide an optimal growth record taking into account the user's areas of interest. Furthermore, the recording unit can analyze the user's current situation and areas of interest and suggest an optimal growth record. This allows for customization based on the user's current situation and areas of interest to provide a highly relevant growth record for the user. Some or all of the above-described processing in the recording unit can be performed using, or without, a generation AI. For example, the recording unit can input the user's situation data into the generation AI and have the generation AI customize the growth record.

[0161] When digitally managing the growth record, the recording unit can support multiple languages ​​according to the user's language setting. Multilingual support includes, but is not limited to, the types of languages ​​supported and the accuracy of translation. For example, the recording unit can automatically translate the growth record based on the user's language setting. The recording unit can also provide a language switching function when the user speaks multiple languages. Furthermore, the recording unit can provide the growth record in the optimal language based on the user's language setting. This allows for multilingual support according to the user's language setting, making it possible to provide a growth record that is easy for the user to understand. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input the growth record into the generation AI and have the generation AI perform translation and language switching.

[0162] The recording unit can estimate the user's emotions and prioritize the growth records based on the estimated user emotions. Growth record priorities include, but are not limited to, importance, urgency, and user interest. For example, when the user is stressed, the recording unit can prioritize displaying important growth records. Furthermore, when the user is relaxed, the recording unit can prioritize displaying detailed growth records. Furthermore, when the user is in a hurry, the recording unit can prioritize displaying growth records that highlight key points. Thus, by prioritizing the growth records according to the user's emotions, important growth records can be prioritized. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the growth records.

[0163] When digitally managing the growth record, the recording unit can prioritize recording highly relevant information taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the recording unit can prioritize recording highly relevant growth records based on the user's geographical location information. The recording unit can also record optimal growth records taking into account the user's geographical location information. Furthermore, the recording unit can analyze the user's geographical location information and prioritize recording highly relevant growth records. This allows for providing a growth record that is beneficial to the user by prioritizing recording highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input geographical location information to the generation AI and cause the generation AI to record highly relevant information.

[0164] When digitally managing the growth record, the recording unit can analyze the user's social media activity and record related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the recording unit can prioritize recording relevant growth records based on the user's social media activity. The recording unit can also record optimal growth records taking the user's social media activity into consideration. Furthermore, the recording unit can analyze the user's social media activity and prioritize recording relevant growth records. In this way, by analyzing the user's social media activity and recording relevant information, a growth record that is relevant to the user can be provided. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input social media data into the generation AI and cause the generation AI to record relevant information.

[0165] When digitally managing the growth record, the recording unit can customize the recording method by reflecting the user's past feedback. Feedback includes, but is not limited to, the user's ratings, comments, and usage history. The recording unit can customize the growth record method based on, for example, the user's past feedback. The recording unit can also analyze the user's past feedback and suggest an optimal growth record method. Furthermore, the recording unit can also improve the growth record method by reflecting the user's past feedback. By customizing the recording method by reflecting the user's past feedback, the growth record can be provided in an optimal manner for the user. Some or all of the above-described processing in the recording unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the recording unit can input feedback data into the generation AI and have the generation AI customize the recording method. === Hard Collateral 1-1 === Each of the multiple elements, including the management unit, notification unit, reception unit, response unit, community creation unit, analysis unit, alert unit, and recording unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit is implemented by either the data processing device 12 or the smart device 14. For example, the management unit manages vaccination information and schedules using the specific processing unit 290 of the data processing device 12 and notifies the user via the control unit 46A of the smart device 14. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 and sends reminders via a smartphone app. The reception unit is implemented, for example, by the control unit 46A of the smart device 14 and accepts questions in text, audio, image, or other formats. The response unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides expert answers using a collaboration system with experts. The community creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate communities based on the user's birth date and region. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the child's health information in real time. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends an alert to the parents when an abnormal value is detected and suggests an organization that can respond in an emergency. The recording unit is realized, for example, by the control unit 46A of the smart device 14, and records weight, height, newly acquired skills, etc., and saves photos and videos. === Hard Collateral 1-2 === Each of the multiple elements, including the management unit, notification unit, reception unit, response unit, community formation unit, analysis unit, alert unit, and recording unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the management unit is realized by either the data processing device 12 or the smart glasses 214. For example, the management unit manages vaccination information and schedules using the specific processing unit 290 of the data processing device 12 and notifies the user via the control unit 46A of the smart glasses 214. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends reminders. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and accepts questions in text, audio, image, or other formats. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides expert answers using a collaboration system with experts. The community formation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate communities based on the user's birth date and region information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the child's health information in real time. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends an alert to the parent when an abnormal value is detected and suggests an emergency response agency. The recording unit is realized, for example, by the control unit 46A of the smart glasses 214, and records weight, height, newly acquired skills, etc., and saves photos and videos. === Hard Collateral 1-3 === Each of the multiple elements, including the management unit, notification unit, reception unit, response unit, community creation unit, analysis unit, alert unit, and recording unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the management unit is implemented by either the data processing device 12 or the headset terminal 314. For example, the management unit manages vaccination information and schedules using the specific processing unit 290 of the data processing device 12 and notifies the user via the control unit 46A of the headset terminal 314. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 and sends reminders. The reception unit is implemented, for example, by the control unit 46A of the headset terminal 314 and accepts questions in text, audio, image, or other formats. The response unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides expert answers using a collaboration system with experts. The community creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate communities based on the user's birth date and region. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the child's health information in real time. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends an alert to the parents when an abnormal value is detected and suggests organizations that can respond in an emergency. The recording unit is realized, for example, by the control unit 46A of the headset terminal 314, and records weight, height, newly acquired skills, etc., and saves photos and videos. === Hard Collateral 1-4 === Each of the multiple elements, including the management unit, notification unit, reception unit, response unit, community formation unit, analysis unit, alert unit, and recording unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit is realized by either the data processing device 12 or the robot 414. For example, the management unit manages vaccination information and schedules using the specific processing unit 290 of the data processing device 12 and notifies the user using the control unit 46A of the robot 414. The notification unit is realized, for example, by the control unit 46A of the robot 414 and sends reminders. The reception unit is realized, for example, by the control unit 46A of the robot 414 and accepts questions in text, audio, image, or other formats. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides expert answers using a collaboration system with experts. The community formation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate communities based on the user's birth date and region information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the child's health information in real time. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends an alert to the parents when an abnormal value is detected and suggests an organization that can respond in an emergency. The recording unit is realized, for example, by the control unit 46A of the robot 414, and records weight, height, newly acquired skills, etc., and saves photos and videos.

[0166] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0167] The management unit can analyze the user's lifestyle and optimize the timing of vaccination information notifications. For example, it can select the time when notifications are most effectively received, taking into account the user's wake-up time, bedtime, meal times, etc. It can also identify the time periods when notifications are most likely to be received based on the user's past notification history and adjust the notification timing. Furthermore, if the user's lifestyle changes, it can perform analysis in real time and dynamically change the notification timing. This allows for optimal notifications that match the user's lifestyle.

[0168] The notification unit can estimate the user's emotions and customize the notification content based on the estimated emotions. For example, if the user is feeling stressed, the notification content can be simplified and only important information can be provided. If the user is relaxed, a notification containing detailed information can be sent. Furthermore, if the user is in a hurry, a short notification that focuses on the main points can be sent. This makes it possible to provide optimal notification content according to the user's emotions.

[0169] The reception unit can analyze the user's past question history and suggest the optimal question reception method. For example, for a user who has previously asked many questions in text format, text input can be suggested as a priority. Also, for a user who prefers voice input, the voice input option can be emphasized. Furthermore, for a user who asks many questions in image format, the image upload function can be highlighted. In this way, the optimal question reception method can be provided based on the user's past question history.

[0170] The answering unit can estimate the user's emotions and adjust the tone of the answer based on the estimated emotions. For example, if the user is feeling anxious, the answering unit can provide a gentle, reassuring answer. If the user is relaxed, the answering unit can provide a friendly answer. If the user is in a hurry, the answering unit can provide a concise, to-the-point answer. This makes it possible to provide the optimal answer according to the user's emotions.

[0171] The community creation unit can analyze the user's social media activity and suggest related communities. For example, if the user frequently participates in a specific childcare group, it can suggest communities related to that group. Also, if the user shows interest in a specific childcare topic, it can suggest communities related to that topic. Furthermore, it can preferentially suggest active communities based on the frequency of the user's social media activity. This makes it possible to provide the most suitable community based on the user's social media activity.

[0172] The analysis unit can estimate the user's emotions and customize the health information analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results that focus on the main points. This makes it possible to provide optimal health information analysis results according to the user's emotions.

[0173] The alert unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. For example, it can optimize the abnormal value detection algorithm by referring to past medical records and test results. It can also improve the accuracy of detecting abnormal values ​​by analyzing past health data and finding specific patterns. It can also adjust the parameters required for detecting abnormal values ​​based on past health data. This makes it possible to more accurately detect abnormal values ​​by utilizing the user's past health data.

[0174] The recording unit can estimate the user's emotions and adjust the display method of the growth record based on the estimated emotions. For example, if the user is feeling stressed, a simple and visually easy-to-understand display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a concise display method that focuses on the main points can be provided. In this way, it is possible to provide an optimal display method of the growth record according to the user's emotions.

[0175] The recording unit can customize the growth record based on the user's geographic location information. For example, if the user lives in a specific area, childcare information and event information for that area can be added to the growth record. Information about local medical institutions can also be reflected in the growth record based on the user's geographic location information. Furthermore, the user's geographic location information can be analyzed to include region-specific childcare topics in the growth record. This makes it possible to provide an optimal growth record based on the user's geographic location information.

[0176] The recording unit can estimate the user's emotions and determine the priority of growth records based on the estimated emotions. For example, if the user is feeling stressed, important growth records can be displayed with priority. Also, if the user is relaxed, detailed growth records can be displayed with priority. Furthermore, if the user is in a hurry, growth records that focus on the main points can be displayed with priority. This makes it possible to provide optimal growth record priorities according to the user's emotions.

[0177] The processing flow of the second embodiment will be briefly explained below.

[0178] Step 1: The administration department manages vaccination information or schedules. For example, it manages information such as vaccination dates, vaccination sites, and the type of vaccine used, as well as the next vaccination date and alternative dates in case of changes. Step 2: The notification department notifies the next vaccination date or change based on the information managed by the management department, for example, by sending a reminder via a smartphone app. Step 3: The reception unit accepts questions about pregnancy, childbirth, and childcare. For example, questions can be accepted in text, audio, or image format. Step 4: The answering unit provides a professional answer based on the question received by the receiving unit. For example, the professional answer can be provided using a system for collaborating with experts. Step 5: The community building unit connects users who gave birth around the same time. For example, it can suggest appropriate communities based on the user's birth time and region. Step 6: The analysis unit analyzes the child's health information, such as temperature, weight, height, and medical history, in real time. Step 7: The alert unit detects abnormal values ​​or preventive measures based on the information analyzed by the analysis unit and sends an alert to the parent. For example, if an abnormal value is detected, an alert can be sent to the parent and an emergency response agency can be suggested. Step 8: The recorder will record your child's growth, such as weight, height, newly acquired skills, etc., and can save photos and videos.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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 AI 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.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0200] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0210] 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.

[0211] 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.

[0212] 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 AI 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.

[0213] 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.

[0214] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0215] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0216] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0227] 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.

[0228] 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.

[0229] 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 AI 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.

[0230] 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.

[0231] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0237] 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."

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] [Explanation of symbols]

[0251] 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 administrative department that manages vaccination information or schedules; A notification unit that notifies the next vaccination date or change based on the information managed by the management unit; A reception desk to answer questions about pregnancy, childbirth, and childcare, an answering unit that provides professional answers based on the questions received by the receiving unit; A community building department that connects users who gave birth around the same time, An analysis department that analyzes children's health information; an alert unit that detects abnormal values ​​or preventive measures based on the information analyzed by the analysis unit and sends an alert to a parent; A recording unit for recording the child's growth. A system characterized by:

2. The management unit Automatically acquire data from medical institutions 2. The system of claim 1.

3. The notification unit Send reminders through a smartphone app 2. The system of claim 1.

4. The answering section Providing expert answers using a system of collaboration with experts 2. The system of claim 1.

5. The community formation unit Suggesting appropriate communities based on the user's birth period or region 2. The system of claim 1.

6. The analysis unit Real-time monitoring of children's health information 2. The system of claim 1.

7. The alert unit If an abnormal value is detected, the system will send an alert to parents and suggest possible emergency response agencies.

2. The system of claim 1.

8. The recording unit Record your weight, height, newly acquired skills, and save photos and videos 2. The system of claim 1.

Images